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At least 19 recordsLinked to original sources
PubMed2025

ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response.

BACKGROUND: Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability. METHODS: Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis. RESULTS: Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets. CONCLUSIONS: Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.

Humans↗
PubMed2026

Impact of precision oncology research in pediatric poor prognosis cancer: patient, parent and healthcare provider perspectives.

BACKGROUND: Comprehensive genomic analyses are increasingly accessible to children, adolescents and young adults (AYAs) with poor prognosis cancers. Challenges and successes of pediatric precision oncology studies from the perspectives of AYA patients, parents and healthcare providers (HCPs) are poorly described. METHODS: Between March 2021 and May 2023, we interviewed AYA patients (12-21 years), parents and HCPs who participated in pediatric precision oncology studies for poor prognosis cancers in British Columbia. Interviews followed an investigator-developed semi-structured topic guide. Data were coded inductively and deductively by one qualitative researcher and one trainee, supported by two additional team members. Analytic themes were established using qualitative thematic analysis. RESULTS: We interviewed 9 AYAs, 10 parents, and 17 HCPs. We identified five analytic themes: importance of clear communication of study information between patients, families and multidisciplinary HCPs; a need to support disclosure, understanding and clinical integration of research results; barriers to accessing innovative therapy and mitigation strategies; approaches to managing parent, patient and HCP hopes and expectations; personal challenges and stressors related to participation. CONCLUSIONS: We highlight unmet needs and offer practical considerations for integrating precision oncology into clinical practice. Considerations include educating and supporting oncologists through genomics results disclosure, increasing engagement with multidisciplinary HCPs, streamlining access to study information and results, coordinating efforts to clinically validate results and access therapies, and establishing real-world outcome data to inform clinical decision-making. Implementation of these strategies will optimize care for patients and families who are navigating poor prognosis cancers.

Humans↗
PubMed2026

Deep learning-based multimodal pathogenomics integration for precision cancer prognosis.

BACKGROUND: Recent studies have revealed valuable prognostic insights in haematoxylin and eosin (H&E)-stained histological sections and transcriptomic profiles, suggesting potential applications in machine learning. However, existing methods lack sufficient intra- and inter-modal interactions, and face challenges in clinical validation due to incomplete multimodal data. METHODS: We proposed PathoGems (PathoGenomics-based integrative survival prediction), a weakly-supervised, interpretable multimodal learning framework that integrates histology and genomic profiles for precise cancer prognosis prediction. To evaluate the robustness of PathoGems, we initially curated a dataset of 1965 cases across four cohorts from The Cancer Genome Atlas (TCGA), including breast, colorectal, glioblastoma, and esophageal cancers. For external validation, PathoGems was further evaluated on four independent cohorts, consisting of 76 breast cancer and 41 esophageal squamous cell carcinoma cases from Zhejiang Cancer Hospital, as well as 102 colorectal cancer and 58 glioblastoma cases from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). RESULTS: PathoGems effectively stratified patients into favorable and unfavorable risk groups, revealing significant differences in histological patterns, genomic features, and overall survival (log-rank test, p&#x2009;<&#x2009;0.05). Moreover, the model&#x2019;s predictions are further supported by visualization and transcriptomic analysis, enhancing interpretability and reliability. CONCLUSIONS: By fusing histological and clinicogenomic multimodal models, PathoGems will provide a solid foundation for developing an innovative tool that aids clinicians in making informed decisions and selection personalized treatment strategies for cancer patients.

Humans↗
PubMed2026

Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.

Non-coding RNAs (ncRNAs), once considered genomic dark matter, are now established as key regulators of gene expression with widespread roles in cellular homeostasis and disease. In cancer, ncRNA expression is frequently and systematically dysregulated, and many of these molecules circulate in stable, protected form within biofluids, offering a compelling basis for non-invasive or minimally invasive diagnostic strategies. However, their clinical translation remains substantially hindered to date due to biological complexity, technical noise, and high dimensionality inherent to ncRNA expression datasets. In this context, machine learning (ML) has emerged as a powerful analytical tool to address these challenges, enabling the identification of subtle, reproducible ncRNA signatures predictive of diverse malignancies. This review critically evaluates ML-driven frameworks for cancer diagnosis and prognosis across four ncRNA subclasses, namely miRNAs, lncRNAs, circRNAs, and piRNAs, while also acknowledging the biophysical and thermodynamic models that reinforce ncRNA bioinformatics. Despite substantial methodological progress in ML-based cancer diagnosis and prognosis, key challenges persist, including tumor biological heterogeneity, limited multicenter validation, and the lack of widely adopted standardized protocols for preprocessing, normalization, and reporting workflows. Furthermore, many current ML models lack interpretability in biological or clinical context, constraining their translational utility. By synthesizing recent advances and identifying unresolved barriers, this review charts a roadmap for developing a robust, clinically actionable ncRNA biomarker platform for cancer detection. With global cancer incidence projected to exceed 35 million annual cases by 2050, validated ncRNA-ML-driven frameworks hold potential to revolutionize early-stage detection and personalized therapeutic strategies, thereby reducing the escalating socio-economic burden of cancer worldwide.

Humans↗
PubMed1976

Immunocompetence, immunodeficiency and prognosis in cancer.

Immunocompetence and prognosis are related in solid tumors, malignant lymphomas, and acute leukemia. Among the parameters of immunocompetence vigorous delayed-type hypersensitivity responses to recall antigens or to primary immunization with Keyhole limpet hemocyanin, vigorous in vitro lymphocyte blastogenic responses to mitogens such as PHA, and relatively high B-lymphocyte levels, all correlate with a good prognosis. The spectrum of immune reactivity as measured by established delayed-type hypersensitivity to recall antigens and in vitro blastogenic responses to mitogens and antigens is similar in melanoma patients and their nontumor-bearing spouses. In melanoma, only patients with widespread inoperable metastatic disease show severe immunological deficiency and this is selective for certain antigens. There are highly significant differences in response to specific antigens when patients with melanoma and lung cancer are compared. Immunotherapy with BCG and C. parvum can boost immunocompetence as measured by recall DTH skin testing. However, the relationship between the initial immunocompetence and prognosis still holds in patients receiving BCG immunotherapy to prevent recurrence of melanoma. These data indicate that a broader survey of immunological reactivity in cancer patients is needed, that immunological testing is useful in cancer prognosis clinically, and that the results of immunological testing can be used to evaluate therapy and to indicate new pathways for improved treatment.

B-Lymphocytes↗
PubMed1979

Statistical evaluation of factors influencing prognosis of gastric cancer patients: Predication of prognosis on patient clusters.

We found ten clusters of gastric cancer patients in Imanaga's group under a cancer research project organized by the Ministry of Health and Welfare, and evaluated the prediction of prognosis of those patients in each cluster by using the censored regression of postsurgical survival time on a "prognosic" factor which has been extracted from nine explanatory variables observed mainly at the time of surgery. Consequently, the ten clusters were interpreted and confirmed to be useful for prediction of the patient prognosis by comparison of the failure rates among those clusters and between treated (administration of chemotherapy) group and control group.

Adult↗
PubMed1977

[Breast cancer: histological prognosis from biopsy material].

Two histological factors to be taken into consideration for prognosis in pretreatment schedules of breast cancer have been studied on a group of 352 cases treated by non-mutilating therapeutics at the Fondation Curie between 1960 and 1970. The tumour material the slides of which we have reexamined "blindly", i.e. ignoring the evolution of the case had been obtained mostly by drill-biopsy. Histological groups and types have been determined following an analytical classification for computer purpose. The degree of malignancy was calculated with the method of Scarff-Bloom-Richardson. The analyzed data have been memorized on computer and then confronted with the elements of the T.N.M. classification and the survival of the patients involved. It appeared that if drill-biopsie have been performed correctly the histological type may be defined in eighty percent of cases. And it is likewise possible to calculate the histological grade of malignancy for each mammary cancer. With such a material the value for prognosis by means of the Scarff-Bloom-Richardson method still remains if applied only to adenocarcinoma of the "common infiltrating type".

Adenocarcinoma↗
PubMed2026

Integrin &#x3b1;3 (ITGA3) expression across breast cancer subtypes: Prognosis and therapeutic relevance.

BACKGROUND: Integrin &#x3b1;3 (ITGA3), which heterodimerizes with integrin &#x3b2;1, has emerged as a potential biomarker and therapeutic target in several epithelial malignancies; however, its clinical relevance in breast cancer remains incompletely characterized. This study evaluated ITGA3 expression across breast cancer molecular subtypes and assessed its prognostic and predictive significance. METHODS: Immunohistochemistry (IHC) was performed on archival breast cancer specimens using tissue microarrays (n = 148) and whole-tissue sections (n = 21). Complete clinicopathologic and outcome data were available for 108 patients, including hormone receptor-positive/human epidermal growth factor receptor 2-negative, HER2-positive, and triple-negative breast cancer (TNBC) subtypes. ITGA3 expression was quantified using H-scores and correlated with clinicopathologic features and survival outcomes. Independent transcriptomic analyses were conducted using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohorts to evaluate ITGA3 mRNA expression, co-expressed signaling pathways, and associations with therapeutic response. RESULTS: ITGA3 protein expression was detected in 85.2% of breast cancer specimens and was significantly higher in HR-positive/HER2-negative and HER2-positive tumors compared with TNBC (p < 0.0050). High ITGA3 expression was associated with shorter recurrence-free survival (p < 0.0001). In the METABRIC cohort, tumors with ITGA3 alterations demonstrated significantly worse relapse-free survival (p < 0.0001) and overall survival (p < 0.0500). Transcriptomic analyses revealed that ITGA3 co-expressed with estrogen receptor 1(ESR1), erb-b2 receptor tyrosine kinase 2 (ERBB2), and luminal markers, along with enrichment of estrogen receptor and phosphoinositide 3-kinase-protein kinase B-mechanistic target of rapamycin (PI3K/AKT/mTOR) signaling pathways. ITGA3 expression was not predictive of response to tamoxifen or trastuzumab. CONCLUSION: Elevated ITGA3 expression is associated with breast cancer recurrence and poor clinical outcomes, supporting its potential role as a prognostic biomarker and candidate therapeutic target.

Biomarkers↗
PubMed1979

Leukoerythroblastosis and cancer frequency, prognosis, and physiopathologic significance.

This investigation was carried out on 100 bone marrow biopsies with metastases and 56 autopsies on patients with evidence of cancer. Leukoerythroblastosis was found in 44% of the patients with bone marrow mestastases and was more frequent in prostatic and gastric carcinoma. Moreover, the postmortem study of patients who died with cancer showed that leukoerythroblastosis was always the sign of bone marrow metastasis. A significant correlation was found between these blood changes and bone marrow fibrosis around the metastasis. Furthermore, leukoerythroblastosis seems caused by hepatosplenic extra medullary hematopoiesis.

Anemia, Myelophthisic↗