Building a Statistical Model for Predicting Cancer Genes

More than 400 cancer genes have been identified in the human genome. The list is not yet complete. Statistical models predicting cancer genes may help with identification of novel cancer gene candidates. We used known prostate cancer (PCa) genes (identified through KnowledgeNet) as a training set to build a binary logistic regression model identifying PCa genes. Internal and external validation of the model was conducted using a validation set (also from KnowledgeNet), permutations, and external data on genes with recurrent prostate tumor mutations. We evaluated a set of 33 gene characteristics as predictors. Sixteen of the original 33 predictors were significant in the model. We found that a typical PCa gene is a prostate-specific transcription factor, kinase, or phosphatase with high interindividual variance of the expression level in adjacent normal prostate tissue and differential expression between normal prostate tissue and primary tumor. PCa genes are likely to have an antiapoptotic effect and to play a role in cell proliferation, angiogenesis, and cell adhesion. Their proteins are likely to be ubiquitinated or sumoylated but not acetylated. A number of novel PCa candidates have been proposed. Functional annotations of novel candidates identified antiapoptosis, regulation of cell proliferation, positive regulation of kinase activity, positive regulation of transferase activity, angiogenesis, positive regulation of cell division, and cell adhesion as top functions. We provide the list of the top 200 predicted PCa genes, which can be used as candidates for experimental validation. The model may be modified to predict genes for other cancer sites

Current perspectives on bone metastases in castrate-resistant prostate cancer

Prostate cancer is the most frequent noncutaneous cancer occurring in men. On average, men with localized prostate cancer have a high 10-year survival rate, and many can be cured. However, men with metastatic castrate-resistant prostate cancer have incurable disease with poor survival despite intensive therapy. This unmet need has led to recent advances in therapy aimed at treating bone metastases resulting from prostate cancer. The bone microenvironment lends itself to metastases in castrate-resistant prostate cancer, as a result of complex interactions between the microenvironment and tumor cells. The development of 223radium dichloride (Ra-223) to treat symptomatic bone metastases has improved survival in men with metastatic castrate-resistant prostate cancer. Moreover, Ra-223 may have effects on the tumor microenvironment that enhance its activity. Ra-223 treatment has been shown to prolong survival, and its effects on the immune system are under investigation. Because prostate cancer affects a sizable portion of the adult male population, understanding how it metastasizes to bone is an important step in advancing therapy. Clinical trials that are underway should yield new information on whether Ra-223 synergizes effectively with immunotherapy agents and whether Ra-223 has enhancing effects on the immune system in patients with prostate cancer

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression

Predicting disease progression is one of the most challenging problems in prostate cancer research. Adding gene expression data to prediction models that are based on clinical features has been proposed to improve accuracy. In the current study, we applied a logistic regression (LR) model combining clinical features and gene co-expression data to improve the accuracy of the prediction of prostate cancer progression. The top-scoring pair (TSP) method was used to select genes for the model. The proposed models not only preserved the basic properties of the TSP algorithm but also incorporated the clinical features into the prognostic models. Based on the statistical inference with the iterative cross validation, we demonstrated that prediction LR models that included genes selected by the TSP method provided better predictions of prostate cancer progression than those using clinical variables only and/or those that included genes selected by the one-gene-at-a-time approach. Thus, we conclude that TSP selection is a useful tool for feature (and/or gene) selection to use in prognostic models and our model also provides an alternative for predicting prostate cancer progression

Subtype and Site Specific-Induced Metabolic Vulnerabilities in Prostate Cancer

Aberrant metabolic functions play a crucial role in prostate cancer progression and lethality. Currently, limited knowledge is available on subtype-specific metabolic features and their implications for treatment. We therefore investigated the metabolic determinants of the two major subtypes of castration-resistant prostate cancer [androgen receptor-expressing prostate cancer (ARPC) and aggressive variant prostate cancer (AVPC)]. Transcriptomic analyses revealed enrichment of gene sets involved in oxidative phosphorylation (OXPHOS) in ARPC tumor samples compared with AVPC. Unbiased screening of metabolic signaling pathways in patient-derived xenograft models by proteomic analyses further supported an enrichment of OXPHOS in ARPC compared with AVPC, and a skewing toward glycolysis by AVPC. In vitro, ARPC C4-2B cells depended on aerobic respiration, while AVPC PC3 cells relied more heavily on glycolysis, as further confirmed by pharmacologic interference using IACS-10759, a clinical-grade inhibitor of OXPHOS. In vivo studies confirmed IACS-10759\u27s inhibitory effects in subcutaneous and bone-localized C4-2B tumors, and no effect in subcutaneous PC3 tumors. Unexpectedly, IACS-10759 inhibited PC3 tumor growth in bone, indicating microenvironment-induced metabolic reprogramming. These results suggest that castration-resistant ARPC and AVPC exhibit different metabolic dependencies, which can further undergo metabolic reprogramming in bone