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Abstract Pseudoxanthoma elasticum (PXE) is a genetic disorder characterized by ectopic calcification of tissues rich in elastic fibres (OMIM 264800). To date, PXE is considered a metabolic disease linked to an imbalance between pro‐ and anti‐calcifying factors. The occurrence of sporadic erythematous flareups of PXE skin lesions is a complaint that we heard about on several occasions at the French PXE reference centre. However, this rare clinical aspect had never been extensively studied. We have had the opportunity to investigate a 13‐year‐old patient experiencing an erythematous flareup of his PXE lesions. We conducted this work to identify what type of inflammation was implicated in his lesions. An incisional skin biopsy on a recent erythematous inguinal PXE lesion was performed and sent for histological and transcriptomic analyses. The findings were compared to a non‐erythematous PXE‐affected skin biopsy obtained from another young PXE patient. Histological examination revealed perivascular T‐cell infiltrates with Th1 polarity and elevated expression of cytotoxicity markers in RNAseq and RT‐qPCR analyses. There was no increase in Th17 or Th2 markers. Our findings support the previous evidence of a possible inflammatory component in the development of PXE. Whether Th1‐dependent inflammation contributes to the pathology as an active process or is an aggravating factor requires further investigations.

Background/Objectives: Ovarian cancer is a complex disease with poor outcomes that affects women worldwide. The lack of successful therapeutic options for this malignancy has led to the need to identify novel biomarkers for patient stratification. Here, we aim to develop the outcome predictors based on the gene expression data as they may serve to identify categories of patients who are more likely to respond to certain therapies. Methods: We used The Cancer Genome Atlas (TCGA) ovarian cancer transcriptomic data from 372 patients and approximately 16,600 genes to train and evaluate the deep learning survival models. In addition, we collected an in-house validation dataset of 12 patients to assess the performance of the trained survival models for their direct use in clinical practice. Despite deceptive generalization capabilities, we demonstrated how our model can be interpreted to uncover biological processes associated with survival. We calculated the contributions of the input genes to the output of the best trained model and derived the corresponding molecular pathways. Results: These pathways allowed us to stratify the TCGA patients into high-risk and low-risk groups (p-value 0.025). We validated the stratification ability of the identified pathways on the in-house dataset consisting of 12 patients (p-value 0.229) and on the external clinical and molecular dataset consisting of 274 patients (p-value 0.006). Conclusions: The deep learning-based models for survival prediction with RNA-seq data could be used to detect and interpret the gene-sets associated with survival in ovarian cancer patients and open a new avenue for future research.

Background Papillary renal cell carcinoma (pRCC) is the most common non-clear cell RCC, and associated with poor outcomes in the metastatic setting. In this study, we aimed to comprehensively evaluate the immune tumor microenvironment (TME), largely unknown, of patients with metastatic pRCC and identify potential therapeutic targets.Methods We performed quantitative gene expression analysis of TME using Microenvironment Cell Populations-counter (MCP-counter) methodology, on two independent cohorts of localized pRCC (n=271 and n=98). We then characterized the TME, using immunohistochemistry (n=38) and RNA-sequencing (RNA-seq) (n=30) on metastatic pRCC from the prospective AXIPAP trial cohort.Results Unsupervised clustering identified two “TME subtypes”, in each of the cohorts: the “immune-enriched” and the “immune-low”. Within AXIPAP trial cohort, the “immune-enriched” cluster was significantly associated with a worse prognosis according to the median overall survival to 8 months (95% CI, 6 to 29) versus 37 months (95% CI, 20 to NA, p=0.001). The two immune signatures, Teff and JAVELIN Renal 101 Immuno signature, predictive of response to immune checkpoint inhibitors (CPI) in clear cell RCC, were significantly higher in the “immune-enriched” group (adjusted p

We report the case of a 32-year-old man with a caecal adenocarcinoma with major lymph node extension and peritoneal carcinomatosis, presenting a BRAF-K601E mutation. A triplet (5FU plus oxaliplatin plus irinotecan) combination with bevacizumab achieved tumor control but the disease progressed immediately after cessation and the patient died 8 months after the diagnosis. A short review of BRAF non-V600E mutations shows that outcome and clinical features depend on the mutation.

The phase of neural oscillatory signals aligns to the predicted onset of upcoming stimulation. Whether such phase alignments represent phase resets of underlying neural oscillations or just rhythmically evoked activity, and whether they can be observed in a rhythm-free visual context, however, remains unclear. Here, we recorded the magnetoencephalogram while participants were engaged in a temporal prediction task, judging the visual or tactile reappearance of a uniformly moving stimulus. The prediction conditions were contrasted with a control condition to dissociate phase adjustments of neural oscillations from stimulus-driven activity. We observed stronger delta band inter-trial phase consistency (ITPC) in a network of sensory, parietal and frontal brain areas, but no power increase reflecting stimulus-driven or prediction-related evoked activity. Delta ITPC further correlated with prediction performance in the cerebellum and visual cortex. Our results provide evidence that phase alignments of low-frequency neural oscillations underlie temporal predictions in a non-rhythmic visual and crossmodal context.

Background: Fluoropyrimidines (FPs) carry around 20% risk of G3-5 toxicity and 0.2-1% risk of death, due to dihydropyrimidine dehydrogenase (DPD) deficiency. Several screening approaches exist for predicting toxicity, however there is ongoing debate over which method is best. This study compares 4 screening approaches. Method: 472 patients treated for colorectal, head-and-neck, breast, or pancreatic cancers, who had not been tested pre-treatment for FP toxicity risk, were screened using: DPYD genotyping (G); phenotyping via plasma Uracil (U); phenotyping via plasma-dihydrouracil/uracil ratio (UH 2 /U); and a Multi-Parametric Method (MPM) using genotype, phenotype, and epigenetic data. Performance was compared, particularly the inability to detect at-risk patients (false negatives). Results: False negative rates for detecting G5 toxicity risk were 51.2%, 19.5%, 9.8% and 2.4%, for G, U, UH 2 /U and MPM, respectively. False negative rates for detecting G4-5 toxicity risk were 59.8%, 36.1%, 21.3% and 4.7%, respectively. MPM demonstrated significantly (p < 0.001) better prediction performance. Conclusion: MPM is the most effective method for limiting G4-5 toxicity. Its systematic implementation is cost-effective and significantly improves the risk-benefit ratio of FP-treatment. The use of MPM, rather than G or U testing, would avoid nearly 8,000 FP-related deaths per year globally (500 in France), and spare hundreds of thousands from G4 toxicity.

Learning and memorizing sequences of events is an important function of the human brain and the basis for forming expectations and making predictions. Learning is facilitated by repeating a sequence several times, causing rhythmic appearance of the individual sequence elements. This observation invites to consider the resulting multitude of rhythms as a spectral “fingerprint” which characterizes the respective sequence. Here we explore the implications of this perspective by developing a neurobiologically plausible computational model which captures this “fingerprint” by attuning an ensemble of neural oscillators. In our model, this attuning process is based on a number of oscillatory phenomena that have been observed in electrophysiological recordings of brain activity like synchronization, phase locking, and reset as well as cross-frequency coupling. We compare the learning properties of the model with behavioral results from a study in human participants and observe good agreement of the errors for different levels of complexity of the sequence to be memorized. Finally, we suggest an extension of the model for processing sequences that extend over several sensory modalities.

Targeted therapies and, more precisely, EGFR tyrosine kinase inhibitors (TKIs) have been a major improvement in the therapeutic management of EGFR-mutated non-small-cell lung cancers (NSCLCs). Earlier administration of these TKIs throughout tumor progression is imperative to improve patient outcomes. Consequently, studies have focused on refining the characterization of biomarkers, especially concerning the resistance mutation p.Thr790Met of EGFR. Herein, we developed peptide nucleic acid (PNA)-mediated PCR clamping followed by pyrosequencing, favoring enrichment of the mutated fraction. A preamplification step was first added to increase the amplifiable DNA fraction. Throughout the application of our method on DNA extracted from FFPE samples of 46 patients with NSCLC who had relapsed under first-generation EGFR TKI, we evaluated a sensitivity of 93.3% and a specificity of 100%. All 19 patients who were positive for the p.Thr790Met mutation with NGS were also found to be positive with our protocol. The only discordant case was a sample with no mutation detected with NGS, but which was positive with PNA. This protocol allows for the detection of the p.Thr790Met mutation with a sensitivity of 0.5% which will permit earlier detection and an improvement of therapeutic management.