bioRxiv Science⌕ Search

Biology subjects

Baras, A. S.

Publications and source records attributed to Baras, A. S..

13 recordsLinked to original sources
bioRxiv2026

Quantifying the Recoverability of V and J Genes from TCR CDR3 Sequences Using Generative Repertoire Models

Introduction: How much of the variable (V) and joining (J) gene identity of a T-cell receptor is recoverable from its third complementarity-determining region (CDR3) amino-acid sequence alone? Immune repertoire studies often report the CDR3 with V and J annotation that is missing, low-confidence, or inconsistent, so what the CDR3 alone can and cannot fix is both a basic question about the receptor and a practical one for reading those repertoires. Methods: For each of 118,096 pooled human rearrangements (37,687 and 80,409 {beta}) we computed the posterior distribution over candidate genes under a generative model of V(D)J recombination and under its post-selection counterpart, and measured recoverability by conditional entropy, the candidate-list size needed to contain the annotated gene, the fraction of sequences admitting a high-confidence single-gene call, and the structure of gene-by-gene confusion. Results: The J gene was nearly determined by the CDR3 in both chains. The V gene was only partially recoverable, and behaved as a group rather than a gene: junctional trimming and non-templated insertion, together with the loss of synonymous codon information in translation, leave sets of mutually confusable V genes whose grouping departs sharply from germline family nomenclature (adjusted Rand index 0.05 for and 0.21 for {beta}). Selection sharpened the V posterior modestly (usage-controlled entropy shift -0.06 nats for and -0.28 for {beta}) and redistributed which V gene was most probable, a locus-scale rewrite in {beta} against a mild reweight in . Both the recoverability measurements and the confusion grouping reproduced in two held-out tumor cohorts. Discussion: V identity is an emergent, system-level property of the repertoire, set jointly by recombination and selection and invisible in any single rearrangement, so it should be reported as a calibrated group rather than a single gene. We also release the pipeline with a computational tool which can output a set of candidate genes with confidence values given a CDR3 sequence.

immunology↗
bioRxiv2026

A Foundation Model for the Cancer Genome

AO_SCPLOWBSTRACTC_SCPLOWCancer is a disease of the genome, in which somatic mutations and copy-number alterations determine tumour identity, clinical behaviour, and response to therapy. Consortium-scale sequencing has profiled hundreds of thousands of tumours,1,2 yet clinical interpretation still proceeds one alteration at a time against hand-curated knowledgebases,3,4 often ignoring co-occurring alterations and the genome-wide copy-number pattern. Self-supervised foundation models pretrained on unlabelled corpora5 have produced transferable representations in adjacent biological domains6-8 by learning joint structure across many features, yet no comparable model exists for the cancer genome. Here we present TESSERA (Tumour Embeddings via Self-Supervised Encoding and Reconstruction of Alterations), a foundation model for the cancer genome; we pretrain it on somatic single-nucleotide variants and copy-number segments through masked-token reconstruction within each modality and a contrastive objective across modalities. A single representation, produced once and reused without retraining, supports variant pathogenicity prediction, pan-cancer tumour typing, unsupervised molecular subtyping, prognostic stratification, and counterfactual treatment-effect estimation that yields predictive chemotherapy-selection biomarkers in real-world cohorts. These biomarkers are interpretable: each surfaces the co-occurring alterations underlying the prediction, exposing biology that single-gene rules miss. In metastatic colorectal cancer, where the FOLFOX-vs-FOLFIRI choice is currently guided by toxicity rather than tumour biology, the model uncovers a candidate predictive biomarker: a three-feature rule (TP53+/KRAS+/17p-) selecting patients who derive substantially greater benefit from FOLFOX than FOLFIRI.

bioinformatics↗
bioRxiv2024

Characterization of non-monotonic relationships between tumor mutational burden and clinical outcomes

Potential clinical biomarkers are often assessed with Cox regressions or their ability to differentiate two groups of patients based on a single cutoff. However, both of these approaches assume a monotonic relationship between the potential biomarker and survival. Tumor mutational burden (TMB) is currently being studied as a predictive biomarker for immunotherapy, and a single cutoff is often used to divide patients. In this study we introduce a two-cutoff approach that allows splitting of patients when a non-monotonic relationship is present, and explore the use of neural networks to model more complex relationships of TMB to outcome data. Using real-world data we find that while in most cases the true relationship between TMB and survival appears monotonic, that is not always the case and researchers should be made aware of this possibility. SignificanceWhen a non-monotonic relationship to survival is present it is not possible to divide patients by a single value of a predictor. Neural networks allow for complex transformations and can be used to correctly split patients when a non-monotonic relationship is present.

bioinformatics↗
bioRxiv2023

Improved bladder cancer antitumor efficacy with a recombinant BCG that releases a STING agonist

Despite the introduction of several new agents for the treatment of bladder cancer (BC), intravesical BCG remains a first line agent for the management of non-muscle invasive bladder cancer. In this study we evaluated the antitumor efficacy in animal models of BC of a recombinant BCG known as BCG-disA-OE that releases the small molecule STING agonist c-di-AMP. We found that compared to wild-type BCG (BCG-WT), in both the orthotopic, carcinogen-induced rat MNU model and the heterotopic syngeneic mouse MB-49 model BCG-disA-OE afforded improved antitumor efficacy. A mouse safety evaluation further revealed that BCG-disA-OE proliferated to lesser degree than BCG-WT in BALB/c mice and displayed reduced lethality in SCID mice. To probe the mechanisms that may underlie these effects, we found that BCG-disA-OE was more potent than BCG-WT in eliciting IFN-{beta} release by exposed macrophages, in reprogramming myeloid cell subsets towards an M1-like proinflammatory phenotypes, inducing epigenetic activation marks in proinflammatory cytokine promoters, and in shifting monocyte metabolomic profiles towards glycolysis. Many of the parameters elevated in cells exposed to BCG-disA-OE are associated with BCG-mediated trained innate immunity suggesting that STING agonist overexpression may enhance trained immunity. These results indicate that modifying BCG to release high levels of proinflammatory PAMP molecules such as the STING agonist c-di-AMP can enhance antitumor efficacy in bladder cancer.

cancer biology↗
bioRxiv2022

DeepTMB: An uncertainty-aware deep calibration of tumor mutational burden with a synthetic tumor-only dataset

BackgroundTumor mutational burden (TMB) has been investigated as a biomarker for immune checkpoint blockade (ICB) therapy. Increasingly, TMB is being estimated with gene panel-based assays (as opposed to full exome sequencing) and different gene panels cover overlapping but distinct genomic coordinates, making comparisons across panels difficult. Previous studies have suggested that standardization and calibration to exome-derived TMB be done for each panel to ensure comparability. With TMB cutoffs being developed from panel-based assays, there is a need to understand how to properly estimate exomic TMB values from different panel-based assays. Design: Our approach to calibration of panel-derived TMB to exomic TMB proposes the use of probabilistic mixture models that allow for nonlinear relationships along with heteroscedastic error. We examined various inputs including nonsynonymous, synonymous, and hotspot counts along with genetic ancestry. Using the TCGA cohort we generated a tumor-only version of the panel-restricted data by reintroducing private germline variants. Results: We were able to model more accurately the distribution of both tumor-normal and tumor-only data using the proposed probabilistic mixture models as compared to linear regression. Applying a model trained on tumor-normal data to tumor-only input results in biased TMB predictions. Including synonymous mutations resulted in better regression metrics across both data types, but ultimately a model able to dynamically weight the various input mutation types exhibited optimal performance. Including genetic ancestry improved model performance only in the context of tumor-only data, wherein private germline variants are observed. SignificanceA probabilistic mixture model better models the nonlinearity and heteroscedasticity of the data as compared to linear regression. Tumor-only panel data is needed to properly calibrate tumor-only panels to exomic TMB. Leveraging the uncertainty of point estimates from these models better informs cohort stratification in terms of TMB.

bioinformatics↗
bioRxiv2022

Read depth correction for somatic mutations

The ability to accurately detect mutations is a function of read depth and variant allele frequency (VAF). While the read depth distribution of a sample is observable, the true VAF distribution of all mutations in a sample is uncertain when there is low coverage depth. We propose to estimate the VAF distributions that would be observed with high-depth sequencing for samples with low sequencing depth by grouping samples with similar clonality and purity and using the VAF distributions observed with the high-depth mutations that are available. With these estimated high-depth VAF distributions we then calculate what the expected VAF distributions would be at a given depth and compare against the observed VAF distributions at that depth. Using this procedure we estimate that The Cancer Genome Atlas (TCGA) MC3 dataset only reports on average 83% of the mutations in a sample which would have been detected with high-depth sequencing. These results have important implications for comparing tumor mutational burden (TMB) estimates when samples are sequenced at different depths and for modeling high-depth, gene panel-based sequencing from the TCGA MC3 dataset.

bioinformatics↗
bioRxiv2021

The proposed promiscuity value of an HLA can vary significantly depending on the source data used

Immune checkpoint blockade, a form of immunotherapy, mobilizes a patients own immune system against cancer cells by releasing some of the natural brakes on T cells. Although our understanding of this process is evolving, it is thought that a patient response to immunotherapy requires tumor presentation of neoantigens to T cells and patients whose tumors present a wider array of neoantigens are more likely to derive benefit from immune checkpoint blockade1-4. Manczinger et al.5 recently reported findings that would appear contrarian to this notion in that they suggested patients with HLA alleles which bind more diverse peptides (higher promiscuity) are less likely to respond to immunotherapy. To estimate HLA promiscuity they looked at the HLA-peptide binding repertoires for class I alleles contained in the IEDB6, and obtained consistent results when performing robustness checks and subsequent analyses. Here we show that the proposed HLA promiscuity values can vary significantly across source data types and individual experiments.

cancer biology↗
bioRxiv2020

Aggregation Tool for Genomic Concepts (ATGC): A deep learning framework for sparse genomic measures and its application to tumor mutational burden

Deep learning can extract meaningful features from data given enough training examples. Large-scale genomic data are well suited for this class of machine learning algorithms; however, for many of these data the labels are at the level of the sample instead of at the level of the individual genomic measures. Conventional approaches to this data statically featurise and aggregate the measures separately from prediction. We propose to featurise, aggregate, and predict with a single trainable end-to-end model by turning to attention-based multiple instance learning. This allows for direct modelling of instance importance to sample-level classification in addition to trainable encoding strategies of genomic descriptions, such as mutations. We first demonstrate this approach by successfully solving synthetic tasks conventional approaches fail. Subsequently we applied the approach to somatic variants and achieved best-in-class performance when classifying tumour type or microsatellite status, while simultaneously providing an improved level of model explainability. Our results suggest that this framework could lead to biological insights and improve performance on tasks that aggregate information from sets of genomic data.

bioinformatics↗
bioRxiv2020

Analysis of SARS-CoV-2 specific T-cell receptors in ImmuneCode reveals cross-reactivity to immunodominant Influenza M1 epitope

Adaptive Biotechnologies and Microsoft have recently partnered to release ImmuneCode, a database containing SARS-CoV-2 specific T-cell receptors derived through MIRA, a T-cell receptor (TCR) sequencing based sequencing approach to identify antigen-specific TCRs. Herein, we query the extent of cross reactivity between these derived SARS-CoV-2 specific TCRs and other known antigens present in McPas-TCR, a manually curated catalogue of pathology-associated TCRs. We reveal cross reactivity between SARS-CoV-2 specific TCRs and the immunodominant Influenza GILGFVFTL M1 epitope, suggesting the importance of further work in characterizing the implications of prior Influenza exposure or co-exposure to the pathology of SARS-CoV-2 illness.

immunology↗
bioRxiv2018

DeepTCR: a deep learning framework for revealing structural concepts within TCR Repertoire

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks, such as in image and vocal recognition 1,2. The ability to learn complex patterns in data has tremendous implications in the genomics and immunology worlds, where sequence motifs become learned features that can be used to predict functionality, guiding our understanding of disease and basic biology 3-6. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system, where complex structural patterns in the TCR can be used to model its antigenic interaction. We present DeepTCR, a broad collection of unsupervised and supervised deep learning methods able to uncover structure in highly complex and large TCR sequencing data by learning a joint representation of a given TCR by its CDR3 sequences, V/D/J gene usage, and HLA background in which the T-cells reside. We demonstrate the utility of deep learning to provide an improved featurization of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs in both unsupervised and supervised learning tasks, understanding immunotherapy-related shaping of repertoire in the murine setting, and predicting response to checkpoint blockade immunotherapy from pre-treatment tumor biopsies in a clinical trial of melanoma. Our results show the flexibility and capacity for deep neural networks to handle the complexity of high-dimensional TCR genomic data for both descriptive and predictive purposes across basic science and clinical research.

bioinformatics↗
bioRxiv2018

miRge 2.0: An updated tool to comprehensively analyze microRNA sequencing data

miRNAs play important roles in the regulation of gene expression. The rapidly developing field of microRNA sequencing (miRNA-seq; small RNA-seq) needs comprehensive bioinformatics tools to analyze these large datasets. We present the second iteration of miRge, miRge 2.0, with multiple enhancements. miRge 2.0 adds new functionality including novel miRNA detection, A-to-I editing analysis, better output files, and improved alignment to miRNAs. Our novel miRNA detection method is the first to use both miRNA hairpin sequence structure and composition of isomiRs resulting in a more specific capture of potential miRNAs. Using known miRNA data, our support vector machine (SVM) model predicted miRNAs with an average Matthews correlation coefficient (MCC) of 0.939 over 32 human cell datasets and outperformed miRDeep2 and miRAnalyzer regarding phylogenetic conservation. The A-to-I editing analysis implementation strongly correlated with a reference datasets prior analysis with adjusted R2 = 0.96. miRge 2.0 comes with alignment libraries to both miRBase v21 and MirGeneDB for 6 species: human, mouse, rat, fruit fly, nematode and zebrafish; and has a tool to create custom libraries. With the redevelopment of the tool in Python, it is now incorporated into bcbio-nextgen and implementable through Bioconda. miRge 2.0 is freely available at: https://github.com/mhalushka/miRge.

bioinformatics↗
bioRxiv2017

Towards the human cellular microRNAome

microRNAs are short RNAs that serve as master regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell autonomously and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data has caused considerable confusion and comprehensive cell-level data does not yet exist. Here we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent super-enhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 2,632 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder suggesting Drosha and Dicer-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to deconvolute variable cellular composition of adipose tissue samples highlighting one use of this cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

genomics↗