Characterization of FAP⁺ mesenchymal cell heterogeneity in pancreatic cancer identifies a tumor-suppressive interferon-response cancer-associated fibroblast subtype that can be induced by stimulating type I interferon signaling using STING agonists.

CAF-secreted LTBP2 binds integrin α6β4 and activates Src signaling to drive metastasis and chemoresistance in esophageal cancer, highlighting LTBP2 as a key regulator of CAF-mediated tumor progression that can be therapeutically targeted.

Chronic stress facilitates lung cancer immune evasion by inducing M2-like macrophage polarization, supporting the potential of combination therapies targeting both tumor cells and the immune microenvironment for treating stress-related cancers.

Ammonia is elevated in the tumor microenvironment and functions as an immunoinhibitory metabolite in cancer by reducing perforin levels, inhibiting NK and T‐cell–mediated immunity and limiting the efficacy of immunotherapies.

Alterations to the gut microbiome induced by mannose engender an immune stimulatory tumor microenvironment responsive to immunotherapy, suggesting that mannose may be an effective and safe adjuvant therapy for stimulating immunotherapy sensitivity.

SCD1 and SLC7A11 are prognostic biomarkers and therapeutic targets for KRAS/STK11/KEAP1 co-mutant lung adenocarcinoma, which will refocus mechanistic studies and lead to treatment strategies for lung cancer.

HRProfiler is a machine learning approach that reliably identifies homologous recombination deficiency in whole-exome–sequenced breast and ovarian cancers, outperforming other tools and providing clinically useful insights.

Integrating spatial transcriptomics, pseudotime, and machine learning analyses is effective for identifying prostate cancer biomarkers that are reliable in different settings and measurable with routine methods, providing potential early diagnosis strategies.

CTSMamba is a multitask deep learning model trained on longitudinal CT images of neoadjuvant chemotherapy-treated locally advanced gastric cancer that accurately predicts lymph node metastasis and overall survival to inform clinical decision-making.

Evolutionary dynamics of the various niches composing the tumor ecosystem can be harnessed for predicting cancer progression, demonstrating how eco-evolutionary–designed approaches can guide biomarkers discovery studies in the era of digital pathology.