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At least 19 recordsLinked to original sources
PubMed2026

Evolution of Precision Oncology, Personalized Medicine, and Molecular Tumor Boards.

With multiple molecular targeted therapies available for patients with cancer that correspond to a specific genetic alteration, the selection of the best treatment is essential to ensure therapeutic efficacy. Molecular tumor boards (MTBs) play a key role in this process to deliver personalized medicine to patients with cancer in a multidisciplinary manner. Historically, personalized medicine has been offered to patients with advanced cancer, but the incorporation of molecular targeted therapies and immunotherapy into the perioperative setting requires clinicians to understand the role of the MTB. Evidence is accumulating to support feasibility and survival benefit in patients treated with matched therapy.

Humans↗
PubMed2026

Gastroenteropancreatic Neuroendocrine Carcinoma (GEP-NEC): An Aggressive Disease Course and Limitations for Personalized Oncology.

Neuroendocrine carcinoma (NEC) is a rare, aggressive malignancy with limited treatment options and poor prognosis. We report a male patient diagnosed with a gastroenteropancreatic (GEP)-NEC with synchronous liver metastasis at the time of surgery who underwent a radical resection attempt. Despite radical-intent surgery followed by adjuvant carboplatin/etoposide, early recurrence developed with progression through multiple subsequent chemotherapy lines. During the treatment process, genetic profiling was performed twice to identify actionable genomic targets, with inclusion in the national IMPRESS study as a last resort. Comprehensive genomic profiling revealed TP53 mutation and RB1 loss but no actionable alterations. A patient-derived organoid (PDO) was successfully established from resected tumor tissue and retained key neuroendocrine and proliferative features, with partial genomic concordance to the primary tumor. Differences between the primary and subsequent PDO in variant allele frequencies suggest clonal selection during culture. Exploratory metabolomic profiling of tryptophan pathway metabolites in patient serum and PDO-culture media indicated tumor-associated metabolic alterations. We present clinical and translational efforts in difficult-to-treat NEC, illustrating both the translational challenges and the potential role of PDOs in advancing personalized treatment strategies for a cancer with very limited treatment options.

Gastroenteropancreatic neuroendocrine carcinoma↗
PubMed2026

Standardized Xenograft Models for Preclinical Cancer Research.

Xenograft models are the principal in vivo platform of preclinical oncology and the most established experimental link between cell culture and clinical investigation. From the carcinogen-exposed rabbit models of the early twentieth century through the current generation of humanized patient-derived xenograft (PDX) systems, these platforms have evolved in response to the demands of translational cancer research. This review critically examines the biological principles, methodological standards, and translational applications of the principal xenograft platforms in current use. Cell line-derived xenograft (CDX) models remain the most widely used and most cost-effective modality for preclinical efficacy testing, offering the reproducibility, scalability, and accessibility that have sustained their role across oncology drug development pipelines for decades. PDX models have emerged as the preferred platform for co-clinical trial design, predictive biomarker discovery, and personalized oncology applications, preserving the genomic landscape, intratumor heterogeneity, and histological architecture of the donor tumor across serial passages. The engraftment biology of PDX systems, including immunodeficient host strain selection, implantation site, tumor source, and passage biology, is reviewed, together with humanized and autologous humanized configurations that extend the platform to immune checkpoint inhibitors, bispecific T-cell engagers, and chimeric antigen receptor T (CAR-T) cell therapy evaluation. This review addresses preclinical-to-clinical translation as a function of immunological divergence, incomplete tumor microenvironment recapitulation, and standardization. Formal frameworks, including the PDX Model Minimal Information (PDX-MI) standard and the Minimal Information for Standardization of Humanized Mice (MISHUM), are examined alongside global biobank infrastructure and emerging AI-driven translational modeling approaches.

Animals↗
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

hPSC models in cancer mechanisms and therapeutic discovery.

Despite major advances in cancer genomics and immunotherapy, the field remains limited by experimental models that fail to faithfully recapitulate human tumor initiation, genetic context, and immune-tumor interactions. Traditional animal and immortalized cell models often lack predictive power for therapeutic response and toxicity. Recent advances in human pluripotent stem cell (hPSC) technology have transformed this landscape, enabling the generation of patient-specific cancer models, multicellular organoids and assembloids, and scalable immune effector cells. These platforms now permit mechanistic dissection of tumorigenesis, reconstruction of human tumor microenvironments, and development of off-the-shelf immunotherapies. This review will synthesize these emerging findings, define key technological and biological gaps, and outline future directions for integrating hPSC-based modeling into precision oncology and translational cancer research.

cancer immunotherapy↗
PubMed2026

Clinical Variable-Based Machine Learning for Predicting Early mCRPC Using Exclusively Clinical Variables: Development and Multicenter External Validation.

BACKGROUND AND OBJECTIVE: Metastatic hormone-sensitive prostate cancer (mHSPC) exhibits heterogeneous progression patterns, with early progression to metastatic castration-resistant prostate cancer (mCRPC) within 12 months indicating aggressive tumor biology and poor prognosis. Current risk stratification tools (CHAARTED, LATITUDE) offer limited individualized prediction. Machine learning approaches are increasingly applied to predict prostate cancer progression, but most models show modest performance (AUC 0.68-0.72), limited external validation, or require genomic variables unavailable in routine practice. This study aimed to develop and externally validate a novel RINH algorithm for predicting early mCRPC progression (≤ 12 months) using exclusively clinical variables, positioning it as a superior alternative to conventional ML classifiers. METHODS: This multicenter study enrolled 412 patients with de novo mHSPC from seven Spanish academic centers using mixed retrospective-prospective data collection. Twenty clinical variables were recorded, including demographics, PSA, ISUP grade, metastatic localization, CHAARTED/LATITUDE classifications, and treatment modalities. Following RINH-based outlier exclusion (55 patients), 357 patients (29 with early progression, 8.1%) were used to train six ML algorithms: RINH, Logistic Regression, Linear Discriminant, Support Vector Machine, Random Forest, and Subspace Discriminant. A two-tiered validation strategy integrated stratified fivefold cross-validation across all centers and formal external validation using center 1 (n = 121, 19 events) for training and centers 2-7 (n = 207, 10 events) for independent testing. Performance metrics included AUC, sensitivity, specificity, accuracy, and F1-score. KEY FINDINGS AND LIMITATIONS: Artificial intelligence and machine learning (ML) are transforming oncology, promising personalized risk stratification beyond traditional clinical criteria. In metastatic hormone-sensitive prostate cancer (mHSPC), early progression to castration resistance (mCRPC) within 12 months signals aggressive biology and poor prognosis, yet current tools (CHAARTED, LATITUDE) offer limited individualized prediction. Multiple ML models have been proposed with variable success: most achieve modest performance (AUC 0.68-0.72), lack robust external validation, or rely on genomic variables inaccessible in routine practice. We propose a novel approach using the Rivality Index Neighborhood (RINH) algorithm, demonstrating superior predictive capacity in an initial multicenter validation with exclusively clinical variables. This study provides rigorous multicenter external validation, advancing toward implementable precision oncology tools. CONCLUSIONS AND CLINICAL IMPLICATIONS: The RINH algorithm achieves superior predictive performance for early mCRPC progression using exclusively clinical variables, representing a significant advance toward implementable risk stratification. However, low reliability scores in external validation underscore that excellent performance metrics alone do not guarantee stability. Before clinical deployment, validation in substantially larger cohorts with higher progression events is essential. If validated, this model could enable personalized, risk-adapted therapeutic strategies, refining patient selection for treatment intensification or de-escalation.

Humans↗
PubMed2026

Clinical translation of senescence-related pan-cancer multi-omics: tools for assessment and immunotherapy prediction.

Cellular senescence (CS) exerts dual roles in tumorigenesis, yet its pan-cancer molecular characteristics and clinical value remain unclear, hindering its translation to oncology and personalized therapy. To address the lack of specific and universal tools for senescence assessment and immunotherapy response prediction, this study systematically analyzed 1259 CS-related genes from the CellAge database across 31 cancer types by integrating multi-omics data, including bulk RNA-seq, single-cell/spatial transcriptomics, and CRISPR screening. We developed a rank-based algorithm SenScoreR (publicly available at https://gxhub.shinyapps.io/SenScoreR/ ) for senescence quantification, validated with 10 independent datasets, and constructed a machine learning-based predictive model CS.Sig for immunotherapy response. Results showed that tumors had significantly lower Rank-based Senescence Score (RSS) than normal tissues across 31 cancers (average diagnostic AUC = 0.895), with low RSS linked to poor survival; high RSS correlated with reduced genomic instability, enriched CD8⁺ T/NK cell/macrophage infiltration, upregulated PD-L1 expression, and elevated immune cytolytic activity. CS.Sig demonstrated robust performance in predicting ICI response (AUC = 0.716 across 10 cohorts), outperforming 13 existing signatures, while CRISPR screening identified 17 senescence-related targets (e.g., CEP55, PPP1CC) whose knockout enhanced anti-tumor immunity. Our findings clarify CS's role in maintaining tumor genomic stability and shaping immune microenvironments, and the developed SenScoreR, CS.Sig, and identified targets bridge basic CS research with clinical oncology, providing a translational resource and hypothesis basis for future experimental and clinical validation.

Journal Article↗
PubMed2026

Personalized medicine strategy for MPNSTs: using precision oncology on PDOX models to inform tumor boards.

BACKGROUND: Malignant peripheral nerve sheath tumors (MPNSTs) are a heterogeneous group of aggressive soft tissue sarcomas with poor prognosis. Currently there is a lack of effective treatments for MPNSTs. Here, we propose a personalized medicine approach that integrates a precision oncology strategy guided by MPNST genomic analysis, with a functional validation of treatment response in an orthotopic xenograft model (PDOX) derived from the same MPNST. METHODS: Comprehensive whole genome sequencing analysis was performed in primary MPNSTs, relapses and (in one case) metastases, following disease progression in two independent individuals. Matched MPNST PDOX models were generated by orthotopically implanting tumor fragments near the sciatic nerve of immunodeficient mice. Candidate targeted combination therapies were prioritized based on genomic alterations and tested in vivo in the PDOX models. RESULTS: The feasibility of the developed strategy is illustrated for two MPNST patients, one Neurofibromatosis type 1 (NF1) individual that developed two independent MPNSTs and another sporadic MPNST case with multiple metastatic relapses. Genomic analysis revealed a remarkable degree of genomic stability across primary MPNSTs and their successive relapses in each patient, and even metastases in one individual. While based on a small number of cases requiring additional analyses, this finding aligns with previous evidence suggesting a fair genomic conservation throughout tumor evolution. This stability supports the identification of consistent therapeutic vulnerabilities throughout disease progression. Among the therapies tested, co-treatment of MEK inhibitor (MEKi) plus bromodomain inhibitor (BETi) elicited the highest antitumor activity, resulting in approximately 60% tumor volume reduction in the sporadic MPNST PDX model, whose patient has been receiving this therapy for eight months with sustained remission. CONCLUSIONS: This study demonstrates the feasibility and clinical utility of integrating genomic-driven precision oncology with PDOX-based functional testing for MPNSTs. This strategy may support molecular tumor boards (MTBs) in their treatment decisions. The observed genomic stability supports the use of longitudinal tumor profiling to guide treatment, and the success of MEKi+BETi highlights its potential as a combination therapy for MPNSTs.

Precision Medicine↗
PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗
PubMed2025

Assessing data size requirements for training generalizable sequence-based TCR specificity models via pan-allelic MHC-I point-mutation ligandome evaluation.

Rapid identification of T cell receptors (TCRs) that specifically bind patient-unique neoepitopes is a critical challenge for personalized TCR-based therapies in oncology. Due to enormous diversity of both TCR and neoepitope repertoires, a machine learning predictor of TCR-pMHC specificity for personalized therapy must generalize to TCRs and epitopes not seen in the training data. We estimate the necessary size of such training data. We first confirm that published models fail to generalize beyond a single-residue dissimilarity to the epitope training set distribution. We then impute the point-mutation ligandome across the 34 most prevalent human MHC alleles and represent it as a graph based on our established dissimilarity cutoff. By finding the dominating set of this graph, we estimate that between one and 100 million epitopes are required to train a generalizable sequence-based TCR specificity prediction model-1000 times the size of current public data.

Humans↗
PubMed2025

A Prospective Validation of the Decipher Genomic Classifier in Men With Early Localized Prostate Cancer: The VANDAAM Study.

BACKGROUND: The emergence of genomic precision oncology has advanced personalized care for some patients with prostate cancer (PCa), while threatening to widen existing disparities due to the historically low recruitment of African American men (AAM), who have the highest disease burden. Here, we report the first prospective validation of a genomic classifier (GC) to predict rapid-onset biochemical recurrence (BCR) in AAM. METHODS: Between 2016 and 2021, this multicenter prospective validation study recruited 243 patients with low- or intermediate-risk PCa who received treatment for their disease. Patients were recruited on a 1:1 basis (AAM:White) and matched by CAPRA score. Patients who elected active surveillance were ineligible for participation. Decipher GC testing was ordered for all patients using their biopsy and/or radical prostatectomy (RP) tumor tissue. The primary outcome was to determine whether the GC could predict 2-year BCR rates-used as a surrogate for disease aggressiveness-following standard treatment. The secondary outcome evaluated the concordance between biopsy- and RP-derived GC risk scores for treatment recommendations. RESULTS: The final analytical cohort included 226 matched patients with genomic information, and 207 evaluable cases (104 AAM, 103 White) with both genomic and complete clinical outcome data. Overall, a high genomic-risk GC score was associated with a 5.25-fold increase in the odds of rapid-onset 2-year BCR compared with the low-risk group (odds ratio, 5.25 [95% CI, 1.27-21.66]; P=.021). In a subset of the surgical cohort (n=74), biopsy- and RP-derived GC scores exhibited a 77% concordance rate, defined as no reclassification in GC risk-based categories. CONCLUSIONS: This study represents the first prospective validation of GC performance in predicting early 2-year BCR in both AAM and White men. The findings provide strong evidence supporting the integration of the GC into clinical practice guidelines to improve risk stratification and management of AAM with early-stage PCa. CLINICALTRIALS: gov identifier: NCT02723734.

Aged↗
PubMed1979

[Personal experience in 121 cases of palliative operations in abdominal oncological surgery].

The Authors analyse 121 cases of patients affected by abdominal malignant tumors, who underwent a palliative operation. Although the number of the examinated cases is small, it appears clear that the longest survival requires, when possible, the removal of most of the malignant tissue. This seems to be in accordance with the results reported by the literature.

Abdominal Neoplasms↗
PubMed2026

EPIC: Event Prototyping via Information Constrained graph learning for personalized cancer driver gene prediction.

MOTIVATION: Precision oncology relies on accurately distinguishing patient-specific driver mutations from the vast background of passenger alterations. While graph-based computational methods have emerged as powerful tools for this task, they often struggle to preserve the distinct genomic context of individual mutations within complex biological networks. Consequently, subtle patient-specific driver signals are frequently obscured by dominant topological patterns, critically impeding the identification of individualized oncogenic events essential for personalized cancer therapy. RESULTS: To address this, we propose EPIC, a novel framework for Event Prototyping via Information Constrained Graph Learning. Unlike traditional node-centric approaches, EPIC redefines driver prediction as a metric learning task in an event embedding space. We introduce an information-constrained learning strategy that imposes explicit geometric constraints on feature variance, effectively preventing feature collapse and ensuring that low-frequency driver signals are distinctively preserved. Experiments on large-scale cancer cohorts demonstrate that EPIC significantly outperforms established baselines. Notably, the model prioritizes low-frequency driver variants typically overlooked by population-based methods, mapping them to critical oncogenic mechanisms associated with drug resistance and metastasis. Furthermore, clinical actionability analysis confirms that EPIC substantially expands the patient population eligible for targeted therapies. EPIC provides a robust and context-aware solution for personalized cancer driver discovery, bridging the gap between genomic data and actionable therapeutic insights. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at https://github.com/spcho-dev/EPIC.

Humans↗
PubMed2025

Radiation Without Borders: Unraveling Bystander and Non-Targeted Effects in Oncology.

Radiotherapy (RT) remains a cornerstone of cancer treatment, offering spatially precise cytotoxicity against malignant cells. However, emerging evidence reveals that ionizing radiation (IR) exerts biological effects beyond the targeted tumor volume, manifesting as radiation bystander effects (BEs) and other non-targeted effects (NTEs). These phenomena challenge the traditional paradigm of RT as a localized intervention, highlighting systemic and long-term consequences in non-irradiated tissues. This comprehensive review synthesizes molecular, cellular, and clinical insights about BEs, elucidating the complex intercellular signaling networks gap junctions, cytokines, extracellular vesicles, and oxidative stress that propagate damage, genomic instability, and inflammation. We explore the role of mitochondrial dysfunction, epigenetic reprogramming, immune modulation, and stem cell niche disruption in shaping BEs outcomes. Clinically, BEs contribute to neurocognitive decline, cardiovascular disease, pulmonary fibrosis, gastrointestinal toxicity, and secondary malignancies, particularly in pediatric and long-term cancer survivors. The review also evaluates countermeasures including antioxidants, COX-2 inhibitors, exosome blockers, and FLASH RT, alongside emerging strategies targeting cfCh, inflammasomes, and senescence-associated secretory phenotypes. We discuss the dual nature of BEs: their potential to both harm and heal, underscoring adaptive responses and immune priming in specific contexts. By integrating mechanistic depth with translational relevance, this work posits that radiation BEs are a modifiable axis of RT biology. Recognizing and mitigating BEs is imperative for optimizing therapeutic efficacy, minimizing collateral damage, and enhancing survivorship outcomes. This review advocates for a paradigm shift in RT planning and post-treatment care, emphasizing precision, personalization, and systemic awareness in modern oncology.

Humans↗
PubMed2026

Genome-wide CRISPR screens map synthetic lethal interactions across recurrent cancer driver alterations.

Synthetic lethality (SL) provides a treatment paradigm for targeting cancer with alterations in driver genes that are not conventionally druggable, including tumor suppressor genes. We execute a series of genome-wide CRISPR screens using functionally validated isogenic cell lines and conduct a large-scale SL analysis using data from the cancer dependency map (DepMap). We chart SL interactions across 15 driver alterations: FBXW7, CCNE1, CDK12, ARID1A, KMT2D, DNMT3A, TET2, KEAP1, STK11, IDH1, SF3B1, SRSF2, U2AF1, chromosome 18q loss, and chromosome 13q loss. We show validation of several SL interactions, including ARID1A and the hexosamine biosynthetic pathway aminotransferase GFPT1, STK11 with CAMK protein kinase MARK2, FBXW7 and the CDK1 regulatory kinase PKMYT1, and CCNE1 amplification and the anaphase-promoting complex or cyclosome (APC/C). In summary, this study offers a rich resource of genetic interactions across cancer drivers enabling the discovery of biological insights and drug targets for future therapeutic development.

CP: cancer↗