Alentorn, A. | bioRxiv Science

bioRxiv2021

Pan-cancer landscape of AID-related mutations, composite mutations and its potential role in the ICI response

Activation-induced cytidine deaminase, AICDA or AID, is a driver of somatic hypermutation and class-switch recombination in immunoglobulins. In addition, this deaminase belonging to the APOBEC family, may have off-target effects genome-wide, but its effects at pan-cancer level are not well elucidated. Here, we used different pan-cancer datasets, totaling more than 50,000 samples analyzed by whole-genome, whole-exome or targeted sequencing. AID synergizes initial hotspot mutations by a second composite mutation. Analysis of 2.5 million cells, normal and oncogenic, revealed AICDA expression activation after oncogenic transformation and cell cycle regulation loss. AID mutational load was found to be independently associated with favorable outcome in immune-checkpoint inhibitors (ICI) treated patients across cancers after analyzing 2,000 samples. Finally, we found that AID related neoepitopes, resulting from mutations at more frequent hotspots if compared to other mutational signatures, enhance CXCL13/CCR5 expression, immunogenicity and T-cell exhaustion, which may increase ICI sensitivity. In BriefA combined bulk and single cell multi-omic analysis of over 50,000 patients and 2.5 million cells across 80 tumor types reveals oncogenic acquired AICDA expression inducing composite mutations and clonal immunogenic neoepitopes that are associated with favorable outcome in patients treated by immune-checkpoint inhibitors. Highlights* Pan-cancer analysis of AID mutations using > 50,000 samples, 2,000 ICI treated cases and 2.5 million cells with genome, exome and transcriptome data * Oncogenic transient AICDA expression induces mutations mainly during transcription of its off-target genes in virtually all cancers * AID is implicated in composite mutations on weakly functional alleles and immunogenic clonal neoepitopes at hotspots with greater positive selection * AID mutational load predicts response and is associated with favorable outcome in ICI treated patients

bioinformatics↗

bioRxiv2020

Learning to leverage salient regions in neuro-oncology using Deap Learning

Developing an automatic tumor detector for MRI medical images is a major challenge in neuro-oncology. The availability of such a tool would be a valuable assistance for the radiologists. Numerous works have tried to segment the tumor tissues, others have attempted to localize the tumor globally. In this work we focus on this second class of methods and we compare two drastically different strategies. The first one is an assumption-free anomaly detector build over a Variational Auto-Encoder (VAE), and the second one is a VGG classifier that embed Attention-Gated (AG) units to focus on the target structures at almost no additional computational cost. This comparison is first conducted on the publicly available BraTS glioma dataset for which published performance results can serve as reference, and extended as such (ie., without transfer learning) to two internal image datasets, namely Primary Central Nervous System Lymphoma (PCNSL) and Metastasis. The results demonstrate that the VAE and AG-VGG strategies can be used, up to a certain extent, to localize brain tumors.

cancer biology↗