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

Abstract

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.

Figure 1

Overview of MSK-IMPACT clinical workflow. Patients provide informed consent for paired tumor-normal sequence analysis, and a blood sample is collected as a source of normal DNA. DNA is extracted from tumor and blood samples using automated protocols, and sequence libraries are prepared and captured using hybridization probes targeting all coding exons of 410 genes and select introns of recurrently rearranged genes. Following sequencing, paired reads are analyzed through a custom bioinformatics pipeline that detects multiple classes of genomic rearrangements. Results are loaded into an in-house developed genomic variants database, MPath, upon which they are manually reviewed for quality and accuracy. Genomic alterations are reported in the electronic medical record, transmitted to an institutional database (Darwin) that facilitates automated clinical trial matching, and automatically uploaded to the cBioPortal for data mining and interpretation.

Figure 2

Overview of the MSK-IMPACT cohort. (a) Distribution of tumor types among cases successfully sequenced from 10,336 patients. Cases represented 62 principal tumor types encapsulating 361 detailed tumor types. (b) Frequency of gene alterations in TCGA and MSK-IMPACT cohorts. Genes that were statistically significantly mutated in TCGA studies are displayed, and genes that show a significant difference between the two cohorts are labeled. (c) Recurrent somatic alterations across common tumor types. Genes with a cohort-level alteration frequency of ≥5% or a tumor type-specific alteration frequency of ≥30% are displayed. Bars indicate the percent of cases within each tumor type harboring different classes of genomic alterations.

Figure 3

Spectrum of TERT promoter mutations in cancer. (a) Location of all TERT promoter mutations relative to the transcription start site (+1). Observed nucleotide changes leading presumptive ETS transcription binding sites are shown for the three most common mutational hotspots. Inset shows the distribution of cancer types harboring mutations at each individual hotspot. (b) Bar plot depicting the percentage of cases in each common principal tumor type (left) and melanoma sub-types (right) harboring a TERT promoter mutation. (c) Kaplan-Meier survival curves for the most prominent detailed tumor types belonging to the principal tumor types with the highest prevalence of TERT promoter mutations. Survival was measured starting from the date of the procedure to obtain the specimen sequenced. Cases where specimens were obtained more than 12 months prior to MSK-IMPACT sequencing were excluded from this analysis.

Figure 4

Spectrum of kinase fusions identified by MSK-IMPACT. (a) Kinase genes recurrently rearranged to form putative gene fusions including the kinase domain, displayed across principal tumor types. (b) List of fusions containing BRAF gene. * novel fusion partner; † complex fusion resolved using an orthogonal RNAseq based assay. (c) In-frame intragenic deletions observed in BRAF, encompassing 5 - 9 exons upstream of the kinase domain.

Figure 5

Mutational signatures derived from MSK-IMPACT targeted sequencing data. (a) Distribution of the somatic tumor mutation burden (TMB), defined as non-synonymous coding mutations per Megabase, for common principal tumor types. (b) Distribution of observed mutation rates across all tumors sequenced was used to identify a threshold of 13.8 mutations/Mb, indicative of high mutation burden. (c) Dominant mutation signatures identified in cases with high mutation burden. The percent of cases harboring a dominant mutation signature is shown for each principal tumor type. POLE: DNA Polymerase ε; MMR: Mismatch repair deficiency; UV: Ultraviolet light; TMZ: Temozolomide. (d) Individual tumors harboring dominant mutation signatures. Bar charts display the total number of coding mutations (gray) and the fraction of mutations explained by the major signature (colored). Tracks below the bar charts indicate: i) POLE mutation status, ii) MMR pathway mutation status, iii) MSIsensor score, iv) indel to SNV ratio, v) reported smoking status, and vi) cancer type. (e) Tumor type distribution for samples with a high mutation burden, dominant MMR signature, and inferred MSI. (f) 55-year-old with castrate and enzalutamide resistant prostate cancer with an MMR signature (19 mutations, including 6 frameshift indels) and no clear underlying somatic or germline MMR pathway lesion. A pathogenic germline MUTYH variant was detected, which may contribute to the MSI phenotype. Upon initiation of treatment on an anti-PD-L1 immunotherapy regimen, significant tumor regression was observed. Line charts show the relative tumor size based on RECIST criteria and serum PSA levels. MRI images show the decreasing tumor size at indicated time points (scale bar = 10 cm).

Figure 6

Clinical actionability of somatic alterations revealed by MSK-IMPACT. (a) Alterations were annotated based on their clinical actionability according to OncoKB, and samples were assigned the level of the most significant alteration. Briefly, levels of evidence varied according to whether mutations are FDA-recognized biomarkers (Level 1), predict response to standard-of-care therapies (Level 2), or predict for response to investigational agents in clinical trials (Level 3). Levels 2 and 3 were subdivided according to whether the evidence existed for the pertinent tumor type (2A, 3A) or a different tumor type (2B, 3B). The distribution of the highest level of actionability across all patients is displayed. (b) Distribution of levels of actionability across tumor types (GNET: gastrointestincal neuroendocrine tumor). (c) Number of patients enrolled on genomically-matched clinical trials on the basis of different gene alterations.