Mutations in valosin-containing protein (VCP) decrease ADP/ATP translocation across the mitochondrial membrane and impair energy metabolism in human neurons

Mutations in the gene encoding valosin-containing protein (VCP) lead to multisystem proteinopathies including frontotemporal dementia. We have previously shown that patient-derived VCP mutant fibroblasts exhibit lower mitochondrial membrane potential, uncoupled respiration, and reduced ATP levels. This study addresses the underlying basis for mitochondrial uncoupling using VCP knockdown neuroblastoma cell lines, induced pluripotent stem cells (iPSCs), and iPSC-derived cortical neurons from patients with pathogenic mutations in VCP. Using fluorescent live cell imaging and respiration analysis we demonstrate a VCP mutation/knockdown-induced dysregulation in the adenine nucleotide translocase, which results in a slower rate of ADP or ATP translocation across the mitochondrial membranes. This deregulation can explain the mitochondrial uncoupling and lower ATP levels in VCP mutation-bearing neurons via reduced ADP availability for ATP synthesis. This study provides evidence for a role of adenine nucleotide translocase in the mechanism underlying altered mitochondrial function in VCP-related degeneration, and this new insight may inform efforts to better understand and manage neurodegenerative disease and other proteinopathies

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient

HRD-related morphology discovery in breast cancer by controlling for confounding factors

Lazard et al. predict homologous recombination deficiency from hematoxylin and eosin-stained slides of breast cancer tissue using deep learning. By controlling for technical artifacts on a curated dataset, the model puts forward novel HRD-related morphologies in luminal breast cancers

DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer

We propose a Deep learning-based weak label learning method for analysing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumorcells not requiring pixel-level or tile-level annotations using Self-supervised pre-training and heterogeneity-aware deep Multiple Instance LEarning (DeepSMILE). We apply DeepSMILE to the task of Homologous recombination deficiency (HRD) and microsatellite instability (MSI) prediction. We utilize contrastive self-supervised learning to pre-train a feature extractor on histopathology tiles of cancer tissue. Additionally, we use variability-aware deep multiple instance learning to learn the tile feature aggregation function while modeling tumor heterogeneity. Compared to state-of-the-art genomic label classification methods, DeepSMILE improves classification performance for HRD from $70.43\pm4.10\%$ to $83.79\pm1.25\%$ AUC and MSI from $78.56\pm6.24\%$ to $90.32\pm3.58\%$ AUC in a multi-center breast and colorectal cancer dataset, respectively. These improvements suggest we can improve genomic label classification performance without collecting larger datasets. In the future, this may reduce the need for expensive genome sequencing techniques, provide personalized therapy recommendations based on widely available WSIs of cancer tissue, and improve patient care with quicker treatment decisions - also in medical centers without access to genome sequencing resources.Comment: Main paper: 16 pages, 5 figures, 2 table