MOTIVATION: Accurate detection of copy number variations (CNVs) from targeted panel sequencing remains challenging due to limited genomic coverage and pronounced sample-specific biases. Existing normalization strategies, including baseline-cohort, matched-control, and single-sample approaches, often struggle to balance noise suppression with adaptability, leading to inconsistent performance across heterogeneous samples. RESULTS: We present PScnv, a personalized self-normalizing framework for robust CNV detection from panel sequencing data. PScnv integrates a pre-built panel-of-normals (PoN) with sample-intrinsic stable chromosomes through ridge-regression normalization to generate individualized log2 ratio profiles with reduced systematic variation. CNVs are then identified using a hierarchical multi-phase segmentation pipeline incorporating z-score pre-partitioning, kernel-based correction, and circular binary segmentation. In 139 clinical tumor samples with orthogonal FISH validation at MET, ERBB2, and MTAP, PScnv showed improved accuracy and robustness over existing methods that do not require patient-matched normal samples, provided that a pre-built PoN cohort is available. AVAILABILITY: Source code is available for academic use at https://github.com/lvws/PScnv.

Copy Number Variation (CNV) is a hallmark of tumor genomes. The performance evaluation and optimization of CNV detection algorithms heavily rely on benchmark datasets with known variations and tumor purity. However, existing simulation tools often generate synthetic sequencing reads based on idealized statistical models, which exhibit significant discrepancies from real sequencing data in terms of error profiles, GC bias, and library complexity, thereby compromising the accuracy of algorithm evaluation. To address this gap, we have designed and developed CNV-Sculptor, a high-fidelity CNV simulation software. CNV -Sculptor innovatively employs a “remove-and-refill” strategy, utilizing real sequencing data from normal samples (BAM files) as building blocks. Through six core steps - base BAM construction, regional read pool extraction, read realignment and deduplication, target read quantification, pure tumor sample synthesis, and optional tumor purity simulation-it precisely introduces variations of specified copy numbers at any genomic location and can simulate varying proportions of tumor cell content. This software not only ensures the high authenticity of the simulated data but also provides users with great flexibility and customizability. CNV-Sculptor offers a powerful, reliable, and user-friendly tool for the validation and comparison of CNV detection algorithms, as well as for bioinformatics education.

Copy number variation (CNV) is a crucial biomarker for many complex traits and diseases. Although numerous CNV detection tools are available, no single method consistently achieves optimal performance across diverse sequencing samples, as each tool has distinct advantages and limitations. Therefore, integrating the strengths of these tools to improve CNV detection accuracy is both a promising strategy and a significant challenge. To address this, we propose EMcnv, a novel deep ensemble framework based on meta-learning. EMcnv combines multiple CNV detection strategies through a three-step approach: (i) leveraging meta-learning and meta-path heterogeneous graphs, employing Relational Graph Convolutional Networks as a specific model within the Heterogeneous Graph Neural Networks framework to develop a probabilistic weight meta-model that ensembles various CNV detection strategies; (ii) assigning probabilistic weights to calls from different CNV detection tools and aggregating them into weighted CNV regions (CNVRs); (iii) refining Copy number variations based on weighted CNVRs. We conducted comprehensive experiments on both simulated and real sequencing data using benchmark datasets. The results demonstrate that EMcnv significantly outperforms popular existing methods, underscoring its superiority and importance in CNV detection. To support further research, the source code is available for academic use at https://github.com/Sherwin-xjtu/EMcnv.

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.

Detection of structural variations (SVs) through circulating tumor DNA (ctDNA) has become a key method for detecting minimal residual disease (MRD). However, the heterogeneity of ctDNA samples, characterized by variable limits of detection (LOD) and diverse structural variant types, significantly impacts detection stability and performance, posing persistent challenges for conventional SV detection tools such as Delly and Manta. These widely used methods require extensive manual parameter tuning, hindered by the combinatorial complexity of multiple parameters and heterogeneous sequencing data. To address this, we propose MRDadaptis, a novel SV detection tool that uniquely incorporates a self-adaptive parameter optimization mechanism. MRDadaptis distinguishes itself by integrating Bayesian optimization with meta-learning techniques to dynamically adjust detection parameters automatically, based on intrinsic features derived from the ctDNA sequencing data itself. This innovative approach not only reduces manual intervention but also effectively captures sample-specific characteristics, significantly improving detection stability, and detection performance. Extensive validation experiments using both simulated and real-world ctDNA datasets demonstrates it distinct advantages, including markedly improved average F1-scores and superior stability (reduced variance, lower RMSE, increased kurtosis). These results highlight the significant advantages of MRDadaptis in addressing sample heterogeneity, underscoring its potential to improve the accuracy and reliability of MRD detecting through ctDNA analysis. https://github.com/aAT0047/MRDadaptis.git.

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).

Immune checkpoint inhibitors (ICIs) have emerged as a cornerstone of modern oncology, necessitating the development of robust biomarkers for optimizing patient stratification and treatment selection. While tumor mutation burden (TMB) has demonstrated prognostic value, conventional quantification methods based on mutation counts fail to reflect immunogenic neoantigen presentation due to intratumoral clonal heterogeneity. Recent efforts have focused on mutation subsets derived from tumor clonality, yet the complex interactions among clones remain a significant obstacle to accurate prognosis. This challenge is further exacerbated by the inherent constraints of limited cohort sizes in clinical studies, which severely compromise model generalizability across heterogeneous cohorts. Therefore, we propose TMBclaw (Tumor Mutation Burden-based Clonal attention with Laplacian Adaptive Weighting), a graph-regularized multi-task learning framework for immunotherapy response prediction. TMBclaw establishes unified integration of group-structured cohorts while enabling cross-cohort knowledge transfer and clonal relationship exploration. For clinical validation, we utilized four cohorts of 238 patients with non-small-cell lung cancer (NSCLC), melanoma, or nasopharyngeal carcinoma treated with ICIs, along with external multicenter validation cohorts (N = 1433) of melanoma and NSCLC patients from public datasets. Comparative analyses demonstrate that TMBclaw significantly outperforms conventional methods in prognostic accuracy and risk stratification. Through systematic quantification of clonal dynamics and discriminative identification of driver clones, TMBclaw shows potential to improve understanding of tumor heterogeneity and provides interpretable insights into the immunotherapy process.

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.

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://github.com/hello-json/TMBstable for academic usage only.