Multi-omics alleviates the limitations of panel-sequencing for cancer drug response prediction

Baranovskii, Artem; Gunduz, Irem B; Franke, Vedran; Uyar, Bora; Akalin, Altuna

doi:10.1101/2022.06.15.496249

Cited by 1 publication

(1 citation statement)

References 29 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, as disease development involves complex interactions and alterations at multiple levels such as genome, epigenome, transcriptome, proteome, and metabolome, multi- omics profilings can describe biological processes comprehensively and systematically (Karczewski and Snyder, 2018). As shown in (Baranovskii et al, 2022), utilizing the transcriptome on top of gene panel features substantially improves drug response prediction performance in cancer. Therefore, biomarkers developed using multi- omics data can more accurately capture inter-patient heterogeneity than single- omics biomarkers (Olivier et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multi-omics biomarkers aid prostate cancer prognostication

Omar

Benedetti

et al. 2022

Preprint

View full text Add to dashboard Cite

Effective biomarkers and diagnostic tools are urgently needed in clinical settings for improved management of prostate cancer patients, especially to reduce over-treatment of indolent tumors and for early identification of aggressive disease. Gene expression signatures are currently the "gold standard" to provide guide clinical decision, however their clinical utility and interpretability is questionable. Multi-modal molecular profiling provides an holistic approach to systematically unravel the biological complexity underlying cancer pathogenesis, hence biomarkers developed using such an integrated approach hold the potential to more accurately capture cancer-driving alterations than signatures based on a single omics modality. Currently, however, robust and reproducible multi-omics biomarkers are still lacking for prostate cancer. In this study, we analyzed transcriptomics and metabolomics profiles jointly in a prostate cancer cohort and identified two prognostic signatures with high statistical powers (signature1: EGLN3, succinate, trans-4-hydroxyprolin; and signature2: IL6, SLC22A2, histamine). Our approach leveraged a priori biological knowledge of the cellular metabolism and gene circuitry, enabling the identification of dysregulated network modules. Functional bioinformatics analyses suggest that these signatures can capture relevant molecular alterations in prostate cancer tissues, including dysregulations of cellular signaling, cell cycle progression, and immune system modulation, stratifying patients in distinct risk groups. Next, we trained two gene expression signatures as a proxy for the multi-omics ones, extending our investigation to publicly available data, further confirming their prognostic values in independent patient cohorts. In summary, the analysis of multi-modal molecular grounded in cellular network biology represents a promising approach for the development of robust prognostic biomarkers of detecting and discriminating high grade disease.

show abstract

Section: Introductionmentioning

confidence: 99%