Alexander Penson

Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically defined tumor types, coupled with an expanding portfolio of molecularly targeted therapies, demands flexible and comprehensive approaches to profile clinically relevant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative using a comprehensive assay, MSK-IMPACT, through which we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel noncoding alterations, and mutational signatures that were shared by common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

The long tail of oncogenic drivers in prostate cancer.

Comprehensive genomic characterization of prostate cancer has identified recurrent alterations in genes involved in androgen signaling, DNA repair, and PI3K signaling, among others. However, larger and uniform genomic analysis may identify additional recurrently mutated genes at lower frequencies. Here we aggregate and uniformly analyze exome sequencing data from 1,013 prostate cancers. We identify and validate a new class of E26 transformation-specific (ETS)-fusion-negative tumors defined by mutations in epigenetic regulators, as well as alterations in pathways not previously implicated in prostate cancer, such as the spliceosome pathway. We find that the incidence of significantly mutated genes (SMGs) follows a long-tail distribution, with many genes mutated in less than 3% of cases. We identify a total of 97 SMGs, including 70 not previously implicated in prostate cancer, such as the ubiquitin ligase CUL3 and the transcription factor SPEN. Finally, comparing primary and metastatic prostate cancer identifies a set of genomic markers that may inform risk stratification.Meta-analysis of exome sequencing data identifies new recurrently mutated driver genes for prostate cancer. Comparison of primary and metastatic tumors further identifies genomic markers for advanced prostate cancer that may inform risk stratification.

Prospective Genomic Profiling of Prostate Cancer Across Disease States Reveals Germline and Somatic Alterations That May Affect Clinical Decision Making

PURPOSE A long natural history and a predominant osseous pattern of metastatic spread are impediments to the adoption of precision medicine in patients with prostate cancer. To establish the feasibility of clinical genomic profiling in the disease, we performed targeted deep sequencing of tumor and normal DNA from patients with locoregional, metastatic non-castrate, and metastatic castration-resistant prostate cancer (CRPC). METHODS Patients consented to genomic analysis of their tumor and germline DNA. A hybridization capture-based clinical assay was employed to identify single nucleotide variations, small insertions and deletions, copy number alterations and structural rearrangements in over 300 cancer-related genes in tumors and matched normal blood. RESULTS We successfully sequenced 504 tumors from 451 patients with prostate cancer. Potentially actionable alterations were identified in DNA damage repair (DDR), PI3K, and MAP kinase pathways. 27% of patients harbored a germline or a somatic alteration in a DDR gene that may predict for response to PARP inhibition. Profiling of matched tumors from individual patients revealed that somatic TP53 and BRCA2 alterations arose early in tumors from patients who eventually developed metastatic disease. In contrast, comparative analysis across disease states revealed that APC alterations were enriched in metastatic tumors, while ATM alterations were specifically enriched in CRPC. CONCLUSION Through genomic profiling of prostate tumors representing the disease clinical spectrum, we identified a high frequency of potentially actionable alterations and possible drivers of disease initiation, metastasis and castration-resistance. Our findings support the routine use of tumor and germline DNA profiling for patients with advanced prostate cancer, for the purpose of guiding enrollment in targeted clinical trials and counseling families at increased risk of malignancy.

Accelerating Discovery of Functional Mutant Alleles in Cancer

Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel AKT1 duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete.Significance: Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. Cancer Discov; 8(2); 174-83. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127.

Small cell carcinomas of the bladder and lung are characterized by a convergent but distinct pathogenesis

Purpose: Small-cell carcinoma of the bladder (SCCB) is a rare and aggressive neuroendocrine tumor with a dismal prognosis and limited treatment options. As SCCB is histologically indistinguishable from small-cell lung cancer, a shared pathogenesis and cell of origin has been proposed. The aim of this study is to determine whether SCCBs arise from a preexisting urothelial carcinoma or share a molecular pathogenesis in common with small-cell lung cancer. Experimental Design: We performed an integrative analysis of 61 SCCB tumors to identify histology- and organ-specific similarities and differences. Results: SCCB has a high somatic mutational burden driven predominantly by an APOBEC-mediated mutational process. TP53, RB1, and TERT promoter mutations were present in nearly all samples. Although these events appeared to arise early in all affected tumors and likely reflect an evolutionary branch point that may have driven small-cell lineage differentiation, they were unlikely the founding transforming event, as they were often preceded by diverse and less common driver mutations, many of which are common in bladder urothelial cancers, but not small-cell lung tumors. Most patient tumors (72%) also underwent genome doubling (GD). Although arising at different chronologic points in the evolution of the disease, GD was often preceded by biallelic mutations in TP53 with retention of two intact copies. Conclusions: Our findings indicate that small-cell cancers of the bladder and lung have a convergent but distinct pathogenesis, with SCCBs arising from a cell of origin shared with urothelial bladder cancer. Clin Cancer Res; 24(8); 1965–73. ©2017 AACR. See related commentary by Oser and Jänne, p. 1775

Genome doubling shapes the evolution and prognosis of advanced cancers

Ploidy abnormalities are a hallmark of cancer, but their impact on the evolution and outcomes of cancers is unknown. Here, we identified whole-genome doubling (WGD) in the tumors of nearly 30% of 9,692 prospectively sequenced advanced cancer patients. WGD varied by tumor lineage and molecular subtype, and arose early in carcinogenesis after an antecedent transforming driver mutation. While associated with TP53 mutations, 46% of all WGD arose in TP53-wild-type tumors and in such cases was associated with an E2F-mediated G1 arrest defect, although neither aberration was obligate in WGD tumors. The variability of WGD across cancer types can be explained in part by cancer cell proliferation rates. WGD predicted for increased morbidity across cancer types, including KRAS-mutant colorectal cancers and estrogen receptor-positive breast cancers, independently of established clinical prognostic factors. We conclude that WGD is highly common in cancer and is a macro-evolutionary event associated with poor prognosis across cancer types.The authors identify whole-genome doubling (WGD) in 30% of ~10,000 sequenced tumors from patients with advanced cancer. WGD correlates with increased risk of death across cancer types.

Unifying cancer and normal RNA sequencing data from different sources.

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data, such as the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA). While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources remains challenging, due to differences in sample and data processing. Here, we developed a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment, gene expression quantification, and batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from GTEx and TCGA and successfully corrected for study-specific biases, enabling comparative analysis between TCGA and GTEx. The normalized datasets are available for download on figshare.

The Genomic Landscape of Endocrine-Resistant Advanced Breast Cancers

We integrated the genomic sequencing of 1,918 breast cancers, including 1,501 hormone receptor-positive tumors, with detailed clinical information and treatment outcomes. In 692 tumors previously exposed to hormonal therapy, we identified an increased number of alterations in genes involved in the mitogen-activated protein kinase (MAPK) pathway and in the estrogen receptor transcriptional machinery. Activating ERBB2 mutations and NF1 loss-of-function mutations were more than twice as common in endocrine resistant tumors. Alterations in other MAPK pathway genes (EGFR, KRAS, among others) and estrogen receptor transcriptional regulators (MYC, CTCF, FOXA1, and TBX3) were also enriched. Altogether, these alterations were present in 22% of tumors, mutually exclusive with ESR1 mutations, and associated with a shorter duration of response to subsequent hormonal therapies.

Enabling cross-study analysis of RNA-Sequencing data

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing. Here, we present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at https://github.com/mskcc/RNAseqDB).