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PubMed2025

Molecular Biomarker Testing Patterns and Turnaround Time in US Patients With Advanced Non-Small Cell Lung Cancer.

BACKGROUND: Patients with advanced non-small cell lung cancer (aNSCLC) are recommended to undergo molecular testing for targetable genomic alterations. However, as high-throughput methods are increasingly used, long test turnaround time (TAT) may lead to lower receipt of appropriate targeted therapy. Guidelines recommend a 2-week TAT for ALK and EGFR testing, 2 prevalent pathogenic alterations with highly effective targeted therapies. PATIENTS AND METHODS: Using an electronic health record-derived, deidentified database, we conducted a retrospective cohort study of patients with aNSCLC diagnosed between 2011 and 2023 who received testing for ≥1 of 8 molecular markers. We assessed the number of biomarkers tested per patient, testing modality, and TAT (defined as the interval between specimen collection and result date) over time. We also evaluated patients with ALK/EGFR-altered aNSCLC who initiated early nontargeted treatment prior to test result availability, examining associations with TAT and clinical outcomes. RESULTS: The study sample comprised 33,945 patients, with a mean age of 68.2 years; 49.4% were female, 58.3% were White, and 83.4% reported a history of smoking. From 2011 to 2023, the mean number of biomarkers tested per patient (range, 2.0-6.8) and the use of next-generation sequencing (NGS) increased, whereas the mean TAT converged to 3 weeks. Fewer than half of the patients with ALK/EGFR-altered aNSCLC had a TAT of ≤2 weeks, and 1 in 8 initiated early nontargeted treatment. Longer TAT was associated with early nontargeted treatment when analyzed as both a continuous variable (odds ratio, 1.83 per week) and a binary variable (TAT >2 vs ≤2 weeks; odds ratio, 6.02). Early treatment was associated with worse median progression-free survival (9 vs 11 months) in patients with ALK/EGFR-altered aNSCLC. CONCLUSIONS: Biomarker testing and NGS use have increased over time in US patients with aNSCLC. TAT has plateaued and remains longer than recommended in consensus guidelines. Longer TAT was associated with early nontargeted therapy in patients with ALK+/EGFR+ aNSCLC, leading to suboptimal first-line treatment and poorer clinical outcomes.

Humans↗
PubMed2025

Proposal of real-world solutions for the implementation of predictive biomarker testing in patients with operable non-small cell lung cancer.

The implementation of biomarker testing for targeted therapies and immune checkpoint inhibitors is a cornerstone in the management of metastatic and locally advanced non-small cell lung cancer (NSCLC), playing a pivotal role in guiding treatment decisions and patient care. The emergence of precision medicine in the realm of operable NSCLC has been marked by the recent approvals of osimertinib, atezolizumab, nivolumab, pembrolizumab and alectinib for early-stage disease, signifying a shift towards more tailored therapeutic strategies. Concurrently, the landscape of this disease is rapidly evolving, with several further pending approvals and numerous clinical trials in progress. To harness the benefits of these innovative neo-adjuvant and adjuvant therapies, the integration of predictive biomarker testing into standard clinical protocols is imperative for patients with operable NSCLC. A multidisciplinary international consortium has identified three primary obstacles impeding the effective testing of patients with operable NSCLC. These challenges encompass the limited number of test requests by physicians, the inadequacy of tissue samples for comprehensive testing, and the prevalence of cost-reduction measures leading to suboptimal testing practices. This review delineates the aforementioned challenges and proposed solutions, and strategic recommendations aimed at enhancing the testing process. By addressing these issues, we strive to optimize patient outcomes in operable NSCLC, ensuring that individuals receive the most appropriate and effective care based on their unique disease profile.

Humans↗
PubMed2026

A Boveri perspective on cancer biomarker testing using artificial intelligence.

Artificial intelligence (AI) can predict genomic alterations from histology, yet its adoption is slowed by a lack of trust. We argue that deliberate morphology (i.e., a cognitive understanding of histological features supported by standardized annotations) creates a bidirectional feedback loop between clinical practice and model outputs.We translate these observations into an actionable hypothesis for clinical and computational teams: that by enhancing explainability, deliberate morphology could facilitate the responsible deployment of AI biomarkers in oncology.

Journal Article↗
PubMed2026

Accelerating Lung Cancer Management Through Previsit Liquid Biopsy: Results From the LUNG-FAST Pilot Study.

BACKGROUND: Timely molecular profiling is essential for treatment selection in non-small cell lung cancer (NSCLC), yet delays in biomarker testing remain common. We evaluated the feasibility and early clinical impact of a nurse navigator-driven workflow to initiate liquid biopsy before the initial oncology visit. METHODS: LUNG-FAST (Liquid Biopsy for Urgent Neoplastic Genomic Profiling Focused Accelerated Stratification and Testing) was a 4-month prospective pilot at a tertiary cancer center. Intake nurse navigators identified eligible patients with suspected or newly diagnosed lung cancer and facilitated previsit liquid biopsy ordering. Feasibility, turnaround times, genomic findings, and early clinical outcomes were assessed. RESULTS: Among 64 patients, intake nurse navigators identified 94% (60/64) of eligible cases. Liquid biopsy was ordered in 58 patients, with 62% (36/58) placed before the initial oncology visit. Median turnaround time from blood draw to results was 8.5 days for commercial testing and 12.5 days for institutional testing. FDA-actionable genomic alterations were identified in 34% (22/64) of patients, while an additional 11% (7/64) harbored clinically relevant, non-FDA-actionable alterations. Overall, FDA-actionable or clinically relevant alterations were identified in 45% (29/64), with 22% detected by liquid biopsy and an additional 23% by tissue-only profiling. Median time from new patient visit to systemic therapy was 26 days. CONCLUSIONS: A nurse navigator-driven workflow enabling previsit liquid biopsy is feasible and identifies actionable genomic alterations in a substantial proportion of patients with lung cancer. Plasma and tissue profiling are complementary, and earlier plasma-based testing may expedite treatment decision-making while highlighting opportunities to optimize biomarker testing workflows.

Humans↗
PubMed2026

Molecular Profiling Across 80,000 Patients With Lung Cancer.

INTRODUCTION: Biomarker testing is an essential component of optimal therapeutic management in NSCLC, enabling the use of both Food and Drug Administration-approved and emerging targeted therapies. Despite well-established biomarker testing guidelines and the availability of many approved targeted therapies, a substantial proportion of patients with advanced NSCLC are not benefiting from precision oncology. In this study, we analyze the distribution of actionable genomic alterations across histologic subtypes and clinicodemographic subgroups of NSCLC using data in 82,328 samples profiled with a single comprehensive genomic profiling assay, aiming to support universal molecular testing across all NSCLC subtypes to ensure equitable access to available therapeutics. METHODS: This is an observational retrospective analysis on histologically confirmed NSCLC cases tested with comprehensive genomic profiling by next-generation sequencing between 2014 and 2022 using Foundation One/Foundation CDx. All cases were centrally reviewed by board-certified anatomic pathologist to determine histologic type and subtype. RESULTS: A total of 82,328 patients with NSCLC were included. An actionable genomic alteration (GA) was found in 35.1% of the cases. Lung adenocarcinoma (LUAD) and adenosquamous carcinoma were more frequently associated with actionable GA (45.8% and 40.9%, respectively) as compared with sarcomatoid (29.1%), not otherwise specified (27.6%), large cell (21.1%), and squamous cell (6.5%) histologies. Sarcomatoid histology had the highest METex14 skipping mutation (mut) frequency (9.95% versus 2.43% in LUAD). Tumor mutation burden more than or equal to 10 mut/Mb was associated with histology (50.91% in large cell, 40.79% in not otherwise specified, 39.08% in squamous cell, and 36.30% in sarcomatoid versus 31.22% in LUAD and 29.22% in adenosquamous carcinoma). Patients with actionable GA had usually a low tumor mutation burden (80.88%). A significant correlation (p < 0.005) between age and actionable GA was reported for BRAF/ERBB2 muts, ALK/RET/ROS1 rearrangements, and MET amplification. EGFR actionable muts and KRAS G12C were more frequently observed in females, whereas no significant correlation between sex and other GA was observed. Finally, genetic ancestry analyses revealed a strong correlation for EGFR actionable muts and South/East Asia and America, but not for other GA. CONCLUSIONS: This is the largest NSCLC data set analyzed for biomarker distribution across histologies, age, sex, and genetic ancestry. This data set confirms sufficient enough biomarker prevalence across many histologic subtypes of NSCLC, providing reassurance that all NSCLC cases should be considered for biomarker workup.

Humans↗
PubMed2026

Prevalence of Claudin 18.2 Expression in Gastric and Gastroesophageal Junction Adenocarcinoma: A Systematic Review and Meta-Analysis.

BACKGROUND: Claudin 18 isoform 2 (CLDN18.2) has emerged as a clinically validated therapeutic target in gastric and gastroesophageal junction (GEJ) adenocarcinoma following the regulatory approval of zolbetuximab in combination with first-line chemotherapy. Accurate prevalence data at the clinically validated immunohistochemical threshold are essential for patient selection, healthcare resource planning, and treatment strategy. Reported prevalence estimates vary widely across studies due to differences in populations, methodologies, and immunohistochemical protocols. This systematic review and meta-analysis aimed to generate a robust pooled prevalence estimate of CLDN18.2 expression at the threshold used in pivotal phase III trials. METHODS: PubMed, Embase, and the Cochrane Library were searched from database inception through March 12th, 2026. Studies reporting CLDN18.2 expression in gastric or gastroesophageal junction adenocarcinoma using the &#x2265;&#x2009;75% moderate-to-strong membranous staining threshold were included. Prevalence proportions were pooled using a random-effects model with logit transformation and restricted maximum-likelihood estimation of between-study variance. Heterogeneity was assessed using the I&#xb2; statistic and Cochran's Q test, and a 95% prediction interval was calculated. Pre-specified subgroup analyses assessed antibody clone and geographic region, with additional exploratory analyses according to disease setting and specimen type. Sensitivity analyses were performed to assess the robustness of the pooled estimate. RESULTS: Twenty-two predominantly retrospective cohort studies comprising 12,173 patients were included. The pooled prevalence of CLDN18.2 positivity using a random-effects model was 33.99% (95% CI: 30.13%-38.07%; 95% prediction interval: approximately 18%-55%), with high between-study heterogeneity (I&#xb2; = 92.4%). Subgroup analysis by antibody clone showed no statistically significant difference between studies using the 43-14&#xa0;A clone (32.79%, 95% CI: 28.86%-36.97%) and those using other reported antibody clones (41.74%, 95% CI: 26.76%-58.42%; p&#x2009;=&#x2009;0.281). One study with an unreported antibody clone was excluded from this subgroup analysis. Geographic subgroup analysis excluding the multinational Shitara et al. cohort demonstrated a non-significant trend toward higher prevalence in non-Asian populations (37.85%, 95% CI: 31.59%-44.54%) compared with Asian populations (32.10%, 95% CI: 27.28%-37.34%; p&#x2009;=&#x2009;0.169). All three sensitivity analyses confirmed robustness of the pooled estimate. No significant evidence of publication bias was detected (Egger's test p&#x2009;=&#x2009;0.56). CONCLUSIONS: Approximately one-third of patients with gastric and GEJ adenocarcinoma express CLDN18.2 at the clinically validated&#x2009;&#x2265;&#x2009;75% threshold. However, because the included studies encompassed heterogeneous disease settings and were predominantly HER2-unselected, the pooled estimate should not be interpreted directly as the proportion of patients eligible for zolbetuximab. The estimate was robust across sensitivity analyses and provides an evidence base for understanding CLDN18.2 prevalence and biomarker-testing requirements. Standardisation of immunohistochemical assessment methods is warranted to reduce between-study heterogeneity in future research.

Humans↗
PubMed2026

AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most common chronic liver disease around the world, affecting 33.6% of the adult population (95% CI: 28.1%-39.5%; I 2&#x2009;=&#x2009;99.9%), or roughly one in three. The extent of the liver damage is variable, from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH, formerly NASH), cirrhosis and hepatocellular carcinoma (HCC). Early diagnosis is essential to prevent serious liver damage. Traditional diagnostic techniques such as liver biopsy, imaging, and biomarker testing are all invasive, costly, reduced sensitive to early-stage disease, and they also have variability among observers. Modern diagnostic and prognostic approaches based on the principles of Artificial Intelligence (AI) and specifically on machine learning (ML) and deep learning (DL) have enabled multimodal approaches integrating clinical, imaging and molecular data. This scoping review conducted per PRISMA-ScR guidelines, synthesizes findings from 73 studies (search window 2020-2026) across three dimensions: clinical data driven models, imaging-based classifiers (ultrasound, CT and MRI), and multi-omics (genomics, transcriptomics and proteomics) techniques. Moreover, emergence of models such as U-Net and LiverNet 2.x, classification models like DeepLiverNet and BiLSTM models, as well as transformer frameworks and the identification of biomarkers models are also described. This study also investigates challenges such as data heterogeneity, data interpretability, fairness and real-world clinical application. Finally, important areas of research opportunities and future directions are highlighted to present a developing clinically applicable, explainable and ethical AI solutions to manage MASLD.

MASLD↗
PubMed2026

Genomic Analysis and Clinical Correlation of Non-Small Cell Lung Cancer with Special Reference to Brain Metastasis.

BACKGROUND: Next-generation sequencing (NGS) has improved genomic analysis depth in precision oncology. This study analyzed genomic biomarker testing in stage IV NSCLC, focusing on brain metastasis and clinicopathological correlations. OBJECTIVE: To study molecular markers and clinicopathological correlations in stage IV NSCLC patients, with and without brain metastasis. METHODS: A total of 169 stage IV NSCLC patients were studied from April 2023 to May 2025. Demographic data, clinical presentations, and mutation analyses were assessed using NGS on tissue blocks or liquid biopsies. RESULTS: Among 169 patients, 41.42% (n = 70) had brain metastasis (NSCLC-BM), while 58.58% (n = 99) had no brain metastasis (mNSCLC). Median ages were 51.5 and 56 years, respectively. Adenocarcinoma comprised 95.27% (n = 161) of cases. The cerebral hemisphere was the most common intracranial metastatic site, while skeletal involvement was the most common extracranial site. Headache was the predominant neurological symptom. EGFR mutations were the most common overall. EGFR > TP53 > ALK > other mutations were observed in NSCLC-BM, while EGFR > TP53 > KRAS > other mutations were seen in mNSCLC. Mutation analysis stratified by smoking history (&#x3c7;&#xb2;(1) = 1.347, p = 0.245) and sex (&#x3c7;&#xb2;(1) = 0.0302, p = 0.862) was not statistically significant. The benefit of gefitinib plus chemotherapy in EGFR exon 19 and exon 21 L858R mutations was greater in mNSCLC (log-rank &#x3c7;&#xb2;(1) = 10.813, p = 0.001) than in NSCLC-BM (log-rank &#x3c7;&#xb2;(1) = 3.100, p = 0.078). Median survival was 11 months (95% CI: 7.506-14.494) for NSCLC-BM versus 21 months (95% CI: 8.365-33.635) for mNSCLC, with a statistically significant difference (log-rank &#x3c7;&#xb2;(1) = 8.639, p = 0.003). CONCLUSION: NSCLC-BM showed higher genomic biomarker enrichment (80% vs. 68.68%) but poorer outcomes than mNSCLC. EGFR was the most common targetable mutation, followed by ALK in NSCLC-BM and KRAS in mNSCLC.

Humans↗
PubMed2026

Intramuscular patient-derived xenografts achieve high engraftment rates in gastric cancer: implications for pharmacodynamic testing and genomic biomarker discovery.

BACKGROUND: Gastric cancer (GC) exhibits marked inter-patient heterogeneity, limiting empirical chemotherapy efficacy. Patient-derived xenograft (PDX) models preserve the molecular features of parental tumors and can serve as pharmacodynamic surrogates, but conventional subcutaneous PDX suffers from low engraftment rates. This study evaluated an optimized intramuscular PDX platform for individualized drug testing in GC and applied whole exome sequencing (WES) for biomarker identification (Clinical trial registry: ChiCTR-OOC-17012731). MATERIALS AND METHODS: Ninety-eight treatment-naive GC patients were enrolled between April 2018 and December 2020. Fresh tumor tissues were engrafted into NCG mice by intramuscular transplantation. Drug efficacy was evaluated using tumor cell necrosis rate and Ki-67 expression. WES was performed on 32 engrafted tumorgrafts to characterize driver mutations in fast- and slow-growing subgroups. RESULTS: An engraftment rate of 71.7% (43/60) was achieved, substantially exceeding rates reported in prior studies. Clinical characteristics were independent of engraftment success and outgrowth time (all p&#x2009;>&#x2009;0.05). Fast- and slow-growing tumorgrafts diverged in frequently altered genes: KMT2C, APOB, CDK12 and MSH2 predominated in fast-growing grafts, whereas TP53, CHD3 and TET2 were enriched in slow-growing grafts. Slow-growing tumorgrafts correlated with longer progression-free survival (p&#x2009;=&#x2009;0.02). PDX-guided treatment was associated with improved prognosis. CONCLUSIONS: Intramuscular transplantation into NCG mice yields high engraftment rates for GC PDX. PDX-guided chemotherapy selection is associated with favorable outcomes. Driver mutation divergence between fast- and slow-growing tumorgrafts provides candidate prognostic biomarkers.

Animals↗
PubMed2026

Takotsubo Syndrome: The First Non-Acute Proteomic Analysis by Remote Dried Blood Microsampling.

Takotsubo syndrome (TTS) is an under-recognized form of acute-onset heart failure typically precipitated by stress. While recovery of cardiac function is described over the course of weeks, adverse outcomes after apparent recovery are increasingly recognized. However, the pathophysiology of non-acute manifestations remains poorly understood. We used mass-spectrometry-based discovery proteomics from remotely collected non-acute dried blood microsamples to perform a case-control study in 62 participants with a prior TTS episode (median of 2.24 years prior to sample collection) and 47 reference controls. We quantified 398 unique proteins, and found that agnostic clustering techniques showed separation between TTS and reference control samples. This represents the first proteomic characterization of non-acute TTS. Pathway analysis of the 52 differentially regulated proteins demonstrated enrichment of proteins involved in complement activation, nitric oxide signaling, and with antioxidant activity. These enriched pathways may be suggestive of a persistent cardiomyopathy resulting from or predisposing to TTS.

Humans↗
PubMed2026

MULTIPREVENT: Integrated screening for smoking-related multimorbidity using low-dose chest computed tomography.

OBJECTIVES: Tobacco consumption, combined with individual genetic predispositions, contributes to an age-dependent risk not only for lung cancer but also for other non-communicable diseases (NCDs) such as cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), osteoporosis, and diabetes. The MULTIPREVENT project aims to validate whether low-dose computed tomography (LDCT) of the chest, combined with simple biomarkers, functional tests, and genomic profiling, can serve as an effective tool for comprehensive health assessment and risk prediction of multimorbidity in adults. STUDY DESIGN: The study is based on a prospective epidemiological design involving 3000 participants from the MOLTEST-BIS lung cancer screening cohort (2016-2018). These participants, aged 50-79 years (during MOLTEST-BIS) and with a smoking history of at least 30 pack-years, will undergo two follow-up assessments in 2025-2027 and 2030-2032. METHODS: Each follow-up includes LDCT, spirometry, standardized blood pressure measurement, anthropometric evaluation, biomarker assessment (lipid profile, lipoprotein(a), glycated haemoglobin), and health-related questionnaires. Genetic profiling will be performed using the Illumina Infinium Global Screening Arrays approach to identify inherited predispositions to major NCDs. All data, clinical, imaging (including radiomics), molecular, and genetic, will be integrated through machine learning algorithms to develop AI-based risk prediction models. RESULTS: The MULTIPREVENT study is expected to generate a wide range of scientific, clinical, and infrastructural results that will serve as a foundation for future public health initiatives in integrated prevention. CONCLUSIONS: By linking imaging and biochemical markers, genetic susceptibility, and clinical parameters within a longitudinal design, MULTIPREVENT will establish data-driven, AI-supported prevention strategies aimed at reducing morbidity and mortality among adults exposed to tobacco. The project will also serve as a model for population-based multimorbidity prevention programs.

Humans↗
PubMed2025

3D Cell Culture Models as a Platform for Studying Tumor Progression, Testing Treatment Responses, and Discovering Biomarkers.

In this chapter, we present a detailed protocol for establishing a three-dimensional (3D) multicellular tumor spheroids (MCTSs) model to simulate the tumor microenvironment (ME) associated with metabolic dysfunction-associated steatotic liver disease (MASLD) for the study of hepatocellular carcinoma (HCC) and colorectal cancer (CRC) cell aggressiveness, growth, and metastasis potential. The MASLD microenvironment (MASLD-ME) is recreated by embedding hepatic stellate cells in a collagen I matrix within a Boyden chamber system. The metabolic medium mimics MASLD conditions, enriched with high glucose, fructose, insulin, and fatty acids, to simulate metabolic stresses associated with the disease.In the protocol, cancer cells are loaded in the upper compartment to analyze their migration toward the MASLD-ME, thereby facilitating studies on cancer cell invasiveness and metastatic capacity. This method offers an adaptable, reproducible model to research disease progression and investigate therapeutic interventions, contributing to preclinical research on MASLD-related liver cancer pathophysiology and potential drug responses.

Humans↗
PubMed2026

Next-generation newborn screening: feasibility of combined genetic and biochemical testing for 95 treatable inherited metabolic disorders.

INTRODUCTION: Next-generation sequencing (NGS) is gaining attention in newborn screening (NBS) for its ability to detect treatable genetic disorders, especially those without a biochemical footprint. However, NGS-NBS requires interpreting variants without phenotype information or family trio analysis. Biochemical tests, preferably in dried blood spots (DBS), are therefore useful to confirm the pathogenicity of variants identified by NGS-NBS and increase its specificity and sensitivity. OBJECTIVES: We aimed to explore the potential of combined genetic-biochemical testing for 95 treatable Inherited Metabolic Disorders (IMD) considered eligible for NGS-NBS (100 genes) previously identified by our research group. METHODS: We reviewed the Collaborative Laboratory Integrated Reports (CLIR) and carried out systematic literature reviews in PubMed and Embase to identify biochemical tests for 95 IMD. Biochemical tests conducted on DBS were differentiated from tests that require referral. RESULTS: We identified DBS-biochemical tests for 72 of the 95 IMD (77/100 genes). DBS-based biochemical tests for 55 IMD (60 genes) are already implemented in NBS. For the other 23 IMD, biochemical tests in non-DBS specimens are reported, although some are less sensitive when measured at neonatal age in presymptomatic infants. CONCLUSION: We present a comprehensive overview of current biochemical tests for 95 IMD. These tests can be used to confirm inconclusive NGS-NBS results, and combined genetic-biochemical testing is expected to improve both the negative and positive predictive values of NBS programs.

Humans↗
PubMed2026

Association of immune and proliferation gene signatures and stromal tumor-infiltrating lymphocytes with clinical outcomes in patients with stage I triple-negative breast cancer.

BACKGROUND: One-third of patients with triple-negative breast cancer (TNBC) are diagnosed with stage I tumors. Biomarkers to stratify prognosis in this setting remain a major unmet need. METHODS: Tissue samples and clinicopathologic data were retrieved from consecutive patients with stage I TNBC (defined as ER <10% and HER2-negative) who underwent upfront breast surgery and received standard of care adjuvant systemic therapy at Dana-Farber/Brigham Cancer Center between 2016 and 2021. The TNBC-DX assay (Core Immune Gene [CIG] signature, proliferation signature) was applied to tumor tissue, and stromal tumor-infiltrating lymphocytes (sTILs) were centrally reviewed. Both biomarkers were tested for association with clinical outcomes using the Kaplan-Meier method. RESULTS: A total of 253 patients with stage I TNBC were included. Most tumors were ductal (88.9%) and high-grade (73.1%); 65.2% of patients received adjuvant chemotherapy. With 18 recurrence events observed, the 3-year recurrence-free survival (RFS) in the overall cohort was 95.0% (95% confidence interval [CI]: 92.1% - 98.1%) and the 3-year overall survival was 97.9% (95% CI: 95.9% - 100.0%). No significant differences in RFS were observed by TNBC-DX (n&#x202f;=&#x202f;117 patients) or sTILs (n&#x202f;=&#x202f;123 patients) category. However, a 3-year RFS of 100% (95% CI: 100% - 100%) was observed among the 29 patients with the highest CIG score quartile. A favorable prognosis was also observed in patients with high sTILs (>20%), who experienced a 3-year RFS of 97.0% (95% CI: 90% - 100%). Conversely, a high TNBC-DX proliferation score was numerically associated with poor outcomes, with a 3-year RFS of 83% (95% CI: 68% - 100%). CONCLUSIONS: In this retrospective study, immune and proliferative features showed opposing prognostic trends in stage I TNBC. Their integration may improve risk stratification and warrants further investigation.

Stromal tumor infiltrating lymphocytes (sTILs)↗
PubMed2026

Frequency and clinical features of germline pathogenic variants in sarcoma: a case-control study.

BACKGROUND: Germline multigene panel testing is not yet integrated into standard care for patients with sarcoma. This study aimed to assess the frequency and distribution of germline pathogenic variants in patients with sarcoma compared with cancer-free controls and identify differences between patients with and without germline pathogenic variants. METHODS: This retrospective cohort included 488 sarcoma patients and 2440 cancer-free controls matched 1:5 by age, sex, and ethnicity. Multigene panel testing was performed between 2016 and 2024 at a single germline testing laboratory. The frequency of germline pathogenic variants in selected genes was compared using Fisher exact test with odds ratios (ORs) and 95% confidence intervals. Additionally, within the case-only cohort, clinical characteristics were evaluated to assess associations with the presence of germline pathogenic variants in any gene. RESULTS: Among 488 patients with sarcoma, 67.8% (n&#x2009;=&#x2009;331) were female, with a median age at sarcoma diagnosis of 47&#x2009;years (range = 0.5-87.5 years). Cases had a higher frequency of germline pathogenic variants compared with controls (26.2% vs 10.5%; OR = 3.05, P&#x2009;<&#x2009;.001). We observed a higher frequency of germline pathogenic variants in TP53, BRCA2, CHEK2, NF1, SDHA, BRIP1, POT1, RB1, and CDH1 among patients with sarcoma compared with controls. Age at sarcoma diagnosis did not differ between groups. CONCLUSIONS: This study confirms the high detection rate of germline pathogenic variants in patients with sarcoma and describes several associated genes. These findings indicate that age at sarcoma diagnosis may not reliably predict germline pathogenic variants. Expanding germline testing for patients with sarcoma would enhance personalized treatment strategies and familial risk assessment.

Humans↗
PubMed2026

Large-Scale Plasma Proteomics Enhances Prediction of Liver-Related Events Among Individuals With Prediabetes and Type 2 Diabetes: A Prospective Cohort Study in the UK Biobank.

OBJECTIVE: To develop a protein risk score (ProRS) for predicting liver-related events (LREs) in patients with diabetes and compare its predictive performance with the Fibrosis-4 Index (FIB-4) and an established polygenic risk score. RESEARCH DESIGN AND METHODS: This prospective cohort study included 13&#x2009;516 individuals with prediabetes and type 2 diabetes (T2D) from the UK Biobank. Cox proportional hazards models and LASSO regression were applied to identify proteins associated with incident LREs and construct the ProRS. Predictive performance was assessed using Harrell's C-index, time-dependent area under the receiver operating characteristic curve, net reclassification improvement and integrated discrimination improvement. RESULTS: Over a median follow-up of 13.5&#x2009;years, 171 (1.3%) incident LREs occurred. We identified 877 proteins associated with LRE risk, primarily enriched in inflammatory signalling, extracellular matrix remodelling and complement/coagulation cascades. In the training set, we developed a 24-protein ProRS (C-index, 0.842; 95% CI 0.797-0.884) that stratified individuals into low-, medium- and high-risk groups, with 10-year cumulative incidences of LREs of 0.2%, 1.2% and 14.2%, respectively. Compared with the low-risk group, the hazard ratio for LREs was 57.1 (95% CI 31.9-102) in the high-risk group. In the internal validation set, the ProRS model (C-index, 0.876; 95% CI 0.827-0.920) accurately predicted both short- and long-term LREs and outperformed FIB-4 index (C-index, 0.733; 95% CI 0.657-0.807) and polygenic risk score (C-index, 0.636; 95% CI 0.564-0.706). CONCLUSIONS: The protein risk score demonstrated superior performance compared with the FIB-4 index and the polygenic risk score in predicting incident LREs among individuals with prediabetes and T2D. The score allows stratification of individuals according to liver-related risk, though external validation in multi-ethnic cohorts is warranted.

Humans↗
PubMed2026

Machine Learning-Driven Prediction of Coronary Artery Disease Risk Based on UK Biobank Plasma Proteomics.

BACKGROUND: Coronary artery disease (CAD) is a leading global cause of mortality, yet the predictive accuracy of conventional risk models is limited. Here, we integrate conventional risk factors, polygenic risk scores, and large-scale proteomics to develop a unified model for enhanced CAD risk prediction. METHODS: Using data from UK Biobank, participants with plasma proteomics and genetic risk data were included after excluding prevalent CAD. Participants from England were split into training (n=32&#x2009;330) and internal validation (n=13&#x2009;857) sets, and Scotland/Wales participants formed an external validation set (n=5775). Incident CAD was ascertained from linked health records. A 202-protein proteomic risk score was derived by least absolute shrinkage and selection operator Cox regression, and CatBoost models were trained using conventional risk factors alone and with incremental addition of polygenic risk scores and protein proteomic risk scores; Shapley Additive Explanations-guided forward selection identified a compact protein panel. RESULTS: Across cohorts, the median age was 58&#x2009;years and &#x223c;45% were men. Protein proteomic risk score was dose-dependently associated with CAD risk. Compared with conventional risk factors alone, integrating polygenic risk scores and protein proteomic risk scores improved discrimination, with the area under the curve increasing from 0.750 (95% CI, 0.732-0.767) to 0.789 (95% CI, 0.772-0.805) in internal validation and from 0.717 (95% CI, 0.683-0.750) to 0.762 (95% CI, 0.732-0.791) in external validation. A 9-protein panel (GDF15 [growth differentiation factor 15], MMP12 [matrix metalloproteinase 12], NPPB [natriuretic peptide B], PGF [placental growth factor], REN [renin], ADGRG2 [adhesion G-protein coupled receptor], ACE2 [angiotensin-converting enzyme 2], CDCP1 [CUB domain-containing protein 1], CXCL17 [C-X-C motif chemokine ligand 17)]) captured most proteomic predictive information. CONCLUSIONS: Our findings demonstrate that integrating conventional risk factors, polygenic risk scores, and proteomic data improves CAD risk prediction. This study highlights the utility of proteomics in precision cardiovascular medicine and simplified risk stratification tools.

Humans↗