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Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions
Open accessAbstract
Small cell lung cancer (SCLC) is characterized by rapid growth and high metastatic capacity. It has strong epidemiologic and biologic links to tobacco carcinogens. Although the majority of SCLCs exhibit neuroendocrine features, an important subset of tumors lacks these properties. Genomic profiling of SCLC reveals genetic instability, almost universal inactivation of the tumor suppressor genes TP53 and RB1 , and a high mutation burden. Because of early metastasis, only a small fraction of patients are amenable to curative‐intent lung resection, and these individuals require adjuvant platinum‐etoposide chemotherapy. Therefore, the vast majority of patients are currently being treated with chemoradiation with or without immunotherapy. In patients with disease confined to the chest, standard therapy includes thoracic radiotherapy and concurrent platinum‐etoposide chemotherapy. Patients with metastatic (extensive‐stage) disease are treated with a combination of platinum‐etoposide chemotherapy plus immunotherapy with an anti‐programmed death‐ligand 1 monoclonal antibody. Although SCLC is initially very responsive to platinum‐based chemotherapy, these responses are transient because of the development of drug resistance. In recent years, the authors have witnessed an accelerating pace of biologic insights into the disease, leading to the redefinition of the SCLC classification scheme. This emerging knowledge of SCLC molecular subtypes has the potential to define unique therapeutic vulnerabilities. Synthesizing these new discoveries with the current knowledge of SCLC biology and clinical management may lead to unprecedented advances in SCLC patient care. Here, the authors present an overview of multimodal clinical approaches in SCLC, with a special focus on illuminating how recent advancements in SCLC research could accelerate clinical development.
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Cited by
Khan, S., Cao, L., Wiegand, J., Zhang, P., Zajac-Kaye, M., Kaye, F. J., Zheng, G., & Zhou, D. (2024) · Cells · doi:10.3390/cells13060528 · Cited by 34
Metadata
- Authors
- Megyesfalvi, Z., Gay, C. M., Popper, H., Pirker, R., Ostoros, G., Heeke, S., Lang, C., Hoetzenecker, K., Schwendenwein, A., Boettiger, K., Bunn, P. A., Renyi‐Vamos, F., Schelch, K., Prosch, H., Byers, L. A., Hirsch, F. R., & Dome, B.
- Venue
- CA: A Cancer Journal for Clinicians
- Publisher
- Wiley
- Published
- 2023-06-17
- Volume
- 73
- Issue
- 6
- Pages
- 620-652
- ISSN
- 0007-9235 (ISSN-L)
- 1542-4863
- OpenAlex
- W4381094907
- Cited by
- 369
- References
- 233
- FWCI
- 38.20
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.