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Background <p>Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer (LARC), yet clinically validated biomarkers for predicting response remain lacking. This study aimed to identify candidate molecular events associated with nCRT response and to develop a pretreatment prediction framework integrating genomic and pathological information.</p> Methods <p>Whole-exome sequencing (WES) was performed on pretreatment tumors from 67 patients with LARC, and an additional 22 published WES cases were integrated to compare genomic differences between responders (R) and nonresponders (NR). Using histopathological whole-slide images (WSIs; <i>n</i> = 106) and genome-derived features, a weakly supervised, multimodal deep learning fusion model was developed to predict nCRT response. Multiomics profiling was used for exploratory pathway characterization, and functional assays were conducted in colorectal cancer cell lines and mouse models harboring <i>KRAS</i><sup>G12V</sup> or <i>KRAS</i><sup>A146T</sup>.</p> Results <p>WES identified 41 response-associated hotspot codon events. <i>KRAS</i><sup>G12V</sup> and <i>KRAS</i><sup>A146T</sup> were detected in the NR group in this cohort and were directionally aligned with poor response, indicating an association with nCRT resistance. Because these events are low-frequency alterations, and the study is a single-center retrospective cohort with limited numbers of carriers, multivariable adjustment for key covariates (including stage and T/N status) was not feasible; thus, these findings should be interpreted as exploratory candidate signals. The multimodal fusion model showed good discrimination within the cohort (AUC = 0.882). Mechanistically, exploratory multiomics analyses and orthogonal functional assays were consistent with <i>KRAS</i> variants being associated with altered DNA damage repair signaling and increased repair capacity, with the functional assays providing the main support for this interpretation.</p> Conclusions <p>The proposed genome–pathology fusion model provides a research-oriented framework for pretreatment prediction and risk stratification of nCRT response in LARC. <i>KRAS</i><sup>G12V</sup> and <i>KRAS</i><sup>A146T</sup> are presented as candidate molecular events aligned with poor response, but their independent predictive value and the clinical usability of the model require validation in larger, multicenter prospective cohorts that include external WSI data, together with systematic evaluation of thresholding and calibration before clinical translation.</p> Graphical abstract <p></p>

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KRASG12V/A146T mutations are associated with nCRT resistance via enhanced DNA double-strand break repair and support a deep learning prediction framework in LARC

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Hengchang Liu,
  • Dechao Bu,
  • Guanhua Yu,
  • Ran Wei,
  • Hui Jin,
  • Yixiao Liu,
  • Xu Guan,
  • Zhixun Zhao,
  • Haipeng Chen,
  • Yi Zhao,
  • Zheng Jiang

摘要

Background

Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer (LARC), yet clinically validated biomarkers for predicting response remain lacking. This study aimed to identify candidate molecular events associated with nCRT response and to develop a pretreatment prediction framework integrating genomic and pathological information.

Methods

Whole-exome sequencing (WES) was performed on pretreatment tumors from 67 patients with LARC, and an additional 22 published WES cases were integrated to compare genomic differences between responders (R) and nonresponders (NR). Using histopathological whole-slide images (WSIs; n = 106) and genome-derived features, a weakly supervised, multimodal deep learning fusion model was developed to predict nCRT response. Multiomics profiling was used for exploratory pathway characterization, and functional assays were conducted in colorectal cancer cell lines and mouse models harboring KRASG12V or KRASA146T.

Results

WES identified 41 response-associated hotspot codon events. KRASG12V and KRASA146T were detected in the NR group in this cohort and were directionally aligned with poor response, indicating an association with nCRT resistance. Because these events are low-frequency alterations, and the study is a single-center retrospective cohort with limited numbers of carriers, multivariable adjustment for key covariates (including stage and T/N status) was not feasible; thus, these findings should be interpreted as exploratory candidate signals. The multimodal fusion model showed good discrimination within the cohort (AUC = 0.882). Mechanistically, exploratory multiomics analyses and orthogonal functional assays were consistent with KRAS variants being associated with altered DNA damage repair signaling and increased repair capacity, with the functional assays providing the main support for this interpretation.

Conclusions

The proposed genome–pathology fusion model provides a research-oriented framework for pretreatment prediction and risk stratification of nCRT response in LARC. KRASG12V and KRASA146T are presented as candidate molecular events aligned with poor response, but their independent predictive value and the clinical usability of the model require validation in larger, multicenter prospective cohorts that include external WSI data, together with systematic evaluation of thresholding and calibration before clinical translation.

Graphical abstract