MSKCC

New York City, NY, United States
New York City, NY, United States
SEARCH FILTERS
Time filter
Source Type

Cold Spring Harbor, NY - Over the last decade, it has made good sense to study the genetic drivers of cancer by sequencing a tiny portion of the human genome called the exome - the 2% of our three billion base pairs that "spell out" the 21,000 genes in our chromosomes. If cancer is a disease precipitated by changes in genes, after all, we need to know lots about how and when different genes change in the many distinctive subtypes of cancer. But a new wave of research, exemplified by a study published today in Nature Genetics by a team at Cold Spring Harbor Laboratory (CSHL), is significantly improving our ability to target cancer cells by studying "the other 98%" of DNA in human chromosomes, sometimes called the genome's "dark matter." Research led by Michael Feigin, Ph.D., a postdoctoral researcher in the laboratory of CSHL Professor David Tuveson, M.D., Ph.D., looked closely at cells sampled from 308 people with pancreatic cancer, one of the most lethal malignancies, with a 5-year survival rate of only 8%. Importantly, the full genome of the sampled pancreatic cancer cells was sequenced, not just the 2% that comprises the exome. This enabled Feigin and colleagues including computational biologist Tyler Garvin, Ph.D., formerly of Adjunct Associate Professor Michael Schatz's lab, to focus narrowly on genome segments called gene promoters. These segments of DNA typically lie adjacent to, but not within, the sequences of the genes that they regulate. Therefore, promoters are "invisible" when only the exomes of cells are sequenced, as has been commonplace in cancer genetics research. "Promoters are important in determining when specific genes are turned on and off," says Feigin, "and I became interested in figuring out whether mutations within promoters - as opposed to within the genes they regulate -consistently affects the way cancers develop and sustain themselves." The team "looked all across the genome," Feigin says, "and, interestingly, while we did find mutations in promoters, we never found clusters of these mutations near any of the genes that prior research had already told us were typically mutated in pancreatic cancer." Genes called KRAS and p53 are mutated in the majority of pancreas cancer cells, for example. But mutations in promoters sifted out of mountains of data by the team's novel mathematical formula, or algorithm, called GECCO, lay in genes never before implicated in pancreatic cancer. Feigin points out that mutations in a promoter can affect how much protein is generated by the gene its regulates. In this way these mutations are unlike those usually found in KRAS and p53, for example, which impair or otherwise alter the function of the proteins they encode. While the promoter mutations were not near known pancreatic cancer genes, the team found that they affected some of the same biological pathways in cells. Most prominent among these were promoters affecting genes involved in cell adhesion and axon guidance. Both pathways involve cascades of interactions among dozens or hundreds of proteins, each one encoded by a different gene. The new data thus "adds depth to our understanding of things that go awry in these critical pathways, sometimes promoting cancer formation, other times providing cancer cells with advantages that enable them to crowd out healthy cells," comments Dr. Tuveson, who in addition to leading a lab at CSHL is the Director of CSHL's NCI-designated Cancer Center and Director of Research for the Lustgarten Foundation, the nation's largest philanthropic funder of pancreatic cancer research. The cell adhesion pathway affected by newly discovered mutations in gene promoter regions is important for obvious reasons in cancer: cancer cells want to grow and proliferate, a process that can culminate in their migration from their tissue of origin. Once they have broken free, they can travel via the bloodstream to other places in the body, a process called metastasis that is often responsible for cancer fatalities. The axon guidance pathway associated with promoter mutations has a less obvious but no less important role in pancreatic cancer. "In pancreas cancer, nerves are often attracted to or get attracted to the tumor," explains Feigin, "and sometimes they grow right through the tumor. This is one of the reasons pancreas cancer is so painful." It's possible, Feigin says, that axon guidance signals - and indeed cell adhesion signals - "are actually being used by tumor cells" to gain advantages over healthy cells. "Tumors, for example, can actually spread via nerves; this is called peri-neural invasion." A question naturally arises: if these and several other pathways were already implicated in pancreatic cancer, what is the advantage of the new knowledge about promoter mutations? The answer, the team explains, has to do with finding ways to fight pancreatic cancer, one of the major cancer types that remains profoundly resistant to all existing treatments. The more that is known about defects in specific pathways in specific cancer types, the more specific molecular targets - pathway components - appear in the sights of researchers trying to disable or enhance a given pathway. The research discussed here was supported by The Lustgarten Foundation, Cold Spring Harbor Laboratory Association, the V Foundation, and the David Rubinstein Center for Pancreatic Cancer Research at MSKCC. Other support was provided by the STARR Foundation, DOD, Louis Morin Charitable Trust and the National Institutes of Health. "Recurrent noncoding regulatory mutations in pancreatic ductal adenocarcinoma" appears XXXXXXXX in Nature Genetics. The authors are: Michael E. Feigin, Tyler Garvin, Peter Bailey, Nicola Waddell, David K. Chang, David R. Kelley, Shimin Shuai, Steven Gallinger, John D. McPherson, Sean M. Grimmond, Ekta Khurana, Lincoln D. Stein, Andrew V. Biankin, Michael C. Schatz, and David A. Tuveson. The paper can be accessed at: http://www. Founded in 1890, Cold Spring Harbor Laboratory has shaped contemporary biomedical research and education with programs in cancer, neuroscience, plant biology and quantitative biology. Home to eight Nobel Prize winners, the private, not-for-profit Laboratory employs 1,100 people including 600 scientists, students and technicians. The Meetings & Courses Program hosts more than 12,000 scientists from around the world each year on its campuses in Long Island and in Suzhou, China. The Laboratory's education arm also includes an academic publishing house, a graduate school and programs for middle and high school students and teachers. For more information, visit http://www.


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

TCR transgenic mice (B6.Cg-Tg(TcraY1,TcrbY1)416Tev/J)12, Cre-ERT2 (B6.129-Gt(ROSA)26Sortm1(cre/ERT2)Tyj/J), Alb-Cre (B6.Cg-Tg(Alb-cre)21Mgn/J), TCR-OT1 (C57BL/6-Tg(TcraTcrb)1100Mjb/J), Ly5.1 (B6.SJL-Ptprca Pepcb/BoyJ), and C57BL/6J Thy1.1 mice were purchased from The Jackson Laboratory. TCR mice were crossed to Thy.1.1 mice to generate TCR Thy.1.1 mice. TCR-OT1 were crossed to Ly5.1 mice to generate TCR-OT1 Ly5.1 mice. AST (Albumin-floxStop-SV40 large T antigen (TAG))33 were crossed to Cre-ERT2 or Alb-Cre mice to obtain AST-Cre-ERT2 and AST-Alb-Cre mice, respectively6. Both female and male mice were used for studies. Mice were age- and sex-matched and between 1.5–3 months old when used for experiments. Animals were assigned randomly to experimental groups. All mice were bred and maintained in the animal facility at Memorial Sloan Kettering Cancer Center (MSKCC). Experiments were performed in compliance with the MSKCC Institutional Animal Care and Use Committee (IACUC) regulations. Fluorochrome-conjugated antibodies were purchased from BD Biosciences, eBioscience, Biolegend, and Cell Signaling Technology. Tamoxifen (Sigma) stock solution was prepared by warming tamoxifen in 1 ml sterile corn oil at 50 °C for 15 min, then further diluted in corn oil to obtain the stock concentration (5 mg ml−1 in corn oil). A single dose of tamoxifen (1 mg) was administered intraperitoneally (i.p.) into AST-Cre-ERT2 mice. Intracellular cytokine staining was performed using the Cytofix/Cytoperm Plus kit (BD Biosciences) per the manufacturer’s instructions. In brief, T cells were mixed with 2 × 106 congenically marked splenocytes and incubated with Tag-I peptide (0.5 μg ml−1) or OVA peptide (0.1 μg ml−1) for 4–5 h at 37 °C in the presence of GolgiPlug (brefeldin A). After staining for cell-surface molecules, the cells were fixed, permeabilized, and stained with antibodies against IFNγ (XMG1.2) and TNFα (MP6-XT22). Flow cytometric analysis was performed using Fortessa and LSR FACS analysers (BD Biosciences); cells were sorted using BD FACS Aria (BD Biosciences) at the MSKCC Flow Core Facility. Flow data were analysed with FlowJo v. 10 software (Tree Star Inc.). The Listeria monocytogenes (Lm) ΔactA ΔinlB strain13 expressing the Tag-I epitope (SAINNYAQKL, SV40 large T antigen ) was generated by Aduro Biotech as previously described34. Experimental vaccination stocks were prepared by growing bacteria to early stationary phase, washing in phosphate buffered saline, formulated at approximately 1 × 1010 colony-forming units (c.f.u.) ml−1, and stored at −80 °C. Mice were infected i.p. with 5 × 106 c.f.u. of LmTAG. For the generation of effector and memory TCR CD8+ T cells, 105 CD8+ splenocytes from TCR Thy1.1 transgenic mice were adoptively transferred into B6 (Thy1.2) mice; one day later, mice were infected with 5 × 106 c.f.u. LmTAG. Effector TCR CD8+ T cells were isolated from the spleens of B6 host mice and analysed 5 or 7 days after LmTAG immunization; memory TCR CD8+ T cells were isolated from spleens of B6 host mice and analysed at least 2–3 months after LmTAG immunization. For the transfer of naive TCR T cells into AST-Cre-ERT2 mice, 1 × 105 to 2.5 × 106 CD8+ splenocytes from TCR Thy1.1 transgenic mice were adoptively transferred into AST-Cre-ERT2 mice; 1 day later, mice were treated with 1 mg tamoxifen and donor T cells isolated for subsequent analyses. For memory TCR transfer experiments (3–4) × 104 TCR Thy1.1+CD44hiCD62Lhi sorted central memory CD8 T cells were adoptively transferred into AST-Alb-Cre mice; one day later, mice were infected with 5 × 106 c.f.u. LmTAG (105 central memory T cells were sorted and transferred for experiments without subsequent listeria immunization). 5 × 105 to 1 × 106 B16 tumour cells expressing OVA (full-length or cytosolic as previously described35) were injected into C57BL/6J wild-type mice. Once tumours were established (1–2 weeks later) naive Ly5.1 congenically marked TCR CD8 T cells were adoptively transferred and isolated from tumours at indicated time points. Tumour volumes did not exceed the permitted volumes specified by the MSKCC IACUC protocol. The B16 cell line was obtained from ATCC. It was tested negative for all rodent pathogens including Mycoplasma pulmonis. Spleens were mechanically disrupted with the back of a 3-ml syringe, filtered through a 70-μm strainer, and red blood cells were lysed with ammonium chloride potassium buffer. Cells were washed twice with cold RPMI 1640 media supplemented with 2 μM glutamine, 100 U ml−1 penicillin/streptomycin, and 5–10% FCS (cRPMI). Liver tissue was mechanically disrupted to a single-cell suspension using a 150 μ metal mesh and glass pestle in ice-cold 3% FCS/HBSS and passed through a 70-μm strainer. The liver homogenate was spun down at 400g for 5 min at 4 °C, and the pellet was resuspended in 30 ml 3% FCS/HBSS, 500 μl (500 U) heparin, and 17 ml Percoll (GE), mixed by inversion, and spun at 500g for 10 min at 4 °C. Pellet was lysed with ammonium chloride potassium buffer and cells were further processed for downstream applications. TCR or TCR cells were isolated from tumours at various time points after transfer and cultured in vitro in the presence of IL-15 (100 ng ml−1) in cRPMI for 3–4 days. Naive TCR (Thy1.1+) cells were transferred into AST-Cre-ERT2 (Thy1.2+) mice which were treated with tamoxifen one day later. On days 2–9, mice were treated with the calcineurin inhibitor FK506 (Prograf, 5 mg ml−1) (2.5 mg per kg per mouse i.p. once daily) alone, or in combination with the GSK3β inhibitor TWS119 (Sigma; 0.75 mg per mouse i.p. once daily; days 5–8). Control mice were treated with PBS and/or DMSO. Human tumour samples and healthy donor peripheral blood lymphocytes were obtained as per protocols approved by the MSKCC Institutional Review Board (IRB), and all patient and healthy donors provided informed consent. Peripheral blood lymphocytes were flow-sorted for naive, effector memory-like and central memory-like phenotypes as described in Extended Data Fig. 10a. Human melanoma and lung tumours were mechanically disrupted as described for solid tumours in mice, and CD45RO+PDhiCD8+ T cells were flow-sorted for subsequent ATAC-seq analysis. Statistical analyses on flow cytometric data were performed using unpaired two-tailed Student’s t tests (Prism 6.0, GraphPad Software). A P value of <0.05 was considered statistically significant. Mouse samples: replicate samples were isolated from spleens or livers and sorted as follows. (i) Naive TCR Thy1.1+ T cells were sorted by flow cytometry (CD8+CD44lo) from spleens of TCR Thy1.1 transgenic mice. (ii) Day 5 and day 7 effector, and memory TCR Thy1.1+ T cells were sorted by flow cytometry (CD8+Thy1.1+) from spleens of infected B6 (Thy1.2) host mice (see above) 5 and 7 days or 2–3 months after listeria infection. (iii) TCR Thy1.1+ T cells from pre/early malignant liver lesions: naive TCR Thy1.1+ T cells were adoptively transferred into AST-Cre-ERT2 mice. 1 day later, mice were given 1 mg tamoxifen i.p. At given time points after tamoxifen treatment, T cells were isolated and sorted (CD8+Thy1.1+) from livers as described above. (iv) TCR Thy1.1+ memory T cells from established hepatocellular carcinomas in AST-Alb-Cre mice: TCR memory T cells were isolated from tumours and flow sorted (CD8+Thy1.1+) as described above. Human samples: samples were flow-sorted as described in Extended Data Fig. 10a. After flow-sorting, all samples for downstream ATAC-seq analysis were frozen in 10% DMSO/FCS and stored at −80 °C; samples for RNA-seq were directly sorted into Trizol and frozen and stored at −80 °C. RNA from sorted cells was extracted using RNeasy mini kit (Qiagen) as per instructions provided by the manufacturer. After ribogreen quantification and quality control of Agilent BioAnalyzer, 6–15 ng of total RNA was amplified (12 cycles) using the SMART-seq V4 (Clontech) ultralow input RNA kit for sequencing. 10 ng of amplified cDNA was used to prepare Illumina hiseq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR. Samples were barcoded and run on a Hiseq 2500 1T in a 50 bp/50 bp Paired end run, using the TruSeq SBS Kit v3 (Illumina). An average of 51 million paired reads were generated per sample and the percent of mRNA bases was 62.5% on average. Chromatin profiling was performed by ATAC-seq as described previously11. In brief, 12,000 to 50,000 cells were washed in cold PBS and lysed. Transposition was performed at 42 °C for 45 min. After purification of the DNA with the MinElute PCR purification kit (Qiagen), material was amplified for 5 cycles. Additional PCR cycles were evaluated by real time PCR. Final product was cleaned by Ampure Beads at a 1.5× ratio. Libraries were sequenced on a Hiseq 2500 1T in a 50 bp/50 bp Paired end run, using the TruSeq SBS Kit v3 (Illumina). An average of 47 × 106 paired reads was generated per sample. Raw ATAC-seq reads were trimmed and filtered for quality using Trim Galore! v0.4.0 (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), powered by CutAdapt v1.8.1 (http://dx.doi.org/10.14806/ej.17.1.200) and FastQC v0.11.3 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Paired-end reads were aligned using Bowtie2 v2.2.5 (ref. 36) against either mm10 or hg38 and non-uniquely mapping reads were removed. To correct for the fact that the Tn5 transposase binds as a dimer and inserts two adapters in the Tn5 tagmentation step37, all positive-strand reads were shifted 4 bp downstream and all negative-strand reads were shifted 5 bp upstream to centre the reads on the transposon binding event11. We then pooled the shifted reads by sample type and identified peaks using MACS2 (ref. 38) with a threshold of FDR-corrected P < 1 × 10−2 using the Benjamini–Hochberg procedure for multiple hypothesis correction. As called peaks may be caused by noise in the assay and not reflect true chromatin accessibility, we calculated an irreproducible discovery rate (IDR)39 for all pairs of replicates across a cell type. The IDR is an estimate of the threshold where two ranked lists of results, in this case peak calls ranked by P value, no longer represent reproducible events. Using this measure, we excluded peaks that were not reproducible (IDR < 5 × 10−3) across at least one pair of replicates in each mouse or human cell type. Peaks found reproducibly in each mouse cell type were combined to create a genome-wide atlas of accessible chromatin regions. Reproducible peaks from different samples were merged if they overlapped by more than 75%. To create the atlas of accessible peaks for the human samples, reproducible peaks from the normal human cell types (HN, HCM, and HEM) and the tumour-derived cells (PD1hi) were combined. There was greater variation between the human TIL samples than between T cell samples from healthy donors; this led to fewer reproducible peaks being called in the TIL samples. Like the mouse atlas, peaks overlapping by more than 75% were merged in the human atlas. Numbers of called peaks and reproducible peaks for each sample type are listed in Supplementary Data. The RefSeq transcript annotations of the hg38 version of the human genome and the mm10 version of the mouse genome were used to define the genomic location of transcription units. For genes with multiple gene models, the longest transcription unit was used for the gene locus definition. ATAC peaks located in the body of the transcription unit, together with the 2-kb regions upstream of the TSS and downstream of the 3′ end, were assigned to the gene. If a peak was found in the overlap of the transcription units of two genes, one of the genes was chosen arbitrarily. Intergenic peaks were assigned to the gene with a TSS or 3′ end that was closest to the peak. In this way, each peak was unambiguously assigned to one gene. Peaks were annotated as promoter peaks if they were within 2 kb of a transcription start site. Non-promoter peaks were annotated as intergenic, intronic or exonic according to the relevant RefSeq transcript annotation. We found a total of 75,689 reproducible ATAC-seq peaks in the mouse samples. Examining genomic locations, 39.6% of the peaks were found in introns, 36.3% were found in intergenic regions, 22.1% were found in promoters and 2.1% were found in exons. In the human samples, we found a total of 42,104 reproducible ATAC-seq peaks. Among these peaks, 34.0% were found in introns, 29.9% were found in intergenic regions, 34.0% were found in promoters, and 2.0% were found in exons. Chromosome-wide genomic coverage for all (autosomal) chromosomes and all samples was examined and no systemic bias was observed. PCA plots were generated using read counts against all mouse or human atlas peaks. These read counts were processed using the variance-stabilizing transformation built into the DESeq2 package40. Reads aligning to atlas peak regions were counted using the summarizeOverlaps function of the R packages GenomicAlignments v1.2.2 and GenomicRanges v1.18.4 (ref. 41). Differential accessibility of these peaks was then calculated for all pairwise comparisons of cell types using DESeq2 v1.6.3 (ref. 40). The ATAC-seq peak heat maps were created by pooling the DESeq size-factor normalized read counts per atlas peak across replicates of ATAC-seq data and binning the region ±1 kb around the peak summit in 20 bp bins. To improve visibility, bins with read counts greater than the 75th percentile + 1.5 × IQR were capped at that value. All analysis was performed using the original uncapped read counts. Genome coverage plots were generated for each replicate of ATAC-seq and RNA-seq by calculating genome-wide coverage of aligned reads using the bedtools function genomecov42. For ATAC-seq samples, this coverage was calculated after shifting the reads to account for the Tn5-induced bias. The coverage values were then normalized using DESeq2-derived size factors and replicates were combined to create one signal track for each sample type. ATAC-seq and RNA-seq coverage plots were generated using the Integrated Genomics Viewer (Broad)43. Using the MEME44-curated CisBP45 transcription factor binding motif (TFBM) reference, we scanned the mouse ATAC-seq peak atlas with FIMO46 to find peaks likely to contain each TFBM (P < 10−4). The MEME cisBP reference for direct and inferred motifs for Mus musculus was curated by the MEME suite developers as follows: to reduce redundancy, for each transcription factor a single motif was selected according to the following precedence rules. The direct motif was chosen if there was one, otherwise the inferred motif with the highest DNA binding domain (DBD) similarity (according to CisBP) to a transcription factor in another species with a direct motif was chosen. If there was more than one direct motif or inferred motif with the highest DBD similarity, a motif was chosen according to its provenance (CisBP ‘Motif_Type’ attribute) in the following order: ChIP-seq, HocoMoco, DeBoer11, PBM, SELEX, B1H, High-throughput Selex CAGE, PBM:CSA:DIP-chip, ChIP-chip, COMPILED, DNaseI footprinting. Each motif thus determined was linked to a single transcription factor in the CisBP database, following the same precedence rules. The final reference contained 718 motifs between 6 and 30 bp in width (average width, 10.7 bp). Transcription factors with similar FIMO-predicted target peaks were combined into transcription factor families. Similarity of predicted target peak sets was measured using the Jaccard index (size of intersection/size of union). Transcription factors with Jaccard indices greater than 0.7 were combined for further analyses. Relative transcription factor accessibility was calculated using two one-sided Wilcoxon rank-sign tests comparing the distributions of peak heights for peaks containing FIMO-predicted transcription factor binding sites. Peak height was defined as the maximum observed number of reads overlapping at any point in the defined peak region. ATAC-seq footprints containing FIMO-predicted transcription factor binding sites (P < 1 × 10−4) were selected. Positive- and negative-strand ATAC-seq cut sites were counted 100 bp up- and down-stream of the centre of the motif site in each of the selected peaks. The mean number of ATAC-seq cut sites across matching atlas peaks was then plotted to generate the footprint figures. In these plots, each gene is represented by a stack of diamonds corresponding accessible chromatin regions of the same gene. The bottom-most peak in this stack corresponds to the log fold change in expression of the gene. The diamonds are coloured according to the accessibility change of their ATAC-seq peak with blue indicating closing and red indicating opening. The colour scale was based on the rank-order of the peak accessibility changes. In Extended Data Fig. 6d, the colour scale ranges from a log fold change of −3.92 to 4.96 (L14/L7). The UCSC liftOver tool47 was used to convert the mouse ATAC-seq peak atlas from mm10 coordinates to hg38 coordinates. The converted mouse atlas was then compared to the human atlas and 20,642 mouse peaks were within 100 bp of a human peak. We compared the results from the UCSC liftover tool and an alternative method, bnMapper48, and confirmed that the set of peaks mapped by bnMapper and by the UCSC liftOver tool was nearly identical (57,383 out of 75,689 by liftOver and 58,299 out of 75,689 by bnMapper). Additionally, all 57,223 peaks mapped to hg38 by both tools were mapped to the same chromosomal positions. The majority of these conserved peaks were found in promoter regions (56.4%), whereas relatively fewer were found in intergenic (22.4%), intronic (19.6%), and exonic (1.5%) regions. For non-promoter peaks conserved between human and mouse, Spearman correlations of log (FC) were calculated between human N and human EM, CM or PD1hi TIL versus log (FC) between mouse N and functional E5, E7, M and dysfunctional L5 to L60. Raw ATAC-seq reads were trimmed and filtered for quality using Trim Galore! v0.4.0 (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), powered by CutAdapt v1.8.1 (http://dx.doi.org/10.14806/ej.17.1.200) and FastQC v0.11.3 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Paired-end reads were aligned using STAR49 against either mm10 or hg38. The RefSeq transcript annotations of the hg38 version of the human genome and the mm10 version of the mouse genome were used for the genomic location of transcription units. Reads aligning to annotated exon regions were counted using the summarizeOverlaps function of the R packages GenomicAlignments v1.2.2 and GenomicRanges v1.18.4 (ref. 41). Differential expression of genes across cell types was calculated using DESeq2 v1.6.3 (ref. 40). FDR correction of 0.05 was imposed unless otherwise stated. A log fold change cutoff of 1 was used in some analyses as indicated. Enrichment of gene ontology terms in sets of ATAC-seq peaks was calculated using GREAT (Genomic Regions Enrichment of Annotations Tool) using default parameters50. The full ATAC-seq atlas was used as the background set. To identify membrane proteins that distinguished early (L5–L7) from late (L14–L60) dysfunctional TST, RNA-seq data was analysed for genes contained within the gene ontology category 0016020 (membrane proteins). The top 50 most up- and downregulated genes (size-factor normalized RPKM) when compared between L5–L7 and L14–L60 were plotted in a heat map (row-normalized). Protein expression was assessed by flow cytometry for those membrane proteins for which monoclonal antibodies were available. Mouse targets (clone; supplier): CD5 (53-7.3; eBioscience), CD30L (RM153; eBioscience), CD38 (90; Biolegend), and CD101 (Moushi101; eBioscience). Human targets: CD5 (L17F12; Biolegend), CD38 (HB7; eBioscience), CD101 (BB27; Biolegend). No statistical methods were used to predetermine sample size. The investigators were not blinded to allocation during experiments and outcome assessment. Mice or human samples were excluded if donor or tumour-infiltrating CD8 T cells could not be found. All data generated and supporting the findings of this study are available within the paper. The RNA-seq and ATAC-seq data have been deposited in the Gene Expression Omnibus (GEO Super-Series accession number GSE89309 (GSE89307 for RNA-seq, GSE89308 for ATAC-seq). Source Data for Figs 1 and Extended Data Figs 1, 3 and 7 are provided with the online version of the paper. Additional information and materials will be made available upon request.


News Article | November 30, 2016
Site: www.eurekalert.org

An international group of researchers report success in mice of a method of using positron emission tomography (PET) scans to track, in real time, an antibody targeting a hormone receptor pathway specifically involved in prostate cancer. This androgen receptor pathway drives development and progression of the vast majority of prostate cancers. The technique shows promise, the investigators say, as a novel way to use such an antibody to detect and monitor prostate and other hormone-sensitive cancers, as well as to guide therapy in real time. "The findings show that individually tailored imaging agents can provide a unique way of looking at disease progression in real time and in a noninvasive manner," says Daniel Thorek, Ph.D., assistant professor of radiology and radiological science at the Johns Hopkins University School of Medicine, and the paper's first author. "Perhaps someday we can put a personalized antibody such as the one we created in our study on a therapeutic agent and conduct cancer treatment using imaging with very high specificity." A summary of the findings was published in Science Translational Medicine on Nov. 30. Thorek says the success is especially important given the challenges of working with small-animal models. "We managed to very accurately and precisely monitor the mouse prostate, and that leads us to hope that a similar approach can be used to guide treatment in people," he adds. Current clinical practice detects prostate cancer by tracking the androgen receptor pathway -- a marker for the cancer -- by testing blood for prostate-specific antigen (PSA). Presence of elevated PSA indicates that the androgen receptor pathway is active and may indicate prostate cancer is present. PSA concentration in the blood, however, is affected by numerous factors, such as age and type of tumor, making it difficult to determine true androgen receptor pathway activation. Furthermore, attempts to target PSA with an antibody is complicated by the "washing out" of the antibody-PSA complex, a process in which the complex is formed but does not remain near the disease site, thus making it difficult to definitively identify and measure disease sites. In the new study, the investigators developed a new antibody called 11B6 to target human kallikrein-related peptidase 2 (hK2), another antigen that indicates androgen receptor pathway activation. Unlike PSA, hK2 is specifically active only in the prostate and an aggressive type of breast cancer. By binding free, unbound hK2 to 11B6, the research team found that the newly formed 11B6-hK2 complex is taken directly back into the cancerous cell rather than washed out. This biological process relies on a transport mechanism involving the neonatal Fc receptor, in which cells are able to recognize and take in antibodies. The Fc receptor is most well-known for how antibodies in mothers' milk are able to pass from the gut of the baby into newborns' bloodstream to provide them with immunity. As far as the research team is aware, this is the first study to exploit the biological mechanism for imaging purposes, Thorek says. In the next phase of their experiments, the team made 11B6 "light up" during PET and fluorescence imaging by binding it to zirconium-89, creating a traceable radiochemical compound called 89Zr-11B6. By imaging the 89Zr-11B6 using PET, the team showed that binding 11B6 to hK2 can measure activity of cancerous lesions robustly, in both soft tissue and bone. Prostate and breast cancer often metastasize to bone, therefore detection of lesions in all areas of the body is critical. To further demonstrate the potential value of 89Zr-11B6 imaging, the team tested the imaging agent in disease models under standard treatment regimens. In one such case, disease activity was imaged and quantified in mice treated with saline and a second group with enzalutamide, a drug used to treat prostate cancer by inhibiting the androgen receptor hormone activity. All of the mice had prostate cancer. Following initial castration, imaging of 89Zr-11B6 allowed the research team to see lower androgen receptor pathway activity, as one might expect. This effect was augmented in the animals with adjuvant enzalutamide treatment. This may inform current clinical practice as the use of adjuvant enzalutamide after castration may show benefit to patients with prostate cancer. By tracking the antibody localization to disease sites in real time, the team hopes this may be a way to determine optimal dosages that don't compromise efficacy while avoiding negative side effects. Imaging of the antibody uptake before and after treatment could, in theory, aid in the decision of whether to keep a patient on chosen drug. If a response is seen, the imaging agent could be used to choose the right dose, balancing the therapeutic effect and minimizing adverse effects. And if there is not an imaging change with a particular drug, this tool would provide a caregiver with rapid information to discontinue ineffective treatments, saving time and cost. The team is currently conducting preliminary nonhuman primate toxicity tests, the final step before applying for human clinical trials, and has thus far found no adverse effects. Other authors on this paper include Diane S. Abou and Marise R.H. van Voss of the Johns Hopkins University School of Medicine; Philip A. Watson, Sang-Gyu Lee, Anson T. Ku, Kwanghee Kim, Michael G. Doran, Elmer Santos, Darren Veach, Mesruh Turkekul, Emily Casey, Jason S. Lewis, Howard I. Scher, Hans Lilja, Steven M. Larson and David Ulmert of Memorial Sloan Kettering Cancer Center; Stylianos Bournazos of Rockefeller University; Katharina Braun of the University of Bochum; Kjell Sjöström of Innovagen AB; Urpo Lamminmäki of the University of Turku; Sven-Erik Strand of Lund University; and Mary L. Alpaugh of Rowan University. Funding for this study was provided by the National Cancer Institute of the National Institutes of Health (P30 CA008748, P30 CA006973, P30 CA008748-48, S10 RR020892-01, S10 RR028889-01, R33 CA127768-02, P50-CA86438), the National Institutes of Health Molecular Imaging Fellowship Program (5R25CA096945-07), the Geoffrey Beene Cancer Research Center, the W.H. Goodwin and A. Goodwin and their Commonwealth Foundation for Cancer Research, the Experimental Therapeutics Center, the Radiochemistry and Molecular Imaging Probe Core (P50-CA086438), the Steve Wynn Prostate Cancer Foundation Young Investigator Award, the Knut and Alice Wallenberg Foundation, the Bertha Kamprad Foundation and the David H. Koch Fund of the Prostate Cancer Foundation, the Ludwig Center for Cancer Immunotherapy, the Swedish Cancer Society, the Swedish National Health Foundation, the Swedish Research Council (Medicine- 20095), the Memorial Sloan Kettering Cancer Center Specialized Programs of Research Excellence in Prostate Cancer (P50 CA92629), the Sidney Kimmel Center for Prostate and Urologic Cancers and the Hascoe Charitable Foundation. D.L.J.T., D.U., U.L., S.-E.S., and H.L. are shareholders of Diaprost Inc. D.L.J.T., S.-E.S., and D.U. currently serve as board members of Diaprost Inc. D.L.J.T., A.K., S.-E.S., S.M.L., and D.U. are inventors on a patent (62257179) submitted by the MSKCC that covers systems, methods and compositions for imaging AR axis activity in carcinoma. D.U. is also the inventor on a patent (20060182682) held by Diaprost Inc. that covers diagnostic imaging of PCa using 11B6. S.-E.S. and U.L. are inventors on a patent application (WO2015075445) submitted by Diaprost Inc. that covers the humanized anti-hK2 antibody.


HSP90 inhibitors used in this study including PU-H71, PU-DZ13, NVP-AUY922, and SNX-2112 were synthesized as previously reported7, 19. 17-DMAG was purchased from Sigma. HSP90 bait (PU-H71 beads)21, HSP70 bait (YK beads)22, biotinylated YK (YK-biotin)22, fluorescently labelled PU-H71 (PU-FITC)23, the control derivatives PU-TEG and PU-FITC9 (ref. 24), and the radiolabelled PU-H71-derivative 124I-PU-H71 (ref. 25) were generated as previously described. The specificity of PU-H71 for HSP90 and over other proteins was extensively analysed7. Thus binding of PU-H71 in cell homogenates, live cells and organisms denotes binding to HSP90 species characteristic of each analysed tumour or tissue. Combined with the findings that PU-H71 binds more tightly to HSP90 in type 1 than in type 2 cells, an observation true for cell homogenates, live cells, and in vivo, at the organismal level, we propose that labelled versions of PU-H71 are reliable tools to perturb, identify and measure the expression of the high-molecular-weight, multimeric HSP90 complexes in tumours. The specificity of YK probes for HSP70 was previously reported22, 26, 27, 28. Cell lines were obtained from laboratories at WCMC or MSKCC, or were purchased from the American Type Culture Collection (ATCC) or Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSMZ). Cells were cultured as per the providers’ recommended culture conditions. Cells were authenticated using short tandem repeat profiling and tested for mycoplasma. The pancreatic cancer cell lines include: ASPC-1 (CRL-1682), PL45 (CRL-2558), MiaPaCa2 (CRL-1420), SU.86.86 (CRL-1837), CFPAC (CRL-1918), Capan-2 (HTB-80), BxPc-3 (CRL-1687), HPAFII (CRL-1997), Capan-1 (HTB-79), Panc-1 (CRL-1469), Panc05.04 (CRL-2557) and Hs766t (HTB-134) (purchased from the ATCC); 931102 and 931019 are patient derived cell lines provided by Y. Janjigian, MSKCC. Breast cancer cell lines were obtained from ATCC and include MDA-MB-468 (HTB-132), HCC1806 (CRL-2335), MDA-MB-231 (CRM-HTB-26), MDA-MB-415 (HTB-128), MCF-7 (HTB-22), BT-474 (HTB-20), BT-20 (HTB-19), MDA-MB-361 (HTB-27), SK-Br-3 (HTB-30), MDA-MB-453 (HTB-131), T-47D (HTB-133), AU565 (CRL-2351), ZR-75-30 (CRL-1504), ZR-75-1 (CRL-1500). Lymphoma cell lines include: Akata1, Mutu-1 and Rae-1 (provided by W. Tam, WCMC); BCP-1 (CRL-2294), Daudi (CCL-213), EB1 (HTB-60), NAMALWA (CRL-1432), P3HR-1 (HTB-62), SU-DHL-6 (CRL-2959), Farage (CRL-2630), Toledo (CRL-2631) and Pfeiffer (CRL-2632) (obtained from ATCC); HBL-1, MD901 and U2932 (kindly provided by J. Angel Martinez-Climent, Centre for Applied Medical Research, Pamplona, Spain); Karpas422 (ACC-32), RCK8 (ACC-561) and SU-DHL-4 (ACC-495) (obtained from the DSMZ); OCI-LY1, OCI-LY3, OCI-LY4, OCI-LY7 and OCI-LY10 (obtained from the Ontario Cancer Institute); TMD8 (kindly provided by L. M. Staudt, NIH); BC-1 (derived from an AIDS-related primary effusion lymphoma); IBL-1 and IBL-4 (derived from an AIDS-related immunoblastic lymphoma) and BC3 (derived from a non-HIV primary effusion lymphoma). Leukaemia cell lines include: REH (CRL-8286), HL-60 (CCL-240), KASUMI-1 (CRL-2724), KASUMI-4 (CRL-2726), TF-1 (CRL-2003), KG-1 (CCL-246), K562 (CCL-243), TUR (CRL-2367), THP-1 (TIB-202), U937 (CRL-1593.2), MV4-11 (CRL-9591) (obtained from ATCC); KCL-22 (ACC-519), OCI-AML3 (ACC-582) and MOLM-13 (ACC-554) (obtained from DSMZ). The lung cancer cell lines include: NCI-H3122, NCI-H299 (provided by M. Moore, MSKCC); EBC1 (provided by Dr Mellinghoff, MSKCC); PC9 (kindly provided by D. Scheinberg, MSKCC), HCC15 (ACC-496) (DSMZ), HCC827 (CRL-2868), NCI-H2228 (CRL-5935), NCI-H1395 (CRL-5868), NCI-H1975 (CRL-5908), NCI-H1437 (CRL-5872), NCI-H1838 (CRL-5899), NCI-H1373 (CRL-5866), NCI-H526 (CRL-5811), SK-MES-1 (HTB-58), A549 (CCL-185), NCI-H647 (CRL-5834), Calu-6 (HTB-56), NCI-H522 (CRL-5810), NCI-H1299 (CRL-5803), NCI-H1666 (CRL-5885) and NCI-H1703 (CRL-5889) (obtained from ATCC). The gastric cancer cell lines include: MKN74 (obtained from G. Schwarz, Columbia University), SNU-1 (CRL-5971) and NCI-N87 (CRL-5822) (obtained from ATCC), OE19 (ACC-700) (DSMZ). The non-transformed cell lines MRC-5 (CCL-171), human lung fibroblast and HMEC (PCS-600-010), human mammary epithelial cells were obtained from ATCC. NIH-3T3, and NIH-3T3 cell lines stably expressing either mutant MET (Y1248H) or vSRC, were provided by L. Neckers, National Cancer Institute (NCI), USA, and were previously reported29, 30. Patient tissue was obtained with informed consent and authorized through institutional review board (IRB)-approved bio-specimen protocol number 09-121 at Memorial Sloan Kettering Cancer Centre (New York, New York). Specimens were treated for 24 h or 48 h with the indicated concentrations of PU-H71 as previously described31. Following treatment, slices were fixed in 4% formalin solution for 1 h, then stored in 70% ethanol. For tissue analysis, slices were embedded in paraffin, sectioned, slide-mounted, and stained with haematoxylin and eosin (H&E). Apoptosis and necrosis of the tumour cells (as percentage) was assessed by reviewing all the H&E slides of the case (controls and treated ones) in toto, blindly, allowing for better estimation of the overall treatment effect to the tumour. In addition, any effects to precursor lesions (if present) and any off-target effects to benign surrounding tissue, were analysed. Tissue slides were assessed blindly by a breast cancer pathologist who determined the apoptotic events in the tumour, as well as any effect on adjacent normal tissue31. Cryopreserved primary AML samples were obtained with informed consent and Weill Cornell Medical College IRB approval (IRB number 0910010677 and IRB number 0909010629). Samples were thawed and cultured for in vitro treatment as described previously32. The microdose 124I-PU-H71 PET-CT (Dunphy, M. PET imaging of cancer patients using 124I-PUH71: a pilot study available from: http://clinicaltrials.gov; NCT01269593) and phase I PU-H71 therapeutic (Gerecitano, J. The first-in-human phase I trial of PU-H71 in patients with advanced malignancies available from: http://clinicaltrials.gov; NCT01393509) studies were approved by the institutional review board (protocols 10-139 and 11-041, respectively), and conducted under an exploratory investigational new drug (IND) application approved by the US Food and Drug Administration. Patients provided signed informed consent before participation. 124I-PU-H71 tracer was synthesized in-house by the institutional cyclotron core facility at high specific activity. For PU-PET, research PET-CT was performed using an integrated PET-CT scanner (Discovery DSTE, General Electric). CT scans for attenuation correction and anatomic coregistration were performed before tracer injection. Patients received 185 megabecquerel (MBq) of 124I-PU-H71 by peripheral vein over two minutes. PET data were reconstructed using a standard ordered subset expected maximization iterative algorithm. Emission data were corrected for scatter, attenuation, and decay. 124I-PU-H71 scans (PU-PET) were performed at 24 h after tracer administration. Each picture shown in Fig. 4c and Extended Fig. 6a is a scan taken of an individual patient. PET window display intensity scales for FDG and PU-PET fusion PET-CT images are given for both PU-PET and FDG-PET. Numbers in the scale bar indicate upper and lower SUV thresholds that define pixel intensity on PET images. The phase I trial included patients with solid tumours and lymphomas who had undergone prior treatment and currently had no curative treatment options. Patient cohorts were treated with PU-H71 at escalating dose levels determined by a modified continuous reassessment model. Each patient was treated with his or her assigned dose of PU-H71 on day 1, 4, 8, and 11 of each 21-day cycle. Human embryonic stem cells (hESCs) were differentiated with a modified dual-SMAD inhibition protocol towards floor plate-based midbrain dopaminergic (mDA) neurons as described previously33. hESCs were maintained on mouse embryonic fibroblasts and passaged with Dispase (STEMCELL Technologies). For each differentiation, hESCs were harvested with Accutase (Innovative Cell Technology). At day 30 of differentiation, hESC-derived mDA neurons were replated and maintained on dishes precoated with polyornithine (PO; 15 μg ml−1), laminin (1 μg ml−1), and fibronectin (2 μg ml−1) in Neurobasal/B27/l-glutamine-containing medium (NB/B27; Life Technologies) supplemented with 10 μM Y-27632 (until day 32) and with BDNF (brain-derived neurotrophic factor, 20 ng ml−1; R&D), ascorbic acid (AA; 0.2 mM, Sigma), GDNF (glial cell line-derived neurotrophic factor, 20 ng ml−1; R&D), TGFβ3 (transforming growth factor type β3, 1 ng ml−1; R&D), dibutyryl cAMP (0.5 mM; Sigma), and DAPT (10 nM; Tocris). Two days after replating, mDA neurons were treated with 1 μg ml−1 mitomycin C (Tocris) for 1 h to kill any remaining non-post mitotic contaminants. Assays were performed at day 65 of neuron differentiation. The PU-FITC assay was performed as previously described7, 23. Briefly, cells were incubated with 1 μM PU-FITC at 37 °C for 4 h. Then cells were washed twice with FACS buffer (PBS/0.5% FBS), and resuspended in FACS buffer containing 1 μg ml−1 DAPI. HL-60 cells were used as internal control to calculate fold binding for all cell lines tested. The mean fluorescence intensity (MFI) of PU-FITC in treated viable cells (DAPI negative) was evaluated by flow cytometry. For primary AML specimens, cells were also stained with anti-CD45-APC-H7, to identify blasts and lymphocyte populations (BD biosciences). Blasts and lymphocyte populations were gated based on SSC versus CD45. The fold PU-FITC binding of leukaemic blasts (CD45dim) was calculated relative to lymphocytes (CD45hiSSClow). The FITC derivative FITC9 was used as a negative control. Cells were seeded on coverslips in 6-well plate and cultured overnight. Cells were treated with 1 μM PU-FITC or negative control (PU-FITC9, an HSP90 inert PU-H71 derivative labelled with FITC). At 4 h post-treatment, cells were fixed with 4% formaldehyde at room temperature for 30 min, and the coverslips were mounted on slides with DAPI-Fluoromount-G Mounting Media (Southern Biotech). The images were captured using EVOS FL Auto imaging system (ThermoFisher Scientific) or a confocal microscope (Zeiss LSM5). Cells were seeded on coverslips and cultured overnight. Cells were fixed with 4% formaldehyde at room temperature for 30 min, washed three times with PBS, and permeabilized with 0.2% Triton X-100 in blocking buffer (PBS/5% BSA) for 10 min. Cells were incubated in blocking buffer for 30 min, and then incubated with rabbit anti-human HSP90α antibody (1:500, Abcam 2928) and mouse anti-human HSP90β (1:500, Stressmarq H9010), or rabbit and mouse normal IgG, in blocking buffer for 1 h. Cells were washed three times with PBS, and incubated with goat anti-mouse Alexa Fluor 568 and goat anti-rabbit Alexa Fluor 488 (1:1,000, ThermoFisher Scientific) in blocking buffer in the dark for 1 h. Cells were then washed three times with PBS, and the coverslips were removed from the plate, and mounted on slides with DAPI-Fluoromount-G Mounting Media (Southern Biotech). The images were captured using EVOS FL Auto imaging system (ThermoFisher Scientific) or a confocal microscope (Zeiss LSM5). Fluorescence intensity was quantified by the integrated density algorithm as implemented in ImageJ. Assays were carried out in black 96-well microplates (Greiner Microlon Fluotrac 200). A stock of 10 μM PU-FITC (or GM-cy3B34) was prepared in DMSO and diluted with Felts buffer (20 mM Hepes (K), pH 7.3, 50 mM KCl, 2 mM DTT, 5 mM MgCl , 20 mM Na MoO , and 0.01% NP40 with 0.1 mg ml−1 BGG). To each well was added the fluorescent dye-labelled HSP90 ligand (3 nM PU-FITC or 6 nM GM-cy3B), and cell lysates (7.5 μg) in a final volume of 100 μl Felts buffer. For each assay, background wells (buffer only), and tracer controls (PU-FITC only) were included on assay plate. To determine the equilibrium binding of GM-cy3b, increasing amounts of lysate (up to 20 μg of total protein) were incubated with tracer. The assay plate was placed on a shaker at room temperature for 60 min and the FP values in mP were measured every 5 min. At time t = 60 min, dissociation of fluorescent ligand was initiated by adding 1 μM PU-H71 in Felts buffer to each well and then placing the assay plate on a shaker at room temperature and measuring the FP values in mP every 5 min. The assay window was calculated as the difference between the FP value recorded for the bound fluorescent tracer and the FP value recorded for the free fluorescent tracer (defined as mP − mPf). Measurements were performed on a Molecular Devices SpectraMax Paradigm instrument (Molecular Devices, Sunnyvale, CA), and data were imported into SoftMaxPro6 and analysed in GraphPad Prism 5. To identify and separate chaperome complexes in tumours, and to overcome the limitations of classical protein chromatography methods for resolving complexes of similar composition and size, we took advantage of a capillary-based platform that combines isoelectric focusing (IEF) with immunoblotting capabilities35. This methodology uses an immobilized pH gradient to separate native multimeric protein complexes based on their isoelectric point (pI), and allows for subsequent probing of immobilized complexes with specific antibodies. The method uses only minute amounts of sample, thus enabling the interrogation of primary specimens. Cultured cells were lysed in 20 mM HEPES pH 7.5, 50 mM KCl, 5 mM MgCl , 0.01% NP40, 20 mM Na MoO buffer, containing protease and phosphatase inhibitors. Primary specimens were lysed in either Bicine-Chaps or RIPA buffers (ProteinSimple). Total protein assay was performed on an automated system, NanoPro 1000 Simple Western (ProteinSimple), for charge-based separation. Briefly, total cell lysates were diluted to a final protein concentration of 250 ng μl−1 using a master mix containing 1× Premix G2 pH 3-10 separation gradient (Protein simple) and 1× isoelectric point standard ladders (ProteinSimple). Samples diluted in this manner maintained their native charge state, and were loaded into capillaries (ProteinSimple) and separated based on their isoelectric points at a constant power of 21,000 μWatts for 40 min. Immobilization was performed by UV-light embedded in the Simple Western system, followed by incubations with anti-HSP90β (SMC-107A, StressMarq Biosciences), anti-HSP90α (ab2928, Abcam), anti-HSP70 (SPA-810, Enzo), AKT (4691), P-AKT (9271) or BCL2 (2872) from Cell Signaling Technology and subsequently with HRP-conjugated anti-Mouse IgG (1030-05, SouthernBiotech) or with HRP-conjugated anti-Rabbit IgG (4010-05, SouthernBiotech). Protein signals were quantitated by chemiluminescence using SuperSignal West Dura Extended Duration Substrate (Thermo Scientific), and digital imaging and associated software (Compass) in the Simple Western system, resulting in a gel-like representation of the chromatogram. This representation is shown for each figure. Protein was extracted from cultured cells in 20 mM Tris pH 7.4, 150 mM NaCl, 1% NP-40 buffer with protease and phosphatase inhibitors added (Complete tablets and PhosSTOP EASYpack, Roche). Ten to fifty μg of total protein was subjected to SDS–PAGE, transferred onto nitrocellulose membrane, and incubated with indicated antibodies. HSP90β (SMC-107) and HSP110 (SPC-195) antibodies were purchased from Stressmarq; HER2 (28-0004) from Zymed; HSP70 (SPA-810), HSC70 (SPA-815), HIP (SPA-766), HOP (SRA-1500), and HSP40 (SPA-400) from Enzo; HSP90β (ab2927), HSP90α (ab2928), p23 (ab2814), GAPDH (ab8245) and AHA1 (ab56721) from Abcam; cleaved PARP (G734A) from Promega; CDC37 (4793), CHIP (2080), EGFR (4267), S6K (2217), phospho-S6K (S235/236) (4858), P-AKT (S473) (9271), AKT (4691), P-ERK (T202/Y204) (4377), ERK (4695), MCL1 (5453), Bcl-XL (2764), BCL2 (2872), c-MYC (5605) and HER3 (4754) from Cell Signaling Technology; and β-actin (A1978) from Sigma-Aldrich. The blots were washed with TBS/0.1% Tween 20 and incubated with appropriate HRP-conjugated secondary antibodies. Chemiluminescent signal was detected with Enhanced Chemiluminescence Detection System (GE Healthcare) following the manufacturer’s instructions. We screened a panel of anti-chaperome antibodies for those that interacted with the target protein in its native form. We reasoned that these antibodies were more likely to capture stable multimeric forms of the chaperome members. These native-cognate antibodies were used in native-PAGE and IEF analyses of chaperome complexes. HSP90β (SMC-107) and HSP110 (SPC-195) antibodies were purchased from Stressmarq; HSP70 (SPA-810), HSC70 (SPA-815), HOP (SRA-1500), and HSP40 (SPA-400) from Enzo; HSP90β (ab2927), HSP90α (ab2928), and AHA1 (ab56721) from Abcam; CDC37 (4793) from Cell Signaling Technology. Cells were lysed in 20 mM Tris pH 7.4, 20 mM KCl, 5 mM MgCl , 0.01% NP40, and 10% glycerol buffer by a freeze-thaw procedure. Primary samples were lysed in either Bicine-Chaps or RIPA buffers (ProteinSimple). Twenty-five to one hundred μg of protein was loaded onto 4–10% native gradient gel and resolved at 4 °C. The gels were immunoblotted as described above following either incubation in Tris-Glycine-SDS running buffer for 15 min before transfer in regular transfer buffer for 1 h, or directly transferred in 0.1% SDS-containing transfer buffer for 1 h. Cells were plated at 1 × 106 per 6 well-plate and transfected with an siRNA against human AHA1 (AHSA1; 5′-TTCAAATTGGTCCACGGATAA-3′), HSP90α (HSP90AA1; no. 1 5′-ATGGCATGACAACTACTTTAA-3′; no. 2 5′-AACCCTGACCATTCCATTATT-3′; no.3 5′-TGCACTGTAAGACGTATGTAA-3′), HSP90β (HSP90AB1; no., 5′-CAAGAATGATAAGGCAGTTAA-3′; no. 5′-TACGTTGCTCACTATTACGTA-3′; no.3 5′-CAGAAGACAAGGAGAATTACA-3′) HSP90α/β (no.1 5′-CAGAATGAAGGAGAACCAGAA-3′, no.2 5′-CACAACGATGATGAACAGTAT-3′), HSP110 (HSPH1; 5′-AGGCCGCTTTGTAGTTCAGAA-3′) from Qiagen or HOP (STIP1) (Dharmacon; M-019802-01), or a negative control (scramble; 5′-CAGGGTATCGACGATTACAAA-3′) with Lipofectamine RNAiMAX reagent (Invitrogen), incubated for 72 h and subjected to further analysis. Total mRNA was isolated using TRIzol Reagent (Invitrogen) following the manufacturer’s recommended protocol. Reverse transcription of mRNA into cDNA was performed using QuantiTect Reverse Transcription Kit (Qiagen). qRT–PCR was performed using PerfeCTa SYBR (Quanta Bioscience), 10 nM AHSA1 (forward: 5′-GCGGCCGCTTCTAGTAGTTT-3′ and reverse: 5′-CATCTCTCTCCGTCCAGTGC-3′) and GAPDH (forward: 5′-CAAAGGCACAGTCAAGGCTGA-3′ and reverse: 5′-TGGTGAAGACGCCAGTAGATT-3′) primers, or 1× QuantiTect Primers for HSP110 (HSPH1), HSP90α (HSP90AA1), HSP90β (HSP90AB1), HSP70 (HSPA1A), HOP (STIP1) (Qiagen) following recommended PCR cycling conditions. Melting curve analysis was performed to ensure product uniformity. To investigate which of the two HSP70 paralogues is involved in epichaperome formation we performed immunodepletions with HSP70 and HSC70 antibodies. Protein lysates were immunoprecipitated consecutively three times with either an HSP70 (Enzo, SPA-810), HSC70 (Enzo, SPA-815) or HOP (kindly provided by M. B. Cox, University of Texas at El Paso), or with the same species normal antibody as a negative control (Santa Cruz). The resulting supernatant was collected and run on a native or a denaturing gel. Tumour lysates were mixed with 10 M urea (dissolved in Felts buffer) to reach the indicated final concentrations of 2 M, 4 M and 6 M. After incubation for 10 min at room temperature or frozen overnight at −80 °C, the lysates were loaded onto 4–10% native gradient gel and resolved at 4 °C or applied to the IEF capillary. The HSP90β bands were detected by using antibody purchased from Stressmarq (SMC-107). A lentiviral vector expressing the MYC shRNA, as previously described36, was requested from Addgene (Plasmid 29435, c-MYC shRNA sequence: GACGAGAACAGTTGAAACA). Viruses were prepared by co-transfecting the shRNA vector, the packaging plasmid psPAX2 and the envelop plasmid pMD2.G into HEK293 cells. OCI-LY1 cells were then infected with lentiviral supernatants in the presence of 4 μg ml−1 polybrene for 24 h. Following flow cytometry selection for positive cells, cells were expanded for further experiments. The MYC protein level was confirmed at 10 days post-infection by western blot using the anti-MYC antibody (Cell Signaling Technology, 5605). Viruses were prepared by co-transfection of the lentiviral vector expressing the MYC shRNA with pLM-mCerulean-2A-cMyc (Addgene, 23244) or pCDH-puro-cMYC (Addgene, 46970), the packaging plasmid psPAX2, and the envelope plasmid pMD2.G into HEK293 cells. ASPC1 cells were then infected with lentiviral supernatants in the presence of 4 μg ml−1 polybrene for 24 h and sorted for mCerulean positive cells or selected with puromycin treatment. Changes in cell size after infection were monitored by analysing the forward scatter (FSC) of intact cells via flow cytometry. MYC protein levels were analysed at 4 days post-infection by western blot. Whole cell extracts were prepared by homogenizing cells in RIPA buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 1% NP40, 0.25% sodium deoxycholate, 10% glycerol, protease inhibitors). MYC activity was determined using the TransAM c-Myc Kit (Active Motif, 43396), following the manufacturer’s instructions. Cell viability was assessed using CellTiter-Glo luminescent Cell Viability Assay (Promega) after a 72 h PU-H71 treatment. The method determines the number of viable cells in culture based on quantification of the ATP present, which signals the presence of metabolically active cells, and was performed as previously reported37. For the annexin V staining, cells were labelled with Annexin V-PE and 7AAD after PU-H71 treatment for 48 h, as previously reported38. The necrotic cells were defined as annexin V+/7AAD+, and the early apoptotic cells were defined as annexin V+/7AAD−. For the LDH assay the release of lactate dehydrogenase (LDH) into the culture medium only occurs upon cell death. Following indicated treatment, the culture medium was collected and centrifuged to remove living cells and cell debris. The collected medium was incubated at room temperature for 30 min with the Cytotox-96 Non-radioactive Assay kit (Promega) LDH substrate. All animal studies were conducted in compliance with MSKCC’s Institutional Animal Care and Use Committee (IACUC) guidelines. Female athymic nu/nu mice (NCRNU-M, 20–25 g, 6 weeks old) were obtained from Harlan Laboratories and were allowed to acclimatize at the MSKCC vivarium for 1 week before implanting tumours. Mice were provided with food and water ad libitum. Tumour xenografts were established on the forelimbs for PET imaging and on the flank for efficacy studies. Tumours were initiated by sub-cutaneous injection of 1 × 107 cells for MDA-MB-468 and 5 × 106 for ASPC1 in a 200 μl cell suspension of a 1:1 v/v mixture of PBS with reconstituted basement membrane (BD Matrigel, Collaborative Biomedical Products). Before administration, a solution of PU-H71 was formulated in citrate buffer. Sample size was chosen empirically based on published data39. No statistical methods were used to predetermine sample size. Animals were randomly assigned to groups. Studies were not conducted blinded. Imaging was performed with a dedicated small-animal PET scanner (Focus 120 microPET; Concorde Microsystems, Knoxville, TN). Mice were maintained under 2% isoflurane (Baxter Healthcare, Deerfield, IL) anaesthesia in oxygen at 2 litres per min during the entire scanning period. To reduce the thyroid uptake of free iodide arising from metabolism of tracer, mice received 0.01% potassium iodide solution in their drinking water starting 48 h before tracer administration. For PET imaging, each mouse was administered 9.25 MBq (250 μCi) of 124I-PU-H71 via the tail vein. List-mode data (10 to 30 min acquisitions) were obtained for each animal at various time points post-tracer administration. An energy window of 420–580 keV and a coincidence timing window of 6 ns were used. The resulting list-mode data were sorted into 2-dimensional histograms by Fourier rebinning; transverse images were reconstructed by filtered back projection (FBP). The image data were corrected for non-uniformity of scanner response, dead-time count losses, and physical decay to the time of injection. There was no correction applied for attenuation, scatter, or partial-volume averaging. The measured reconstructed spatial resolution of the Focus 120 is 1.6-mm FWHM at the centre of the field of view. Region of interest (ROI) analysis of the reconstructed images was performed using ASIPro software (Concorde Microsystems, Knoxville, TN), and the maximum pixel value was recorded for each tissue/organ ROI. A system calibration factor (that is, μCi per ml per cps per voxel) that was derived from reconstructed images of a mouse-size water-filled cylinder containing 18F was used to convert the 124I voxel count rates to activity concentrations (after adjustment for the 124I positron branching ratio). The resulting image data were then normalized to the administered activity to parameterize the microPET images in terms of per cent injected dose per gram (%ID per g) (corrected for decay of 124I to the time of injection). Post-reconstruction smoothing was applied only for visual representation of images in the figures. Upon euthanasia, radioactivity (124I) was measured in a gamma-counter (Perkin Elmer 1480 Wizard 3 Auto Gamma counter) using a 400–600 keV energy window. Count data were background- and decay-corrected to the time of injection, and the percent injected dose per gram (%ID per g) for each tumour sample was calculated using a calibration curve to convert counts to radioactivity, followed by normalization to the total activity injected. Mice (n = 5) bearing MDA-MB-468 or ASPC1 tumours reaching a volume of 100–150 mm3 were treated i.p. using PU-H71 (75mg per kg) or vehicle, on a 3 times per week schedule, as indicated. Tumour volume (in mm3) was determined by measurement with Vernier calipers, and was calculated as the product of its length × width2 × 0.5. Tumour volume was expressed on indicated days as the median tumour volume ± s.d. indicated for groups of mice. Mice were euthanized after similar PU-H71 treatment periods, and at a time before tumours reached a size that resulted in discomfort or difficulty in physiological functions of mice in the individual treatment group, in accordance with our IUCAC protocol. Frozen tissue was dried and weighed before homogenization in acetonitrile/H O (3:7). PU-H71 was extracted in methylene chloride, and the organic layer was separated and dried under vacuum. Samples were reconstituted in mobile phase. The concentrations of PU-H71 in tissue or plasma were determined by high-performance LC-MS/MS. PU-H71-d was added as the internal standard40. Compound analysis was performed on the 6410 LC-MS/MS system (Agilent Technologies) in multiple reaction monitoring mode using positive-ion electrospray ionization. For tissue samples, a Zorbax Eclipse XDB-C18 column (2.1 × 50 mm, 3.5 μm) was used for the LC separation, and the analyte was eluted under an isocratic condition (80% H O + 0.1% HCOOH: 20% CH CN) for 3 min at a flow rate of 0.4 ml min−1. For plasma samples, a Zorbax Eclipse XDB-C18 column (4.6 × 50 mm, 5 μm) was used for the LC separation, and the analyte was eluted under a gradient condition (H O + 0.1% HCOOH:CH CN, 95:5 to 70:30) at a flow rate of 0.35 ml min−1. Protein extracts were prepared either in 20 mM HEPES pH 7.5, 50 mM KCl, 5 mM MgCl , 1% NP40, and 20 mM Na MoO for PU-H71 beads pull-down, or in 20 mM Tris pH 7.4, 150 mM NaCl, and 1% NP40 for YK beads pull-down. Samples were incubated with the PU-H71 beads (HSP90 bait) for 3–4 h or with the YK beads (HSP70 bait, for chemical precipitation) overnight, at 4 °C, then washed and subjected to SDS–PAGE with subsequent immunoblotting and western blot analysis. For HSP70 proteomic analyses, cells were incubated with a biotinylated YK-derivative, YK-biotin. Briefly, MDA-MB-468 cells were treated for 4 h with 100 μM biotin-YK5 or d-biotin as a negative control. Cells were collected and lysed in 20 mM Tris pH 7.4, 150 mM NaCl, and 1% NP40 buffer. Protein extracts were incubated with streptavidin agarose beads (Thermo Scientific) for 1 h at 4 °C, washed with 20 mM Tris pH 7.4, 150 mM NaCl, and 0.1% NP40 buffer and applied onto SDS–PAGE. The gels were stained with SimplyBlue Coomassie stain (Invitrogen Life Science Technologies). Proteomic analyses were performed using the published protocol7, 18, 22. Control beads contained an inert molecule as previously described7, 18, 22. Affinity-purified protein complexes from type 1 tumours (n = 6; NCI-H1975, MDA-MB-468, OCI-LY1, Daudi, IBL1, BC3), type 2 tumours (n = 3; ASPC1, OCI-LY4, Ramos) and from non-transformed cells (n = 3; MRC5, HMEC and neurons) were resolved using SDS-polyacrylamide gel electrophoresis, followed by staining with colloidal, SimplyBlue Coomassie stain (Invitrogen Life Science Technologies) and excision of the separated protein bands. Control beads that contained an inert molecule were subjected to the same steps as PU-H71 and YK beads and served as a control experiment. To ensure that we captured a majority of the HSP90 complexes in each cell type, we performed these studies under conditions of HSP90-bait saturation. The number of gel sections per lane averaged to be 14. In situ trypsin digestion of gel bound proteins, purification of the generated peptides and LC–MS/MS analysis were performed using our published protocols7, 18, 22. After the acquisition of raw files, Proteowizard (version 3.0.3650)41 was used to create a Mascot Generic Format (mgf) file containing accurate mass for each peak and its corresponding ms2 ions. Each mgf was then subjected to search a human segment of Uniprot protein database (20,273 sequences, European Bioinformatics Institute, Swiss Institute of Bioinformatics and Protein Information Resource) using Mascot (Matrix Science; version 2.5.0; http://www.matrixscience.com). Decoy proteins were added to the search to allow for the calculation of false discovery rates (FDR). The search parameters were as follows: (i) two missed cleavage tryptic sites were allowed; (ii) precursor ion mass tolerance = 10 p.p.m.; (iii) fragment ion mass tolerance = 0.8 Da; and (iv) variable protein modifications were allowed for methionine oxidation, deamidation of asparagine and glutamines, cysteine acrylamide derivatization and protein N-terminal acetylation. MudPit scoring was typically applied using significance threshold score P < 0.01. Decoy database search was always activated and, in general, for merged LS–MS/MS analysis of a gel lane with P < 0.01, false discovery rate averaged around 1%. The Mascot search result was finally imported into Scaffold (Proteome Software, Inc.; version 4_4_1) to further analyse tandem mass spectrometry (MS/MS) based protein and peptide identifications. X! Tandem (The GPM, http://thegpm.org; version CYCLONE (2010.12.01.1) was then performed and its results are merged with those from Mascot. The two search engine results were combined and displayed at 1% FDR. Protein and peptide probability was set at 95% with a minimum peptide requirement of 1. Protein identifications were expressed as Exclusive Spectrum Counts that identified each protein listed. Primary data, such as raw mass spectrometry files, Mascot generic format files and proteomics data files created by Scaffold have been deposited onto the Massive site (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp; MassIVE Accession ID: MSV000079877). In each of the Scaffold files that validate and import Mascot searched files, peptide matches, scoring information (Mascot, as well as X! Tandem search scores) for peptide and protein identifications, MS/MS spectra, protein views with sequence coverage and more, can be easily accessed. To read the Scaffold files, free viewer software can be found at (http://www.proteomesoftware.com/products/free-viewer/). Peptide matches and scoring information that demonstrate the data processing are available in Supplementary Table 1f–q. The exclusive spectrum count values, an alternative for quantitative proteomic measurements42, were used for protein analyses. CHIP and PP5 were examined and used as internal quality controls among the samples. Statistics were performed using R (version 3.1.3) limma package43, 44. For entries with zero spectral counts, and to enable further analyses, we assigned an arbitrary small number of 0.1. The data were then transformed into logarithmic base 10 for analysis. Linear models were fit to the transformed data and moderated standard errors were calculated using empirical Bayesian methods. For Fig. 1f and Extended Data Fig. 5a, a moderated t-statistic was used to compare protein enrichment between type 1 cells and combined type 2 and non-transformed cells45. For Extended Data Fig. 5b, the t-statistic was performed to compare protein enrichment among type 1 cells, type 2 cells and non-transformed cells (see Supplementary Table 1). Heat maps were created to display the selected proteins using the package “gplots” and “lattice”46, 47. See Supplementary Table 1 in which the table tab ‘a’ corresponds to Fig. 1f and contains core chaperome networks in type 1, type 2 and non-transformed cells; the table tab ‘b’ corresponds to Extended Data Fig. 5a and contains comprehensive chaperome networks in type 1, type 2 and non-transformed cells; the table tab ‘c’ corresponds to Extended Data Fig. 5b and Extended Data Fig. 8b and contains the HSP90 interactome as isolated by the HSP90 bait in type 1, type 2 and non-transformed cells; the table tab ‘d’ corresponds to Extended Data Fig. 8a and contains upstream transcriptional regulators that explain the protein signature of type1 tumours and the table tab ‘e’ contains metastasis-related proteins characteristic of type 1 tumours. To understand the physical and functional protein-interaction properties of the HSP90-interacting chaperome proteins enriched in type 1 tumours, we used the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database48. Proteins displayed in the heat map were uploaded in STRING database to generate the PPI networks. STRING builds functional protein-association networks based on compiled available experimental evidence. The thickness of the edges represents the confidence score of a functional association. The score was calculated based on four criteria: co-expression, experimental and biochemical validation, association in curated databases, and co-mentioning in PubMed abstracts48. Proteins with no adjacent interactions were not shown. The colour scale in nodes indicates the average enrichment of the protein (measured as exclusive spectral counts) in type 1, type 2, and non-transformed cells, respectively. The network layout for type 1 tumours was generated using edge-weighted spring-electric layout in Cytoscape with slight adjustments of marginal nodes for better visualization49. The layout for type 2 and non-transformed cells retains that of type 1 for better comparison. Proteins with average relative abundance values less than 1 were deleted from analyses. The biological processes in which they participate and the functionality of proteins enriched in type 1 tumours were assigned based on gene ontology terms and based on their designated interactome from UniProtKB, STRING, and/or I2D databases48, 50, 51, 52, 53. The Upstream Regulator analytic, as implemented in Ingenuity Pathways Analysis (IPA, QIAGEN Redwood City, http://www.qiagen.com/ingenuity), was used to identify the cascade of upstream transcriptional regulators that can explain the observed protein expression changes in type 1 tumours. The analysis is based on prior knowledge of expected effects between transcriptional regulators and their target genes stored in the Ingenuity Knowledge Base. The analysis examines how many known targets of each transcription regulator are present in the data set, and calculates an overlap P value for upstream regulators based on significant overlap between dataset genes and known targets regulated by a transcription regulator. For Extended Data Fig. 8b, proteins were selected based on 3 pre-curated lists (MYC target genes based on the analysis report from INGENUITY, MYC signature genes based on the reported list provided in ref. 54 and MYC expression/function activators were manually curated from UniProt and GeneCards databases). Cell lines with information available in the cBioPortal for cancer genomics (http://www.cbioportal.org) were evaluated for mutations in pathways implicated in cancer: P53, RAS, RAF, PTEN, PIK3CA, AKT, EGFR, HER2, CDK2NA/B, RB, MYC, STAT1, STAT3, JAK2, MET, PDGFR, KDM6A, KIT. Mutations in major chaperome members (HSP90AA1, HSP90AB1, HSPH1, HSPA8, STIP1, AHSA1) were also evaluated. Data were visualized and statistical analyses performed using GraphPad Prism (version 6; GraphPad Software) or R statistical package. In each group of data, estimate variation was taken into account and is indicated in each figure as s.d. or s.e.m. If a single panel is presented, data are representative of 2 or 3 biological or technical replicates, as indicated. P values for unpaired comparisons between two groups with comparable variance were calculated by two-tailed Student’s t-test. Pearson’s tests were used to identify correlations among variables. Significance for all statistical tests was shown in figures for not significant (NS), *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001. No samples or animals were excluded from analysis, and sample size estimates were not used. Animals were randomly assigned to groups. Studies were not conducted blinded, with the exception of all patient specimen histological analyses.


News Article | November 10, 2016
Site: www.eurekalert.org

NOVEMBER 9, 2016, NEW YORK - A Ludwig Cancer Research study shows that an experimental drug currently in clinical trials can reverse the effects of troublesome cells that prevent the body's immune system from attacking tumors. The researchers also establish that it is these suppressive cells that interfere with the efficacy of immune checkpoint inhibitors. This class of immunotherapies lifts the brakes that the body imposes on the immune system's T cells to unleash an attack on cancer cells. "Though checkpoint inhibitors have durable effects when they work, not all patients respond to the treatment," says Taha Merghoub, an investigator at the Ludwig Memorial Sloan Kettering Collaborative Laboratory who led the study with Director Jedd Wolchok. "Part of the reason for this is that some tumors harbor tumor-associated myeloid cells, or TAMCs, that prevent T cells from attacking tumor cells." In a study published online today in Nature, Merghoub and his team used mouse models of cancer to show that the effects of TAMCs can be reversed by an appropriately targeted therapy. To show that TAMCs were indeed involved in resistance to checkpoint blockade, the researchers used a specific growth stimulant to increase their number in melanoma tumors to create a suitable model for their studies. They found that this made the tumors less susceptible to checkpoint blockade. "We were able to make a tumor that was not rich in immune suppressing myeloid cells into one that was," says Merghoub. Having established a link between TAMCs and checkpoint inhibitor resistance, the researchers next set out to test the hypothesis that blocking immune suppressor cell activity would improve immunotherapy response. To do this, they used an experimental drug manufactured by Infinity Pharmaceuticals called IPI-549. The drug, which is available for clinical use, blocks a molecule in the suppressor cells called PI3 kinase-gamma. Blocking this molecule changes the balance of these immune suppressor cells in favor of more immune activation. "We effectively reprogrammed the TAMCs, turning them from bad guys into good guys," Merghoub said. IPI-549 dramatically improved responses to immune checkpoint blockade (ICB) therapy for tumors with high concentrations of TAMCs. When checkpoint inhibitors were administered to mice with suppressed tumors, only 20% of the animals underwent complete remission. When the same drugs were administered with IPI-549, that number jumped to 80%. IPI-549 provided no benefit to tumors lacking the suppressor cells. Merghoub and his team also showed that tumors that were initially sensitive to checkpoint inhibitors were rendered unresponsive when their TAMC concentrations were boosted with growth stimulants. Taken together, these results indicate that TAMCs promote resistance to checkpoint inhibitors and that IPI-549 can selectively block these cells, thereby overcoming their resistance. Merghoub said the findings help pave the way for a precision medicine approach to immunotherapy that will allow cancer treatments to be tailored to a patient's particular tumor profile. "We can now potentially identify patients whose tumors possess immune suppressor cells and add a drug to their treatment regimen to specifically disarm them," he added. IPI-549 is currently undergoing a Phase I trial in the United States to assess its safety when administered alone and in combination with the FDA-approved checkpoint inhibitor drug nivolumab (Opdivo®). Funding and support for this research was provided by Ludwig Cancer Research, Swim Across America, the Parker Institute for Cancer Immunotherapy, the Center for Experimental Therapeutics at MSKCC, the Breast Cancer Research Foundation, the J. Houtard Foundation, Nuovo Soldati Foundation, Wallonie-Bruxelles International, and Infinity Pharmaceutics. Taha Merghoub also co-directs the Swim Across America Laboratory at Memorial Sloan Kettering, is a Parker Institute for Cancer Immunotherapy investigator and leads the biorepository for the Melanoma and Immunotherapeutics Service at MSK. Jedd Wolchok is also Chief of the Melanoma and Immunotherapeutics Service and Director of the Parker Institute for Cancer Immunotherapy at MSK. Ludwig Cancer Research is an international collaborative network of acclaimed scientists that has pioneered cancer research and landmark discovery for more than 40 years. Ludwig combines basic science with the ability to translate its discoveries and conduct clinical trials to accelerate the development of new cancer diagnostics and therapies. Since 1971, Ludwig has invested $2.7 billion in life-changing science through the not-for-profit Ludwig Institute for Cancer Research and the six U.S.-based Ludwig Centers. To learn more, visit http://www. . For further information please contact Rachel Steinhardt, rsteinhardt@licr.org or +1-212-450-1582.


RUTHERFORD, N.J., Nov. 02, 2016 (GLOBE NEWSWIRE) -- Cancer Genetics, Inc. (Nasdaq:CGIX); (“CGI” or “The Company”), a leader in enabling precision medicine for oncology through molecular markers and diagnostics, announced today the successful CLIA validation and approval of its next generation sequencing (NGS) assay that enables an era of precision medicine for renal cancers, Focus::Renal™. Focus::Renal™, a highly-sensitive NGS panel, detects mutations of 76 renal cancer-related genes, as well as genome-wide copy number changes, and critical single nucleotide polymorphisms (SNPs), all in a single test, that enable precision diagnosis, prognosis, and therapy selection for renal cancer patients. Renal cancer accounts for 5% of adult cancers in the United States with an estimated 62,700 new cases and 14,240 deaths in 2016 [1]. The most common renal neoplasm is renal cell carcinoma (RCC). Clear cell RCC (ccRCC) accounts for ~75% of RCC, with malignant subtypes papillary RCC (pRCC) and chromophobe RCC (chrRCC), and the benign neoplasm oncocytoma (OC) mostly comprising the remainder. ccRCC has a poorer prognosis than papillary and chromophobe RCC malignant subtypes. About one fourth of the patients with ccRCC present with metastatic disease at diagnosis while 20-40% of those with locally confined tumor tend to develop metastasis. There are seven FDA-approved targeted therapies (including VEGF-TKIs, anti-VEGF monoclonal antibody, and mTOR inhibitors) and one immunotherapy (anti-PD1 checkpoint inhibitor) available to date to treat metastatic RCC – which makes therapy selection more challenging and yet more critical. Currently, there is a growing body of evidence showing that mutations, copy number changes, and certain polymorphisms correlate with patient outcome and therapy response, and can be critical in enabling precision medicine. In addition, there are over 200 open clinical trials enrolling patients with renal cancers. At present, comprehensive genomic profiling of renal cancer patients has become an important need due to its potential impact on precision diagnostics and the development of tailor-made therapies, resulting in improved cancer care. Focus::Renal™ is a unique NGS panel, developed by CGI in collaboration with leading cancer centers and academic institutions, including MSKCC, Cleveland Clinic, Huntsman Cancer Center at University of Utah, and University Hospital of Paris. Focus::Renal™ is a comprehensive and accurate genomic profiling tool covering the majority of renal cancer markers and pathways. Focus::Renal™ is designed based on the most current scientific literature, TCGA genomic data, and in-house findings as a result of collaborations with leading research institutions, and has undergone multiple independent validations using samples from over 500 patients. The test can be performed on a wide variety of patient specimen types, such as needle biopsies, fine-needle aspirates, and resected specimens using both formalin-fixed paraffin-embedded (FFPE) and fresh/fresh-frozen specimens, including the ones with minimal starting material, giving clinicians a choice on how to incorporate the test into their diagnostic workflow. The Focus::Renal™ NGS panel can be utilized to distinguish among the dominant 3 malignant and 1 benign renal cancer subtypes, which is today largely driven by morphological and immunohistochemical review. "Focus::Renal™ has the ability to significantly facilitate the personalized care for renal cancer patients and also generate future insights; as the deeper understanding of molecular abnormalities will enhance the development of more effective therapies for RCC. Implementing precision medicine for kidney cancer patients requires powerful and targeted tools like Focus::Renal™. Our immediate plans include collaborations with biopharma and academic partners to implement Focus::Renal™ for liquid biopsies so that this critical test can also inform cancer care directly from blood," said Panna Sharma, Chief Executive Officer and President of CGI. ABOUT CANCER GENETICS Cancer Genetics, Inc. is a leader in enabling precision medicine in oncology from bench to bedside through the use of oncology biomarkers and molecular testing. CGI is developing a global footprint with locations in the US, India and China. We have established strong clinical research collaborations with major cancer centers such as Memorial Sloan Kettering, The Cleveland Clinic, Mayo Clinic, Keck School of Medicine at USC and the National Cancer Institute. The Company offers a comprehensive range of laboratory services that provide critical genomic and biomarker information. Its state-of-the-art reference labs are CLIA-certified and CAP-accredited in the US and have licensure from several states including New York State. For more information, please visit or follow CGI at: Internet: www.cancergenetics.com  Twitter: @Cancer_Genetics Facebook: www.facebook.com/CancerGenetics Forward-Looking Statements This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. All statements pertaining to Cancer Genetics Inc.’s expectations regarding future financial and/or operating results and potential for our tests and services, and future revenues or growth in this press release constitute forward-looking statements. Any statements that are not historical fact (including, but not limited to, statements that contain words such as "will," "believes," "plans," "anticipates," "expects," "estimates") should also be considered to be forward-looking statements. Forward-looking statements involve risks and uncertainties, including, without limitation, risks inherent in the development and/or commercialization of potential products, risks of cancellation of customer contracts or discontinuance of trials, risks that anticipated benefits from acquisitions will not be realized, uncertainty in the results of clinical trials or regulatory approvals, need and ability to obtain future capital, maintenance of intellectual property rights and other risks discussed in the Cancer Genetics, Inc. Form 10-K for the year ended December 31, 2015 and the Form 10-Q for the Quarter ended June 30, 2016 along with other filings with the Securities and Exchange Commission. These forward-looking statements speak only as of the date hereof. Cancer Genetics, Inc. disclaims any obligation to update these forward-looking statements.


News Article | September 29, 2016
Site: www.chromatographytechniques.com

Nanoparticles known as Cornell dots, or C dots, have shown great promise as a therapeutic tool in the detection and treatment of cancer. Now, the ultrasmall particles – developed more than a dozen years ago by Ulrich Wiesner, the Spencer T. Olin Professor of Engineering at Cornell University – have shown they can do something even better: kill cancer cells without attaching a cytotoxic drug. The study was led by Michelle Bradbury, director of intraoperative imaging at Memorial Sloan Kettering Cancer Center and associate professor of radiology at Weill Cornell Medicine, and Michael Overholtzer, cell biologist at MSKCC, in collaboration with Wiesner. Their work details how C dots, administered in large doses and with the tumors in a state of nutrient deprivation, trigger a type of cell death called ferroptosis. “If you had to design a nanoparticle for killing cancer, this would be exactly the way you would do it,” Wiesner said. “The particle is well tolerated in normally healthy tissue, but as soon as you have a tumor, and under very specific conditions, these particles become killers.” “In fact,” Bradbury said, “this is the first time we have shown that the particle has intrinsic therapeutic properties.” Wiesner’s fluorescent silica particles, as small as 5 nanometers in diameter, were originally designed to be used as diagnostic tools, attaching to cancer cells and lighting up to show a surgeon where the tumor cells are. Potential uses also included drug delivery and environmental sensing. A first-in-human clinical trial by the Food and Drug Administration, led by Bradbury, deemed the particles safe for humans. In further testing of the particles over the last five years, Bradbury, Overholtzer, Wiesner and their collaborators made this major, unexpected finding. When incubated with cancer cells at high doses – and importantly, with cancer cells in a state of nutrient deprivation – Wiesner’s peptide-coated C dots show the ability to adsorb iron from the environment and deliver this into cancer cells. The peptide, called alpha-MSH, was developed by Thomas Quinn, professor of biochemistry at the University of Missouri. This process triggers ferroptosis, a necrotic form of cell death involving plasma membrane rupture – different from the typical cell fragmentation found during a more commonly observed form of cell death called apoptosis. “The original purpose for studying the dots in cells was to see how well larger concentrations would be tolerated without altering cellular function,” Overholtzer said. “While high concentrations were well-tolerated under normal conditions, we wanted to also know how cancer cells under stress might respond.” To the group’s surprise, in 24 to 48 hours after the cancer cells were exposed to the dots, there was a “wave of destruction” throughout the entire cell culture, Wiesner said. Tumors also shrank when mice were administered multiple high dose injections without any adverse reactions, said Bradbury, co-director with Wiesner of the MSKCC-Cornell Center for Translation of Cancer Nanomedicines. In the ongoing fight against a disease that kills millions worldwide annually – cancer has taken several in Wiesner’s family, making this also a personal crusade for him. Having another weapon can only help, Wiesner said. “We’ve found another tool that people have not thought about at all so far,” he said. “This has changed our way of thinking about nanoparticles and what they could potentially do.” Future work will focus on utilizing these particles in combination with other standard therapies for a given tumor type, Bradbury said, with the hope of further enhancing efficacy before testing in humans. Researchers will also look to tailor the particle to target specific cancers. Their paper, “Ultrasmall Nanoparticles Induce Ferroptosis of Nutrient-Deprived Cancer Cells and Suppress Tumor Growth,” was published Sept. 26 in Nature Nanotechnology.


News Article | November 29, 2016
Site: www.marketwired.com

Interview with Dr. Marjorie Zauderer, MD, of Memorial Sloan Kettering Cancer Center, discussing mesothelioma treatments is now available on demand ALEXANDRIA, VA--(Marketwired - November 29, 2016) - On Tuesday, November 1, the Meso Foundation held a Meet the Mesothelioma Experts session with Marjorie Zauderer, MD, of Memorial Sloan Kettering Cancer Center (MSKCC). During the call, Dr. Zauderer discussed her work as a medical oncologist at MSKCC in an interview with Mary Hesdorffer, the Meso Foundation's executive director and expert nurse practitioner. Dr. Zauderer specializes in the care of patients with lung cancer and mesothelioma. Her practice aims to provide personalized care to patients throughout the course of their treatment. She also conducts clinical trials focused on novel therapies for the treatment of mesothelioma and lung cancer. Several trials with novel agents and new approaches are ongoing in MSKCC's multidisciplinary Mesothelioma Program and Thoracic Oncology Service. The interview is available at www.curemeso.org/experts. Mesothelioma is a malignant tumor of the lining of the lung, abdomen, or heart known to be caused by exposure to asbestos. With the life expectancy of less than one year after diagnosis, medical experts consider it one of the most aggressive and deadly of all cancers. An estimated one-third of those who develop mesothelioma were exposed while serving in the Navy or working in shipyards. Currently, few treatment options exist. There is no cure. ABOUT THE MESOTHELIOMA APPLIED RESEARCH FOUNDATION The Meso Foundation is the only 501(c)(3) nonprofit organization dedicated to eradicating mesothelioma and easing the suffering caused by this cancer. The Meso Foundation actively seeks philanthropic support to fund mesothelioma research; provide patient support services and education; and advocate Congress for increased federal funding for mesothelioma research. The Meso Foundation is the only non-government funder of peer-reviewed scientific research to develop life-saving treatments for this extremely aggressive cancer. To date, the Foundation has awarded over $9.4 million to research. More information is available at www.curemeso.org.

Loading MSKCC collaborators
Loading MSKCC collaborators