Search Results (43)

Circadian rhythms are intrinsic 24 h cycles that regulate metabolic processes across multiple tissues, with skeletal muscle emerging as a central node in this temporal network. Muscle clocks govern gene expression, fuel utilisation, mitochondrial function, and insulin sensitivity, thereby maintaining systemic energy homeostasis. However, circadian misalignment, whether due to behavioural disruption, nutrient excess, or metabolic disease, impairs these rhythms and contributes to insulin resistance, and the development of obesity, and type 2 diabetes mellitus. Notably, the muscle clock remains responsive to non-photic cues, particularly exercise, which can reset and amplify circadian rhythms even in metabolically impaired states. This work synthesises multi-level evidence from rodent models, human trials, and in vitro studies to elucidate the role of skeletal muscle clocks in circadian metabolic health. It explores how exercise entrains the muscle clock via molecular pathways involving AMPK, SIRT1, and PGC-1α, and highlights the time-of-day dependency of these effects. Emerging data demonstrate that optimally timed exercise enhances glucose uptake, mitochondrial biogenesis, and circadian gene expression more effectively than time-agnostic training, especially in individuals with metabolic dysfunction. Finally, findings are integrated from multi-omic approaches that have uncovered dynamic, time-dependent molecular signatures that underpin circadian regulation and its disruption in obesity. These technologies are uncovering biomarkers and signalling nodes that may inform personalised, temporally targeted interventions. By combining mechanistic insights with translational implications, this review positions skeletal muscle clocks as both regulators and therapeutic targets in metabolic disease. It offers a conceptual framework for chrono-exercise strategies and highlights the promise of multi-omics in developing precision chrono-medicine approaches aimed at restoring circadian alignment and improving metabolic health outcomes. Full article

Epidermal growth factor receptor (EGFR) mutations occur in approximately 10–20% of Caucasian and up to 50% of Asian patients with oncogene-addicted non-small cell lung cancer (NSCLC). Most frequently, alterations include exon 19 deletions and exon 21 L858R mutations, which confer sensitivity to EGFR tyrosine kinase inhibitors (TKIs). In the last decade, the third-generation EGFR-TKI osimertinib has represented the first-line standard of care for EGFR-mutant NSCLC. However, the development of acquired mechanisms of resistance significantly impacts long-term outcomes and represents a major therapeutic challenge. The mesenchymal–epithelial transition (MET) gene amplification and MET protein overexpression have emerged as prominent EGFR-independent (off-target) resistance mechanisms, detected in approximately 25% of osimertinib-resistant NSCLC. Noteworthy, variability in diagnostic thresholds, which differ between fluorescence in situ hybridization (FISH) and next-generation sequencing (NGS) platforms, complicates its interpretation and clinical applicability. To address MET-driven resistance, several therapeutic strategies have been explored, including MET-TKIs, antibody–drug conjugates (ADCs), and bispecific monoclonal antibodies, and dual EGFR/MET inhibition has emerged as the most promising strategy. In this context, the bispecific EGFR/MET antibody amivantamab has demonstrated encouraging efficacy, regardless of MET alterations. Furthermore, the combination of the ADC telisotuzumab vedotin and osimertinib has been associated with activity in EGFR-mutant, c-MET protein-overexpressing, osimertinib-resistant NSCLC. Of note, several novel agents and combinations are currently under clinical development. The success of these targeted approaches relies on tissue re-biopsy at progression and accurate molecular profiling. Yet, tumor heterogeneity and procedural limitations may challenge the feasibility of re-biopsy, making biomarker-agnostic strategies viable alternatives. Full article

The human genome has a profound impact on human health and disease detection. Carcinoma (cancer) is one of the prominent diseases that majorly affect human health and requires the development of different treatment strategies and targeted therapies based on effective disease detection. Therefore, our research aims to identify biomarkers associated with distinct cancer types (gastric, lung, and breast) using machine learning. In the current study, we have analyzed the human genomic data of gastric cancer, breast cancer, and lung cancer patients using XGB-BIF (i.e., XGBoost-Driven Biomarker Identification Framework for detecting cancer). The proposed framework utilizes feature selection via XGBoost (eXtreme Gradient Boosting), which captures feature interactions efficiently and takes care of the non-linear effects in the genomic data. The research progressed by training XGBoost on the full dataset, ranking the features based on the Gain measure (importance), followed by the classification phase, which employed support vector machines (SVM), logistic regression (LR), and random forest (RF) models for classifying cancer-diseased and non-diseased states. To ensure interpretability and transparency, we also applied SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), enabling the identification of high-impact biomarkers contributing to risk stratification. Biomarker significance is discussed primarily via pathway enrichment and by studying survival analysis (Kaplan–Meier curves, Cox regression) for identified biomarkers to strengthen translational value. Our models achieved high predictive performance, with an accuracy of more than 90%, to classify and link genomic data into diseased (cancer) and non-diseased states. Furthermore, we evaluated the models using Cohen’s Kappa statistic, which confirmed strong agreement between predicted and actual risk categories, with Kappa scores ranging from 0.80 to 0.99. Our proposed framework also achieved strong predictions on the METABRIC dataset during external validation, attaining an AUC-ROC of 93%, accuracy of 0.79%, and Kappa of 74%. Through extensive experimentation, XGB-BIF identified the top biomarker genes for different cancer datasets (gastric, lung, and breast). CBX2, CLDN1, SDC2, PGF, FOXS1, ADAMTS18, POLR1B, and PYCR3 were identified as important biomarkers to identify diseased and non-diseased states of gastric cancer; CAVIN2, ADAMTS5, SCARA5, CD300LG, and GIPC2 were identified as important biomarkers for breast cancer; and CLDN18, MYBL2, ASPA, AQP4, FOLR1, and SLC39A8 were identified as important biomarkers for lung cancer. XGB-BIF could be utilized for identifying biomarkers of different cancer types using genetic data, which can further help clinicians in developing targeted therapies for cancer patients. Full article

Early diagnosis is critical for improving outcomes in cancer patients; however, the application of diagnostic markers derived from serum proteomic screening remains challenging. Artificial intelligence (AI), encompassing deep learning and machine learning (ML), has gained increasing prominence across various scientific disciplines. In this study, we utilized cervical cancer (CC) as a model to develop an AI-driven pipeline for the identification and validation of serum biomarkers for early cancer diagnosis, leveraging mass spectrometry-based proteomics data. By processing and normalizing serum polypeptide differential peaks from 240 patients, we employed eight distinct ML algorithms to classify and analyze these differential polypeptide peaks, subsequently constructing receiver operating characteristic (ROC) curves and confusion matrices. Key performance metrics, including accuracy, precision, recall, and F1 score, were systematically evaluated. Furthermore, by integrating feature importance values, Shapley values, and local interpretable model-agnostic explanation (LIME) values, we demonstrated that the diagnostic area under the curve (AUC) achieved by our multi-dimensional learning models approached 1, significantly outperforming the diagnostic AUC of single markers derived from the PRIDE database. These findings underscore the potential of proteomics-driven integrated machine learning as a robust strategy to enhance early cancer diagnosis, offering a promising avenue for clinical translation. Full article

This review comprehensively analyzes the current landscape of tumor-agnostic therapies in oncology. Tumor-agnostic therapies are designed to target specific molecular alterations rather than the primary site of the tumor, representing a shift in cancer treatment. We discuss recent approvals by regulatory agencies such as the FDA and EMA, highlighting therapies that have demonstrated efficacy across multiple cancer types sharing common alterations. We delve into the trial methodologies that underpin these approvals, emphasizing innovative designs such as basket trials and umbrella trials. These methodologies present unique advantages, including increased efficiency in patient recruitment and the ability to assess drug efficacy in diverse populations rapidly. However, they also entail certain challenges, including the need for robust biomarkers and the complexities of regulatory requirements. Moreover, we examine the promising prospects for developing therapies for rare cancers that exhibit common molecular targets typically associated with more prevalent malignancies. By synthesizing these insights, this review underscores the transformative potential of tumor-agnostic therapies in oncology. It offers a pathway for personalized cancer treatment that transcends conventional histology-based classification. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and neuropsychiatric symptoms resulting from the loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate the loss of dopaminergic neurons in the striatum. This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson’s Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. By focusing on a single biomarker, our approach simplifies PD diagnosis, integrates seamlessly into clinical workflows, and provides interpretable, actionable insights. Although DATSCAN has limitations in detecting early-stage PD, our study demonstrates the potential of ML to enhance diagnostic precision, contributing to improved clinical decision-making and patient outcomes. Full article

Background: Early-phase clinical trials offer a unique opportunity for patients with cancer. These trials often mandate biopsies to collect tumor tissue for research purposes, requiring patients to undergo invasive procedures. Some trials mandate molecular prescreening, but the success of these analyses relies on the quality and quantity of the tested materials. Additionally, bioptic procedures may result in complications. Methods: We retrospectively examined the records of patients referred to the Early Drug Development (EDD) Unit of the European Institute of Oncology who underwent biopsies for research purposes between January 2014 and December 2022. Our objective was to assess the safety of biopsy procedures and adequacy of the samples for NGS testing. Results: In total, 355 out of 731 patients (48.6%) underwent protocol-mandated biopsies. The most frequent sites of biopsy were the liver, lymph nodes, skin, and breast. Histological diagnosis was achieved in 349 (98%) patients, and NGS testing was successfully conducted in 111/127 (88.4%) cases. Of the 16 unsuccessful NGS attempts, 9 were performed on liver tissue. Unsuccessful NGS testing was attributed to poor sample quality and/or quantity, and the success rate varied significantly based on the specific tests attempted. Complications occurred in a small proportion of patients (4.8%), and none were serious. Conclusions: The non-negligible failure rate of NGS testing highlights the crucial need for implementing specific guidelines and Standard Operating Procedures for samples intended for NGS. With the use of a risk-based biopsy framework to guide clinical decisions, procedure-related complications may be minimized. Full article

Recent advancements in oncology have led to the development of histology-agnostic therapies, which target genetic alterations irrespective of the tumor’s tissue of origin. This review aimed to provide a comprehensive update on the current state of histology-agnostic drug development, focusing on key therapies, including pembrolizumab, larotrectinib, entrectinib, dostarlimab, dabrafenib plus trametinib, selpercatinib, trastuzumab deruxtecan, and reprotrectinib. We performed a detailed analysis of each therapy’s mechanism of action, clinical trial outcomes, and associated biomarkers. The review further explores challenges in drug resistance, such as adaptive signaling pathways and neoantigen variability, as well as diagnostic limitations in identifying optimal patient populations. While these therapies have demonstrated efficacy in various malignancies, significant hurdles remain, including intratumoral heterogeneity and resistance mechanisms that diminish treatment effectiveness. We propose considerations for refining trial designs and emerging biomarkers, such as tumor neoantigen burden, to enhance patient selection. These findings illustrate the transformative potential of histology-agnostic therapies in precision oncology but highlight the need for continued research to optimize their use and overcome existing barriers. Full article

Circulating tumor DNA (ctDNA) is a promising cancer biomarker, but accurately detecting tumor mutations in cell-free DNA (cfDNA) is challenging due to their low frequency and sequencing errors. Our study benchmarked Mutect2, VarScan2, shearwater, and DREAMS-vc using deep targeted sequencing of cfDNA with Unique Molecular Identifiers (UMIs) from 111 colorectal cancer patients. Performance was assessed at both the mutation level (distinguish tumor variants from errors) and the sample level (detect if an individual has cancer). Additionally, we investigated the effects of various UMI grouping and consensus strategies. The shearwater-AND variant calling method demonstrated the highest precision in detecting tumor-derived mutations from plasma, and reached the highest ROC-AUC of 0.984 for sample classification in tumor-informed cfDNA analyses. DREAMS-vc exhibited the highest ROC-AUC of 0.808 for sample classification in tumor-agnostic studies. We also found that sequencing depth differences in PBMCs could lead to false positives, particularly with VarScan2 and Mutect2, which was addressed by downsampling to equivalent mean depths. Additionally, network-based UMI grouping methods outperformed those using identical UMIs when all reads were retained. Our findings emphasize that the optimal variant caller depends on the study context—whether focused on mutation or sample classification, and whether conducted under tumor-informed or tumor-agnostic conditions. Full article

The regulatory approvals of tumor-agnostic therapies have led to the re-evaluation of the drug development process. The conventional models of drug development are histology-based. On the other hand, the tumor-agnostic drug development of a new drug (or combination) focuses on targeting a common genomic biomarker in multiple cancers, regardless of histology. The basket-like clinical trials with multiple cohorts allow clinicians to evaluate pan-cancer efficacy and toxicity. There are currently eight tumor agnostic approvals granted by the Food and Drug Administration (FDA). This includes two immune checkpoint inhibitors, and five targeted therapy agents. Pembrolizumab is an anti-programmed cell death protein-1 (PD-1) antibody that was the first FDA-approved tumor-agnostic treatment for unresectable or metastatic microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR) solid tumors in 2017. It was later approved for tumor mutational burden-high (TMB-H) solid tumors, although the TMB cut-off used is still debated. Subsequently, in 2021, another anti-PD-1 antibody, dostarlimab, was also approved for dMMR solid tumors in the refractory setting. Patients with fusion-positive cancers are typically difficult to treat due to their rare prevalence and distribution. Gene rearrangements or fusions are present in a variety of tumors. Neurotrophic tyrosine kinase (NTRK) fusions are present in a range of pediatric and adult solid tumors in varying frequency. Larotrectinib and entrectinib were approved for neurotrophic tyrosine kinase (NTRK) fusion-positive cancers. Similarly, selpercatinib was approved for rearranged during transfection (RET) fusion-positive solid tumors. The FDA approved the first combination therapy of dabrafenib, a B-Raf proto-oncogene serine/threonine kinase (BRAF) inhibitor, plus trametinib, a mitogen-activated protein kinase (MEK) inhibitor for patients 6 months or older with unresectable or metastatic tumors (except colorectal cancer) carrying a BRAF^V600E mutation. The most recent FDA tumor-agnostic approval is of fam-trastuzumab deruxtecan-nxki (T-Dxd) for HER2-positive solid tumors. It is important to identify and expeditiously develop drugs that have the potential to provide clinical benefit across tumor types. Full article

Cancer accounted for 10 million deaths in 2020, nearly one in every six deaths annually. Despite advancements, the contemporary clinical management of human neoplasms faces a number of challenges. Surgical removal of tumor tissues is often not possible technically, while radiation and chemotherapy pose the risk of damaging healthy cells, tissues, and organs, presenting complex clinical challenges. These require a paradigm shift in developing new therapeutic modalities moving towards a more personalized and targeted approach. The tumor-agnostic philosophy, one of these new modalities, focuses on characteristic molecular signatures of transformed cells independently of their traditional histopathological classification. These include commonly occurring DNA aberrations in cancer cells, shared metabolic features of their homeostasis or immune evasion measures of the tumor tissues. The first dedicated, FDA-approved tumor-agnostic agent’s profound progression-free survival of 78% in mismatch repair-deficient colorectal cancer paved the way for the accelerated FDA approvals of novel tumor-agnostic therapeutic compounds. Here, we review the historical background, current status, and future perspectives of this new era of clinical oncology. Full article

Electromyography (EMG) proves invaluable myoelectric manifestation in identifying neuromuscular alterations resulting from ischemic strokes, serving as a potential marker for diagnostics of gait impairments caused by ischemia. This study aims to develop an interpretable machine learning (ML) framework capable of distinguishing between the myoelectric patterns of stroke patients and those of healthy individuals through Explainable Artificial Intelligence (XAI) techniques. The research included 48 stroke patients (average age 70.6 years, 65% male) undergoing treatment at a rehabilitation center, alongside 75 healthy adults (average age 76.3 years, 32% male) as the control group. EMG signals were recorded from wearable devices positioned on the bicep femoris and lateral gastrocnemius muscles of both lower limbs during indoor ground walking in a gait laboratory. Boosting ML techniques were deployed to identify stroke-related gait impairments using EMG gait features. Furthermore, we employed XAI techniques, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Anchors to interpret the role of EMG variables in the stroke-prediction models. Among the ML models assessed, the GBoost model demonstrated the highest classification performance (AUROC: 0.94) during cross-validation with the training dataset, and it also overperformed (AUROC: 0.92, accuracy: 85.26%) when evaluated using the testing EMG dataset. Through SHAP and LIME analyses, the study identified that EMG spectral features contributing to distinguishing the stroke group from the control group were associated with the right bicep femoris and lateral gastrocnemius muscles. This interpretable EMG-based stroke prediction model holds promise as an objective tool for predicting post-stroke gait impairments. Its potential application could greatly assist in managing post-stroke rehabilitation by providing reliable EMG biomarkers and address potential gait impairment in individuals recovering from ischemic stroke. Full article

Advances in molecular tumor diagnostics have transformed cancer care. However, it remains unclear whether precision oncology has the same impact and transformative nature across all malignancies. We conducted a retrospective analysis of patients with human papillomavirus (HPV)-related gynecologic malignancies who underwent comprehensive molecular profiling and subsequent discussion at the interdisciplinary Molecular Tumor Board (MTB) of the University Hospital, LMU Munich, between 11/2017 and 06/2022. We identified a total cohort of 31 patients diagnosed with cervical (CC), vaginal or vulvar cancer. Twenty-two patients (fraction: 0.71) harbored at least one mutation. Fifteen patients (0.48) had an actionable mutation and fourteen (0.45) received a recommendation for a targeted treatment within the MTB. One CC patient received a biomarker-guided treatment recommended by the MTB and achieved stable disease on the mTOR inhibitor temsirolimus for eight months. Factors leading to non-adherence to MTB recommendations in other patient cases included informed patient refusal, rapid deterioration, stable disease, or use of alternative targeted but biomarker-agnostic treatments such as antibody–drug conjugates or checkpoint inhibitors. Despite a remarkable rate of actionable mutations in HPV-related gynecologic malignancies at our institution, immediate implementation of biomarker-guided targeted treatment recommendations remained low, and access to targeted treatment options after MTB discussion remained a major challenge. Full article

High microsatellite instability (MSI-H) derives from genomic hypermutability due to deficient mismatch repair function. Colorectal (CRC) and endometrial cancers (EC) are the tumor types that more often present MSI-H. Anti-PD(L)-1 antibodies have been demonstrated to be agnostically effective in patients with MSI-H cancer, but 50–60% of them do not respond to single-agent treatment, highlighting the necessity of expanding their treatment opportunities. Ipilimumab (anti-CTLA4) is the only immune checkpoint inhibitor (ICI) non-targeting PD(L)-1 that has been approved so far by the FDA for MSI-H cancer, namely, CRC in combination with nivolumab. Anti-TIM3 antibody LY3321367 showed interesting clinical activity in combination with anti-PDL-1 antibody in patients with MSI-H cancer not previously treated with anti-PD(L)-1. In contrast, no clinical evidence is available for anti-LAG3, anti-TIGIT, anti-BTLA, anti-ICOS and anti-IDO1 antibodies in MSI-H cancers, but clinical trials are ongoing. Other immunotherapeutic strategies under study for MSI-H cancers include vaccines, systemic immunomodulators, STING agonists, PKM2 activators, T-cell immunotherapy, LAIR-1 immunosuppression reversal, IL5 superagonists, oncolytic viruses and IL12 partial agonists. In conclusion, several combination therapies of ICIs and novel strategies are emerging and may revolutionize the treatment paradigm of MSI-H patients in the future. A huge effort will be necessary to find reliable immune biomarkers to personalize therapeutical decisions. Full article

Circulating tumor DNA (ctDNA) has recently emerged as a real-time prognostic and predictive biomarker for monitoring cancer patients. Here, we aimed to ascertain whether tumor-agnostic ctDNA testing would be a feasible strategy to monitor disease progression and therapeutic response in muscle-invasive bladder cancer (MIBC) patients after radical cystectomy (RC). Forty-two MIBC patients who underwent RC were prospectively included. Blood samples from these patients were collected at different follow-up time points. Two specific mutations (TERT c.1-124C>T and ATM c.1236-2A>T) were analyzed in the patients’ plasma samples by droplet digital PCR to determine their ctDNA status. During a median follow-up of 21 months, 24% of patients progressed in a median of six months. ctDNA status was identified as a prognostic biomarker of tumor progression before RC and 4 and 12 months later (HR 6.774, HR 3.673, and HR 30.865, respectively; p < 0.05). Lastly, dynamic changes in ctDNA status between baseline and four months later were significantly associated with patient outcomes (p = 0.045). In conclusion, longitudinal ctDNA analysis using a tumor-agnostic approach is a potential tool for monitoring MIBC patients after RC. The implementation of this testing in a clinical setting could improve disease management and patients’ outcomes. Full article