Whole-genome characterization of lung adenocarcinomas lacking the RTK/RAS/RAF pathway

RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(-) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples. Among the remaining 57 cases, we observe focal deletions targeting the promoter or transcription start site of STK11 (n = 7) or KEAP1 (n = 3), and promoter mutations associated with the increased expression of ILF2 (n = 6). We also identify complex structural variations associated with high-level copy number amplifications. Moreover, an enrichment of focal deletions is found in TP53 mutant cases. Our results indicate that RPA(-) cases demonstrate tumor suppressor deletions and genome instability, but lack unique or recurrent genetic lesions compensating for the lack of RPAs. Larger WGS studies of RPA(-) cases are required to understand this important LUAD subset.

Impact of Lineage Plasticity to and from a Neuroendocrine Phenotype on Progression and Response in Prostate and Lung Cancers

Intratumoral heterogeneity can occur via phenotype transitions, often after chronic exposure to targeted anticancer agents. This process, termed lineage plasticity, is associated with acquired independence to an initial oncogenic driver, resulting in treatment failure. In non-small cell lung cancer (NSCLC) and prostate cancers, lineage plasticity manifests when the adenocarcinoma phenotype transforms into neuroendocrine (NE) disease. The exact molecular mechanisms involved in this NE transdifferentiation remain elusive. In small cell lung cancer (SCLC), plasticity from NE to nonNE phenotypes is driven by NOTCH signaling. Herein we review current understanding of NE lineage plasticity dynamics, exemplified by prostate cancer, NSCLC, and SCLC.

Abstract PO-10: Histone 1 deficiency drives lymphoma through disruption of 3D chromatin architecture

Linker histone H1 binds to nucleosomes and causes chromatin compaction, yet little is known about their biologic function. Somatic missense mutations in histone 1 genes (HIST1H1B-E) occur in ~30% of follicular lymphomas and DLBCL, with significant mutual co-occurrence among these alleles, most frequently involving H1C and H1E. Examining whole exomes in 547 DLBCL patients, we found significant enrichment for H1 gene SNVs and copy number loss in the MCD class of ABC-DLBCLs as compared to other subtypes. Next we performed a genetic driver analysis in 101 DLBCLs with matched germline control to identify mutations significantly enriched over background somatic variation. We find that lymphoma-associated H1 alleles H1C and H1E are true genetic driver mutations in lymphomas. Lymphoma H1 mutations affect the H1 globular domain or C-terminus. We found that the globular domain mutants fail to bind to chromatin whereas C-ter mutants fail to compact chromatin as shown by atomic force microscopy, in vitro assembled nucleosome arrays, and FRAP assays in live cells. Hence both types of mutation confer loss of function. Constitutive H1C/E knockout mice are healthy and have no overt phenotype. However, immunization with T cell-dependent antigen caused significant GC hyperplasia (p=0.013) and disruption of polarity due to expansion in the number of centrocytes. Notably, H1C/EDKO GC B-cells readily outcompeted WT GC B-cells in mixed chimera experiments, indicating that they have superior fitness (p=0.0086). We performed RNA-seq in H1C/EDKO GC B-cells, which revealed an aberrant gene derepression signature. Strikingly, these same genes are upregulated during induced pluripotency (iPS cell) reprogramming and are normally silenced during early development by the PRC2 complex (p <0.05 to 1e-05). Indeed, histone mass spectrometry showed reduced H3K27me3 (p=0.0003) and increased H3K36me2 (p =0.001) in H1C/EDKO GC B cells. This prompted us to characterize the epigenome of purified H1C/EDKO vs WT GC B cells using Hi-C, ATAC-seq, and ChIP-seq for multiple histone marks. H1 deficiency causes profound architectural remodeling of the genome characterized by large-scale yet focal compartment shifts from compartment B to compartment A. The degree of decompaction results in distinct epigenetic states, primarily due to gain of H3K36me2 and/or loss of repressive H3K27me3. Strikingly, the H1C/EDKO primitive stem cell gene expression signature was highly significantly enriched (NES 1.24, FDR<0.001) in the RNA-seq profiles of independent cohorts of DLBCL patients with H1C and H1E mutations. We crossed H1C+/-H1E+/- mice with VavP-Bcl2 transgenic mice and observed significant acceleration of lymphomagenesis (p=0.0001). Consistent with acquisition of stem cell characteristics, H1C+/-H1E+/-;VavP-Bcl2 but not VavP-Bcl2 primary lymphoma cells manifested lymphoma-initiating functionality after secondary transplantation into recipient animals.

Citation Format: Nevin Z. Yusufova, Matt Teater, Andreas Kloetgen, Alexey Soshnev, Adewola Osunade, Christopher Chin, Ashley Doane, Louis Staudt, David Scott, Neil Kelleher, Aristotelis Tsirigos, Marcin Imielinski, Yael David, David Allis, Ethel Cesarman, Ari Melnick. Histone 1 deficiency drives lymphoma through disruption of 3D chromatin architecture [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-10.

Abstract 5895: Whole-genome characterization of lung adenocarcinomas lacking alterations in RTK/RAS/RAF/MAPK pathway

Lung adenocarcinomas (LUAD) are typically characterized by genetic activation of the receptor tyrosine kinase (RTK)/RAS/RAF/MAP kinase (MAPK) pathway. A minority of LUAD cases (20-25%) lack apparent genetic alterations in this pathway, and thus are ineligible for most targeted therapies. These candidate “oncogene negative” LUADs may harbor novel classes of oncogenic drivers or represent a biologically distinct class of tumors. To characterize the genomic landscape of oncogene-negative LUADs, we nominated 98 cases that were found to lack an activating RTK/RAS/RAF/MAPK pathway alteration in a TCGA study utilizing whole exome sequencing, microarray, and transcriptome data. We profiled these tumors with high-depth whole genome sequencing (WGS), with the goal of identifying noncoding and structural variant driver DNA alterations in both known and novel loci. Of the 98 cases, 20 harbored somatic KRAS mutations that had been missed in the prior WES and transcriptome studies because of insufficient coverage, including 8 cases with the recently targetable p.G12C mutation. 16 samples harbored oncogenic or loss-of-function structural variants in FGFR1, MAPK1, EGFR, NF1, RASA1, ARAF, NTRK2 and NRG1. 5 other samples with SNV or indels in EGFR, ERBB2 and SOS1 were reclassified as oncogene positive. Thus via comprehensive genomic analysis, we confirmed that 57 of the 98 WGS cases did not harbor any detectable alterations in genes encoding any known RTK/RAS/RAF/MAPK members, representing 13% cases chosen as “lung adenocarcinomas” for the TCGA study. Among the 57 confirmed oncogene-negative LUADs, we identified focal deletions targeting the promoter and transcription start site of tumor suppressor genes STK11, KEAP1 and SMARCA4 in 10 samples. Expression and methylation profiling suggested an enrichment of the TP53-deficient phenotype, including cell cycle and FOXM1 deregulation, among the oncogene-negative samples. Moreover, novel promoter mutations associated with increased expression were identified in ILF2, which regulates DNA damage response pathways. Finally, a subset of confirmed oncogene-negative LUADs harbored increased expression of neuroendocrine markers, suggesting that these oncogene-negative samples may either be mis-diagnosed as LUAD or represent LUAD with mixed features of other subtypes of lung cancer; indeed, 14 of the 57 confirmed oncogene-negative cases show histological features of large cell neuroendocrine lung carcinoma. This would suggest that 10% of the cases in this study are both lung adenocarcinoma and “oncogene-negative” to date. Our results provide some of the first comprehensive genomic characterization of oncogene-negative LUADs, implicating TP53 and structural variants in the pathogenesis of this common and difficult to treat entity.

Citation Format: Jian Carrot-Zhang, Siddhartha Devarakonda, Nicolas Robine, Xiaotong Yao, Tiago C. Silva, Jeff Damrauer, Aditya Deshpande, Ming-Sound Tsao, Christina Yao, Chris Wong, Lisui Bao, Hyo Young Choi, Ina Felau, Jean C. Zenklusen, Gordon Robertson, Tuan Trieua, Wei-Wei Liang, Meng Zhou, Esther Rheinbay, Neil Hayes, Ekta Khurana, Li Ding, Peter Laird, Olivier Elemento, John Weinstein, David Kwiatkowski, Chris Benz, Josh Stuart, Lixing Yang, Mauro Castro, William Travis, Katherine Hoadley, Ben Berman, TCGA Analysis Network, Matthew Meyerson, Ramaswamy Govindan, Marcin Imielinski. Whole-genome characterization of lung adenocarcinomas lacking alterations in RTK/RAS/RAF/MAPK pathway [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5895.

Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

Patterns of somatic structural variation in human cancer genomes

A key mutational process in cancer is structural variation, in which rearrangements delete, amplify or reorder genomic segments that range in size from kilobases to whole chromosomes1-7. Here we develop methods to group, classify and describe somatic structural variants, using data from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumour types8. Sixteen signatures of structural variation emerged. Deletions have a multimodal size distribution, assort unevenly across tumour types and patients, are enriched in late-replicating regions and correlate with inversions. Tandem duplications also have a multimodal size distribution, but are enriched in early-replicating regions-as are unbalanced translocations. Replication-based mechanisms of rearrangement generate varied chromosomal structures with low-level copy-number gains and frequent inverted rearrangements. One prominent structure consists of 2-7 templates copied from distinct regions of the genome strung together within one locus. Such cycles of templated insertions correlate with tandem duplications, and-in liver cancer-frequently activate the telomerase gene TERT. A wide variety of rearrangement processes are active in cancer, which generate complex configurations of the genome upon which selection can act.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

The evolutionary history of 2,658 cancers

Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.

Abstract 909: Single-cell transcriptomic profiling of non-small cell lung cancer uncovers inter- and intracell population structure across TCGA lung adenocarcinoma and lung squamous cancer subtypes

Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent types of non-all cell lung cancer (NSCLC), a leading cause of cancer death worldwide. In this study, we analyzed the transcriptomes of ~45,000 single cells (scRNA) from 13 NSCLC patients, including 5 LUAD cases which were collected and profiled at our institution. To correlate genomes and transcriptomes we performed Whole-Genome Sequencing (WGS) on 3 of these 5 LUAD cases. By comparing tumor tissue with matched adjacent non-malignant lung tissue we are able to confidently distinguish 13 cell-type specific clusters that unambiguously match previously characterized lineages. We developed algorithms for the identification of malignant cells derived from tumor tissue through scRNA analysis of copy number alterations and single nucleotide variants (SNV). Joint analysis of WGS and scRNA confirmed an enrichment of tobacco-associated SNVs among malignant cells of the tumor. Stromal cell types demonstrated consistent expression patterns across cases, while malignant cells demonstrated both inter- and intra-tumoral heterogeneity in their expression of signatures related to GPCR signaling, 3’ UTR mediated translational regulation, and cell-cell junction organization. In particular, one case displayed a unique pattern of intra-tumoral heterogeneity, as a subset of malignant cells robustly express a marker of pulmonary neuroendocrine cells, CGRP. Employing immunohistochemistry, the spatial organization of these malignant cells is revealed to be mutually exclusive within the tumor microenvironment and overlapping in expression of clinical markers of small-cell lung cancer. Finally, we deconvolved bulk TCGA LUAD and LUSC gene expression samples and analyzed the relationship between cell type specific gene expression in cell types of the lung and passenger mutation topographies. Our results provide insight into the molecular and clinical correlates of deconvolved NSCLC transcriptomes and provide a novel methodology with which to explore genomic variation at a single cell resolution. Furthermore, our dataset provides a resource for illuminating cancer-cell transcriptional changes and revealing key molecular drivers of tumor-stromal interactions in lung cancer.

Citation Format: Kofi E. Gyan, Aditya Deshpande, Shaham Beg, Huasong Tian, Joel Rosiene, Marlon Stoeckius, Peter Smibert, Davide Risso, Juan Miguel Mosquera, Marcin Imielinski. Single-cell transcriptomic profiling of non-small cell lung cancer uncovers inter- and intracell population structure across TCGA lung adenocarcinoma and lung squamous cancer subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 909.

Abstract 218: The evolutionary history of 2,658 cancers

Cancer develops through a continuous process of somatic evolution. Whole genome sequencing provides a snapshot of the tumor genome at the point of sampling, however, the data can contain information that permits the reconstruction of a tumor's evolutionary past.

Here, we apply such life history analyses on an unprecedented scale, to a set of 2,658 tumors spanning 39 cancer types. We estimated the timing of large chromosomal gains during tumor evolution, by comparing the rates of doubled to non-doubled point mutations within gained regions. Although we find that such events typically occur in the second half of clonal evolution, we also observe distinctive and early chromosomal gains in some cancer types, such as gains of chromosomes 7, 19 and 20 in glioblastoma, and isochromosome 17q in medulloblastoma. By integrating these results with the qualitative timing of individual driver mutations, we obtained an overall ranking, from early to late, of frequent somatic events per cancer type, which both identified novel patterns of tumor evolution, and incorporated additional detail into known models, such as the progression of APC-KRAS-TP53 in colorectal cancer proposed by Vogelstein and Fearon.

To estimate how mutational processes acting on the tumor genome change over time, we classified mutations in each sample according to three broad time periods (early clonal, late clonal, and subclonal), and quantified the activity of mutational signatures in each period. Most mutational processes appear to remain remarkably constant, however, certain signatures show clear and consistent changes during clonal evolution. Particularly, mutational signatures associated with exposure to carcinogens, such as smoking and UV light, tend to decrease over time. In contrast, signatures associated with defective endogenous processes, such as APOBEC mutagenesis and defective double strand break repair, show an increase between early and late phases of tumor evolution.

Making use of clock-like mutational signatures, we converted mutational time estimates for large events, such as whole genome duplication (WGD), and the emergence of the most recent common ancestor (MRCA), into real time estimates, which allowed us to combine our analyses into overall timelines of cancer evolution, per tumor type. For example, the typical timeline of ovarian adenocarcinoma development shows that early tumor evolution is characterized by mutations in TP53, and widespread genome instability, with WGD events taking place on average 8 years prior to diagnosis. In later stages of evolution, signatures of defective repair processes increase, and the MRCA emerges on average 1 year before diagnosis.

Taken together, these data reveal the common and divergent evolutionary trajectories available to a cancer, which might be crucial in understanding specific tumor biology, and in providing new opportunities for early detection and cancer prevention.

Citation Format: Clemency Jolly, Moritz Gerstung, Ignaty Leshchiner, Stefan C. Dentro, Santiago Gonzalez, Thomas J. Mitchell, Yulia Rubanova, Pavana Anur, Daniel Rosebrock, Kaixian Yu, Maxime Tarabichi, Amit Deshwar, Jeff Wintersinger, Kortine Kleinheinz, Ignacio Vásquez-García, Kerstin Haase, Subhajit Sengupta, Geoff Macintyre, Salem Malikic, Nilgun Donmez, Dimitri G. Livitz, Mark Cmero, Jonas Demeulemeester, Steve Schumacher, Yu Fan, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Paul C. Boutros, David D. Bowtell, Hongtu Zhu, Gad Getz, Marcin Imielinski, Rameen Beroukhim, S Cenk Sahinalp, Yuan Ji, Martin Peifer, Florian Markowetz, Ville Mustonen, Ke Juan, Wenyi Wang, Quaid D. Morris, Paul T. Spellman, David C. Wedge, Peter Van Loo, PCAWG Evolution and Heterogeneity Working Group. The evolutionary history of 2,658 cancers [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 218.

Abstract NG03: Chromosomal instability promotes metastasis through a cytosolic DNA response

Chromosomal instability (CIN) is a hallmark of cancer and it results from ongoing errors in chromosome segregation during mitosis. While CIN is a major driver of tumor evolution, its role in metastasis has not been established. Here we show that CIN promotes metastasis by sustaining a tumor-cell autonomous response to cytosolic DNA. Errors in chromosome segregation create a preponderance of micronuclei whose rupture spills genomic DNA into the cytosol. This leads to the activation of the cGAS-STING cytosolic DNA-sensing pathway and downstream noncanonical NF-κB signaling. Genetic suppression of CIN significantly delays metastasis even in highly aneuploid tumor models, whereas inducing continuous chromosome segregation errors promotes cellular invasion and metastasis in a STING-dependent manner. Using single-cell RNA sequencing, we uncover a CIN-induced transcriptional switch from a proliferative and metabolically active state to a mesenchymal phenotype associated with inflammatory pathways, offering an opportunity to target chromosome segregation errors for therapeutic benefit. Our work reveals an unexpected link between CIN, cytosolic DNA sensing pathways, and metastasis. The use of an isogenic system has enabled us to dissect the role of CIN from that of aneuploidy. Importantly, while we do not discount the role of CIN in generating karyotypic heterogeneity that can serve as the substrate for natural selection, our work demonstrates that continuous chromosome missegregation is also required to replenish cytosolic DNA pools leading to chronic upregulation of inflammatory pathways. In non-transformed settings, cytosolic DNA sensing is incompatible with viability. Unlike normal cells, chromosomally unstable cells are awash with cytosolic DNA and have adapted to coexist with a chronically active cGAS-STING pathway by suppressing downstream type I interferon signaling and instead upregulating the alternative NF-κB pathway. Persistent STING activation mediates carcinogen-induced tumor formation and we now show that tumor cells co-opt this otherwise lethal program to spread to distant organs. The evolutionary benefit of the noncanonical pathway might justify the scarcity of inactivating mutations in cGAS and STING among human cancers. The emergence, and subsequent tolerance, of CIN represents an important bottleneck during tumor evolution. Our single-cell analysis revealed that CIN induces a transcriptional switch whereby cells shift from a proliferative and highly metabolic state, ideally suited for primary tumor growth, to a chromosomally unstable and mesenchymal state associated with upregulation of inflammatory pathways. These two largely mutually exclusive states likely account for the reversibility in chromosome missegregation rates seen in primary tumors and metastases, and provide an explanation for the negative effect of aneuploidy during early tumorigenesis. Interestingly, this mutual exclusivity was recently observed in a pan-cancer genomic analysis of metastatic tumors, and it leads us to suggest that CIN underlies the subset of metastases that are characterized by EMT and inflammation. By providing a mechanistic link between CIN and metastasis, our work opens new avenues to target chromosomally unstable tumors for therapeutic benefit.

Citation Format: Samuel F. Bakhoum, Bryon Ngo, Ashley L. Bakhoum, Julie-Ann Cavallo, Charles J. Murphy, Peter Ly, Pragya Shah, Roshan K. Sriram, Thomas B.k. Watkins, Neil K. Taunk, Mercedes Duran, Chantal Pauli, Christine Shaw, Kalyani Chadalavada, Vinagolu K. Rajasekhar, Giulio Genovese, Subramanian Venkatesan, Nicolai J. Birkbak, Nicholas McGranahan, Mark Lundquist, Quincy LaPlant, John H. Healey, Olivier Elemento, Christine H. Chung, Nancy Y. Lee, Marcin Imielinski, Gouri Nanjangud, Dana Pe'er, Don W. Cleveland, Simon N. Powell, Jan Lammerding, Charles Swanton, Lewis C. Cantley. Chromosomal instability promotes metastasis through a cytosolic DNA response [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 NG03.

SvABA: genome-wide detection of structural variants and indels by local assembly

Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA's performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs and substantially improves detection performance for variants in the 20–300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (<1000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types and found that short templated-sequence insertions occur in ∼4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized (50–300 bp) SVs.

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie.

The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations

Objective: This paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu), an interactive online application for collaborative editing, maintenance, and sharing of structured clinical-grade cancer mutation interpretations.

Materials and Methods: PMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as the Human Genome Variation Society variant description format, we implemented a data model that links variants to tumor-specific and tissue-specific interpretations. Key features of PMKB include support for all major variant types, standardized authentication, distinct user roles including high-level approvers, and detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically.

Results: At the time of writing, PMKB contains 457 variant descriptions with 281 clinical-grade interpretations. The EGFR, BRAF, KRAS, and KIT genes are associated with the largest numbers of interpretable variants. PMKB’s interpretations have been used in over 1500 AmpliSeq tests and 750 whole-exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API.

Discussion: An accurate and up-to-date knowledge base of genomic alterations of clinical significance is critical to the success of precision medicine programs. The open-access, programmatically accessible PMKB represents an important attempt at creating such a resource in the field of oncology.

Conclusion: The PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitate reporting for clinical cancer genomic testing. The PMKB was also designed to enable the creation of clinical cancer genomics automated reporting pipelines via an API.

Insertions and Deletions Target Lineage-Defining Genes in Human Cancers

Certain cell types function as factories, secreting large quantities of one or more proteins that are central to the physiology of the respective organ. Examples include surfactant proteins in lung alveoli, albumin in liver parenchyma, and lipase in the stomach lining. Whole-genome sequencing analysis of lung adenocarcinomas revealed noncoding somatic mutational hotspots near VMP1/MIR21 and indel hotspots in surfactant protein genes (SFTPA1, SFTPB, and SFTPC). Extrapolation to other solid cancers demonstrated highly recurrent and tumor-type-specific indel hotspots targeting the noncoding regions of highly expressed genes defining certain secretory cellular lineages: albumin (ALB) in liver carcinoma, gastric lipase (LIPF) in stomach carcinoma, and thyroglobulin (TG) in thyroid carcinoma. The sequence contexts of indels targeting lineage-defining genes were significantly enriched in the AATAATD DNA motif and specific chromatin contexts, including H3K27ac and H3K36me3. Our findings illuminate a prevalent and hitherto unrecognized mutational process linking cellular lineage and cancer.

Modeling cancer rearrangement landscapes

Cancer genome sequences contain footprints of somatic mutational processes, whose analysis in large tumor sequencing datasets has revealed novel mutational signatures, correlative features of variant topography, and complex events. Many of these analytic results have yet to reconciled with decades of mechanistic genome integrity research performed in controlled model systems. However, a new generation of genome-integrity experiments combining computational modeling, data analytics, and high-throughput sequencing are emerging to link mechanisms to patterns. Conversely, analytic studies evaluating quantitative footprints of specific genome integrity hypotheses will be critical in fitting naturally occurring mutational patterns to the predictions of a particular mechanistic model. Such quantitative and mechanistic studies will form the foundation of an emerging systems biology of genome integrity.

High-throughput Phenotyping of Lung Cancer Somatic Mutations

Recent genome sequencing efforts have identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood. Here we characterize 194 somatic mutations identified in primary lung adenocarcinomas. We present an expression-based variant-impact phenotyping (eVIP) method that uses gene expression changes to distinguish impactful from neutral somatic mutations. eVIP identified 69% of mutations analyzed as impactful and 31% as functionally neutral. A subset of the impactful mutations induces xenograft tumor formation in mice and/or confers resistance to cellular EGFR inhibition. Among these impactful variants are rare somatic, clinically actionable variants including EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, and multiple BRAF variants, demonstrating that rare mutations can be functionally important in cancer.

Abstract 4368: High-throughput phenotyping of lung cancer somatic mutations

Recent cancer genome sequencing and analysis has identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood, limiting the use of this genetic knowledge for clinical decision-making. Here we describe a new high-throughput approach, expression-based variant impact phenotyping (eVIP), which uses gene expression changes to infer somatic mutation impact. We generated a lentiviral expression library representing 53 genes and 194 somatic mutations identified in primary lung adenocarcinomas. Next, we introduced this library into A549 lung adenocarcinoma cells and 96 hours later performed gene expression profiling using Luminex-based L1000 profiling. We built a computational pipeline, eVIP, to compare mutant and wild-type expression signatures to infer whether variants were gain-of-function, change-of-function, loss-of-function, or neutral. Overall, eVIP identified 69% of mutations as impactful whereas 31% appeared functionally neutral. A very high rate, 92%, of missense mutations in the KEAP1 and STK11 tumor suppressor genes were found to inactivate or diminish protein function. As a complementary approach, we assessed which mutations are epistatic to EGFR or capable of initiating xenograft tumor formation in vivo. A subset of the impactful mutations identified by eVIP could induce xenograft tumor formation in mice and/or confer resistance to cellular EGFR inhibition. Among these mutations were 20 rare or non-canonical somatic variants in clinically-actionable or -relevant oncogenes including EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, and PIK3CA E600K. eVIP can, in principle, characterize any genetic variant, independent of prior knowledge of gene function. Further application of eVIP should significantly advance the pace of functional characterization of mutations identified from genome sequencing.

Citation Format: Alice H. Berger, Angela N. Brooks, Xiaoyun Wu, Yashaswi Shrestha, Candace Chouinard, Federica Piccioni, Mukta Bagul, Atanas Kamburov, Marcin Imielinski, Larson Hogstrom, Cong Zhu, Xiaoping Yang, Sasha Pantel, Ryo Sakai, Nathan Kaplan, David Root, Rajiv Narayan, Ted Natoli, David Lahr, Itay Tirosh, Pablo Tamayo, Gad Getz, Bang Wong, John Doench, Aravind Subramanian, Todd R. Golub, Matthew Meyerson, Jesse S. Boehm. High-throughput phenotyping of lung cancer somatic mutations. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4368.

Abstract PR08: NF1, MET, and RIT1 mutations are RAS-pathway driver events in lung adenocarcinoma

Lung adenocarcinoma is comprised of distinct mutational subtypes characterized by mutually exclusive oncogenic alterations such as EGFR and KRAS mutations. Identification of these oncogenic events has transformed treatment strategies and drug development for lung adenocarcinoma. However, such mutations have been identified in only 50-60% of lung adenocarcinoma cases in the United States.

We analyzed SNP array, exome, and RNA sequencing data from 230 primary lung adenocarcinomas to identify known and novel mutually exclusive driver events. MET exon 14 skipping was present in 10/230 tumors (4.3%) and only RNA sequencing data allowed robust detection of this event. To identify novel driver events, we performed a focused statistical analysis of the 87/230 tumors (38%) that lacked a mutation in MET or other known oncogenes. Copy number analysis revealed rare, focal amplifications of MET and ERBB2 that were enriched in this subset. Exome analysis revealed significant enrichment of TP53, KEAP1, NF1, and RIT1 mutations in the previously oncogene-negative subset. Of these mutated genes, only NF1 and RIT1 displayed mutual exclusivity with the other known and candidate driver events.

Whereas TP53, KEAP1, and NF1 are well-documented cancer genes, little is known about the role of RIT1 in human cancer. RIT1 is a RAS-subfamily small GTPase with significant homology to HRAS, NRAS, and KRAS. Mutations in RIT1 were present in ∼2% of cases and clustered in the switch II domain. Ectopic expression of mutated RIT1 in murine fibroblasts and human immortalized lung epithelial cells conferred anchorage-independent growth and tumor forming capability. cDNA sequencing identified a p.M90I missense mutation in the NCI-H2110 cell line. Knockdown of RIT1 in NCI-H2110 cells revealed the role of RIT1 in regulation of PI3K and MEK signaling. Consistently, combination PI3K/mTOR/MEK inhibition could inhibit cellular transformation induced by ectopic expression of mutated RIT1, and PI3K/mTOR inhibition suppressed in vivo tumor growth of NCI-H2110 cells.

Recently, RIT1 mutations were described in myeloid malignancies and Noonan syndrome. Our data, together with these reported observations, indicate that RIT1 is a RAS-pathway oncogene in lung adenocarcinoma. In addition, PI3K/mTOR inhibition with or without MEK inhibition should be explored as a therapeutic strategy for RIT1-mutated lung or other RIT1-mutated cancers. This work expands the number of U.S. lung adenocarcinoma tumors with identified driver events from 62% to 75% and nominates these additional alterations as candidate therapeutic targets.

This abstract is also presented as Poster B26.

Citation Format: Alice H. Berger, Angela N. Brooks, Marcin Imielinski, Andrew Cherniack, Fujiko Duke, Nathan Kaplan, Jeremiah Wala, Matthew Meyerson. NF1, MET, and RIT1 mutations are RAS-pathway driver events in lung adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference on RAS Oncogenes: From Biology to Therapy; Feb 24-27, 2014; Lake Buena Vista, FL. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(12 Suppl):Abstract nr PR08. doi: 10.1158/1557-3125.RASONC14-PR08

Abstract 4263: Identifying somatic mutation hotspots across protein family alignments

In cancer, somatic driver mutations often target “hotspots” of paralogous residues across evolutionarily related members of a single protein family. These hotspots usually confer a dominant oncogenic function, such as constitutive catalytic activity or binding. These hotspots generally represent the most “druggable” genetic alterations. Examples of protein families targeted in such a manner include the Ras and receptor Tyrosine Kinase family. Protein family hotspots are not directly assessed by standard computational approaches for statistically nominating genes under positive somatic selection, such as MutSig, InVex, or MuSic. These methods only assess single genes for statistical enrichment of total mutation burden or the presence of mutation hotspots. As a result, such approaches are only powered to detect protein family hotspots in cases where each (or at least one) protein family member is very frequently mutated (e.g. KRAS, NRAS, HRAS). They are not powered to detect hotspots in highly functionally redundant families, of which each member may be only mutated in &lt;&lt;1% of a tumor type. To directly identify such protein-family hotspots, we have developed a computational framework, MutPfam. This framework superimposes mutation data onto Pfam (http://pfam.sanger.ac.uk/) (protein family and subfamily multiple sequence alignments and identifies statistically significant hotspots. We demonstate our method on protein families involved in the catalysis of small-molecular catabolism using the TCGA Pan-Cancer dataset (https://tcga-data.nci.nih.gov/) comprising somatic mutation calls for 4608 whole-exome sequenced patients and 21 tumor types. As we demonstrate, our framework enables the de novo discovery of mutation hotspots across protein families. Though developed for somatic mutation analysis, this framework could also be applied to the analysis of inherited mutations. Computational issues meriting future investigation include (1) alternate definitions of protein alignments and sub-families (e.g. involving three dimensional structure, biophysical constraints) (2) improved statistical modeling of the local background mutation rate (incorporating local genomic covariates that mediate heterogeneity) (3) incorporation of variant functional impact scores for missense variants.

Citation Format: Marcin Imielinski, Charles Du, Matthew Meyerson. Identifying somatic mutation hotspots across protein family alignments. [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 4263. doi:10.1158/1538-7445.AM2014-4263