Biology subjects

Karchin, R.

Publications and source records attributed to Karchin, R..

At least 19 recordsLinked to original sources

bioRxiv2026

A generative reference grammar of healthy TCR repertoires reveals cancer-associated immune remodeling

T-cell receptor (TCR) repertoires record how adaptive immunity is organized and how cancer and therapy reshape it, but this signal is hard to read: treatment-associated change is entangled with the V(D)J recombination constraints that shape every repertoire. We present CRAFT (Cancer Repertoire Anomaly Finding Transformer), a conditional sequence-to-sequence transformer that learns a nucleotide-level generative grammar of productive TCR-beta CDR3 sequences from healthy donors, conditioned on germline V(D)J assignments. A dual-head decoder mirrors the independence of V-D and D-J recombination, and curriculum training produces embeddings that define a healthy-reference coordinate system in which cancer-associated change appears as structured, measurable deviation. In proof-of-concept applications to a neoadjuvant checkpoint-blockade cohort sampled longitudinally across blood, and to serial single-cell profiling of T-cell subsets during oncolytic immunotherapy, CRAFT geometric metrics capture response-associated remodeling, including shifts in repertoire organization over time. On antigen-labeled benchmarks, CRAFT organizes specificity classes coherently, recovering structure that reflects shared antigen recognition.

cancer biology↗

bioRxiv2026

Reconstructing clone-resolved transcriptional programs from bulk tumor sequencing

Tumor clones acquire distinct transcriptional programs as they evolve, but bulk RNA-seq averages over clonal mixtures and obscures the lineage-specific biology that DNA-sequencing reveals. We present PICTographPlus, the first method to infer clone-resolved transcriptional programs by integrating bulk DNA-derived clonal phylogenies and proportions with bulk RNA-seq alone, without single-cell data. Benchmarked against experimentally measured ground truth, scDNA/scRNA co-profiled cells from a wellDR-seq cancer dataset, across 320 pseudo-bulk replicates spanning four tumor purities and four sample counts and evaluated under seven regularization models, PICTographPlus recovers clone-level expression at mean Pearson r [≥] 0.92 and localizes pathway gains and losses to correct evolutionary branches (median F1 0.31-0.40, well above a no-deconvolution baseline). Applied to multi-region NSCLC, pancreatic precursor lesions, and rapid-autopsy PDAC, it localizes metabolic reprogramming, precursor-to-invasive transitions, and organ-adapted metastatic states to specific clonal branches. PICTographPlus turns standard bulk assays into clone-resolved transcriptional maps, enabling retrospective analyses where single-cell profiling is impractical.

genomics↗

bioRxiv2025

Do Pseudosequences Matter in Neoantigen Prediction?

Computational prediction of neoantigens that elicit T cell responses is central to the development of personalized cancer vaccines. Many current predictors represent MHC class I alleles using selected subsets of residues, known as pseudosequences, yet the extent to which pseudosequence choice and encoding strategy influence predictive performance has not been systematically examined. This study addresses that gap by evaluating a range of MHC representations within the BigMHC EL framework. We compared pseudosequence definitions based on protein structure and evolutionary diversity, a randomly sampled pseudosequence baseline, pseudosequences of varying lengths, embeddings generated using the ESM-2 protein language model, and a graph-based annotation embedding derived from allele groupings. Models using biologically informed pseudosequences consistently outperformed the random baseline, underscoring the importance of residue selection. Protein structure and evolutionary diversity pseudosequences showed similar performance, likely reflecting overlap in residues near the peptide-binding groove. We also found that pseudosequences of approximately 30 to 35 residues produced the strongest performance. Lastly, ESM-2 and annotation-based embeddings outperformed the random baseline but did not surpass curated pseudosequences under the current setup. Together, these findings indicate that curated pseudosequences remain efficient representations of MHC alleles in neoantigen prediction models, while alternative encodings can approximate but not yet replace residue-level sequence information.

bioinformatics↗

bioRxiv2025

Integrative analysis of genomic and transcriptomic data informs precancer progression in the pancreas

Pancreatic ductal adenocarcinoma (PDAC) arises from heterogeneous precursor lesions, including intraductal papillary mucinous neoplasms (IPMNs), but the features distinguishing indolent from progressive lesions remain unclear. We performed an integrative analysis of transcriptomic, genomic, and microenvironmental profiles of IPMNs to define multi-omic phenotypes. Using transfer learning, we projected IPMN-derived transcriptional programs onto spatial transcriptomic datasets from IPMNs and pancreatic intraepithelial neoplasias (PanINs). We identified two major phenotypes: one associated with cancer-associated fibroblasts and epithelial-to-mesenchymal transition, shared across IPMN, PanIN, and PDAC; and a second, glycolysis-enriched phenotype with a unique somatic mutation profile specific to IPMN. Spatial mapping further revealed grade-specific enrichment of transcriptional programs and distinct interactions with stromal and immune subtypes, underscoring the role of the precancer microenvironment in progression. These findings establish multi-omic phenotypes that unify genetic, transcriptional, and microenvironmental heterogeneity, providing a framework for distinguishing progressive from indolent precancers and a web-based public atlas for future exploration of these data and transcriptional phenotypes.

bioinformatics↗

bioRxiv2025

SVCFit: Inferring structural variant cellular fraction in tumors

Therapeutic pressure shapes tumor evolution by selecting for subclones with distinct genomic architectures. In metastatic castration-resistant prostate cancer (mCRPC), structural variants (SVs) are central to this process but rarely incorporated into clonal reconstruction from bulk DNA sequencing. We describe SVCFit, a computational framework that estimates the cellular fraction of diverse SV classes from whole-genome sequencing and uses these estimates to reconstruct clonal evolutionary relationships. SVCFit explicitly models SV-type-specific breakpoint and breakend patterns and accounts for local copy-number context, enabling accurate SV cellular-fraction estimation across heterogeneous tumor genomes. On simulated datasets and in silico mixtures of metastatic prostate cancer samples, SVCFit achieves lower estimation error than SVclone, the current state-of-the-art for bulk-sequencing SV cellular-fraction estimation. Applied to longitudinal whole-genome sequencing from patients with mCRPC treated with bipolar androgen therapy, SVCFit reveals marked treatment-associated clonal reconfiguration, including contraction of highly rearranged subclones and expansion of resistant populations defined by distinct structural alterations. By enabling reconstruction of SV-defined clonal architecture from routine whole-genome sequencing, SVCFit expands the toolkit for studying tumor evolution under therapy in precision oncology. The COMBAT trial that provided these samples is registered on ClinicalTrials.gov (NCT03554317; first posted 13 June 2018).

bioinformatics↗

bioRxiv2024

Islands of genomic stability in the face of genetically unstable metastatic cancer

AbstractO_ST_ABSIntroductionC_ST_ABSMetastatic cancer affects millions of people worldwide annually and is the leading cause of cancer-related deaths. Most patients with metastatic disease are not eligible for surgical resection, and current therapeutic regimens have varying success rates, some with 5-year survival rates below 5%. Here we test the hypothesis that metastatic cancer can be genetically targeted by exploiting single base substitution mutations unique to individual cells that occur as part of normal aging prior to transformation. These mutations are targetable because [~]10% of them form novel tumor-specific "NGG" protospacer adjacent motif (PAM) sites targetable by CRISPR-Cas9. MethodsWhole genome sequencing was performed on five rapid autopsy cases of patient-matched primary tumor, normal and metastatic tissue from pancreatic ductal adenocarcinoma decedents. CRISPR-Cas9 PAM targets were determined by bioinformatic tumor-normal subtraction for each patient and verified in metastatic samples by high-depth capture-based sequencing. ResultsWe found that 90% of PAM targets were maintained between primary carcinomas and metastases overall. We identified rules that predict PAM loss or retention, where PAMs located in heterozygous regions in the primary tumor can be lost in metastases (private LOH), but PAMs occurring in regions of loss of heterozygosity (LOH) in the primary tumor were universally conserved in metastases. ConclusionsRegions of truncal LOH are strongly retained in the presence of genetic instability, and therefore represent genetic vulnerabilities in pancreatic adenocarcinomas. A CRISPR-based gene therapy approach targeting these regions may be a novel way to genetically target metastatic cancer.

cancer biology↗

bioRxiv2023

SpliceMutr enables pan-cancer analysis of splicing-derived neoantigen burden in tumors

Aberrant alternative splicing can generate neoantigens, which can themselves stimulate immune responses and surveillance. Previous methods for quantifying splicing-derived neoantigens are limited by independent references and potential batch effects. Here, we introduce SpliceMutr, a bioinformatics approach and pipeline for identifying splicing derived neoantigens from paired tumor normal data. SpliceMutr facilitates the identification of tumor-specific antigenic splice variants, predicts MHC-binding affinity, and estimates splicing antigenicity scores per gene. By applying this tool to genomic data from The Cancer Genome Atlas (TCGA), we generate splicing-derived neoantigens and neoantigenicity scores per sample and across all cancer types and find numerous correlations between splicing antigenicity and well-established biomarkers of anti-tumor immunity. Notably, carriers of mutations within splicing machinery genes have higher splicing antigenicity, which provides support for our approach. Further analysis of splicing antigenicity in cohorts of melanoma patients treated with mono- or combined immune checkpoint inhibition suggest that the abundance of splicing antigens is reduced post-treatment from baseline in patients who progress, likely because of an immunoediting process. We also observe increased splicing antigenicity in responders to immunotherapy, which may relate to an increased capacity to mount an immune response to splicing-derived antigens. This new computational tool provides novel analytical capabilities for splicing antigenicity and is openly available for further immuno-oncologic analysis.

cancer biology↗

bioRxiv2023

Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions

Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm3 of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogene KRAS, but no gene other than KRAS was altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multiple KRAS mutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.

cancer biology↗

bioRxiv2022

Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity

Identifying neoepitopes that elicit an adaptive immune response is a major bottleneck to developing personalized cancer vaccines. Experimental validation of candidate neoepitopes is extremely resource intensive, and the vast majority of candidates are non-immunogenic, making their identification a needle-in-a-haystack problem. To address this challenge, we present computational methods for predicting MHC-I epitopes and identifying immunogenic neoepitopes with improved precision. The BigMHC method comprises an ensemble of seven pan-allelic deep neural networks trained on peptide-MHC eluted ligand data from mass spectrometry assays and transfer learned on data from assays of antigen-specific immune response. Compared with four state-of-the-art classifiers, BigMHC significantly improves the prediction of epitope presentation on a test set of 45,409 MHC ligands among 900,592 random negatives (AUROC=0.9733, AUPRC=0.8779). After transfer learning on immunogenicity data, BigMHC yields significantly higher precision than seven state-of-the-art models in identifying immunogenic neoepitopes, making BigMHC effective in clinical settings. All data and code are freely available at https://github.com/KarchinLab/bigmhc.

bioinformatics↗

bioRxiv2022

Evidence-based calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for clinical use of PP3/BP4 criteria

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as Supporting level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (Supporting, Moderate, Strong, Very Strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tools scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved Supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying Moderate and several reached Strong evidence levels. One tool reached Very Strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

bioinformatics↗

bioRxiv2021

Estimation of cancer cell fractions and clone trees from multi-region sequencing of tumors

AO_SCPLOWBSTRACTC_SCPLOWMulti-region sequencing of one or multiple biopsies of solid tumors from a patient can be used to improve our understanding of the diversity of subclones in the patients tumor and shed light on the evolutionary history of the disease. Due to the large number of possible evolutionary relationships between clones and the fundamental uncertainty of the mutational composition of subclones, elucidating the most probable evolutionary relationships poses statistical and computational challenges. We developed a Bayesian hierarchical model called PICTograph to model uncertainty in the assignment of mutations to subclones and an approach to reduce the space of possible graphical models that postulate their evolutionary origin. Compared to available methods, our approach provided more consistent and accurate estimates of cancer cell fractions and better tree topology reconstruction over a range of simulated clonal diversity. Application of PICTograph to whole exome sequencing data of individuals with pancreatic cancer precursor lesions confirmed known early occurring mutations and indicated substantial molecular diversity, including multiple distinct subclones (range 6 - 12) and intra-sample mixing of subclones. As the complete evolutionary history for some patients was not identifiable, we used ensemble-based visualizations to distinguish between highly probable evolutionary relationships recovered in multiple models from uncertain relationships occurring in a small subset of models. These analyses indicate that PICTograph provides a useful approximation to evolutionary inference, particularly when the evolutionary course of a patients cancer is complex.

bioinformatics↗

bioRxiv2021

HLA Gene Expression Mediates Tumor Immunogenicity and Escape

Human Leukocyte Antigen (HLA) expression contributes to the activation of anti-tumor immunity through interactions with T cell receptors. However, pan-cancer HLA-mediated immunogenicity and immunoediting mechanisms has not been systematically studied. In a retrospective analysis of 33 tumor types from the Cancer Genome Atlas, we uncovered HLA class I and class II differential expression, which outperformed traditional clinical metrics in predicting patient survival. We also characterized the distribution of HLA supertypes across cancers and showed that patients with high HLA allelic diversity and gene expression had better prognosis. Immune microenvironments with varied survival outcomes could be predicted using a neural network model trained on HLA expression data. Furthermore, we identified a subset of tumors which upregulated HLA class I but not class II genes and exploited HLA-mediated escape strategies. Our results suggest the potential of using HLA expression data to predict patient prognosis. Taken together, we emphasize the crucial role of HLA upregulation in shaping prolonged anti-tumor immunity. SynopsisIn a retrospective analysis of 11080 patients of 33 TCGA cancer types, we showed HLA class I and class II differential expression shape various immune microenvironments and tumor immunoediting mechanisms, predicting tumor immunogenicity and patient survival.

systems biology↗

bioRxiv2019

OpenCRAVAT, an open source collaborative platform for the annotation of human genetic variation

PURPOSEThe modern researcher is confronted with hundreds of published methods to interpret genetic variants. There are databases of genes and variants, phenotype-genotype relationships, algorithms that score and rank genes, and in silico variant effect prediction tools. Because variant prioritization is a multi-factorial problem, a welcome development in the field has been the emergence of decision support frameworks, which make it easier to integrate multiple resources in an interactive environment. Current decision support frameworks are typically limited by closed proprietary architectures, access to a restricted set of tools, lack of customizability, web dependencies that expose protected data, or limited scalability.\n\nMETHODSWe present OpenCRAVAT, a new open source, scalable decision support system for variant and gene prioritization. We have designed the resource catalog to be open and modular to maximize community and developer involvement, and as a result the catalog is being actively developed and growing every month. Resources made available via the store are well-suited for analysis of cancer, as well as Mendelian and complex diseases.\n\nRESULTSOpenCRAVAT offers both command line utility and dynamic GUI, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines, and explore results in a richly detailed viewing environment. We present several case studies to illustrate the design of custom workflows to prioritize genes and variants.\n\nCONCLUSIONOpenCRAVAT is distinguished from similar tools by its capabilities to access and integrate an unprecedented amount of diverse data resources and computational prediction methods, which span germline, somatic, common, rare, coding and non-coding variants. OpenCRAVAT is freely available at https://opencravat.org

bioinformatics↗

bioRxiv2019

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique candidate immunogenic missense mutations (IMMs). Predicted-IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted-IMM load was significantly associated with increased immune cell infiltration (p<2e-16) including CD8+ T cells. Notably, only 0.16% of predicted immunogenic missense mutations were observed in >2 patients, with 61.7% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.\n\nSynopsisWe developed a new in silico predictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.

immunology↗

bioRxiv2019

Assessing aneuploidy with repetitive element sequencing.

We report a sensitive PCR-based assay that can detect aneuploidy in samples containing as little as 3 picograms of DNA. Using a single primer pair, we amplified [~]750,000 amplicons distributed throughout the genome Aneuploidy was detected in 49% of liquid biopsies from a total of 883 non-metastatic cancers of eight different types. Combining aneuploidy with somatic mutation detection and eight standard protein biomarkers yielded a median sensitivity of 80% at 99% specificity.

cancer biology↗

bioRxiv2018

Enhanced context reveals the scope of somatic missense mutations driving human cancers

Large-scale cancer sequencing studies of patient cohorts have statistically implicated many genes driving cancer growth and progression, and their identification has yielded substantial translational impact. However, a remaining challenge is to increase the resolution of driver prediction from the gene level to the mutation level, because mutation-level predictions are more closely aligned with the goal of precision cancer medicine. Here we present CHASMplus, a computational method, that is uniquely capable of identifying driver missense mutations, including those specific to a cancer type, as evidenced by significantly superior performance on diverse benchmarks. Applied to 8,657 tumor samples across 32 cancer types in The Cancer Genome Atlas, CHASMplus identifies over 4,000 unique driver missense mutations in 240 genes, supporting a prominent role for rare driver mutations. We show which TCGA cancer types are likely to yield discovery of new driver missense mutations by additional sequencing, which has important implications for public policy. SignificanceMissense mutations are the most frequent mutation type in cancers and the most difficult to interpret. While many computational methods have been developed to predict whether genes are cancer drivers or whether missense mutations are generally deleterious or pathogenic, there has not previously been a method to score the oncogenic impact of a missense mutation specifically by cancer type, limiting adoption of computational missense mutation predictors in the clinic. Cancer patients are routinely sequenced with targeted panels of cancer driver genes, but such genes contain a mixture of driver and passenger missense mutations which differ by cancer type. A patients therapeutic response to drugs and optimal assignment to a clinical trial depends on both the specific mutation in the gene of interest and cancer type. We present a new machine learning method honed for each TCGA cancer type, and a resource for fast lookup of the cancer-specific driver propensity of every possible missense mutation in the human exome.

bioinformatics↗

bioRxiv2017

Non-invasive detection of upper tract urothelial carcinomas through the analysis of driver gene mutations and aneuploidy in urine

Upper tract urothelial carcinomas (UTUC) of the renal pelvis or ureter can be difficult to detect and challenging to diagnose. Here, we report the development and application of a non-invasive test for UTUC based on molecular analyses of DNA recovered from cells shed into the urine. The test, called UroSEEK, incorporates assays for mutations in eleven genes frequently mutated in urologic malignancies and for allelic imbalances on 39 chromosome arms. At least one genetic abnormality was detected in 75% of urinary cell samples from 56 UTUC patients but in only 0.5% of 188 samples from healthy individuals. The assay was considerably more sensitive than urine cytology, the current standard-of-care. UroSEEK therefore has the potential to be used for screening or to aid in diagnosis in patients at increased risk for UTUC, such as those exposed to herbal remedies containing the carcinogen aristolochic acid.

cancer biology↗

bioRxiv2017

Non-invasive detection of bladder cancer through the analysis of driver gene mutations and aneuploidy

Current non-invasive approaches for bladder cancer (BC) detection are suboptimal. We report the development of non-invasive molecular test for BC using DNA recovered from cells shed into urine. This \"UroSEEK\" test incorporates assays for mutations in 11 genes and copy number changes on 39 chromosome arms. We first evaluated 570 urine samples from patients at risk for BC (microscopic hematuria or dysuria). UroSEEK was positive in 83% of patients that developed BC, but in only 7% of patients who did not develop BC. Combined with cytology, 95% of patients that developed BC were positive. We then evaluated 322 urine samples from patients soon after their BCs had been surgically resected. UroSEEK detected abnormalities in 66% of the urine samples from these patients, sometimes up to 4 years prior to clinical evidence of residual neoplasia, while cytology was positive in only 25% of such urine samples. The advantages of UroSEEK over cytology were particularly evident in low-grade tumors, wherein cytology detected none while UroSEEK detected 67% of 49 cases. These results establish the foundation for a new, non-invasive approach to the detection of BC in patients at risk for initial or recurrent disease.

cancer biology↗