The Cancer Genome Atlas Research Research Articles

LncRNA expression signature identified using genome-wide transcriptomic profiling to predict lymph node metastasis in patients with stage T1 and T2 gastric cancer

BackgroundLymph node (LN) status is vital to evaluate the curative potential of relatively early gastric cancer (GC; T1–T2) treatment (endoscopic or surgery). Currently, there is a lack of robust and convenient methods to identify LN metastasis before therapeutic decision-making.MethodsGenome-wide expression profiles of long noncoding RNA (lncRNA) in primary T1 gastric cancer data from The Cancer Genome Atlas (TCGA) was used to identify lncRNA expression signature capable of detecting LN metastasis of GC and establish a 10-lncRNA risk-prediction model based on deep learning. The performance of the lncRNA panel in diagnosing LN metastasis was evaluated both in silico and clinical validation methods. In silico validation was conducted using TCGA and Asian Cancer Research Group (ACRG) datasets. Clinical validation was performed on T1 and T2 patients, and the panel’s efficacy was compared with that of traditional tumor markers and computed tomography (CT) scans.ResultsProfiling of genome-wide RNA expression identified a panel of lncRNA to predict LN metastasis in T1 stage gastric cancer (AUC = 0.961). A 10-lncRNA risk-prediction model was then constructed, which was validated successfully in T1 and T2 datasets (TCGA, AUC = 0.852; ACRG, AUC = 0.834). Thereafter, the clinical performance of the lncRNA panel was validated in clinical cohorts (T1, AUC = 0.812; T2, AUC = 0.805; T1 + T2, AUC = 0.764). Notably, the panel demonstrated significantly better performance compared with CT and traditional tumor markers. ConclusionsThe novel 10-lncRNA could diagnose LN metastasis robustly in relatively early gastric cancer (T1–T2), with promising clinical potential.

Chromosomal imbalances detected via RNA-sequencing in 28 cancers.

MotivationRNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA.ResultsWe present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings.Availability and implementationThe analyses presented use the data publicly available from TCGA Research Network (http://cancergenome.nih.gov/). See Methods for details regarding data downloads. hapLOHseq software is freely available under The MIT license and can be downloaded from http://scheet.org/software.html.Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 622: Gene panel-based immune profiling of human cancers

Abstract It is now well accepted that patients with inflamed tumors are more likely to benefit from immunotherapies targeting immune checkpoints such as CTLA4, PD1 and PD-L1.Several factors determine a tumor's immune status and identifying patients who may benefit from immunotherapy and determining which immune therapy to administer depends largely on the patient's tumor immunophenotype. We have developed an approach for immunoprofiling using a focused gene set (115 genes) and scoring system that assesses cell specific types and immune functions. This approach was developed in a preclinical cancer models and is being evaluated and tested in human cancers. We previously extracted data for the gene set and employed unsupervised network clustering to differentiate and classify cancer samples of patients from The Cancer Genome Atlas (TCGA) into immunologically distinct clusters. Here continue or examination and overview clinical features associated with individual clusters. A total of 8477 patients from the TCGA research network were profiled which included 28 solid cancer types. A total of 10 immune clusters (IC 1-10) were identified with a median cluster size of 971 patients with sizes ranging from 178 to 1570. All cancer types were represented in the ICs with mostly normal distribution except for IC 6 which was composed of 40% breast cancer patients and IC10 of which 1/5 of the cases were head-neck squamous cell carcinomas. ICs 2, 5 and 8 were associated with the poorest survival with median times at 73, 51 and 65 months, respectively. Conversely, patients in IC 9 experienced the longest overall survival, median time 148 months. The aggregate inflammation score, which measures overall tumor inflammation was positively correlated to overall survival (P=0.0109). IC 9 was correlated with the highest survival times and was positively associated with primary signatures related antitumor immunity such as high IFN gamma, cytotoxicity, and CD8 T cell and NK cell signatures. IC 8 was characterized with poor survival times and was associated with signatures related with immunosuppressive cells including granulocytic myeloid derived suppressor cells, M2 polarized macrophages and TH17 T cells, and an adenosine high signature. In addition, cases from this cluster also had low CTL signatures. Our findings show that gene expression data from a focused gene and our scoring algorithm could be used to differentiate and classify patients into various groups of immunologically distinct tumors types. Citation Format: Marco A. De Velasco, Yurie Kura, Kazuko Sakai, Kazutoshi Fujita, Masahiro Nozawa, Kazuhiro Yoshimura, Kazuto Nishio, Hirotsugu Uemura. Gene panel-based immune profiling of human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 622.

The functional GRHL3-filaggrin axis maintains a tumor differentiation potential and influences drug sensitivity

Current therapies for treating heterogeneous cancers such as head and neck squamous cell carcinoma (HNSCC) are non-selective and are administered independent of response biomarkers. Therapy resistance subsequently emerges, resulting in increased cellular proliferation that is associated with loss of differentiation. Whether a cancer cell differentiation potential can dictate therapy responsiveness is still currently unknown. A multi-omic approach integrating whole-genome and whole-transcriptome sequencing with drug sensitivity was employed in a HNSCC mouse model, primary patients’ data, and human cell lines to assess the potential of functional differentiation in predicting therapy response. Interestingly, a subset of HNSCC with effective GRHL3-dependent differentiation was the most sensitive to inhibitors of PI3K/mTOR, c-Myc, and STAT3 signaling. Furthermore, we identified the GRHL3-differentiation target gene Filaggrin (FLG) as a response biomarker and more importantly, stratified HNSCC subsets as treatment resistant based on their FLG mutational profile. The loss of FLG in sensitive HNSCC resulted in a dramatic resistance to targeted therapies while the GRHL3-FLG signature predicted a favorable patient prognosis. This study provides evidence for a functional GRHL3-FLG tumor-specific differentiation axis that regulates targeted therapy response in HNSCC and establishes a rationale for clinical investigation of differentiation-paired targeted therapy in heterogeneous cancers.

Cytolytic activity score as a biomarker for antitumor immunity and clinical outcome in patients with gastric cancer.

BackgroundA simple measure of immune cytolytic activity (CYT) base on mRNA expression levels of two genes, GZMA and PRF1, was recently reported. Here, we aimed to evaluate the CYT score's potential as a measure of antitumor immunity and predictor of clinical outcome in gastric cancer (GC) patients.Materials and MethodsWe evaluated the correlations between tumor‐infiltrating immune cells and the CYT score in 238 GC samples from The Cancer Genome Atlas (TCGA). Next, we investigated CYT score associations with molecular subtypes, somatic mutation load, and immune checkpoint molecules in GC samples from TCGA and Asian Cancer Research Group (ACRG). Moreover, we evaluated the clinical significance of the CYT score calculated by reverse transcription (RT)‐quantitative PCR (qPCR) data in 123 GC samples and the association of the CYT score with the response to anti‐PD‐1 therapy in 7 GC samples from Kyushu University Hospital.ResultsThe CYT score positively correlated with the proportions of tumor‐infiltrating CD8+ T cells and macrophages and negatively correlated with the proportion of regulatory T cells in GC tissues. A high CYT score was associated with common immune checkpoint molecules, a high mutation, the Epstein–Barr virus subtype, and the microsatellite instability subtype in GC. Moreover, a low CYT score was a poor prognosis factor in patients with GC. Finally, the CYT score was higher in a responder to anti‐PD‐1 therapy compared to nonresponders.ConclusionThe CYT score reflects antitumor immunity and predicts clinical outcome in GC patients.

Tumor ADAM10/ADAM17-Mediated PD-L1 Loss May Predict Poor Outcomes in Diffuse Large B Cell Lymphoma

Introduction Tumor surface matrix metalloproteases ADAM10 and ADAM17 are associated with poor outcomes in multiple malignancies. We previously showed that these proteases shed PD-L1 from lymphoma cell line Karpas-299 to produce soluble PD-L1 (sPD-L1) that induces CD8+ T cell apoptosis (1). While higher levels of soluble PD-L1 are associated with worse prognosis in patients with non-Hodgkin lymphoma treated with standard chemoimmunotherapy (cutoff of 1.52 ng/ml, 3-year OS of 76% versus 89%) (2), it is unknown whether the loss of PD-L1 on the surface of B cell lymphoma cells affects clinical outcomes. We hypothesized that tumors expressing low PD-L1 levels despite high PD-L1 mRNA levels would (1) produce higher ADAM10 or ADAM17 transcript levels and (2) predict poorer prognosis in diffuse large B cell lymphomas. Methods We queried the cancer genome atlas (TCGA) public database for cases of diffuse large B cell lymphoma (DLBCL) for which PD-L1 (CD274) protein levels are reported by reverse phase protein array (RPPA). We then queried TCGA for RNA-Seq data for CD274, ADAM10, and ADAM17 expressed as transcripts per million (FPKM). We re-normalized RPPA data and calculated a PD-L1 protein-to-mRNA ratio for each tumor sample. We then compared groups of high and low PD-L1 protein-to-mRNA ratios for ADAM10 and ADAM17 mRNA expression. We further compared groups of high and low PD-L1 protein-to-mRNA ratios for survival (adjusted for age at diagnosis). All statistical analyses were performed using R Statistical Software (R Foundation). Unpaired student's t-test assessed statistical differences in experimental groups except where otherwise indicated. Figures comprise box plots showing quartile values and individual data points. P<0.05 was considered statistically significant. In figures, p values are denoted <0.05 with *, <0.01 with **, and <0.001 with ***. Results Data were available for a total of 33 diffuse large B cell (DLBC) lymphomas. 18 of those samples were categorized as having low PD-L1 protein-to-mRNA ratios (cutoff 6.38e-6) with the remaining categorized as high ratio. DLBCL tumors demonstrating low PD-L1 protein-to-mRNA ratios expressed significantly higher ADAM10 (p=0.0237) and ADAM17 (p<0.0001) transcripts than tumors expressing low PD-L1 protein-to-mRNA ratios (Figure 1). Furthermore, patients with tumors demonstrating low PD-L1 protein-to-mRNA ratios experienced significantly poorer survival according to likelihood ratio (6.6, p=0.01) and log rank (4.67, p=0.03) tests (Figure 2). Conclusions Our findings may explain a crucial phenomenon seen in diffuse large B cell lymphoma, namely that many purportedly PD-L1-positive tumors appear to express PD-L1 but do not respond to PD-(L)1 inhibitor therapy (3,4). This may be caused by the activity of ADAM10 and ADAM17 to cleave PD-L1, which we previously showed in lymphoma cell line Karpas-299 (1). This posits pre-treatment ADAM10/ADAM17 inhibition to "boost" PD-(L)1 inhibitor therapy in lymphoma. Our results contrast with previous findings that PD-L1-positive DLBCL progression free survival is inferior to PD-L1-negative DLBCL (5). This may be due to different methods of protein detection (immunohistochemistry versus RPPA) and the lack of localization of PD-L1 expression in tumor cells versus the microenvironment. Notably, this study is only correlative with a limited case number. Larger prospective studies will be needed to further elucidate this relationship. Acknowledgments The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

Capturing heterogeneity of covariate effects in hidden subpopulations in the presence of censoring and large number of covariates

The advent of modern technology has led to a surge of high-dimensional data in biology and health sciences such as genomics, epigenomics and medicine. The high-grade serous ovarian cancer (HGS-OvCa) data reported by The Cancer Genome Atlas (TCGA) Research Network is one example. The TCGA and other research groups have analyzed several aspects of these data. Here we study the relationship between Disease Free Time (DFT) after surgery among ovarian cancer patients and their DNA methylation profiles of genomic features. Such studies pose additional challenges beyond the typical big data problem due to population substructure and censoring. Despite the availability of several methods for analyzing time-to-event data with a large number of covariates but a small sample size, there is no method available to date that accommodates the additional feature of heterogeneity. To this end, we propose a regularized framework based on the finite mixture of accelerated failure time model to capture intangible heterogeneity due to population substructure and to account for censoring simultaneously. We study the properties of the proposed framework both theoretically and numerically. Our data analysis indicates the existence of heterogeneity in the HGS-OvCa data, with one component of the mixture capturing a more aggressive form of the disease, and the second component capturing a less aggressive form. In particular, the second component portrays a significant positive relationship between methylation and DFT for BRCA1. By further unearthing the negative relationship between expression and methylation for this gene, one may provide a biologically reasonable explanation that sheds light on the relationship between DNA methylation, gene expression and mutation.

The prognostic significance of estrogen and progesterone receptors in grade I and II endometrioid endometrial adenocarcinoma: hormone receptors in risk stratification.

ObjectivesAlthough patients with grade I and II endometrioid endometrial adenocarcinoma (EEA) are considered with good prognosis, among them 15%–25% died in 5 years. It is still unknown whether integrating estrogen receptor (ER) and progesterone receptor (PR) into clinical risk stratification can help select high-risk patients with grade I–II EEA. This study was to investigate the prognostic value of ER and PR double negativity (ER/PR loss) in grade I–II EEA, and the association between ER/PR loss and The Cancer Genome Atlas (TCGA) classification.MethodsER and PR were assessed by immunohistochemistry on hysterectomy specimens of 903 patients with grade I–II EEA. ER and PR negativity were determined when <1% tumor nuclei were stained. Gene expression data were obtained from the TCGA research network.ResultsCompared with ER or PR positive patients (n=868), patients with ER/PR loss (n=35) had deeper myometrial infiltration (p=0.012), severer FIGO stage (p=0.004), and higher rate of pelvic lymph node metastasis (p=0.020). In univariate analysis, ER/PR loss correlated with a shorter progression-free survival (PFS; hazard ratio [HR]=5.25; 95% confidence interval [CI]=2.21–12.52) and overall survival (OS; HR=7.59; 95% CI=2.55–22.60). In multivariate analysis, ER/PR loss independently predicted poor PFS (HR=3.77; 95% CI=1.60–10.14) and OS (HR=5.56; 95% CI=1.37–22.55) for all patients, and poor PFS for patients in stage IA (n=695; HR=5.54; 95% CI=1.28–23.89) and stage II–IV (n=129; HR=5.77; 95% CI=1.57–21.27). No association was found between ER/PR loss and TCGA classification.ConclusionIntegrating ER/PR evaluation into clinical risk stratification may improve prognosis for grade I–II EEA patients.

Abstract 3302: The molecular landscape of oncogenic signaling pathways in The Cancer Genome Atlas

Abstract Over the past decade, The Cancer Genome Atlas (TCGA) has profiled more than 11,000 tumors spanning 33 distinct cancer types. The TCGA PanCanAtlas is a collaborative project by the TCGA Research Network that aims to address relevant overarching questions in oncology based on a cross-cancer analysis of the full, uniformly reprocessed TCGA data set. Here, we present results from our analysis of genetic alterations in mitogenic signaling pathways across cancer. Genetic alterations in signaling pathways that control cell cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations and copy-number changes in 9,125 tumor samples profiled by TCGA, we analyzed the mechanisms and patterns of alterations in 10 canonical pathways: cell cycle, Hippo, Myc, Notch, beta-catenin / WNT, PI-3-Kinase / Akt, receptor-tyrosine kinase / RAS / MAP-kinase signaling, TP53, and TGF-beta signaling, as well as oxidative stress response. For each of these pathways, we propose an expert-curated description (or “template”) that includes the relevant (altered) genes and the connections between them, as well as a detailed catalogue of the driver mutations and copy number changes with known oncogenic relevance. We provide a high-level map of pathway alteration frequencies across tissues and relevant cancer subtypes as well as detailed frequencies of alteration at the gene level for each individual pathway. We also investigate relationships of co-occurrence and mutual exclusivity across pathways and evaluate therapeutic implications, including drug combinations. Forty-nine percent of tumors had at least one potentially targetable alteration in the evaluated pathways, and 31% of tumors had multiple targetable alterations, making them candidates for combination therapy. Our work delineates the full landscape of oncogenic alterations in mitogenic signaling pathways across cancer, and the pathway templates as well as the richly annotated data set that we provide will constitute an invaluable public resource for future use by the cancer genomics and precision oncology communities. Citation Format: Francisco Sanchez-Vega, Marco Mina, Joshua Armenia, Walid K. Chatila, Augustin Luna, Konnor La, Sofia Dimitriadoy, David L. Liu, Havish S. Kantheti, Zachary Heins, Angelica Ochoa, Benjamin Gross, Jianjiong Gao, Hongxin Zhang, Ritika Kundra, Cyriac Kandoth, Istemi Bahceci, Leonard Dervishi, Ugur Dogrusoz, Wanding Zhou, Hui Shen, Peter W. Laird, Alice H. Berger, Trever G. Bivona, Alexander J. Lazar, Gary Hammer, Thomas Giordano, Lawrence Kwong, Grant McArthur, Chenfei Huang, Mitchell J. Frederick, Frank McCormick, Matthew Meyerson, The Cancer Genome Atlas Research Network, Eliezer Van Allen, Andrew D. Cherniack, Giovanni Ciriello, Chris Sander, Nikolaus Schultz. The molecular landscape of oncogenic signaling pathways in The Cancer Genome Atlas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3302.

Molecular analysis of thymic epithelial tumors (TETs): A report by The Cancer Genome Atlas (TCGA) research network.

8520 Background: Thymoma (T) and thymic carcinoma (TC) are the most common malignancies of the anterior mediastinum. Additionally, thymoma has an unique association with autoimmune disorders, notably myasthenia gravis (MG). Histologic classification of TETs has been largely based on the gross description of the epithelial cell appearance and the relative abundance of associated lymphocytes. A comprehensive molecular analysis of TETs has not heretofore been conducted. Methods: The TCGA Research Network conducted multi-platform analyses of 117 TETs (T = 105; TC = 10 and micronodular T = 2), which included whole-exome, transcriptome, methylome and targeted proteome analysis. Patient characteristics: median age = 60 years (range 17-84); M:F (%) = 52:48; Masaoka Stage (I-36, IIA-39, IIB-19; III-15; IVA-1; IVB-5); mg was present in 32 patients. No patient had prior therapy for metastatic disease, but 14 had prior chemotherapy and 39 had prior radiation therapy in adjuvant setting. WHO histologic classification (blinded review) revealed A = 10; A/B = 48, B1 = 12, B2 = 25, B3 = 10, micronodular T = 2 and TC = 10. Results: T has one of the lowest mutational loads of any tumor in the TCGA. A unique transcription factor, GTF2I, was the most commonly observed mutation in WHO Types A and A/B. All GTF2I mutations were exclusively at the amino acid 424 locus. This is the only tumor with this specific mutation within the entire TCGA database. HRAS, NRAS and TP53 mutations were less commonly noted among all TETs. Four distinct molecular-driven subtypes of TETs were identified that strongly correlated with the current WHO histologic classification. Increased aneuploidy and overexpression of muscle auto-antigens were associated with mg phenotype. Conclusions: Based on molecular analysis, four clusters were identified that correlated strongly with the current WHO Histologic Classification. Also identified was a unique mutation in GTF2I, which was associated with WHO Type A and A/B thymoma. This international effort represents the largest and most comprehensive molecular analysis of TETs conducted to date is expected to have important clinical and translational implications for this rare disease.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.

The Impact of Age and Sex in DLBCL: Determination of Biologic Dynamics and Delineation of Inter-Connected Signaling Networks Via a Systems Biology Analysis

Background: Older age has been shown to consistently correlate with inferior survival in diffuse large B-cell lymphoma (DLBCL). This is likely attributable in part to poorer performance status and inability to tolerate therapy, however, potential molecular differences associated with age are not well defined. In addition, the impact of sex has been shown to be important in DLBCL. Models that identify adverse tumor biology related to sex remain largely unexplored in DLBCL. Via a comprehensive systems biology approach (e.g., Yang Y, Nature Commun. 2014; Kuchenbaecker KB, Nature Genet. 2015), we performed detailed global transcriptome analyses from the Cancer Genome Atlas (TCGA) to investigate the biologic dynamics from pre-treatment/baseline DLBCL based on age and sex. Furthermore, a novel unbiased method was used to identify "key genes" and related signaling networks most strongly associated with adverse DLBCL biology.Methods: From TCGA research network (cancergenome.nih.gov), lymphoid neoplasm DLBCL mRNA level 3 data type with a total of 48 data sets from untreated DLBCL patients (pt) was available with 33 data sets containing relevant age and sex. Since median age was 58 years for this data set, we defined older pts as ≥58 years (vs ≤57 for younger patients). Significant genes were determined by T-test with p-value <0.05 for all comparisons to take to pathway analysis. Pathway analysis of selected genes were performed using fold change > ±1.2 comparing old to young pts and observing pathway relationships using Ingenuity Pathway Analysis (IPA) software. Upstream regulator and biofunction analysis was done with IPA. Gene Set Enrichment Analysis (GSEA) was performed with FDR <0.05 for functional analysis. Furthermore, a novel unbiased systems biology method for determining key genes and association with adverse tumor biology (ie, prediction of tumor progression) were determined as previously reported (Beheshti A, Cancer Res 2015). 'Tumor progression' herein is defined as the presence of adverse tumor dynamics based on the milieu of biologic factors present (eg, tumor suppressors and oncogenes) at diagnosis.Results: Both sex and age revealed striking, independent transcriptional differences. There were several distinct genes associated with DLBCL at older age including JUN, CR1, DNAH10, and C20orf54. Nine distinct genes were modulated by sex, regardless of age. This included XIST, which was significantly upregulated in females and DDX3Y, KDM5D, and PRKY that were downregulated in females. Furthermore, GSEA demonstrated that older age was associated with decreased metabolism and telomere functions and that globally female sex was associated with decreases in interferon signaling, transcription, cell cycle, and PD-1 signaling. In addition, through DAVID gene function classification, we discovered that the key genes for most groups strongly regulated immune function activity. Surprisingly, we also identified global downregulation of genes for older females, while older males had overall upregulation, which was in agreement with the GSEA data. Moreover, older females were predicted to have more favorable tumor dynamics vs older males as well as young females (Fig. 1). Finally, systems biology analyses revealed that JUN and CYCS signaling were the most critical inter-connected factors associated with adverse biology and likelihood of tumor progression in older and male patients, respectively (Fig. 1).Conclusions: Collectively, these findings reinforce the importance of a detailed understanding of biology in DLBCL and the inter-connectivity of genes and signaling pathways. JUN was a predicted upstream regulator, and moreover, it was the only specific key gene that was commonly upregulated for older DLBCL patients (independent of sex). For sex, CYCS was shown to be a critically connected factor. Additionally, the majority of key genes were strongly connected to immune function activity and associated signaling networks. Altogether, understanding how molecular factors interact and change as a function of age and sex, and how this impacts tumor biology, may improve our understanding of lymphomagenesis and potentially lead to enhanced therapeutic strategies. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Integrated genomics of thyroid cancer

Objective Advances in DNA sequencing technology have permitted comprehensive genomic characterization of the most common types of cancer, including those of the thyroid gland. The Cancer Genome Atlas (TCGA) program of the U.S. National Institutes of Health (a joint NCI and NHGRI effort) has performed the first of several attempts to define the genomic landscape of thyroid cancer by identifying the somatic genetic changes of a large cohort of papillary thyroid carcinoma (PTC). Methods The TCGA Research Network performed an integrated genomic analysis of nearly 500 PTCs, the most common type, using next generation DNA sequencing and other pan-genomic methods, together with detailed clinical and pathologic data. Results Despite significant pre-existing knowledge about the genetics of PTC, the TCGA project illuminated many novel aspects of the genetics of PTC. Novel driving alterations, both novel single nucleotide variants and gene fusions, were identified. Driving events were nearly mutually exclusive. Tumors were divided into BRAF-V600E-like and RAS-like groups based on their gene expression profiles and were shown to have distinct differentiation and signaling properties. Several differentially expressed miRs were identified and hypothesized to play a prognostic role in PTC. Discussion The overarching conclusion of this study is PTC is much more genetically diverse than previously recognized, with BRAF-driven tumors being genetically diverse and also fundamentally different from RAS-driven tumors.

Abstract 2969: Progress in The Cancer Genome Atlas bladder cancer project

Abstract In 2014, as part of The Cancer Genome Atlas (TCGA) Project, we reported molecular profiling of 131 chemotherapy-naive, muscle-invasive urothelial bladder cancers for DNA copy number variants (CNVs), somatic mutations by whole exome sequencing (WES), DNA methylation, mRNA expression, microRNA expression, protein expression and phosphorylation, transcript splicing, gene fusion, viral integration, pathway perturbation, clinical correlates, and histopathology (TCGA Research Network, Nature 507:315, 2014). Since that time, the number of tumors (from 19 tissue source sites) profiled comprehensively has doubled, and the final analyzed cohort will total 412 by February 2015. Unsupervised consensus clustering for the data from miRNAseq on the first 310 tumor samples shows five robust clusters, with miR-99a, miR-100, and miR-200 family members differentially abundant across the clusters. mRNA clusters I-IV remained stable as well. For the 266 tumour samples for which we have both miRNA and mRNA data, potential miRNA-mRNA targeting relationships supported by functional validation publications include miR-100-5p targeting FGFR3. The data also highlight the miR-200 family, miR-29abc, miR-17-92 and 106b-25 complexes, as well as miR-34a, miR-155, and miR-21. At this time, mutation analysis of WES data has been completed for 238 samples (including the first 131). Seven additional significantly mutated genes (using MutSig) have been identified: ASXL2 (11%), HSP90AA1 (7%), PSIP1 (5%), ZFP36L2 (5%), ZNF513 (5%), PTEN (4%), CEBPB (2%) (mutant frequency in parentheses). A sample with a POLE exonuclease domain mutation (P286R) exhibited an ultra-mutant phenotype (mutation rate ∼100 per MB). Many of the 38 (total) SMGs have not previously been described in bladder cancer. Combining copy number variation and somatic mutation data, 69% of tumors harbor one or more potentially actionable targets. Three mutation clusters were identified and characterized as: 1) “Focally amplified” - enriched in focal copy number alterations (e.g., 3p loss/PPARG) and MLL2 mutations; 2) Enriched for TP53 and RB1 mutations, E2F3 amplifications; and 3) Papillary histology, FGFR3 mutant CDKN2A-deficient. This work was supported by the following grants from the United States National Institutes of Health: U54HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24CA143840, U24 CA143843, U24 CA143845, U24 CA143848, U24 CA143858, U24CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025 and P01 CA120964, as well as multiple other funding sources for the individual authors. Citation Format: John N. Weinstein, Jaegil Kim, Chad J. Creighton, Rehan Akbani, Katherine A. Hoadley, William Y. Kim, Margaret B. Morgan, Toshinori Hinoue, Andrew Cherniack, Xiaoping Su, Andrew J. Mungall, Michael C. Ryan, Dean F. Bajorin, Jonathan E. Rosenberg, Bogdan Czerniak, Donna Hansel, Victor E. Reuter, Brian D. Robinson, Hikmat A. Al-Ahmadie, Jeffrey S. Damrauer, Wei Zhang, Yuexin Liu, Dmitry R. Gordenin, Joshua M. Stuart, Nikolaus Schultz, Gordon Robertson, Steven JM Jones, Raju R. Kucherlapati, David J. McConkey, Peter W. Laird, Gordon B. Mills, David J. Kwiatkowski, Seth P. Lerner, TCGA Bladder Cancer Working Group, TCGA Research Network. Progress in The Cancer Genome Atlas bladder cancer project. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2969. doi:10.1158/1538-7445.AM2015-2969

The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge.

The Cancer Genome Atlas (TCGA) is a public funded project that aims to catalogue and discover major cancer-causing genomic alterations to create a comprehensive “atlas” of cancer genomic profiles. So far, TCGA researchers have analysed large cohorts of over 30 human tumours through large-scale genome sequencing and integrated multi-dimensional analyses. Studies of individual cancer types, as well as comprehensive pan-cancer analyses have extended current knowledge of tumorigenesis. A major goal of the project was to provide publicly available datasets to help improve diagnostic methods, treatment standards, and finally to prevent cancer. This review discusses the current status of TCGA Research Network structure, purpose, and achievements.

Abstract 987: Comprehensive characterization of urothelial bladder cancer: a TCGA Project update

Abstract Urothelial carcinoma (UC) is a major cause of morbidity and mortality for which there are no approved molecularly targeted agents and few good treatment options beyond cisplatin-based chemotherapy. As part of The Cancer Genome Atlas (TCGA) Project, we analyzed 131 chemotherapy-naive, muscle-invasive UC tumors for somatic mutations, DNA copy number variants (CNVs), mRNA and microRNA expression, protein expression and phosphorylation, DNA methylation, transcript splicing, gene fusion, viral integration, pathway perturbation, clinical correlates, and histopathology (TCGA Research Network, Nature, in press). Whole-exome sequencing showed 29 recurrently mutated genes. Potential therapeutic targets include altered PIK3CA, ERBB2, FGFR3, TSC1, and ERBB3, plus mutated chromatin-regulating genes MLL, MLL2, MLL3, CREBBP, CHD7, SRCAP, ARID1A, KDM6A (UTX), and EP300. There were 22 arm-level CNVs and 27 focally amplified or deleted regions. CDKN2A was deleted in 47%. Low-pass whole genome sequencing identified FGFR3-TACC3 fusions. Viral DNA was identified in 6% (CMV, HHV6B, HPV16, BK polyoma), and viral transcripts were identified in 4% (CMV, BK polyoma, HPV16). . mRNA-seq identified 4 tumor clusters. Cluster I shows papillary morphology and FGFR3 dysregulation. Clusters I and II express high HER2 (ERBB2) and estrogen receptor beta signaling signature, sharing features with Luminal A breast cancer. Cluster III shows similarities to Basal-like breast and squamous cell head and neck carcinomas. . Integrated analyses confirm alteration of multiple pathways, including cell cycle regulation (93%), kinase and PI3-K signaling (72%), and epigenetic regulation (histone-modifiers: 89%; SWI/SNF nucleosome remodeling complex: 64%). Recurrent alterations in the PI3-kinase/AKT/mTOR pathway (42%) and RTK/RAS pathway (44%) are potentially actionable. . Overall, this study and others have identified multiple druggable targets in UC. FGFR3 is activated by mutation, gene fusion, and overexpression, suggesting clinical trials of FGFR3 inhibitors. PI3-kinase/mTOR/AKT/TSC1 pathway alterations are frequent, and mutation in TSC1 has been associated with response to mTOR inhibitors. ERBB2 amplifications and activating mutations may be targetable with agents such as trastuzumab, trastuzumab-DM1, lapatinib, and neratinib. The frequent alterations in epigenetic regulatory pathways suggest trials of agents such as the bromodomain inhibitors. The project is now being updated on the basis of data on 117 additional tumors. Citation Format: John N. Weinstein, Jaegil Kim, Chad J. Creighton, Rehan Akbani, Katherine A. Hoadley, William Y. Kim, Margaret B. Morgan, Toshinori Hinoue, Andrew Cherniack, Xiaoping Su, Andrew J. Mungall, Michael C. Ryan, Jonathan E. Rosenberg, Dean F. Bajorin, Bogdan Czerniak, Donna Hansel, Victor E. Reuter, Brian D. Robinson, Hikmat A. Al-Ahmadie, Jeffrey S. Damrauer, Wei Zhang, Yuexin Liu, Dmitry Gordenin, Joshua M. Stuart, Nikolaus Schultz, Gordon Robertson, Raju Kucherlapati, Peter W. Laird, Gordon B. Mills, David J. Kwiatkowski, Seth P. Lerner, representing TCGA's Bladder Cancer Working Group. Comprehensive characterization of urothelial bladder cancer: a TCGA Project update. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 987. doi:10.1158/1538-7445.AM2014-987

Impact of Recurrent Copy Number Alterations and Cancer Gene Mutations on the Predictive Accuracy of Prognostic Models in Clear Cell Renal Cell Carcinoma

Several recently reported recurrent genomic alterations in clear cell renal cell carcinoma are linked to pathological and clinical outcomes. We determined whether any recurrent cancer gene mutations or copy number alterations identified in the TCGA (The Cancer Genome Atlas) clear cell renal cell carcinoma data set could add to the predictive accuracy of current prognostic models. In 413 patients who underwent nephrectomy/partial nephrectomy we investigated whole exome, copy number array analyses and clinical variables. We identified 65 recurrent genomic alterations based on prevalence and combined them into 35 alterations, including 12 cancer gene mutations. Genomic markers were modeled using the elastic net algorithm with preoperative variables (tumor size plus patient age) and in the postoperative setting using the externally validated Mayo Clinic SSIGN (stage, size, grade and necrosis) prognostic scoring system. These models were subjected to internal validation using bootstrap. Median followup in survivors was 45 months. Several markers correlated with adverse cancer specific survival and time to recurrence on univariate analysis. However, most of them lost significance when controlling for tumor size with or without age in the preoperative models or for SSIGN score in the postoperative setting. Adding multiple genomic markers selected by the elastic net algorithm failed to substantially add to the predictive accuracy of any preoperative or postoperative model for cancer specific survival or time to recurrence. While recurrent copy number alterations and cancer gene mutations are biologically significant, they do not appear to improve the predictive accuracy of existing models of clinical cancer specific survival or time to recurrence for clear cell renal cell carcinoma.

Adverse Outcomes in Clear Cell Renal Cell Carcinoma with Mutations of 3p21 Epigenetic Regulators BAP1 and SETD2: A Report by MSKCC and the KIRC TCGA Research Network

To investigate the impact of newly identified chromosome 3p21 epigenetic tumor suppressors PBRM1, SETD2, and BAP1 on cancer-specific survival (CSS) of 609 patients with clear cell renal cell carcinoma (ccRCC) from 2 distinct cohorts. Select sequencing on 3p tumor suppressors of 188 patients who underwent resection of primary ccRCC at the Memorial Sloan-Kettering Cancer Center (MSKCC) was conducted to interrogate the genotype-phenotype associations. These findings were compared with analyses of the genomic and clinical dataset from our nonoverlapping The Cancer Genome Atlas (TCGA) cohort of 421 patients with primary ccRCC. 3p21 tumor suppressors are frequently mutated in both the MSKCC (PBRM1, 30.3%; SETD2, 7.4%; BAP1, 6.4%) and the TCGA (PBRM1, 33.5%; SETD2, 11.6%; BAP1, 9.7%) cohorts. BAP1 mutations are associated with worse CSS in both cohorts [MSKCC, P = 0.002; HR 7.71; 95% confidence interval (CI)2.08-28.6; TCGA, P = 0.002; HR 2.21; 95% CI 1.35-3.63]. SETD2 are associated with worse CSS in the TCGA cohort (P = 0.036; HR 1.68; 95% CI 1.04-2.73). On the contrary, PBRM1 mutations, the second most common gene mutations of ccRCC, have no impact on CSS. The chromosome 3p21 locus harbors 3 frequently mutated ccRCC tumor suppressor genes. BAP1 and SETD2 mutations (6%-12%) are associated with worse CSS, suggesting their roles in disease progression. PBRM1 mutations (30%-34%) do not impact CSS, implicating its principal role in the tumor initiation. Future efforts should focus on therapeutic interventions and further clinical, pathologic, and molecular interrogation of this novel class of tumor suppressors.