Biology subjects

Ghersi, D.

Publications and source records attributed to Ghersi, D..

10 recordsLinked to original sources

bioRxiv2025

Form Follows Function: A New Paradigm of Pancreatic Cancer Progression

Pancreatic ductal adenocarcinoma (PDAC) exhibits a distinctive propensity to invade nearby organs and infiltrate large blood vessels, even in the absence of distant metastasis. While the genetic and transcriptomic drivers of PDAC progression have been well studied, the mechanisms by which these molecular changes translate into functional, invasive behavior remain largely unknown. Here, we uncover a striking level of tissue organization, characterized by previously unrecognized spatial and geometric properties within and among tumor structures. Leveraging the first large-scale, AI-assisted, human-curated PDAC atlas from hematoxylin and eosin (H&E) images, we annotated, classified, and characterized 144,474 malignant and normal structures from treatment-naive (n=118) and neoadjuvant-treated PDAC patients (n=50). Additionally, we developed a new computational tool, SHAPE, to investigate PDAC aggressiveness through a comprehensive "geometrization" of cancer progression. Using traditional H&E-stained slides and three-dimensional (3D) tissue reconstruction experiments, we observed that invading tumor structures display an eccentric morphology with pronounced local angular coherence. These geometric and spatial properties revealed coherent architectural patterns, with invasive structures closely tracking vessels and nerves as they infiltrate surrounding tissue. Mechanistically, integration of morphological features from 39,045 annotated tumor structures with whole-genome and RNA sequencing data revealed that PDACs with numerous eccentric structures exhibit increased copy number alterations (CNAs), loss of heterozygosity (LoH) on the p-arm of chromosome 17, and a quasi-mesenchymal/basal-like molecular subtype. Spatial transcriptomic analysis of 1,650 tumor structures from six additional PDAC patients further confirmed upregulation of invasive cellular programs within highly eccentric structures, such as epithelial-to-mesenchymal transition (EMT), angiogenesis, coagulation, and complement pathways, underscoring their infiltrative nature. Finally, cross-validation of our AI-based method enabled a fully automated, highly interpretable computational approach to assist pathologists and clinicians in evaluating neoadjuvant chemotherapy response, predicting patient survival, and guiding chemotherapy in adjuvant settings. Collectively, these findings deepen our understanding of PDAC progression, identify a new hallmark of tumor architecture, and pave the way for full integration of AI-driven morphology-based approaches into clinical workflows to improve the management of PDAC patients.

cancer biology↗

bioRxiv2024

Towards Equitable MHC Binding Predictions: Computational Strategies to Assess and Reduce Data Bias

Deep learning tools that predict peptide binding by major histocompatibility complex (MHC) proteins play an essential role in developing personalized cancer immunotherapies and vaccines. In order to ensure equitable health outcomes from their application, MHC binding prediction methods must work well across the vast landscape of MHC alleles. Here we show that there are alarming differences across individuals in different racial and ethnic groups in how much binding data are associated with their MHC alleles. We introduce a machine learning framework to assess the impact of this data disparity for predicting binding for any given MHC allele, and apply it to develop a state-of-the-art MHC binding prediction model that additionally provides per-allele performance estimates. We demonstrate that our MHC binding model successfully mitigates much of the data disparities observed across racial groups. To address remaining inequities, we devise an algorithmic strategy for targeted data collection. Our work lays the foundation for further development of equitable MHC binding models for use in personalized immunotherapies.

bioinformatics↗

bioRxiv2022

Co-evolution of human influenza A and Epstein Barr virus-specific CD8 ex vivo memory T cell receptor BV repertoires with increasing age

CD8 memory T cells are generated during primary infection with intracellular pathogens, such as viruses. These cells play an important role in the protection of the host upon re-infection with the same pathogen. In this study, we compare CD8 memory T cell receptor (TCR) BV repertoires directly ex vivo for two common human viruses, influenza A virus (IAV), an RNA virus that frequently re-infects due to a high rate of genetic mutation, and Epstein-Barr virus (EBV), a DNA virus, which persists in B cells for life, in the 95% of people that become infected. In cross- sectional and longitudinal studies of EBV seropositive, HLA-A2+, young (18-22 years), middle age (25-59 years), and older (>60 years) donors, we demonstrate that CD8 memory TCR repertoires to three immunodominant epitopes, known to have cross-reactive responses, IAV- M158-66 (M1), EBV-BMLF1280-288 (BM), and EBV-BRLF109-117 (BR) co-evolve as individuals age. Cross-sectional studies showed that IAV-M1- and both EBV-specific repertoires narrowed their TRBV usage with increasing age manifesting to different degrees for each epitope. In fact, narrowing of EBV-BM and EBV-BR-specific TRBV usage correlated with increasing age. IAV- M1-specific TRBV usage was significantly narrowed by middle-age. There was evidence that TRBV usage was changing with increasing age. For instance, IAV-M1-specific dominant BV19 usage appeared to become bimodal showing either high or low frequency of usage in the older age group, while BV30 usage frequency directly correlated with age. For the EBV epitope-specific responses there was preferential usage of particular TRBV and changes in the hierarchy of BV family usage in the different age groups. There appeared to be focusing of the TRBV repertoire by all 3 epitopes to three common BV in the older donors, which would be consistent with retention of crossreactive TCR suggesting co-evolution. Longitudinal studies tracking two donors over 14- 15 years (middle age to older) showed that there were continuous modulations in the TCR repertoire of IAV-M1, EBV-BM and EBV-BR-specific responses over time. There was evidence that acute IAV infection could contribute to these changes in TCR repertoire. This could be occurring by the TCR cross-reactivity that is known to exist between these 3 epitopes, and which appeared to be enhanced during acute IAV infection based on increased usage of common shared TRBV. These studies suggest that virus-specific TCR repertoires change over time as individuals age leading to narrowing of the repertoire favoring retention of potentially crossreactive TCR.

immunology↗

bioRxiv2022

A novel computational approach to identify cancer cells in scRNA-seq data

Single-cell RNA-seq is an invaluable research tool that allows for the investigation of gene expression in heterogeneous cancer cell populations in ways that bulk RNA-seq cannot. However, normal (i.e., non tumor) cells in cancer samples have the potential to confound the downstream analysis of single-cell RNA-seq data. Several existing methods for identifying tumor cells use copy number variation inference. This work aims to extend existing approaches for identifying cancer cells in single-cell RNA-seq samples by incorporating putative driver alterations. We found that putative driver alterations can be detected in single-cell RNA-seq data and that a subset of cells in tumor samples are enriched in putative driver alterations as compared to normal cells. Furthermore, we show that the number of putative driver alterations and inferred copy number variation are not correlated in all samples. Taken together, our findings suggest that combining copy number variation inference with putative driver mutation load can augment the number of tumor cells that can be confidently included in downstream analyses of single-cell RNA-seq datasets.

bioinformatics↗

bioRxiv2020

SwarmTCR: a computational approach to predict the specificity of T Cell Receptors

MotivationComputationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to tackle this problem is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance. ResultsWe compared the performance of SwarmTCR against a state-of-the-art method (TCRdist) and showed that SwarmTCR performed significantly better on epitopes EBV-BRLF1300, EBV-BRLF1109, NS4B214-222 with single cell data and epitopes EBV-BRLF1300, EBV-BRLF1109, IAV-M158 with bulk sequencing data ( and {beta} chains). In addition, we show that the weights returned by SwarmTCR are biologically interpretable. AvailabilitySwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub (https://github.com/thecodingdoc/SwarmTCR) Contactdghersi@unomaha.edu

bioinformatics↗

bioRxiv2020

1q21.3 amplification modulates therapy sensitivity in Triple-Negative Breast Cancer

The contrast in therapy sensitivity and response across triple negative breast cancer (TNBC) patients suggest underlying genotypic heterogeneity. Using publicly available data, we found significant associations between DNA-level copy number alterations of 1q21.3 locus and therapy sensitivity. We show that in spite of their aggressive nature, 1q21.3 amplified tumors are more responsive to commonly used cytotoxic therapies, highlighting the relevance of 1q21.3 copy number status as a genetic marker for risk stratification, therapy selection and response.

cancer biology↗

bioRxiv2020

ACE2 fragment as a decoy for novel SARS-Cov-2 virus

Novel SARS-Cov-2 enters human cells via interaction between the surface spike (S) glycoprotein and the cellular membrane receptor angiotensin-converting enzyme 2 (ACE2). Using a combination of comparative structural analyses of the binding surface of the S protein to ACE2, docking experiments, and molecular dynamics simulations we computationally identified a minimal, stable fragment of ACE2. This fragment binds to the S protein, is soluble, and appears not to bind to the physiological ligand angiotensinII. These results suggest a possible use of the ACE2 fragment as a decoy that could interfere with viral binding by competition.

bioinformatics↗

bioRxiv2020

Epstein Barr virus epitope/MHC interaction combined with convergent recombination drive selection of diverse T cell receptor a and b repertoires

Recognition modes of individual T cell receptors (TCR) are well studied, but factors driving the selection of TCR repertoires from primary through persistent human virus infections are less well understood. Using deep sequencing, we demonstrate a high degree of diversity of EBV-specific clonotypes in acute infectious mononucleosis. Only 9% of unique clonotypes detected in AIM persisted into convalescence; the majority (91%) of unique clonotypes detected in AIM were not detected in convalescence and were seeming replaced by equally diverse "de-novo" clonotypes. The persistent clonotypes had a greater probability of being generated than non-persistent due to convergence recombination of multiple nucleotide sequences to encode the same amino acid sequence, as well as the use of shorter CDR3 regions with fewer nucleotide additions (i.e. sequences closer to germline). Moreover, the two most immunodominant HLA-A2-restricted EBV epitopes, BRLF1109 and BMLF1280, show highly distinct antigen-specific public (i.e. shared between individuals) features. In fact, TCR CDR3 motifs played a dominant role, while TCR{beta} played a minimal role, in the selection of TCR repertoire to an immunodominant EBV epitope, BRLF1. This contrasts with the majority of previously reported repertoires, which appear to be selected either on TCR{beta} CDR3 interactions with peptide/MHC or in combination with TCR CDR3. Understanding of how TCR/peptide/MHC complex interactions drive repertoire selection can be used to develop optimal strategies for vaccine design or generation of appropriate adoptive immunotherapies for viral infections in transplant settings or for cancer. ImportanceSeveral lines of evidence suggest that TCR and {beta} repertoires play a role in disease outcomes and treatment strategies during viral infections in transplant patients, and in cancer and autoimmune disease therapy. Our data suggests that it is essential that we understand the basic principles of how to drive optimum repertoires for both TCR chains, and {beta}. We address this important issue by characterizing the CD8 TCR repertoire to a common persistent human viral infection (EBV), which is controlled by appropriate CD8 T cell responses. The ultimate goal would be to determine if the individuals who are infected asymptomatically develop a different TCR repertoire than those that develop the immunopathology of AIM. Here, we begin by doing an in depth characterization of both CD8 T cell TCR and {beta} repertoires to two immunodominant EBV epitopes over the course of AIM identifying potential factors that may be driving their selection.

immunology↗

bioRxiv2019

Uncovering and characterizing splice variants associated with survival in lung cancer patients

Splice variants have been shown to play an important role in tumor initiation and progression and can serve as novel cancer biomarkers. However, the clinical importance of individual splice variants and the mechanisms by which they can perturb cellular functions are still poorly understood. To address these issues, we developed an efficient and robust computational method to: (1) identify splice variants that are associated with patient survival in a statistically significant manner; and (2) predict rewired protein-protein interactions that may result from altered patterns of expression of such variants. We applied our method to the lung adenocarcinoma dataset from TCGA and identified splice variants that are significantly associated with patient survival and can alter protein-protein interactions. Among these variants, several are implicated in DNA repair through homologous recombination. To computationally validate our findings, we characterized the mutational signatures in patients, grouped by low and high expression of a splice variant associated with patient survival and involved in DNA repair. The results of the mutational signature analysis are in agreement with the molecular mechanism suggested by our method. To the best of our knowledge, this is the first attempt to build a computational approach to systematically identify splice variants associated with patient survival that can also generate experimentally testable, mechanistic hypotheses. Code for identifying survival-significant splice variants using the Null Empirically Estimated P-value method can be found at https://github.com/thecodingdoc/neep. Code for construction of Multi-Granularity Graphs to discover potential rewired protein interactions can be found at https://github.com/scwest/SINBAD. Presentation slides are found at https://github.com/scwest/RECOMB-CBB_2019_NEEP.\n\nAuthor summaryIn spite of many recent breakthroughs, there is still a pressing need for better ways to diagnose and treat cancer in ways that are specific to the unique biology of the disease. Novel computational methods applied to large-scale datasets can help us reach this goal more effectively. In this work we shed light on a still poorly understood biological process that is often aberrant in cancer and that can lead to tumor formation, progression, and invasion. This mechanism is alternative splicing and is the ability of one gene to code for many different variants with distinct functions. We developed a fast and statistically robust approach to identify splice variants that are significantly associated with patient survival. Then, we computationally characterized the protein products of these splice variants by identifying potential losses and gains of protein interactions that could explain their biological role in cancer. We applied our method to a lung adenocarcinoma dataset and identified several splice variants associated with patient survival that lose biologically important interactions. We conducted case studies and computationally validated some of our results by finding mutation signatures that support the molecular mechanism suggested by our method.

bioinformatics↗

bioRxiv2018

High diversity, turnover, and structural constraints characterize TCR α and β repertoire selection

Recognition modes of individual T-cell receptors (TCR) are well studied, but how TCR repertoires are selected during acute through persistent human virus infections is less clear. Here, we show that persistent EBV-specific clonotypes account for only 9% of unique clonotypes but are highly expanded in acute infectious mononucleosis, and have distinct antigen-specific public features that drive selection into convalescence. The other 91% of highly diverse unique clonotypes disappear and are replaced in convalescence by equally diverse \"de-novo\" clonotypes. These broad fluctuating repertoires lend plasticity to antigen recognition and potentially protect against T-cell clonal loss and viral escape.

immunology↗