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Singh G.,Ohio State University | Pratt G.,Institute for Genomic Medicine | Pratt G.,University of California at San Diego | Yeo G.W.,Institute for Genomic Medicine | And 3 more authors.
Annual Review of Biochemistry | Year: 2015

Throughout their lifetimes, messenger RNAs (mRNAs) associate with proteins to form ribonucleoproteins (mRNPs). Since the discovery of the first mRNP component more than 40 years ago, what is known as the mRNA interactome now comprises >1,000 proteins. These proteins bind mRNAs in myriad ways with varying affinities and stoichiometries, with many assembling onto nascent RNAs in a highly ordered process during transcription and precursor mRNA (pre-mRNA) processing. The nonrandom distribution of major mRNP proteins observed in transcriptome-wide studies leads us to propose that mRNPs are organized into three major domains loosely corresponding to 5′ untranslated regions (UTRs), open reading frames, and 3′ UTRs. Moving from the nucleus to the cytoplasm, mRNPs undergo extensive remodeling as they are first acted upon by the nuclear pore complex and then by the ribosome. When not being actively translated, cytoplasmic mRNPs can assemble into large multi-mRNP assemblies or be permanently disassembled and degraded. In this review, we aim to give the reader a thorough understanding of past and current eukaryotic mRNP research. Copyright © 2015 by Annual Reviews. All rights reserved.


News Article | November 16, 2016
Site: www.nature.com

We analysed TCGA data for association between mRNA expression level of 16 candidate immune-related genes (ARG1, IL10, FOXP3, CD68, IL12A, IL12B, IFNG, CD8A, CD4, ITGAM (also known as CD11B), CD14, TNF, IL1A, IL1B, IL6 and CCL5) and 5 year overall survival. Illumina HiSeq RNaseqV2 mRNA expression and clinical data for 520 head and neck squamous cell carcinoma samples were downloaded from the TCGA data portal. Median follow-up from diagnosis was 1.8 years with a range of 0.01 years to 17.6 years. Follow-up time was truncated at 5-years for analysis and 200 deaths occurred in this period. For each of the 16 candidate immune response genes, we scored subjects as above (high) or below (low) the median expression and compared survival using a log-rank test at 5% significance. HPV+ patients were stratified into a favourable immune profile if they had expression above the median for the significant genes IL12A, IL12B, IFNG, CD8A and below the median for IL6. Kaplan–Meier curves were plotted for these two groups. Similar methods were used to examine association of these 16 genes with 720 lung adenocarcinoma and 876 gastric carcinoma samples using the publically available data from KM Plotter29. In lung adenocarcinomas, 12 genes were significantly associated with survival; patients were scored as having a favourable immune profile if 7 or more of the 12 significant genes had expression in the favourable direction. In 876 gastric cancer samples, 8 genes were significantly associated with survival. Patients were scored as having a favourable immune profile if 5 out of the 8 genes had expression in the favourable direction. We investigated 66 immune-related genes in four functional classes, 17 genes related to antigen presentation (HLA class I and II molecules), 24 genes surveying T cell activation, 20 innate immune response genes (IL6, CCL7 and others) and 5 genes related to cancer cell signalling. These genes changed expression in response to PI3Kγ inhibition for association with survival in HPV+ and HPV− TCGA HNSCC and lung adenocarcinoma cohorts. Within each cancer type, we scored subjects as above or below the median expression for each gene and compared survival using a log-rank test, using 10% false discovery rate (FDR) within each class as the significance threshold. HPV+ and HPV− HNSCC survival were investigated separately, as HPV− HNSCC generally has a worse prognosis. Within each cohort, patients were classified as having a favourable PI3Kγ immune response profile if they had expression levels above or below the median in the direction of low PI3Kγ activity for the genes identified as significant. We compared the survival experience of favourable versus less-favourable profiles of patients using Kaplan–Meier curves. Out of the 66 experimentally identified PI3Kγ-regulated genes, 43 showed significant association with overall survival in the HPV+ cohort (FDR < 10% within each functional class). Comparison of these genes between HPV+ and HPV− cohorts showed that HPV− samples generally had significantly (P < 0.05) lower expression of 42 genes in the antigen presentation and T cell activation classes, consistent with a pattern of adaptive immune suppression, and higher expression of genes in the innate immune response and cancer cell signalling classes, which were negatively associated with survival. Only MALT1 was not differentially expressed between the two groups (P = 0.7). Pik3cg−/− and Pik3cg−/−,PyMT mice were generated as previously described13. Cd8−/− and Cd4−/− mice with a C57Bl/6J background were purchased from the Jackson Laboratory and crossed with syngeneic Pik3cg−/− mice. All animal experiments were performed with approval from the Institutional Animal Care and Use Committee of the University of California. Animals were euthanized before the IACUC maximum allowable tumour burden of 2 cm3 per mouse was exceeded. Wild-type or Pik3cg−/− 6–8 week-old female or male syngeneic C57Bl/6J (LLC lung, PyMT breast and MEER HPV+ HNSCC) or C3He/J (SSCVII HPV− HNSCC) mice were implanted with 106 tumour cells by subcutaneous injection (LLC, MEER, SCCVII) or by orthotopic injection (PyMT) (n = 10–15) and tumour growth was monitored for up to 30 days. Tumour dimensions were measured once when tumours were palpable. Tumour volumes were calculated using the equation (l2 × w)/2. In some studies, wild-type and Pik3cg−/− mice with LLC tumours were treated with gemcitabine (150 mg kg−1) or saline by intraperitoneal (i.p.) injection on day 7 and day 14 (n = 10). LLC were acquired from ATCC, PyMT were from L. Ellies (University of California), HPV+ MEER were from J. Lee (Cancer Biology Research Center, Sanford Research/USD) and SCCVII squamous carcinoma cells were from S. Schoenberger (La Jolla Institute for Allergy and Immunology). All cell lines were tested for mycoplasma and mouse pathogens and checked for authenticity against the International Cell Line Authentication Committee (ICLAC; http://iclac.org/databases/cross-contaminations/) list. In some studies, mice bearing LLC, PyMT, HPV+ MEER or HPV− HNSCC tumour cells were treated once daily by oral gavage with vehicle (5% NMP and 95% PEG 400), 15 mg kg−1 per day of the PI3Kγ inhibitor IPI-549 or by i.p. injection with 2.5 mg kg−1 twice per day of TG100-115 (ref. 13) beginning on day 8 post-tumour injection and continuing daily until euthanasia. IPI-549 is an orally bioavailable PI3Kγ inhibitor with a long plasma half-life and a K value of 0.29 nM for PI3Kγ with >58-fold weaker binding affinity for the other class I PI3K isoforms17. Enzymatic and cellular assays confirmed the selectivity of IPI-549 for PI3Kγ (>200-fold in enzymatic assays and >140-fold in cellular assays over other class I PI3K isoforms17). To study the effect of IPI-549 on lung tumour growth, LLC tumour cells were passaged three times in C57BL/6 albino male mice. When tumour volume reached 1,500 mm3, tumours were collected and single-cell suspensions were prepared. This tumour cell suspension was implanted subcutaneously in the hind flank of C57BL/6 albino male mice at 106 cells per mouse. Prior to initiating treatment with once daily IPI-549 (15 mg kg−1 orally), groups were normalized on the basis of tumour volume. In some studies, wild-type- and Pik3cg−/−-tumour-bearing mice were treated with 100 μg of anti-CD8 (clone YTS 169.4) or an isotype-control clone (LTF-2) from Bio X Cell administered by i.p. injections on day 7, 10 and 13 of tumour growth. For all tumour experiments, tumour volumes and weights were recorded at death. C57Bl/6J (wild-type) or Pik3cg−/− 6–8 week-old male or female mice (MEER HPV+ HNSCC) or C3He/J (SCCVII HPV− HNSCC) were implanted with tumour cells by subcutaneous injection (106 MEER or 105 SCCVII). In HPV+ MEER studies, wild-type and Pik3cg−/− mice were treated with four doses of 250 μg of anti-PD-1 antibody (clone RMP-14, Bio X Cell) or rat IgG2a isotype control (clone 2A3, Bio X Cell) every 3 days, starting when tumours became palpable on day 11 (n = 12–14 mice per group). Wild-type mice bearing HPV+ tumours were also treated with the PI3Kγ inhibitor TG100-115 (ref. 13) twice per day by i.p. injection, beginning on day 11. Tumour regressions were calculated as a percentage of the difference in tumour volume between the date treatment was initiated and the first date of death of the control group. For HPV− SCCVII studies, C3He/J mice were treated with PI3Kγ inhibitor (2.5 mg kg−1 TG100-115 i.p.) beginning on day 6 post-tumour inoculation and with six doses of anti-PD-1 antibody (250 μg clone RMP-14, Bio X Cell) or rat IgG2a isotype control (clone 2A3, Bio X Cell) every 3 days beginning on day 3 (n = 12 mice per group) or with a combination of the two. Alternatively, mice were treated with 5 mg kg−1 TG100-115 twice per day ± anti-PD-1 (250 μg every 3 days) beginning on day 1 (Fig. 4). Mice that completely cleared HPV+ MEER tumours were re-injected with HPV+ tumour cells contralateral to the initial tumour injection and tumour growth was monitored. The growth and metastasis of spontaneous mammary tumours in female PyMT+ (n = 13) and Pik3cg−/−,PyMT+ (n = 8) mice was evaluated over the course of 0–15 weeks. Total tumour burden was determined by subtracting the total mammary gland mass in PyMT− mice from the total mammary gland mass in PyMT+ mice. Lung metastases were quantified macroscopically and microscopically in H&E tissue sections at week 15. Septic shock was induced in wild-type and Pik3cg−/− mice via i.p. injection of 25 mg kg−1 LPS (Sigma, B5:005). Survival was monitored every 12 h and liver, bone marrow and serum were collected 24 h after LPS injection. C57Bl/6J female mice were implanted with 106 LLC tumour cells by subcutaneous injection. When the average tumour size was 250 mm3, mice were treated by i.p. injection with 1 mg per mouse clodronate or control liposomes (www.clodronateliposomes.com) every 4 days for 2 weeks in combination with daily administration of vehicle or IPI-549 (15 mg kg−1 per day orally). In other studies, 6-week-old female BALB/c mice were injected subcutaneously with 2.5 × 105 CT26 mouse colon carcinoma cells in 100 μl phosphate buffered saline (PBS) in the right flank. Eight days later, tumour-bearing mice were arranged into four groups (n = 15) with an average tumour volume of 70 mm3. Oral administration of IPI-549 (15 mg kg−1) or vehicle (5% NMP and 95% PEG 400) and anti-CSF-1R antibody (50 mg kg−1 i.p. 3× per week, clone AFS98, Bio X Cell) began on day 8 after tumour injection via oral gavage at a 5 ml kg−1 dose volume and continued daily for a total of 18 doses. Six-week-old female BALC/c mice were injected subcutaneously with 2.5 × 105 CT26 mouse colon carcinoma cells in 100 μl PBS in the right flank. On day 8 after tumour injection, tumour-bearing mice were grouped and treated daily with IPI-549 (15 mg kg−1, orally) or vehicle (5%NMP and 95% PEG 400). In addition, mice were injected i.p. with 50 mg kg−1 anti-CD115 (Bio X Cell clone AFS98) or 50 mg kg−1 rat IgG2a isotype control (Bio X Cell clone 2A3) antibodies as described above for a total of three injections. Two days after the final injection mice were euthanized, tumours were digested in a mixture of 0.5 mg ml−1 collagenase IV and 150 U ml−1 DNase I in RPMI-1640 for 30 min at 37 °C and tumour-infiltrating myeloid cells were analysed by flow cytometry. CD11b+Gr1− cells were isolated from single-cell suspensions of LLC tumours from donor mice by fluorescence-activated cell sorting (FACS) or serial magnetic bead isolation. Additionally, for some experiments, primary bone-marrow-derived macrophages were polarized and collected into a single-cell suspension. Purified cells were admixed 1:1 with LLC tumour cells and 5 × 105 total cells were injected subcutaneously into new host mice. Tumour dimensions were measured three times per week beginning on day 7. In antibody blocking studies, CD11b+Gr1− cells were incubated with 5 μg anti-IL12 (clone RD1-5D9) or isotype (clone LTF-2, Bio X Cell) for 30 min before the addition of tumour cells. Mice were additionally treated intradermally with 5 μg of antibody 3 and 6 days after tumour cell inoculation. In some studies, CD11b+Gr1− cells were pre-incubated with inhibitors of arginase (nor-NOHA, 50 μM, Cayman Chemical), iNOS (1400W dihydrocholoride, 100 μM, Tocris), mTOR (rapamycin, 10 μM Calbiochem), or IκKβ (ML120B, 30 μM, Tocris) for 30 min before the addition of tumour cells. Inoculated mice were further treated by intradermal injection with inhibitors at 3 and 6 days after inoculation. Donor C57Bl/6J (WT) or Pik3cg−/− mice were implanted with 106 LLC tumour cells by subcutaneous injection. On day 14 after tumour implantation, CD90.2+, CD4+ or CD8+ cells were harvested by magnetic bead isolation (Miltenyi Biotec). T cells were mixed 1:1 with viable LLC tumour cells. Cell mixtures containing 5 × 105 total cells were injected into the flanks of naive wild-type or Pik3cg−/− mice (n = 8–10 per group). Tumour growth, intratumoral apoptosis and necrosis were investigated over 0–16 days. In other studies, wild-type T cells were incubated at 37 °C and 5% CO for 6 h with 10 or 100 nM IPI-549 (Infinity Pharmaceuticals) or Cal-101 (Selleck Chem). After 6 h, T cells were washed, admixed 1:1 with LLC tumour cells, and 106 total cells were injected subcutaneously into recipient mice. Tumour growth was monitored for 14 days. Tumours were isolated, minced in a Petri dish on ice and then enzymatically dissociated in Hanks balanced salt solution containing 0.5 mg ml−1 collagenase IV (Sigma), 0.1 mg ml−1 hyaluronidase V (Sigma), 0.6 U ml−1 dispase II (Roche) and 0.005 MU ml−1 DNase I (Sigma) at 37 °C for 5–30 min. The duration of enzymatic treatment was optimized for greatest yield of live CD11b+ cells per tumour type. Cell suspensions were filtered through a 70-μm cell strainer. Red blood cells were solubilized with red cell lysis buffer (Pharm Lyse, BD Biosciences) and the resulting suspension was filtered through a cell strainer to produce a single-cell suspension. Cells were washed once with PBS before use in flow cytometry analysis or magnetic bead purification. Thioglycollate-elicited peritoneal macrophages were collected 96 h after i.p. injection of a 3% thioglycollate solution. Cells were collected from the peritoneal cavity in 10 ml of PBS and macrophage enrichment was performed by plating cells in RPMI with 10% FBS and 1% penicillin/streptomycin for 2 h at 37 °C and 5% CO . After 2 h, non-adherent cells were removed with three PBS washes, and cells were analysed via flow cytometry and qPCR analysis. Single-cell suspensions (106 cells in 100 μl total volume) were incubated with aqua live dead fixable stain (Life Technologies), FcR-blocking reagent (BD Biosciences) and fluorescently labelled antibodies and incubated at 4 °C for 1 h. Primary antibodies to cell surface markers directed against F4/80 (BM8), CD45 (30-F11), CD11b (M1/70), Gr1 (RB6-8C5), CD3 (145-2C11), CD4 (GK1.5), CD8 (53-6.7), CD273 (B7-DC), CD274 (B7-H1) were from eBioscience; Ly6C (AL-21), Ly6G (1A8), CD11c (HL3), and MHC-II (AF6-120.1) from BD Pharmingen, CCR2 (475301) from R&D Systems and CD206 (MR5D3) from AbD Serotech. For intracellular staining, cells were fixed, permeabilized using transcription factor staining buffer set (eBioscience) and then incubated with fluorescently labelled antibodies to FoxP3 (FJK-16 s) from eBioscience. Multicolour FACS analysis was performed on a BD Canto RUO 11 colour analyser. All data analysis was performed using the flow cytometry analysis program FloJo (Treestar). Single-cell preparations from bone marrow or tumours were incubated with FcR-blocking reagent (BD Biosciences) and then with 20 μl magnetic microbeads conjugated to antibodies against CD11b, Gr1, CD90.2, CD4 and CD8 (Miltenyi Biotech MACS Microbeads) per 107 cells for 20 min at 4 °C. Cells bound to magnetic beads were then removed from the cell suspension according to the manufacturer’s instructions. For cell sorting, single-cell suspensions were stained with aqua live dead fixable stain (Life Technologies) to exclude dead cells and anti-CD11b-APC (M1/70, eBioscience) and anti-Gr1-FITC (RB6-8C5, eBioscience) antibodies. FACS sorting was performed on a FACS Aria 11 colour high speed sorter at the Flow Cytometry Core at the UC San Diego Center for AIDS Research. Live cells were sorted into the following populations: CD11b+Gr1−, CD11b+Gr1lo, CD11b+Gr1hi and CD11b−Gr1− cells. CD11b-positive cells were defined by increased staining over the isotype control, and Gr1 levels were defined both by comparison to the isotype control and relative staining to other populations. Bone-marrow-derived cells were aseptically collected from 6–8 week-old female mice by flushing leg bones of euthanized mice with PBS, 0.5% BSA, 2 mM EDTA, incubating in red cell lysis buffer (155 mM NH Cl, 10 mM NaHCO and 0.1 mM EDTA) and centrifuging over Histopaque 1083 to purify the mononuclear cells. Approximately 5 × 107 bone-marrow-derived cells were purified by gradient centrifugation from the femurs and tibias of a single mouse. Purified mononuclear cells were cultured in RPMI + 20% serum + 50 ng ml−1 mCSF (PeproTech). Human leukocytes from apheresis blood products were obtained from the San Diego Blood Bank. Cells were diluted in PBS, 0.5% BSA, 2 mM EDTA, incubated in red cell lysis buffer (155 mM NH Cl, 10 mM NaHCO and 0.1 mM EDTA) and centrifuged over Histopaque 1077 to purify mononuclear cells. Approximately 109 bone-marrow-derived cells were purified by gradient centrifugation from one apheresis sample. Purified mononuclear cells were cultured in RPMI + 20% serum + 50 ng ml−1 Human mCSF (PeproTech). Non-adherent cells were removed after 2 h by washing and adherent cells were cultured for 6 days to differentiate macrophages fully. Bone-marrow-derived macrophages were polarized with IFNγ (20 ng ml−1, Peprotech) + LPS (100 ng ml−1, Sigma) or LPS alone for 24 h, or IL4 (20 ng ml−1, Peprotech) for 24–48 h. For inhibitor studies, PI3Kγ inhibitors (1 μM) (IPI-549, Infinity Pharmaceuticals and TG100-115, Targegen/Sanofi-Aventis), rapamycin (10 μM, Selleck), or ML120B (30 μM) were incubated with macrophages 1 h before the addition of polarizing stimuli. Total RNA was harvested from macrophages using the RNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. Freshly isolated mouse bone marrow cells from nine wild-type and nine Pik3cg−/− mice were pooled into three replicates sets of wild-type or Pik3cg−/− cells and differentiated into macrophages for 6 days in RPMI + 20% FBS+ 1% penicillin/streptomycin + 50 ng ml−1 mCSF. Each replicate set of macrophages was then treated with mCSF, IL4 or IFNγ/LPS. Macrophages were removed from dishes, and RNA was collected using Qiagen Allprep kit. In addition, RNA was harvested from day 14 (500 mm3) LLC tumours or purified CD11b+Gr1-F480+ TAMs from wild-type (C57BL/6) and Pik3cg−/− mice. RNA was collected using the Qiagen Allprep kit. RNA libraries were prepared from 1 μg RNA per sample for sequencing using standard Illumina protocols. RNA sequencing was performed by the University of California, San Diego Institute for Genomic Medicine. mRNA profiles were generated by single read deep sequencing, in triplicate, using Illumina HiSeq2000. Sequence analysis was performed as previously described16. Sequence files from Illumina HiSeq that passed quality filters were aligned to the mouse transcriptome (mm9 genome build) using the Bowtie2 aligner4. Gene-level count summaries were analysed for statistically significant changes using DESeq. Individual P values were adjusted for multiple testing by calculating Storey’s q values using fdrtooltrimmer. For each gene, the q value is the smallest false discovery rate at which the gene is found significant. We analysed biological processes as defined by the Gene Ontology Consortium. Each gene ontology term defines a set of genes. The entire list of genes, sorted by the q value in ascending order, was subjected to a non-parametric variant of the gene set enrichment analysis (GSEA), in which the parametric Kolmogorov–Smirnov P value was replaced with the exact rank-order P value. We perform a Bonferroni adjustment of gene set P values for the number of gene sets tested. Heat maps of expression levels were created using in-house hierarchical clustering software that implements Ward clustering. The colours qualitatively correspond to fold changes. cDNA was prepared using 1 μg RNA with the qScript cDNA Synthesis Kit (Quanta Biosciences). Sybr green-based qPCR was performed using human and mouse primers to Arg1, Ifng, Il10, Il12p40, Il1b, Il6, Ccl2, Vegfa, Gapdh, Nos2, Tgfb1, Tnfa and mouse H2-Aa, H2-Ab1, H2-Eb1, and H60a (Qiagen QuantiTect Primer Assay). mRNA levels were normalized to Gapdh and reported as relative mRNA expression or fold change. Freshly isolated bone-marrow-derived CD11b+ myeloid cells or differentiated macrophages were transfected by electroporation using an AMAXA mouse macrophage nucleofection kit with 100 nM of siRNA or 2 μg Pik3cgCAAX or pcDNA control plasmid. Non-silencing (Ctrl_AllStars_1) siRNA and Cebpb (MmCebpb_4 and MmCebpb_6), and Mtor (Mm_Frap1_1 and Mm_Frap1_2) siRNAs were purchased from Qiagen. After transfection, cells were cultured for 36–48 h in RPMI containing 10% serum and 10 ng ml−1 mCSF (PeproTech) or polarized as described above. Whole tumours, CD11b+Gr1− cells, CD90.2+ cells, CD4+ cells and CD8+ cells isolated from LLC tumours were lysed in RIPA buffer and total protein concentrations were determined using a BCA protein assay (Pierce). Macrophage supernatants (100 μl) or 500 μg of total protein lysate from tumours were used in ELISAs to detect CCL2, TGFβ, IL1β, TNFα, IL6, IFNγ, IL10, IL12 and granzyme B (ready set go ELISA, eBioscience). Protein expression was normalized to total volume (supernatants) or mg total protein (tumour lysates). The QuantiChrom arginase assay kit (DARG-200, BioAssay Systems) was used to measure arginase activity in primary mouse bone-marrow-derived macrophages from wild-type and Pik3cg−/− mice according to the manufacturer’s instructions. For all conditions, cells were harvested and lysed in 10 mM Tris (pH 7.4) containing 1 μM pepstatin A, 1 μM leupeptin, and 0.4% (w/v) Triton X-100. Samples were centrifuged at 20,000g at 4 °C for 10 min. To measure NFκB and C/EBPβ activation, TransAM NFκB family and C/EBP transcription factor assay kits (43296 and 44196, Active Motif) were used according to the manufacturer’s protocol. Briefly, wild-type and Pik3cg−/− bone-marrow-derived macrophages were stimulated with LPS (100 ng ml−1) or IL4 (20 ng ml−1) and nuclear extracts were prepared in lysis buffer AM2 (Active Motif). Nuclear extracts were incubated with the immobilized consensus sequences and RelA, cRel or C/EBPβ were detected using specific primary antibodies. Quantification was performed via colourimetric readout of absorbance at 450 nm. IL4 and LPS macrophage cultures were solubilized in RIPA buffer containing protease and phosphatase inhibitors. Thirty micrograms of protein was electrophorezed on Biorad precast gradient gels and electroblotted onto PVDF membranes. Proteins were detected by incubation with 1:1,000 dilutions of primary antibodies, washed and incubated with goat anti-rabbit-HRP antibodies and detected after incubation with a chemiluminescent substrate. Primary antibodies directed against Akt (11E7), p-Akt (244F9), IκBα (L35A5), IκKβ (D30C6), p-IκKα/β (16A6), RelA (D14E12), pRelA (93H1), C/EBPβ (#3087), p-CEBPβ (#3082), IRAK1 (D51G7), TBK1 (D1B4) and PI3Kγ (#4252) were from Cell Signaling Technology and pTBK1 (EPR2867(2)) was from Abcam. CD90.2+ tumour-derived T cells were purified from LLC tumour-bearing wild-type and Pik3cg−/− or TG100-115 and control treated mice and then co-incubated with LLC tumour cells (target cells) at 2.5:1, 5:1 and 10:1 ratios of T cells to tumour cells (2 × 103 LLC tumour cells per well) for 6 h. Target cell killing was assayed by collecting the supernatants from each well for measurement of the lactate dehydrogenase release (Cytotox96 non-radioactive cytotoxicity assay kit, Promega). Tumour samples were collected and cryopreserved in OCT, sections (5 μm) were fixed in 100% cold acetone, blocked with 8% normal goat serum for 2 h, and incubated anti-CD8 (53-6.7, 1:50 BD Biosciences) for 2 h at room temperature. Sections were washed three times with PBS and incubated with Alexa594-conjugated secondary antibodies. Slides were counterstained with 4′,6-diamidino-2-phenylindole (DAPI) to identify nuclei. Immunofluorescence images were collected on a Nikon microscope (Eclipse TE2000-U) and analysed using Metamorph image capture and analysis software (Version 6.3r5, Molecular Devices). The detection of apoptotic cells was performed using a TUNEL-assay (ApopTag fluorescein in situ apoptosis detection kit, Promega) according to the manufacturer’s instructions. Slides were washed and mounted in DAKO fluorescent mounting medium. Immunofluorescence images were collected on a Nikon microscope (Eclipse TE2000-U) and analysed with MetaMorph software (version 6.3r5) or SPOT software (version 4.6). Pixels per field or cell number per field were quantified in five 100× fields from ten biological replicates. Primary tumour samples with mRNA expression data were scored as above or below the median expression level, and tested for association with patient survival using a log-rank test at 5% significance. For studies evaluating the effect of drugs on tumour size, tumour dimensions were measured directly before the start of treatment, tumour volumes were computed and mice were randomly assigned to groups so that the mean volume ± s.e.m. of each group was identical. A sample size of ten mice per group provided 80% power to detect a mean difference of 2.25 standard deviation (s.d.) between two groups (based on a two-sample t-test with two-sided 5% significance level). Sample sizes of 15 mice per group provided 80% power to detect one s.d. difference between two groups. Data were normalized to the standard (control). Analysis for significance was performed by one-way ANOVA with a Tukey’s post-hoc test for multiple pairwise testing with more than two groups and by parametric or nonparametric Student’s t-test when only two groups were compared. We used a two-sample t-test (two groups) and ANOVA (multiple groups) when data were normally distributed and a Wilcoxon rank sum test (two groups) when data were not normally distributed. All mouse studies were randomized and blinded; assignment of mice to treatment groups, tumour measurement and tumour analysis was performed by coding mice with randomly assigned mouse number, with the key unknown to operators until experiments were completed. In tumour studies for which tumour size was the outcome, mice removed from the study owing to health concerns were not included in endpoint analyses. All experiments were performed at least twice; n refers to biological replicates. RNA sequencing data can be accessed using numbers GSE58318 (in vitro macrophage samples) and GSE84535 (in vivo tumour and tumour-associated macrophages samples) at www.ncbi.nlm.nih.gov/geo.


News Article | November 16, 2016
Site: www.eurekalert.org

LA JOLLA--(November 16, 2016) Salk Institute researchers have discovered a holy grail of gene editing--the ability to, for the first time, insert DNA at a target location into the non-dividing cells that make up the majority of adult organs and tissues. The technique, which the team showed was able to partially restore visual responses in blind rodents, will open new avenues for basic research and a variety of treatments, such as for retinal, heart and neurological diseases. "We are very excited by the technology we discovered because it's something that could not be done before," says Juan Carlos Izpisua Belmonte, a professor in Salk's Gene Expression Laboratory and senior author of the paper published on November 16, 2016 in Nature. "For the first time, we can enter into cells that do not divide and modify the DNA at will. The possible applications of this discovery are vast." Until now, techniques that modify DNA--such as the CRISPR-Cas9 system--have been most effective in dividing cells, such as those in skin or the gut, using the cells' normal copying mechanisms. The new Salk technology is ten times more efficient than other methods at incorporating new DNA into cultures of dividing cells, making it a promising tool for both research and medicine. But, more importantly, the Salk technique represents the first time scientists have managed to insert a new gene into a precise DNA location in adult cells that no longer divide, such as those of the eye, brain, pancreas or heart, offering new possibilities for therapeutic applications in these cells. To achieve this, the Salk researchers targeted a DNA-repair cellular pathway called NHEJ (for "non-homologous end-joining"), which repairs routine DNA breaks by rejoining the original strand ends. They paired this process with existing gene-editing technology to successfully place new DNA into a precise location in non-dividing cells. "Using this NHEJ pathway to insert entirely new DNA is revolutionary for editing the genome in live adult organisms," says Keiichiro Suzuki, a senior research associate in the Izpisua Belmonte lab and one of the paper's lead authors. "No one has done this before." First, the Salk team worked on optimizing the NHEJ machinery for use with the CRISPR-Cas9 system, which allows DNA to be inserted at very precise locations within the genome. The team created a custom insertion package made up of a nucleic acid cocktail, which they call HITI, or homology-independent targeted integration. Then they used an inert virus to deliver HITI's package of genetic instructions to neurons derived from human embryonic stem cells. "That was the first indication that HITI might work in non-dividing cells," says Jun Wu, staff scientist and co-lead author. With that feat under their belts, the team then successfully delivered the construct to the brains of adult mice. Finally, to explore the possibility of using HITI for gene-replacement therapy, the team tested the technique on a rat model for retinitis pigmentosa, an inherited retinal degeneration condition that causes blindness in humans. This time, the team used HITI to deliver to the eyes of 3-week-old rats a functional copy of Mertk, one of the genes that is damaged in retinitis pigmentosa. Analysis performed when the rats were 8 weeks old showed that the animals were able to respond to light, and passed several tests indicating healing in their retinal cells. "We were able to improve the vision of these blind rats," says co-lead author Reyna Hernandez-Benitez, a Salk research associate. "This early success suggests that this technology is very promising." The team's next steps will be to improve the delivery efficiency of the HITI construct. As with all genome editing technologies, getting enough cells to incorporate the new DNA is a challenge. The beauty of HITI technology is that it is adaptable to any targeted genome engineering system, not just CRISPR-Cas9. Thus, as the safety and efficiency of these systems improve, so too will the usefulness of HITI. "We now have a technology that allows us to modify the DNA of non-dividing cells, to fix broken genes in the brain, heart and liver," says Izpisua Belmonte. "It allows us for the first time to be able to dream of curing diseases that we couldn't before, which is exciting." Other researchers on the study were Euiseok J. Kim, Fumiyuki Hatanaka, Mako Yamamoto, Toshikazu Araoka, Masakazu Kurita, Tomoaki Hishida, Mo Li, Emi Aizawa, April Goebl, Rupa Devi Soligalla, Concepcion Rodriguez Esteban, Travis Berggren and Edward M. Callaway of the Salk Institute; Yuji Tsunekawa and Fumio Matsuzaki of RIKEN Center for Developmental Biology; Pierre Magistretti of King Abdullah University of Science and Technology; Jie Zhu, Tingshuai Jiang, Xin Fu, Maryam Jafari and Kang Zhang of Shiley Eye Institute and Institute for Genomic Medicine, University of California San Diego; Zhe Li, Shicheng Guo, Song Chen and Kun Zhang of Institute of Engineering in Medicine, University of California San Diego; Jing Qu and Guang-Hui Liu of Chinese Academy of Sciences; Jeronimo Lajara, Estrella Nuñez and Pedro Guillen of Universidad Catolica San Antonio de Murcia; and Josep M. Campistol of the University of Barcelona. The work and the researchers involved were supported in part by the National Institutes of Health, The Leona M. and Harry B. Helmsley Charitable Trust, the G. Harold and Leila Y. Mathers Charitable Foundation, The McKnight Foundation, The Moxie Foundation, the Dr. Pedro Guillen Foundation and Universidad Catolica San Antonio de Murcia, Spain. Every cure has a starting point. The Salk Institute embodies Jonas Salk's mission to dare to make dreams into reality. Its internationally renowned and award-winning scientists explore the very foundations of life, seeking new understandings in neuroscience, genetics, immunology and more. The Institute is an independent nonprofit organization and architectural landmark: small by choice, intimate by nature and fearless in the face of any challenge. Be it cancer or Alzheimer's, aging or diabetes, Salk is where cures begin. Learn more at: salk.edu.


News Article | April 27, 2016
Site: www.nature.com

NOD/SCID Il2rgnull mice (Jackson Laboratory) were bred and maintained in the Stem Cell Unit animal barrier facility at McMaster University. All procedures were approved by the Animal Research Ethics Board at McMaster University. All patient samples were obtained with informed consent and with the approval of local human subject research ethics boards at McMaster University. Human umbilical cord blood mononuclear cells were collected by centrifugation with Ficoll-Paque Plus (GE), followed by red blood cell lysis with ammonium chloride (StemCell Technologies). Cells were then incubated with a cocktail of lineage-specific antibodies (CD2, CD3, CD11b, CD11c, CD14, CD16, CD19, CD24, CD56, CD61, CD66b, and GlyA; StemCell Technologies) for negative selection of Lin− cells using an EasySep immunomagnetic column (StemCell Technologies). Live cells were discriminated on the basis of cell size, granularity and, as needed, absence of viability dye 7-AAD (BD Biosciences) uptake. All flow cytometry analysis was performed using a BD LSR II instrument (BD Biosciences). Data acquisition was conducted using BD FACSDiva software (BD Biosciences) and analysis was performed using FlowJo software (Tree Star). To quantify MSI2 expression in human HSPCs, Lin− cord blood cells were stained with the appropriate antibody combinations to resolve HSC (CD34+ CD38− CD45RA− CD90+), MPP (CD34+ CD38− CD45RA− CD90−), CMP (CD34+ CD38+ CD71−) and EP (CD34+ CD38+ CD71+) fractions as similarly described previously18, 19 with all antibodies from BD Biosciences: CD45RA (HI100), CD90 (5E10), CD34 (581), CD38 (HB7) and CD71 (M-A712). Cell viability was assessed using the viability dye 7AAD (BD Biosciences). All cell subsets were isolated using a BD FACSAria II cell sorter (BD Biosciences) or a MoFlo XDP cell sorter (Beckman Coulter). HemaExplorer20 analysis was used to confirm MSI2 expression in human HSPCs and across the hierarchy. For all qRT–PCR determinations total cellular RNA was isolated with TRIzol LS reagent according to the manufacturer’s instructions (Invitrogen) and cDNA was synthesized using the qScript cDNA Synthesis Kit (Quanta Biosciences). qRT–PCR was done in triplicate with PerfeCTa qPCR SuperMix Low ROX (Quanta Biosciences) with gene-specific probes (Universal Probe Library (UPL), Roche) and primers: MSI2 UPL-26, F-GGCAGCAAGAGGATCAGG, R-CCGTAGAGATCGGCGACA; HSP90 UPL-46, F-GGGCAACACCTCTACAAGGA, R-CTTGGGTCTGGGTTTCCTC; CYP1B1 UPL-20, F-ACGTACCGGCCACTATCACT, R-CTCGAGTCTGCACATCAGGA; GAPDH UPL-60, F-AGCCACATCGCTCAGACAC, R-GCCCAATACGACCAAATCC; ACTB (UPL Set Reference Gene Assays, Roche). The mRNA content of samples compared by qRT–PCR was normalized based on the amplification of GAPDH or ACTB. MSI2 shRNAs were designed with the Dharmacon algorithm (http://www.dharmacon.com). Predicted sequences were synthesized as complimentary oligonucleotides, annealed and cloned downstream of the H1 promoter of the modfied cppt-PGK-EGFP-IRES-PAC-WPRE lentiviral expression vector18. Sequences for the MSI2 targeting and control RFP targeting shRNAs were as follows: shMSI2, 5′-GAGAGATCCCACTACGAAA-3′; shRFP, 5′-GTGGGAGCGCGTGATGAAC-3′. Human MSI2 cDNA (BC001526; Open Biosystems) was subcloned into the MA bi-directional lentiviral expression vector21. Human CYP1B1 cDNA (BC012049; Open Biosystems) was cloned in to psMALB22. All lentiviruses were prepared by transient transfection of 293FT (Invitrogen) cells with pMD2.G and psPAX2 packaging plasmids (Addgene) to create VSV-G pseudotyped lentiviral particles. All viral preparations were titrated on HeLa cells before use on cord blood. Standard SDS–PAGE and western blotting procedures were performed to validate the effects of knockdown on transduced NB4 cells (DSMZ) and overexpression on 293FT cells. Immunoblotting was performed with anti-MSI2 rabbit monoclonal IgG (EP1305Y, Epitomics) and β-actin mouse monoclonal IgG (ACTBD11B7, Santa Cruz Biotechnology) antibodies. Secondary antibodies used were IRDye 680 goat anti-rabbit IgG and IRDye 800 goat anti-mouse IgG (LI-COR). 293FT and NB4 cell lines tested negative for mycoplasma. NB4 cells were authenticated by ATRA treatment before use. Cord blood transductions were conducted as described previously18, 23. Briefly, thawed Lin− cord blood or flow-sorted Lin− CD34+ CD38− or Lin− CD34+ CD38+ cells were prestimulated for 8–12 h in StemSpan medium (StemCell Technologies) supplemented with growth factors interleukin 6 (IL-6; 20 ng ml−1, Peprotech), stem cell factor (SCF; 100 ng ml−1, R&D Systems), Flt3 ligand (FLT3-L; 100 ng ml−1, R&D Systems) and thrombopoietin (TPO; 20 ng ml−1, Peprotech). Lentivirus was then added in the same medium at a multiplicity of infection of 30–100 for 24 h. Cells were then given 2 days after transduction before use in in vitro or in vivo assays. For in vitro cord blood studies biological (experimental) replicates were performed with three independent cord blood samples. Human clonogenic progenitor cell assays were done in semi-solid methylcellulose medium (Methocult H4434; StemCell Technologies) with flow-sorted GFP+ cells post transduction (500 cells per ml) or from day seven cultured transduced cells (12,000 cells per ml). Colony counts were carried out after 14 days of incubation. CFU-GEMMs can seed secondary colonies owing to their limited self-renewal potential24. Replating of MSI2-overexpressing and control CFU-GEMMs for secondary CFU analysis was performed by picking single CFU-GEMMs at day 14 and disassociating colonies by vortexing. Cells were spun and resuspended in fresh methocult, mixed with a blunt-ended needle and syringe, and then plated into single wells of a 24-well plate. Secondary CFU analysis for shMSI2- and shControl-expressing cells was performed by harvesting total colony growth from a single dish (as nearly equivalent numbers of CFU-GEMMs were present in each dish), resuspending cells in fresh methocult by mixing vigorously with a blunt-ended needle and syringe and then plating into replicate 35-mm tissue culture dishes. In both protocols, secondary colony counts were done following incubation for 10 days. For primary and secondary colony forming assays performed with the AHR agonist FICZ (Santa Cruz Biotechnology), 200 nM FICZ or 0.1% DMSO was added directly to H4434 methocult medium. Two-way ANOVA analysis was performed to compare secondary CFU output and FICZ treatment for MSI2-overexpressing or control conditions. Colonies were imaged with a Q-Colour3 digital camera (Olympus) mounted to an Olympus IX5 microscope with a 10× objective lens. Image-Pro Plus imaging software (Media Cybernetics) was used to acquire pictures and subsequent image processing was performed with ImageJ software (NIH). Transduced human Lin− cord blood cells were sorted for GFP expression and seeded at a density of 105 cells per ml in IMDM 10% FBS supplemented with human growth factors IL-6 (10 ng ml−1), SCF (50 ng ml−1), FLT3-L (50 ng ml−1), and TPO (20 ng ml−1) as previously described25. To generate growth curves, every seven days cells were counted, washed, and resuspended in fresh medium with growth factors at a density of 105 cells per ml. Cells from suspension cultures were also used in clonogenic progenitor, cell cycle and apoptosis assays. Experiments performed on transduced Lin− CD34+ cord blood cells used serum-free conditions as described in the cord blood transduction subsection of Methods. For in vitro cord blood studies, biological (experimental) replicates were performed with three independent cord blood samples. Cell cycle progression was monitored with the addition of BrdU to day 10 suspension cultures at a final concentration of 10 μM. After 3 h of incubation, cells were assayed with the BrdU Flow Kit (BD Biosciences) according to the manufacturer’s protocol. Cell proliferation and quiescence were measured using Ki67 (BD Bioscience) and Hoechst 33342 (Sigma) on day 4 suspension cultures after fixing and permeabilizing cells with the Cytofix/Cytoperm kit (BD Biosciences). For apoptosis analysis, Annexin V (Invitrogen) and 7-AAD (BD Bioscience) staining of day 7 suspension cultures was performed according to the manufacturer’s protocol. Lin− cord blood cells were initially stained with anti-CD34 PE (581) and anit-CD38 APC (HB7) antibodies (BD Biosciences) then fixed with the Cytofix/Cytoperm kit (BD Biosciences) according to the manufacturer’s instructions. Fixed and permeabilized cells were immunostained with anti-MSI2 rabbit monoclonal IgG antibody (EP1305Y, Abcam) and detected by Alexa-488 goat anti-rabbit IgG antibody (Invitrogen). CD34+ cells were transduced with an MSI2-overexpression or MSI2-knockdown lentivirus along with their corresponding controls and sorted for GFP expression 3 days later. Transductions for MSI2 overexpression or knockdown were each performed on two independent cord blood samples. Total RNA from transduced cells (>1 × 105) was isolated using TRIzol LS as recommended by the manufacturer (Invitrogen), and then further purified using RNeasy columns (Qiagen). Sample quality was assessed using Bioanalyzer RNA Nano chips (Agilent). Paired-end, barcoded RNA-seq sequencing libraries were then generated using the TruSeq RNA Sample Prep Kit (v2) (Illumina) following the manufacturer’s protocols starting from 1 μg total RNA. The quality of library generation was then assessed using a Bioanalyzer platform (Agilent) and Illumina MiSeq-QC run was performed or quantified by qPCR using KAPA quantification kit (KAPA Biosystems). Sequencing was performed using an Illumina HiSeq2000 using TruSeq SBS v3 chemistry at the Institute for Research in Immunology and Cancer’s Genomics Platform (University of Montreal) with cluster density targeted at 750,000 clusters per mm2 and paired-end 2 × 100-bp read lengths. For each sample, 90–95 million reads were produced and mapped to the hg19 (GRCh37) human genome assembly using CASAVA (version 1.8). Read counts generated by CASAVA were processed in EdgeR (edgeR_3.12.0, R 3.2.2) using TMM normalization, paired design, and estimation of differential expression using a generalized linear model (glmFit). The false discovery rate (FDR) was calculated from the output P values using the Benjamini–Hochberg method. The fold change of logarithm of base 2 of TMM normalized data (logFC) was used to rank the data from top upregulated to top downregulated genes and FDR (0.05) was used to define significantly differentially expressed genes. RNA-seq data have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE70685. iRegulon26 was used to retrieve the top 100 AHR predicted targets with a minimal occurrence count threshold of 5. The data were analysed using GSEA27 with ranked data as input with parameters set to 2,000 gene-set permutations. The GEO dataset GSE28359, which contains Affymetrix Human Genome U133 Plus 2.0 Array gene expression data for CD34+ cells treated with SR1 at 30 nM, 100 nM, 300 nM and 1,000 nM was used to obtain lists of genes differentially expressed in the treated samples compared to the control ones (0 nM)2. Data were background corrected using Robust Multi-Array Average (RMA) and quantile normalized using the expresso() function of the affy Bioconductor package (affy_1.38.1, R 3.0.1). Lists of genes were created from the 150 top upregulated and downregulated genes from the SR1-treated samples at each dose compared to the non-treated samples (0 nM). The data were analysed using GSEA with ranked data as input with parameters set to 2,000 gene-set permutations. The normalized enrichment score (NES) and false discovery rate (FDR) were calculated for each comparison. The GEO data set GSE24759, which contains Affymetrix GeneChip HT-HG_U133A Early Access Array gene expression data for 38 distinct haematopoietic cell states4, was compared to the MSI2 overexpression and knockdown data. GSE24759 data were background corrected using Robust Multi-Array Average (RMA), quantile normalized using the expresso() function of the affy Bioconductor package (affy_1.38.1, R 3.0.1), batch corrected using the ComBat() function of the sva package (sva_3.6.0) and scaled using the standard score. Bar graphs were created by calculating for significantly differentially expressed genes the number of scaled data that were above (>0) or below (<0) the mean for each population. Percentages indicating for how long the observed value (set of up- or downregulated genes) was better represented in that population than random values were calculated from 1,000 trials. A unique list of genes closest to AHR-bound regions previously identified from TCDD-treated MCF7 ChIP–seq data14 was used to calculate the overlap with genes showing >1.5-fold downregulation in response to treatment with UM171 (35 nM) or SR1 (500 nM) relative to DMSO-treated samples3 as well as with genes significantly downregulated in MSI2-overexpressing versus control treated samples (FDR < 0.05). The percentage of downregulated genes with AHR-bound regions was then plotted for each gene set. P values were generated with Fisher’s exact test for comparisons between gene lists. AHR transcription factor binding sites in downregulated gene sets were identified with oPOSSUM-328. Genes showing >1.5-fold downregulation in response to treatment with UM171 (35 nM) or SR1 (500 nM) relative to DMSO-treated samples3 were used along with significantly downregulated genes (FDR < 0.05) with EdgeR-analysed MSI2-overexpressing versus control-treated samples. The three gene lists were uploaded into oPOSSUM-3 and the AHR:ARNT transcription factor binding site profile was used with the matrix score threshold set at 80% to analyse the region 1,500 bp upstream and 1,000 bp downstream of the transcription start site. The percentage of downregulated genes with AHR-binding sites in their promoters was then plotted for each gene set. Fisher’s exact test was used to identify significant overrepresentation of AHR-binding sites in gene lists relative to background. Eight- to 12-week-old male or female NSG mice were sublethally irradiated (315 cGy) one day before intrafemoral injection with transduced cells carried in IMDM 1% FBS at 25 μl per mouse. Injected mice were analysed for human haematopoietic engraftment 12–14 weeks after transplantation or at 3 and 6.5 weeks for STRC experiments. Mouse bones (femurs, tibiae and pelvis) and spleen were removed and bones were crushed with a mortar and pestle then filtered into single-cell suspensions. Bone marrow and spleen cells were blocked with mouse Fc block (BD Biosciences) and human IgG (Sigma) and then stained with fluorochrome-conjugated antibodies specific to human haematopoietic cells. For multilineage engraftment analysis, cells from mice were stained with CD45 (HI30) (Invitrogen), CD33 (P67.6), CD15 (HI98), CD14 (MφP9), CD19 (HIB19), CD235a/GlyA (GA-R2), CD41a (HIP8) and CD34 (581) (BD Biosciences). For MSI2 knockdown in HSCs, 5.0 × 104 and 2.5 × 104 sorted Lin− CD34+ CD38− cells were used per short-hairpin transduction experiment, leading to transplantation of day zero equivalent cell doses of 10 × 103 and 6.25 × 103, respectively, per mouse. For STRC LDA transplantation experiments, 105 sorted CD34+CD38+ cells were used per control or MSI2-overexpressing transduction. After assessing levels of gene transfer, day zero equivalent GFP+ cell doses were calculated to perform the LDA. Recipients with greater than 0.1% GFP+CD45+/− cells were considered to be repopulated. For STRC experiments that read out extended engraftment at 6.5 weeks, 2 × 105 CD34+ CD38+ cells were used per overexpressing or control transduction to allow non-limiting 5 × 104 day zero equivalent cell doses per mouse. For HSC expansion and LDA experiments, CD34+CD38− cells were sorted and transduced with MSI2-overexpressing or control vectors (50,000 cells per condition) for 3 days and then analysed for gene-transfer levels (% GFP+/−) and primitive cell marker expression (% CD34 and CD133). To ensure that equal numbers of GFP+ cells were transplanted into both control and MSI2-overexpressing recipient mice, we added identically cultured GFP− cells to the MSI2 culture to match the % GFP+ of the control culture (necessary owing to the differing efficiency of transduction). The adjusted MSI2-overexpressing culture was recounted and aliquoted (63,000 cells) to match the output of half of the control culture. Three day 0 equivalent GFP+ cell doses (1,000, 300 and 62 cells) were then transplanted per mouse to perform the D3 primary LDA. A second aliquot of the adjusted MSI2-overexpressing culture was then taken and put into culture in parallel with the remaining half of the control culture to perform another LDA after 7 days of growth (10 days total growth, D10 primary LDA). Altogether, four cell doses were transplanted; when converted back to day 0 equivalents these equalled approximately 1,000, 250, 100, and 20 GFP+ cells per mouse, respectively. Pooled bone marrow from six engrafted primary mice that received D10 cultured control or MSI2-overexpressing cells (from the two highest doses transplanted) was aliquoted into five cell doses of 15 million, 10 million, 6 million, 2 million and 1 million cells. The numbers of GFP+ cells within primary mice was estimated from nucleated cell counts obtained from NSG femurs, tibias and pelvises and from Colvin et al.29. The actual numbers of GFP+ cells used for determining numbers of GFP+ HSCs and the number of mice transplanted for all LDA experiments is shown in Supplementary Tables 3–5. The cut-off for HSC engraftment was a demonstration of multilineage reconstitution that was set at bone marrow having >0.1% GFP+ CD33+ and >0.1% GFP+ CD19+ cells. HSC and STRC frequency was assessed using ELDA software30. For all mouse transplantation experiments, mice were age- (6–12 week) and sex-matched. All transplanted mice were included for analysis unless mice died from radiation sickness before the experimental endpoint. No randomization or blinding was performed for animal experiments. Approximately 3–6 mice were used per cell dose for each cord blood transduction and transplantation experiment. CLIP–seq was performed as previously described15. Briefly, 25 million NB4 cells (a transformed human cell line of haematopoietic origin) were washed in PBS and UV-cross-linked at 400 mJ cm−2 on ice. Cells were pelleted, lysed in wash buffer (PBS, 0.1% SDS, 0.5% Na-deoxycholate, 0.5% NP-40) and DNase-treated, and supernatants from lysates were collected for immunoprecipitation. MSI2 was immunoprecipitated overnight using 5 μg of anti-MSI2 antibody (EP1305Y, Abcam) and Protein A Dynabeads (Invitrogen). Beads containing immunoprecipated RNA were washed twice with wash buffer, high-salt wash buffer (5× PBS, 0.1% SDS, 0.5% Na-Deoxycholate, 0.5% NP-40), and PNK buffer (50 mM Tris-Cl pH 7.4, 10 mM MgCl , 0.5% NP-40). Samples were then treated with 0.2 U MNase for 5 min at 37° with shaking to trim immunopreciptated RNA. MNase inactivation was then carried out with PNK + EGTA buffer (50 mM Tris-Cl pH 7.4, 20 mM EGTA, 0.5% NP-40). The sample was dephosphorylated using alkaline phosphatase (CIP, NEB) at 37° for 10 min followed by washing with PNK+EGTA, PNK buffer, and then 0.1 mg ml−1 BSA in nuclease-free water. 3′RNA linker ligation was performed at 16° overnight with the following adaptor: 5′P-UGGAAUUCUCGGGUGCCAAGG-puromycin. Samples were then washed with PNK buffer, radiolabelled using P32-y-ATP (Perkin Elmer), run on a 4–12% Bis-Tris gel and then transferred to a nitrocellulose membrane. The nitrocellulose membrane was developed via autoradiography and RNA–protein complexes 15–20 kDa above the molecular weight of MSI2 were extracted with proteinase K followed by RNA extraction with acid phenol-chloroform. A 5′RNA linker (5′HO-GUUCAGAGUUCUACAGUCCGACGAUC-OH) was ligated to the extracted RNA using T4 RNA ligase (Fermentas) for 2 h and the RNA was again purified using acid phenol-chloroform. Adaptor ligated RNA was re-suspended in nuclease-free water and reverse transcribed using Superscript III reverse transcriptase (Invitrogen). Twenty cycles of PCR were performed using NEB Phusion Polymerase using a 3′PCR primer that contained a unique Illumina barcode sequence. PCR products were run on an 8% TBE gel. Products ranging between 150 and 200 bp were extracted using the QIAquick gel extraction kit (Qiagen) and re-suspended in nuclease-free water. Two separate libraries were prepared and sent for single-end 50-bp Illumina sequencing at the Institute for Genomic Medicine at the University of California, San Diego. 47,098,127 reads from the first library passed quality filtering, of which 73.83% mapped uniquely to the human genome. 57,970,220 reads from the second library passed quality filtering, of which 69.53% mapped uniquely to the human genome. CLIP-data reproducibility was verified through high correlation between gene RPKMs and statistically significant overlaps in the clusters and genes within replicates. CLIP–seq data have been deposited in NCBI’s GEO and are accessible through GEO Series accession number GSE69583. Before sequence alignment of CLIP–seq reads to the human genome was performed, sequencing reads from libraries were trimmed of polyA tails, adapters, and low quality ends using Cutadapt with parameters–match-read-wildcards–times 2 -e 0 -O 5–quality-cutoff' 6 -m 18 -b TCGTATGCCGTCTTCTGCTTG -b ATCTCGTATGCCGTCTTCTGCTTG -b CGACAGGTTCAGAGTTCTACAGTCCGACGATC -b TGGAATTCTCGGGTGCCAAGG -b AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA-b TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT. Reads were then mapped against a database of repetitive elements derived from RepBase (version 18.05). Bowtie (version 1.0.0) with parameters -S -q -p 16 -e 100 -l 20 was used to align reads against an index generated from Repbase sequences31. Reads not mapped to Repbase sequences were aligned to the hg19 human genome (UCSC assembly) using STAR (version 2.3.0e)32 with parameters–outSAMunmapped Within –outFilterMultimapNmax 1 –outFilterMultimapScoreRange 1. To identify clusters in the genome of significantly enriched CLIP–seq reads, reads that were PCR replicates were removed from each CLIP–seq library using a custom script of the same method as in ref. 33; otherwise, reads were kept at each nucleotide position when more than one read’s 5′-end was mapped. Clusters were then assigned using the CLIPper software with parameters–bonferroni–superlocal–threshold-34. The ranked list of significant targets was calculated assuming a Poisson distribution, where the observed value is the number of reads in the cluster, and the background is the number of reads across the entire transcript and or across a window of 1000 bp ± the predicted cluster. Transcriptomic regions and gene classes were defined using annotations found in gencode v17. Depending on the analysis, clusters were associated by the Gencode-annotated 5′UTR, 3′UTR, CDS or intronic regions. If a cluster overlapped multiple regions, or a single part of a transcript was annotated as multiple regions, clusters were iteratively assigned first as CDS, then 3′UTR, 5′UTR and finally as proximal (<500 bases from an exon) or distal (>500 bases from an exon) introns. Overlapping peaks were calculated using bedtools and pybedtools35, 36. Significantly enriched gene ontology (GO) terms were identified using a hypergeometric test that compared the number of genes that were MSI2 targets in each GO term to genes expressed in each GO term as the proper background. Expressed genes were identified using the control samples in SRA study SRP012062. Mapping was performed identically to CLIP–seq mapping, without peak calling and changing the STAR parameter outFilterMultimapNmax to 10. Counts were calculated with featureCounts37 and RPKMs were then computed. Only genes with a mean RPKM > 1 between the two samples were used in the background expressed set. Randomly located clusters within the same genic regions as predicted MSI2 clusters were used to calculate a background distribution for motif and conservation analyses. Motif analysis was performed using the HOMER algorithm as in ref. 34. For evolutionary sequence conservation analysis, the mean (mammalian) phastCons score for each cluster was used. CD34+ cells (>5 × 104) were transduced with an MSI2-overexpression or control lentivirus. Three days later, GFP+ cells were sorted and then put back in to StemSpan medium containing growth factors IL-6 (20 ng ml−1), SCF (100 ng ml−1), FLT3-L (100 ng ml−1) and TPO (20 ng ml−1). A minimum of 10,000 cells were used for immunostaining at culture days 3 and 7 after GFP sorting. Cells were fixed in 2% PFA for 10 min, washed with PBS and then cytospun on to glass slides. Cytospun cells were then permeabilized (PBS, 0.2% Triton X-100) for 20 min, blocked (PBS, 0.1% saponin, 10% donkey serum) for 30 min and stained with primary antibodies (CYP1B1 (EPR14972, Abcam); HSP90 (68/hsp90, BD Biosciences)) in PBS with 10% donkey serum for 1 h. Detection with secondary antibody was performed in PBS 10% donkey serum with Alexa-647 donkey anti-rabbit antibody or Alexa-647 donkey anti-mouse antibodies for 45 min. Slides were mounted with Prolong Gold Antifade containing DAPI (Invitrogen). Several images (200–1,000 cells total) were captured per slide at 20× magnification using an Operetta HCS Reader (Perkin Elmer) with epifluorescence illumination and standard filter sets. Columbus software (Perkin Elmer) was used to automate the identification of nuclei and cytoplasm boundaries in order to quantify mean cell fluorescence. A 271-bp region of the CYP1B1 3′UTR that flanked CLIP–seq-identified MSI2-binding sites was cloned from human HEK293FT genomic DNA using the forward primer GTGACACAACTGTGTGATTAAAAGG and reverse primer TGATTTTTATTATTTTGGT AATGGTG and placed downstream of renilla luciferase in the dual-luciferase reporter vector pGL4 (Promega). A 271-bp geneblock (IDT) with 6 TAG > TCC mutations was cloned in to pGL4 using XbaI and NotI. The HSP90 3′UTR was amplified from HEK293FT genomic DNA with the forward primer TCTCTGGCTGAGGGATGACT and reverse primer TTTTAAGGCCAAGGAATTAAGTGA and cloned into pGL4. A geneblock of the HSP90 3′UTR (IDT) with 14 TAG > TCC mutations was cloned in to pGL4 using SfaAI and NotI. Co-transfection of wild-type or mutant luciferase reporter (40 ng) and control or MSI2-overexpressing lentiviral expression vector (100 ng) was performed in the NIH-3T3 cell line, which does not express MSI1 or MSI2 (50,000 cells per co-transfection). Reporter activity was measured using the Dual-Luciferase Reporter Assay System (Promega) 36–40 h later. For MSI2-overexpressing cultures with the AHR antagonist SR1, Lin− CD34+ cells were transduced with MSI2-overexpression or control lentivirus in medium supplemented with SR1 (750 nM; Abcam) or DMSO vehicle (0.1%). GFP+ cells were isolated (20,000 cells per culture) and allowed to proliferate with or without SR1 for an additional 7 days at which point they were counted and immunophenotyped for CD34 and CD133 expression. For MSI2-overexpressing cultures with the AHR agonist FICZ, Lin− CD34+ cells were transduced with MSI2-overexpression or control lentivirus. GFP+ cells were isolated (20,000 cells per culture) and allowed to proliferate with FICZ (200 nM; Santa Cruz Biotechnology) or DMSO (0.1%) for an additional 3 days, at which point they were immunophenotyped for CD34 and CD133 expression. Lin− CD34+ cells were cultured for 72 h (lentiviral treated but non-transduced flow-sorted GFP− cells) in StemSpan medium containing growth factors IL-6 (20 ng ml−1), SCF (100 ng ml−1), FLT3-L (100 ng ml−1) and TPO (20 ng ml−1) before the addition of the CYP1B1 inhibitor TMS (Abcam) at a concentration of 10 μM or mock treatment with 0.1% DMSO. Equal numbers of cells (12,000 per condition) were then allowed to proliferate for 7 days at which point they were counted and immunophenotyped for CD34 and CD133 expression. Unless stated otherwise (that is, analysis of RNA–seq and CLIP–seq data sets), all statistical analysis was performed using GraphPad Prism (GraphPad Software version 5.0). Unpaired student t-tests or Mann–Whitney tests were performed with P < 0.05 as the cut-off for statistical significance. No statistical methods were used to predetermine sample size.


News Article | November 10, 2016
Site: www.sciencedaily.com

The same algorithms that personalize movie recommendations and extract topics from oceans of text could bring doctors closer to diagnosing, treating and preventing disease on the basis of an individual's unique genetic profile. In a study to be published in Nature Genetics, researchers at Columbia and Princeton universities describe a new machine-learning algorithm for scanning massive genetic data sets to infer an individual's ancestral makeup, which is key to identifying disease-carrying genetic mutations. On simulated data sets of 10,000 individuals, TeraStructure could estimate population structure more accurately and twice as fast as current state-of-the art algorithms, the study said. TeraStructure alone was capable of analyzing 1 million individuals, orders of magnitude beyond modern software capabilities, researchers said. The algorithm could potentially characterize the structure of world-scale human populations. "We're excited to scale some of our recent machine learning tools to real-world problems in genetics," said David Blei, a professor of computer science and statistics at Columbia University and member of the Data Science Institute. The cost of genetic sequencing has fallen sharply since the first complete mapping of the human genome in 2003. More than a million people now have sequenced genomes, and by 2025 that number could rise to 2 billion. The technology to put this data into context, however, has lagged and remains one of the barriers to tailoring healthcare to an individual's DNA. To identify disease-causing variants in a genome, one of the goals of personalized medicine, researchers need to know something about his or her ancestry to control for normal genetic variation within a subpopulation. "We can run software on a few thousand people, but if we increase our sample size to a few hundred thousand, it can take months to infer population structure," said Kai Wang, director of clinical informatics at Columbia's Institute for Genomic Medicine, who was not involved in the study. "This new tool addresses these limitations, and will be very useful for analyzing the genomes of large populations." The researchers' algorithm, called TeraStructure, builds on the widely used and adapted STRUCTURE algorithm first described in the journal Genetics in 2000. The STRUCTURE algorithm cycles through an entire data set, genome by genome, one million variants at a time, before updating its model to both characterize ancestral populations and estimate their proportion in each individual. The model gets refined after repeated passes through the data set. TeraStructure, by contrast, updates the model as it goes. It samples one genetic variant at one location, and compares it to all variants in the data set at the same location across the data set, producing a working estimate of population structure. "You don't have to painstakingly go through all the points each time to update your model," said Blei. STRUCTURE is mathematically similar to a topic-modeling algorithm Blei developed independently in 2003 that made it possible to scan large numbers of documents for overarching themes. Blei's algorithm and its underlying LDA model have been used, among other things, to analyze published research in the journal Science to understand the evolution of scientific ideas and review regulatory meeting transcripts for insight into how the U.S. Federal Reserve sets interest rates. More recently, Blei has experimented with statistical techniques to extend probabilistic models to massive data sets. One technique, stochastic optimization, developed in 1951 by statistician Herbert Robbins just before arriving at Columbia, uses a small, random subset of observations to compute a rough update for the model's parameters. Continuously refining the model with each new observation, stochastic optimization algorithms have been enormously successful in scaling up machine learning approaches used in deep learning, recommendation systems and social network analysis. In a 2010 paper, Online Learning for LDA, Blei and his colleagues applied stochastic optimization to Blei's earlier LDA model. In a later paper, Stochastic Variational Inference, they showed that stochastic optimization could be applied to a range of models. As Matthew Hoffman, a coauthor of both papers, now a senior research scientist at Adobe Research explains, "Stochastic optimization algorithms often find a good solutions before they've even analyzed the whole dataset." In the Nature Genetics study, they apply these ideas to the STRUCTURE method. In their analysis of two real-world data sets -- 940 individual genomes from Stanford's Human Genome Diversity Project and 1,718 genomes from the 1000 Genomes Project -- they found that TeraStructure performed comparably to the more recent ADMIXTURE and fastSTRUCTURE algorithms. But when they ran TeraStructure on a simulated data set of 10,000 genomes, it was more accurate and two to three times faster at estimating population structure, the study said. The researchers also showed that TeraStructure alone could analyze data sets as large as 100,000 genomes and 1 million genomes. Matthew Stephens, a genetics researcher at University of Chicago who helped develop the STRUCTURE algorithm, called TeraStructure's performance impressive. "I think these results will motivate future applications of this kind of algorithm in challenging inferences problems," he said The study also received praise from other researchers working with big genetic data sets. "We now have the technology to create the data," said Itsik Pe'er, a computational geneticist at Columbia Engineering who was not involved in the study. "But this paper really allows us to use it."


News Article | November 7, 2016
Site: www.eurekalert.org

The same algorithms that personalize movie recommendations and extract topics from oceans of text could bring doctors closer to diagnosing, treating and preventing disease on the basis of an individual's unique genetic profile. In a study to be published Monday, Nov. 7 in Nature Genetics, researchers at Columbia and Princeton universities describe a new machine-learning algorithm for scanning massive genetic data sets to infer an individual's ancestral makeup, which is key to identifying disease-carrying genetic mutations. On simulated data sets of 10,000 individuals, TeraStructure could estimate population structure more accurately and twice as fast as current state-of-the art algorithms, the study said. TeraStructure alone was capable of analyzing 1 million individuals, orders of magnitude beyond modern software capabilities, researchers said. The algorithm could potentially characterize the structure of world-scale human populations. "We're excited to scale some of our recent machine learning tools to real-world problems in genetics," said David Blei, a professor of computer science and statistics at Columbia University and member of the Data Science Institute. The cost of genetic sequencing has fallen sharply since the first complete mapping of the human genome in 2003. More than a million people now have sequenced genomes, and by 2025 that number could rise to 2 billion. The technology to put this data into context, however, has lagged and remains one of the barriers to tailoring healthcare to an individual's DNA. To identify disease-causing variants in a genome, one of the goals of personalized medicine, researchers need to know something about his or her ancestry to control for normal genetic variation within a subpopulation. "We can run software on a few thousand people, but if we increase our sample size to a few hundred thousand, it can take months to infer population structure," said Kai Wang, director of clinical informatics at Columbia's Institute for Genomic Medicine, who was not involved in the study. "This new tool addresses these limitations, and will be very useful for analyzing the genomes of large populations." The researchers' algorithm, called TeraStructure, builds on the widely used and adapted STRUCTURE algorithm first described in the journal Genetics in 2000. The STRUCTURE algorithm cycles through an entire data set, genome by genome, one million variants at a time, before updating its model to both characterize ancestral populations and estimate their proportion in each individual. The model gets refined after repeated passes through the data set. TeraStructure, by contrast, updates the model as it goes. It samples one genetic variant at one location, and compares it to all variants in the data set at the same location across the data set, producing a working estimate of population structure. "You don't have to painstakingly go through all the points each time to update your model," said Blei. STRUCTURE is mathematically similar to a topic-modeling algorithm Blei developed independently in 2003 that made it possible to scan large numbers of documents for overarching themes. Blei's algorithm and its underlying LDA model have been used, among other things, to analyze published research in the journal Science to understand the evolution of scientific ideas and review regulatory meeting transcripts for insight into how the U.S. Federal Reserve sets interest rates. More recently, Blei has experimented with statistical techniques to extend probabilistic models to massive data sets. One technique, stochastic optimization, developed in 1951 by statistician Herbert Robbins just before arriving at Columbia, uses a small, random subset of observations to compute a rough update for the model's parameters. Continuously refining the model with each new observation, stochastic optimization algorithms have been enormously successful in scaling up machine learning approaches used in deep learning, recommendation systems and social network analysis. In a 2010 paper, Online Learning for LDA, Blei and his colleagues applied stochastic optimization to Blei's earlier LDA model. In a later paper, Stochastic Variational Inference, they showed that stochastic optimization could be applied to a range of models. As Matthew Hoffman, a coauthor of both papers, now a senior research scientist at Adobe Research explains, "Stochastic optimization algorithms often find a good solutions before they've even analyzed the whole dataset." In the Nature Genetics study, they apply these ideas to the STRUCTURE method. In their analysis of two real-world data sets--940 individual genomes from Stanford's Human Genome Diversity Project and 1,718 genomes from the 1000 Genomes Project--they found that TeraStructure performed comparably to the more recent ADMIXTURE and fastSTRUCTURE algorithms. But when they ran TeraStructure on a simulated data set of 10,000 genomes, it was more accurate and two to three times faster at estimating population structure, the study said. The researchers also showed that TeraStructure alone could analyze data sets as large as 100,000 genomes and 1 million genomes. Matthew Stephens, a genetics researcher at University of Chicago who helped develop the STRUCTURE algorithm, called TeraStructure's performance impressive. "I think these results will motivate future applications of this kind of algorithm in challenging inferences problems," he said The study also received praise from other researchers working with big genetic data sets. "We now have the technology to create the data," said Itsik Pe'er, a computational geneticist at Columbia Engineering who was not involved in the study. "But this paper really allows us to use it." The study is titled, "Scaling probabilistic models of genetic variation to millions of humans." Other authors are Prem Gopalan, Wei Hao, and John Storey, of Princeton. The Data Science Institute at Columbia University is training the next generation of data scientists and developing innovative technology to serve society. http://datascience.


News Article | December 22, 2016
Site: www.eurekalert.org

NEW YORK NY (December 22, 2016)--Columbia University Medical Center (CUMC) researchers have created a computational tool that can rapidly predict which genes are implicated in an individual's cancer and recommend treatments. It is among the most comprehensive tools of its kind, and the first that incorporates a user-friendly web interface that requires little knowledge of bioinformatics. The researchers found that iCAGES identified personal cancer "drivers" 77 percent of the time when presented with a pair of randomly chosen driver genes and non-driver genes, compared with about 51 percent for other computational tools. The study was published online today in Genome Medicine. Most cancers are caused by the accumulation of somatic (versus inherited) genetic mutations, or variants. Many of the variants involved in numerous types of cancer have been identified with genetic sequencing studies of large numbers of patients. However, this information is not always clinically useful on an individual level. Cancer "drivers" can vary from patient to patient, and there are no practical clinical tools for predicting which variants in an individual's genome are driving his or her disease and which are present but not causing disease. "Even when the genes driving cancer are known, clinicians don't have an efficient way to choose among the hundreds of possible drug therapies," said study leader Kai Wang, PhD, associate professor of biomedical informatics and director of clinical informatics at the Institute for Genomic Medicine at CUMC. To address this shortfall, Dr. Wang and his colleagues developed a computational tool called integrated CAncer GEnome Score (iCAGES). First, iCAGES analyzes the patient's entire genome, comparing it to the genomic sequence of the patient's tumor to identify possible cancer-causing variants. Next, iCAGES cross-references these variants to databases of known cancer-causing genes, using statistical analyses and machine learning techniques to prioritize the most likely driver genes. Finally, iCAGES matches the variants to FDA-approved and experimental drug therapies that specifically address those variants or genes. The entire process takes about 30 minutes. In contrast, conventional approaches require many separate steps involving human input, taking as long as several weeks. In a test designed to show how the tool would be used in actual practice, Dr. Wang retrospectively tested iCAGES using detailed sequencing data from a patient with lung cancer. Out of 129 possible cancer drivers, iCAGES focused on a gene called ARAF. iCAGES used the genomic sequencing data to select sorafenib as the top drug candidate out of 122 possible treatments. The patient's oncologists had reached the same conclusions, but they used a much more complex and time-consuming approach, involving expert knowledge throughout the decision-making process. "The patient was given sorafenib and had an extraordinary clinical response," said Dr. Wang. "It's worth noting that sorafenib is not FDA-approved for this indication. Nonetheless, the result suggests that iCAGES may help to identify novel treatment strategies and off-label use of existing approved drugs." When tested on various cancer patient databases, iCAGES was found to be superior to other computational tools at predicting cancer drivers from personal genomes and at identifying beneficial treatment. "We hope that iCAGES can help clinicians take full advantage of the massive amounts of data on genomic sequencing and cancer variants, and shed light on personalized cancer therapy," said Dr. Wang. A pilot clinical trial is now being planned to evaluate the translational potential of iCAGES. Clinicians and researchers can access the tool at icages.wglab.org. The study is titled, "iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes." The other contributors are: Chengliang Dong (University of Southern California, Los Angeles, CA), Yunfei Guo (University of Southern California), Hui Yang (University of Southern California), Zeyu He (New York University, New York, NY), and Xiaoming Liu (University of Texas Health Science Center at Houston, Houston, TX). The study was supported by grants from the National Institutes of Health (HG006465 and MH108728). Previously, Dr. Wang was a board member and shareholder in Tute Genomics, a bioinformatics software company. The remaining authors declare that they have no competing interests. Columbia University Medical Center provides international leadership in basic, preclinical, and clinical research; medical and health sciences education; and patient care. The medical center trains future leaders and includes the dedicated work of many physicians, scientists, public health professionals, dentists, and nurses at the College of Physicians and Surgeons, the Mailman School of Public Health, the College of Dental Medicine, the School of Nursing, the biomedical departments of the Graduate School of Arts and Sciences, and allied research centers and institutions. Columbia University Medical Center is home to the largest medical research enterprise in New York City and State and one of the largest faculty medical practices in the Northeast. The campus that Columbia University Medical Center shares with its hospital partner, NewYork-Presbyterian, is now called the Columbia University Irving Medical Center. For more information, visit cumc.columbia.edu or columbiadoctors.org.


News Article | December 23, 2016
Site: www.biosciencetechnology.com

Columbia University Medical Center (CUMC) researchers have created a computational tool that can rapidly predict which genes are implicated in an individual's cancer and recommend treatments. It is among the most comprehensive tools of its kind, and the first that incorporates a user-friendly web interface that requires little knowledge of bioinformatics. The researchers found that iCAGES identified personal cancer "drivers" 77 percent of the time when presented with a pair of randomly chosen driver genes and non-driver genes, compared with about 51 percent for other computational tools. The study was published online in Genome Medicine. Most cancers are caused by the accumulation of somatic (versus inherited) genetic mutations, or variants. Many of the variants involved in numerous types of cancer have been identified with genetic sequencing studies of large numbers of patients. However, this information is not always clinically useful on an individual level. Cancer "drivers" can vary from patient to patient, and there are no practical clinical tools for predicting which variants in an individual's genome are driving his or her disease and which are present but not causing disease. "Even when the genes driving cancer are known, clinicians don't have an efficient way to choose among the hundreds of possible drug therapies," said study leader Kai Wang, PhD, associate professor of biomedical informatics and director of clinical informatics at the Institute for Genomic Medicine at CUMC. To address this shortfall, Dr. Wang and his colleagues developed a computational tool called integrated CAncer GEnome Score (iCAGES). First, iCAGES analyzes the patient's entire genome, comparing it to the genomic sequence of the patient's tumor to identify possible cancer-causing variants. Next, iCAGES cross-references these variants to databases of known cancer-causing genes, using statistical analyses and machine learning techniques to prioritize the most likely driver genes. Finally, iCAGES matches the variants to FDA-approved and experimental drug therapies that specifically address those variants or genes. The entire process takes about 30 minutes. In contrast, conventional approaches require many separate steps involving human input, taking as long as several weeks. In a test designed to show how the tool would be used in actual practice, Dr. Wang retrospectively tested iCAGES using detailed sequencing data from a patient with lung cancer. Out of 129 possible cancer drivers, iCAGES focused on a gene called ARAF. iCAGES used the genomic sequencing data to select sorafenib as the top drug candidate out of 122 possible treatments. The patient's oncologists had reached the same conclusions, but they used a much more complex and time-consuming approach, involving expert knowledge throughout the decision-making process. "The patient was given sorafenib and had an extraordinary clinical response," said Dr. Wang. "It's worth noting that sorafenib is not FDA-approved for this indication. Nonetheless, the result suggests that iCAGES may help to identify novel treatment strategies and off-label use of existing approved drugs." When tested on various cancer patient databases, iCAGES was found to be superior to other computational tools at predicting cancer drivers from personal genomes and at identifying beneficial treatment. "We hope that iCAGES can help clinicians take full advantage of the massive amounts of data on genomic sequencing and cancer variants, and shed light on personalized cancer therapy," said Dr. Wang. A pilot clinical trial is now being planned to evaluate the translational potential of iCAGES. Clinicians and researchers can access the tool at icages.wglab.org.


Krishnaswami S.R.,Institute for Genomic Medicine | Kumar S.,Institute for Genomic Medicine | Ordoukhanian P.,Scripps Research Institute | Yu B.D.,Institute for Genomic Medicine
Journal of Investigative Dermatology | Year: 2015

Germline and somatic mutations in RAS and its downstream effectors are found in several congenital conditions affecting the skin. Here we demonstrate that activation of BRAF in the embryonic mouse ectoderm triggers both craniofacial and skin defects, including hyperproliferation, loss of spinous and granular keratinocyte differentiation, and cleft palate. RNA sequencing of the epidermis confirmed these findings but unexpectedly revealed evidence of continued epidermal maturation, expression of >80% of epidermal differentiation complex genes, and formation of a hydrophobic barrier. Spinous and granular differentiation were restored by pharmacologic inhibition of MAPK/ERK kinase or BRAF. However, restoration of epidermal differentiation was non-cell autonomous and required dermal tissue to be present in tissue recombination studies. These studies indicate that early activation of the RAF signaling pathway in the ectoderm has effects on specific steps of epidermal differentiation, which may be amenable to treatment with currently available pharmacologic inhibitors. © 2015 The Society for Investigative Dermatology.

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