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Immune checkpoint inhibitors have been successful across several tumor types; however, their efficacy has been uncommon and unpredictable in glioblastomas (GBM), where <10% of patients show long-term responses. To understand the molecular determinants of immunotherapeutic response in GBM, we longitudinally profiled 66 patients, including 17 long-term responders, during standard therapy and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations associated with immunosuppressive expression signatures in non-responders, and an enrichment of MAPK pathway alterations ( PTPN11 , BRAF ) in responders. Responsive tumors were also associated with branched patterns of evolution from the elimination of neoepitopes as well as with differences in T cell clonal diversity and tumor microenvironment profiles. Our study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor’s clonal evolution during treatment. Genomic, transcriptomic, and microenvironmental analyses of samples from patients with glioblastoma treated with nivolumab or pembrolizumab identifies features associated with treatment response that may help in refining patient stratification.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. This leads to considerable difficulties for users trying to navigate the selection and application of VEPs. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs.

Background Efficient presentation of mutant peptide fragments by the human leukocyte antigen class I (HLA-I) genes is necessary for immune-mediated killing of cancer cells. According to recent reports, patient HLA-I genotypes can impact the efficacy of cancer immunotherapy, and the somatic loss of HLA-I heterozygosity has been established as a factor in immune evasion. While global deregulated expression of HLA-I has also been reported in different tumor types, the role of HLA-I allele-specific expression loss — that is, the preferential RNA expression loss of specific HLA-I alleles — has not been fully characterized in cancer. Methods Here, we use RNA and whole-exome sequencing data to quantify HLA-I allele-specific expression (ASE) in cancer using our novel method arcasHLA-quant . Results We show that HLA-I ASE loss in at least one of the three HLA-I genes is a pervasive phenomenon across TCGA tumor types. In pancreatic adenocarcinoma, tumor-specific HLA-I ASE loss is associated with decreased overall survival specifically in the basal-like subtype, a finding that we validated in an independent cohort through laser-capture microdissection. Additionally, we show that HLA-I ASE loss is associated with poor immunotherapy outcomes in metastatic melanoma through retrospective analyses. Conclusions Together, our results highlight the prevalence of HLA-I ASE loss and provide initial evidence of its clinical significance in cancer prognosis and immunotherapy treatment.

In the version of this article originally published, the graph in Extended Data Fig. 2c was a duplication of Extended Data Fig. 2b. The correct version of Extended Data Fig. 2c is now available online.

Interpreting the functional consequences of genetic variants is a central challenge in human genomics. Despite the rapid acceleration of sequencing technologies, the majority of protein-coding variants remain uncharacterized and classified as variants of unknown significance (VUSs). Variant effect predictors (VEPs) aim to address this gap by scoring the potential impact of variants, but most current models are limited by biases in training data, lack of interpretability, and poor generalizability beyond known disease genes. These limitations are particularly problematic in clinical settings where accurate, genome-wide prediction of variant effect is essential.To promote the development of clinically useful and generalizable VEPs, we worked to establish best-practice guidelines to address transparency, training data sourcing, and evaluation design. These guidelines identify common pitfalls such as circularity, overfitting to clinical labels, and limited benchmarking scope. Emphasis is placed on rigorous separation between training and evaluation data and open-source availability of models and scores. Building on these principles, we introduced a large-scale benchmarking resource, ProteinGym, to harmonize data from hundreds of deep mutational scanning (DMS) experiments and clinical variant annotations, enabling robust comparisons across predictors.We developed a novel model, popEVE, to integrate evolutionary sequence data with human population variation in a probabilistic framework. By calibrating missense variant scores against gene-level constraint derived from population data, popEVE enables comparison of variant deleteriousness across the proteome without relying on clinical labels or allele frequency-based heuristics. Applied to rare disease cohorts, popEVE identifies over 100 novel candidate developmental disorder genes and successfully ranks causal variants without parental data. When extended to phenome-wide burden testing in population cohorts, our model uncovers hundreds of novel gene–phenotype associations and enables the construction of disease-specific polygenic risk scores from rare missense variants alone. These results demonstrate the utility of combining deep evolutionary context with human-specific constraint to build generalizable, clinically meaningful models of variant effect.

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

Human leukocyte antigen (HLA) locus makes up the major compatibility complex (MHC) and plays a critical role in host response to disease, including cancers and autoimmune disorders. In the clinical setting, HLA typing is necessary for determining tissue compatibility. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between the HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional splicing and bias due to amplification. Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two field precision for MHC class I genes, and over 99.7% for MHC class II. Importantly, arcasHLA takes as its input pre-aligned BAM files, and outputs three-field resolution for all HLA genes in less than 2 minutes. Finally, we discuss evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. arcasHLA is available at https://github.com/RabadanLab/arcasHLA.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

Disease-associated genetic variants occur extensively in noncoding regions like promoters, but current methods focus primarily on single nucleotide variants (SNVs) that typically have small regulatory effect sizes. Expanding beyond single nucleotide events is essential with insertions and deletions (indels) representing the logical next step as they are readily identifiable in population data and more likely to disrupt regulatory elements. However, existing methods struggle with indel prediction, and clinical interpretation often requires assessing complete promoter haplotypes rather than individual variants. We present LOL-EVE (Language Of Life for Evolutionary Variant Effects), a conditional autoregressive transformer trained on 13.6 million mammalian promoter sequences that enables both zero-shot indel prediction and complete promoter sequence scoring. We introduce three benchmarks for promoter indel prediction: ultra rare variant prioritization, causal eQTL identification, and transcription factor binding site disruption analysis. LOL-EVE’s superior performance demonstrates that evolutionary patterns learned from indels enable accurate assessment of broader promoter function. Application to Genomics England clinical data shows that LOL-EVE can prioritize promoter haplotypes in known developmental disorder genes, suggesting potential utility for clinical variant assessment. LOL-EVE bridges individual variant prediction with haplotype-level analysis, demonstrating how evolution-based genomic language models may assist in evaluating regulatory variants in complex genetic cases.

Genetic studies reveal extensive disease-associated variation across the human genome, predominantly in noncoding regions, such as promoters. Quantifying the impact of these variants on disease risk is crucial to our understanding of the underlying disease mechanisms and advancing personalized medicine. However, current computational methods struggle to capture variant effects, particularly those of insertions and deletions (indels), which can significantly disrupt gene expression. To address this challenge, we present LOL-EVE (Language Of Life across EVolutionary Effects), a conditional autoregressive transformer model trained on 14.6 million diverse mammalian promoter sequences. Leveraging evolutionary information and proximal genetic context, LOL-EVE predicts indel variant effects in human promoter regions. We introduce three new benchmarks for indel variant effect prediction in promoter regions, comprising the identification of causal eQTLs, prioritization of rare variants in the human population, and understanding disruptions of transcription factor binding sites. We find that LOL-EVE achieves state-of-the-art performance on these tasks, demonstrating the potential of region-specific large genomic language models and offering a powerful tool for prioritizing potentially causal non-coding variants in disease studies.Competing Interest StatementThe authors have declared no competing interest.