Gastric cancer, a leading cause of cancer-related deaths, is a heterogeneous disease. We aim to establish clinically relevant molecular subtypes that would encompass this heterogeneity and provide useful clinical information. We use gene expression data to describe four molecular subtypes linked to distinct patterns of molecular alterations, disease progression and prognosis. The mesenchymal-like type includes diffuse-subtype tumors with the worst prognosis, the tendency to occur at an earlier age and the highest recurrence frequency (63%) of the four subtypes. Microsatellite-unstable tumors are hyper-mutated intestinal-subtype tumors occurring in the antrum; these have the best overall prognosis and the lowest frequency of recurrence (22%) of the four subtypes. The tumor protein 53 (TP53)-active and TP53-inactive types include patients with intermediate prognosis and recurrence rates (with respect to the other two subtypes), with the TP53-active group showing better prognosis. We describe key molecular alterations in each of the four subtypes using targeted sequencing and genome-wide copy number microarrays. We validate these subtypes in independent cohorts in order to provide a consistent and unified framework for further clinical and preclinical translational research.
INTRODUCTION: Immunotherapy targeting the programmed cell death protein–1 (PD-1) axis elicits durable antitumor responses in multiple cancer types. However, clinical responses vary, and biomarkers predictive of response may help to identify patients who will derive the greatest therapeutic benefit. Clinically validated biomarkers predictive of response to the anti–PD-1 monoclonal antibody pembrolizumab include PD-1 ligand 1 (PD-L1) expression in specific cancers and high microsatellite instability (MSI-H) regardless of tumor type. Tumor mutational burden (TMB) and T cell–inflamed gene expression profile (GEP) are emerging predictive biomarkers for pembrolizumab. Both PD-L1 and GEP are inflammatory biomarkers indicative of a T cell–inflamed tumor microenvironment (TME), whereas TMB and MSI-H are indirect measures of tumor antigenicity generated by somatic tumor mutations. However, the relationship between these two categories of biomarkers is not well characterized. RATIONALE: This study assessed the potential for TMB and a T cell–inflamed GEP to jointly predict clinical response to pembrolizumab in >300 patient samples with advanced solid tumors and melanoma across 22 tumor types from four KEYNOTE clinical trials. To assess the individual and joint clinical utility of TMB and GEP, patients were stratified in four biomarker–defined clinical response groups [GEP low and TMB low (GEPlo TMBlo), GEP low and TMB high (GEPlo TMBhi), GEPhi TMBlo, and GEPhi TMBhi] based on predefined cutoffs for TMB and GEP. These patient–defined biomarker groups were further used to guide transcriptome and exome analyses of tumors in a large molecular database [The Cancer Genome Atlas (TCGA)] (n = 6384 tumors) to identify targetable patterns of biology that may modulate response and resistance. RESULTS: TMB and GEP exhibited only modest correlation and were independently predictive of response across the KEYNOTE clinical datasets. We found that objective response rates were strongest in patients with GEPhi TMBhi (37 to 57%), moderate in those with GEPhi TMBlo (12 to 35%) and GEPlo TMBhi (11 to 42%), and reduced or absent in those with GEPlo TMBlo (0 to 9%) (see the figure). Additionally, longer progression–free survival times were seen in patients with higher levels of both TMB and GEP. Findings were comparable when TMB and PD-L1 expression were jointly assessed. Within TCGA database, GEP and TMB again had a low correlation, demonstrating the potential to jointly stratify transcriptomic and genomic features across cancer types. Specific gene expression patterns reflective of TME biology showed significant associations with TMB, GEP, or both. In particular, gene set enrichment analysis identified proliferative and stromal, myeloid, and vascular biology corresponding to specific TMB-defined subgroups within GEPhi tumors. In TMBhi tumors, indication-dependent somatic DNA alterations in key cancer driver genes showed a strong negative association with GEP. CONCLUSION: This analysis shows that TMB and inflammatory biomarkers (T cell–in...
We hypothesized that immune gene-related signatures would predict the presence of unique histological features of lymphoid cell infiltrates in colorectal carcinoma (CRC) that correlate with clinical parameters. Metagene analysis with gene chip technology was performed on 326 CRCs, which were then sorted by low versus high gene scores. Microscopically, CRCs with a high gene score revealed a marked host immune response organized, remarkably, as lymphoid follicles. Proliferation involved both B and T cells. In every case, the presence of CD79a(+) B-cell precursors was identified, suggesting that the lymphoid follicles represent newly formed, ectopic lymph node-like structures. CD21(+) dendritic cells were present within the follicular germinal centers, and CD3(+) T cells were localized mainly in the parafollicular cortex zone surrounding the B-cell area of the follicles. A strong correlation between a 12-chemokine gene subset of the molecular profile and the presence of ectopic lymph node-like structures was associated with better patient survival independent of tumor staging, site location, microsatellite instability or stability, and patient treatment. These findings suggest beneficial, intratumoral immune cell priming and raise the possibility of immunotherapy intervention decisions based on molecular signatures that can identify the presence of tumor-localized, ectopic lymph node-like structures.
BackgroundColon cancer has been classically described by clinicopathologic features that permit the prediction of outcome only after surgical resection and staging.MethodsWe performed an unsupervised analysis of microarray data from 326 colon cancers to identify the first principal component (PC1) of the most variable set of genes. PC1 deciphered two primary, intrinsic molecular subtypes of colon cancer that predicted disease progression and recurrence.ResultsHere we report that the most dominant pattern of intrinsic gene expression in colon cancer (PC1) was tightly correlated (Pearson R = 0.92, P < 10-135) with the EMT signature-- both in gene identity and directionality. In a global micro-RNA screen, we further identified the most anti-correlated microRNA with PC1 as MiR200, known to regulate EMT.ConclusionsThese data demonstrate that the biology underpinning the native, molecular classification of human colon cancer--previously thought to be highly heterogeneous-- was clarified through the lens of comprehensive transcriptome analysis.
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