Mitochondrial DNA mutations drive aerobic glycolysis to enhance checkpoint blockade response in melanoma

The mitochondrial genome (mtDNA) encodes essential machinery for oxidative phosphorylation and metabolic homeostasis. Tumor mtDNA is among the most somatically mutated regions of the cancer genome, but whether these mutations impact tumor biology is debated. We engineered truncating mutations of the mtDNA-encoded complex I gene, Mt-Nd5, into several murine models of melanoma. These mutations promoted a Warburg-like metabolic shift that reshaped tumor microenvironments in both mice and humans, consistently eliciting an anti-tumor immune response characterized by loss of resident neutrophils. Tumors bearing mtDNA mutations were sensitized to checkpoint blockade in a neutrophil-dependent manner, with induction of redox imbalance being sufficient to induce this effect in mtDNA wild-type tumors. Patient lesions bearing >50% mtDNA mutation heteroplasmy demonstrated a response rate to checkpoint blockade that was improved by ~2.5-fold over mtDNA wild-type cancer. These data nominate mtDNA mutations as functional regulators of cancer metabolism and tumor biology, with potential for therapeutic exploitation and treatment stratification

DMTs and Covid-19 severity in MS: a pooled analysis from Italy and France

We evaluated the effect of DMTs on Covid-19 severity in patients with MS, with a pooled-analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid-19 severity was assessed by multivariate ordinal-logistic models and pooled by a fixed-effect meta-analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti-CD20 therapies were significantly associated (OR = 2.05, 95%CI = 1.39–3.02, p < 0.001) with Covid-19 severity, whereas interferon indicated a decreased risk (OR = 0.42, 95%CI = 0.18–0.99, p = 0.047). This pooled-analysis confirms an increased risk of severe Covid-19 in patients on anti-CD20 therapies and supports the protective role of interferon

Malformations artérioveineuses cérébrales (effet des caractéristiques morphologiques sur le risque hémorragique initial et post intervention endovasculaire)

PARIS7-Xavier Bichat (751182101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Prediction and Prioritization of Rare Oncogenic Mutations in the Cancer Kinome Using Novel Features and Multiple Classifiers

<div><p>Cancer is a genetic disease that develops through a series of somatic mutations, a subset of which drive cancer progression. Although cancer genome sequencing studies are beginning to reveal the mutational patterns of genes in various cancers, identifying the small subset of “causative” mutations from the large subset of “non-causative” mutations, which accumulate as a consequence of the disease, is a challenge. In this article, we present an effective machine learning approach for identifying cancer-associated mutations in human protein kinases, a class of signaling proteins known to be frequently mutated in human cancers. We evaluate the performance of 11 well known supervised learners and show that a multiple-classifier approach, which combines the performances of individual learners, significantly improves the classification of known cancer-associated mutations. We introduce several novel features related specifically to structural and functional characteristics of protein kinases and find that the level of conservation of the mutated residue at specific evolutionary depths is an important predictor of oncogenic effect. We consolidate the novel features and the multiple-classifier approach to prioritize and experimentally test a set of rare unconfirmed mutations in the epidermal growth factor receptor tyrosine kinase (EGFR). Our studies identify T725M and L861R as rare cancer-associated mutations inasmuch as these mutations increase EGFR activity in the absence of the activating EGF ligand in cell-based assays.</p></div

Description des résultats rapportés par les patients (PROs) à l’inclusion dans une étude de phase IV évaluant ocrelizumab dans la sclérose en plaques (SEP) récurrente active (PRO-MSACTIVE)

International audienc

Top predicted unconfirmed mutations.

<p>Probability scores and rankings of the top predicted mutations. Scores were calculated with the multiple classifier trained on COSMIC v.50 data. Asterisks indicate the five mutations selected for cell-based assays.</p

Structural location of selected EGFR mutation sites.

<p>Protein crystal structure [PDB∶2JIU] shown as cartoon, with sites G724, T725, L858 and L861 shown as spheres. Structural regions highlighted in yellow are kinase subdomain I and the activation loop. The structure image was generated using PyMOL <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003545#pcbi.1003545-Delano1" target="_blank">[75]</a>.</p

Quantified tyrosine auto-phosphorylation levels of wild-type and mutant-type EGFR.

<p>Quantified phosphorylation levels are shown in the form of histograms. Quantification was done using Image J.</p

Feature values of selected mutations.

<p>Feature values of selected mutations.</p

Comparison of performance of individual and combined classifiers.

<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Values are average of the metrics evaluated with respect to the positive and negative classes.</p