ThermoFisher Scientific Inc.

Logan, UT, United States

ThermoFisher Scientific Inc.

Logan, UT, United States
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All NSCLC cell lines (A549, H1355, H157, H2122, Hcc515, H460, H1395, H1437, H1755, H1993, H2023, H2073, H1373, H2347, H358, H441, Calu-1 and Calu-6) used in this study were obtained from the Hamon Cancer Center Collection (University of Texas–Southwestern Medical Center). Cells were maintained in RPMI-1640 supplemented with penicillin–streptomycin, and 5% fetal bovine serum (FBS) at 37 °C in a humidified atmosphere containing 5% CO and 95% air. All cell lines have been DNA fingerprinted for provenance using the PowerPlex 1.2 kit (Promega) and were mycoplasma-free using the e-Myco kit (Boca Scientific). H460-EV, H460-LKB1-WT and H460-LKB1-KD were generated by infecting H460 cells with pBABE retroviral vectors expressing no cDNA or cDNAs encoding wild-type or kinase-dead (K78I mutant) LKB1, respectively. pBABE-FLAG-LKB1-WT and -KD were from L. Cantley (Addgene plasmid 8592 and 8593, respectively) and pAMPK alpha2 delta312X (constitutively active AMPK) was from M. Birnbaum (Addgene plasmid 60127). For mouse CPS1 (mCPS1) cloning, mCPS1 cDNA was purchased from GE healthcare (cloneID 40098767), and subcloned into pWPXL lentiviral plasmid (Addgene, plasmid 12257). Stable integrants were sorted by flow cytometry (FACS Aria II SORP) for further analyses. To generate H460-shREN and -shCPS1 cells, parental H460 cells were infected by TRMPVIR retroviral vectors expressing Tet-shRNA targeting Renilla luciferase (REN) as negative control or Tet-shRNAs targeting CPS1, and stable integrants were obtained by flow cytometry (FACS Aria II SORP). The primers used to generate shCPS1 constructs were as follows: shCPS1-1 forward, 5′-TGCTGTTGACAGTGAGCGCAACCAAGGATGTCAAAGTGTATAGTGAAGCCACA-3′, shCPS1-2 forward, 5′-TGCTGTTGACAGTGAGCGCACCAAGGATGTCAAAGTGTACTAGTGAAGCCACA-3′. All nucleosides (uridine, thymidine and adenosine), citrulline, ornithine and NaNO were purchased from Sigma-Aldrich. Doxycycline was from Research Products International (RPI), Torin 1 and cisplatin were from Selleckchem and A769662 was from Tocris Bioscience. NSCLC cell lines were plated at 3–5 × 106 cells per 10-cm plate for 16 h before collection. Two hours before collection, cells were incubated with fresh media. At the time of collection, cells were washed with ice-cold saline, lysed with 80% methanol in water and quickly scraped into an Eppendorf tube followed by three freeze–thaw cycles in liquid nitrogen. The insoluble material was pelleted in a cooled centrifuge (4 °C) and the supernatant was transferred to a new tube and evaporated to dryness using a SpeedVac concentrator (Thermo Savant). Metabolites were reconstituted in 100 μl of 0.03% formic acid in LCMS-grade water, vortex-mixed and centrifuged to remove debris. For human NSCLC metabolomics, frozen tissues were weighed and divided into 3–9 fragments (around 3 mg per fragment) for technical replicates. Fragments were homogenized in 80% methanol in water and centrifuged at 14,000g for 15 min (4 °C). The supernatant was transferred to a new tube and evaporated to dryness as described above for cell lysates. Samples were randomized and blinded before analysing by LC–MS/MS. LC–MS/MS and data acquisition were performed using an AB QTRAP 5500 liquid chromatography/triple quadrupole mass spectrometer (Applied Biosystems SCIEX) as described previously24 with an injection volume of 20 μl. Carbamoyl phosphate was detected in negative mode by monitoring ions 140 and 79 in Q1 and Q3, respectively. Chromatogram review and peak area integration were performed using MultiQuant software version 2.1 (Applied Biosystems SCIEX), and the peak area for each detected metabolite was normalized against the total ion count of that sample to correct for any variations introduced by sample handling through instrument analysis. The normalized areas were used as variables for the multivariate and univariate statistical data analysis. All multivariate analyses and modelling on the normalized data were carried out using Metaboanalyst 3.0 (http://www.metaboanalyst.ca). Univariate statistical differences of the metabolites between two groups were analysed using a two-tailed Student’s t-test. Cells were plated at 24 × 106 cells per 2 × 15-cm plate for 16 h before labelling. The next day, cells were incubated in labelling media containing 10 mM 15NH Cl for 4 h before collection. At the time of collection, the cells were washed with ice-cold saline, lysed with 40% methanol:40% acetonitrile:20% water with 0.1 M formic acid and processed as described above (Metabolomics). Metabolites were reconstituted in 100 μl of 0.1% formic acid in LCMS-grade water, vortex-mixed and centrifuged to remove debris. Samples were randomized and blinded before analysing by LC–MS/MS. LC–MS/MS and data acquisition were performed as described previously on an AB SCIEX QTRAP 5500 (ref. 24) with slight modifications. In brief, the mobile phases used were 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The gradient program was as follows: 0–3 min, 0% B; 3–4 min, 0%–100% B; 4–5 min, 100% B; 5–5.1 min, 100%–0% B; 5.1–6 min, 0% B. The column was maintained at 35 °C and the samples were kept in the autosampler at 4 °C. The flow rate was 0.5 ml min−1 and the injection volume was 20 μl. Sample analysis was performed in positive mode. Declustering potential (DP), collision energy (CE) and collision cell exit potential (CXP) were optimized by direct infusion of reference standards using a syringe pump before sample analysis. Q1, Q3, DP, CE, CXP, retention time and dwell time for each transition of thymidine are in Supplementary Table 10. The MRM MS/MS detector conditions were set as follows: curtain gas 30 psi; ion spray voltages 1,200 V; temperature 650 °C; ion source gas 1, 50 psi; ion source gas 2, 50 psi; interface heater on; entrance potential 10 V. Dwell time for each transition was set at 3 ms. Samples were analysed in a randomized order, and MRM data was acquired using Analyst 1.6.1 software (Applied Biosystems SCIEX). To measure NOS activity, 10,000 cells were cultured in a 96-well plate for 16 h before the assay, and free NO was quantified with a spectrophotometric assay (Sigma-Aldrich). For ammonia secretion, cells were cultured in fresh RPMI for 6 h and ammonia was measured with a spectrophotometric assay (Megazyme). In this assay, glucose deprivation induces ammonia secretion25 and was used as a positive control for the effect of CPS1 silencing on ammonia secretion in Extended Data Fig. 9b. For arginine deprivation and metabolite rescue experiments (citrulline, ornithine and NaNO ), NSCLC cells were plated in 96-well plates at 3,000–5,000 cells per well. The following day, the culture medium was changed either to complete RPMI or arginine-depleted RPMI with or without 1 mM citrulline, 1 mM ornithine or 3 mM NaNO . Cell viability was assayed three days later using CellTiter-Glo (Promega). To monitor proliferation in a monolayer culture, 1–3 × 105 cells were seeded in a 6-cm dish. Every three days, cells were trypsinized and counted with a haemocytometer. The live cell content was estimated using a CellTiter-Glo assay (Promega). To examine cell death, cells were treated as indicated in the figure legend and stained with propidium iodide or with Annexin-V–FITC and propidium iodide. Cells were then analysed by flow cytometry (FACS Aria II SORP). Four days after Dox induction, cells (1,000 per well) were suspended in 0.375% agar (Noble agar, Difco) pre-equilibrated with growth medium, over a 0.75% bottom agar layer in each well of a 6-well plate. Colonies were allowed to form for 20–22 days with intermittent medium supplementation (a few drops twice a week). Images were acquired with G box-Syngene (Syngene) and colonies were detected with GeneTools software (Syngene). Cells were labelled with BrdU labelling (10 μM) for 1 h followed by fixation. Incorporated BrdU was detected by immunostaining and quantified by FACS analysis. Cells were labelled with 100 μM iododeoxyuridine (IdU) for 10 min, then with 100 μM chlorodeoxyuridine (CldU) for 20 min. DNA fibres were spread as described previously26 and stained with primary antibodies (mouse anti-BrdU/IdU from BD Bioscience; rat anti-BrdU/CldU from Accurate Chemical) and fluorescence-conjugated secondary antibodies (Alexa Fluor 488-anti-rat and Texas-Red-conjugated anti-mouse from Invitrogen). Fibres were imaged using Zeiss Axio Imager M2 and measured using AxioVision software (SE64 version 4.9.1). RNA was extracted in TRIzol (Invitrogen) and isolated according to the manufacturer’s protocol. cDNA was generated using the iScript synthesis kit (Bio-Rad), and abundance was measured on a Thermo qPCR instrument. Data were normalized to β-actin (ACTB) or GAPDH. Primers used for qRT–PCR were as follows: CPS1 forward, 5′-ATTCCTTGGTGTGGCTGAAC-3′, reverse, 5′-ATGGAAGAGAGGCTGGGATT-3′; ARG2 forward, 5′-GAGAAGCTGGCTTGATGAAA-3′, reverse, 5′-CAGCTCTGCTAACCACCTCA-3′; ASS1 forward, 5′-CTGATGGAGTACGCAAAGCA-3′, reverse, 5′-CTCGAGAATGTCAGGGGTGT-3′; ADC forward, 5′-CCTCAGGCCTATGCTCAGTC-3′, reverse, 5′-CTGAGTTGATCACGGAAGCA-3′; AGMAT forward, 5′-CGACCTTGGATCCCTACAGA-3′, reverse, 5′-ACCAGCAATTTCAGGTGTCC-3′; CAD forward, 5′-TCAAGGTGACCCAGCACCTG-3′, reverse, 5′-TCAGGCAAAGGGATGCCCAA-3′; actin forward, 5′-AGAGCTACGAGCTGCCTGAC-3′, reverse, 5′-AGCACTGTGTTGGCGTACAG-3′, GAPDH forward, 5′-ACCCAGAAGACTGTGGATGG-3′, reverse, 5′-TTCAGCTCAGGGATGACCTT-3′. Transient gene-silencing experiments were performed with endoribonuclease-prepared siRNAs (esiRNA, Sigma-Aldrich) for CPS1 and CAD, and with ON-TARGETplus-SMART pools (Dharmacon) for LKB1, CREB1, FOXA1, TEAD4, ODC, TSC-1 and TSC-2, AMPKα-1 (PRKAA1) and AMPKα-2 (PRKAA2). In brief, siRNA oligos were transfected into cells with RNAiMAX transfection reagent (Invitrogen); esiRNA oligos targeting eGFP or siRNA universal negative controls were used as a negative control (Sigma-Aldrich). For Extended Data Figs 6e, 10d, triple transfections were performed (every other day, repeated three times) and western blots were assayed 144 h after the first transfection. Viability assays were performed after 96 h and cell death analyses and all other western blots were performed after 48 h. BrdU incorporation was measured after 24 h in H460 cells and after 36 h in H2122 cells. For inducible RNAi experiments, shREN and shCPS1-1 and shCPS1-2 were induced using doxycycline concentrations of 1.0–2.0 mg ml−1. ChIP experiments were performed as described27 with modifications. In brief, around 1–2 × 107 cells were crosslinked with 1% formaldehyde for 5 min at room temperature. Chromatin was sonicated to around 500 bp in RIPA buffer (10 mM Tris-HCl, 1 mM EDTA, 0.1% sodium deoxycholate, 0.1% SDS, 1% Triton X-100, 0.25% sarkosyl, pH 8.0) with 0.3 M NaCl. Sonicated chromatin samples were incubated with 5 μg antibody at 4 °C. After overnight incubation, protein A or G Dynabeads (Invitrogen) were added to the ChIP reactions and incubated for four additional hours at 4 °C to collect the immunoprecipitated chromatin. Subsequently, Dynabeads were washed twice with 1 ml of RIPA buffer, twice with 1 ml of RIPA buffer with 0.3 M NaCl, twice with 1 ml of LiCl buffer (10 mM Tris-HCl, 1 mM EDTA, 0.5% sodium deoxycholate, 0.5% NP-40, 250 mM LiCl, pH 8.0), and twice with 1 ml of TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0). The chromatin was eluted in SDS elution buffer (1% SDS, 10 mM EDTA, 50 mM Tris-HCl, pH 8.0) followed by reverse crosslinking at 65 °C overnight. ChIP DNA were treated with RNaseA (5 μg ml−1) and protease K (0.2 mg ml−1) and purified using QIAquick Spin Columns (Qiagen). The purified ChIP DNA was quantified by real-time PCR using the iQ SYBRGreen Supermix (Bio-Rad). The following antibodies were used: H3K27ac (Abcam, ab4729), H3K4me3 (Millipore, 04-745,), RNAPII (Santa Cruz Biotechnology, sc-899), FOXA1 (Abcam, ab23738), TEAD4 (Abcam, ab58310), CREB1 (Santa Cruz Biotechnology, sc-186) and IgG (Millipore, 12-370). Other ChIP–seq datasets were obtained from previous publications or the ENCODE project. Primers used for CPS1 qPCR were as follows: control forward, 5′-AAACCCACGTCCAGCACAGTGTC-3′, reverse, 5′-AATAGCGGGTAAGGATGTAGACAGG-3′; promoter forward, 5′-TTAACCCACCCGGACAAAGAGG-3′, reverse, 5′-AATAGCCCTTCTGTTACTGTCC-3′; enhancer 1 forward, 5′-CCTGCCCTATGACTCAACTTAC-3′, reverse, 5′-GGAAATCGGAAATAGGACCCGTGC-3′; enhancer 2 forward, 5′-CCACATGCTTCTCTGTGATCCTC-3′, reverse, 5′-ATTCTAAAGAGCAACCCTAGCTG-3′. Primers used for CAD qPCR were as follows: promoter 1 forward, 5′-TCCTTCCCGCTTCTCCGTACTCG-3′, reverse, 5′-CACAGAGTGGGATAAGGTCTGC-3′; promoter 2 forward, 5′-AGCCCAGCCCTGCTTCTTTCTTGC-3′, reverse, 5′-GGGATGCCATAGTTGCCGATCAGAG-3′. CPS1-deficient H460 clones for isotope tracing were generated using the original CRISPR–Cas9 system28 and pools for cell viability assays were generated using the lentiCRISPR V2 system29. To control for variations among individual clones in the tracing experiments, 4–5 clones were pooled together. Guide-RNA oligos were as follows: Protein lysates were prepared in either RIPA or CHAPS buffer and quantified using the BCA protein assay (Thermo Scientific). Proteins were separated on 4–20% SDS–PAGE gels, transferred to PVDF membranes, and probed with antibodies against CPS1 (Abcam, ab3682), β-actin (Abcam, ab8227), ASS1 (clone 2B10, Abcam, ab124465), cyclophilin B (clone EPR12703(B), Abcam, ab178397), total AMPK (Cell Signaling, 2603), pAMPK (Cell signaling, 2531), total ACC (Cell Signaling, 3662), pACC (Cell Signaling, 11818), LKB1 (Cell Signaling, 3050), γH2AX (Cell Signaling, 9718), total CAD (Cell Signaling, 11933), pCAD (Cell Signaling, 12662), NOS3 (BD, 610298), pS6 (Cell Signaling, 2211), p4E-BP1 (Cell Signaling, 2855), CREB1 (Santa Cruz, sc-186X), FOXA1 (ab23738), TEAD4 (ab58310). Animal procedures were performed with the approval of the UT Southwestern IACUC. Tumour size must not exceed 20 mm at the largest diameter and this tumour threshold was never exceeded in any experiment. H460 shREN or shCPS1-1 and shCPS1-2 cells were suspended in RPMI (107 per ml), mixed 1:1 with Matrigel (Becton Dickinson), and 105 cells for H460 and 106 cells for H2122 were implanted subcutaneously into 6-week-old NCRNU mice. After tumour cell injection, mice were randomized and then allocated into cages. Mice were fed regular chow or doxycycline-containing chow (200 mg kg−1, Bio-Serv), starting one day after implantation. For cisplatin treatment, tumour-bearing mice were intraperitoneally injected with cisplatin at 2 mg kg−1 or PBS when the xenografted tumours measured around 100 mm3. Injections were performed every other day for a total of 5–6 doses. Tumour size was measured every other day with electronic callipers. Tumour volumes were calculated every 3–4 days by calliper measurements of the short (a) and long (b) tumour diameters (volume = a2 × b / 2) or of tumour height (h), short (a) and long (b) tumour width (volume = h × a × b / 2) depending on tumour shape. Paraffin-embedded tumour sections from mouse xenografts were deparaffinized with xylene followed by ethanol rehydration, fixed in 4% paraformaldehyde and antigens were retrieved with 10 mM sodium citrate pH 6.0. Sections were then subjected to endogenous peroxidase blocking with 0.3% H O . Bovine serum albumin (BSA, 3%) in 0.1% PBST was used as the blocking agent and antibody dilution solution. After 1 h blocking, samples were incubated overnight at 4 °C with the primary antibody (Cell Signaling, 9718) followed by incubation with fluorescence-conjugated secondary antibodies (ThermoFisher Scientific, A-21206). Images were acquired as a series of 0.4-μm stacks with a DeltaVision system (Applied Precision). Raw images were deconvolved using the iterative algorithm implemented in the softWoRx software (Applied Precision). Cell death was detected in xenografts using the In Situ Cell Death Detection kit, Fluorescein (Sigma-Aldrich) according to the manufacturer’s protocol. In brief, tissue sections were deparaffinized with xylene and rehydrated with ethanol, then treated with proteinase K (5 μg ml−1, New England Biolabs). After washing in PBS, sections were incubated with reaction solution for 1 h at 37 °C in a humidified atmosphere in the dark. Images were acquired with an Olympus IX81 microscope. A tissue microarray with 180 human NSCLC samples (MD Anderson Cancer Center) was probed with antibodies against CPS1 (Sigma-Aldrich, HPA021400) and LKB1 (Cell Signaling, 13752). Immunocytochemistry was performed in a Leica Bond Max (Leica Biosystem) with an antibody dilution at 1:800 for CPS1 and 1:250 for LKB1. Liver tissue was used as a control. Staining intensity was graded as: 0 (no staining); 1+ (weak staining); 2+ (moderate staining); and 3+ (intense staining) by one pathologist, then reviewed by a second pathologist independently. The percentage of stained tumour cells was recorded, and the H-score was assigned using the following formula: 1 × (% cells 1+) + 2 × (% cells 2+) + 3 × (% cells 3+). A final H-score of 0 was assigned as negative; 1–100 as weak; 101–200 as medium; and 201–300+ as strong. Differences in survival based on either LKB1 mutation or CPS1 mRNA expression was determined in lung adenocarcinoma tumours from The Cancer Genome Atlas (TCGA) (TCGA LUAD provisional). The analysis was restricted to the 230 tumours that had undergone both whole-exome sequencing and mRNA profiling. Methods for data generation, normalization and bioinformatics analyses were previously described in the TCGA LUAD publication30. For the present analysis, data from this cohort was downloaded and analysed using cBioPortal (www.cbioportal.org). mRNA data used for this analysis was RNA Seq V2 RSEM with a z-score threshold of 2.0 applied to identify tumours with high levels of CPS1 upregulation. No statistical methods were used to predetermine sample size. Metabolomics and flux analysis samples were randomized before LC–MS/MS analysis. For xenograft experiments, mice injected with tumour cells were randomized before being allocated to cages. All other experiments were not randomized, and the investigators were not blinded to allocation during experiments or to outcome assessment. Experiments in Figs 1a, 2b, 3g with shREN and shCPS1-2, Extended Data Figs 1c, 2, 7i, 8c, 9i, 10j, k were performed once, and experiments in Figs 1d, 3g with shCPS1-1, Fig. 4e, g with shCPS1-2 and Extended Data Figs 6b, 7f, 9e, j, 10b were performed twice. All other experiments were performed three times or more. Variation for xenograft tumour volume is indicated using the standard error of the mean, and variation in all other experiments is indicated using the standard deviation. To assess the significance of differences between two conditions, a two-tailed Welch’s unequal variances t-test was used. Where the data points showed a skewed distribution (for example, Extended Data Fig. 1c), a Wilcoxon signed-rank test was performed. For comparisons among three or more groups, a one-way ANOVA followed by Tukey’s multiple comparisons test was performed. To examine significance in xenograft tumour growth between two or among three or more groups, a two-way ANOVA followed by Tukey’s multiple comparisons test was performed. Before applying an ANOVA, we first tested whether there was homogeneity of variation among the groups (as required for ANOVA) using the Brown–Forsythe test. In all xenograft assays, we injected 6–7-week-old NCRNU mice (both male and female), 10 mice per treatment (except Extended Data Fig. 10j, k: n = 4 per group), as we expected based on previous pilot experiments to observe differences in tumour size after two weeks. When mice died before the end of experiments, data from those mice were excluded (Fig. 3g for shCPS1-2 −Dox). All primary data are included in the Source Data associated with each figure accompanying this Letter. Any additional information required to interpret, replicate or build upon the Methods or findings reported in the manuscript are available upon request from the corresponding author.


"New York State approval for our entire menu is a key component of our quality and regulatory strategy," says David Margulies, MD and Claritas Executive Chair. "Today's approval, Claritas' combination of NYS, ISO 15189, and CLIA Certification demonstrates our commitment to Total Quality Management Practice to ensure the best possible results for patients and families." All the tests employ Claritas' innovative dual-capture, dual sequencing whole exome platform method that is unique to the industry. This "Orthogonal Approach", provides the highest confidence in clinical results, while the whole exome platform allows to add additional testing without additional sequencing costs. Clinical interpretation is facilitated by WuxiNextCODE's  software. The WuXi NextCODE CSA is the world's most widely-used system for sequence-based rare disease diagnosis. It provides access through every interface to an always-on, fully-harmonized knowledgebase of all major global databases and reference sets; the ability to conduct queries according to a range of modes of inheritance without specialized informatics expertise; and instant visualization of mutations in raw sequence data. More information regarding the Claritas' test menu can be found at www.claritasgenomics.com. For ordering information, email info@claritasgenomics.com, visit our website at claritasgenomics.com or call Client Services at (617)553-5880/(855)373-9003(toll free). Claritas Genomics was created by leading pediatric medical centers Boston Children's Hospital and Cincinnati Children's Hospital in partnership with Cerner Corp, WuxiNextCode Genomics, and ThermoFisher Scientific to serve children affected with complex genetic disorders by providing timely and accurate results, resolving families' long search for answers.  By combining clinical expertise of the world's best pediatric specialists with innovative best in class information and genomic platform solutions, Claritas' mission is to improve patient care and enable new discoveries for pediatric precision medicine. Now is the time to integrate genomics into clinical practice to inform, guide and improve medical treatment for kids around the world. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/claritas-genomics-receives-new-york-state-nys-approval-for-bone-marrow-failure-hlhmas-nephrotic-syndrome-and-mitochondrial-dna-tests-300473290.html


Viola Davis won an Academy Award for Supporting Actress for her role in the 2016 film adaptation of August Wilson's play Fences, directed by Denzel Washington. Davis also earned a Golden Globe, a Critics Choice Award, and a SAG Award for her portrayal of Rose Maxson. Earlier, she won a Tony Award for the 2010 revival of Fences on Broadway. Davis currently stars in the hit ABC drama How to Get Away with Murder, for which she became the first African American to win the Emmy for Best Actress in a Drama Series and also earned two Screen Actors Guild Award and an NAACP Image Award. Adam Grant, the Wharton School's top-rated teacher for five straight years, has been recognized as one of the world's 25 most influential management thinkers, one of the 100 most creative people in business and one of the 40 best business professors under 40. Grant serves on the Lean In board and wrote a New York Times series on women and work with Sheryl Sandberg, including "Speaking while female" and "Madam C.E.O., get me a coffee." He is the New York Times best-selling author of three books, including Give and Take and Originals. "We are delighted to welcome Meryl Streep, Viola Davis and Adam Grant to the main stage in Boston," said Gloria Larson, board chair of the Massachusetts Conference for Women. "This standout lineup of speakers, icons in the arts and in business, are sure to inspire attendees in ways they will long remember." The 13th annual Massachusetts Conference for Women will host over 11,000 attendees for a full day of networking, inspiration, professional development and personal growth. In addition to a standout lineup of keynote speakers, the nonpartisan, nonprofit event will feature breakout sessions led by more than 150 experts and industry leaders about topical issues including building networks, branding, work-life balance, managing up, happiness and civility in the workplace and life. For the third year, the Conference will feature the Workplace Summit on Wednesday, December 6th, the day prior to the Conference. Designed to teach men and women how to promote gender partnerships and advance equity within workplaces, the summit will feature an address by Adam Grant on how to create a culture of originality. That evening, the Conference will also present the third annual Opening Night event from 5 to 8 p.m., featuring an address from Bethenny Frankel, Skinnygirl founder, author, and branding guru, and an inspiring talk and live cooking demo with Barbara Lynch, restaurateur and award-winning chef. Opening Night tickets are available for $25 and can be purchased here. The Massachusetts Conference for Women is presented by: State Street Corporation. It is generously sponsored by Dell; Merck KGaA; Cisco; Hologic; Johnson & Johnson; Liberty Mutual; Raytheon; Tyco; Bank of America; Bose Corporation; Boston Children's Hospital; Boston Scientific ; Fresenius Medical Care; Harvard Business School Executive Education; Rapid7; Mastercard; MFS Investment Management; Poo-Pourri; Reebok; Riverbed; ThermoFisher Scientific; Acadian Asset Management; Amtrak; Applied Materials; Blue Cross Blue Shield of Massachusetts; Comcast; Kate Spade & Company; Merck; QVC, Inc.; Staples, Inc.; Weber Shandwick; and media sponsors AMP Radio 103.3; Mix 104.1; WBZ 1030 News Radio; The Boston Globe; and WCVB-TV Boston. "The Massachusetts Conference for Women continues to set itself apart by bringing together change-makers and pioneers at the top of their fields," said Kathy Horgan, EVP, chief human resources and citizenship officer for State Street Corporation. "State Street is proud to sponsor this year's conference, which shares the goals of our company to promote leadership and gender equity within the workplace and beyond." Registration is now open for the Conference and more exciting speaker announcements are to come. To register or learn more about the Conference, Opening Night and the Workplace Summit, visit www.maconferenceforwomen.org. To apply for media credentials, please contact Michelle Clark, mclark@conferenceforwomen.org. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/meryl-streep-viola-davis-and-adam-grant-to-headline-massachusetts-conference-for-women-300470128.html


News Article | May 24, 2017
Site: www.nature.com

The cohort of 100 patients evaluated within this study comprises the first 100 patients prospectively analysed by the lung TRACERx study (https://clinicaltrials.gov/ct2/show/NCT01888601, approved by an independent Research Ethics Committee, 13/LO/1546) and mirrors the prospective 100 patient cohort described in ref. 9. Multi-region tumour sampling was performed as previously described9. Relapse tissue samples, excess to diagnostic requirements, were acquired via clinical procedures detailed in Supplementary Table 3. For patient CRUK0063 post-mortem examination was performed through the PEACE study 24 h after death (https://clinicaltrials.gov/ct2/show/NCT03004755, approved by an independent Research Ethics Committee, 13/LO/0972). Informed consent was obtained from all subjects for procedures conducted in these studies. The experiments were not randomized. Tissue microarrays were created for 100 NSCLC cases for Ki67+ immunohistochemistry. Representative primary tumour areas were defined by examination of sections stained with haematoxylin and eosin from TRACERx cases. Two 2-mm cores were selected from different regions within each specimen and re-embedded in recipient blocks. This resulted in a tissue microarray of 200 cores with four normal lung cores as negative control. 2–5 μm sections from tissue microarrays containing tumours were cut. Immunohistochemistry with anti-Ki67 monoclonal antibody (dilution, 1:100; clone MIB-1; DAKO Agilent Technologies LDA) was performed using BenchMark Ultra (Ventana/Roche). The percentage of Ki67+ cells was averaged across two tumour sections for each case. Detection was performed using the peroxidase-based detection reagent conjugate (OptiView DAB IHC Detection kit; Ventana). Digital images of diagnostic tumour sections from all cases were reviewed in detail centrally by at least one pathologist, and in cases of uncertainty, by two. Histological subtype, percentage of necrosis and the presence of lymphovascular invasion were all evaluated on digital images from scanned diagnostic slides blinded to the ctDNA detection status of the patient in question. 92 out of 96 anonymized diagnostic PET–CT scans were reviewed by a nuclear medicine physician, blinded to the initial PET–CT reports. Scan images were not available in three cases (CRUK0025, CRUK0039 and CRUK0023) and in one case a preoperative PET–CT was not performed (CRUK0082). CT and PET images were matched and fused into transaxial, coronal and sagittal images and reviewed on a dedicated PET–CT software visualizer (AW 4.1/4.2 GE medical systems). The semiquantitative parameter standardized uptake value (SUV) maximum for the primary tumour mass was calculated and recorded along with the SUV of mediastinal background uptake. The tumour-to-background ratio (TBR) was calculated on the basis of the SUV of the tumour divided by the mediastinal background uptake24, 25. Tumour volume was determined on the basis of tumour CT scans. CT slices of the primary tumour were measured with 3D Slicer applying the ‘lung algorithm window’ settings, tumour contours were segmented on each axial CT slice. These steps were performed by an experienced resident (W.L.B.), and all contours were confirmed and edited where necessary, by a radiologist with 14 years of experience in cancer imaging (F.M.F.). Cancer cell volume was defined as tumour volume multiplied by the mean purity of the tumour on the basis of the M-seq results, purity estimates derived from the ASCAT analysis as previously described9. Effective subclone size was defined as the mean cancer cell fraction (CCF) across the regions of the mutation cluster multiplied by tumour volume and mean tumour purity. Whole-exome sequencing was performed on DNA purified from tumour tissue and normal blood as previously described9, with the exception of CRUK0063_BR_T1-R1. This capture was performed according to the manufacturer’s 200 ng DNA protocol (Agilent). Annotated SNV calls from primary tumours are available in ref. 9. For this study, metastatic tissue biopsies from each of four patients (CRUK0035, CRUK0041, CRUK0044 and CRUK0063) and six metastatic samples acquired at post-mortem examination of CRUK0063 were obtained. Genomic DNA was purified from all tissue samples, and processed through the TRACERx bioinformatics pipeline as previously described9. Annotated SNV calls are available in Supplementary Table 4. Blood samples were collected in K -EDTA tubes. Samples were processed within 2 h of collection by double centrifugation of the blood, first for 10 min at 1,000g, then the plasma for 10 min at 2000g. Plasma was stored in 1 ml aliquots at −80 °C. Up to 5 ml of plasma per case was available for this study (range, 1–5 ml; median 5 ml). The entire volume of plasma was used for cfDNA extraction. cfDNA was extracted using the QIAamp Circulating Nucleic Acid kit (Qiagen) and eluted into 50 μl DNA Suspension Buffer (Sigma). Every cfDNA sample was analysed on the Bioanalyzer High Sensitivity (Agilent) and quantified by interpolation of the mononucleosomal peak height on a calibration curve prepared from a pure cfDNA sample that was quantified previously. Subsequently, 40 μl of cfDNA from each plasma sample, which is present as fragments of mononucleosomal and polynucleosomal length, was used as input into Library Prep using the Natera Library Prep kit; in two samples with extremely high cfDNA amounts, input was restricted to approximately 50,000 genome equivalents (165 ng). cfDNA was end-repaired and A-tailed. Natera custom adapters were ligated. The libraries were amplified for 15 cycles to plateau and then purified using Ampure beads following the manufacturer’s protocol. The purified libraries were run on the LabChip. Successful libraries had a single peak at around 250 bp. Natera’s standard assay design pipeline was used to generate forward and reverse PCR primers for somatic SNVs detected in tumour samples. For every pair of primers, the probability of forming a primer-dimer was calculated and assays were combined into pools such that any primer combination in a pool is not predicted to form primer-dimers. For each patient, assays were prioritized such that (1) assays covering driver SNVs had highest priority and (2) there was uniform sampling of the phylogenetic tree. For the baseline cohort, 10 balanced pools were created, each containing on average 18 assays for 10 patients’ SNVs. For the longitudinal cohort, up to 10 extra assays were generated for samples. For patient CRUK0063 post-mortem analysis, new assays were designed on the basis of the M-seq of the metastatic biopsy retrieved on day 467 and of metastatic lesions collected post-mortem. A total of 103 new assays were designed compared with the 19 that were based on the primary tumour alone. Primer details are available in Supplementary Table 5 (baseline, preoperative cohort), Supplementary Table 6 (longitudinal cohort) and Supplementary Table 7 (extended longitudinal assays for CRUK0063). SNV assays were ordered from IDT. Each pool was optimized by running the multiplex-PCR and sequencing protocol using one plasma cfDNA library from a healthy subject. For optimization, PCR parameters (primer concentration and annealing temperature) that yielded the best percentage of on-target reads, depth-of-read uniformity (measured as the ratio of the 80th percentile to the 20th percentile), and number of drop-out assays (defined as assays with <1,000 reads) were determined by sequencing. The PCR conditions that yield the best percentage of on-target reads, depth-of-read uniformity, and the lowest number of drop-outs were determined. For all pools, the optimal conditions were 10 nM primers and 60 °C or 62.5 °C annealing temperatures. Primer pairs contributing to dimer formation were removed from each final pool. Synthetic spikes representing twenty SNVs that were randomly selected from primer assay pool 1 were designed and synthesized (IDT) as 160-bp oligos with the respective SNV placed in the middle (position 80). These synthetic spikes were mixed at equimolar ratios and used to prepare a library. This library was titrated into a library prepared from mononucleosomal DNA (10,000 copies) from a normal cell line (AG16778 from Coriell). The library of 20 synthetic spikes was titrated into the mononucleosomal DNA library at 2.5%, 0.5%, 0.25%, 0.1%, 0.05% and 0% (each in triplicate), and 0.01%, 0.005% and 0.001% (each in quadruplicate). Because preparing spiked samples at such low levels is either subject to sampling noise (0.01% spikes into 10,000 genomic copies background is equivalent to one mutant copy), or is not possible (at levels less than 0.01%), samples were mixed as libraries. Following library mixing and sequencing, data was analysed to detect all the targets in assay pool 1 using the same parameters as used for the patient samples. The measured VAF of each spike for the samples with 2.5% nominal input was used to calculate an input correction factor (measured VAF/2.5%). This correction factor was applied to the other inputs of the corresponding spike titration series. The measured VAF differed from the nominal input most likely because the mononucleosomal fragmentation pattern is not entirely random. Because of this, the actual input levels differ from the nominal input levels. Therefore, analytical sensitivity and specificity were measured on the basis of the corrected input intervals (see Extended Data Fig. 1a). The library material from each plasma sample was used as input into the multiplex-PCR (mPCR) using the relevant assay pool and an optimized plasma mPCR protocol. Optimal mPCR conditions were as previously described10. Each PCR assay pool was used to amplify the SNV targets from the 10 corresponding samples and 20 negative control samples (plasma libraries prepared from healthy subjects; BioMed IRB 601-01 and E&I West Coast Board IRB00007807, study 13090-01A and 13090-04A). The mPCR products were barcoded in a separate PCR step. Each amplicon pool was sequenced on one Illumina HiSeq 2500 Rapid Run with 50 cycles paired-end reads using the Illumina Paired End v1 kit with an average target read depth of around 40,000 per assay. All the paired-end reads were merged using Pear26. Merged reads were mapped to the hg19 reference genome with Novoalign version 2.3.4 (http://www.novocraft.com/) and sorted and indexed using SAMtools27. Bases that did not match in forward and reverse reads or that have Phred quality score <20 were filtered out to minimize sequencing errors in subsequent steps. Merged reads with mapping quality >30 and at most one mismatch under the sequence of the primers were marked as on-target. Targets with <1,000 reads were considered to have failed and were filtered from further analyses. Quality control was performed using an in-house program checking for a wide list of statistics per sample that included total numbers of reads, mapped reads, on-target reads, number of failed targets and average error rate. For each target SNV a position-specific error model was built (see Methods section ‘SNV calling algorithm’). Samples with high plasma VAF (>20%) among the putative negatives were considered to have possible germline mutations and were excluded from the error model. A confidence score was calculated for each target SNV on the basis of the error model and a positive plasma SNV call was made if the confidence score passed a threshold of 95% for transitions and 98% for transversions. There was no difference in read depth between called and not called SNVs (Extended Data Fig. 1c). Because the post-mortem analysis of CRUK0063 involved a larger number of target SNVs per time point being analysed (103 versus 19 targets in previous samples), updated calling thresholds were applied to control for false positives. The new updated thresholds were chosen such that the average number of false positives in the 30 negative samples in the run becomes around 1 per sample. All multiplex-PCR NGS ctDNA SNV assays with confidence score data are available in Supplementary Table 5 (baseline, preoperative cohort assays), Supplementary Table 6 (longitudinal assays), and Supplementary Table 7 (extended longitudinal assays for CRUK0063). The PCR process was modelled as a stochastic process, estimating the error parameters using a set of 28–30 control plasma samples and making the final SNV calls on the target cancer samples. For each target SNV, we built a target-specific background-error model by estimating the following parameters from the control samples: PCR efficiency (p), probability that each molecule is replicated in a PCR cycle; error rate (p ), error rate per cycle for mutation type e (for example, wild-type allele A to mutant allele G); initial number of molecules (X ). The target-specific error propagation model was used to characterize the distribution of error molecules. As a molecule is replicated over the course of the PCR process, more errors occur. If an error occurs in cycle i and there are X wild-type molecules in the system, that error molecule is duplicated in next cycle with probability p, and new error molecules are produced from the wild-type background molecules according to a binomial process, B(X , p ). Using a recursive relation, we computed the mean and variance of the number of total molecules X and number of error molecules E after n PCR cycles. Algorithm steps are as follows. (1) Estimate the PCR efficiency and per-cycle error rate using the normal control samples. (2) Using the efficiency estimate, compute the starting number of molecules in the test set. (3) Use the starting number of molecules and the prior efficiency distribution from the training set to estimate the PCR efficiency in the test sample. (4) For a range of potential real mutant fraction values θ between 0 and 1 (we used 0.15 as upper bound), estimate the mean and variance for the total number of molecules, background error molecules and real mutation molecules using the described error propagation model and parameters estimated in steps (1)–(3). (5) Use the mean and variance estimated in step (4) to compute the likelihood L(θ) for each potential real mutant fraction, select the value of θ that maximizes this likelihood (denoted by ) and compute the confidence score as . (6) Call a mutation positive if the confidence score passes a predefined threshold. Cross-platform validation was performed in 28 patients with M-seq-confirmed SNV(s) within one or more hotspots targeted by a generic multiplex PCR NGS panel (Extended Data Table 2a, b and Supplementary Table 8). 20 ng of isolated cfDNA was used for library preparation using the Oncomine Lung cfDNA assay (ThermoFisher Scientific), according to the manufacturer’s instructions. Automated template preparation and chip loading was conducted on the Ion Chef instrument using the Ion 520 & Ion 530 Kit-Chef (ThermoFisher Scientific). Ultimately, samples were sequenced on Ion 530 chips using the Ion S5 System (ThermoFisher Scientific). Sequencing data was accessed on the Torrent suite version 5.2.2. Reads were aligned against the human genome (hg19) using Alignment version 4.0-r77189, and variants were called using the coverage Analysis version 4.0-r77897 plugin. All 18 bespoke-panel ctDNA-negative patients had no tumour SNVs detectable in plasma preoperatively by the generic panel, supporting biological specificity of the bespoke targeted approach, 7 out of 10 bespoke-panel ctDNA-positive patients had tumour SNVs detected in plasma by the generic panel (Extended Data Table 2a, b). SNVs detected by the hotspot panel not identified by M-seq are displayed in Extended Data Table 2c. Biopsies from multiple regions from the primary tumour (n = 327), metastatic biopsies (n = 4) and matching blood germline samples (n = 100) were subjected to multi-region whole exome sequencing and analysis including estimation of copy number, purity and ploidy, and phylogenetic tree construction as previously described9. In brief, phylogenetic analysis was performed on the basis of the CCF determined for SNVs and clustered across tumour regions using a modified version of Pyclone9 into clusters with similar CCF values, filtered and processed as previously described9. Mutation clusters are assumed to represent tumour subclones, either current or ancestral, and are used as input for construction of the phylogenetic relationship. Phylogenetic trees were primarily constructed using the published tool CITUP (0.1.0)28. However, in a small number of cases, including all relapse/autopsy cases, manual tree construction was required and performed as previously described9. Complete details of primary tumour tree construction can be found in ref. 9. Relapse tree construction was performed as follows. CRUK0063: clustering was performed twice, once across five primary tumour regions and once across five primary, one relapse, and six autopsy regions. To ensure consistency, when deriving a phylogenetic tree based on all tumour regions, for CCF clusters based on clustering, only the primary tumour regions were maintained for mutations not involved in metastatic relapse. A phylogenetic tree was constructed based on 17 mutation clusters. CRUK0035: clustering primary tumour regions with the relapse region revealed one cluster private to the relapse, and one cluster shared with the relapse and all other regions. CRUK0044: clustering primary tumour regions with the relapse region revealed a cluster private to the relapse, descended from a cluster private to region 1 in the primary tumour. CRUK0041: clustering primary tumour regions with the relapse region revealed cluster 4 as private to the relapse. This cluster must have evolved from cluster 3 only found in the relapse and in region 4. A private cluster 6 in region 4 must have evolved from cluster 4. However, this conflicts with clusters 2 and 5, found in the relapse and regions 1–3, but not region 4. This can be reconciled by assuming a polyclonal relapse, seeded primarily from regions 1–3, but with some contribution from cluster 3, private to region 4. Cluster data are available in Supplementary Table 4 under ‘PyClonePhyloCluster’. No statistical methods were used to predetermine sample size. Analysis was performed in the R statistical environment version 3.2.3 and SPSS version 24. All statistical tests were two-sided, unless expressly stated. Multivariate logistic regression used detection of ctDNA (the dependent variable) classified as detection of two or more patient-specific variants in ctDNA and the covariates listed in Supplementary Table 1. All predictors were entered simultaneously into the regression. All continuous independent variables were found to be linearly related to the logit of the dependent variable (assessed using the Box–Tidwell procedure). The logistic regression model was statistically significant,  = 81.35, P < 0.001 and the Hosmer–Lemeshow P value was 0.9858, indicating that the model was not a poor fit. To determine the ability of PET TBR to predict whether or not tumour ctDNA was identified in plasma, PET TBR estimates were analysed by receiver operating characteristic (ROC) curve analysis against binary detection of ctDNA in plasma at baseline on the basis of at least two variants detected; significance was based on theWilcoxon rank-sum test. For analysis involving longitudinally detected variants (Fig. 4 and Extended Data Fig. 5), only subclonal variants from Pyclone clusters present in phylogenetic trees were displayed; this did not affect ctDNA detection status of any time points. In non-relapse cases presented in Extended Data Fig. 6 all detected subclonal SNVs were plotted. To determine the relationship between tumour volume and ctDNA VAF, ctDNA assays against clonal SNVs were selected. For each patient, the mean ctDNA VAF of the clonal SNVs was determined as baseline for 38 out of 46 patients with at least two SNVs detected in plasma. As detailed in Extended Data Fig. 4c, 8 out of 46 patients were not included in the analysis: CRUK0036 had no preoperative CT scan available; CRUK0087 had a large cavity inside the primary cancer; CRUK0099 had a collapsed lung making volume assessment inaccurate; CRUK0100, CRUK0077 and CRUK0052 had a CT slice spacing of >5 mm (CT slice spacing for all volumetric analyses detailed in Supplementary Table 1); and finally CRUK0088 and CRUK0091 had a total tumour volume of <3.5 cm3. Linear regression was performed on log-transformed mean VAF and tumour volume. The log transformation was justified as it symmetrized the residuals in the model. An independent analysis was performed where tumour volume was multiplied with tumour purity to estimate the cancer cell volume. The same log transformation and analysis was applied to data acquired from ref. 16, where ctDNA VAF was determined based on CAPP-seq analysis with matched tumour volume data available. To analyse clone size versus ctDNA VAF for subclonal SNVs, the mean CCF of the mutations within a subclonal mutation cluster was multiplied with tumour volume, and as a second independent analysis, with tumour purity. Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the The European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG), under accession numbers EGAS00001002247 (primary tumour data) and EGAS00001002415 (metastatic tumour data). Further information about EGA can be found at https://ega-archive.org (the European Genome-phenome Archive of human data consented for biomedical research).


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No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. The codon-optimized human GCGR gene (Genewiz) was cloned into a modified pFastBac1 vector with hemagglutinin (HA) signal sequence at the N terminus and a PreScission protease site followed by a 10×His tag and a Flag tag at the C terminus. To facilitate crystallization, T4L was inserted into the second intracellular loop (ICL2) of GCGR between A256 and E260. To further improve thermostability, 45 residues (H433–F477) were truncated at the C terminus. Our ligand-binding assay showed that protein engineering had little effect on the binding affinity of GCGR to NNC0640 (Extended Data Fig. 2a). The optimized GCGR construct was expressed in Spodoptera frugiperda (Sf9) insect cells (obtained from Invitrogen) using the Bac-to-Bac Baculovirus Expression System (Invitrogen). Cells were infected at a density of 2 × 106 cells per ml with high-titre viral stock at MOI (multiplicity of infection) of 5.0. Cells were collected 48 h after infection and stored at −80 °C until use. The cells expressing the GCGR–T4L protein were lysed in a buffer containing 10 mM HEPES, pH 7.5, 20 mM KCl, 10 mM MgCl and EDTA-free protease inhibitor cocktail tablets (Roche), then prepared with three washes of high salt buffer containing 10 mM HEPES, pH 7.5, 1 M NaCl, 20 mM KCl and 10 mM MgCl . Purified membranes were resuspended in 10 ml lysis buffer with 40% glycerol and stored at −80 °C. Purified membranes were thawed in 30 ml buffer containing 10 mM HEPES, pH 7.5, 20 mM KCl, 10 mM MgCl , 13% glycerol, 200 μM NNC0640 and EDTA-free protease inhibitor cocktail (Roche) at 4 °C for 1 h. The GCGR protein was extracted by adding 10 ml solubilization buffer containing 4% (w/v) n-dodecyl-β-d-maltopyranoside (DDM, Anatrace) and 0.8% (w/v) cholesteryl hemisuccinate (CHS, Sigma) at 4 °C for 3 h. The supernatant was isolated by ultracentrifugation at 160,000g for 30 min, and the final concentrations of NaCl and DDM were adjusted to 0.8 M and 0.5% by adding 40 ml buffer containing 50 mM HEPES, pH 7.5 and 1.6 M NaCl. The supernatant was incubated with TALON resin (Clontech) overnight at 4 °C. The TALON resin was washed with 25 column volumes of wash buffer 1 containing 25 mM HEPES, pH 7.5, 500 mM NaCl, 0.05% (w/v) DDM, 0.01% (w/v) CHS, 10% glycerol, 30 μM NNC0640 and 30 mM imidazole, and then followed by incubation with mAb1 at a molar ratio of 1:1.2 in 2 ml of wash buffer 2 containing 25 mM HEPES, pH 7.5, 150 mM NaCl, 0.05% (w/v) DDM, 0.01% (w/v) CHS, 10% glycerol, 30 μM NNC0640 and 10 mM imidazole at 4 °C for 2 h. The unbound mAb1 was removed by washing the resin with 13 column volumes of wash buffer 2. The GCGR–NNC0640–mAb1 complex was eluted with 5 column volumes of 25 mM HEPES, pH 7.5, 150 mM NaCl, 0.05% (w/v) DDM, 0.01% (w/v) CHS, 10% glycerol, 30 μM NNC0640 and 300 mM imidazole. The PD MiniTrap G-25 column (GE Healthcare) was used to remove imidazole. The sample was treated overnight with His-tagged PreScission protease (custom-made) to remove the C-terminal His- and Flag-tags, and His-tagged PNGase F (custom-made) was also added to deglycosylate the receptor. The Ni-NTA resin (Qiagen) was incubated with the sample at 4 °C for 1 h to remove the cleaved His-tag and PreScission protease. The purified GCGR–NNC0640–mAb1 complex was concentrated to 20–30 mg ml−1 with a 100 kDa molecular weight cut-off concentrator (Millipore). Crystallization was performed using the lipidic cubic phase (LCP) method21. The GCGR–NNC0640–mAb1 protein (20–30 mg ml−1) was mixed with lipid (monoolein/cholesterol 10:1 by mass) at weight ratio of 2:3 using a syringe mixer. The LCP mixture was dispensed onto 96-well glass sandwich plates (Shanghai FAstal BioTech) in 35 nl drop and overlaid with 800 nl precipitant solution using a Mosquito LCP robot (TTP Labtech). Protein reconstitution in LCP and crystallization trials were performed at room temperature (19–22 °C). Plates were incubated and imaged at 20 °C using an automated incubator/imager (RockImager, Formulatrix). Crystals grew in 100 mM HEPES, pH 7.0, 200–300 mM potassium phosphate monobasic, 20–30% (v/v) PEG 500DME and 10–60 mM Gly-Gly-Gly, and reached to a full size of 60–100 μm after 15 days. Crystals were harvested using 75–100 μm MiTeGen micromounts (M2-L19-50/150, MiTeGen) and immediately flash-frozen in liquid nitrogen. Data collection was performed at the SPring-8 beam line 41XU, Hyogo, Japan, using a Pilatus3 6M detector (X-ray wavelength 1.0000 Å). The crystals were exposed with a 10 μm × 8 μm mini-beam for 0.2 s and 0.2° oscillation per frame. Owing to radiation damage, data collection was limited to 10–15° per crystal. Diffraction data from 26 crystals were integrated and scaled using XDS22. The crystals for X-ray free-electron laser (XFEL) data collection were obtained23 by injecting 6–8 μl of LCP sample as a continuous column into a syringe filled with 80 μl precipitant solution comprised of 100 mM HEPES, pH 7.0, 300 mM potassium phosphate monobasic, 25% (v/v) PEG 500DME and 100 mM Gly-Gly-Gly. The syringe was sealed up and incubated at 20 °C. The excess precipitant was removed after the crystals appeared. The 7.9 MAG was added to absorb the residual precipitant solution and avoid the problem of lipid freezing upon injecting LCP in vacuum24. LCP-SFX experiments were carried out at the Coherent X-ray Imaging (CXI) instrument25 at the Linac Coherent Light Source (LCLS) in the SLAC National Accelerator Laboratory. X-ray pulses of 40 fs duration at a wavelength of 1.3 Å (9.5 keV) were attenuated to around 6% (9 × 1010 photons per pulse) and focused to approximately 1.5 μm diameter at the interaction point using Kirkpatrick–Baez mirrors26. GCGR–NNC0640–mAb1 complex crystals in LCP were injected across the XFEL beam using an LCP injector24 with a 50 μm diameter nozzle at a flow rate of approximately 0.2 μl min−1. Diffraction patterns were collected at 120 Hz using the Cornell-SLAC Pixel Array Detector (CSPAD). Over 1 million data frames were collected corresponding to around 2.3 h of data acquisition time. Of these frames, approximately 6.5% contained potential crystal hits as identified using Cheetah27 (more than 15 Bragg peaks of minimum 2 pixels in size and a signal to noise ratio better than 7 after local background subtraction). Of the 91,626 potential crystal hits, 57,573 diffraction patterns could be auto-indexed by CrystFEL28 (indexing rate of 63%) using a combination of MOSFLM29, asdf28 and DirAx30. Reflections from different crystals in random orientations were merged using a Monte Carlo integration of each reflection by CrystFEL28. The data used for the structure refinement were truncated at 3.0 Å based on the criteria of data correlation coefficient (CC*) cut-off of 0.5. The statistics of the final data used in structure refinement are shown in Extended Data Table 1. Both the synchrotron data and XFEL data were initially merged according to the apparent Laue group of mmm. Molecular replacement searches were performed in all possible space groups of mmm, but no satisfying structure solution was found. The data were then reprocessed with the Laue group of 2/m, and the axis length of 245.3 Å was selected as the 2 screw axis based on systematic absences of the merged synchrotron data, with a β angle very close to 90° (90.01°). Both the large crystals for synchrotron data collection and the small crystals for XFEL data collection appeared to be pseudo-merohedrally twinned based on the L-test analysis by Phenix Xtriage31 with the multivariate score of 7.8. Despite the challenge of twinned data, the GCGR–NNC0640–mAb1 complex structure was solved by molecular replacement (MR) implemented in Phaser32 using the models of the GCGR TMD, mAb1-bound ECD of GCGR, and T4L (PDB IDs: 4L6R, 4LF3 and 2RH1, respectively). Two molecules of GCGR TMD, two molecules of mAb1-bound GCGR ECD and one molecule of T4L were found sequentially by MR search. The second T4L was partially resolved based on the electron density. The structure was initially solved and refined against the synchrotron data without using a twin law to an R of approximately 33% with REFMAC33 and BUSTER34. The model maps from the data were of sufficient quality to interpret the overall structure of the GCGR–NNC0640–mAb1 complex, and both the stalk and ECL1 were built based on the electron map. The model then underwent iterated cycles of manual building into |2F | − |F | maps with Coot35 and refinement with REFMAC33, where rigid body, individual positions and TLS refinements were used along with NCS restraints and a twin law (h, -k, -l). The final structure refined by synchrotron data was then used as a search model for the XFEL data, and the XFEL structure was refined in a similar strategy as described above. Both structures have been carefully refined and the ramachandran plot analysis indicates that 100% of the residues are in favourable or allowed regions (no outliers). The structures of the GCGR–NNC0640–mAb1 complex were determined to 3.0 and 3.2 Å resolution using the XFEL data and synchrotron data, respectively (Extended Data Table 1). The two structures are similar with C r.m.s.d. of 0.6 Å. Structure analysis and discussion are based on the structure solved using the XFEL data at higher resolution. The genes of light chain and heavy chain of mAb1 Fab fragment were synthesized with CD33 signal peptide and cloned into the vector pJSV002 for mammalian cell expression. The plasmids were then transfected into HEK293-6E cells (obtained from Invitrogen) at a density of 1.0 × 106 cells per ml with DNA molar ratio of 1:1. Cells were routinely tested for mycoplasma contamination. The transfection was performed following Invitrogen’s Freestyle_293 expression manual. The cell culture supernatant was filtered 120 h after transfection and applied to a protein G affinity column (GE Healthcare) that was pre-equilibrated in phosphate-buffered saline (PBS). The bound Fab was eluted with 100 mM glycine-HCl, pH 2.8. Fractions were collected and neutralized immediately with 1/20 volume of 2 M Tris-HCl, pH 9.0. The pooled fraction was then diluted into 20 mM Na-acetate, pH 5.5 and applied to a SP HP column (GE Healthcare). The bound Fab was eluted with a 100–300 mM linear gradient of NaCl in 20 mM Na-acetate, pH 5.5 and buffer-exchanged to PBS on a G25 desalting column (GE Healthcare). Purified protein was sterilized by filtration through a 0.2 mm filter unit (Sartorius). The purity of the protein sample was analysed by SDS–PAGE and size-exclusion chromatography. The Fab identity was confirmed by mass spectrometry. The complementary DNA (cDNA) encoding the human GCGR was originally obtained from GeneCopoeia and cloned into the expression vector pcDNA3.1/V5-His-TOPO (Invitrogen) at the HindIII and EcoRI sites. The double cysteine mutants were constructed by PCR-based site-directed mutagenesis. CHO-K1 cells (obtained from ATCC) were seeded onto 96-well poly-d-lysine-treated cell culture plates (PerkinElmer) at a density of 3 × 104 cells per well. Cells were routinely tested for mycoplasma contamination. After overnight culture, the cells were transiently transfected with wild-type or mutant GCGR DNA using Lipofectamine 2000 transfection reagent (Invitrogen). CHO-K1 cells were cultured in F-12 medium with 10% (v/v) fetal bovine serum and collected 24 h after transfection, washed twice, and incubated with blocking buffer (F12 supplemented with 33 mM HEPES, pH 7.4 and 0.1% bovine serum albumin (BSA)) for 2 h at 37 °C. Cells were treated with PBS or 1 mM DTT for 10 min before homogeneous binding. They were then washed twice with PBS and incubated in binding buffer (PBS supplemented with 10% BSA, pH 7.4) with constant concentration of 125I-labelled glucagon (40 pM) and different concentrations of unlabelled glucagon (3.57 pM to 1 μM) at room temperature for 3 h. Cells were washed three times with ice-cold PBS and lysed by 50 μl lysis buffer (PBS supplemented with 20 mM Tris-HCl, 1% Triton X-100, pH 7.4). The plates were subsequently counted for radioactivity (counts per minute, CPM) in a scintillation counter (MicroBeta2 Plate Counter, PerkinElmer) using a scintillation cocktail (OptiPhase SuperMix, PerkinElmer). HEK293T cells (obtained from and certified by the Cell Bank at the Chinese Academy of Sciences) were cultured in Dulbecco’s Modified Eagle Medium supplemented with 10% (v/v) fetal bovine serum, 50 IU ml−1 penicillin and 50 μg ml−1 streptomycin. Cells were routinely tested for mycoplasma contamination. Cells were maintained at 37 °C in 5% CO incubator and seeded onto six-well cell culture plates before transfection. After overnight culture, the cells were transiently transfected with wild-type or mutant GCGR DNA using Lipofectamine 2000 transfection reagent (Invitrogen). The transfected cells were seeded onto 384-well plates (8,000 cells per well) 24 h after transfection. cAMP accumulation was measured using the LANCE cAMP kit (PerkinElmer) according to the manufacturer’s instructions. In brief, transfected cells were incubated for 30 min in assay buffer (DMEM, 1 mM 3-isobutyl-1-methylxanthine) with different concentrations of glucagon (0.001 pM to 10 nM) at 37 °C. The reactions were stopped by adding lysis buffer containing LANCE reagents. Plates were then incubated for 60 min at room temperature and time-resolved FRET signals were measured at 620 nm and 650 nm by an EnVision multilabel plate reader (PerkinElmer). NNC0640 binding was analysed using plasma membranes prepared from HEK293T cells transiently expressing wild-type or mutant GCGRs. Approximately 1.2 × 108 transfected HEK293T cells were collected, suspended in 10 ml ice-cold membrane buffer (20 mM HEPES-NaOH and 10 mM EDTA, pH 7.4) and centrifuged for 5 min at 200g. The resulting pellet was resuspended in cold membrane buffer then homogenized and centrifuged for 15 min at 40,000g. The pellet was resuspended, homogenized and centrifuged again, and the precipitate containing the plasma membranes was suspended in the membrane buffer containing protease inhibitor (Sigma-Aldrich) and stored at −80 °C. Protein concentration was determined using a protein BCA assay kit (Pierce Biotechnology). For binding, cell membrane homogenates (10 μg protein per well) were incubated in membrane binding buffer with constant concentration of [3H]-NNC0640 (50 nM, labelled by PerkinElmer) and serial dilutions of unlabelled NNC0640 (0.26 nM to 100 μM) at room temperature for 3 h. Nonspecific binding was determined in the presence of 100 μM NNC0640. Following incubation, the samples were filtered rapidly in vacuum through glass fibre filter plates (PerkinElmer). After soaking and rinsing four times with ice-cold PBS, the filters were dried and counted for radioactivity in a scintillation counter (PerkinElmer). HDX experiments on the antibody-bound and antibody-free GCGRs were carried out at 4 °C using a system as previously described7. The GCGR construct used in the HDX studies lacks the T4L fusion protein. The ligand NNC0640 was added during protein purification to improve protein stability. In brief, 15 μM of the receptor protein was incubated in a D O containing buffer (25 mM HEPES, pH 7.5, 150 mM NaCl, 0.05% (w/v) DDM, 0.01% (w/v) CHS, 10% glycerol) for a range of exchange times from 10 s to 1 h before quenching the deuterium exchange reaction with an acidic quench solution (pH 2.4). All mixing and digestions were carried out on a LEAP Technologies Twin HTS PAL liquid handling robot housed inside a temperature-controlled cabinet. Digestion was performed in-line with chromatography using an immobilized pepsin column. Mass spectra were acquired on a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (ThermoFisher Scientific) and peptide identification from the MSMS data was done using Mascot. HDX experiments for each pairwise comparison (antibody-free vs. mAb1-bound GCGR or antibody-free vs. mAb23-bound GCGR) were run separately under the same conditions and per cent deuterium exchange values for peptide isotopic envelopes at each time point were calculated and processed using the Workbench software36. We performed three 1-μs all-atom molecular dynamics simulations on the full-length GCGR extracted from the GCGR–NNC0640–mAb1 complex structure to investigate the conformational dynamics of the apo receptor. The apo GCGR structure without the ligand NNC0640 and mAb1 was used as the starting model for the molecular dynamics simulation, which was embedded in a 90 Å × 90 Å palmitoyl oleoyl phosphatidyl choline (POPC) bilayer and the lipids located within 1 Å of the receptor were removed. The system was solvated in a box (90 × 90 × 160 Å) with TIP3P waters and 0.15 M NaCl, including 124,478 atoms. Three parallel molecular dynamics simulations were performed using the GROMACS 5.0.4 package37 with isothermal–isobaric (NPT) ensemble and periodic boundary condition. The CHARMM36-CAMP force field38 was used for the protein, the POPC phospholipids, ions and water molecules. Energy minimizations were first performed to relieve unfavourable contacts in the system, followed by equilibration steps of 50 ns in total to equilibrate the lipid bilayer and the solvent with restraints on the main chain of GCGR. Subsequently, three 1-μs production runs were performed. The temperature of the systems was maintained at 310 K using the v-rescale method39 with a coupling time of 0.1 ps. The pressure was kept at 1 bar using the Parrinello–Rahman40 with τ  = 1.0 ps and a compressibility of 4.5 × 10−5 bar−1. SETTLE41 constraints and LINCS42 constraints were applied to the hydrogen-involved covalent bonds in water molecules and in other molecules, respectively, and the time step was set to 2 fs. Electrostatic interactions were calculated with the particle-mesh Ewald (PME) algorithm43 with a real-space cut-off of 1.2 nm. Atomic coordinates and structure factor files for the GCGR–NNC0640–mAb1 complex structures solved using the XFEL data and synchrotron data have been deposited in the Protein Data Bank with accession codes 5XEZ and 5XF1, respectively.


News Article | June 7, 2017
Site: www.prnewswire.com

LONDON, June 7, 2017 /PRNewswire/ -- INTRODUCTION Cancer is an extremely complex disease and medical science is still struggling to figure out the reasons and factors that influence the disease origin, propagation, spread (metastasis) and relapse. In 2017, a total of 1.7 million new cancer cases are estimated to be diagnosed in the US alone; during the same time period, close to 0.6 million patients are estimated to die due to cancer. The high cancer mortality rate is primarily due to delay in detection of the disease. Download the full report: https://www.reportbuyer.com/product/4936648/ Therefore, in addition to satisfying the unmet market need for advanced and efficient treatment interventions, early cancer diagnosis and screening form an important component of disease prevention and cure. Early diagnosis increases the survival rate that is unlikely to happen if the disease is identified at an advanced stage. Invasive cancer diagnostic methods such as tissue biopsies have been the gold standard to determine the clinico-pathological characteristics of cancer tissues for many years. The procedure is not only cost-intensive but is also a traumatic experience for the patients. Additionally, endoscopies such as colonoscopy, gastroscopy and laparoscopy are also employed for cancer diagnosis. However, biopsies and endoscopies only offer insights of the disease state at a single point of time. They are unable to measure the disease progression or monitor the effects of the administered therapy over the treatment period. Therefore, the current cancer diagnostics market faces a pressing need for more accurate non-invasive methods of diagnosis to ensure better patient care. There are several advanced approaches that are not only non-invasive / minimally invasive but also outweigh the limitations posed by invasive diagnostic procedures. Liquid biopsy has emerged as a promising non-invasive cancer diagnostic tool that analyzes biofluids (blood, urine or plasma) to detect rare cells and biomarkers such as circulating tumor cells (CTCs), circulating tumor DNA / RNA (ctNAs) or exosomes. Moreover, liquid biopsies are capable of not only analyzing the tumor state at the time of sample extraction but can also monitor and track changes in tumor genetics over the course of treatment. In addition to liquid biopsy, the market is gradually witnessing the emergence of several other non-invasive diagnostic technologies that exploit skin lesions, bronchial fluid and exhaled breath as samples to trace signatures of cancer. These tests use gene expression profiles, biomarker analysis, volatile organic compound detection and other advanced techniques of molecular genetics to identify a particular cancer indication. These non-invasive diagnostic techniques, backed by patient success stories, awareness and the availability of successful clinical validation data for several cancer indications, hold a significant promise and are anticipated to replace the existing invasive diagnostic tools in the coming few years. In fact, social media platforms, such as Twitter, have witnessed an increasing volume of tweets over the years. Between 2010 and 2016, we were able to identify over 7,000 tweets; this clearly indicates an upsurge in the popularity of these non-invasive tests in the given time period. With liquid biopsy on the forefront, the overall non-invasive cancer diagnostics market is likely to receive a significant boost in the near future. SCOPE OF THE REPORT The 'Non-Invasive Cancer Diagnostics Market (2nd Edition), 2017-2030' report provides an extensive study on liquid biopsy kits / assays that are either commercialized or are under development for diagnosis and / or monitoring of different types of cancer. The market is characterized by the presence of several companies that have proprietary technologies / platforms for either isolation / enrichment / enumeration of CTCs or for molecular characterization / sequencing of the genetic material extracted from the CTCs / exosomes. Based on these platforms, a number of liquid biopsy kits and systems are being developed for non-invasive diagnosis, prognosis, and patient and recurrence monitoring of different cancer indications. Such kits are likely to transform the cancer diagnostics market with many commercial success stories in the near future. The market is primarily led by start-ups / small companies, such as (in alphabetical order) CellMax Life, Celsee Diagnostics, Datar Genetics, DiaDx, EONE-DIAGNOSTICS Genome Center, Exosome Sciences, iCellate Medical, Inivata, IVDiagnostics, LCM Genect and MDNA Life Sciences. It also has presence of mid to large-sized pharma players; notable examples include (in alphabetical order) Biocartis, Counsyl, Foundation Medicine, Genomic Health and NeoGenomic Laboratories. In addition to the aforementioned players, a number of pharma giants are also developing assets in this field. Prominent players under this category include (in alphabetical order) Affymetrix, Menarini Silicon Biosystems, Myriad Genetics, QIAGEN, Roche, Siemens Healthineers and ThermoFisher Scientific. As companies continue to initiate and expand their research programs and platforms in this area, one of the key objectives of this report was to understand the future potential of the market. Amongst other elements, the report provides information on: - The overall landscape of liquid biopsies and other novel non-invasive diagnostic tests with respect to the stage of development, type of markers (CTCs / ctNA / exosomes), test sample source (blood / urine / others), indications and type of application (early diagnosis / recurrence monitoring / patient monitoring). Additionally, the market overview highlights the geographical distribution and coverage of the tests across the globe, depicting the activity of this domain in different regions of the world. - Comprehensive profiles of the popular tests and systems highlighting details on development status, specifications and advantages, clinical information, and related collaborations. Additionally, we have provided detailed profiles of the key players involved in the domain. - The impact of venture capital funding in this area. It is important to mention that since the industry has witnessed the emergence of several start-ups, funding is a key enabler that is likely to drive both innovation and product development in the coming years. - An elaborate valuation analysis of start-ups and small players that are involved in the liquid biopsy domain. We built a multi-variable dependent valuation model to estimate the current valuation of a number of companies focused in this domain. - The emerging trends and the popularity of liquid biopsy on social media platforms, such as Twitter, over the last few years. The volume of tweets has witnessed an increasing trend in the last six years, influenced by the approval and launch of several liquid biopsy tests in the market. - The competitive landscape of the players involved in the space. This is represented as an illustrative bubble analysis, which is based on parameters such as the liquid biopsy portfolio of a company, its number of employees and geographical coverage of the tests. - Contribution of the other non-invasive cancer diagnostics market, taking into account the number of tests and the sales registered by the marketed tests. In addition, we have provided a comprehensive market estimation to determine the global evolution of the liquid biopsy market. This has been done by evaluating the likely success of key applications of early diagnosis, recurrence monitoring and patient monitoring. We have included insights on the likely regional evolution of the market covering US, EU5 and rest of the world. In addition, we have estimated the likely contribution of different target patient populations to the global market; this covered key indications including (in alphabetical order) bladder cancer, breast cancer, colorectal cancer, gastric cancer, lung cancer, melanoma, ovarian cancer, pancreatic cancer and prostate cancer. Further, we segmented the market by the type of markers (CTCs, ctNAs, exosomes) and the sample source (blood, urine, saliva) used in different liquid biopsies. In order to account for uncertainties associated with some of the key parameters and to add robustness to our model, we have provided three market forecast scenarios for the time period 2017-2030. The conservative, base and optimistic scenarios represent three different tracks of the industry's evolution. The research, analysis and insights presented in this report are backed by the deep understanding of key insights gathered from both secondary and primary research. Our opinions and insights presented in this study were influenced by discussions that we conducted with several experts in this area. These included contributions from (in alphabetical order of companies) Burkhard Jansen (Chief Medical Officer, DermTech), Christer Ericsson (Chief Scientific Officer, iCellate Medical), Frank Szczepanski (President and CEO, IVDiagnostics), Riccardo Razzini (Sales and Marketing Manager, LCM Genect), Philippe Nore (CEO and Co-founder, MiNDERA Corporation), Nathalie Bernard (Marketing Director, OncoDNA), Abizar Lakdawalla (Founder, ProXeom), Mark Li (CEO, Resolution Bioscience) and Jake Micallef (Chief Scientific Officer, VolitionRx). All actual figures have been sourced and analyzed from publicly available information and discussions with industry experts. The figures mentioned in this report are in USD, unless otherwise specified. RESEARCH METHODOLOGY Most of the data presented in this report has been gathered via secondary and primary research. For all our projects, we conduct interviews with experts in the area (academia, industry, medical practice and other associations) to solicit their opinions on emerging trends in the market. This is primarily useful for us to draw out our own opinion on how the market will evolve across different regions and technology segments. Where possible, the available data has been checked for accuracy from multiple sources of information. The secondary sources of information include - Annual reports - Investor presentations - SEC filings - Industry databases - News releases from company websites - Government policy documents - Industry analysts' views While the focus has been on forecasting the market over the coming 10-15 years, the report also provides our independent view on various technological and non-commercial trends emerging in the industry. This opinion is solely based on our knowledge, research and understanding of the relevant market gathered from various secondary and primary sources of information. CHAPTER OUTLINES Chapter 2 presents an executive summary of the report. It offers a high level view on where the market for liquid biopsy and other novel non-invasive diagnostics is headed in the mid to long term. Chapter 3 provides a general introduction to cancer statistics and the global burden of the disease. In this section, we have also highlighted the importance of early detection of the disease through diagnosis and asymptomatic screening. The chapter outlines the conventional invasive diagnostic tests, which are widely used for cancer diagnosis and prognosis. Chapter 4 discusses, in detail, the need for non-invasive cancer diagnostics and their importance. We have highlighted the underlying concept of liquid biopsy and other non-invasive tests. In addition, we have mentioned the principle behind common imaging tests that are deployed in cancer diagnosis. Chapter 5 provides a holistic view of the liquid biopsy market. It lists the liquid biopsy tests, technologies and systems. In addition, the chapter includes a detailed analysis of the liquid biopsy tests based on the development stage (available for patients / RUO / under development), sample type (blood / plasma / urine / multiple), type of markers detected (CTCs / ctNA (DNA) / exosomes / multiple / others), indication and type of application (early detection / patient monitoring / recurrence monitoring). Chapter 6 offers an illustrative bubble analysis representing the competitive landscape of the players involved in the space, based on their liquid biopsy portfolio, number of employees and geographical coverage. In addition, it presents a Venn diagram depicting the distribution of liquid biopsy products across different application areas. The chapter elucidates the emerging trends and the popularity of liquid biopsy on social media platforms, such as Twitter, over the last few years. Chapter 7 offers a comprehensive discussion on liquid biopsy. We have talked about the emerging need for liquid biopsy highlighting its advantages, and the related challenges. In addition, we have provided profiles of several liquid biopsy products and technologies. These products belong to those companies that were identified as active players in the bubble analysis presented in Chapter 6. The chapter also mentions the liquid biopsy companion diagnostics, which are being co-developed as part of strategic collaborations. Chapter 8 presents details on the investments and grants received by companies working in the field of liquid biopsy. The analysis highlights the growing interest of the venture capital community and other strategic investors in this market. Chapter 9 features a comprehensive valuation analysis of the companies that are developing liquid biopsy tests / technologies / systems / platforms / instruments. The chapter provides insights based on a multi-variable dependent valuation model. The model is based on the future potential of the companies' uniqueness, their current popularity, funding received, year of establishment and the employed workforce. Chapter 10 provides detailed company profiles of the leading players that are involved in the development of liquid biopsy tests. Amongst other details, each profile includes information such as the company overview, financial performance, product portfolio (technical specifications and clinical information) and recent collaborations. Chapter 11 provides an overview of the other non-invasive diagnostics for oncology. It highlights different diagnostics tests, including non-blood based biomarker detection tests (saliva-based biomarker detection, stool-based metabolic biomarker detection, skin-based biomarker detection, semen-based biomarker detection and urine based biomarker detection), DNA methylation detection test, fecal occult blood test and fecal immunochemical test, MicroRNA (miRNA) based test, pigmented lesion assay, stool DNA (sDNA) testing, and Volatile Organic Compound (VOC) detection test. The diagnostic procedures discussed in this chapter are backed up by several examples. In addition, the chapter illustrates a detailed analysis on the survey conducted for gaining a deeper understanding on the nature of products and services offered by the companies. Chapter 12 highlights the market forecast and sizing of the overall non-invasive cancer diagnostics market. This chapter discusses, in detail, the parameters that are likely to influence the evolution of liquid biopsy market. It features detailed insights on the likely market evolution for across different application areas such as early diagnosis, patient monitoring and recurrence monitoring. Additionally, the chapter presents a detailed market segmentation by the key indications, types of markers (CTCs, ctNAs, exosomes) and types of samples (blood, urine, saliva). The chapter also highlights the likely distribution of the market across the US, EU5 and RoW (including Asia) regions. We have also presented an informed view on the contribution of the other non-invasive tests in the overall non-invasive cancer diagnostics market. Chapter 13 summarizes the overall report. In this chapter, we have listed the key takeaways and have provided our independent opinion based on the research and analysis described in previous chapters. Chapter 14 is a collection of interview transcripts of the discussions held with key stakeholders in this market. In this chapter, we have presented the insights provided to us by Burkhard Jansen (Chief Medical Officer, DermTech), Christer Ericsson (Chief Scientific Officer, iCellate Medical), Frank Szczepanski (President and CEO, IVDiagnostics), Riccardo Razzini (Sales and Marketing Manager, LCM Genect), Philippe Nore (CEO and Co-founder, MiNDERA Corporation), Nathalie Bernard (Marketing Director, OncoDNA), Abizar Lakdawalla (Founder, ProXeom), Mark Li (CEO, Resolution Bioscience) and Jake Micallef (Chief Scientific Officer, VolitionRx) Chapter 15 is an appendix, which provides tabulated data and numbers for all the figures provided in the report. Chapter 16 is an appendix, which provides the list of companies and organizations mentioned in the report. EXAMPLE HIGHLIGHTS 1. We identified over 110 liquid biopsy tests; of these, 60% of the tests are currently available for patients while the remaining are either available for research use only (RUO) or are under development. Further, of the total tests, 36% detect the presence of ctDNA in the sample, nearly 41% detect CTCs and 4% validate the presence of exosomes. Close to 19% of the liquid biopsy tests identify the presence of multiple / other markers. 2. It is worth highlighting that there are seven liquid biopsy tests that are being explored for use as companion diagnostics. Many partnerships have been inked to co-develop liquid biopsy companion diagnostic products; notable examples include collaborations between BMS and GRAIL (2017), Merck and Sysmex Inostics (2016), Biocept and Baylor College of Medicine (2015), QIAGEN and Tokai Pharmaceuticals (2015), ANGLE and MD Anderson Cancer Center (2015), and AstraZeneca and QIAGEN (2015). 3. Considering the future potential of these diagnostic kits and systems, several investors have made substantial capital commitment to drive future development. We identified approximately 196 funding instances amounting to capital investments (equity / debt / grants) of over USD 3.8 billion between 2011 and 2017; these investments were made either directly or indirectly to support the development of liquid biopsy products. In 2016 alone, driven by increased venture capital activity, amount worth USD 770 million was invested in different companies. 4. It is worth highlighting that with the rising activity in the field of cancer diagnostics, pharmaceutical players have been keen to explore the acquisition opportunities in this domain. In the Roots Analysis proprietary valuation analysis of over 40 start-ups / small companies, we identified nine companies that are likely to achieve valuation of over USD 500 million. 5. The market, primarily driven by the sales generated by liquid biopsy tests, is anticipated to register a growth rate of 19% between 2017 and 2030. The current market is dominated by the tests that offer patient monitoring (63% share). Specifically, catering to the high unmet need for timely treatment, the share of early diagnosis is likely to increase from 20% in 2017 to over 40% by 2030. 6. Among specific indications, we believe that prostate cancer is likely to capture the largest share of the market (~15%). Subsequently, owing to the large target patient population, breast cancer is likely to capture a share of 14% by 2030. In addition, lung cancer is also likely to capture a significant market share of 12% by 2030. It is worth highlighting that, despite the low prevalence of lung cancer, the demand of liquid biopsies is significantly high as tissue biopsy is extremely difficult in case of lungs. 7. With regard to the geographical activity, in 2017, the US is likely to capture the maximum share (43%) followed by EU5 (34%) and Rest of the World (RoW, 23%). The overall trend is unlikely to change significantly in 2030; however, regions in RoW are likely to occupy a larger share in 2030 (29%). It is important to highlight that, within RoW, Asian countries such as China, India, Japan, have several developers and distributors of liquid biopsy tests, demonstrating an elevating activity within these regions. Download the full report: https://www.reportbuyer.com/product/4936648/ About Reportbuyer Reportbuyer is a leading industry intelligence solution that provides all market research reports from top publishers http://www.reportbuyer.com   For more information: Sarah Smith Research Advisor at Reportbuyer.com Email: query@reportbuyer.com   Tel: +44 208 816 85 48 Website: www.reportbuyer.com To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/non-invasive-cancer-diagnostics-market-2nd-edition-2017---2030-300470232.html


TAMPA, Fla.--(BUSINESS WIRE)--Pilgrim Quality Solutions, a leading global provider of enterprise quality management software and services for the Life Sciences, today announced it is sponsoring a series of live webinars, presented by The Knowledge Group, on current risk- and compliance-centric trends impacting quality processes within the Pharmaceutical industry. The webinars will be presented by a panel of key thought leaders and practitioners who will offer insights into best practice solutions for addressing those industry trends. The series begins June 22, 2017, at 3:00 pm (EDT), with a live 90-minute telecast, Strategic Approaches in Integrating Risk and Quality Management in Life Sciences. The panelists include Florian Czaszewicz, Pharmaceutical industry veteran and Industry Solutions Consultant for Pilgrim Quality Solutions; Lee Young, Director of Supplier Quality, Life Sciences Solutions for ThermoFisher Scientific; and, Dr. Carmine Jabri, President & CEO of E.M.M.A. International Consulting Group, Inc. They will outline strategic approaches for managing risk and compliance and ways to streamline an adaptive quality management process. Key topics include finding common ground between risk and quality management, and how to integrate the two practices within the Life Sciences, specifically the Pharmaceutical industry. According to the Knowledge Group, in today’s highly-regulated environment, Life Sciences and Pharmaceutical quality activities are often associated with maintaining regulatory compliance. However, both regulators and quality professionals are beginning to understand that compliance alone will not ensure high product quality and patient safety. In the Life Sciences industry, integrating risk and quality management is becoming the new standard for measuring and monitoring quality and compliance activities. The benefits in successfully aligning these two fields can optimize resources to effectively achieve quality goals and find an optimal balance between operational costs and common risks. The Life Sciences’ Supplier and Contractor Qualification and Control Framework Explored will be presented in July. Practical Tips in Managing Supplier Risks in the Life Sciences Industry is slated for September. All live webcasts will feature presentations from Czaszewicz and other industry experts, with an emphasis on topical trends and issues impacting the Pharmaceutical industry segment. The Knowledge Congress was established with the mission to produce unbiased, objective, and educational live webinars that examine industry trends and regulatory changes from a variety of different perspectives. The goal is to deliver a unique multilevel analysis of an important issue affecting business in a highly focused format. To contact or register to an event, please visit: http://theknowledgegroup.org/ Pilgrim Quality Solutions is the leader in quality compliance management software and services for Life Sciences. For more than 20 years, our solutions have automated thousands of processes across global company sites to manage the quality and compliance of life’s most important products. Our cloud-based and on-premise solutions include in-the-box best practice workflows, document and process management, dashboards, electronic signatures, audit trails, and automated validation – helping companies more easily achieve quality system compliance and pass regulatory audits. Pilgrim Quality Solutions is majority owned by Boston-based private equity firm, Riverside Partners LLC. With Pilgrim Quality Solutions as your partner, you are prepared to succeed. For more information, visit www.pilgrimquality.com.


Compelling Advantages of the Company's Patented PCT Platform Highlighted in Two Different Mass Spectrometry User Meetings on Day One SOUTH EASTON, MA--(Marketwired - Jun 6, 2017) - Pressure BioSciences, Inc. ( : PBIOD) ("PBI" or the "Company"), a leader in the development and sale of broadly enabling, pressure cycling technology ("PCT")-based sample preparation solutions to the worldwide life sciences industry, today announced that the Company's patented PCT platform will be featured in multiple presentations at the annual conference of the American Society for Mass Spectrometry ("ASMS"), being held from June 4-8, 2017 in Indianapolis, IN. Dr. Nate Lawrence, Vice President of Marketing and Sales for PBI, said: "It is encouraging that in less than one year from the initial shipment of the Barocycler 2320EXT, the advantages of this next-generation PCT-based instrument will be highlighted in multiple presentations by well-respected scientific groups from around the world. These groups include researchers from such noteworthy institutions/companies as the U.S. Food and Drug Administration, Novo Nordisk A/S (Denmark), SCIEX (U.S. and Australia), ETH Zurich (Switzerland), the University of Cologne (Germany), and the Inova Schar Cancer Institute. Dr. Lawrence continued: "Dr. Thomas Conrads, a nationally-acclaimed protein chemist, is the Associate Director of Scientific Technologies at the Inova Schar Cancer Institute. Dr. Conrads and his group were invited to participate in the SCIEX and ThermoFisher Scientific User Meetings on June 4th. During their presentations, they highlighted the use of the Barocycler 2320EXT for the digestion of tumor and other samples to be analyzed by their group as part of discovery proteomics for the APOLLO Consortium of the Cancer Moonshot program." "Proteins comprise most of the biomarkers that are measured to detect cancers, they constitute the antigens that drive immune response and the inter- and intra-cellular communications, and they are the drug targets for nearly every targeted therapy that is being evaluated in cancer trials today," commented Dr. Conrads. "We believe that a combined systems biology view of the tumor microenvironment that orients cancer studies back to the functional proteome, phosphoproteome, and biochemistry of the cell will be essential to delivering on the promise of the Cancer Moonshot program." Dr. Conrads continued: "Standardized, reproducible, high quality preparation of samples to be analyzed is critical to the success of transformative research studies. We spent many months investigating multiple aspects of PCT-enhanced protein extraction and digestion. We subsequently concluded that PCT was an enabling tool that met our high standards for critical sample preparation. We also concluded that the PCT platform could make profiling of our laser micro-dissected tumor tissue samples possible at the throughput required by the APOLLO Consortium of the Cancer Moonshot program." Mr. Richard T. Schumacher, President and CEO of PBI, commented: "The ASMS Conference is one of the largest annual meetings of mass spectrometry professionals worldwide. At the 2016 ASMS Conference, we unveiled the newest addition to our PCT-based instrument line, the Barocycler 2320EXT. Designed with a number of new and enhanced features and benefits to enable scientists better access to biomolecules (e.g., proteins, lipids, nucleic acids) in samples being studied, we believe the 2320EXT offers the potential to result in new biological insights and discoveries, and rapid growth for PBI." Mr. Schumacher continued: "In February 2017, the Barocycler 2320EXT gained CE Mark approval, enabling it to be marketed throughout all 31 countries in the European Economic Area. In March 2017, the 2320EXT received the 2017 North American Excellence Award for 'Best New Instrument for Sample Preparation' by Corporate America News, a leading business publication. The Barocycler 2320EXT has become the centerpiece of our co-marketing agreement with global life sciences analytical technologies leader SCIEX. We believe it will continue to find a significant role in transformative research efforts worldwide, such as the Cancer Moonshot program." About Pressure BioSciences, Inc. Pressure BioSciences, Inc. ("PBI") ( : PBIOD) develops, markets, and sells proprietary laboratory instrumentation and associated consumables to the estimated $6 billion life sciences sample preparation market. Our products are based on the unique properties of both constant (i.e., static) and alternating (i.e., pressure cycling technology, or PCT) hydrostatic pressure. PCT is a patented enabling technology platform that uses alternating cycles of hydrostatic pressure between ambient and ultra-high levels to safely and reproducibly control bio-molecular interactions. To date, we have installed over 270 PCT systems in approximately 160 sites worldwide. There are over 100 publications citing the advantages of the PCT platform over competitive methods, many from key opinion leaders. Our primary application development and sales efforts are in the biomarker discovery and forensics areas. Customers also use our products in other areas, such as drug discovery & design, bio-therapeutics characterization, soil & plant biology, vaccine development, histology, and counter-bioterror applications. Forward Looking Statements Statements contained in this press release regarding PBI's intentions, hopes, beliefs, expectations, or predictions of the future are "forward-looking'' statements within the meaning of the Private Securities Litigation Reform Act of 1995. These statements are based upon the Company's current expectations, forecasts, and assumptions that are subject to risks, uncertainties, and other factors that could cause actual outcomes and results to differ materially from those indicated by these forward-looking statements. These risks, uncertainties, and other factors include, but are not limited to, the risks and uncertainties discussed under the heading "Risk Factors" in the Company's Annual Report on Form 10-K for the year ended December 31, 2016, and other reports filed by the Company from time to time with the SEC. The Company undertakes no obligation to update any of the information included in this release, except as otherwise required by law. For more information about PBI and this press release, please click on the following website link: http://www.pressurebiosciences.com Please visit us on Facebook, LinkedIn, and Twitter


Patients who were included in the study all had Goodpasture disease and fulfilled the following key diagnostic criteria: (1) serum anti-α3(IV)NC1 IgG by enzyme-linked immunosorbent assay (ELISA), (2) linear IgG staining of the GBM and (3) necrotizing and crescentic glomerulonephritis. HLA-DR15 typing of patients was done by monoclonal antibody staining (BIH0596, One Lambda) and flow cytometry. Blood from HLA-typed healthy humans was collected via the Australian Bone Marrow Donor Registry. HLA-DR15, HLA-DR1 and HLA-DR15/DR1 donors were molecularly typed and were excluded if they expressed DQB1*03:02, which is potentially weakly associated with susceptibility to anti-GBM disease2. Studies were approved by the Australian Bone Marrow Donor Registry and Monash Health Research Ethics Committees, and informed consent was obtained from each individual. Mouse MHCII deficient, DR15 transgenic mice and mouse MHCII deficient, DR1 transgenic mice were derived from existing HLA transgenic colonies and intercrossed so that they were on the same background as previously described4. The background was as follows: 50% C57BL/10, 43.8% C57BL/6, 6.2% DBA/2; or with an Fcgr2b−/− background: 72% C57BL/6, 25% C57BL/10 and 3% DBA/2. To generate mice transgenic for both HLA-DR15 and HLA-DR1, mice transgenic for either HLA-DR15 or HLA-DR1 were intercrossed. FcγRIIb intact HLA transgenic mice and cells were used for all experiments, except those in experimental Goodpasture disease, where Fcgr2b−/− HLA transgenic strains were used. While DR15+ mice readily break tolerance to α3(IV)NC1 when immunized with human α3 or mouse α3 , renal disease is mild4. As genetic changes in fragment crystallizable (Fc) receptors have been implicated in the development of nephritis in rodents and in humans18, Fcgr2b−/− HLA transgenic strains were used when end organ injury was an important endpoint. For in vitro experiments, cells from either male or female mice were used. For in vivo experiments both male and female mice were used, for immunization aged 8–12 weeks and for the induction of experimental Goodpasture disease aged 8–10 weeks. Experiments were approved by the Monash University Animal Ethics Committee (MMCB2011/05 and MMCB2013/21). HLA-DR15-α3 and HLA-DR1-α3 were produced in High Five insect cells (Trichoplusia ni BTI-Tn-5B1-4 cells, Invitrogen) using the baculovirus expression system essentially as described previously for HLA-DQ2/DQ8 proteins19, 20. Briefly, synthetic DNA (Integrated DNA Technologies, Iowa, USA) encoding the α- and β-chain extracellular domains of HLA-DR15 (HLA-DR1A*0101, HLA-DRB1*15:01), HLA-DR1 (HLA-DR1A*0101, HLA-DRB1*01:01) and the α3 peptide were cloned into the pZIP3 baculovirus vector19, 20. To promote correct pairing, the carboxy (C) termini of the HLA-DR15 and HLA-DR1 α- and β-chain encoded enterokinase cleavable Fos and Jun leucine zippers, respectively. The β-chains also encoded a C-terminal BirA ligase recognition sequence for biotinylation and a poly-histidine tag for purification. HLA-DR15-α3 and HLA-DR1-α3 were purified from baculovirus-infected High Five insect cell supernatants through successive steps of immobilized metal ion affinity (Ni Sepharose 6 Fast-Flow, GE Healthcare), size exclusion (S200 Superdex 16/600, GE Healthcare) and anion exchange (HiTrap Q HP, GE Healthcare) chromatography. For crystallization, the leucine zipper and associated tags were removed by enterokinase digestion (Genscript, New Jersey, USA) further purified by anion exchange chromatography, buffer exchanged into 10 mM Tris, pH 8.0, 150 mM NaCl and concentrated to 7 mg ml−1. Purified HLA-DR15-α3 and HLA-DR1-α3 proteins were buffer exchanged into 10 mM Tris pH 8.0, biotinylated using BirA ligase and tetramers assembled by addition of Streptavidin-PE (BD Biosciences) as previously described19. In mice, 107 splenocytes or cells from kidneys were digested with 5 mg ml−1 collagenase D (Roche Diagnostics, Indianapolis, Indiana, USA) and 100 mg ml−1 DNase I (Roche Diagnostics) in HBBS (Sigma-Aldrich) for 30 min at 37 °C, then filtered, erythrocytes lysed and the CD45+ leukocyte population isolated by MACS using mouse CD45 microbeads (Miltenyi Biotec); they were then surface stained with Pacific Blue-labelled anti-mouse CD4 (BD), antigen-presenting cell (APC)-Cy7-labelled anti-mouse CD8 (BioLegend) and 10 nM PE-labelled tetramer. Cells were then incubated with a Live/Dead fixable Near IR Dead Cell Stain (Thermo Scientific), permeabilized using a Foxp3 Fix/Perm Buffer Set (BioLegend) and stained with Alexa Fluor 647-labelled anti-mouse Foxp3 antibody (FJK16 s). To determine Vα2 and Vβ6 usage, cells were stained with PerCP/Cy5.5 anti-mouse Vα2 (B20.1, Biolegend) and antigen-presenting cell labelled anti-mouse Vβ6 (RR4-7, Biolegend). For each mouse a minimum of 100 cells were analysed. The tetramer+ gate was set on the basis of the CD8+ population. In humans, 3 × 107 white blood cells were surface stained with BV510-labelled anti-human CD3 (BioLegend), Pacific Blue-labelled anti-human CD4 (BioLegend), PE-Cy7-labelled anti-human CD127 (BioLegend), FITC-labelled anti-human CD25 (BioLegend) and 10 nM PE-labelled tetramer. Then, cells were incubated with a Live/Dead fixable Near IR Dead Cell Stain (Life Technologies), permeabilized using a Foxp3 Fix/Perm Buffer Set (BioLegend) and stained with Alexa Fluor 647-labelled anti-human Foxp3 antibody (150D). The tetramer+ gate was set on the basis of the CD3+CD4− population. As validation controls, we found that HLA-DR1-α3 tetramer+ cells did not bind to HLA-DR1-CLIP tetramers (data not shown). The human α3 peptide (GWISLWKGFSF), the mouse α3 peptide (DWVSLWKGFSF) and control OVA peptide (ISQAVHAAHAEINEAGR) were synthesized at >95% purity, confirmed by high-performance liquid chromatography (Mimotopes). Recombinant murine α3(IV)NC1 was generated using a baculovirus system21 and recombinant human α3(IV)NC1 expressed in HEK 293 cells22. The murine α3(IV)NC1 peptide library, which consists of 28 20-amino-acid long peptides overlapping by 12 amino acids, was synthesized as a PepSet (Mimotopes). To measure peptide specific recall responses, IFN-γ and IL-17A ELISPOTs and [3H]thymidine proliferation assays were used (Mabtech for human ELISPOTs and BD Biosciences for mouse ELISPOTs). To measure pro-inflammatory responses of HLA-DR15-α3 tetramer+ CD4+ T cells in patients with Goodpasture disease, HLA-DR15-α3 tetramer+ CD4+ T cells were enumerated then isolated from peripheral blood mononuclear cells of patients with Goodpasture disease (frozen at the time of presentation) by magnetic bead separation (Miltenyi Biotec) then co-cultured at a frequency of 400 HLA-DR15-α3 tetramer+ CD4+ T cells per well with 2 × 106 HLA-DR15-α3 tetramer-depleted mitomycin C-treated white blood cells and stimulated with either no antigens, α3 (10 μg ml−1) or whole recombinant human α3(IV)NC1 (10 μg ml−1) in supplemented RPMI media (10% male AB serum, 2 mM l-glutamine, 50 μM 2-ME, 100 U ml−1 penicillin and 0.1 mg ml−1 streptomycin) (Sigma-Aldrich). Cells were cultured for 18 h at 37 °C, 5% CO and the data expressed as numbers of IFN-γ or IL-17A spots per well. To measure pro-inflammatory responses of HLA-DR15-α3 tetramer+ CD4+ T cells in DR15+ transgenic mice, HLA-DR15-α3 tetramer+ CD4+ T cells were enumerated then isolated from pooled spleen and lymph node cells of DR15+ transgenic mice, immunized with mouse α3 10 days previously by magnetic bead separation. They were then co-cultured at a frequency of 400 HLA-DR15-α3 tetramer+ CD4+ T cells per well with 106 HLA-DR15-α3 tetramer-depleted mitomycin C-treated white blood cells and stimulated with either no antigens, mouse α3 (10 μg ml−1), human α3 (10 μg ml−1), whole recombinant mα3(IV)NC1 (10 μg ml−1) or whole recombinant hα3(IV)NC1 (10 μg ml−1) in supplemented RPMI media (10% FCS, 2 mM l-glutamine, 50 μM 2-ME, 100 U ml−1 penicillin and 0.1 mg ml−1 streptomycin). Cells were cultured for 18 h at 37 °C, 5% CO and the data expressed as numbers of IFN-γ or IL-17A spots per well. To determine the immunogenic portions of α3(IV)NC1, mice were immunized subcutaneously with peptide pools (containing α3 amino acids 1–92, 81–164, or 153–233; 10 μg per peptide per mouse), the individual peptide or in some experiments mα3 at 10 μg per mouse in Freund’s complete adjuvant (Sigma-Aldrich). Draining lymph node cells were harvested 10 days after immunization and stimulated in vitro (5 × 105 cells per well) with no antigen, peptide (10 μg ml−1) or whole α3(IV)NC1 (10 μg ml−1) in supplemented RPMI media (10% FCS, 2 mM l-glutamine, 50 μM 2-ME, 100 U ml−1 penicillin and 0.1 mg ml streptomycin). For [3H]thymidine proliferation assays, cells were cultured in triplicate for 72 h with [3H]thymidine added to culture for the last 16 h. To measure human α3 - or mouse α3 -specific responses in CD4+ T cells from naive transgenic mice or blood of healthy humans, we used a modification of a previously published protocol23. One million CD4+ T cells were cultured with 106 mitomycin-treated CD4-depleted splenocytes for 8 days in 96-well plates with or without 100 μg ml−1 of human α3 or mouse α3 . T cells were depleted from mouse cultures by sorting out CD4+CD25+ and in humans by sorting out CD4+CD25hiCD127lo cells using antibodies and a cell sorter. Cytokine secretion was detected in the cultured supernatants by cytometric bead array (BD Biosciences) or ELISA (R&D Systems). To determine proliferation, magnetically separated CD4+ T cells were labelled with CellTrace Violet (CTV; Thermo Scientific) before culture. To measure the expansion of T cells, mice were immunized with 100 μg of α3 emulsified in Freund’s complete adjuvant, then boosted 7 days later in Freund’s incomplete adjuvant. Draining lymph node cells were stained with the HLA-DR15-α3 tetramer, CD3, CD4, CXCR5, PD-1, CD8 and Live/Dead Viability dye. To determine the potency of HLA-DR1-α3 tetramer+ T cells, 106 cells per well of CD4+CD25− T effectors isolated by CD4+ magnetic beads and CD25− cell sorting from naive DR15+DR1+ mice were co-cultured with CD4+CD25+ T cells with or without depletion of HLA-DR1-α3 tetramer+ T cells from DR1+ mice at different concentrations: 0, 12.5 × 103, 25 × 103, 50 × 103 and 100 × 103 cells per well in the presence of 106 CD4-depleted mitomycin C-treated spleen and lymph node cells from DR15+DR1+mice in supplemented RPMI media (10% FCS, 2 mM l-glutamine, 50 μM 2-ME, 100 U ml−1 penicillin and 0.1 mg ml−1 streptomycin) containing 100 μg ml−1 of mouse α3 . To determine proliferation, the CD4+CD25− T effector cells were labelled with CTV before culture. Cells were cultured in triplicate for 8 days in 96-well plates. HLA transgenic mice, on an Fcgr2b−/− background, were immunized with 100 μg of α3 or mα3 subcutaneously on days 0, 7 and 14, first in Freund’s complete, and then in Freund’s incomplete, adjuvant. Mice were killed on day 42. Albuminuria was assessed in urine collected during the last 24 h by ELISA (Bethyl Laboratories) and expressed as milligrams per micromole of urine creatinine. Blood urea nitrogen and urine creatinine were measured using an autoanalyser at Monash Health. Glomerular necrosis and crescent formation were assessed on periodic acid-Schiff (PAS)-stained sections; fibrin deposition using anti-murine fibrinogen antibody (R-4025) and DAB (Sigma); CD4+ T cells, macrophages and neutrophils were detected using anti-CD4 (GK1.5), anti-CD68 (FA/11) and anti-Gr-1 (RB6-8C5) antibodies. The investigators were not blinded to allocation during experiments and outcome assessment, except in histological and immunohistochemical assessment of kidney sections. To deplete regulatory T cells, mice were injected intraperitoneally with 1 mg of an anti-CD25 monoclonal antibody (clone PC61) or rat IgG (control) 2 days before induction of disease. In these experiments, mice were randomly assigned to receive control or anti-CD25 antibodies. Individual DR15-α3 -specific CD4+ T cells were sorted into wells of a 96-well plate. Multiplex single-cell reverse transcription and PCR amplification of TCR CDR3α and CDR3β regions were performed using a panel of TRBV- and TRAV-specific oligonucleotides, as described24, 25. Briefly, mRNA was reverse transcribed in 2.5 μl using a Superscript III VILO cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, Massachusetts, USA) (containing 1× Vilo reaction mix, 1× superscript RT, 0.1% Triton X-100), and incubated at 25 °C for 10 min, 42 °C for 120 min and 85 °C for 5 min. The entire volume was then used in a 25 μl first-round PCR reaction with 1.5 U Taq DNA polymerase, 1× PCR buffer, 1.5 mM MgCl , 0.25 mM dNTPs and a mix of 25 mouse TRAV or 40 human TRAV external sense primers and a TRAC external antisense primer, along with 19 mouse TRBV or 28 human TRBV external sense primers and a TRBC external antisense primer (each at 5 pmol μl−1), using standard PCR conditions. For the second-round nested PCR, a 2.5 μl aliquot of the first-round PCR product was used in separate TRBV- and TRAV-specific PCRs, using the same reaction mix described above; however, a set of 25 mouse TRAV or 40 human TRAV internal sense primers and a TRAC internal antisense primer, or a set of 19 mouse TRBV or 28 human TRBV internal sense primers and a TRBV internal antisense primer, were used. Second-round PCR products were visualized on a gel and positive reactions were purified with ExoSAP-IT reagent. Purified products were used as template in sequencing reactions with internal TRAC or TRBC antisense primers, as described. TCR gene segments were assigned using the IMGT (International ImMunoGeneTics) database26. In mouse experiments, three mice were pooled per HLA and the number of sequences obtained were as follows. For TRAV: DR15, n = 81; DR1 n = 84; for TRBV: DR15, n = 100; DR1 n = 87; for TRAJ: DR15, n = 81; DR1 n = 84; and for TCR beta joining (TRBJ): DR15, n = 100; DR1 n = 87. Red-blood-cell-lysed splenocytes from DR1+ and DRB15+DR1+ mice were sorted on the basis of surface expression of CD4 and CD25 and being either DR1-α3 tetramer positive or negative into three groups: (1) CD4+CD25−HLA-DR1-α3 tetramer− T cells; (2) CD4+CD25+HLA-DR1-α3 tetramer− T cells; and (3) CD4+CD25+HLA-DR1-α3 tetramer+ T cells. A minimum of 1,000 cells were sorted. Immediately after sorting, the RNA was isolated and complementary DNA (cDNA) generated using a Cells to Ct Kit (Ambion) followed by a preamplification reaction using Taqman Pre Amp Master Mix (Applied Biosystems), which preamplified the following cDNAs: Il2ra, Foxp3, Ctla4, Tnfrsf18, Il7r, Sell, Pdcd1, Entpd1, Cd44, Tgfb3, Itgae, Ccr6, Lag3, Lgals1, Ikzf2, Tnfrsf25, Nrp1, Il10. The preamplified cDNA was used for RT–PCR reactions in duplicate using Taqman probes for the aforementioned genes. Each gene was expressed relative to 18S, logarithmically transformed and presented as a heat map. The Epstein-Barr-virus-transformed human B lymphoblastoid cell lines IHW09013 (SCHU, DR15-DR51-DQ6) and IHW09004 (JESTHOM, DR1-DQ5) were maintained in RPMI (Invitrogen) supplemented with 10% FCS, 50 IU ml−1 penicillin and 50 μg ml−1 streptomycin. Confirmatory tissue typing of these cells was performed by the Victorian Transplantation and Immunogenetics Service. The B-cell hybridoma LB3.1 (anti-DR) was grown in RPMI-1640 with 5% FCS at 37 °C and secreted antibody purified using protein A sepharose (BioRad). HLA-DR-presented peptides were isolated from naive DR15+Fcgr2b+/+ or DR1+Fcgr2b+/+ mice. Spleens and lymph nodes (pooled from five mice in each group) or frozen pellets of human B lymphoblastoid cell lines (triplicate samples of 109 cells) were cryogenically milled and solubilized as previously described12, 27, cleared by ultracentrifugation and MHC peptide complexes purified using LB3.1 coupled to protein A (GE Healthcare). Bound HLA complexes were eluted from each column by acidification with 10% acetic acid. The eluted mixture of peptides and HLA heavy chains was fractionated by reversed-phase high-performance liquid chromatography as previously described10. Peptide-containing fractions were analysed by nano-liquid chromatography–tandem mass spectrometry (nano-LC–MS/MS) using a ThermoFisher Q-Exactive Plus mass spectrometer (ThermoFisher Scientific, Bremen, Germany) operated as described previously10. LC–MS/MS data were searched against mouse or human proteomes (Uniprot/Swissprot v2016_11) using ProteinPilot software (SCIEX) and resulting peptide identities subjected to strict bioinformatic criteria including the use of a decoy database to calculate the false discovery rate28. A 5% false discovery rate cut-off was applied, and the filtered data set was further analysed manually to exclude redundant peptides and known contaminants as previously described29. The mass spectrometry data have been deposited in the ProteomeXchange Consortium via the PRIDE30 partner repository with the data set identifier PXD005935. Minimal core sequences found within nested sets of peptides with either N- or C-terminal extensions were extracted and aligned using MEME (http://meme.nbcr.net/meme/), where motif width was set to 9–15 and motif distribution to ‘one per sequence’31. Graphical representation of the motif was generated using IceLogo32. Crystal trials were set up at 20 °C using the hanging drop vapour diffusion method. Crystals of HLA-DR15-α3 were grown in 25% PEG 3350, 0.2 M KNO and 0.1 M Bis-Tris-propane (pH 7.5), and crystals of HLA-DR1-α3 were grown in 23% PEG 3350, 0.1 M KNO , and 0.1 M Bis-Tris-propane (pH 7.0). Crystals were washed with mother liquor supplemented with 20% ethylene glycol and flash frozen in liquid nitrogen before data collection. Data were collected using the MX1 (ref. 33) and MX2 beamlines at the Australian Synchrotron, and processed with iMosflm and Scala from the CCP4 program suite34. The structures were solved by molecular replacement in PHASER35 and refined by iterative rounds of model building using COOT36 and restrained refinement using Phenix37 (see Extended Data Table 2 for data collection and refinement statistics). No statistical methods were used to predetermine sample size. For normally distributed data, an unpaired two-tailed t-test (when comparing two groups). For non-normally distributed data, non-parametric tests (Mann–Whitney U-test for two groups or a Kruskal–Wallis test with Dunn’s multiple comparison) were used. Statistical analyses, except for TCR usage, was by GraphPad Prism (GraphPad Software). For each TCR type/region (TRAV, TRBV, TRAJ, TRBJ), we compared the TCR distribution (frequencies of different TCRs) between DR15 and DR1 using Fisher’s exact test. This was applied both to mice and to human samples. The P values associated with those TCR distributions are indicated above the pie-charts. To correct for multiple testing for individual TCRs, we used Holm’s method. *P < 0.05, **P < 0.01, ***P < 0.001. The data that support the findings of this study are available from the corresponding authors upon request. Self-peptide repertoires have been deposited in the Proteomics Identifications Database archive with the accession code PXD005935. Structural information has been deposited in the Protein Data Bank under accession numbers 5V4M and 5V4N.


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HEK293 cells stably transfected with the STF plasmid encoding the firefly luciferase reporter under the control of a minimal promoter, and a concatemer of 7 LEF/TCF binding sites32, were obtained from J. Nathans. Mouse L cells stably transfected with the STF plasmid and a constitutively expressed Renilla luciferase (control reporter) were obtained from C. Kuo. L cells transfected with a mouse WNT3A expression vector to produce conditioned media were obtained from the ATCC. A375, SH-SY5Y and A549 cells were stably transfected with the BAR plasmid encoding the firefly luciferase reporter under the control of a minimal prompter and a concatemer of 12 TCF/LEF binding sites and a constitutively expressed Renilla luciferase (control reporter) using a lentiviral-based approach33. All reporter cell lines were cultured in complete DMEM medium (Gibco) supplemented with 10% FBS, 1% penicillin, streptomycin, and l-glutamine (Gibco), at 37 °C and 5% CO and cultured in the presence of antibiotics for selection of the transfected reporter plasmid. C3H10T1/2 cells were obtained from the ATCC. Human primary MSCs were obtained from Cell Applications, Inc. Mouse primary MSCs were obtained from Invitrogen. Cell lines have not been tested for mycoplasma contamination. The coding sequence of B12 containing a C-terminal 6×His-tag was cloned into the pET28 vector (Novagen) for bacterial cytoplasmic protein expression. Protein expression was performed in transformed BL21 cells, expression was induced with 0.7 mM IPTG at an OD   of 0.8 for 3–4 h. Cells were pelleted, lysed by sonication in lysis buffer (20 mM HEPES, pH 7.2, 300 mM NaCl, 20 mM imidazole), and soluble fraction was applied to Ni-NTA agarose (QIAGEN). After washing the resin with lysis buffer containing 500 mM NaCl, B12 was eluted with 300 mM imidazole, and subsequently purified on a Superdex 75 size-exclusion column (GE Healthcare) equilibrated in HBS (10 mM HEPES, pH 7.2, 150 nM NaCl). XWnt8 was purified from a stably transfected Drosophila S2 cell line co-expressing XWnt8 and mouse FZD8 CRD–Fc described previously4. Cells were cultured in complete Schneider’s medium (Thermo Fisher Scientific), containing 10% FBS and supplemented with 1% l-glutamine, penicillin and streptomycin (Gibco), and expanded in Insect-Xpress medium (Lonza). A complex of XWnt8 and FZD8 CRD–Fc was captured from the conditioned media on Protein A agarose beads (Sigma). After washing with 10 column volumes of HBS, XWnt8 was eluted with HBS containing 0.1% n-dodecyl-β-d-maltoside (DDM) and 500 mM NaCl, while the FZD8 CRD–Fc remained bound to the beads. All other proteins were expressed in High Five (Trichoplusia ni) cells (Invitrogen) using the baculovirus expression system. To produce the B12-based surrogate, the coding sequences of B12, a flexible linker peptide comprising of 0, 1, 2 or 3 GSGSG-linker repeats, followed by the C-terminal domain of human DKK1 (residues 177–266), and a C-terminal 6×His-tag, were cloned into the pAcGP67A vector (BD Biosciences). To clone the scFv-based surrogate ligand, the sequence of the Vantictumab was retrieved from the published patent, reformatted into a scFv, and cloned at the N terminus of the surrogate variant containing the GSGSG linker peptide. To produce recombinant FZD CRD for crystallization, surface plasmon resonance measurements, SEC-MALS experiments and functional assays, the CRDs of human FZD1 (residues 113–182), human FZD4 (residues 42–161), human FZD5 (residues 30–150), human FZD7 (residues 36–163), human FZD8 (residues 32–151) and human FZD10 (residues 30–150), containing a C-terminal 3C protease cleavage site (LEVLFQ/GP), a biotin acceptor peptide (BAP)-tag (GLNDIFEAQKIEWHE) and a 6×His-tag were cloned into the same vector. The human FZD8 CRD used for crystallization contained only a C-terminal 6×His-tag, in addition to a Asn49Gln mutation to mutate the N-linked glycosylation site. FZD1/FZD8 CRD for inhibition assay contained a C-terminal 3C protease cleavage site, Fc-tag (constant region of human IgG), and a 6×His-tag. Human DKK1 (residues 32–266) with a C-terminal BAP-tag and 6×His-tag, and the two furin-like repeats of human RSPO2 (residues 36–143) with a N-terminal Fc-tag and a C-terminal 6×His-tag, were cloned also into the pAcGP67A vector. All proteins were secreted from High Five insect cells grown in Insect-Xpress medium, and purified using Ni-NTA affinity purification, and size-exclusion chromatography equilibrated in HBS (10 mM HEPES, pH 7.3, 150 nM NaCl). Enzymatic biotinylation was performed in 50 mM bicine, pH 8.3, 10 mM ATP, 10 mM magnesium acetate, 0.5 mM d-biotin with recombinant glutathione S-transferase (GST)-tagged BirA ligase overnight at 4 °C, and proteins were subsequently re-purified on a Superdex 75 size-exclusion column to remove excess biotin. We attempted to mimic the native Wnt–FZD lipid–protein interaction with a de novo designed protein–protein binding interface. A 13-residue alanine helix was docked against the lipid-binding cleft using Foldit34. This structural element was grafted onto a diverse set of native helical proteins using the Rosetta Epigraft35 application to discover scaffolds with compatible, shape-complementary backbones. Prototype designs were selected by interface size and optimized using RosettaScripts36 to perform side-chain redesign. 50 selected designs were further manually designed to ensure charge complementarity and non-essential mutations were reverted to the wild-type amino acid identity to maximize stability. DNA was obtained from Gen9 and screened for binding via yeast surface display as previously described with 1 μM biotinylated FZD8 CRD pre-incubated with 025 µM SAPE (Life Technologies)37. A design based on the scaffold with PDB code 2QUP, a uncharacterized four-helix bundle protein from Bacillus halodurans, demonstrated binding activity under these conditions, whereas knockout mutants Ala52Arg and Ala53Asd made using the Kunkel method38 abrogated binding, verifying that the functional interface used the predicted residues. Wild-type scaffold 2QUP did not bind, confirming that activity was specifically due to design. To improve the affinity of the original design, a full-coverage site-saturation mutagenesis library was constructed for design based on the 2QUP scaffold via the Kunkel mutagenesis method38 using forward and reverse primers containing a ‘NNK’ degenerate codon and 21-bp flanking regions (IDT). A yeast library was transformed as previously described39 and sorted for three rounds, collecting the top 1% of binders using the BD Influx cell sorter. Naive and selected libraries were prepared and sequenced, and the data was processed as previously described37 using a Miseq (Illumina) according to manufacturer protocols. The most enriched 11 mutations were identified by comparison of the selected and unselected pools of binders and were combined in a degenerate library containing all enriched and wild-type amino acid identities at each of these positions. This combination library was assembled from the oligonucleotides (IDT) listed below for a final theoretical diversity of around 800 k distinct variants. This library was amplified, transformed, and selected to convergence over five rounds, yielding the optimized variant B12. The B12–FZD8 CRD(N49Q) complex was formed by mixing purified B12 and FZD8 CRD(N49Q) in stoichiometric quantities. The complex was then treated with 1:100 (w/w) carboxypeptidase A (Sigma) overnight at 4 °C, and purified on a Superdex 75 (GE Healthcare Life Sciences) size-exclusion column equilibrated in HBS. Purified complex was concentrated to around 15 mg ml−1 for crystallization trials. Crystals were grown by hanging-drop vapour diffusion at 295 K, by mixing equal volumes of the complex and reservoir solution containing 42–49% PEG 400, 0.1 M Tris, pH 7.8–8.2, 0.2 M NaCl, or 20% PEG 3000, 0.1 M sodium citrate, pH 5.5. While the PEG 400 condition is already a cryo-protectant, the crystals grown in the PEG 3000 condition were cryoprotected in reservoir solution supplemented with 20% glycerol before flash freezing in liquid nitrogen. Crystals grew in space groups P2 (PEG 400 condition) and P2 (PEG 3000 condition), respectively, with 2 and 4 complexes in the asymmetric units. Cell dimensions are listed in Supplementary Table 1. Data were collected at beamline 8.2.2 at the Advanced Light Source (ALS), Lawrence Berkeley National Laboratory. All data were indexed, integrated, and scaled with the XDS package40. The crystal structures in both space groups were solved by molecular replacement with the program PHASER41 using the structure of the FZD8 CRD (PDB code 1IJY) and the designed model of a minimal core of B12 as search models. Missing residues were manually build in COOT42 after initial rounds of refinement. Several residues at the N terminus (residues 1 to 16/17/20/21), at the C terminus (residues after 117) and several residues within loop regions were unstructured and could not been modelled. Furthermore, we observed that in both crystal forms, B12 underwent domain swapping, and one B12 molecule lent helix 3 and 2 to another B12, resulting in a closely packed B12 homodimer. The density of the loops connecting helixes 1 and 2, and 3 and 4 were clearly visible, and folded into helical turns. Yet, SEC-MALS experiments confirmed that B12 existed as a monomer in solution, and complexed FZD8 CRD with a 1:1 stoichiometry. PHENIX Refine43 was used to perform group coordinate refinement (rigid body refinement), followed by individual coordinate refinement using gradient-driven minimization applying stereo-chemical restraints, NCS restraints, and optimization of X-ray/stereochemistry weight, and individual B-factor refinement. Initial rounds of refinement were aided by restraints from the high-resolution mouse FZD8 CRD structure as a reference model. Real space refinement was performed in COOT into a likelihood-weighted SigmaA-weighted 2mF  − DF map calculated in PHENIX. The final model in the P2 space group was refined to 3.20 Å with R and R values of 0.2002 and 0.2476, respectively (Supplementary Table 1). The quality of the structure was validated with MolProbity44. 99.5% of residues are in the favoured region of the Ramachandran plot, and no residue in the disallowed region. The structure within the P2 space group was refined to 2.99 Å with R and R values of 0.2253 and 0.2499, respectively, with 99.2% of residues in the favoured region of the Ramachandran plot, and no residues in the disallowed region. See Supplementary Table 1 for data and refinement statistics. Structure figures were prepared with the program PYMOL. Binding measurements were performed by surface plasmon resonance on a BIAcore T100 (GE Healthcare) and all proteins were purified on SEC before experiments. Biotinylated FZD1 CRD, FZD5 CRD, FZD7 CRD and FZD8 CRD were coupled at a low density to streptavidin on a SA sensor chip (GE Healthcare). An unrelated biotinylated protein was captured at equivalent coupling density to the control flow cells. Increasing concentrations of B12 and scFv–DKK1c were flown over the chip in HBS-P (GE Healthcare) containing 10% glycerol and 0.05% BSA at 40 μl ml−1. The chip surface was regenerated after each injection with 2 M MgCl in HBS-P or 50% ethylene glycol in HBS-P (scFv–DKK1c measurements), or 4 M MgCl in HBS-P (B12 measurements) for 60 s. Curves were reference-subtracted and all data were analysed using the Biacore T100 evaluation software version 2.0 with a 1:1 Langmuir binding model to determine the K values. To characterize the FZD-specificity of B12, the yeast display vector encoding B12 was transformed into EBY100 yeast. To induce the display of B12 on the yeast surface, cells were growing in SGCAA medium45, 46 for 2 days at 20 °C. 1 × 106 yeast cells per condition were washed with PBE (PBS, 0.5% BSA, 2 mM EDTA), and stained separately with 0.06–1,000 nM biotinylated FZD1/4/5/7/8/10 CRDs for 2 h at 4 °C. After washing twice with ice-cold PBE, bound FZD CRDs were labelled with 10 nM strepdavidin-Alexa647 for 20 min. Cells were fixed with 4% paraformaldehyde, and bound FZD CRD was analysing on an Accuri C6 flow cytometer. FZD8 fused to an N-terminal HaloTag47 and LRP6 fused to an N-terminal SNAP-tag48 were cloned into the pSEMS-26m vector (Covalys Biosciences) by cassette cloning49, 50. The template pSEMS-26m vectors had been coded with DNA sequences of the SNAP-tag or the HaloTag, respectively, together with an Igκ leader sequence (from the pDisplay vector, Invitrogen) as described previously50. The genes of full-length mouse Fzd8 or human LRP6 without the N-terminal signal sequences were inserted into pSEMS-26m via the XhoI and AscI or AscI and NotI, restriction sites, respectively. A plasmid encoding a model transmembrane protein, maltose-binding protein fused to a transmembrane domain, fused to an N-terminal HaloTag was prepared as described recently13. HeLa cells were cultivated at 37 °C, 5% CO in MEM Earle’s (Biochrom AG, FG0325) supplemented with 10% fetal calf serum and 1% nonessential amino acids. Cells were plated in 60-mm cell culture dishes to a density of 50% confluence and transfected via calcium phosphate precipitation49. 8–10 h after transfection, cells were washed twice with PBS and the medium was exchanged, supplied with 2 μM porcupine inhibitor IWP-2 for inhibiting maturation of endogenous Wnt in HeLa cells51. 24 h after transfection, cells were plated on glass coverslips pre-coated with PLL-PEG-RGD52 for reducing nonspecific binding of dyes during fluorescence labelling. After culturing for 12 h, coverslips were mounted into microscopy chambers for live-cell imaging. SNAP-tag and HaloTag were labelled by incubating cells with 50 nM benzylguanine-DY649 (SNAP-Surface 649, New England Biolabs) and 80 nM of HaloTag tetramethylrhodamine ligand (HTL-TMR, Promega) for 20 min at 37 °C. Under these conditions, effective degrees of labelling estimated from single molecule assays with a HaloTag–SNAP-tag fusion protein were ~40% for the SNAP-tag and ~25% for the HaloTag13. After washing three times with PBS, the chamber was refilled with MEM containing 2 μM IWP-2 for single-molecule fluorescence imaging. Single-molecule fluorescence imaging was carried out by using an inverted microscope (Olympus IX71) equipped with a triple-line total internal reflection (TIR) illumination condenser (Olympus) and a back-illuminated EMCCD camera (iXon DU897D, 512 × 512 pixel from Andor Technology). A 561-nm diode solid state laser (CL-561-200, CrystaLaser) and a 642-nm laser diode (Luxx 642-140, Omicron) were coupled into the microscope for excitation. Laser lights were reflected by a quad-line dichroic beam splitter (Di R405/488/561/647, Semrock) and passed through a TIRF objective (UAPO 150×/1.45, Olympus). For simultaneous dual-colour detection, a DualView microimager (Optical Insight) equipped with a 640 DCXR dichroic beamsplitter (Chroma) in combination with bandpass filters FF01-585/40 and FF01 670/30 (Semrock), respectively, was mounted in front of the camera. The overlay of the two channels was calibrated by imaging fluorescent microbeads (TetraSpeck microspheres 0.1 μm, T7279, Invitrogen), which were used for calculating a transformation matrix. After channel alignment, the deviation between the channels was below 10 nm. For single-molecule imaging, typical excitation powers of 1 mW at 561 nm and 0.7 mW at 642 nm measured at the objective were used. Time series of 150–300 frames were recorded at 30 Hz (4.8–9.6 s). An oxygen scavenging system containing 0.5 mg ml−1 glucose oxidase, 40 mg ml−1 catalase, and 5% (w/v) glucose, together with 1 μM ascorbic acid and 1 μM methyl viologene, was added to minimize photobleaching53. Receptor dimerization was initiated by incubating with 100 nM Wnt proteins or surrogates. Images were acquired after 5 min incubation in the presence of the ligands. All live-cell imaging experiments were carried out at room temperature. A 2D Gaussian mask was used for localizing single emitters54, 55. For colocalization analysis to determine the heterodimerization fraction, particle coordinates from two channels were aligned by a projective transformation (cp2tform of type ‘projective’, MATLAB 2012a) according to the transformation matrix obtained from microbead calibration measurement. Particles colocalized within a distance of 150 nm were selected. Only co-localized particles, which could be tracked for at least 10 consecutive frames (that is, molecules co-locomoting for at least 0.32 s) were accepted as receptor heterodimers or hetero-oligomers, which has been previously found to be a robust criterion for protein dimerization13. The fraction of heterodimerization or hetero-oligomerization was determined as the number of co-locomotion trajectories with respect to the number of the receptor trajectories. Since the receptor expression level of FZD8 or LRP6 was variable in the transiently transfected cells, only cells with similar receptor expression levels were considered (less than three times the excess of one subunit over the other). The smaller number of trajectories of either FZD8 or LRP6 was regarded as the limiting factor and therefore taken as a reference for calculating the heterodimerized/hetero-oligomerized fraction. Oligomerization values were not corrected for the degree of labelling. Single-molecule trajectories were reconstructed using the multi-target tracing (MTT) algorithm56. The detected trajectories were evaluated with respect to their step length distribution to determine the diffusion coefficients. For a reliable quantification of local mobilities, we estimated diffusion constants from the displacements with three frames (96 ms). Step-length histograms were obtained from all single molecule trajectories and fitted by a two-component model of Brownian diffusion, thus taking into account the intrinsic heterogeneity of protein diffusion in the plasma membrane57, 58. A bimodal probability density function p(r) was used for a nonlinear least square fit of the step-length histogram: where is the percentage of the fraction, contains the diffusion coefficient of each fraction (nδt = 96 ms). Average diffusion coefficients were determined by weighting the diffusion coefficients with the corresponding fractions. Single-molecule intensity distribution of individual diffraction-limited spots was extracted from the first 50 images of the recorded time lapse image sequence, in which photobleaching of dyes was kept below 10%13. Oligomerization of receptors was evaluated by fitting the obtained single molecule intensity with a multi-component Gaussian distribution function59. To ensure a reliable analysis, monomeric receptors were first distinguished based on the observation that monomers diffused much faster than oligomers. Therefore, the characteristic intensity distribution of monomeric receptor subunits was obtained by tracking of the fast mobile fraction. Fractions of the monomer, dimer, trimer and higher oligomers were then de-convoluted from the single molecule intensity distribution, presuming that intensities of clusters were multiples of the monomer intensity distribution. Immortal cells were seeded in triplicate for each condition in 96-well plates, and stimulated with surrogates, XWnt8, WNT3A conditioned media, control proteins, or other treatments for 20–24 h. After washing cells with PBS, cells in each well were lysed in 30 μl passive lysis buffer (Promega). 10 μl per well of lysate was assayed using the Dual Luciferase Assay kit (Promega) and normalized to the Renilla luciferase signal driven constitutively by the human elongation factor-1 alpha promoter to account for cell variability. A375 BAR, SH-SY5Y BAR, L STF and HEK293 STF cells were plated at a density of 10,000–20,000 cells per well, and treatment was started after 24 h in fresh medium. A549 BAR cells were plated at a density of 5,000 cells per well in the presence of 2 μM IWP-2 (Calbiochem) to suppress endogenous Wnt secretion, and treatment was started after 48 h in fresh medium containing fresh IWP-2. To induce β-catenin accumulation, SH-SY5Y BAR cells were treated for 2 h with scFv–DKK1c, WNT3A conditioned media (positive control), B12 (negative control protein) and mock conditioned media (from untransfected L cells, negative control) at 37 °C, 5% CO . After, cells were washed twice with PBS. For β-catenin stabilization assay, cells were scraped into hypotonic lysis buffer (10 mM Tris-HCl pH 7.4, 0.2 mM MgCl , supplemented with protease inhibitors), incubated on ice for 10 min, and homogenized using a hypodermic needle. Sucrose and EDTA were added to final concentration of 0.25 M and 1 mM, respectively. For LRP6 phosphorylation assay, cells were lysed in RIPA buffer (50 mM Tris pH 8.0, 150 mM NaCl, 0.5% sodium deoxylate, 1% Triton X-100), supplemented with protease inhibitor and phosphatase inhibitor for 1 h at 4 °C. Lysates were centrifuged at 12,000g for 1 h at 4 °C. Supernatants were then diluted into SDS sample buffer. For immunoblotting, samples were resolved on a 12% Mini-PROTEAN(R)TGX precast protein gel (Bio-Rad) and transferred to a PVDF membrane. The membranes were cut horizontally approx. at the 64 kDa mark of the SeeBlue plus 2 molecular mass marker (Invitrogen). Top half of the blot was incubated with anti-β-catenin primary antibody ((D10A8)XP, rabbit, Cell Signaling 8480), LRP6 antibody ((C47E12), rabbit, Cell Signaling 3395), and P-LRP6 (S1490) antibody (rabbit, Cell Signaling 2568), and the bottom part with the anti-α-tubulin primary antibody (mouse, DM1A, Sigma) in PBS containing 0.1% Tween-20 and 5% BSA overnight at 4 °C. Blots were then washed, incubated with the corresponding secondary antibodies in the same buffer, before washing and developing using the ECL prime western blotting detection reagent (GE Healthcare). To induce β-catenin accumulation, K562 and cells were stimulated for 0, 15, 30, 45, 60, 90 and 120 min with 10 nM scFv–DKK1c, recombinant Wnt3a (R&D Systems), B12 (negative control protein) or plain complete growth medium at 37 °C, 5% CO . After, cells were washed twice with PBS, fixed with 4% PFA for 10 min at room temperature, and permeabilized in 100% methanol for at least 30 min at −80 °C. The cells were than stained with Alexa-647 conjugated anti-β-catenin antibody (L54E2) (Cell Signaling Technology, 1:100–1: 50 dilution). Fluorescence was analysed on an Accuri C6 flow cytometer. Total RNA was isolated using either TRIZOL (Invitrogen) or RNeasy plus micro kit (QIAGEN) according to manufacturer’s protocols. A total of 2 μg RNA were used to generate cDNA using the RevertAid RT kit (Life Technologies) using oligo(dT)18 mRNA primers (Life Technologies) according to manufacturer’s protocol. 12 ng of cDNA per reaction were used. qPCR was performed using SYBR Green-based detection (Applied Biosystems) according to the manufacturer’s protocol on a StepOnePlus real-time PCR system (ThermoFisher Scientific). All primers were published, or validated by us. Transcript copy numbers were normalized to GAPDH for each sample, and fold induction compared to control was calculated. The following gene-specific validated primers were used: human FZD1: F: 5′-ATCCTGTGTGCTCCTCTTTTGG-3′, R: 5′-GATTGCTTTTCTCCTCTTCTTCAC-3′; human FZD2: F: 5′-CTGGGCGAGCGTGATTGT-3′, R: 5′-GTGGTGACAGTGAAGAAGGTGGAAG-3′; human FZD3: F: 5′-TCTGTATTTTGGGTTGGAAGCA-3′, R: 5′-CGGCTCTCATTCACTATCTCTTT-3′; human FZD4: F: 5′-TGGGCACTTTTTCGGTATTC-3′, R: 5′-TGCCCACCAACAAAGACATA-3′; human FZD5: F: 5′-CCATGATTCTTTAAGGTGAGCTG-3′, R: 5′-ACTTATTCAAGACACAACGATGG-3′; human FZD6: F: 5′-CGATAGCACAGCCTGCAATA-3′, R: 5′-ACGGTGCAAGCCTTATTTTG-3′; human FZD7: F: 5-TACCATAGTGAACGAAGAGGA-3′, R: 5′-TGTCAAAGGTGGGATAAAGG-3′; human FZD8: F: 5′-ACCCAGCCCCTTTTCCTCCATT-3′, R: 5′-GTCCACCCTCCTCAGCCAAC-3′; human FZD9: F: 5′-GCTGTGACTGGAATAAACCCC, R: 5′-GCTCTGCTTACAAGAAAGACTCC-3′; human FZD10: F: 5′-CTCTTCTCTGTGCTGTACACC, R: 5′-GTCTTGGAGGTCCAAATCCA-3′; mouse Fzd1: F: 5′-GCGACGTACTGAGCGGAGTG, R: 5′-TGATGGTGCGGATGCGGAAG-3′60; mouse Fzd2: F: 5′-CTCAAGGTGCCGTCCTATCTCAG, R: GCAGCACAACACCGACCATG-3′60; mouse Fzd3: F: 5′-GGTGTCCCGTGGCCTGAAG-3′, R: 5′-ACGTGCAGAAAGGAATAGCCAAG-3′60; mouse Fzd4: F: 5′-GACAACTTTCACGCCGCTCATC-3′, R: 5′-CAGGCAAACCCAAATTCTCTCAG-3′60; mouse Fzd5: F: 5′-AAGCTGCCTTCGGATGACTA-3′, R: 5′-TGCACAAGTTGCTGAACTCC-3′60; mouse Fzd6: F: 5′-TGTTGGTATCTCTGCGGTCTTCTG-3′, R: 5′-CTCGGCGGCTCTCACTGATG-3′60; mouse Fzd7: F: 5′-ATATCGCCTACAACCAGACCATCC-3′, R: 5′-AAGGAACGGCACGGAGGAATG-3′60; mouse Fzd8: F: 5′-GTTCAGTCATCAAGCAGCAAGGAG-3′, R: 5′-AAGGCAGGCGACAACGACG-3′60; mouse Fzd9: F: 5′-ATGAAGACGGGAGGCACCAATAC-3′, R: 5′-TAGCAGACAATGACGCAGGTGG-3′60; mouse Fzd10: F: 5′-ATCGGCACTTCCTTCATCCTGTC-3′, R: 5′-TCTTCCAGTAGTCCATGTTGAG-3′60; human AXIN2: F: 5′-CTCCCCACCTTGAATGAAGA-3′, R: 5′-TGGCTGGTGCAAAGACATAG-3′; human GAPDH: F: 5′-TGAAGGTCGGAGTCAACGGA-3′, R: 5′-CCATTGATGACAAGCTTCCCG-3′; mouse Gapdh: F: 5′-CCCCAATGTGTCCGTCGTG-3′, R: 5′-GCCTGCTTCACCACCTTCT-3′. Differentiation of C3H10T1/2, and human and mouse primary MSCs were performed essentially as described previously61. In brief, approximately 10,000 cells cm−2 were plated in normal culture medium (αMEM + FBS + penicillin/streptomycin), and allowed to adhere overnight. The following day, the medium was replaced with osteogenic medium (αMEM, 10% FBS, 1% penicillin/streptomycin, 50 μg ml−1 ascorbic acid, 10 mM β-glycerol phosphate (βGP), and replaced every other day. To determine alkaline phosphatase enzymatic activity, cells were fixed for 10 min with 10% formalin in PH7 PBS, before incubation in NBT-BCIP solution (1-Step(tm) NBT/BCIP Substrate Solution (Thermo Fisher Scientific, 34042) for 30 min. qPCR reactions were done with the SYBR method using the following primers: human ACTB F: 5′-GTTGTCGACGACGAGCG-3′, R: 5′-GCACAGAGCCTCGCCTT-3′; human ALPL: F: 5′-GATGTGGAGTATGAGAGTGACG-3′, R: 5′-GGTCAAGGGTCAGGAGTTC-3′; mouse Alpl: F: 5′-AAGGCTTCTTCTTGCTGGTG-3′, R: 5′-GCCTTACCCTCATGATGTCC-3′; mouse Actb: F: 5′-GGAATGGGTCAGAAGGACTC-3′, R: 5′-CATGTCGTCCCAGTTGGTAA-3′; mouse Col2a1 F: 5′-GTGGACGCTCAGGAGAAACA-3′, R: 5′-TGACATGTCGATGCCAGGAC-3′. P26N, normal adult human colon organoids, were established from a tumour-free colon segment of a patient diagnosed with CRC as described18, 62, 63. CFTR-derived colorectal organoids were obtained from a patient at Wilhelmina Children’s Hospital WKZ-UMCU. Informed consent for the generation and use of these organoids for experimentation was approved by the ethical committee at University Medical Center Utrecht (UMCU) (TcBio 14-008). Human stomach organoids, derived from normal corpus and pylorus, were from patients that underwent partial or total gastrectomy at the University Medical Centre Utrecht (UMCU) and were established as described19, 64, 65. Pancreas organoids were obtained from the healthy part of the pancreas of patients undergoing surgical resection of a tumour at the University Medical Centre Utrecht Hospital (UMC) and were established as described66, 67. The liver organoids were derived from freshly isolated normal liver tissue from a patient with metastatic CRC who presented at the UMC hospital (ethical approval code TCBio 14-007) and were established as described20, 68. For the performance of 3D cultures, Matrigel (BD Biosciences) was used and overlaid with a liquid medium consisting of DMEM/F12 advanced medium (Invitrogen), supplemented with additional factors as outlined below. 2% RSPO3-CM (produced via the r-PEX protein expression platform at U-Protein Express BV), WNT3A conditioned medium (50%, produced using stably transfected L cells in the presence of DMEM/F12 advanced medium supplemented with 10% FBS), and Wnt and Wnt/RSPO2 surrogates at different concentrations were added as indicated. Single-cell suspensions of normal human organoids were cultured in duplicate or triplicate in round-bottom 96-well plates to perform a cell viability test using Cell Titer-Glo 3D (Promega). In brief, organoids were trypsinized to single-cell suspension and plated in 100 μl medium in the presence of the different reagents. 3 μM IWP-2 was added to inhibit endogenous Wnt lipidation and secretion. After 12 days, 100 μl of Cell Titer-Glo 3D was added, plates were shaken for 5 min, incubated for an additional 25 min and centrifuged before luminescence measurement. All animal experiments were conducted in accordance with procedures approved by the IACUC at Stanford University. Experiments were not randomized, the investigators were not blinded, and all samples/data were included in the analysis. Group sample sizes were chosen based on (1) previous experiments, (2) performance of statistics analysis, and (3) logistical reasons with respect to full study size, to accommodate all groups. Adenoviruses (E1 and E3 deleted, replication deficient) were constructed to express scFv–DKK1c or scFv–DKK1c–RSPO2 with an N-terminal signal peptide and C-terminal 6×His-tag (Ad-scFv–DKK1c or Ad-scFv–DKK1c–RSPO2), respectively. Adenoviruses expressing mouse IgG2α Fc (Ad-Fc), human RSPO2–Fc fusion protein (Ad-RSPO2–Fc) and mouse WNT3A (Ad-Wnt3a) were constructed and described in the companion paper by Yan et al.26 The adenoviruses were cloned, purified by CsCl gradient, and titred as previously described69. Adult C57Bl/6J mice were purchased from Taconic Biosciences. Adult C57Bl/6J mice between 8–10 weeks old were injected intravenously with a single dose of adenovirus at between 1.2 × 107 p.f.u. to 6 × 108 p.f.u. per mouse in 0.1 ml PBS. Serum expression of Ad-scFv–DKK1c or Ad-scFv–DKK1c–RSPO2 were confirmed by immunoblotting using mouse anti-6×His (Abcam ab18184, 1:2,000) or rabbit anti-6×His (Abcam ab9108, 1:1,000), respectively. All experiments used n = 4 mice per group and repeated at least twice. qRT–PCR on liver samples were performed as following. Total cDNA was prepared from each liver sample using Direct-Zol RNA miniprep kit (Zymo Research) and iScript Reverse Transcription Supermix for RT-qPCR (BIO-RAD). Gene expression was analysed by -ΔΔC or fold change (2−ΔΔCt). Unpaired Student’s t-test (two tailed) was used to analyse statistical significance. Primers for mouse Axin2 and Cyp2f2 were previously published70. Additional primers used were listed as below: For the parabiosis experiment, age- and gender-matched C57Bl/6J mice were housed together for at least 2 weeks before surgery. At 2 days before surgery, the ‘donor’ mice were injected intravenously with a single dose of adenovirus at between 1.2 × 107 pfu to 6 × 108 pfu per mouse in 0.1 ml PBS and were separated from the ‘recipient’ mice until surgery. The parabiosis surgery was performed as described previously71. The establishment of shared circulation was confirmed at day 5 after surgery by presence of adenovirus-expressed proteins in the serum of both donors and recipients. Mouse livers were collected and fixed in 4% paraformaldehyde. 5 μm paraffin-embedded sections were stained with the following antibodies after citrate antigen retrieval and blocking with 10% normal goat serum: mouse anti-glutamine synthetase antibody (Millipore MAB302, 1:200), mouse anti-PCNA (BioLegend 307902, 1:200), and rabbit anti-HNF4α (Cell Signaling 3113S, 1:500). The immunostained tissue sections were analysed and images were captured on a Zeiss Axio-Imager Z1 with ApoTome attachment. Atomic structure factors and coordinates have been deposited to the Protein Data Bank (PDB) under accession numbers 5UN5 and 5UN6. All other data are available from the corresponding author upon reasonable request.

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