Biology subjects

Heidersbach, A.

Publications and source records attributed to Heidersbach, A..

3 recordsLinked to original sources

bioRxiv2026

Systemic neoantigen-specific T cells reveal central determinants of PD-(L)1 blockade efficacy

The contribution of neoantigen-specific T cells to PD-(L)1 efficacy has largely been inferred from tumor mutational burden. We functionally profiled circulating T cell responses against 7,038 predicted HLA-I-restricted and 21,453 HLA-II-restricted neopeptides in 27 patients with advanced non-small cell lung cancer treated with anti-PD-(L)1. CD4 responses were frequent and correlated with neoantigen availability but not clinical benefit. In contrast, the magnitude and breadth of neoantigen-specific CD8 T cell responses were associated with clinical benefit, progression-free and overall survival, independently of tumor mutational burden. Patients mounting coordinated CD4 and CD8 responses experienced improved progression-free survival. Tumors from CD8 responders displayed immune signatures indicative of both T cell priming and effector functions. Circulating neoantigen-specific CD8 T cells recognized endogenously processed antigens, trafficked to tumors, and selectively expanded under therapy while retaining CD28, CD226, and CXCR3 expression. These findings identify coordinated, functionally engaged neoantigen-specific T cell responses as central determinants of PD-(L)1 efficacy.

cancer biology↗

bioRxiv2025

A novel, high-density CRISPR activation platform for mapping cancer dependencies and resistance pathways ex vivo and in vivo

CRISPR activation (CRISPRa) enables precise, locus-specific upregulation of gene expression, offering potential for both ex vivo and in vivo applications. However, the lack of scalable, high-coverage tools has limited its use in comprehensive genetic screens, particularly in murine models. Here, we introduce Partita, a next-generation, whole-genome CRISPRa sgRNA platform designed for unparalleled efficiency and depth in gene activation studies. Partita employs a high-density targeting strategy, deploying 10 sgRNAs per transcription start site, structured into five gene family-specific sub-libraries to maximize transcriptional induction. To demonstrate its capabilities, we performed a series of large-scale screens: an in vitro enrichment/depletion screen in iBMDMs, whole-genome CRISPRa screens in a double-hit lymphoma model to uncover genes driving resistance to pro-apoptotic drugs (venetoclax, nutlin-3a, etoposide), and an in vivo whole-genome screen identifying accelerators of Myc-driven lymphomagenesis. Each experiment revealed both expected and novel regulators of cellular phenotypes, with a high validation rate in secondary assays. By enabling robust, high-throughput gain-of-function screening, Partita unlocks new avenues for functional genomics and expands the toolkit for discovering key drivers of biological processes across diverse research fields.

cancer biology↗

bioRxiv2022

HLApollo: A superior transformer model for pan-allelic peptide-MHC-I presentation prediction, with diverse negative coverage, deconvolution and protein language features.

Antigen presentation on MHC class I (MHC-I) is key to the adaptive immune response to cancerous cells. Computational prediction of peptide presentation by MHC-I has enabled individualized cancer immunotherapies. Here, we introduce HLApollo, a transformer-based approach with end-to-end modeling of MHC-I sequence, deconvolution, and flanking sequences. To achieve this, we develop a novel training strategy, negative set switching, which greatly reduces overfitting to falsely presumed negatives that are necessarily found in presentation datasets. HLApollo shows a meaningful improvement compared to recent MHC-I models on peptide presentation (20.19% average precision (AP)) and immunogenicity (4.1% AP). As expected, adding gene expression boosts the performance of HLApollo. More interestingly, we show that introduction of features from a protein language model, ESM 1b, remarkably recoups much of the benefits of gene expression in absence of true expression measurements. Finally, we demonstrate excellent pan-allelic generalization, and introduce a framework for estimating the expected accuracy of HLApollo for untrained alleles. This guides the use of HLApollo in a clinical setting, where rare alleles may be observed in some subjects, particularly for underrepresented minorities.

immunology↗