Weijie Zhang

Germline–Somatic Interactions in BRCA-Associated Cancers: Unique Molecular Profiles and Clinical Outcomes Linking ATM to TP53 Synthetic Essentiality

Abstract Purpose: Germline alterations in homologous recombination repair (gHRR) genes affect the pathogenesis, treatment options, and survival of patients with cancer. However, distinct gHRR gene alterations may differentially affect treatment response and oncogenic signaling. In this study, we interrogated genomic and transcriptomic data and assessed clinical outcomes of patients with gHRR mutations across four BRCA-associated cancers (breast, ovarian, pancreatic, and prostate cancers) to identify therapeutic vulnerabilities. Experimental Design: We assessed 24,309 patients undergoing matched tumor/normal next-generation DNA and RNA sequencing. Annotated gHRR gene variants [germline BRCA1, germline BRCA2, germline PALB2, germline ATM (gATM), and germline CHEK2] were analyzed. HRs were used to assess survival outcomes comparing germline versus sporadic groups. Somatic alterations and their frequencies were compared across gHRR-altered groups. Differential gene expression and gene set enrichment analysis were used to compare transcriptomic profiles. Results: Somatic TP53 mutations were depleted in gATM carriers (P < 0.05) across all four BRCA-associated cancers by up to 2.5-fold. Tumors with germline BRCA1/2 mutations were associated with improved survival in patients with ovarian cancer and had consistent enrichment of TP53 mutations in all four cancers. gATM mutations displayed elevated p53 transcriptional activity in all four cancers, with significance reached in breast and prostate cancers (P < 0.01). In breast, ovarian, and prostate cancers, gATM tumors demonstrated significantly increased inflammatory pathways (P < 0.001). Finally, using gene dependency data, we found that cell lines that were highly dependent on ATM were co-dependent on canonical p53 function. Conclusions: gATM-associated cancers seem to require intact p53 activity and this synthetic essentiality may be used to guide targeted therapies that perturb canonical TP53 function.

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Abstract Background Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. Objective This study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. Methods We explored the data-driven CCP subgroups by applying unsupervised machine learning to a deeply phenotyped, population-based discovery cohort. We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. Results This study included 551,934 women (median age, 49 years) in the discovery cohort and 47,130 women (median age, 37 years) in the external cohort. Phenotyping identified 5 CCP subgroups, with CCP4 showing the highest carcinoma prevalence. CCP2–4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03‐2.12], CCP3: 3.88 [3.78‐3.97], and CCP4: 4.47 [4.33‐4.63]) and CIN3+ (CCP2: 2.10 [2.05‐2.14], CCP3: 3.92 [3.82‐4.02], and CCP4: 4.45 [4.31‐4.61]) compared to CCP1 (P<.001), consistent with the direction of results observed in the external cohort. The proposed triple strategy was validated as clinically relevant, prioritizing high-risk subgroups (CCP3-4) for colposcopies and scaling human papillomavirus screening for CCP1-2. Conclusions This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.