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PubMed2026

Age and sex: dual drivers remodeling the anti-tumor immune microenvironment and shaping personalized immuno-oncology.

Despite breakthrough advancements in cancer immunotherapy, significant inter-individual heterogeneity in clinical outcomes persists, bringing the regulatory roles of intrinsic host biological variables into sharp focus. Accumulating fundamental and clinical evidence indicates that age and sex play crucial roles in determining tumor susceptibility, disease progression, and the remodeling of the anti-tumor immune microenvironment. This review systematically delineates the profound impacts of the dual dimensions of age and sex on anti-tumor immune responses and immune evasion mechanisms. In the dimension of age, this article outlines the progressive functional decline of T/B lymphocytes and innate immune subsets driven by immunosenescence, and emphatically reveals how inflammaging and its associated senescence-associated secretory phenotype (SASP) orchestrate the formation of an immunosuppressive tumor microenvironment. In the dimension of sex, we deeply explore four core mechanisms comprising sex chromosome genomics (e.g., escape from X-chromosome inactivation and loss of Y chromosome), sex hormone networks, microenvironmental metabolic reprogramming, and the host gut microbiome, elucidating the molecular basis driving the disparities in innate and adaptive immunity between males and females. In summary, thoroughly deciphering the complex immune regulatory networks driven by age and sex not only helps elucidate the disparities in efficacy and toxicity observed in patients undergoing immune checkpoint inhibitors, but also provides crucial theoretical foundations and translational insights for the future development of "age-tailored" and "sex-specific" strategies in personalized immuno-oncology.

Humans↗

PubMed2026

Immune Markers and Risk of Pancreatic Cancer in the European EPIC Cohort.

The immune system is a major driver in pancreatic cancer development. Several prospective cohort studies have found associations for single immune system-derived proteins such as IL6 or CRP, but results are inconclusive, and Omics-based research is scarce. Hence, we aimed to investigate associations of a comprehensive protein panel with the risk of pancreatic cancer. Within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, 92 immune proteins were measured in baseline blood samples of 406 incident pancreatic cancer cases and 406 sex- and age-matched controls, using the Olink Immuno-Oncology panel. Multivariable adjusted conditional logistic regression was used to estimate odds ratios (OR, 95% CI) for protein levels in association with pancreatic cancer risk. Eight biomarkers were associated with pancreatic cancer risk (MMP12, LAMP3, CD28, IL-6, IL-12, FASLG, PD-L2, and PDCD1) but only MMP12 was significantly associated after multivariable adjustments for confounders and the seven proteins, with OR = 1.56 (95% CI: 1.20-2.03) for a doubling in protein concentration. After correction for multiple testing, none of the proteins were associated with risk. Restricting analyses to cases diagnosed within the first 4 years and 4-8 years after recruitment resulted in OR of 1.89 (95% CI: 1.28-2.80) and 1.37 (95% CI: 1.01-1.86) for MMP12, respectively. Higher levels of MMP12 were associated with pancreatic cancer risk specifically in those diagnosed shortly after recruitment, while other immune-related factors were not associated with risk. Further cohort studies are needed to confirm our initial findings.

Humans↗

PubMed2027

Systematic Analysis of Tumor Microenvironment Using IOBR.

The Immuno-Oncology Biological Research (IOBR) package is an R-based analysis tool for exploring the tumor microenvironment (TME) and its influence on anti-tumor immunity. Built for high-throughput data-spanning both transcriptomic and genomic profiles-IOBR integrates six analytical modules, including transcriptomic data preprocessing, TME profiling, TME pattern identification, ligand-receptor interaction analysis, genome-TME interaction assessment, and visualization. In this chapter, we walk through a multi-omics workflow using example datasets, illustrating data preparation, distribution analyses, result interpretation, and graphical output. IOBR is open source and is available at https://github.com/IOBR/IOBR and a detailed GitBook ( https://iobr.github.io/book/ ) offers a complete manual and analysis guide for each function.

Tumor Microenvironment↗

PubMed2026

Improved quality of life and prolonged survival with add-on homeopathic treatment in patients with non-small cell lung cancer: a prospective, randomized, placebo-controlled, double-blind, three-arm, multicenter study.

BACKGROUND: Alongside conventional anticancer treatment, add-on homeopathy might help to alleviate adverse effects of conventional therapy. AIM: The aim of this study was to replicate previous studies on the effect of adjunctive homeopathy on quality of life (QoL) and survival in non-small cell lung cancer (NSCLC) patients. METHOD: In this prospective, randomized, placebo-controlled, double-blind, three-arm multicenter phase III study with quadruple-checked data analysis, we investigated the potential effects of an add-on homeopathic treatment compared to placebo in patients with stage IV NSCLC in terms of QoL. Ninety-eight received either individualized homeopathic medicinal products (HMPs; n = 51) or placebo (n = 47) in a double-blinded fashion. Fifty-two control patients without homeopathic treatment were only observed in terms of their survival rate. The ingredients of the various HMPs were mainly prepared of plant, mineral, or animal origin. The data entry and statistical analysis were subject to an exceptional quadruple-checked data analysis process. The analysis presented in this article was inspired by our earlier report of this trial published in The Oncologist in 2020, which was retracted by that journal in November 2025 after two corrections; a majority of the co-authors disagreed with this decision. The present article is based on the same trial dataset but was deliberately designed to highlight the unique research methodology: design and preparation by a lead statistician, data entry, data clearing and independent statistical evaluation were performed in four mutually independent steps, reporting follows the CONSORT statement, and the interpretation of the findings has been reframed conservatively. RESULTS: Global health status (QoL) was higher in the homeopathy group than in the placebo group after 9 weeks and after 18 weeks (p < 0.001). With the exception of cognitive functioning at 9 weeks and of pain, diarrhea and financial difficulties at 9 weeks, all functional and symptom scales of the EORTC QLQ-C30 favored the homeopathy group (p < 0.001 for the multivariate comparisons), with between-group differences exceeding the threshold of 10 points that is generally regarded as clinically meaningful. Median survival time over the 730-day observation period was 435 days in the homeopathy group, 257 days in the placebo group (p = 0.010), and 228 days in the non-randomized control group (p < 0.001); the corresponding 2-year survival rates were 45.1%, 23.4%, and 13.5% (homeopathy vs. placebo p = 0.020; homeopathy vs. control p < 0.001). The difference between the placebo group and the non-randomized control group was not statistically significant (p = 0.154). CONCLUSION: In this trial, add-on homeopathy was associated with better quality of life across most functional and symptom domains, with clinically meaningful effect sizes congruently to a previous open study. Survival time was significantly longer in the homeopathy group compared to both the placebo and control groups. Independent replication, ideally within contemporary immuno-oncological treatment regimens is required. TRIALS REGISTRATION: ClinicalTrials.gov; No.: NCT01509612; January 7, 2012.

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PubMed2026

Recent advances in immunotherapy for breast cancer: An updated review.

Immunotherapy has revolutionized the treatment landscape of breast cancer, particularly for triple-negative breast cancer (TNBC), yet primary and acquired resistance remain formidable obstacles limiting durable clinical benefit. This review provides a comprehensive update on recent advances in breast cancer immunotherapy, with a focused emphasis on the molecular and cellular mechanisms driving treatment resistance and emerging strategies to overcome them. We dissect tumor-intrinsic resistance pathways, including loss of tumor antigens, defects in antigen processing and presentation machinery, insensitivity to interferon-γ signaling, metabolic reprogramming, and epigenetic dysregulation. Tumor-extrinsic mechanisms, such as infiltration of immunosuppressive cells, abnormal angiogenesis, extracellular matrix remodeling, and FGF/FGFR genomic amplification, are highlighted as key barriers to effective immune checkpoint blockade. Emerging evidence implicates novel resistance mediators, including the DUSP22-LGALS1 axis, THSD4-driven T cell exclusion, and the MTDH-SND1 complex impairing antigen presentation, etc. We critically evaluate current strategies to surmount resistance, encompassing combination regimens with chemotherapy, targeted therapies, radiotherapy, and novel immunomodulators. The review also addresses challenges in managing immune-related adverse events, controversies surrounding patient selection biomarkers, and the urgent need for optimized efficacy evaluation systems beyond RECIST criteria. Finally, we discuss future directions, including novel immune checkpoints, microbiome modulation, artificial intelligence-assisted decision-making, and innovative trial designs. By integrating mechanistic insights with clinical evidence, this review provides a framework for understanding and overcoming immunotherapy resistance, advancing the paradigm from "effective" to "precise" immuno-oncology in breast cancer.

Humans↗

PubMed2025

Epigenetic orchestration of cancer-immune dynamics: mechanisms, technologies, and clinical advancements.

BACKGROUND: Epigenetic dysregulation plays a pivotal role in cancer immune evasion by orchestrating tumour antigen silencing, immune cell dysfunction, and the formation of an immunosuppressive microenvironment. By disrupting successive phases of the cancer-immunity cycle-from antigen presentation to T cell exhaustion-these aberrations facilitate immune escape and tumour progression, highlighting the need for targeted epigenetic intervention. AIM OF REVIEW: This review systematically dissects how epigenetic alterations impair anti-tumour immunity at each stage of the CI cycle. It not only integrates fragmented mechanistic evidence but also emphasizes underexplored crosstalk between specific epigenetic regulators and immune cell types. It further highlights emerging technologies-such as single-cell epigenomics, spatial multi-omics, and CRISPR-based screens-that are driving discovery of novel therapeutic targets and refining patient stratification. Key scientific concepts of review. We discuss how epigenetic interventions, alone or in combination with immunotherapies, can reinvigorate immune responses and overcome resistance to current treatments. A particular focus is given to how integrative high-resolution platforms are mapping immunoepigenetic landscapes, enabling mechanism-informed, precision immunotherapy strategies. By bridging epigenetic regulation with translational immuno-oncology, this review outlines a future where epigenetic reprogramming becomes central to overcoming immune evasion in cancer.

Humans↗

PubMed2026

Machine learning-based analysis of oral rinse samples to identify candidate proteomic signatures for severe periodontitis: a pilot study.

This pilot study investigated whether candidate protein signatures from oral rinse samples can distinguish patients with severe periodontitis (stage III/IV) and its subtypes, generalized and localized periodontitis, from non-periodontitis controls. Participants rinsed with phosphate-buffered saline, and samples were analyzed using a Proximity Extension Assay targeting 92 inflammatory and 92 immuno-oncology proteins. A machine learning approach using repeated nested cross-validation and SHAP was implemented to identify protein signatures. The study included 38 patients (18 with localized periodontitis and 20 with generalized periodontitis) and 16 controls. After data preprocessing, 54 samples and 141 proteins were retained. Proteins Gal-1, HGF, TNFSF14, CD27, and ARG1 distinguished periodontitis from controls (ROC-AUC = 0.85, 95% CI 0.82, 0.87). For generalized periodontitis, we found a protein signature including TNFSF14, Gal-1, STAMBP, MUC-16, S100A12, HGF, CASP-8, CD27, LAP TGF-β1, TNFRSF9, and uPA (ROC-AUC = 0.92, 95% CI 0.90, 0.94). For localized periodontitis, we identified ARG1 (ROC-AUC = 0.72, 95% CI 0.68, 0.76). No proteomic signature distinguishing generalized periodontitis from localized periodontitis was identified. This pilot study indicated that oral rinses are suitable for proteomic profiling, and there was a putative protein signature that could differentiate periodontitis, generalized periodontitis, and localized periodontitis from controls. These findings warrant validation in larger independent cohorts, including a clearly defined gingivitis group, before real-world non-invasive screening applications can be considered.

Humans↗

PubMed2026

Profiling Dectin-2-Positive Tumor-Associated Macrophages Across Human Cancers by Immunohistochemistry.

PURPOSE: To characterize the prevalence and distribution of Dectin-2-positive macrophages across human tumors and develop a research immunohistochemistry (IHC) assay to assess Dectin-2 in cancer tissues. MATERIALS AND METHODS: C-type lectin domain family 6 member A (CLEC6A), the gene encoding Dectin-2, was evaluated across 38 tumor types using The Cancer Genome Atlas. A fit-for-purpose Dectin-2 IHC assay was developed using a monoclonal antibody selected from screening 11 anti-Dectin-2 antibodies. Assay performance was supported by Dectin-2-expressing and parental cell line controls, macrophage-associated staining patterns, and comparison with an orthogonal CLEC6A in situ hybridization method using RNAscope. Dectin-2 expression was assessed in tissue microarrays (n = 553 samples) across 6 cancer types and whole tissue sections (n = 137) across 7 cancer types. RESULTS: The Cancer Genome Atlas analysis identified enriched CLEC6A expression in several tumor types, including non-small cell lung cancer (NSCLC), triple-negative breast cancer (TNBC), and subsets of head and neck cancer (HNC) and colorectal cancer (CRC). By IHC, Dectin-2-positive macrophages were detected across tumor types, with notable heterogeneity within and across cancer types. In tissue microarrays, NSCLC showed the highest frequency of Dectin-2-positive macrophage infiltration, with 38% of cases with staining ≥1% of tumor area. Whole tissue section analysis confirmed and expanded these findings, with ≥50% of NSCLC, melanoma, HNC, TNBC, and CRC samples showing Dectin-2-positive macrophages in ≥1% tumor area. CONCLUSIONS: Dectin-2 expression was observed in subsets of tumor-associated macrophages across multiple human cancers, with relatively enriched expression in NSCLC, melanoma, HNC, TNBC, and CRC. To our knowledge, this study represents the first broad protein-level characterization of Dectin-2 across multiple human tumor types, identifies cancers with relatively enriched Dectin-2-positive macrophage infiltration, and provides a foundation for future translational studies of Dectin-2-targeted therapies.

Humans↗

PubMed2026

Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.

BACKGROUND: Stromal tumour-infiltrating lymphocytes (sTILs) are prognostic in early-stage HER2-positive breast cancer, but their role in the context of dual HER2 blockade remains undefined. We evaluated manual, digital, and artificial intelligence (AI)-based sTIL quantification, together with AI-derived spatial metrics, for prognostic and treatment-benefit stratification using tumour samples from the phase 3 APHINITY trial. METHODS: In the APHINITY trial, 4805 patients were randomly assigned to receive chemotherapy plus trastuzumab with pertuzumab or chemotherapy plus trastuzumab with placebo. Median follow-up was 74·1 months (IQR 68·3-75·4). We analysed 4262 haematoxylin and eosin-stained images using manual assessment, an automated digital approach, AI-based lymphocyte quantification (AI percentage lymphocytes), and two AI-derived spatial features (AI-TIL and immune hotspot). Interobserver reproducibility was assessed in 262 randomly chosen tumour samples scored independently by five pathologists. Multivariable Cox models were used to assess associations between TIL levels and invasive disease-free survival (primary outcome in APHINITY), distant recurrence-free interval, and overall survival. The heterogeneity of pertuzumab benefit was evaluated using subgroup analyses, subpopulation treatment effect pattern plot analyses, and nested Cox models with treatment-by-biomarker interaction terms. FINDINGS: Manual scoring showed high interobserver reproducibility (intraclass correlation coefficient 0·84 [95% CI 0·79-0·88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11·6%) of 1035 node-positive tumours from immune-low (by manual scoring) to immune-high; this subgroup of patients showed greater separation of 5-year invasive disease-free survival curves between pertuzumab and placebo groups compared with patients whose tumours were concordantly classified as immune-low by both manual and AI-based approaches. Higher levels of TILs were associated with improved invasive disease-free survival for all sTIL measurement approaches and spatial measurements (hazard ratios [HRs] 0·41-0·93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0·36-0·48), but was not associated with higher values of spatial measures. The largest 6-year absolute improvements with pertuzumab were observed in patients with node-positive disease whose tumours scored in the highest level of immune infiltration of manual sTIL scoring (≥70·0%; mean absolute improvement 12·1 percentage points [SD 2·8]). In nested prognostic and predictive models, AI-based immune hotspot scores provided the most consistent additional information when combined with any sTIL measurement (all p<0·010). INTERPRETATION: Standardised manual sTIL scoring was reproducible, and digital and AI-based methods showed consistent prognostic stratification and potential for treatment-benefit stratification despite only modest correlation between platforms. AI spatial metrics provided complementary information beyond sTIL density and could support more scalable immune assessment. Future studies are needed to validate these approaches in independent cohorts and to clarify their clinical utility for stratifying contemporary HER2-directed therapies. FUNDING: None.

Humans↗

PubMed2026

Artificial intelligence-based tumour infiltrating lymphocyte quantification in patients with triple-negative breast cancer: an independent validation study.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are a robust prognostic marker in patients with triple-negative breast cancer. Artificial intelligence (AI)-derived computational tools assessing TILs could improve efficiency, but require independent validation against clinical outcomes. We aimed to compare the prognostic performance of AI-derived TIL scores with pathologist-scored TILs in a large, prospectively collected dataset pooled from randomised controlled trials. METHODS: CATALINA was an independent, external validation study using prospectively collected long-term clinical outcome data pooled from seven randomised clinical trials conducted at multiple sites. We independently evaluated two previously validated AI pipelines that generate five computationally assessed tumour-infiltrating lymphocyte (cTIL) scores by masked, independent deployment of locked models. cTIL scores were correlated with the mean of the pathologist-scored stromal TILs (sTILs) in 220 digitised haematoxylin and eosin whole slide images in a cohort of patients with early-stage triple-negative or HER-2 positive breast cancer, previously scored by trained pathologists in a TIL-reproducibility study. Prognostic performance was assessed in a separate cohort of patients with early triple-negative breast cancer pooled from seven prospective, randomised adjuvant trials. Multivariable Cox regression models adjusted for clinicopathological factors and study heterogeneity assessed associations of cTIL score and sTIL score with invasive disease-free survival, distant disease-free survival, and overall survival. 5-year discrimination was estimated using time-dependent area under the receiver operating characteristic curve (AUC). FINDINGS: Individual data were collated from 1759 patients, of whom 1356 had complete clinicopathological data, pathologist sTIL scores, and cTIL scores available. Modest correlation (r 0·375-0·473) was observed between cTIL scores and the mean pathologist sTIL score. Both sTIL and cTIL were independently associated with 5-year invasive disease-free survival, distant disease-free survival, and overall survival after adjustment for clinicopathological factors (hazard ratio for invasive disease-free survival was 0·73 [95% CI 0·66-0·82]; q<0·0001, distant disease-free survival was 0·70 [0·61-0·79]; q<0·0001, and overall survival was 0·72 [0·63-0·82]; q<0·0001 for sTIL scores and 0·80 [0·73-0·89]; q<0·0001, 0·77 [0·69-0·86]; q<0·0001, and 0·79 [0·70-0·88]; q=0·0002, respectively, for percentage_lymphocyte scores). In models adjusted for clinicopathological variables and sTIL score, cTIL score did not maintain a statistically significant prognostic association. Both sTIL and cTIL scores improved the 5-year AUC over clinicopathological variables alone, while cTIL score did not significantly further improve AUC when combined with clinicopathological variables and sTIL score. INTERPRETATION: Two cTIL models deployed entirely without retraining or modification provided statistically significant prognostic information and improved risk discrimination compared with clinicopathological variables alone in this large, platform-based, independent validation study. Although cTIL score did not incrementally improve prognostication compared with models combining clinicopathological variables with sTIL score, these findings support the application of cTILs as a reproducible prognostic biomarker, particularly in settings where routine or widespread pathologist assessment is unavailable. FUNDING: Breast Cancer Research Foundation (USA).

Humans↗

PubMed2024

Circadian rhythms of macrophages are altered by the acidic tumor microenvironment.

Tumor-associated macrophages (TAMs) are prime therapeutic targets due to their pro-tumorigenic functions, but varying efficacy of macrophage-targeting therapies highlights our incomplete understanding of how macrophages are regulated within the tumor microenvironment (TME). The circadian clock is a key regulator of macrophage function, but how circadian rhythms of macrophages are influenced by the TME remains unknown. Here, we show that conditions associated with the TME such as polarizing stimuli, acidic pH, and lactate can alter circadian rhythms in macrophages. While cyclic AMP (cAMP) has been reported to play a role in macrophage response to acidic pH, our results indicate pH-driven changes in circadian rhythms are not mediated solely by cAMP signaling. Remarkably, circadian disorder of TAMs was revealed by clock correlation distance analysis. Our data suggest that heterogeneity in circadian rhythms within the TAM population level may underlie this circadian disorder. Finally, we report that circadian regulation of macrophages suppresses tumor growth in a murine model of pancreatic cancer. Our work demonstrates a novel mechanism by which the TME influences macrophage biology through modulation of circadian rhythms.

Tumor Microenvironment↗