Low expression of ANT1 confers oncogenic properties to rhabdomyosarcoma tumor cells by modulating metabolism and death pathways.

Rhabdomyosarcoma (RMS) is the most frequent form of pediatric soft-tissue sarcoma. It is divided into two main subtypes: ERMS (embryonal) and ARMS (alveolar). Current treatments are based on chemotherapy, surgery, and radiotherapy. The 5-year survival rate has plateaued at 70% since 2000, despite several clinical trials. RMS cells are thought to derive from the muscle lineage. During development, myogenesis includes the expansion of muscle precursors, the elimination of those in excess by cell death and the differentiation of the remaining ones into myofibers. The notion that these processes may be hijacked by tumor cells to sustain their oncogenic transformation has emerged, with RMS being considered as the dark side of myogenesis. Thus, dissecting myogenic developmental programs could improve our understanding of RMS molecular etiology. We focused herein on ANT1, which is involved in myogenesis and is responsible for genetic disorders associated with muscle degeneration. ANT1 is a mitochondrial protein, which has a dual functionality, as it is involved both in metabolism via the regulation of ATP/ADP release from mitochondria and in regulated cell death as part of the mitochondrial permeability transition pore. Bioinformatics analyses of transcriptomic datasets revealed that ANT1 is expressed at low levels in RMS. Using the CRISPR-Cas9 technology, we showed that reduced ANT1 expression confers selective advantages to RMS cells in terms of proliferation and resistance to stress-induced death. These effects arise notably from an abnormal metabolic switch induced by ANT1 downregulation. Restoration of ANT1 expression using a Tet-On system is sufficient to prime tumor cells to death and to increase their sensitivity to chemotherapy. Based on our results, modulation of ANT1 expression and/or activity appears as an appealing therapeutic approach in RMS management.

Improving survival in end-stage renal disease: A case study.

Background: With the increase of life expectancy, *On behalf of the REIN registry. end-stage renal disease (ESRD) is affecting a growing number of people. Simultaneously, renal replacement therapies (RRTs) have considerably improved patient survival. We investigated the way current RRT practices would affect patients' survival. Methods: We used a multi-state model to represent the transitions between RRTs and the transition to death. The concept of "crude probability of death" combined with this model allowed estimating the proportions of ESRD-related and ESRD-unrelated deaths. Estimating the ESRD-related death rate requires comparing the mortality rate between ESRD patients and the general population. Predicting patients' courses through RRTs and Death states could be obtained by solving a system of Kolmogorov differential equations. The impact of practice on patient survival was quantified using the restricted mean survival time (RMST) which was compared with that of healthy subjects with same characteristics. Results: The crude probability of ESRD-unrelated death was nearly zero in the youngest patients (18-45 years) but was a sizeable part of deaths in the oldest (≥70 years). Moreover, in the oldest patients, the proportion of expected death was higher in patient without vs. with diabetes because the former live older. In men aged 75 years at first RRT, the predicted RMSTs in patients with and without diabetes were, respectively, 61% and 69% those of comparable healthy men. Conclusion: Using the concept of "crude probability of death" with multi-state models is feasible and useful to assess the relative benefits of various treatments in ESRD and help patient long-term management.

Statistical method to compare massive parallel sequencing pipelines.

BACKGROUND: Today, sequencing is frequently carried out by Massive Parallel Sequencing (MPS) that cuts drastically sequencing time and expenses. Nevertheless, Sanger sequencing remains the main validation method to confirm the presence of variants. The analysis of MPS data involves the development of several bioinformatic tools, academic or commercial. We present here a statistical method to compare MPS pipelines and test it in a comparison between an academic (BWA-GATK) and a commercial pipeline (TMAP-NextGENe®), with and without reference to a gold standard (here, Sanger sequencing), on a panel of 41 genes in 43 epileptic patients. This method used the number of variants to fit log-linear models for pairwise agreements between pipelines. To assess the heterogeneity of the margins and the odds ratios of agreement, four log-linear models were used: a full model, a homogeneous-margin model, a model with single odds ratio for all patients, and a model with single intercept. Then a log-linear mixed model was fitted considering the biological variability as a random effect. RESULTS: Among the 390,339 base-pairs sequenced, TMAP-NextGENe® and BWA-GATK found, on average, 2253.49 and 1857.14 variants (single nucleotide variants and indels), respectively. Against the gold standard, the pipelines had similar sensitivities (63.47% vs. 63.42%) and close but significantly different specificities (99.57% vs. 99.65%; p < 0.001). Same-trend results were obtained when only single nucleotide variants were considered (99.98% specificity and 76.81% sensitivity for both pipelines). CONCLUSIONS: The method allows thus pipeline comparison and selection. It is generalizable to all types of MPS data and all pipelines.

Estimating the parameters of multi-state models with time-dependent covariates through likelihood decomposition.

BACKGROUND: Multi-state models become complex when the number of states is large, when back and forth transitions between states are allowed, and when time-dependent covariates are inevitable. However, these conditions are sometimes necessary in the context of medical issues. For instance, they were needed for modelling the future treatments of patients with end-stage renal disease according to age and to various treatments. METHODS: The available modelling tools do not allow an easy handling of all issues; we designed thus a specific multi-state model that takes into account the complexity of the research question. Parameter estimation relied on decomposition of the likelihood and separate maximisations of the resulting likelihoods. This was possible because there were no interactions between patient treatment courses and because all exact times of transition from any state to another were known. Poisson likelihoods were calculated using the time spent at risk in each state and the observed transitions between each state and all others. The likelihoods were calculated on short time intervals during which age was considered as constant. RESULTS: The method was not limited by the number of parameters to estimate; it could be applied to a multi-state model with 10 renal replacement therapies. Supposing the parameters of the model constant over each of seven time intervals, this method was able to estimate one hundred age-dependent transitions. CONCLUSIONS: The method is easy to adapt to any disease with numerous states or grades as long as the disease does not imply interactions between patient courses.