Dataset Information

Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities.

ABSTRACT: Despite molecular and clinical heterogeneity, small cell lung cancer (SCLC) is treated as a single entity with predictably poor results. Using tumor expression data and non-negative matrix factorization, we identify four SCLC subtypes defined largely by differential expression of transcription factors ASCL1, NEUROD1, and POU2F3 or low expression of all three transcription factor signatures accompanied by an Inflamed gene signature (SCLC-A, N, P, and I, respectively). SCLC-I experiences the greatest benefit from the addition of immunotherapy to chemotherapy, while the other subtypes each have distinct vulnerabilities, including to inhibitors of PARP, Aurora kinases, or BCL-2. Cisplatin treatment of SCLC-A patient-derived xenografts induces intratumoral shifts toward SCLC-I, supporting subtype switching as a mechanism of acquired platinum resistance. We propose that matching baseline tumor subtype to therapy, as well as manipulating subtype switching on therapy, may enhance depth and duration of response for SCLC patients.

SUBMITTER: Gay CM

PROVIDER: S-EPMC8143037 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities.

Gay Carl M CM Stewart C Allison CA Park Elizabeth M EM Diao Lixia L Groves Sarah M SM Heeke Simon S Nabet Barzin Y BY Fujimoto Junya J Solis Luisa M LM Lu Wei W Xi Yuanxin Y Cardnell Robert J RJ Wang Qi Q Fabbri Giulia G Cargill Kasey R KR Vokes Natalie I NI Ramkumar Kavya K Zhang Bingnan B Della Corte Carminia M CM Robson Paul P Swisher Stephen G SG Roth Jack A JA Glisson Bonnie S BS Shames David S DS Wistuba Ignacio I II Wang Jing J Quaranta Vito V Minna John J Heymach John V JV Byers Lauren Averett LA

Cancer cell 20210121 3

Despite molecular and clinical heterogeneity, small cell lung cancer (SCLC) is treated as a single entity with predictably poor results. Using tumor expression data and non-negative matrix factorization, we identify four SCLC subtypes defined largely by differential expression of transcription factors ASCL1, NEUROD1, and POU2F3 or low expression of all three transcription factor signatures accompanied by an Inflamed gene signature (SCLC-A, N, P, and I, respectively). SCLC-I experiences the great ...[more]

PMID: 33482121

Similar Datasets

Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Project description:The inhibitory epidermal growth factor receptor (EGFR) antibody, cetuximab, is an approved therapy for head and neck squamous cell carcinoma (HNSCC). Despite tumor response observed in some HNSCC patients, cetuximab alone or combined with radio- or chemotherapy fails to yield long-term control or cures. We hypothesize that a flexible receptor tyrosine kinase coactivation signaling network supports HNSCC survival in the setting of EGFR blockade, and that drugs disrupting this network will provide superior tumor control when combined with EGFR inhibitors. In this work, we submitted EGFR-dependent HNSCC cell lines to RNA interference-based functional genomics screens to identify, in an unbiased fashion, essential protein kinases for growth and survival as well as synthetic lethal targets for combined inhibition with EGFR antagonists. Mechanistic target of rapamycin kinase (MTOR) and erythroblastosis oncogene B (ERBB)3 were identified as high-ranking essential kinase hits in the HNSCC cell lines. MTOR dependency was confirmed by distinct short hairpin RNAs (shRNAs) and high sensitivity of the cell lines to AZD8055, whereas ERBB3 dependency was validated by shRNA-mediated silencing. Furthermore, a synthetic lethal kinome shRNA screen with a pan-ERBB inhibitor, AZD8931, identified multiple components of the extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase pathway, consistent with ERK reactivation and/or incomplete ERK pathway inhibition in response to EGFR inhibitor monotherapy. As validation, distinct mitogen-activated protein kinase kinase (MEK) inhibitors yielded synergistic growth inhibition when combined with the EGFR inhibitors, gefitinib and AZD8931. The findings identify ERBB3 and MTOR as important pharmacological vulnerabilities in HNSCC and support combining MEK and EGFR inhibitors to enhance clinical efficacy in HNSCC. SIGNIFICANCE STATEMENT: Many cancers are driven by nonmutated receptor tyrosine kinase coactivation networks that defy full inhibition with single targeted drugs. This study identifies erythroblastosis oncogene B (ERBB)3 as an essential protein kinase in epidermal growth factor receptor-dependent head and neck squamous cell cancer (HNSCC) cell lines and a synthetic lethal interaction with the extracellular signal-regulated kinase mitogen-activated protein kinase pathway that provides a rationale for combining pan-ERBB and mitogen-activated protein kinase inhibitors as a therapeutic approach in subsets of HNSCC.

Dataset Information

Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities.

Publications

Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets