Faculty Bibliography

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.

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

Epigenetic alterations, such as promoter hypermethylation, may drive cancer through tumor suppressor gene inactivation. However, we have limited ability to differentiate driver DNA methylation (DNAme) changes from passenger events. We developed DNAme driver inference-MethSig-accounting for the varying stochastic hypermethylation rate across the genome and between samples. We applied MethSig to bisulfite sequencing data of chronic lymphocytic leukemia (CLL), multiple myeloma, ductal carcinoma in situ, glioblastoma, and to methylation array data across 18 tumor types in TCGA. MethSig resulted in well-calibrated quantile-quantile plots and reproducible inference of likely DNAme drivers with increased sensitivity/specificity compared with benchmarked methods. CRISPR/Cas9 knockout of selected candidate CLL DNAme drivers provided a fitness advantage with and without therapeutic intervention. Notably, DNAme driver risk score was closely associated with adverse outcome in independent CLL cohorts. Collectively, MethSig represents a novel inference framework for DNAme driver discovery to chart the role of aberrant DNAme in cancer. SIGNIFICANCE: MethSig provides a novel statistical framework for the analysis of DNA methylation changes in cancer, to specifically identify candidate DNA methylation driver genes of cancer progression and relapse, empowering the discovery of epigenetic mechanisms that enhance cancer cell fitness.This article is highlighted in the In This Issue feature, p. 2113.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has infected over 115 million people and caused over 2.5 million deaths worldwide. Yet, the molecular mechanisms underlying the clinical manifestations of COVID-19, as well as what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome (ARDS), remains poorly understood. To address these challenges, we combined transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues, matched with spatial protein and expression profiling (GeoMx) across 357 tissue sections. These results define both body-wide and tissue-specific (heart, liver, lung, kidney, and lymph nodes) damage wrought by the SARS-CoV-2 infection, evident as a function of varying viral load (high vs. low) during the course of infection and specific, transcriptional dysregulation in splicing isoforms, T cell receptor expression, and cellular expression states. In particular, cardiac and lung tissues revealed the largest degree of splicing isoform switching and cell expression state loss. Overall, these findings reveal a systemic disruption of cellular and transcriptional pathways from COVID-19 across all tissues, which can inform subsequent studies to combat the mortality of COVID-19, as well to better understand the molecular dynamics of lethal SARS-CoV-2 infection and other viruses.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

Importance:Recommendations for adjuvant therapy after surgical resection of lung adenocarcinoma (LUAD) are based solely on TNM classification but are agnostic to genomic and high-risk clinicopathologic factors. Creation of a prediction model that integrates tumor genomic and clinicopathologic factors may better identify patients at risk for recurrence. Objective:To identify tumor genomic factors independently associated with recurrence, even in the presence of aggressive, high-risk clinicopathologic variables, in patients with completely resected stages I to III LUAD, and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict risk of recurrence, compared with the TNM system. Design, Setting, and Participants:This prospective cohort study included 426 patients treated from January 1, 2008, to December 31, 2017, at a single large cancer center and selected in consecutive samples. Eligibility criteria included complete surgical resection of stages I to III LUAD, broad-panel next-generation sequencing data with matched clinicopathologic data, and no neoadjuvant therapy. External validation of the PRecur prediction model was performed using The Cancer Genome Atlas (TCGA). Data were analyzed from 2014 to 2018. Main Outcomes and Measures:The study end point consisted of relapse-free survival (RFS), estimated using the Kaplan-Meier approach. Associations among clinicopathologic factors, genomic alterations, and RFS were established using Cox proportional hazards regression. The PRecur prediction model integrated genomic and clinicopathologic factors using gradient-boosting survival regression for risk group generation and prediction of RFS. A concordance probability estimate (CPE) was used to assess the predictive ability of the PRecur model. Results:Of the 426 patients included in the analysis (286 women [67%]; median age at surgery, 69 [interquartile range, 62-75] years), 318 (75%) had stage I cancer. Association analysis showed that alterations in SMARCA4 (clinicopathologic-adjusted hazard ratio [HR], 2.44; 95% CI, 1.03-5.77; P = .042) and TP53 (clinicopathologic-adjusted HR, 1.73; 95% CI, 1.09-2.73; P = .02) and the fraction of genome altered (clinicopathologic-adjusted HR, 1.03; 95% CI, 1.10-1.04; P = .005) were independently associated with RFS. The PRecur prediction model outperformed the TNM-based model (CPE, 0.73 vs 0.61; difference, 0.12 [95% CI, 0.05-0.19]; P < .001) for prediction of RFS. To validate the prediction model, PRecur was applied to the TCGA LUAD data set (n = 360), and a clear separation of risk groups was noted (log-rank statistic, 7.5; P = .02), confirming external validation. Conclusions and Relevance:The findings suggest that integration of tumor genomics and clinicopathologic features improves risk stratification and prediction of recurrence after surgical resection of early-stage LUAD. Improved identification of patients at risk for recurrence could enrich and enhance accrual to adjuvant therapy clinical trials.

Telomere crisis contributes to cancer genome evolution, yet only a subset of cancers display breakage-fusion-bridge (BFB) cycles and chromothripsis, hallmarks of experimental telomere crisis identified in previous studies. We examine the spectrum of structural variants (SVs) instigated by natural telomere crisis. Eight spontaneous post-crisis clones did not show prominent patterns of BFB cycles or chromothripsis. Their crisis-induced genome rearrangements varied from infrequent simple SVs to more frequent and complex SVs. In contrast, BFB cycles and chromothripsis occurred in MRC5 fibroblast clones that escaped telomere crisis after CRISPR-controlled telomerase activation. This system revealed convergent evolutionary lineages altering one allele of chromosome 12p, where a short telomere likely predisposed to fusion. Remarkably, the 12p chromothripsis and BFB events were stabilized by independent fusions to chromosome 21. The data establish that telomere crisis can generate a wide spectrum of SVs implying that a lack of BFB patterns and chromothripsis in cancer genomes does not indicate absence of past telomere crisis.

Uterine leiomyosarcomas (uLMS) are aggressive tumors arising from the smooth muscle layer of the uterus. We analyzed 83 uLMS sample genetics, including 56 from Yale and 27 from The Cancer Genome Atlas (TCGA). Among them, a total of 55 Yale samples including two patient-derived xenografts (PDXs) and 27 TCGA samples have whole-exome sequencing (WES) data; 10 Yale and 27 TCGA samples have RNA-sequencing (RNA-Seq) data; and 11 Yale and 10 TCGA samples have whole-genome sequencing (WGS) data. We found recurrent somatic mutations in TP53, MED12, and PTEN genes. Top somatic mutated genes included TP53, ATRX, PTEN, and MEN1 genes. Somatic copy number variation (CNV) analysis identified 8 copy-number gains, including 5p15.33 (TERT), 8q24.21 (C-MYC), and 17p11.2 (MYOCD, MAP2K4) amplifications and 29 copy-number losses. Fusions involving tumor suppressors or oncogenes were deetected, with most fusions disrupting RB1, TP53, and ATRX/DAXX, and one fusion (ACTG2-ALK) being potentially targetable. WGS results demonstrated that 76% (16 of 21) of the samples harbored chromoplexy and/or chromothripsis. Clinically actionable mutational signatures of homologous-recombination DNA-repair deficiency (HRD) and microsatellite instability (MSI) were identified in 25% (12 of 48) and 2% (1 of 48) of fresh frozen uLMS, respectively. Finally, we found olaparib (PARPi; P = 0.002), GS-626510 (C-MYC/BETi; P < 0.000001 and P = 0.0005), and copanlisib (PIK3CAi; P = 0.0001) monotherapy to significantly inhibit uLMS-PDXs harboring derangements in C-MYC and PTEN/PIK3CA/AKT genes (LEY11) and/or HRD signatures (LEY16) compared to vehicle-treated mice. These findings define the genetic landscape of uLMS and suggest that a subset of uLMS may benefit from existing PARP-, PIK3CA-, and C-MYC/BET-targeted drugs.