Molecular and clinical characterization of a claudin-low subtype of gastric cancer

Purpose Claudin-low molecular subtypes have been identified in breast and bladder cancers and are characterized by low expression of claudins, enrichment for epithelial-to-mesenchymal transition (EMT), and tumor-initiating cell (TIC) features. We evaluated whether the claudin-low subtype also exists in gastric cancer. Materials and Methods Four hundred fifteen tumors from The Cancer Genome Atlas (TCGA) gastric cancer mRNA data set were clustered on the claudin, EMT, and TIC gene sets to identify claudin-low tumors. We derived a 24-gene predictor that classifies gastric cancer into claudin-low and non-claudin-low subtypes. This predictor was validated with the Asian Cancer Research Group(ACRG)data set. We characterized molecular and clinical features of claudin-low tumors. Results We identified 46 tumors that had consensus enrichment for claudin-low features in TCGA data set. Claudin-low tumors were most commonly diffuse histologic type (82%) and originally classified as TCGA genomically stable(GS)subtype (78%). Compared with GS subtype, claudin-low subtype had significant activation in Rho family of GTPases signaling, which appears to play a key role in its EMT and TIC properties. In the ACRG data set, 28 of 300 samples were classified as claudin-low tumors by the 24-gene predictor and were phenotypically similar to the initially derived claudin-low tumors. Clinically, claudin-low subtype had the worst overall survival. Of note, the hazard ratios that compared claudin-low versus GS subtype were 2.10 (95% CI, 1.07 to 4.11) in TCGA and 2.32 (95% CI, 1.18 to 4.55) in the ACRG cohorts, with adjustment for age and pathologic stage. Conclusion We identified a gastric claudin-low subtype that carries a poor prognosis likely related to therapeutic resistance as a result of its EMT and TIC phenotypes

Neoadjuvant pazopanib and molecular analysis of tissue response in renal cell carcinoma

BACKGROUND. Surgery remains the frontline therapy for patients with localized clear cell renal cell carcinoma (ccRCC); however, 20%–40% recur. Angiogenesis inhibitors have improved survival in metastatic patients and may result in responses in the neoadjuvant setting. The impact of these agents on the tumor genetic heterogeneity or the immune milieu is largely unknown. This phase II study was designed to evaluate safety, response, and effect on tumor tissue of neoadjuvant pazopanib. METHODS. ccRCC patients with localized disease received pazopanib (800 mg daily; median 8 weeks), followed by nephrectomy. Five tumors were examined for mutations by whole exome sequencing from samples collected before therapy and at nephrectomy. These samples underwent RNA sequencing; 17 samples were available for posttreatment assessment. RESULTS. Twenty-one patients were enrolled. The overall response rate was 8 of 21 (38%). No patients with progressive disease. At 1-year, response-free survival and overall survival was 83% and 89%, respectively. The most frequent grade 3 toxicity was hypertension (33%, 7 of 21). Sequencing revealed strong concordance between pre- and posttreatment samples within individual tumors, suggesting tumors harbor stable core profiles. However, a reduction in private mutations followed treatment, suggesting a selective process favoring enrichment of driver mutations. CONCLUSION. Neoadjuvant pazopanib is safe and active in ccRCC. Future genomic analyses may enable the segregation of driver and passenger mutations. Furthermore, tumor infiltrating immune cells persist during therapy, suggesting that pazopanib can be combined with immune checkpoint inhibitors without dampening the immune response. FUNDING. Support was provided by Novartis and GlaxoSmithKline as part of an investigator-initiated study

Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma

Human endogenous retroviruses (hERVs) are remnants of exogenous retroviruses that have integrated into the genome throughout evolution. We developed a computational workflow, hervQuant, which identified more than 3,000 transcriptionally active hERVs within The Cancer Genome Atlas (TCGA) pan-cancer RNA-Seq database. hERV expression was associated with clinical prognosis in several tumor types, most significantly clear cell renal cell carcinoma (ccRCC). We explored two mechanisms by which hERV expression may influence the tumor immune microenvironment in ccRCC: (i) RIG-I-like signaling and (ii) retroviral antigen activation of adaptive immunity. We demonstrated the ability of hERV signatures associated with these immune mechanisms to predict patient survival in ccRCC, independent of clinical staging and molecular subtyping. We identified potential tumor-specific hERV epitopes with evidence of translational activity through the use of a ccRCC ribosome profiling (Ribo-Seq) dataset, validated their ability to bind HLA in vitro, and identified the presence of MHC tetramer-positive T cells against predicted epitopes. hERV sequences identified through this screening approach were significantly more highly expressed in ccRCC tumors responsive to treatment with programmed death receptor 1 (PD-1) inhibition. hervQuant provides insights into the role of hERVs within the tumor immune microenvironment, as well as evidence that hERV expression could serve as a biomarker for patient prognosis and response to immunotherapy. © 2018 American Society for Clinical Investigation. All rights reserved

The immune landscape of cancer

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes—wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant—characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field

The Immune Landscape of Cancer

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes\u2014wound healing, IFN-\u3b3 dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-\u3b2 dominant\u2014characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field. Thorsson et al. present immunogenomics analyses of more than 10,000 tumors, identifying six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis. This work provides a resource for understanding tumor-immune interactions, with implications for identifying ways to advance research on immunotherapy

The Immune Landscape of Cancer (Immunity (2018) 48 (812–832), (S1074-7613(18)30121-3), (10.1016/j.immuni.2018.03.023))

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.