TMB (Tumor Mutational Burden)

Our studies reveal that CMV-specific viral memory T cells can be re-directed to control a solid tumor normally refractory to immunotherapy via a simple, intravenous injection of T-cell peptide epitopes. This mutation-agnostic approach has significant potential for the development of "off-the-shelf" therapeutics by stimulating pre-existing antiviral memory, and it is widely applicable due to the high prevalence of CMV.

Compared to results from established architectures such as Inception-v3 on the same WSIs, our model demonstrated significantly improved performance for most features. With further optimization, our model could support triaging for molecular testing and inform precision treatment strategies for NS-LUAD patients.

AlphaTMB improves survival prediction beyond TMB alone, better captures immunogenic tumor profiles, and reflects more accurate patient stratification. This AI derived somatic mutations pathogenicity scoring represents a step toward personalized immuno-oncology and merits further validation in prospective studies.

CS4 showed the highest immune infiltration, elevated tumor mutational burden, and enhanced sensitivity to 5-fluorouracil and cetuximab. This integrative multi-omics framework refines the molecular taxonomy of COAD, revealing immunologically active and therapeutically distinct subgroups. The classification not only bridges genomic, epigenomic, and transcriptomic regulation but also provides a practical roadmap for precision oncology by linking molecular features to potential treatment strategies.

A super-high TMB threshold of ≥ 25 mut/Mb by MSK-IMPACT identifies a distinct subset of melanoma patients who achieve truly exceptional benefit, with nearly one in two attaining clinical cure following ICB therapy. These data support prospective validation of "hypermutation" as a clinically actionable biomarker, refine patient counseling, and inform trial stratification in the era of personalized cancer immunotherapy.

Plasma ctDNA detected ≥14 days post-surgery identified 91.3% of recurrences, with lead times up to 500 days before clinical confirmation. These results highlight the value of serial ctDNA monitoring for early relapse detection and potentially improved treatment guidance in HNSCC.

Combining biomarker-driven immunotherapy and targeted approaches such as PD-1 blockade in MSI-H or EBV-positive tumors, HER2-directed therapy, and CLDN18.2 inhibition, has demonstrated a paradigm shift in the clinical management. This review highlights a pathologist-centered perspective on molecularly defined subgroups, actionable biomarkers, and evolving therapeutic paradigms in CRC and GEJ carcinoma, advancing precision oncology.

We evaluate the factors that influence DNAmeH via these metrics, including the cell cycle phase, tumor mutational burden (TMB), cellular stemness, copy number variation (CNV), tumor subtype, tumor characteristics, tumor stage, state of tumor cells, hypoxia, and tumor purity. Finally, we highlight the deconvolution of TME cellular components and the application of predictive methylation biomarkers in cancer clinical research.

These findings suggest that CSF cfDNA effectively represents the genetic profile of gliomas and serves as a sensitive measure for surgical resection efficacy and patient prognosis, highlighting its potential as a non-invasive biomarker for enhancing post-operative management in glioma patients.

Furthermore, the review explores emerging frontiers such as neoantigen-based vaccines, microbiome modulation, and bispecific antibodies, underscoring their capacity to convert immunologically "cold" tumours into "hot", responsive lesions. By bridging preclinical insights with clinical trial evidence, this review speculates that the precise modulation of the TME is indispensable for unlocking durable, broad-spectrum antitumor immunity and defining the next generation of cancer immunotherapies.

This study underscores the significant expression differences and prognostic implications of SEC13 in various cancers, emphasizing its potential as a biomarker and therapeutic target.

On the other hand, agent-based models (ABMs) naturally capture stochasticity, interactions at an individual level, and discrete events that lie beyond the scope of differential-equation formulations. As such, we also convert our ODE model, with parameters calibrated to experimental data, to an ABM, preserving its dynamics while providing a flexible platform for future mechanistic investigation and modelling.