Biology subjects

Ayyoub, M.

Publications and source records attributed to Ayyoub, M..

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↗

bioRxiv2020

Colon-specific immune microenvironment regulates cancer progression versus rejection

BackgroundImmunotherapies have achieved clinical benefit in many types of cancer but remain limited to a subset of patients in colorectal cancer (CRC). Resistance to immunotherapy can be attributed in part to tissue-specific factors constraining antitumor immunity. Thus, a better understanding of how the colon microenvironment shapes the immune response to CRC is needed to identify mechanisms of resistance to immunotherapies and guide the development of novel therapeutics. MethodsIn an orthotopic mouse model of MC38-CRC, tumor progression was monitored by bioluminescence imaging and the immune signatures were assessed at a transcriptional level using NanoString and at a cellular level by flow cytometry. ResultsDespite initial tumor growth in all mice, only 25 to 35% of mice developed a progressive lethal CRC while the remaining animals spontaneously rejected their solid tumor. No tumor rejection was observed in the absence of adaptive immunity, nor when MC38 cells were injected in non-orthotopic locations, subcutaneously or into the liver. We observed that progressive CRC tumors exhibited a protumor immune response, characterized by a regulatory T-lymphocyte pattern, discernible shortly post-tumor implantation, as well as suppressive myeloid cells. In contrast, tumor-rejecting mice presented an early inflammatory response and an antitumor microenvironment enriched in CD8+ T cells. ConclusionsTaken together, our data demonstrate the role of the colon microenvironment in regulating the balance between anti or protumor immune responses. While emphasizing the relevance of the CRC orthotopic model, they set the basis for exploring the impact of the identified signatures in colon cancer response to immunotherapy.

cancer biology↗

bioRxiv2019

Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets

The development of single cell transcriptomic technologies yields large datasets comprising multimodal informations such as transcriptomes and immunophenotypes. Currently however, there is no software to easily and simultaneously analyze both types of data. Here, we introduce Single-Cell Virtual Cytometer, an open-source software for flow cytometry-like visualization and exploration of multi-omics single cell datasets. Using an original CITE-seq dataset of PBMC from an healthy donor, we illustrate its use for the integrated analysis of transcriptomes and phenotypes of functional maturation in peripheral T lymphocytes from healthy donors. So this free and open-source algorithm constitutes a unique resource for biologists seeking for a user-friendly analytic tool for multimodal single cell datasets.

bioinformatics↗