Chromosomal imbalances detected via RNA-sequencing in 28 cancers.

MOTIVATION: RNA-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. RESULTS: We 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 IMPLEMENTATION: The 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.

System for Quality-Assured Data Analysis: Flexible, reproducible scientific workflows.

The reproducibility of scientific processes is one of the paramount problems of bioinformatics, an engineering problem that must be addressed to perform good research. The System for Quality-Assured Data Analysis (SyQADA), described here, seeks to address reproducibility by managing many of the details of procedural bookkeeping in bioinformatics in as simple and transparent a manner as possible. SyQADA has been used by persons with backgrounds ranging from expert programmer to Unix novice, to perform and repeat dozens of diverse bioinformatics workflows on tens of thousands of samples, consuming over 80 CPU-months of computing on over 300,000 individual tasks of scores of projects on laptops, computer servers, and computing clusters. SyQADA is especially well-suited for paired-sample analyses found in cancer tumor-normal studies. SyQADA executable source code, documentation, tutorial examples, and workflows used in our lab is available from http://scheet.org/software.html.

Driver Mutations in Normal Airway Epithelium Elucidate Spatiotemporal Resolution of Lung Cancer.

Rationale: Uninvolved normal-appearing airway epithelium has been shown to exhibit specific mutations characteristic of nearby non-small cell lung cancers (NSCLCs). Yet, its somatic mutational landscape in patients with early-stage NSCLC is unknown.Objectives: To comprehensively survey the somatic mutational architecture of the normal airway epithelium in patients with early-stage NSCLC.Methods: Multiregion normal airways, comprising tumor-adjacent small airways, tumor-distant large airways, nasal epithelium and uninvolved normal lung (collectively airway field), matched NSCLCs, and blood cells (n = 498) from 48 patients were interrogated for somatic single-nucleotide variants by deep-targeted DNA sequencing and for chromosomal allelic imbalance events by genome-wide genotype array profiling. Spatiotemporal relationships between the airway field and NSCLCs were assessed by phylogenetic analysis.Measurements and Main Results: Genomic airway field carcinogenesis was observed in 25 cases (52%). The airway field epithelium exhibited a total of 269 somatic mutations in most patients (n = 36) including key drivers that were shared with the NSCLCs. Allele frequencies of these acquired variants were overall higher in NSCLCs. Integrative analysis of single-nucleotide variants and allelic imbalance events revealed driver genes with shared "two-hit" alterations in the airway field (e.g., TP53, KRAS, KEAP1, STK11, and CDKN2A) and those with single hits progressing to two in the NSCLCs (e.g., PIK3CA and NOTCH1).Conclusions: Tumor-adjacent and tumor-distant normal-appearing airway epithelia exhibit somatic driver alterations that undergo selection-driven clonal expansion in NSCLC. These events offer spatiotemporal insights into the development of NSCLC and, thus, potential targets for early treatment.

XPAT: a toolkit to conduct cross-platform association studies with heterogeneous sequencing datasets.

High-throughput sequencing data are increasingly being made available to the research community for secondary analyses, providing new opportunities for large-scale association studies. However, heterogeneity in target capture and sequencing technologies often introduce strong technological stratification biases that overwhelm subtle signals of association in studies of complex traits. Here, we introduce the Cross-Platform Association Toolkit, XPAT, which provides a suite of tools designed to support and conduct large-scale association studies with heterogeneous sequencing datasets. XPAT includes tools to support cross-platform aware variant calling, quality control filtering, gene-based association testing and rare variant effect size estimation. To evaluate the performance of XPAT, we conducted case-control association studies for three diseases, including 783 breast cancer cases, 272 ovarian cancer cases, 205 Crohn disease cases and 3507 shared controls (including 1722 females) using sequencing data from multiple sources. XPAT greatly reduced Type I error inflation in the case-control analyses, while replicating many previously identified disease-gene associations. We also show that association tests conducted with XPAT using cross-platform data have comparable performance to tests using matched platform data. XPAT enables new association studies that combine existing sequencing datasets to identify genetic loci associated with common diseases and other complex traits.

Strategies for identification of somatic variants using the Ion Torrent deep targeted sequencing platform.

BACKGROUND: 'Next-generation' (NGS) sequencing has wide application in medical genetics, including the detection of somatic variation in cancer. The Ion Torrent-based (IONT) platform is among NGS technologies employed in clinical, research and diagnostic settings. However, identifying mutations from IONT deep sequencing with high confidence has remained a challenge. We compared various computational variant-calling methods to derive a variant identification pipeline that may improve the molecular diagnostic and research utility of IONT. RESULTS: Using IONT, we surveyed variants from the 409-gene Comprehensive Cancer Panel in whole-section tumors, intra-tumoral biopsies and matched normal samples obtained from frozen tissues and blood from four early-stage non-small cell lung cancer (NSCLC) patients. We used MuTect, Varscan2, IONT's proprietary Ion Reporter, and a simple subtraction we called "Poor Man's Caller." Together these produced calls at 637 loci across all samples. Visual validation of 434 called variants was performed, and performance of the methods assessed individually and in combination. Of the subset of inspected putative variant calls (n=223) in genomic regions that were not intronic or intergenic, 68 variants (30%) were deemed valid after visual inspection. Among the individual methods, the Ion Reporter method offered perhaps the most reasonable tradeoffs. Ion Reporter captured 83% of all discovered variants; 50% of its variants were visually validated. Aggregating results from multiple packages offered varied improvements in performance. CONCLUSIONS: Overall, Ion Reporter offered the most attractive performance among the individual callers. This study suggests combined strategies to maximize sensitivity and positive predictive value in variant calling using IONT deep sequencing.

Integrated case-control and somatic-germline interaction analyses of melanoma susceptibility genes.

While a number of genes have been implicated in melanoma susceptibility, the role of protein-coding variation in melanoma development and progression remains underexplored. To better characterize the role of germline coding variation in melanoma, we conducted a whole-exome case-control and somatic-germline interaction study involving 322 skin cutaneous melanoma cases from The Cancer Genome Atlas and 3607 controls of European ancestry. We controlled for cross-platform technological stratification using XPAT and conducted gene-based association tests using VAAST 2. Four established melanoma susceptibility genes achieved nominal statistical significance, MC1R (p = .0014), MITF (p = .0165) BRCA2 (p = .0206), and MTAP (p = .0393). We also observed a suggestive association for FANCA (p = .002), a gene previously implicated in melanoma survival. The association signal for BRCA2 was driven primarily by likely gene disrupting (LGD) variants, with an Odds Ratio (OR) of 5.62 (95% Confidence Interval (CI) 1.03-30.1). In contrast, the association signals for MC1R and MITF were driven primarily by predicted pathogenic missense variants, with estimated ORs of 1.4 to 3.0 for MC1R and 4.1 for MITF. MTAP exhibited an excess of both LGD and predicted damaging missense variants among cases, with ORs of 5.62 and 3.72, respectively, although neither category was significant. For individuals with known or predicted damaging variants, age of disease onset was significantly lower for two of the four genes, MC1R (p = .005) and MTAP (p = .035). In an analysis of germline carrier status and overlapping copy number alterations, we observed no evidence to support a two-hit model of carcinogenesis in any of the four genes. Although MC1R carriers were represented proportionally among the four molecular tumor subtypes, these individuals accounted for 69% of ultraviolet (UV) radiation mutational signatures among triple-wild type tumors (p = .040), highlighting the increased sensitivity to UV exposure among individuals with loss-of-function variants in MC1R.

Assessing inter-component heterogeneity of biphasic uterine carcinosarcomas.

OBJECTIVE: Uterine carcinosarcoma (UCS) is a rare and aggressive form of uterine cancer. It is bi-phasic, exhibiting histological features of both malignant epithelial (carcinoma) and mesenchymal (sarcoma) elements, reflected in ambiguity in accepted treatment guidelines. We sought to study the genomic and transcriptomic profiles of these elements individually to gain further insights into the development of these tumors. METHODS: We macro-dissected carcinomatous, sarcomatous, and normal tissues from formalin fixed paraffin embedded uterine samples of 10 UCS patients. Single nucleotide polymorphism microarrays, targeted DNA sequencing and whole-transcriptome RNA-sequencing were performed. Somatic chromosomal alterations (SCAs), point mutation and gene expression profiles were compared between carcinomatous and sarcomatous components. RESULTS: In addition to TP53, other recurrently mutated genes harboring putative driver or loss-of-function mutations included PTEN, FBXW7, FGFR2, KRAS, PIK3CA and CTNNB1, genes known to be involved in UCS. Intra-patient somatic mutation and SCA profiles were highly similar between paired carcinoma and sarcoma samples. An epithelial-mesenchymal transition (EMT) signature tended to differentiate components, with EMT-like status more common in advanced-stage patients exhibiting higher inter-component SCA heterogeneity. CONCLUSIONS: From DNA analysis, our results indicate a monoclonal disease origin for this cohort. Yet expression-derived EMT statuses of the carcinomatous and sarcomatous components were often discrepant, and advanced cases displayed greater genomic heterogeneity. Therefore, separately-profiled components of UCS tumors may better inform disease progression or potential.

Development of Kras mutant lung adenocarcinoma in mice with knockout of the airway lineage-specific gene Gprc5a.

Despite the urgency for prevention and treatment of lung adenocarcinoma (LUAD), we still do not know drivers in pathogenesis of the disease. Earlier work revealed that mice with knockout of the G-protein coupled receptor Gprc5a develop late onset lung tumors including LUADs. Here, we sought to further probe the impact of Gprc5a expression on LUAD pathogenesis. We first surveyed GPRC5A expression in human tissues and found that GPRC5A was markedly elevated in human normal lung relative to other normal tissues and was consistently downregulated in LUADs. In sharp contrast to wild-type littermates, Gprc5a-/- mice treated chronically with the nicotine-specific carcinogen NNK developed LUADs by 6 months following NNK exposure. Immunofluorescence analysis revealed that the LUADs exhibited abundant expression of surfactant protein C and lacked the clara cell marker Ccsp, suggesting that these LUADs originated from alveolar type II cells. Next, we sought to survey genome-wide alterations in the pathogenesis of Gprc5a-/- LUADs. Using whole exome sequencing, we found that carcinogen-induced LUADs exhibited markedly higher somatic mutation burdens relative to spontaneous tumors. All LUADs were found to harbor somatic mutations in the Kras oncogene (p. G12D or p. Q61R). In contrast to spontaneous lesions, carcinogen-induced Gprc5a-/- LUADs exhibited mutations (variants and copy number gains) in additional drivers (Atm, Kmt2d, Nf1, Trp53, Met, Ezh2). Our study underscores genomic alterations that represent early events in the development of Kras mutant LUAD following Gprc5a loss and tobacco carcinogen exposure and that may constitute targets for prevention and early treatment of this disease.

Rapid and powerful detection of subtle allelic imbalance from exome sequencing data with hapLOHseq.

MOTIVATION: The detection of subtle genomic allelic imbalance events has many potential applications. For example, identifying cancer-associated allelic imbalanced regions in low tumor-cellularity samples or in low-proportion tumor subclones can be used for early cancer detection, prognostic assessment and therapeutic selection in cancer patients. We developed hapLOHseq for the detection of subtle allelic imbalance events from next-generation sequencing data. RESULTS: Our method identified events of 10 megabases or greater occurring in as little as 16% of the sample in exome sequencing data (at 80×) and 4% in whole genome sequencing data (at 30×), far exceeding the capabilities of existing software. We also found hapLOHseq to be superior at detecting large chromosomal changes across a series of pancreatic samples from TCGA. AVAILABILITY AND IMPLEMENTATION: hapLOHseq is available at scheet.org/software, distributed under an open source MIT license. CONTACT: pscheet@alum.wustl.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A whole-exome case-control association study to characterize the contribution of rare coding variation to pancreatic cancer risk.

Pancreatic cancer is a deadly disease that accounts for approximately 5% of cancer deaths worldwide, with a dismal 5-year survival rate of 10%. Known genetic risk factors explain only a modest proportion of the heritable risk of pancreatic cancer. We conducted a whole-exome case-control sequencing study in 1,591 pancreatic cancer cases and 2,134 cancer-free controls of European ancestry. In our gene-based analysis, ATM ranked first, with a genome-wide significant p value of 1 × 10-8. The odds ratio for protein-truncating variants in ATM was 24, which is substantially higher than prior estimates, although ours includes a broad 95% confidence interval (4.0-1000). SIK3 was the second highest ranking gene (p = 3.84 × 10-6, false discovery rate or FDR = 0.032). We observed nominally significant association signals in several genes of a priori interest, including BRCA2 (p = 4.3 × 10-4), STK11 (p = 0.003), PALB2 (p = 0.019), and TP53 (p = 0.037), and reported risk estimates for known pathogenic variants and variants of uncertain significance (VUS) in these genes. The rare variants in established susceptibility genes explain approximately 24% of log familial relative risk, which is comparable to the contribution from established common susceptibility variants (17%). In conclusion, this study provides new insights into the genetic susceptibility of pancreatic cancer, refining rare variant risk estimates in known pancreatic cancer susceptibility genes and identifying SIK3 as a novel candidate susceptibility gene. This study highlights the prominent importance of ATM truncating variants and the underappreciated role of VUS in pancreatic cancer etiology.

Pan cancer patterns of allelic imbalance from chromosomal alterations in 33 tumor types.

Somatic copy number alterations (SCNAs) serve as hallmarks of tumorigenesis and often result in deviations from one-to-one allelic ratios at heterozygous loci, leading to allelic imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation algorithm, providing segment mean copy number estimates from single-nucleotide polymorphism DNA microarray total intensities (log R ratio), but not allele-specific intensities ("B allele" frequencies) that inform of AI. Our approach provides more sensitive identification of SCNAs by modeling the "B allele" frequencies jointly, thereby bolstering the catalog of chromosomal alterations in this widely utilized resource. Here we present AI summaries for all 33 tumor sites in TCGA, including those induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH). We identified AI in 94% of the tumors, higher than in previous reports. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p, as well as mixed event types on 8p and 13q. We also observed both site-specific and pan-cancer (spanning 17p) cnLOH, patterns which have not been comprehensively characterized. The identification of such cnLOH events elucidates tumor suppressors and multi-hit pathways to carcinogenesis. We also contrast the landscapes inferred from AI- and total intensity-derived SCNAs and propose an automated procedure to improve and adjust SCNAs in TCGA for cases where high levels of aneuploidy obscured baseline intensity identification. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.

Genomic landscape of allelic imbalance in premalignant atypical adenomatous hyperplasias of the lung.

BACKGROUND: Genomic investigation of atypical adenomatous hyperplasia (AAH), the only known precursor lesion to lung adenocarcinomas (LUAD), presents challenges due to the low mutant cell fractions. This necessitates sensitive methods for detection of chromosomal aberrations to better study the role of critical alterations in early lung cancer pathogenesis and the progression from AAH to LUAD. METHODS: We applied a sensitive haplotype-based statistical technique to detect chromosomal alterations leading to allelic imbalance (AI) from genotype array profiling of 48 matched normal lung parenchyma, AAH and tumor tissues from 16 stage-I LUAD patients. To gain insights into shared developmental trajectories among tissues, we performed phylogenetic analyses and integrated our results with point mutation data, highlighting significantly-mutated driver genes in LUAD pathogenesis. FINDINGS: AI was detected in nine AAHs (56%). Six cases exhibited recurrent loss of 17p. AI and the enrichment of 17p events were predominantly identified in patients with smoking history. Among the nine AAH tissues with detected AI, seven exhibited evidence for shared chromosomal aberrations with matched LUAD specimens, including losses harboring tumor suppressors on 17p, 8p, 9p, 9q, 19p, and gains encompassing oncogenes on 8q, 12p and 1q. INTERPRETATION: Chromosomal aberrations, particularly 17p loss, appear to play critical roles early in AAH pathogenesis. Genomic instability in AAH, as well as truncal chromosomal aberrations shared with LUAD, provide evidence for mutation accumulation and are suggestive of a cancerized field contributing to the clonal selection and expansion of these premalignant lesions. FUND: Supported in part by Cancer Prevention and Research Institute of Texas (CPRIT) grant RP150079 (PS and HK), NIH grant R01HG005859 (PS) and The University of Texas MD Anderson Cancer Center Core Support Grant.

Oral microbiota reveals signs of acculturation in Mexican American women.

The oral microbiome has been linked to a number of chronic inflammatory conditions, including obesity, diabetes, periodontitis, and cancers of the stomach and liver. These conditions disproportionately affect Mexican American women, yet few studies have examined the oral microbiota in this at-risk group. We characterized the 16S rDNA oral microbiome in 369 non-smoking women enrolled in the MD Anderson Mano a Mano Mexican American Cohort Study. Lower bacterial diversity, a potential indicator of oral health, was associated with increased age and length of US residency among recent immigrants. Grouping women by overarching bacterial community type (e.g., "Streptococcus," "Fusobacterium," and "Prevotella" clusters), we observed differences across a number of acculturation-related variables, including nativity, age at immigration, time in the US, country of longest residence, and a multi-dimensional acculturation scale. Participants in the cluster typified by higher abundance of Streptococcus spp. exhibited the lowest bacterial diversity and appeared the most acculturated as compared to women in the "Prevotella" group. Computationally-predicted functional analysis suggested the Streptococcus-dominated bacterial community had greater potential for carbohydrate metabolism while biosynthesis of essential amino acids and nitrogen metabolism prevailed among the Prevotella-high group. Findings suggest immigration and adaption to life in the US, a well-established mediator of disease risk, is associated with differences in oral microbial profiles in Mexican American women. These results warrant further investigation into the joint and modifying effects of acculturation and oral bacteria on the health of Mexican American women and other immigrant populations. The oral microbiome presents an easily accessible biomarker of disease risk, spanning biological, behavioral, and environmental factors.

Genomic Landscape of Atypical Adenomatous Hyperplasia Reveals Divergent Modes to Lung Adenocarcinoma.

There is a dearth of knowledge about the pathogenesis of premalignant lung lesions, especially for atypical adenomatous hyperplasia (AAH), the only known precursor for the major lung cancer subtype adenocarcinoma (LUAD). In this study, we performed deep DNA and RNA sequencing analyses of a set of AAH, LUAD, and normal tissues. Somatic BRAF variants were found in AAHs from 5 of 22 (23%) patients, 4 of 5 of whom had matched LUAD with driver EGFR mutations. KRAS mutations were present in AAHs from 4 of 22 (18%) of patients. KRAS mutations in AAH were only found in ever-smokers and were exclusive to BRAF-mutant cases. Integrative analysis revealed profiles expressed in KRAS-mutant cases (UBE2C, REL) and BRAF-mutant cases (MAX) of AAH, or common to both sets of cases (suppressed AXL). Gene sets associated with suppressed antitumor (Th1; IL12A, GZMB) and elevated protumor (CCR2, CTLA-4) immune signaling were enriched in AAH development and progression. Our results reveal potentially divergent BRAF or KRAS pathways in AAH as well as immune dysregulation in the pathogenesis of this premalignant lung lesion. Cancer Res; 77(22); 6119-30. ©2017 AACR.

Mutational Heterogeneity in APC and KRAS Arises at the Crypt Level and Leads to Polyclonality in Early Colorectal Tumorigenesis.

Purpose: The majority of genomic alterations causing intratumoral heterogeneity (ITH) in colorectal cancer are thought to arise during early stages of carcinogenesis as a burst but only after truncal mutations in APC have expanded a single founder clone. We have investigated if the initial source of ITH is consequent to multiple independent lineages derived from different crypts harboring distinct truncal APC and driver KRAS mutations, thus challenging the prevailing monoclonal monocryptal model.Experimental Design: High-depth next-generation sequencing and SNP arrays were performed in whole-lesion extracts of 37 familial adenomatous polyposis colorectal adenomas. Also, ultrasensitive genotyping of hotspot mutations of APC and KRAS was performed using nanofluidic PCRs in matched bulk biopsies (n = 59) and crypts (n = 591) from 18 adenomas and seven carcinomas and adjacent normal tissues.Results: Multiple co-occurring truncal APC and driver KRAS alterations were uncovered in whole-lesion extracts from adenomas and subsequently confirmed to belong to multiple clones. Ultrasensitive genotyping of bulk biopsies and crypts revealed novel undetected APC mutations that were prominent among carcinomas, whereas abundant wild-type APC crypts were detected in adenomas. KRAS mutational heterogeneity within crypts was evident in both adenomas and carcinomas with a higher degree of concordance between biopsy and crypt genotyping in carcinomas. Nonrandom heterogeneity among crypts was also observed.Conclusions: The striking degree of nonrandom intercrypt heterogeneity in truncal and driver gene mutations observed in adenomas and carcinomas is consistent with a polycryptal model derived from multiple independent initiation linages as the source of early ITH in colorectal carcinogenesis. Clin Cancer Res; 23(19); 5936-47. ©2017 AACR.

Genomic Landscape of Colorectal Mucosa and Adenomas.

The molecular basis of the adenoma-to-carcinoma transition has been deduced using comparative analysis of genetic alterations observed through the sequential steps of intestinal carcinogenesis. However, comprehensive genomic analyses of adenomas and at-risk mucosa are still lacking. Therefore, our aim was to characterize the genomic landscape of colonic at-risk mucosa and adenomas. We analyzed the mutation profile and copy number changes of 25 adenomas and adjacent mucosa from 12 familial adenomatous polyposis patients using whole-exome sequencing and validated allelic imbalances (AI) in 37 adenomas using SNP arrays. We assessed for evidence of clonality and performed estimations on the proportions of driver and passenger mutations using a systems biology approach. Adenomas had lower mutational rates than did colorectal cancers and showed recurrent alterations in known cancer driver genes (APC, KRAS, FBXW7, TCF7L2) and AIs in chromosomes 5, 7, and 13. Moreover, 80% of adenomas had somatic alterations in WNT pathway genes. Adenomas displayed evidence of multiclonality similar to stage I carcinomas. Strong correlations between mutational rate and patient age were observed in at-risk mucosa and adenomas. Our data indicate that at least 23% of somatic mutations are present in at-risk mucosa prior to adenoma initiation. The genomic profiles of at-risk mucosa and adenomas illustrate the evolution from normal tissue to carcinoma via greater resolution of molecular changes at the inflection point of premalignant lesions. Furthermore, substantial genomic variation exists in at-risk mucosa before adenoma formation, and deregulation of the WNT pathway is required to foster carcinogenesis. Cancer Prev Res; 9(6); 417-27. ©2016 AACR.

Genomic Landscape Established by Allelic Imbalance in the Cancerization Field of a Normal Appearing Airway.

Visually normal cells adjacent to, and extending from, tumors of the lung may carry molecular alterations characteristics of the tumor itself, an effect referred to as airway field of cancerization. This airway field has been postulated as a model for early events in lung cancer pathogenesis. Yet the genomic landscape of somatically acquired molecular alterations in airway epithelia of lung cancer patients has remained unknown. To begin to fill this void, we sought to comprehensively characterize the genomic architecture of chromosomal alterations inducing allelic imbalance (AI) in the airway field of the most common type of lung tumors, non-small cell lung cancer (NSCLC). To do so, we conducted a genome-wide survey of multiple spatially distributed normal-appearing airways, multiregion tumor specimens, and uninvolved normal tissues or blood from 45 patients with early-stage NSCLC. We detected alterations in airway epithelia from 22 patients, with an increased frequency in NSCLCs of squamous histology. Our data also indicated a spatial gradient of AI in samples at closer proximity to the NSCLC. Chromosome 9 displayed the highest levels of AI and comprised recurrent independent events. Furthermore, the airway field AI included oncogenic gains and tumor suppressor losses in known NSCLC drivers. Our results demonstrate that genome-wide AI is common in the airway field of cancerization, providing insights into early events in the pathogenesis of NSCLC that may comprise targets for early treatment and chemoprevention. Cancer Res; 76(13); 3676-83. ©2016 AACR.

Cancer in silico drug discovery: a systems biology tool for identifying candidate drugs to target specific molecular tumor subtypes.

Large-scale cancer datasets such as The Cancer Genome Atlas (TCGA) allow researchers to profile tumors based on a wide range of clinical and molecular characteristics. Subsequently, TCGA-derived gene expression profiles can be analyzed with the Connectivity Map (CMap) to find candidate drugs to target tumors with specific clinical phenotypes or molecular characteristics. This represents a powerful computational approach for candidate drug identification, but due to the complexity of TCGA and technology differences between CMap and TCGA experiments, such analyses are challenging to conduct and reproduce. We present Cancer in silico Drug Discovery (CiDD; scheet.org/software), a computational drug discovery platform that addresses these challenges. CiDD integrates data from TCGA, CMap, and Cancer Cell Line Encyclopedia (CCLE) to perform computational drug discovery experiments, generating hypotheses for the following three general problems: (i) determining whether specific clinical phenotypes or molecular characteristics are associated with unique gene expression signatures; (ii) finding candidate drugs to repress these expression signatures; and (iii) identifying cell lines that resemble the tumors being studied for subsequent in vitro experiments. The primary input to CiDD is a clinical or molecular characteristic. The output is a biologically annotated list of candidate drugs and a list of cell lines for in vitro experimentation. We applied CiDD to identify candidate drugs to treat colorectal cancers harboring mutations in BRAF. CiDD identified EGFR and proteasome inhibitors, while proposing five cell lines for in vitro testing. CiDD facilitates phenotype-driven, systematic drug discovery based on clinical and molecular data from TCGA.