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Selection bias due to delayed comprehensive genomic profiling in Japan
Taichi Tamura1, Masachika Ikegami2, Yusuke Kanemasa1,3
1Department of Medical Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, Tokyo, Japan.
Cancer Science
|November 12, 2022
Summary
Length bias in cancer genomic profiling impacts survival analysis. Adjusting for this bias using a novel simulation model reveals crucial prognostic factors and improves the interpretation of real-world data for advanced cancer patients.
Area of Science:
- Oncology
- Genomics
- Biostatistics
Background:
- Comprehensive genomic profiling (CGP) in advanced cancer patients in Japan is often delayed until after other treatments fail.
- This delay introduces length bias, a selection bias that skews survival analysis and hinders accurate prognostic assessment.
- Existing methods for adjusting length bias in CGP data are insufficient.
Purpose of the Study:
- To develop and validate a simulation-based model for adjusting length bias in overall survival analysis for advanced cancer patients undergoing CGP.
- To assess the impact of length bias on survival outcomes across various cancer subtypes.
- To identify prognostic oncogenic mutations and evaluate treatment efficacy after adjusting for length bias.
Main Methods:
- Utilized clinicogenomic data from 8813 advanced cancer patients in Japan (June 2019-April 2022).
- Employed a simulation-based model and conditional Kendall τ statistics to estimate and adjust for length bias.
- Analyzed overall survival, prognostic mutations, and treatment outcomes post-adjustment.
Main Results:
- Significant positive length bias was observed in 13 of 15 cancer subtypes, indicating a worse prognosis for early-tested patients.
- Adjusted median overall survival times were 937 days (colorectal), 1225 days (breast), and 585 days (pancreatic cancer).
- Identified 12 tumor-specific oncogenic mutations associated with poor survival post-adjustment; no survival difference found between FOLFIRINOX and gemcitabine/nab-paclitaxel for pancreatic cancer.
Conclusions:
- Adjusting for length bias is critical for accurate survival analysis using real-world clinicogenomic data in advanced cancer.
- The developed model enables better assessment of prognostic relevance of mutations and treatments.
- This approach enhances the utility of CGP data for improving patient outcomes.
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