Biology subjects

Vonderheide, R. H.

Publications and source records attributed to Vonderheide, R. H..

3 recordsLinked to original sources

bioRxiv2023

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (CD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict CD40 treatment status and disease-free survival (DFS) following CD40 therapy based upon TME features. Through downstream interpretation of the machine learning models predictions, we found CD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following CD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of CD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for CD40-treated patients with PDAC.

cancer biology↗

bioRxiv2022

Agonistic anti-CD40 converts Tregs into Type 1 effectors within the tumor micro-environment

Multiple cell types, molecules, and processes contribute to inhibition of anti-tumor effector responses, often frustrating effective immunotherapy. Among these, Foxp3+ CD4+ cells (Tregs) are well-recognized to play an immunosuppressive role in the tumor microenvironment. The first clinically successful checkpoint inhibitor, anti-CTLA-4 antibody, may deplete Tregs at least in part by antibody-dependent cellular cytotoxicity (ADCC), but this effect is unreliable in mice, including in a genetically engineered mouse model of pancreatic ductal adenocarcinoma (PDAC). In contrast, agonistic CD40 antibody, which serves as an effective therapy, is associated with notable Treg disappearance in the PDAC model. The mechanism of CD40-mediated Treg loss is poorly understood, as Tregs are CD40-negative. Here we have explored the mechanistic basis for the loss of Foxp3 T cells upon anti-CD40 treatment and find, using tissue-level multiplex immunostaining and orthogonal dissociated cell analyses, that Tregs are not depleted but converted into interferon-{gamma} (IFN-{gamma}) producing, Type I CD4+ T effector cells. This process depends on IL-12 and IFN-{gamma} signaling evoked by action of the anti-CD40 antibody on dendritic cells (DCs), especially BATF3-dependent cDC1s. These findings provide insight into a previously unappreciated mechanism of CD40 agonism as a potent anti-tumor intervention that promotes the re-programming of Tregs into tumor-reactive CD4+ effector T cells, markedly augmenting the anti-tumor response.

immunology↗

bioRxiv2022

An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences

T cell receptor (TCR) repertoires can be profiled using next generation sequencing (NGS) to monitor dynamical changes in response to disease and other perturbations. Several strategies for profiling TCRs have been recently developed with different benefits and drawbacks. Genomic DNA-based bulk sequencing, however, remains the most cost-effective method to profile TCRs. The major disadvantage of this method is the need for multiplex target amplification with a large set of primer pairs with potentially very different amplification efficiencies. One approach addressing this problem is by iteratively adjusting the concentrations of the primers based on their efficiencies, and then computationally correcting any remaining bias. Yet there are no standard, publicly available protocols to process and analyze raw sequencing data generated by this method. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both approaches, we show that the concordance between bulk clonality metrics obtained from using the commercial kits and that developed herein is high. Therefore, we suggest the method presented here as an inexpensive and non-commercial alternative for measuring and monitoring adaptive dynamics in TCR clonotype repertoire.

bioinformatics↗