Tyler Alban Laboratory

Research

Overview

The Alban Laboratory operates at the intersection of computational biology, data science, and translational immuno-oncology. Our laboratory functions as a collaborative team science hub focused on driving translational efforts across the institute, managing and supporting the informatics and quantitative pipelines for complex clinical trials. Our overarching mission is to decrypt the immunologic recognition of cancer and overcome mechanisms of tumor mediated immune evasion. By integrating high-dimensional multi-omics data with artificial intelligence and advanced computational algorithms, we aim to identify actionable biomarkers, decode neoantigen immunogenicity, and translate these computational insights into next-generation precision immunotherapies.

Neoantigen Discovery & Quantum AI

A major focus of our laboratory is the application of advanced artificial intelligence and emerging quantum computing frameworks to model tumor-immune interactions. The accurate prediction of neoantigen presentation and subsequent T-cell receptor (TCR) engagement remains a significant computational challenge. To address this, we developed a comprehensive neoantigen immunogenicity atlas (highlighted in our recent Nature Medicine publication PMID: 39349627) which provides a critical foundation for understanding the rules of tumor recognition.

Building upon this atlas, we are pushing the limits of computational methods to unravel one of immunology's most complex problems: how immune cells recognize and ultimately kill cancer cells. Our goal is to model and mimic this biological phenomenon to drive the discovery of novel therapeutics. To achieve this, we developed Q-CHIPP (Quantum Convolutional HLA Immunogenic Peptide Prediction), a first of its kind quantum convolutional neural network designed to predict immunogencity.  This research is part of a broader quantum initiative at the Cleveland Clinic through the Discovery Accelerator partnership with IBM, where we leverage the first commercially deployed onsite quantum computer.

We are actively expanding upon these computational foundations to translate these insights into clinical applications. As highlighted in our recent collaborative with IBM, we are also utilizing advanced AI frameworks to rationally design novel T cell receptors, laying the groundwork for highly personalized cancer vaccines and engineered cellular therapies.

Multi-Omics & The Tumor Microbiome

Tumor-mediated immune suppression is a highly dynamic process driven by complex cellular and microbial networks. Our lab utilizes high-throughput methodologies including single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and comprehensive immune profiling to comprehensively interrogate the tumor immune microenvironment (TIME). Crucially, our profiling efforts extend beyond host immune cells to include the impact of the intra-tumoral microbiome. In two recent Nature Cancer publications (highlighted here and here), we integrated comprehensive multi-omics profiling from a large phase 3 clinical trial to identify how intra-tumoral bacterial burden is a primary driver of resistance to immunotherapy.

Building on these findings, our current research seeks to delineate the specific neutrophil and polymorphonuclear myeloid (PMN) subsets that are primarily driven by this intra-tumoral bacterial presence. By characterizing these bacteria-driven immune phenotypes, we aim to identify novel therapeutic targets to disrupt this suppressive axis. To rapidly translate these discoveries into clinical impact, we are collaborating with Dr. Natalie Silver, who has initiated pioneering clinical trials utilizing antibiotics in head and neck (HN) cancer patients. Our laboratory leads the correlative genomic and multi-omic analyses for these trials, directly investigating how the therapeutic reduction of bacterial load reshapes the microenvironment to restore immune activation and enhance anti-tumor recognition.

Translational Correlative Science

Translating complex computational biology into actionable clinical insights requires robust infrastructure and novel predictive tools. Through our leadership of the Translational and Correlative Sciences Service, the Alban Lab develops bioinformatics pipelines that directly support early-phase and ongoing clinical trials. Building directly on our multi-omics discoveries, we are actively developing advanced artificial intelligence and machine learning models designed to predict various biomarkers such as intra-tumoral bacterial burden non-invasively using H&E images. By bridging these predictive AI workflows with clinical outcomes and large-scale trial data, we systematically identify novel biomarkers of immunotherapy response and toxicity, facilitating real-time correlative analyses that directly impact patient care and future trial design.

Selected Publications

Ryan Peters, Kahn Rhrissorrakrai, Prerana Bangalore Parthasarathy, Vadim Ratner, Tanvi P. Gujarati, Meltem Tolunay, Jie Shi, Jeffrey K. Weber, Timothy A. Chan, Laxmi Parida, Capponi, Filippo Utro, Alban TJ, Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP): Next-Generation Neoantigen Prediction with Quantum Neural Networks, Accepted, Science Advances, 2026

Weber JK, Parajuli G, Wang S, Ratner V, Ma X, Shoshan Y, Zhang L, Morrone JA, Raboh M, Hexter E, Parthasarathy P, Pavicic P, Gaughan C, Makarov V, Chu L, Hasgur S, Juric I, Diaz-Montero CM, Srivastava R, Knauf JA, Hassan KA, Cornell WD, Alban TJ, Chan TA., Engineering Endogenous T Cell Receptors to Recognize Cancer Neoantigens Using a Hybrid Physics-AI Approach., Under Review, Advanced Science, 2026

Weber JK, Morrone JA, Kang SG, Zhang L, Lang L, Chowell D, Krishna C, Huynh T, Parthasarathy P, Luan B, Alban TJ, Cornell WD, Chan TA. Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity. Brief Bioinformatics. 2023, PMID: 38233090

Alban TJ, Alvarado AG, Sorensen MD, Bayik D, Volovetz J, Serbinowski E, Mulkearns-Hubert EE, Sinyuk M, Hale JS, Onzi GR, McGraw M, Huang P, Grabowski MM, Walthen CA, Ahluwalia MS, Radivoyevitch T, Kornblum HI, Kristensen BW, Vogelbaum MA+, Lathia JD+: Global immune fingerprinting in glioblastoma patient peripheral blood reveals immune-suppression signatures associated with prognosis. JCI Insight. 2018. Nov 2; 3(21): PMCID: PMC6238746.

Alban, TJ*, Riaz N*, Parthasarathy P, Makarov V, Kendall S, Yoo SK, Shah R,  Weinhold N, Srivastava R, Ma X, Krishna C, Mok JY, Esch WJ, Garon E, Akerley W, Creelan B, Aanuren N, Chowell D, Geese WJ, Rizvi NA, and Chan TA. Neoantigen Immunogenicity Landscapes and Evolution of Tumor Ecosystems During Immunotherapy with Nivolumab, Nature Medicine, 2024, PMID: 39349627

Riaz N*, Alban, TJ*, Haddad R, Saul M, Makarov V, Cohen E, Ferris R, Chang PM, Lin JC, Pyrri A, Parthasarathy P, Novaj A, Gawali M, Hoen D, Hamilton F, Silver NL, Juric I, Chawla D, Gradissimo A, Ko J, McGrail DJ, Davis C, Lee NY, Chan TA, Genetic and Tumor Microbiome Features and Outcomes from Avelumab plus Chemoradiotherapy in a Phase III Randomized Trial, Nature Cancer, 2026, PMID: 41482527