Omics-based molecular classifications empowering in precision oncology.

BACKGROUND: In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine. CONCLUSION: This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma.

Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.

Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients.