BackgroundHigh tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors and has been shown to be more significantly associated with response to PD-1 and PD-L1 blockade immunotherapy than PD-1 or PD-L1 expression, as measured by immunohistochemistry (IHC). The distribution of TMB and the subset of patients with high TMB has not been well characterized in the majority of cancer types.MethodsIn this study, we compare TMB measured by a targeted comprehensive genomic profiling (CGP) assay to TMB measured by exome sequencing and simulate the expected variance in TMB when sequencing less than the whole exome. We then describe the distribution of TMB across a diverse cohort of 100,000 cancer cases and test for association between somatic alterations and TMB in over 100 tumor types.ResultsWe demonstrate that measurements of TMB from comprehensive genomic profiling are strongly reflective of measurements from whole exome sequencing and model that below 0.5 Mb the variance in measurement increases significantly. We find that a subset of patients exhibits high TMB across almost all types of cancer, including many rare tumor types, and characterize the relationship between high TMB and microsatellite instability status. We find that TMB increases significantly with age, showing a 2.4-fold difference between age 10 and age 90 years. Finally, we investigate the molecular basis of TMB and identify genes and mutations associated with TMB level. We identify a cluster of somatic mutations in the promoter of the gene PMS2, which occur in 10% of skin cancers and are highly associated with increased TMB.ConclusionsThese results show that a CGP assay targeting ~1.1 Mb of coding genome can accurately assess TMB compared with sequencing the whole exome. Using this method, we find that many disease types have a substantial portion of patients with high TMB who might benefit from immunotherapy. Finally, we identify novel, recurrent promoter mutations in PMS2, which may be another example of regulatory mutations contributing to tumorigenesis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0424-2) contains supplementary material, which is available to authorized users.

As more clinically relevant cancer genes are identified, comprehensive diagnostic approaches are needed to match patients to therapies, raising the challenge of optimization and analytical validation of assays that interrogate millions of bases of cancer genomes altered by multiple mechanisms. Here we describe a test based on massively parallel DNA sequencing to characterize base substitutions, short insertions and deletions (indels), copy number alterations and selected fusions across 287 cancer-related genes from routine formalin-fixed and paraffin-embedded (FFPE) clinical specimens. We implemented a practical validation strategy with reference samples of pooled cell lines that model key determinants of accuracy, including mutant allele frequency, indel length and amplitude of copy change. Test sensitivity achieved was 95–99% across alteration types, with high specificity (positive predictive value >99%). We confirmed accuracy using 249 FFPE cancer specimens characterized by established assays. Application of the test to 2,221 clinical cases revealed clinically actionable alterations in 76% of tumors, three times the number of actionable alterations detected by current diagnostic tests.

Purpose: Molecular classification of breast cancer has been proposed based on gene expression profiles of human tumors. Luminal, basal-like, normal-like, and erbB2+ subgroups were identified and were shown to have different prognoses. The goal of this research was to determine if these different molecular subtypes of breast cancer also respond differently to preoperative chemotherapy. Experimental Design: Fine needle aspirations of 82 breast cancers were obtained before starting preoperative paclitaxel followed by 5-fluorouracil, doxorubicin, and cyclophosphamide chemotherapy. Gene expression profiling was done with Affymetrix U133A microarrays and the previously reported ''breast intrinsic''gene set was used for hierarchical clustering and multidimensional scaling to assign molecular class. Results: The basal-like and erbB2+ subgroups were associated with the highest rates of pathologic complete response (CR), 45% [95% confidence interval (95% CI), 24-68] and 45% (95% CI, 23-68), respectively, whereas the luminal tumors had a pathologic CR rate of 6% (95% CI, 1-21). No pathologic CR was observed among the normal-like cancers (95% CI, 0-31). Molecular class was not independent of conventional cliniocopathologic predictors of response such as estrogen receptor status and nuclear grade. None of the 61genes associated with pathologic CR in the basal-like group were associated with pathologic CR in the erbB2+ group, suggesting that the molecular mechanisms of chemotherapy sensitivity may vary between these two estrogen receptor^negative subtypes. Conclusions: The basal-like and erbB2+ subtypes of breast cancer are more sensitive to paclitaxel-and doxorubicin-containing preoperative chemotherapy than the luminal and normallike cancers.Breast cancer is a clinically heterogeneous disease. Histologically similar tumors may have different prognoses and may respond to therapy differently. It is believed that these differences in clinical behavior are due to molecular differences between histologically similar tumors. DNA microarray technology is ideally suited to reveal such molecular differences. A novel molecular classification of breast cancer based on gene expression profiles was recently proposed (1). The investigators identified a set of stably expressed genes (''intrinsic gene set''; n = 534) that accounted for much of the molecular differences between 42 breast cancers and did hierarchical cluster analysis to identify subgroups of cancers with separate gene expression profiles. Luminal, basal-like, normal-like, and erbB2+ subgroups were identified and were shown to have different prognoses (1 -4). These results were confirmed in follow-up experiments by the same group and others using larger numbers of cases. The basal-like (mostly estrogen receptor negative) and erbB2+ (mostly HER-2 amplified and estrogen receptor negative) subgroups had the shortest relapse-free and overall survival, whereas the luminal-type (estrogen receptorpositive) tumors had a more favorable clinical outcome (2 -4).

is the most common oncogenic driver in lung adenocarcinoma (LUAC). We previously reported that (KL) or (KP) comutations define distinct subgroups of -mutant LUAC. Here, we examine the efficacy of PD-1 inhibitors in these subgroups. Objective response rates to PD-1 blockade differed significantly among KL (7.4%), KP (35.7%), and K-only (28.6%) subgroups ( < 0.001) in the Stand Up To Cancer (SU2C) cohort (174 patients) with -mutant LUAC and in patients treated with nivolumab in the CheckMate-057 phase III trial (0% vs. 57.1% vs. 18.2%; = 0.047). In the SU2C cohort, KL LUAC exhibited shorter progression-free ( < 0.001) and overall ( = 0.0015) survival compared with ; LUAC. Among 924 LUACs, alterations were the only marker significantly associated with PD-L1 negativity in TMB LUAC. The impact of alterations on clinical outcomes with PD-1/PD-L1 inhibitors extended to PD-L1-positive non-small cell lung cancer. In-mutant murine LUAC models, loss promoted PD-1/PD-L1 inhibitor resistance, suggesting a causal role. Our results identify alterations as a major driver of primary resistance to PD-1 blockade in -mutant LUAC. This work identifies alterations as the most prevalent genomic driver of primary resistance to PD-1 axis inhibitors in-mutant lung adenocarcinoma. Genomic profiling may enhance the predictive utility of PD-L1 expression and tumor mutation burden and facilitate establishment of personalized combination immunotherapy approaches for genomically defined LUAC subsets. .