arXiv subjects

Robert Amevor

Publications and source records attributed to Robert Amevor.

3 recordsLinked to original sources

arXiv2026

Adaptive Weighting for Time-to-Event Continual Reassessment Method: Improving Safety in Phase I Dose-Finding Through Data-Driven Delay Distribution Estimation

Background: Phase I dose-finding trials increasingly encounter delayed-onset toxicities, especially with immunotherapies and targeted agents. The time-to-event continual reassessment method (TITE-CRM) handles incomplete follow-up using fixed linear weights, but this ad hoc approach doesn't reflect actual delay patterns and may expose patients to excessive risk during dose escalation. Methods: We replace TITE-CRM's fixed weights with adaptive weights, posterior predictive probabilities derived from the evolving toxicity delay distribution. Under a Weibull timing model, we get closed-form weight updates through maximum likelihood estimation, making real-time implementation straightforward. We tested our method (AW-TITE) against TITE-CRM and standard designs (3+3, mTPI, BOIN) across three dose-toxicity scenarios through simulation (N = 30 patients, 2,000 replications). We also examined robustness across varying accrual rates, sample sizes, shape parameters, observation windows, and priors. Results: Our AW-TITE reduced patient overdosing by 40.6% compared to TITE-CRM (mean fraction above MTD: 0.202 vs 0.340; 95% CI: -0.210 to -0.067, p < 0.001) while maintaining comparable MTD selection accuracy (mean difference: +0.023, p = 0.21). Against algorithm-based methods, AW-TITE achieved higher MTD identification: +32.6% vs mTPI, +19.8% vs 3+3, and +5.6% vs BOIN. Performance remained robust across all sensitivity analyses. Conclusions: Adaptive weighting offers a practical way to improve Phase I trial safety while preserving MTD selection accuracy. The method requires minimal computation and is ready for real-time use.

stat.ME↗

arXiv2026

Time-Varying Hazard Patterns and Co-Mutation Profiles of KRAS G12C and G12D in Real-World NSCLC

Background: KRAS mutations are the largest oncogenic subset in NSCLC. While KRAS G12C is now targetable, no approved therapies exist for G12D. We examined time-to-next-treatment (TTNT) and overall survival (OS) differences between G12C and G12D, allowing for time-varying hazard effects. Methods: De-identified data from AACR Project GENIE BPC NSCLC v2.0-public were analyzed. TTNT served as a real-world surrogate for progression-free survival. Co-mutations (TP53, STK11, KEAP1, SMARCA4, MET), TMB, and PD-L1 were harmonized. Kaplan-Meier, multivariable Cox, and a pre-specified piecewise Cox model (split at median TTNT = 23 months) were applied. Schoenfeld residuals assessed proportional hazards; bootstrap resampling (B=1000) evaluated stability. Results: Among 162 TTNT-evaluable patients (G12C n=130; G12D n=32), median TTNT was 28.6 versus 32.0 months (log-rank p=0.79). Adjusted Cox regression showed no overall hazard difference (HR=0.85; 95% CI 0.53-1.37; p=0.50), but Schoenfeld testing indicated borderline non-proportionality (p=0.053). Piecewise Cox modeling revealed time-varying effects: early TTNT hazard favored G12D (HR=0.41; 95% CI 0.17-0.97; p=0.043) with significant KRAS x period interaction (HR=3.33; p=0.021) and late-period attenuation (HR=1.38; 95% CI 0.77-2.47; p=0.285). Bootstrap resampling confirmed this pattern (median HRearly=0.39; HRlate=1.41). Among 278 OS-evaluable patients (133 deaths), G12D showed improved OS (adjusted HR=0.63; 95% CI 0.39-0.99; p=0.048). G12C tumors exhibited higher TMB (9.79 vs 7.83 mut/Mb; p=0.002) and greater STK11/KEAP1 enrichment. Conclusions: KRAS G12D demonstrated early TTNT advantage and improved OS. Late-period TTNT differences were non-significant (post-hoc power: 12.3%). These exploratory findings require validation in larger cohorts but support allele-specific therapeutic development for G12D.

q-bio.QM↗

arXiv2025

Integrative Prognostic Modeling of Breast Cancer Survival with Gene Expression and Clinical Data

Background: Accurate survival prediction in breast cancer is essential for patient stratification and personalized therapy. Integrating gene expression data with clinical factors may enhance prognostic performance and support precision medicine. Objective: To develop an integrative survival prediction model combining clinical variables and gene expression signatures, and to assess their contributions using penalized Cox regression and machine learning. Methods: We analyzed 1,867 patients from the METABRIC cohort with clinical annotations and microarray-based gene expression profiles. The top 5,000 most variable genes were retained. Elastic Net-penalized Cox regression identified 75 predictors (70 genes and 5 clinical variables: tumor size, stage, surgery type, age at diagnosis, and Nottingham Prognostic Index). Model performance was evaluated with Harrell's concordance index (C-index) and 36-month time-dependent AUC. Random Survival Forests (RSF) trained on the top 20 genes assessed nonlinear effects and validated variable importance. PCA and heatmaps visualized gene expression patterns across risk groups. Results: The integrative Cox model achieved a C-index of 0.922 and a 36-month AUC of 0.94, outperforming clinical-only models (C=0.64). RSF confirmed the prognostic value of top genes (e.g., OR2T27, TBATA, LINC01165, SLC10A4), yielding a 36-month AUC of 0.88. Conclusions: Combining gene expression signatures with clinical variables substantially improves survival prediction in breast cancer and provides a framework for individualized prognostic assessment and clinical decision-making.

q-bio.QM↗