Background: Concurrent chemoradiotherapy (CRT) remains the primary treatment for limited stage (LS) small cell lung cancer (SCLC). Although most patients respond, the majority relapse and die from the disease, and treatment-related toxicity is common. Molecular subtyping has been extensively investigated in SCLC, but its clinical relevance remains uncertain, and most studies have focused on systemic treatment of extensive-stage SCLC. We analysed baseline tumor samples from a subset of participants (n = 77) in a randomized trial (n = 170) comparing high-dose (60 Gy/40 fractions) with standard-dose (45 Gy/30 fractions) twice-daily thoracic radiotherapy given concurrently with platinum-etoposide chemotherapy for LS SCLC, aiming to characterize molecular subtypes of LS SCLC and evaluate their association with time to progression (TTP) and overall survival (OS). Methods: Molecular subtypes were defined in 81 samples using non-negative matrix factorization (NMF) on gene expression data, supported by differential expression and known SCLC markers. Results: Inflamed subtypes were associated with better treatment outcomes. Univariable analysis indicate that SCLC-I-NE (inflamed neuroendocrine) was associated with longer TTP (HR 0.10, 95 % CI 0.01–0.80, p = 0.03), and SCLC-I-nNE (inflamed non-neuroendocrine) was associated with improved OS (HR 0.16, 95 % CI 0.03–0.77, p = 0.02). By contrast, SCLC-N (NEUROD1-driven) was consistently associated with both shorter TTP (HR 6.90, 95 % CI 1.49–31.92, p = 0.01) and OS (HR 4.89, 95 % CI 1.24–19.21, p = 0.02). Conclusions: Among patients receiving chemoradiotherapy for LS SCLC, there were indications of improved outcomes among those with tumors of inflamed subtypes, whereas SCLC-N was linked to unfavorable TTP and OS.
@article{ro2026,title={Subtyping limited stage small cell lung cancer (LS SCLC) reveals inflammatory subtypes with improved chemoradiotherapy outcomes},author={Oliveira, Rafael and Halvorsen, Tarje and Killingberg, Kristin and Emdal, Elisabeth Fritzke and Dale, Vibeke Grotnes and Grønberg, Bjørn Henning and Sætrom, Pål},journal={Lung Cancer},volume={221},pages={109606},year={2026},month=nov,doi={10.1016/j.lungcan.2026.109606},url={https://doi.org/10.1016/j.lungcan.2026.109606}}
To be published: Genomic Radiosensitivity Reveal Subtype Differences and Predict Outcomes in SCLC
Rafael Oliveira, Tarje Halvorsen, Kristin Killingberg, and 2 more authors
Radiomics and radiogenomics are still new fields to be explored in oncology, although there are several platforms and tools already developed, or in development. This review aimed to identify the current state of the art of radiomics and radiogenomics in terms of platforms and tools integrating machine learning techniques to support the analysis of medical imaging studies of oncological patients. A systematic literature search was performed, and 19 studies were included in the review. These studies were published between 2016 and 2022 and considered several platforms and tools integrating machine learning techniques for the analysis of medical imaging studies of oncological patients together with molecular and clinical endpoints. However, the included articles present scarce evidence related to the translation of the reported platforms and tools to the clinical practice.
@article{ro,title={Radiomics and Radiogenomics Platforms Integrating Machine Learning Techniques: A Review},author={Oliveira, Rafael and Martinho, Beatriz},year={2024},month=feb,journal={Information Systems and Technologies},doi={10.1007/978-3-031-45648-0_42},url={https://link.springer.com/chapter/10.1007/978-3-031-45648-0_42},publisher={Information Systems and Technologies,}}
