With the increasing number of indications for immune checkpoint inhibitors in early and advanced cancers, the prospect of a tumor-agnostic biomarker to prioritize patients is compelling. Tumor mutation burden (TMB) is a widely endorsed biomarker that quantifies nonsynonymous mutations within tumor DNA, essential for neoantigen production, which, in turn, correlates with the immune response and guides decision-making. However, the general clinical application of TMB-relying on simple mutational counts targeted at a single endpoint-does not adequately capture the complex clonal structure of tumors nor the multifaceted nature of prognostic indicators. This recognition has spurred the exploration of sophisticated high-dimensional regression techniques. Unfortunately, the limited cohort sizes in immunotherapy trials have hindered the full potential of these advanced methods. Our approach considers patient subgroups as related yet distinct entities, enabling precise tailoring and refinement to address subgroup-specific dynamics. Given the deficiencies and the constraints, we introduce a TMB heterogeneity-optimized regression (THOR). This innovative model enhances the predictive capabilities of TMB by integrating tumor clonality and a diverse spectrum of clinical endpoints, further augmented by fusion techniques across subgroups to facilitate robust data sharing and interpretation. Our simulations validate THOR's superiority in parameter estimation for statistical inference. Clinically, we assess the utility of THOR in a structured cohort of 238 cancer patients undergoing immunotherapy, supplemented by 2212 patients across 19 subgroups from public datasets. The forecast of the responses and comparison of survival hazards demonstrate that THOR significantly enhances patient stratification and prognostic predictions by incorporating complex immunogenetic biology and subgroup-specific dynamics.

Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets. We benchmarked TMBquant against nine widely used variant callers, including traditional tools (e.g. Mutect2, VarScan2, Strelka2) and recent AI-based methods (DeepSomatic, Octopus), using 706 whole-exome sequencing tumor-control pairs. To evaluate clinical relevance, we further assessed TMBquant through survival analyses across immunotherapy-treated cohorts of non-small cell lung cancer (NSCLC), nasopharyngeal carcinoma (NPC), and the two NSCLC subtypes: lung adenocarcinoma and lung squamous cell carcinoma. In each cohort, TMBquant consistently achieved the highest hazard ratios, demonstrating superior patient stratification compared to all other methods. Importantly, TMBquant maintained robust predictive performance across both high-TMB (NSCLC) and low-TMB (NPC) settings, highlighting its generalizability across cancer types with distinct biological characteristics. These findings establish TMBquant as a reliable, reproducible, and clinically actionable tool for precision oncology. The software is open source and freely available at https://github.com/SomaticCaller/SomaticCaller. To enhance reproducibility, we provide detailed usage instructions and representative code snippets for TMBquant in the Methods section (see Code Availability).

Molecular residual disease (MRD) detection, initially developed for hematologic malignancies, has become a critical biomarker for monitoring solid tumors. MRD detection primarily relies on circulating tumor DNA (ctDNA) analysis using next-generation sequencing, offering high sensitivity and broad genomic coverage. However, challenges remain in designing cost-effective panels that maximize mutation detection while maintaining biological relevance. Fixed panels often lack sufficient patient-specific mutation coverage, while WES-based personalized MRD assays, despite their high sensitivity, are costly and less accessible. We developed a tumor comprehensive genomic profiling (CGP)-informed personalized MRD assay to detect tumor-derived mutations, which allowed us to design patient-specific personalized panels and meanwhile, provide a cost-effective alternative to whole exome sequencing (WES). To address these limitations, we developed MRDtarget, a heuristic multivariate Gaussian model-based targeted capture region selection method. By expanding beyond traditional hotspot regions, MRDtarget optimizes variant tracking for MRD detection, significantly improving sensitivity. Using a Bayesian inference-based heuristic approach, MRDtarget integrates multi-feature informativeness rates to identify optimal genomic regions for capture. Experimental results demonstrate that MRDtarget enables the detection of more variants per patient. This study underscores the importance of rational panel design to improve MRD sensitivity and provides a novel approach to enhance precision diagnostics and treatment for solid tumor patients.

Monitoring rare health-related events using control charts is crucial for timely detecting potential changes in healthcare scenarios. For example, sequentially testing the level of changes in infectious disease patient numbers helps prepare before an epidemic. Unlike general health-related events, the observation of rare ones often involves an excess of zeros, making it more appropriate to use the zero-inflated Poisson (ZIP) distribution rather than the classical Poisson. Although residual-based charts have attracted significant attention in this field, few studies have explored how to appropriately select residuals with different advantages in the complex situations like healthcare scenarios. Therefore, in this paper, we propose LMR-EWMA, a least absolute shrinkage and selection operator (LASSO)-based multivariate residual exponentially weighted moving average (EWMA) control chart, to automatically select the optimal residuals for monitoring changes (i.e., shifts) in the number of rare health-related events. Additionally, we have innovatively designed a bi-directional moving mechanism to address the limitation of current research in distinguishing the practical significance of shifts. Experimental results on three simulation cases and two real datasets demonstrate that LMR-EWMA outperforms existing charts in monitoring performance.

Copy number variations are crucial in cancer research, but their detection through next-generation sequencing is often hindered by read biases, particularly in complex genomic regions. Existing bias-correction methods address common issues like GC content but often fail in regions with repetitive sequences or segmental duplications, leading to false-positive CNVs. We propose refMask, a hybrid Gaussian model-based method that dynamically identifies low-confidence regions in the reference genome, correcting read biases and improving CNV detection accuracy. By integrating features from hg38 and T2T genomes, refMask tailors a custom blacklist for each sequencing sample, enhancing the reliability of CNV detection across diverse conditions. Our method provides a more accurate and flexible solution compared to current fixed blacklists, offering improved performance in challenging genomic regions.