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Updated: Jul 15, 2025
06:46
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
297
Evolution of predictive risk factor analysis for chemotherapy-related toxicity
Daniel L Hertz1, Maryam B Lustberg2, Stephen Sonis3
1Department of Clinical Pharmacy, University of Michigan College of Pharmacy, 428 Church St., Room 3054 College of Pharmacy, Ann Arbor, MI, 48109-1065, USA. DLHertz@med.umich.edu.
Summary
Predicting cancer treatment toxicity requires looking beyond germline genomics. An integrated biomarker approach, combining various "omic" and non-omic factors, offers a promising path for personalized oncology and minimizing patient side effects.
Area of Science:
- Biomarker discovery in oncology
- Pharmacogenetics and treatment toxicity
Background:
- Patient response to cancer treatment varies significantly due to unknown factors.
- Germline genomics initially promised personalized treatment but yielded limited success in predicting toxicity.
- Translating pharmacogenetic predictors into clinical practice remains a significant challenge.
Purpose of the Study:
- To highlight the limitations of current toxicity predictor discovery and translation.
- To propose an integrated biomarker discovery approach for predicting treatment-related toxicity.
- To discuss opportunities and challenges in utilizing multi-omic and non-omic factors for personalized oncology.
Main Methods:
- Review of limited success in clinical and pharmacogenetic toxicity predictor discovery and translation.
- Illustration using taxane-induced peripheral neuropathy as a case study.
- Discussion of non-genomic (metabolomic, lipidomic, transcriptomic, proteomic, microbiomic, medical, behavioral, environmental) and integrated biomarker opportunities.
Main Results:
- Limited success in discovering and translating germline pharmacogenetic predictors for treatment toxicity.
- Taxane-induced peripheral neuropathy exemplifies common, debilitating treatment side effects.
- Non-genomic and integrated biomarkers show potential for improved toxicity prediction.
Conclusions:
- Predicting cancer treatment toxicity necessitates moving beyond germline genomics.
- An integrated biomarker approach, considering diverse factors, may enhance toxicity prediction.
- This approach could advance precision oncology, maximizing treatment benefits while minimizing toxicity.
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