Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 28 appraisals
Histochemistry and cell biology
Molecular plasticity of LAMA3 across the disease spectrum: pathogenic mechanisms and clinical translation.
The laminin α3 chain, encoded by LAMA3, constitutes a principal component of laminin-332 (LN-332), an essential extracellular matrix (ECM) glycoprotein governing cell adhesion, proliferation, and tissue homeostasis. This systematic review consolidates current evidence on the molecular features and regulatory mechanisms of LAMA3, including its roles in PI3K/Akt, epithelial-mesenchymal transition (EMT), and Hippo-YAP signaling, as well as epigenetic and post-transcriptional modulation. Its context-dependent functions across distinct pathological states are delineated. In malignancies, including colorectal and ovarian cancers, LAMA3 functions as an oncogenic determinant that enhances invasion, metastatic dissemination, and chemotherapeutic resistance. In contrast, in hereditary diseases such as junctional epidermolysis bullosa (JEB) and chronic disorders such as idiopathic pulmonary fibrosis (IPF), LAMA3 deficiency or dysfunction constitutes a structural basis of tissue pathology. From a translational standpoint, elevated LAMA3 expression has been recognized as an independent prognostic indicator in pancreatic ductal adenocarcinoma, whereas LAMA3 promoter methylation is a candidate biomarker for platinum resistance in ovarian cancer. LAMA3-directed gene therapy for JEB has progressed to clinical evaluation. Current limitations in the field are critically examined, and emerging therapeutic approaches, including proteolysis-targeting chimeras (PROTACs), are discussed. Collectively, an integrated framework that connects LAMA3 biology with clinical applications is presented to inform future investigations and precision therapeutic strategies targeting this multifunctional molecule.
29 July 2026
Read appraisal →Journal of chemical information and modeling
Protein Language Model-Based Fitness Estimates Facilitate Resistance Mutation Identification
Drug resistance is a major challenge in cancer therapy. Cancer cells with pre-existing or acquired mutations that confer resistance to a given drug treatment outgrow the susceptible cell population and cause cancer recurrence after an initial successful treatment response. Knowledge about resistance mutations before they occur in the clinic could prevent unnecessary patient treatment with ineffective drugs, in clinical trials as well as clinical practice, or potentially speed up the development of follow-up compounds. Here, we focused on on-target amino acid mutations that confer resistance to an inhibitor compound with a known binding mode. We evaluated whether a combination of physics-based free energy perturbation (FEP) affinity estimates and protein language model-based protein fitness estimates could improve the in silico identification of resistance mutations. Validation was done with data from deep mutational scanning (DMS) experiments that tested for resistance to single amino acid mutations. A public data set testing ERK2 resistance against the inhibitor SCH772984 and an internal data set testing resistance of an EGFR_exon20 mutant against a Bayer small molecule inhibitor were used. Our results show that protein fitness estimates can facilitate the identification of resistance mutations by filtering mutations with a low estimated fitness. Even though FEP has flagged such mutations as affinity-decreasing and thus potentially resistant, they were not resistant according to the DMS experiment and therefore correctly filtered out. This indicates that protein language model-based protein fitness estimates could be a computationally efficient method to filter mutations without having to model the negative impact of mutations on native function or protein stability, which is error-prone and computationally expensive.
28 July 2026
Read appraisal →Molecular genetics and genomics : MGG
The use of artificial intelligence in advancing molecular biology in Africa: a narrative review
Artificial intelligence (AI) is rapidly becoming a core methodological pillar of molecular biology and precision medicine, and Africa is a uniquely consequential setting for this transition because the continent combines the world's greatest human genomic diversity with the most severe underrepresentation of that diversity in the datasets and reference resources on which AI models are built and benchmarked. This narrative review examines, for a genetics and genomics readership, where AI-driven methods are already strengthening African molecular biology, where the supporting evidence remains preliminary, and what is required to translate technical capability into scientifically robust and equitable benefit. The central argument is that AI is especially consequential in African molecular biology, not simply because it automates analysis, but because it can help unlock insight from African genomic diversity, pathogen biology, and clinically relevant multi-omics data that remain underrepresented in global models. Across core molecular domains, AI is accelerating protein structure prediction, high-throughput variant calling and pan-genomic reference construction, genome-wide association analysis, transcriptomic interpretation, drug discovery, and CRISPR guide design. African initiatives such as H3Africa, the African Genome Variation Project, H3ABioNet, and the H3D Centre show that locally generated datasets and African-led computational pipelines can already support meaningful discovery, from improved variant interpretation to structure-guided therapeutic prioritization. At the same time, persistent barriers remain, including underrepresentation of African genomes in training data and reference genomes, uneven computational infrastructure, limited interdisciplinary training, fragmented governance, and the risk that AI-derived benefits will remain inaccessible to the populations whose data enable them. We conclude that the future impact of AI in African molecular biology will depend less on adopting global tools in the abstract and more on building African-led datasets, validation pipelines, governance frameworks, and translational pathways that make molecular discovery both scientifically robust and equitably useful. Looking ahead, the central perspective offered by this review is that Africa's exceptional genomic diversity should be treated as a scientific asset rather than an analytical liability: realising this will require population-representative pan-genome references, sustained computational capacity, and governance structures that ensure African populations are not only the source of the underlying data but also the principal beneficiaries of the discoveries it enables.
23 July 2026
Read appraisal →Nucleic acids research
FABIAN-variant 2026: improved prediction of the effects of DNA variants on transcription factor binding
Variants in promoters and enhancers can alter the binding of transcription factors (TFs), but their functional assessment remains difficult. FABIAN-variant is a web application that predicts the effects of DNA variants on TF binding by comparing position weight matrix (PWM) and transcription factor flexible model (TFFM) scores between reference and variant alleles. Here, we present FABIAN-variant 2026, a major update that expands the prediction model library from ~5000 to over 40 000 models for >1500 human TFs, sourced from nine PWM databases and including 1290 TFFMs. The application now supports the mouse genome (GRCm38 and GRCm39) with over 35 000 models for >1100 mouse TFs. An optional BPNet deep learning scorer provides neural network-based binding predictions for 240 human TFs. Known TF binding site information has been expanded from three to five sources. Predictions for over 1400 heterodimer TF complexes have been added. The web server has been rewritten in Rust and the scoring engine optimized, reducing runtime by ~70%. A RESTful JSON API and a standalone command-line version enable programmatic access and local high-throughput analysis. FABIAN-variant 2026 is available at https://fabianapp.org/variant26/. The web server is free and open to all users and there is no login requirement.
13 July 2026
Read appraisal →Nucleic acids research
SNPnexus: an enhanced web platform for large-scale and multi-sample variant analysis (2025 update)
SNPnexus is a long-standing web-based platform for the functional annotation and prioritization of genetic variants. Since its previous release, SNPnexus has undergone substantial backend re-engineering resulting in major performance improvements and a complete restructuring of the underlying datasets. A redesigned interface now enables larger queries and multi-sample analysis enabling comparative workflows such as identifying shared pathogenic variants in disease cohorts and divergent mutations in cancer evolution studies. SNPnexus increased its capacity to 150 000 variants per query and introduced pre-annotation filters that allow users to restrict analyses to selected genes or genomic regions, improving efficiency while enabling targeted interrogation of high-priority targets. SNPnexus integrates updated and expanded annotations across both GRCh37 and GRCh38, covering genomic consequences, in silico pathogenicity predictions, population allele frequencies, evolutionary conservation, regulatory elements, biological pathways, and clinical associations. The refreshed result interface provides interactive visualizations for single-sample and cohort-level outputs, together with advanced filtering and export options. Optional user accounts now support query history and real-time job monitoring while preserving full, unregistered access for all users. SNPnexus remains free and open to all users without login requirements at https://snpnexus.org/.
13 July 2026
Read appraisal →Journal of gastrointestinal cancer
Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis
BACKGROUND: The evaluation of genetic mutations is crucial for personalized therapy in colorectal cancer (CRC), but the invasive tissue biopsy is subject to sampling bias and other complications. Radiomics has emerged as a non-invasive tool to predict these mutations from standard medical images. In this systematic review and meta-analysis, we aimed to evaluate the diagnostic accuracy and methodological quality of radiomics models for predicting key genetic mutations in CRC. METHODS: A comprehensive search of PubMed, Scopus, Web of Science, and Embase was conducted in accordance with PRISMA guidelines. Studies evaluating radiomics models for predicting genetic mutations in CRC patients using pre-operative CT, MRI, or PET/CT were included. A meta-analysis of diagnostic accuracy was performed to calculate the pooled sensitivity, specificity. Methodological quality was assessed using the Radiomics Quality Score (RQS) and QUADAS-2 tools. RESULTS: Sixteen studies were included in the quantitative analysis. The pooled sensitivity and specificity were 0.75 (95% CI, 0.67-0.81) and 0.78 (95% CI, 0.70-0.85), respectively, with an overall AUC of 0.79. Subgroup analyses revealed that radio-clinical models integrating both clinical and radiomics features achieved superior sensitivity compared to models with only radiological input. However, the overall methodological quality of the included studies was low, with a mean RQS of 45%. CONCLUSION: Conventional radiomics models demonstrate promising results for the non-invasive prediction of genetic mutations in CRC, with sensitivity enhanced by the integration of clinical data. Despite this potential, significant methodological shortcomings and heterogeneity across studies highlight the need for standardized protocols and large-scale, prospective validation before these models can be translated into routine clinical practice.
10 July 2026
Read appraisal →BioEssays : news and reviews in molecular, cellular and developmental biology
AI in Genomics: From Variant Calling to Multi-Omics Integration
Artificial intelligence (AI) strategies are revolutionizing genomics by extracting complex patterns that traditional statistical pipelines are likely to miss. This mini-review aims to provide a concise overview of how AI is transforming major genomic technologies including variant calling, gene expression analysis, single-cell transcriptomics, CRISPR-Cas9 optimization, and multi-omics integration. In genome sequencing, machine learning variant callers greatly improve the accuracy and the rate at which single nucleotide and structural variants are called. In bulk RNA-Seq, AI augmented quantification, denoising, and differential expression modules complement the highly established STAR-featureCounts-DESeq2 pipeline, revealing subtle signals in big data sets. In single cell transcriptomics, deep learning approaches enhance batch correction, automate cell type annotation, and track developmental trajectories, hence clarifying cellular heterogeneity. AI-assisted guide RNA design, outcome prediction, and nuclease engineering enable more efficient CRISPR-Cas9 editing, reducing experimental cycles, and off-target effects. Finally, integrated platforms that combine genomic, transcriptomic, epigenomic, proteomic, and metabolomic layers provide an integrative view of cellular regulation and disease mechanisms. The review also covers current limitations, sparsity of data, model bias, privacy, and the need for standardized benchmarks and offers future directions in the form of interpretable models, collaborative learning, and open science practices. Together, these developments render AI an indispensable partner to unravel genomic complexity and accelerate precision medicine applications.
3 July 2026
Read appraisal →Molecular biology reports
Update of Mexican population databases for complex kinship interpretation with twelve and seven X-STRs
BACKGROUND: X-chromosome short tandem repeats (X-STRs) are valuable for resolving complex kinship cases. Most population databases are based on the ArgusX12 kit (Qiagen), using PCR and capillary electrophoresis (CE) to identify length-based (LB) alleles, and are formatted for FamlinkX software. However, haplotype diversity is often underrepresented by population studies, compelling database updates ( https://famlink.se/fx_databases.html ). The ForenSeq DNA Signature Prep kit (Verogen) uses massive parallel sequencing (MPS) to analyze 7 X-STRs and report sequence-based (SB) alleles, for which population databases are currently unavailable. Because these 7 markers are a subset of the ArgusX12 panel, existing LB-based databases can support kinship interpretation of MPS-derived data. METHODS AND RESULTS: We therefore updated a Mexican population database for 12 X-STRs and generated a corresponding 7-marker subset with FamlinkX-compatible files. Using 500 haplotypes from the Investigator Argus X-12 kit QS (Qiagen), we estimated forensic parameters and evaluated the impact of the updated database by calculating likelihood ratios (LRs) in four representative kinship scenarios. The updated database (n = 1433) increased LRs by an average of 55% compared to the previous version (n = 933). CONCLUSIONS: These results support improved forensic interpretation in Mexican and Latin American populations lacking comprehensive national X-STR databases based on ArgusX12 and ForenSeq DNA Signature kits.
29 June 2026
Read appraisal →Nucleic acids research
SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction
Accurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely understood. Aberrant splicing caused by mutations can lead to severe diseases, including cancer and genetic disorders, underscoring the need for accurate computational tools to predict splice sites and detect disruptions. Existing methods have made significant advances in splice site prediction but are often limited in handling long-range dependencies due to high computational costs, a factor critical to splicing regulation. Moreover, many models lack interpretability, hindering efforts to elucidate the underlying biological mechanisms. Here, we present SpliceSelectNet (SSNet), a hierarchical Transformer-based deep learning model that predicts splice sites from DNA sequences spanning up to 100 kb. By integrating local and global attention mechanisms, SSNet efficiently captures both proximal and distal regulatory signals while maintaining single-nucleotide resolution. Across multiple benchmark datasets, SSNet achieves state-of-the-art performance in splice site prediction and aberrant splicing detection. Systematic in silico mutagenesis demonstrates that attention scores reflect functional sequence importance, supporting their biological relevance. Long-range sequence perturbation experiments further show that SSNet captures distal regulatory effects beyond conventional receptive fields. Together, these results establish SSNet as a biologically interpretable framework for modeling long-range splicing regulation from genomic sequence.
23 June 2026
Read appraisal →Mammalian genome : official journal of the International Mammalian Genome Society
A novel biological function-based method for mining core genes in rare disease with limited cases
Conventional high - throughput sequencing data analysis methods, relying on large cohorts, are unsuitable for rare diseases with few cases and poor at explaining gene biological functions. This study proposes a biological-function-based method for mining core genes under limited-sample conditions. Using colorectal cancer (CRC) and its rare subtype signet-ring cell carcinoma (SRCC) as examples, differential expression analysis was performed separately for each SRCC sample against CRC samples. Shared genes obtained through progressive intersections were defined as the initial core gene set (List 1). Non-core genes were subjected to functional enrichment analysis, and pathway-associated genes were integrated to construct the non-core gene set. Two independent SRCC samples were subsequently used for secondary mining to obtain the final biologically significant core gene set (List 2). In addition, RankProd (RP), Robust Rank Aggregation (RRA), and an external mucinous adenocarcinoma dataset (GSE281917) were used for validation. First-round mining identified 246 core genes in SRCC, while secondary mining generated 66 and 65 biologically significant core genes from two validation samples with a high degree of overlap. RP identified 1,850 candidate genes and RRA identified 601 candidate genes, among which 64 genes overlapped with the 65 genes identified by this method. In the external mucinous adenocarcinoma dataset, first-round mining identified 144 core genes, while secondary mining generated 122 and 119 core genes with high overlap between validation samples. This method enables the identification of biologically significant core genes in rare diseases under limited-sample conditions, improves the biological interpretability of gene-mining results, demonstrates stable performance in both SRCC and external mucinous adenocarcinoma datasets, and provides a reliable experimental basis for the advancement of precision medicine in rare disease research.
15 June 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Amivantamab Monotherapy in Chemorefractory RAS/BRAF Wild-Type Metastatic Colorectal Cancer: Results From OrigAMI-1, an Open-Label, Phase Ib/II Study
PURPOSE: Amivantamab, an EGFR-MET bispecific antibody with immune cell-directing activity, is approved in non-small cell lung cancer (NSCLC). Effective treatments are limited for chemorefractory metastatic colorectal cancer (mCRC). METHODS: OrigAMI-1 (ClinicalTrials.gov identifier: NCT05379595) is a phase Ib/II study evaluating amivantamab monotherapy in chemorefractory (2-3 prior lines) mCRC. Participants had centrally confirmed RAS/BRAF/EGFR ectodomain wild-type status, without ERBB2/HER2 amplification. Participants with left-sided mCRC without (cohort A) or with (cohort B) prior anti-EGFR antibody treatment, or right-sided mCRC (cohort C) regardless of prior anti-EGFR treatment, received intravenous amivantamab 1,050 mg (1,400 mg for ≥80 kg) once every 2 weeks. The primary end point was objective response rate (ORR) per RECIST v1.1. RESULTS: By October 31, 2024, 94 participants received amivantamab monotherapy (median follow-up, 11.9 months). The median age was 60 years, and 65% of participants were male, with a median of 2 prior lines (94%, prior bevacizumab). In left-sided cohorts, the ORR was 29% (5 of 17) in cohort A and 19% (10 of 54) in cohort B; the median duration of response (DoR) was 9.0 months and 6.1 months, and the median progression-free survival (PFS) was 5.7 months and 4.6 months, respectively. In the right-sided cohort, the ORR was 22% (10 of 23; 43% had prior anti-EGFR), the median DoR was 9.8 months, and the median PFS was 3.7 months. Most frequent treatment-related grade ≥3 adverse events (AEs) were rash (7%), dermatitis acneiform (4%), and hypoalbuminemia (4%). One participant discontinued amivantamab because of a treatment-related AE. CONCLUSION: Amivantamab monotherapy demonstrated promising, durable antitumor activity in chemorefractory mCRC, regardless of prior anti-EGFR therapy and the primary tumor location. The amivantamab safety profile in mCRC is consistent with experience in NSCLC. Amivantamab plus chemotherapy is currently being explored in two phase III studies in first-line and second-line mCRCs.
11 June 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Tissue-Free Circulating Tumor DNA Assay and Patient Outcome in a Phase III Trial of FOLFOX-Based Adjuvant Chemotherapy (Alliance N0147)
PURPOSE: Detection of molecular residual disease using circulating tumor DNA (ctDNA) may enable postoperative risk stratification and guide adjuvant therapy. We evaluated the prognostic value of a tissue-free, epigenomic ctDNA assay in patients with stage III colon cancer (CC) enrolled in a phase III adjuvant chemotherapy trial. METHODS: Plasma samples were collected after surgery and before adjuvant infusional fluorouracil, leucovorin, and oxaliplatin alone or combined with cetuximab. ctDNA was analyzed using a tissue-free assay; in ctDNA-positive patients, tumor fraction (TF) was quantified and genotyping was performed with a 739-gene panel. Associations with disease-free survival (DFS), time to recurrence (TTR), and overall survival (OS) were assessed using multivariable Cox models adjusted for covariates. RESULTS: Among 2,260 evaluable patients, 461 (20.4%) were ctDNA-positive with significantly higher detection in advanced T-/N-stage, high-grade, obstruction/perforation, and BRAFV600E tumors. At a median follow-up of 6.1 years, ctDNA positivity was independently associated with shorter TTR (hazard ratio [HR], 5.96 [95% CI, 5.11 to 6.96]), DFS (HR, 5.03 [95% CI, 4.36 to 5.81]), and OS (HR, 4.45 [95% CI, 3.76 to 5.27]; all P < .0001). The 5-year DFS was 27.7% (95% CI, 23.8 to 32.2) v 77.1% (95% CI, 75.1 to 79.1) in ctDNA-positive versus ctDNA-negative patients, and adverse prognostic impact was greater in lower T/N stage, low-risk, and dMMR subsets (interaction P = .0012-.041). Among ctDNA-positive patients, TF was nearly double in those who recurred or died (P = .0002) and stratified patients for TTR, DFS, and OS (all adjusted P < .002). Genotyping identified mutations in FLT1 (OR, 8.99) and PREX2 (OR, 7.73) genes that were most strongly associated with recurrence (P < .03). CONCLUSION: Evaluation of ctDNA in resected stage III CC using a tissue-free assay provided robust and independent prognostic value. Higher ctDNA burden, dMMR, and specific mutations defined poor prognostic groups among ctDNA-positive patients.
20 May 2026
Read appraisal →Cell reports methods
A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer
Predicting treatment response remains challenging in oncology, particularly given the growing diversity of therapeutic options. Despite efforts using gene expression signatures, or integrative multi-omics frameworks, robust and interpretable biomarkers remain limited. We present SubNetDL, a deep learning framework that integrates subclonal mutation profiles and protein-protein interaction networks via network propagation. Unlike condition-specific approaches, SubNetDL leverages somatic mutations alone and is applicable across diverse cancer types and treatment modalities. Applied to 10 TCGA cancer-drug combinations, SubNetDL achieved consistently strong performance (median area under the receiver operating characteristic curve [AUROC] = 0.74) and successfully generalized to two independent immunotherapy datasets (median AUROC = 0.77). Importantly, it identified candidate biomarker genes with treatment-specific relevance. SubNetDL prioritized genes that were not central in the network, highlighting its ability to capture context-specific patterns beyond traditional metrics. In conclusion, our approach offers a robust and interpretable framework for identifying predictive biomarkers and stratifying patients based on mutation profiles and network context.
20 May 2026
Read appraisal →Journal of clinical pathology
Clinicopathological and molecular characterisation of superficial CD34-positive fibroblastic tumour: A systematic review
AIM: This systematic review aims to comprehensively evaluate the clinicopathological and molecular features of superficial CD34-positive fibroblastic tumour (SCD34FT), a recently described intermediate-grade soft-tissue neoplasm recognised in the 5th edition of the WHO classification of soft tissue tumours. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we systematically searched for English-language studies in PubMed, Scopus, Google Scholar and Web of Science. Retrospective or original case series with ≥3 histologically confirmed cases were selected for review, whereas review articles, single case reports and conference abstracts were excluded. Risk of bias was assessed using the Joanna Briggs Institute checklist. RESULTS: A total of 190 patients across 14 selected studies met the inclusion criteria. SCD34FT mainly affected middle-aged adults, with the most common site being the lower extremity. Diffuse CD34 expression was universal across all studies. A high number of cases displayed SynCAM/CADM3 positivity, while WT1 and Pan-CK (focal) were frequent. PRDM10 fusion was detected in 73% of cases, and the most common fusions were PRDM10::MED12 (66%) and PRDM10::CITED2 (28%). Only 9/169 (5.3%) had local recurrences, whereas 4/190 (2.1%) cases had lymph node metastasis, with no distant metastasis or disease-related death. CONCLUSION: SCD34FT is a novel entity with consistent CD34 expression and recurrent PRDM10 rearrangements and shows indolent behaviour. A high index of suspicion for SCD34FT should be warranted in a superficially located tumour with striking nuclear pleomorphism but an unexpectedly low mitotic index. Molecular studies demonstrating PRDM10 rearrangement may be helpful in atypical cases.
19 May 2026
Read appraisal →Journal of neuro-oncology
Should checkpoint inhibitors be reserved for biomarker-selected pediatric brain tumors?
PURPOSE: Pediatric brain tumors, including high-grade gliomas (HHG), medulloblastomas (MB), and ependymomas (EPN), are a leading cause of death in children. They are often immunologically "cold" with low tumor mutational burden (TMB) and very few tumor-infiltrating lymphocytes (TILs), which may limit the role of immune checkpoint inhibitors (ICI). METHODS: We performed a PRISMA‑guided systematic review of PubMed/MEDLINE, Embase, and Scopus from inception to September 17, 2025, for English-language studies of patients ≤ 21 years with primary CNS tumors treated with PD‑1/PD‑L1 or CTLA‑4 inhibitors. Eligible reports included prospective trials, retrospective series, and observational/case reports with extractable data on efficacy and/or toxicity. Of 479 records identified, 386 unique citations were screened, 127 underwent full‑text review, and 40 met inclusion criteria for qualitative synthesis. RESULTS: Prospective and institutional studies in biomarker-unselected diffuse midline glioma, high-grade glioma, medulloblastoma, and ependymoma showed low objective response rates (generally ≤ 6%), short median progression-free survival (1-3 months), and overall survival similar to historical controls, despite acceptable safety (grade ≥ 3 treatment-related adverse events ~ 15-25% with anti-PD-1 ± anti-CTLA-4). In contrast, across germline MMR-deficient and broader RRD/hypermutant cohorts, PD-1 blockade produced clinically meaningful and sometimes durable responses, including complete remissions in malignant glioma and approximate 2-year overall survival near 50%, often with delayed responses and pseudoprogression. Histology-specific profiling highlighted marked variation in immune contexture: pediatric gliomas segregate into immune "hot," "altered," and "cold" subtypes; medulloblastoma is largely PD-L1-low with prominent B7-H3 and myeloid programs; checkpoint expression is also observed in germ cell and selected sellar tumors. Evidence quality is limited by small, heterogeneous, predominantly non-comparative designs. CONCLUSION: ICIs show manageable safety but limited efficacy in unselected pediatric CNS tumors. Durable benefit is most evident in RRD/hypermutant biology and possibly PD-L1-high niches (e.g., some low-grade gliomas and CNS-GCT). Future trials should be biomarker-driven and pair ICIs with priming combinations (e.g., radiation, epigenetic modulators, metronomic chemotherapy) to convert "cold" tumors into responders.
18 May 2026
Read appraisal →PloS one
Analyzing the performance of deep learning splice prediction algorithms
SpliceAI is the leading tool for predicting splice-altering variants, but restrictive licensing limits clinical adoption. While open-source implementations have been published with author-reported comparisons, independent benchmarking across diverse datasets is needed to establish equivalence. We compared the original SpliceAI with two open-source implementations (OpenSpliceAI and CI-SpliceAI) and a legacy ensemble baseline across six datasets: a curated set of 1,316 validated variants, 213 variants with splice-assay data, 99,601 variants from the SPiP splicing prediction study, 242 manually curated deep intronic pathogenic variants, and two ClinVar-derived datasets comprising 53,600 intronic variants and 58,064 variants spanning all genomic contexts. The deep learning models were also evaluated against an ensemble of four legacy splice-prediction tools. Across all datasets, the deep learning algorithms outperformed the legacy ensemble. All three deep learning algorithms showed similar performance on the larger datasets dominated by canonical splice site variants (balanced accuracies 0.889-0.977). On the deep intronic benchmark, the original SpliceAI achieved the highest balanced accuracy (0.940), outperforming both CI-SpliceAI (0.890) and OpenSpliceAI (0.841). Critically, optimal thresholds for deep intronic variants were an order of magnitude lower than standard recommendations, indicating that default thresholds would miss the majority of pathogenic deep intronic variants. A correlation analysis showed that CI-SpliceAI maintained balanced concordance across event types, whereas OpenSpliceAI showed stronger correlation for loss events than gain events. Both implementations showed high positional agreement with SpliceAI, with exact splice-site match rates exceeding 90% across event types. Together, these results demonstrate that both open-source reimplementations of SpliceAI successfully reproduce the predictive behavior of the original algorithm across multiple evaluation contexts, while consistently outperforming traditional splice prediction methods. However, performance diverges on deeply intronic variants, and standard score thresholds are poorly calibrated for this variant class regardless of algorithm choice.
15 May 2026
Read appraisal →Analytical chemistry
Precise Artificial Intelligence Nanomotor Tracking-Powered Amplification-Free Digital MicroRNA Sensing
Digital assays have become a state-of-the-art technology for the sensing of nucleic acid molecules. Nevertheless, most of the prevalent digital nucleic acid assays heavily rely on sealed microchambers and sophisticated nucleic acid amplification strategies to achieve an end-point digital signal readout. Herein, we proposed an artificial intelligence (AI)-facilitated, digital fluorescence nanomotor tracking-powered assay (AI-dFNA) for one-step, amplification-free, and highly sensitive detection of microRNAs (miRNAs). In this design, even a single target miRNA-templated amplification-free ligation can introduce a sufficient driving force on the surface of a fully open fluorescence nanoparticle to cause remarkable motion behaviors. With the assistance of a self-developed AI algorithm, clear discrimination of positive and negative fluorescent nanomotors can be achieved, enabling real-time digital miRNA sensing. The AI-dFNA avoids the use of enclosed microchambers and intricate signal amplification techniques, which could even identify varying fluorescence colors of nanomotors to allow for the analysis of different kinds of miRNA molecules, pioneering a new direction toward next-generation digital nucleic acid sensing.
11 May 2026
Read appraisal →Medical image analysis
PH2ST: Prompt-guided hypergraph learning for spatial transcriptomics prediction in whole slide images
Spatial Transcriptomics (ST) reveals the spatial distribution of gene expression in tissues, offering critical insights into biological processes and disease mechanisms. However, the high cost, limited coverage, and technical complexity of current ST technologies restrict their widespread use in clinical and research settings, making obtaining high-resolution transcriptomic profiles across large tissue areas challenging. Predicting ST from H&E-stained histology images has emerged as a promising alternative to address these limitations but remains challenging due to the heterogeneous relationship between histomorphology and gene expression, which is affected by substantial variability across patients and tissue sections. In response, we propose PH2ST, a prompt-guided hypergraph learning framework, which leverages limited ST signals to guide multi-scale histological representation learning for accurate and robust spatial gene expression prediction. Extensive evaluations on two public ST datasets and multiple prompt sampling strategies simulating real-world scenarios demonstrate that PH2ST not only outperforms existing state-of-the-art methods, but also shows strong potential for practical applications such as imputing missing spots, ST super-resolution, and local-to-global prediction, highlighting its value for scalable and cost-effective spatial gene expression mapping in biomedical contexts.
5 May 2026
Read appraisal →Nano letters
Seeing and Feeling DNA Methylation: Single-Molecule Biophysics Meets Machine Learning
DNA methylation at 5-methylcytosine (5mC) is crucial for embryonic development and cellular function, while aberrant patterns strongly drive disease onset and progression. Its reversible nature offers substantial therapeutic potential, emphasizing the need for precise, context-specific genome wide 5mC mapping. Conventional techniques such as bisulfite sequencing and ensemble biosensor assays are hindered by DNA degradation, amplification bias, high cost, and inability to resolve single-molecule structural and mechanical effects of methylation. This review examines advances in single-molecule biophysical methods (nanopore sensing, smFRET, optical/magnetic tweezers, and AFM) that provide direct, label-free/minimally invasive 5mC detection, along with quantitative insights into DNA conformation, mechanics, and protein-DNA interactions. These techniques complement traditional methylome mapping by linking genomic localization to molecular mechanisms. Emerging machine-learning approaches are revolutionizing analysis, particularly in nanopore sensing, while promising applications in smFRET, tweezers, and AFM address throughput and reproducibility challenges. Their convergence promises scalable, high-resolution epigenetic profiling, advancing precision epigenomics toward clinical application.
4 May 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
CALYPSO: Final Results of Savolitinib and Durvalumab Combination in Metastatic Papillary Renal Cancer
PURPOSE: The CALYPSO study demonstrated activity of savolitinib and durvalumab in MET-driven papillary renal cancer (PRC). We report final efficacy outcomes and exploratory circulating tumor DNA (ctDNA) biomarker analysis. METHODS: This single-arm phase II study evaluated savolitinib and durvalumab in treatment-naïve or pretreated PRC. End points included overall response rate (ORR), progression-free survival (PFS), and overall survival (OS). FoundationOne CDx assessed DNA alterations and MET/PD-L1 or MET/tumor mutational burden (TMB) copositivity. ctDNA collected at baseline and on treatment was correlated with outcomes. RESULTS: At 41-months median follow-up, ORR was 34% (95% CI, 20.0 to 51.0) in the intention-to-treat (ITT) population (N = 41) and 53% (95% CI, 28.0 to 77.0) in MET-driven patients (n = 17). Median PFS was 6.5 (95% CI, 2.7 to 12.0) versus 13.9 months (95% CI, 2.9 to 23.8), and OS was 18.3 (95% CI, 7.3 to 30.7) versus 27.4 months (95% CI, 9.3 to 37.4), in the ITT population and the MET-driven population, respectively. PD-L1 (66% positive) and TMB (median 2.5 mut/Mb) status did not correlate with response. Baseline ctDNA positivity (10/21) correlated with shorter OS (median 7.3 v 33.3 months), while ctDNA clearance and mean variant allele frequency reduction correlated with improved OS (median 31.3 v 7.2 and 31.3 v 15.5 months, respectively). CONCLUSION: Savolitinib plus durvalumab shows OS in MET-driven PRC, supporting the ongoing SAMETA RIII trial (ClinicalTrials.gov identifier: NCT05043090). ctDNA may be a useful predictive biomarker.
21 Apr 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Recurrent Resistance Mutations to Lirafugratinib Inform Treatment Sequencing in FGFR2-Driven Tumors
PURPOSE: The use of reversible fibroblast growth factor receptor 2 (FGFR) inhibitors leads to the emergence of "undruggable" FGFR2 kinase domain mutations, hampering sequential treatment strategies. Lirafugratinib and futibatinib are irreversible FGFR inhibitors with the most promising clinical activity against FGFR2-driven tumors. EXPERIMENTAL DESIGN: We characterized resistance to lirafugratinib with circulating tumor DNA, tissue whole-exome sequencing, and bulk RNA sequencing in 30 patients with FGFR2-driven cancers, treated in the phase I/II ReFocus trial (NCT04526106) and enrolled in the UNLOCK program at Gustave Roussy. RESULTS: Among the 30 patients included, 18 (60%) had intrahepatic cholangiocarcinoma and 12 (40%) had other tumor types. Twenty-two patients (73%) were FGFR inhibitor-naïve. Among those experiencing primary resistance to lirafugratinib, we identified potential resistance mechanisms in five of six pretreatment samples. Patients with acquired lirafugratinib resistance manifested an unprecedented emergence of FGFR2 mutations in the M538 and/or L618 residues of the kinase domain, documented in 11 of 16 cases (69%). Compared with futibatinib resistance, FGFR2 molecular brake (N550) and gatekeeper (V565) mutations were rare. Leveraging the spectrum of FGFR2 kinase domain mutations at resistance to lirafugratinib and futibatinib, respectively, we identified the complementarity of the two irreversible inhibitors. On the basis of viability assays in FGFR2::BICC1-dependent Ba/F3 models and in vivo studies on patient-derived xenografts, we propose treatment sequences with the two agents. After lirafugratinib progression, three patients received futibatinib and experienced prolonged disease response. CONCLUSIONS: The complementary activity of lirafugratinib and futibatinib against FGFR2 kinase domain mutations supports their sequential use when precise resistance mutations are detected in patients.
17 Apr 2026
Read appraisal →Cancer research
Mutant and Wild-Type RAS Cross-talk and Stoichiometric Deficiencies Are Determinants of Sensitivity to Targeted Therapies in KRASG12R Pancreatic Ductal Adenocarcinoma
UNLABELLED: Therapies targeting the RAF-MEK-ERK pathway are generally considered to have limited efficacy in KRAS-mutant cancers. However, specific KRAS mutants exhibit distinct behaviors. Notably, KRASG12R pancreatic ductal adenocarcinoma (PDAC) tumors have shown sensitivity to MEK inhibitors (MEKi) in combination with autophagy inhibitors, but a better understanding of the underlying mechanisms is needed to optimize this treatment strategy. Using a systems-level approach, we uncovered a mechanistic explanation for this phenomenon. Due to distinct biophysical properties, KRASG12R had an impaired ability to activate wild-type HRAS and NRAS (WT-RAS) compared with other KRAS mutants, such as KRASG12D. This reduced activation stemmed from the weaker interaction between KRASG12R and guanine exchange factors (SOS), as well as the tumor suppressor neurofibromin (NF1), crucial in regulating WT-RAS activity. The impaired ability to activate WT-RAS led to weaker holistic MAPK signaling in KRASG12R-driven tumors, which conferred increased sensitivity to MEKi. To substantiate the preclinical findings, the utility of MEKi in combination with the autophagy inhibitor hydroxychloroquine was analyzed in patients with KRASG12R-mutated metastatic PDAC. Five of the eight (62.5%) patients treated in first- or second-line settings had a progression-free survival exceeding 6 months. Three patients had impressive disease control: two had stable disease of 11 and 22.7 months, and one achieved a partial response with an 83% decrease in tumor size that lasted for 8.9 months. Overall, this work highlights how systems-based approaches in precision medicine can uncover mechanistic insights to guide the identification of patients with PDAC most likely to benefit from tailored therapeutic strategies. SIGNIFICANCE: The unique sensitivity of KRASG12R-mutant cancers to MEK inhibitors offers a critical advancement in understanding MAPK signaling and paving the way for precision-targeted therapies in previously untreatable contexts. See related commentary by Tiriac and Engle, p. 1817 See related article by Burge et al., p. 1854 See related article by Burge et al., p. 1868.
17 Apr 2026
Read appraisal →Signal transduction and targeted therapy
STRATEGIC-1: multiple-line, randomized, open-label GERCOR-PRODIGE-39 phase III trial in unresectable RAS/BRAF wild-type metastatic colorectal cancer
Managing unresectable metastatic colorectal cancer (mCRC) requires a comprehensive strategy. While chemotherapy, anti-angiogenic, and anti-epidermal growth factor receptor (EGFR) agents are available, strategy trials are needed to optimize their use and sequencing. The STRATEGIC-1 phase III trial (NCT01910610) was designed to determine the optimal treatment sequence in patients with untreated, unresectable wild-type RAS/BRAFV600E mCRC. Patients were randomized (1:1) to FOLFIRI-cetuximab then mFOLFOX6-bevacizumab (arm A) or OPTIMOX-bevacizumab then FOLFIRI-bevacizumab followed by EGFR monoclonal antibody +/- irinotecan (arm B). The primary endpoint was the duration of disease control (DDC). Secondary endpoints were overall survival (OS), time to failure of strategy (TFS), progression-free survival (PFS), overall response rate (ORR), salvage surgery rate, safety, and health-related quality of life (HRQoL). Overall, 263 patients (arm A:131, arm B:132) were randomized. After 68.4 months of median follow-up (95% CI, 76.5-98.0), the median DDC was 22.8 months (95% CI, 20.4-28.8) in arm A and 23.5 months (95% CI, 17.9-26.3) in arm B (HR = 1.01, 95% CI, 0.76-1.34; log-rank P = 0.945). The median OS was 40.4 months (95% CI, 32.4-51.1) in arm A and 34.4 months (95% CI, 27.5-42.2) in arm B (HR = 1.30, 95% CI, 0.99-1.72). The ORR was higher in arm A (82.4% versus 65.4%) in the first-line group but not in the second-line group (20.7% versus 16.4%). Adverse events were consistent with the well-known safety profiles. STRATEGIC-1 did not meet its primary endpoint and was inconclusive in identifying the optimal treatment strategy in wild-type RAS/BRAFV600E mCRC.
16 Apr 2026
Read appraisal →Medicine
Glioma biomarker analysis and drug targeting prediction based on ferroptosis-related genes
Glioma represents a significant challenge in neuro-oncology due to its highly invasive nature and poor prognosis. Recent studies indicate that ferroptosis, a form of regulated cell death, plays a crucial role in the pathogenesis of glioma. Modulating ferroptosis may offer a novel therapeutic approach for glioma treatment. This study aims to identify ferroptosis-related biomarkers and patterns of immune infiltration in glioma using bioinformatics techniques, and to predict potential therapeutic drugs targeting these biomarkers. Gene expression profiles (GSE16011, GSE50161) were retrieved from the GEO database, while ferroptosis-related genes were sourced from the FerrDb database. Intersection genes were identified using the CytoHubba algorithm and a consensus clustering method. Immune infiltration was assessed using the CIBERSORT algorithm. Functional enrichment analysis and gene set enrichment analysis were conducted with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Drug targeting predictions were made based on identified hub genes. We identified 10 hub genes: TP53, RRM2, EZH2, CDKN1A, MYCN, KIF20A, BLM, GLS2, HMOX1, and GOT1. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that ferroptosis is primarily associated with apoptosis, reactive oxygen metabolism, the p53 signaling pathway, and immune regulation. Immune infiltration analysis showed significant differences in the expression of plasma cells, CD8 T cells, follicular helper T cells, activated NK cells, and macrophages between groups. qRT-PCR validation in glioma cell lines confirmed the differential expression of key ferroptosis-related genes, supporting the robustness of our bioinformatic findings. Furthermore, molecular docking demonstrates excellent binding properties between the drug components and key targets, and these key targets were further validated by PCR analysis in vitro. In summary, this study highlights the potential of ferroptosis-related biomarkers in glioma and suggests novel drug targets, providing a foundation for future therapeutic strategies.
12 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Resistance to neoadjuvant talazoparib in triple-negative breast cancer by BRN2-induced ATR/STAT3 pathways or SHLD2 subclone expansion
Intrinsic and acquired resistance to poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) remains a major barrier in treating homologous recombination (HR) repair-deficient tumors, including those with germline or somatic BRCA1/2 mutations. Although PARPi are FDA approved for adjuvant treatment of locally advanced or metastatic breast cancer in patients with germline BRCA1/2 mutations, emerging data support their use as monotherapy in the neoadjuvant setting. Promising safety profiles of newer-generation PARPi further support this potential. However, resistance mechanisms specific to the neoadjuvant setting are poorly understood. To address this gap, we leveraged resources from a phase II neoadjuvant clinical trial (NCT03499353), analyzing tumors from patients with germline BRCA1/2 mutant breast tumors before and after six months of talazoparib monotherapy. Whole-transcriptome analyses were performed on these samples. Additionally, we established orthotopic patient-derived xenograft models from a subset of the patient tumors and conducted whole-exome and whole-transcriptome analysis. This integrative approach revealed both known and previously unknown PARPi resistance mechanisms. In one case, overexpression of BRN2, encoding a transcription factor that plays a critical role in neurogenesis, led to activation of ATR/RAD51 and STAT3 pathways, restoring HR repair. BRN2-driven resistance could be reversed with ATR and STAT3 inhibitors, resensitizing cells to talazoparib. In another, an HR repair proficient tumor subclone lacking Shieldin 2 expression expanded during treatment and accounted for intrinsic resistance. Our findings highlight the need to determine intrinsic and anticipate acquired resistance pathways in treatment-naïve tumors and support combining PARPi with targeted agents to improve outcomes in the neoadjuvant setting.
12 Apr 2026
Read appraisal →The New England journal of medicine
Setidegrasib in Advanced Non-Small-Cell Lung Cancer and Pancreatic Cancer
BACKGROUND: The KRAS p.G12D variant occurs in 5% of patients with non-small-cell lung cancer (NSCLC) and is the most common substitution variant in pancreatic ductal adenocarcinoma, occurring in 40% of patients, but no targeted therapies directed against this variant are currently approved for clinical use. Setidegrasib (ASP3082) is a first-in-class KRAS G12D-targeted protein degrader. METHODS: We conducted this phase 1 study to evaluate the safety, pharmacokinetics, pharmacodynamics, and antitumor activity of setidegrasib in patients with previously treated advanced solid tumors harboring KRAS p.G12D variants. The primary objectives were to evaluate the safety profile, as indicated by dose-limiting toxic effects and adverse events (the primary end points), and to determine the phase 2 dose. Setidegrasib was administered intravenously once weekly at doses of 10 to 800 mg. RESULTS: Overall, 203 patients were enrolled. Among the 76 patients who received setidegrasib at a dose of 600 mg, which was ultimately selected as the phase 2 dose, adverse events occurred during treatment in all the patients, with events of grade 3 or higher in 42%. Treatment-related adverse events occurred in 93% of the patients; the most common were transient infusion-related reactions (in 80%) and nausea (in 30%). Adverse events led to discontinuation in 2 patients. Among the 45 patients with NSCLC who received the 600-mg dose, 36% (95% confidence interval [CI], 22 to 51) had a partial response, the median progression-free survival was 8.3 months (95% CI, 4.1 to could not be estimated), and the estimated 12-month overall survival was 59% (95% CI, 40 to 74). Among the 21 patients with metastatic pancreatic ductal adenocarcinoma who received the 600-mg dose as second- or third-line treatment (of whom 67% received setidegrasib as third-line treatment), 24% (95% CI, 8 to 47) had a response, the median progression-free survival was 3.0 months (95% CI, 1.4 to 6.9), and the median overall survival was 10.3 months (95% CI, 4.2 to 13.0). CONCLUSIONS: Setidegrasib was associated with antitumor activity and a low incidence of treatment discontinuation due to adverse events in patients with previously treated advanced KRAS p.G12D-mutated NSCLC or pancreatic ductal adenocarcinoma. (Funded by Astellas Pharma; ClinicalTrials.gov number, NCT05382559.).
9 Apr 2026
Read appraisal →Nature reviews. Clinical oncology
MicroRNAs in oncology: a translational perspective in the era of AI
Over the past three decades, knowledge of microRNA (miRNA) biology has advanced from the initial discovery of their regulatory functions to the finding of abnormal activity in leukaemias, and then to a comprehensive understanding of the roles of miRNAs in both normal physiology and most diseases, with cancer being extensively studied. miRNA dysregulation contributes to tumorigenesis, with certain miRNAs acting as either tumour suppressors or oncogenic factors in a context-dependent manner. A subset of miRNAs have shown promise as tumour biomarkers and therapeutic targets in preclinical studies, with several miRNA-based diagnostic tools and treatments progressing to clinical trials. Artificial intelligence (AI) and machine learning techniques began to be introduced into cancer research and oncology a decade ago and are now on the verge of revolutionizing biomarker identification and clinical trials. In this Review, we highlight important roles of miRNAs in cancer biology and their potential as diagnostic tools and therapeutic targets. In particular, we discuss emerging challenges and opportunities presented by AI-driven data analysis and combinatorial strategies, and how advances in these areas have addressed previous doubts on the clinical translation of miRNA-based biomarkers and therapeutics.
4 Apr 2026
Read appraisal →The Journal of investigative dermatology
A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma
Melanoma is a highly aggressive cutaneous malignancy characterized by a strong propensity for metastasis and therapy resistance, with its progression being closely linked to metabolic reprogramming. This study integrated multiomics data (The Cancer Genome Atlas, Gene Expression Omnibus, European Nucleotide Archive) and advanced machine learning to develop prognostic and immunotherapy prediction models for melanoma, focusing on 114 metabolism-related pathways. Cox regression identified 70 genes linked to survival, with functional enrichment revealing key metabolic pathway alterations. A metabolism-related prognostic model (MRPM) was constructed using 101 combinations of machine learning algorithms, demonstrating superior predictive accuracy across 4 cohorts. High-risk patients showed worse survival and immunotherapy response in melanoma and other cancers. Tumor microenvironment analysis revealed MRPM's negative correlation with immune infiltration and positive association with tumor purity. Single-cell sequencing highlighted MRPM gene enrichment in melanocytes. Mechanistically, GYS1 (the key gene in MRPM) emerged as a pivotal prognostic gene that promotes melanoma proliferation and metastasis. Regulatory studies uncovered SP1's transcriptional control of GYS1- and PSMD14-mediated stabilization of SP1 through K48-linked ubiquitination removal. In vivo validation confirmed that PSMD14 knockdown suppressed tumor growth through SP1-GYS1 axis disruption. This work establishes MRPM as a robust predictive tool and elucidates the PSMD14-SP1-GYS1 regulatory network as a potential therapeutic target in melanoma metabolism.
4 Apr 2026
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