Comprehensive Analysis of Fraction of Genome Altered in Prostate Cancer Treatment

Dear Editor,

We read with great interest the recent article by Yukihiro et al. [1], “Comprehensive Analysis of Fraction of Genome Altered in Prostate Cancer Treatment” published in The Prostate. The authors are to be commended for conducting a timely and comprehensive evaluation of the fraction of genome altered (FGA) as a potential biomarker of tumor aggressiveness and treatment resistance. By leveraging multiple large public genomic‐clinical datasets, the study provides valuable insights into how genomic instability may relate to clinical outcomes and highlights the promise of FGA in prostate cancer prognostication. Nevertheless, there are several additional limitations worth discussing.

First, heterogeneity in data collection, sequencing platforms, and clinical annotation across databases (MSK, SU2C/PCF, GENIE) may introduce batch effects and confounding, potentially biasing associations between FGA and survival outcomes. For instance, Pugh et al. [2] demonstrated that Project GENIE exhibits substantial variability in sequencing methods and annotation across contributing institutions. Second, the assumption that high FGA mechanistically drives aggressiveness or therapy resistance is unproven without functional experiments. Observed correlations may reflect downstream genomic chaos rather than causal drivers. For example, Beltran et al. [3] have shown that neuroendocrine prostate cancers harbor functional vulnerabilities beyond copy number changes. Third, FGA alone likely underrepresents the full genomic complexity of tumors. Without integrating tumor mutational burden (TMB), microsatellite instability (MSI), methylation, or transcriptomic features, its prognostic strength may be overstated. Spurr et al. [4] illustrated how tumor aneuploidy interacts with TMB to predict outcomes across cancers. Fourth, the lack of ancestry‐stratified analysis is notable. Prostate cancer exhibits genomic and outcome disparities across racial groups ignoring ancestry raises concerns about generalizability and bias. For instance, Stopsack et al. [5] found that patients of African ancestry carry distinct burdens of genomic alteration. Fifth, dichotomizing FGA into “low” versus “high” using a fixed cutoff may oversimplify continuous biological variation. This approach risks misclassification. Hieronymus et al. [6] showed that optimal prognostic thresholds of copy number burden differ across patient subgroups. Future research should employ harmonized, prospectively collected cohorts with uniform sequencing and clinical annotation, use experimental models (e.g., CRISPR‐based copy number manipulation in organoids or xenografts) to probe causality, integrate multiomic data (FGA, TMB, MSI, transcriptomics) for composite biomarkers, stratify analyses by ancestry using diverse genomic cohorts and apply data‐driven methods (machine learning, ROC curve optimization, continuous modeling) rather than rigid cutoffs to refine prognostic thresholds.

In conclusion, while Yukihiro et al. make an important contribution by demonstrating associations between FGA and clinical outcome, addressing methodological heterogeneity, functional validation, multiomic integration, ancestry analysis, and threshold refinement will be critical to fully establish the biomarker's clinical utility in prostate cancer.

Author Contributions

All the authors meet the ICMJE authorship criteria and have made significant and equal contributions to this manuscript. All authors approved the final version and agree to be accountable for all aspects of the work, ensuring the accuracy and integrity of the data and interpretation. All authors have read and approved the final version of the manuscript. They took complete responsibility for the data's integrity and the accuracy of the data analysis.

Acknowledgments

The authors received no specific funding for this work.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Transparency Statement

The authors affirm that this manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted, and that any discrepancies from the study as planned (and if relevant, registered) have been explained.

Data Availability Statement

Data sharing does not apply to this article as no datasets were generated during the current study; all data were sourced from published literature.