Dylan Kotliar

An Integrated Approach to Uncover Drivers of Cancer

Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma data set. Our analysis correctly identified known drivers of melanoma and predicted multiple tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer.

Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers

Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10(-14)). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helioss exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer.

Transcriptomic Characterization of SF3B1 Mutation Reveals Its Pleiotropic Effects in Chronic Lymphocytic Leukemia

Mutations in SF3B1, which encodes a spliceosome component, are associated with poor outcome in chronic lymphocytic leukemia (CLL), but how these contribute to CLL progression remains poorly understood. We undertook a transcriptomic characterization of primary human CLL cells to identify transcripts and pathways affected by SF3B1 mutation. Splicing alterations, identified in the analysis of bulk cells, were confirmed in single SF3B1-mutated CLL cells and also found in cell lines ectopically expressing mutant SF3B1. SF3B1 mutation was found to dysregulate multiple cellular functions including DNA damage response, telomere maintenance, and Notch signaling (mediated through KLF8 upregulation, increased TERC and TERT expression, or altered splicing of DVL2 transcript, respectively). SF3B1 mutation leads to diverse changes in CLL-related pathways.

Medical education in the United States: do residents feel prepared?

BackgroundMedical schools face a growing challenge in providing a comprehensive educational experience. Students must graduate with not only the medical knowledge but also the requisite skills to care for patients and serve as physicians-in-training.ObjectiveTo assess whether residents felt prepared by their medical school training.MethodWe developed a questionnaire to assess resident attitudes towards various aspects of their medical school training and electronically distributed it among 107 United States training institutions.ResultsA total of 2287 residents responded. Overall, a majority (53.8 %) agreed that ‘medical school prepared me well to be a resident.’ Most residents felt very well or mostly prepared in medical knowledge and clinical skills such as collecting a history (92.3 %), presenting a physical exam (86.1 %), or pathophysiology (81.6 %), but not for applied medical and psychosocial practices including end-of-life care (41.7 %), dealing with a patient death (46.3 %), and considering cost-effective care (28.7 %). Additionally, many residents reported feeling underprepared for time and fatigue management, debt, and medical-legal issues.ConclusionsMedical school graduates generally feel well prepared for residency. However, they may be less prepared to face important psychosocial, cultural and professional issues. Ultimately, a greater emphasis on skills and psychosocial experience may yield graduates who feel better prepared for todays residency challenges.

Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia

Intra-tumoral genetic heterogeneity has been characterized across cancers by genome sequencing of bulk tumors, including chronic lymphocytic leukemia (CLL). In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype-phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. By clearly resolving phylogenic relationships, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Integrative analysis of somatic mutations with transcriptional states prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells, leading to increased understanding of driving events in malignancy.

Field validation of recombinant antigen immunoassays for diagnosis of Lassa fever

Lassa fever, a hemorrhagic fever caused by Lassa virus (LASV), is endemic in West Africa. It is difficult to distinguish febrile illnesses that are common in West Africa from Lassa fever based solely on a patient’s clinical presentation. The field performance of recombinant antigen-based Lassa fever immunoassays was compared to that of quantitative polymerase chain assays (qPCRs) using samples from subjects meeting the case definition of Lassa fever presenting to Kenema Government Hospital in Sierra Leone. The recombinant Lassa virus (ReLASV) enzyme-linked immunosorbant assay (ELISA) for detection of viral antigen in blood performed with 95% sensitivity and 97% specificity using a diagnostic standard that combined results of the immunoassays and qPCR. The ReLASV rapid diagnostic test (RDT), a lateral flow immunoassay based on paired monoclonal antibodies to the Josiah strain of LASV (lineage IV), performed with 90% sensitivity and 100% specificity. ReLASV immunoassays performed better than the most robust qPCR currently available, which had 82% sensitivity and 95% specificity. The performance characteristics of recombinant antigen-based Lassa virus immunoassays indicate that they can aid in the diagnosis of LASV Infection and inform the clinical management of Lassa fever patients.

Identifying Gene Expression Programs of Cell-type Identity and Cellular Activity with Single-Cell RNA-Seq

Identifying gene expression programs underlying cell-type identity and cellular processes is a crucial step toward understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell’s expression may derive both from programs determining cell-type and from programs facilitating dynamic cellular activities such as cell-division or apoptosis, which cannot be easily disentangled with current methods. Here, we introduce clustered nonnegative matrix factorization (cNMF) as a solution to this problem. We show with simulations that it deconvolutes scRNA-Seq profiles into interpretable programs corresponding to both cell-types and cellular activities. Applied to published brain organoid and visual cortex datasets, cNMF refines the hierarchy of cell-types and identifies both expected (e.g. cell-cycle and hypoxia) and intriguing novel activity programs. In summary, we show that cNMF can increase the accuracy of cell-type identification while simultaneously inferring interpretable cellular activity programs in scRNA-Seq data, thus providing useful insight into how cells vary dynamically within cell-types.

Analysis of CD8+ T cell response during the 2013–2016 Ebola epidemic in West Africa

Significance Zaire ebolavirus (EBOV) is a viral pathogen of significant global health concern best exemplified by more than 28,000 human infections during the recent West African epidemic. Examining immunity in EBOV disease survivors has been historically difficult due to the occurrence of only small outbreaks in remote regions of central Africa. Consequently, little data exist describing EBOV-specific T cell responses during human infection. We examined virus-specific CD8+ T cell immunity in 32 Sierra Leonean survivors of the 2013–2016 epidemic. CD8+ T cells against the nucleoprotein dominated the EBOV-specific responses in this group, while a minority of individuals harbored memory CD8+ T cells against the EBOV-GP. Our data have implications in designing EBOV vaccines that can elicit cell-mediated immunity in a large group of individuals. The recent Ebola epidemic exemplified the importance of understanding and controlling emerging infections. Despite the importance of T cells in clearing virus during acute infection, little is known about Ebola-specific CD8+ T cell responses. We investigated immune responses of individuals infected with Ebola virus (EBOV) during the 2013–2016 West Africa epidemic in Sierra Leone, where the majority of the >28,000 EBOV disease (EVD) cases occurred. We examined T cell memory responses to seven of the eight Ebola proteins (GP, sGP, NP, VP24, VP30, VP35, and VP40) and associated HLA expression in survivors. Of the 30 subjects included in our analysis, CD8+ T cells from 26 survivors responded to at least one EBOV antigen. A minority, 10 of 26 responders (38%), made CD8+ T cell responses to the viral GP or sGP. In contrast, 25 of the 26 responders (96%) made response to viral NP, 77% to VP24 (20 of 26), 69% to VP40 (18 of 26), 42% (11 of 26) to VP35, with no response to VP30. Individuals making CD8+ T cells to EBOV VP24, VP35, and VP40 also made CD8+ T cells to NP, but rarely to GP. We identified 34 CD8+ T cell epitopes for Ebola. Our data indicate the immunodominance of the EBOV NP-specific T cell response and suggest that its inclusion in a vaccine along with the EBOV GP would best mimic survivor responses and help boost cell-mediated immunity during vaccination.

Abstract 3168: Helios identifies novel oncogenes in breast cancer by integrating genomic characterization of primary tumors and functional shRNA-screens.

Genomic profiling of tumors has uncovered a staggering diversity of recurrent aberrations. However, inferring functionally important driver genes from this data remains difficult_particularly in the case of copy-number aberrations (CNAs) that often span many genes. Genome-wide functional shRNA screens have been a useful orthogonal approach for discovering drivers. The integration of observational data from primary tumors with functional data on cell lines provides an unprecedented opportunity for the identification of driver genes. Unfortunately, most current analysis is limited to naive intersection of top scoring candidates and thus more powerful computational methods are needed. We have developed Helios, a novel Bayesian algorithm that integrates genomic data from primary tumors with functional shRNA screens in cell lines, gaining unprecedented sensitivity and specificity in identifying drivers. Applying Helios to TCGA breast cancer data led to the recapitulation of many known oncogenes as well as to the identification and validation of two novel oncogenes involved in chromatin regulation. Importantly, many of the drivers pinpointed by Helios were not identified on the basis of any one data type alone. Helios uses shRNA data in a novel fashion by employing a new score measuring oncogene addiction, a phenotype associated with many key cancer drivers. It integrates this with CNA, sequence mutation, and RNA expression data into a single probabilistic score for each gene which is then used to assess the most-likely driver gene in a region of recurrent CNA. We applied Helios to TCGA breast cancer data paired with two independent genome-wide shRNA screens on breast cancer cell lines. This identified many previously known oncogenes including FOXA1, ERBB2, PIK3CA, CCND1, IGF1R, BCL2, CDK4, ESR1, MYC, EGFR, GAB1, CCNE1, FGFR2, FGFR3, MYC as the top-scoring candidates in their respective amplified regions. We validated a number of novel predictions in vitro and selected two candidate oncogenes involved in chromatin remodeling for in depth follow up. The contribution of both genes to cancer was confirmed in vitro and in vivo, enhancing colony formation in agar and increasing tumor size in mouse models. One novel oncogene showed evidence of association with invasion and metastasis in a lung-cancer model. Another novel oncogene resides in a frequently amplified region in several epithelial cancers such as lung, bladder, stomach and ovarian carcinomas. Taken together, we have demonstrated that Helios is a powerful “in-silico” screen that can accelerate discovery of driver mutations in cancer. Citation Format: Felix Sanchez Garcia, Patricia Villagrasa, Junji Matsui, Bo-Juen Chen, Dylan Kotliar, Veronica Castro, Jose M. Silva, Dana Pe9er. Helios identifies novel oncogenes in breast cancer by integrating genomic characterization of primary tumors and functional shRNA-screens. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3168. doi:10.1158/1538-7445.AM2013-3168

Abstract A26: Multi-Reg: An integrative parallel approach to uncover drivers of cancer

Tumor samples harbor a vast number of genomic alterations of various kinds, and it is not easy to distinguish driver alterations that contribute to oncogenesis from passenger alterations. Most computational methods attempt to identify driver alternations by focusing on the most frequent alterations. Previous work in our lab has led to the development of CONEXIC, a Bayesian framework for integrating copy number and gene expression to identify candidate driver genes in cancer and to link them to gene expression signatures they regulate (Akavia et al, Cell, 2010). This framework was applied to data from melanoma cell lines, where it correctly identified known drivers (MITF) and connected them to their known targets. In addition, it predicted novel tumor dependencies not previously implicated in melanoma, which were confirmed experimentally. In general, current algorithms identify only one driver (the strongest) controlling a gene expression signature. However, drivers may act in parallel, where not only the strongest one is important. For example, either PTEN deletion or AKT activation can lead to a similar expression signature and phenotype. Therefore, we developed a new algorithm, based on the same principles as CONEXIC with multiple improvements. The new algorithm (called Multi-Reg) is capable of detecting multiple candidate regulators that can all act in parallel to regulate an expression signature. This algorithm can integrate mutations in addition to copy number and expression. Finally, we have designed Multi-Reg to be easier and quicker to run and more robust. We applied Multi-Reg to glioblastoma data from The Cancer Genome Atlas (TCGA). This data includes copy number, gene expression and mutations for hundreds of primary tumor samples. We found 84 candidate regulators that were missed by CONEXIC, but discovered by Multi-Reg. Because of Multi-Reg9s ability to search for genes working in parallel, it identified FGFR3, PDGFRA and NF1, in addition to EGFR & MET (identified by CONEXIC), as important candidate drivers. Additionally, Multi-Reg results identified RHPN2 as a novel oncogenic factor controlling a gene expression signature related to invasion and migration. Validation has shown that RHPN2 has limited effect on cell proliferation but induces invasiveness in glioblastoma cell lines. Our results correctly identify known drivers of glioblastoma progression, including oncogenes such as EGFR, MET, CEBPB and tumor suppressors such as p16 and NF1. Multi-Reg also has the capability to identify more regulators than CONEXIC, and has correctly linked RHPN2 to the oncogenic phenomena it controls. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the Second AACR International Conference on Frontiers in Basic Cancer Research; 2011 Sep 14-18; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2011;71(18 Suppl):Abstract nr A26.