ArticleCancer research2017
Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity.
Article in Cancer research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed, 107 citations in OpenAlex.
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- Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways.NPJ systems biology and applications · 2023Article
- Toward a systems-level probing of tumor clonality.iScience · 2023Review
- Involvement of Epithelial-Mesenchymal Transition Genes in Small Cell Lung Cancer Phenotypic Plasticity.Cancers · 2023Article
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- NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity.Genome biology · 2022Article
- Multivalent state transitions shape the intratumoral composition of small cell lung carcinoma.Science advances · 2022Article
- Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer.Cell systems · 2022Article
- Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.International journal of molecular sciences · 2022Review
- Intrinsic and acquired drug resistance to LSD1 inhibitors in small cell lung cancer occurs through a TEAD4-driven transcriptional state.Molecular oncology · 2022Article
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- TIAM1-RAC1 promote small-cell lung cancer cell survival through antagonizing Nur77-induced BCL2 conformational change.Cell reports · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Small cell lung cancer (SCLC) is a devastating disease due to its propensity for early invasion and refractory relapse after initial treatment response. Although these aggressive traits have been associated with phenotypic heterogeneity, our understanding of this association remains incomplete. To fill this knowledge gap, we inferred a set of 33 transcription factors (TF) associated with gene signatures of the known neuroendocrine/epithelial (NE) and non-neuroendocrine/mesenchymal-like (ML) SCLC phenotypes. The topology of this SCLC TF network was derived from prior knowledge and was simulated using Boolean modeling. These simulations predicted that the network settles into attractors, or TF expression patterns, that correlate with NE or ML phenotypes, suggesting that TF network dynamics underlie the emergence of heterogeneous SCLC phenotypes. However, several cell lines and patient tumor specimens failed to correlate with either the NE or ML attractors. By flow cytometry, single cells within these cell lines simultaneously expressed surface markers of both NE and ML differentiation, confirming the existence of a "hybrid" phenotype. Upon exposure to standard-of-care cytotoxic drugs or epigenetic modifiers, NE and ML cell populations converged toward the hybrid state, suggesting possible escape from treatment. Our findings indicate that SCLC phenotypic heterogeneity can be specified dynamically by attractor states of a master regulatory TF network. Thus, SCLC heterogeneity may be best understood as states within an epigenetic landscape. Understanding phenotypic transitions within this landscape may provide insights to clinical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.