Differential expression of miR200a-3p and miR21 in grade II–III and grade IV gliomas

International audienc

Comparative genomic analysis of primary tumors and paired brain metastases in lung cancer patients by whole exome sequencing: a pilot study

International audienceLung cancer brain metastases (BMs) are frequent and associated with poor prognosis despite a better knowledge of lung cancer biology and the development of targeted therapies. The inconstant intracranial response to systemic treatments is partially due to tumor heterogeneity between the primary lung tumor (PLT) and BMs. There is therefore a need for a better understanding of lung cancer BMs biology to improve treatment strategies for these patients. We conducted a study of whole exome sequencing of paired BM and PLT samples. The number of somatic variants and chromosomal alterations was higher in BM samples. We identified recurrent mutations in BMs not found in PLT. Phylogenic trees and lollipop plots were designed to describe their functional impact. Among the 13 genes mutated in ≥ 1 BM, 7 were previously described to be associated with invasion process, including 3 with recurrent mutations in functional domains which may be future targets for therapy. We provide with some insights about the mechanisms leading to BMs. We found recurrent mutations in BM samples in 13 genes. Among these genes, 7 were previously described to be associated with cancer and 3 of them (CCDC178, RUNX1T1, MUC2) were described to be associated with the metastatic process

Machine learning random forest for predicting oncosomatic variant NGS analysis

International audienceSince 2017, we have used IonTorrent NGS platform in our hospital to diagnose and treat cancer. Analyzing variants at each run requires considerable time, and we are still struggling with some variants that appear correct on the metrics at first, but are found to be negative upon further investigation. Can any machine learning algorithm (ML) help us classify NGS variants? This has led us to investigate which ML can fit our NGS data and to develop a tool that can be routinely implemented to help biologists. Currently, one of the greatest challenges in medicine is processing a significant quantity of data. This is particularly true in molecular biology with the advantage of next-generation sequencing (NGS) for profiling and identifying molecular tumors and their treatment. In addition to bioinformatics pipelines, artificial intelligence (AI) can be valuable in helping to analyze mutation variants. Generating sequencing data from patient DNA samples has become easy to perform in clinical trials. However, analyzing the massive quantities of genomic or transcriptomic data and extracting the key biomarkers associated with a clinical response to a specific therapy requires a formidable combination of scientific expertise, biomolecular skills and a panel of bioinformatic and biostatistic tools, in which artificial intelligence is now successful in developing future routine diagnostics. However, cancer genome complexity and technical artifacts make identifying real variants challenging. We present a machine learning method for classifying pathogenic single nucleotide variants (SNVs), single nucleotide polymorphisms (SNPs), multiple nucleotide variants (MNVs), insertions, and deletions detected by NGS from different types of tumor specimens, such as: colorectal, melanoma, lung and glioma cancer. We compared our NGS data to different machine learning algorithms using the k-fold cross-validation method and to neural networks (deep learning) to measure the performance of the different ML algorithms and determine which one is a valid model for confirming NGS variant calls in cancer diagnosis. We trained our machine learning with 70% of our data samples, extracted from our local database (our data structure had 7 parameters: chromosome, position, exon, variant allele frequency, minor allele frequency, coverage and protein description) and validated it with the 30% remaining data. The model offering the best accuracy was chosen and implemented in the NGS analysis routine. Artificial intelligence was developed with the R script language version 3.6.0. We trained our model on 70% of 102,011 variants. Our best error rate (0.22%) was found with random forest machine learning (ntree = 500 and mtry = 4), with an AUC of 0.99. Neural networks achieved some good scores. The final trained model with the neural network achieved an accuracy of 98% and an ROC-AUC of 0.99 with validation data. We tested our RF model to interpret more than 2000 variants from our NGS database: 20 variants were misclassified (error rate < 1%). The errors were nomenclature problems and false positives. After adding false positives to our training database and implementing our RF model routinely, our error rate was always < 0.5%. The RF model shows excellent results for oncosomatic NGS interpretation and can easily be implemented in other molecular biology laboratories. AI is becoming increasingly important in molecular biomedical analysis and can be very helpful in processing medical data. Neural networks show a good capacity in variant classification, and in the future, they may be useful in predicting more complex variants

Regional molecular genetics centers in thoracic oncology: what and who should be tested?

International audienceManagement of NSCLC patients is more and more individualized especially on the base of bioguided treatments. In order to guarantee an access for all the patients too this type of strategy, the French NCI supports since 2006 a nationwide network of 28 regional genetics center. The financial support is based on public funds. The French NCI recommends today the assessment of seven biomarkers for all stage IV non squamous NSCLC patients. Due to financial and technical reasons, this recommendation must be followed. However, the molecular profiling of lung cancer patients would ideally be extended across all stages and all histological types of the disease in order to improve our knowledge in this field and provides the patient with an opportunity to access a bioguided treatment as frequently as possible

Extreme Gradient Boosting Tuned with Metaheuristic Algorithms for Predicting Myeloid NGS Onco-Somatic Variant Pathogenicity

International audienceThe advent of next-generation sequencing (NGS) technologies has revolutionized the field of bioinformatics and genomics, particularly in the area of onco-somatic genetics. NGS has provided a wealth of information about the genetic changes that underlie cancer and has considerably improved our ability to diagnose and treat cancer. However, the large amount of data generated by NGS makes it difficult to interpret the variants. To address this, machine learning algorithms such as Extreme Gradient Boosting (XGBoost) have become increasingly important tools in the analysis of NGS data. In this paper, we present a machine learning tool that uses XGBoost to predict the pathogenicity of a mutation in the myeloid panel. We optimized the performance of XGBoost using metaheuristic algorithms and compared our predictions with the decisions of biologists and other prediction tools. The myeloid panel is a critical component in the diagnosis and treatment of myeloid neoplasms, and the sequencing of this panel allows for the identification of specific genetic mutations, enabling more accurate diagnoses and tailored treatment plans. We used datasets collected from our myeloid panel NGS analysis to train the XGBoost algorithm. It represents a data collection of 15,977 mutations variants composed of a collection of 13,221 Single Nucleotide Variants (SNVs), 73 Multiple Nucleoid Variants (MNVs), and 2683 insertion deletions (INDELs). The optimal XGBoost hyperparameters were found with Differential Evolution (DE), with an accuracy of 99.35%, precision of 98.70%, specificity of 98.71%, and sensitivity of 1

1693P Incidence of NTRK genes fusion in adult brain tumours: A prospective cohort of 140 patients with cerebral gliomas and brain metastases

International audienceBackground: ImmTAC molecules are TCR-anti-CD3 bispecific fusion proteins that can redirect polyclonal T cell activation against cancer cells. Tebentafusp, a gp100-directed ImmTAC, has demonstrated survival benefit in metastatic uveal melanoma 1. Here, patient-derived tumour organoids (TO) 2 and 3-dimensional multicellular melanospheres were evaluated as tumour models to study ImmTAC activity. Methods: 3D melanospheres were generated by low adherence culture of melanoma cell lines (MEL202, MEL624, 92.1, MP-41, A375, IGR-1, Mewo) and their melanin synthesis genes were quantified by qPCR. The capacity of ImmTAC to redirect T cells against melanospheres was assessed in vitro using IFNg and granzyme B Elispots in the presence or absence of commercially sourced IFNa2 or IFNb. Patient derived TO were generated by Tempus 2 and screened for HLA-A*02:01 and antigen positivity. ImmTAC-mediated killing of relevant TO was assessed by 3D high content imaging. Results: Melanospheres formed from all melanoma cell lines tested, and upregulated melanin synthesis genes including PMEL, MITF, and MLANA. This translated into visible but heterogenous melanin expression. ImmTAC were capable of redirecting T cells against cells from melanospheres to produce IFNg (EC 50 23 pM-4.8 nM) and granzyme B (EC 50 130 pM-1.4 nM). Treatment of 3D cultures with IFNa2 or IFNb for 48 hours augmented ImmTAC-mediated redirection of IFNg secretion by a mean of 4.42-fold. To further assess ImmTAC-mediated T cell redirection against more complex in vitro cultures, TO were derived from patient tumours and co-cultured with PBMC for 96 hours. In these settings, clinically relevant doses of ImmTAC redirected T cells to induce cell lysis of antigen positive TO (EC 50 29.3 pM). Conclusions: 3D melanospheres and TO may provide useful models to study tumour biology, heterogeneity, and ImmTAC activity. ImmTAC were capable of redirecting T cells against these in vitro tumour models, which will allow for better understanding of mechanism of action. 1

Deep Learning Architecture Optimization with Metaheuristic Algorithms for Predicting BRCA1/BRCA2 Pathogenicity NGS Analysis

International audienceMotivation, BRCA1 and BRCA2 are genes with tumor suppressor activity. They are involved in a considerable number of biological processes. To help the biologist in tumor classification, we developed a deep learning algorithm. The question when we want to construct a neural network is how many hidden layers and neurons should we use. If the number of inputs and outputs is defined by the problem, the number of hidden layers and neurons is difficult to define. Hidden layers and neurons that make up each layer of the neural network influence the performance of system predictions. There are different methods for finding the optimal architecture. In this paper, we present the two packages that we have developed, the genetic algorithm (GA) and the particle swarm optimization (PSO) to optimize the parameters of the neural network for predicting BRCA1 and BRCA2 pathogenicity; Results, we will compare the results obtained by the two algorithms. We used datasets collected from our NGS analysis of BRCA1 and BRCA2 genes to train deep learning models. It represents a data collection of 11,875 BRCA1 and BRCA2 variants. Our preliminary results show that the PSO provided the most significant architecture of hidden layers and the number of neurons compared to grid search and GA; Conclusions, the optimal architecture found by the PSO algorithm is composed of 6 hidden layers with 275 hidden nodes with an accuracy of 0.98, precision 0.99, recall 0.98, and a specificity of 0.99

Prognostic Impact of CD133 mRNA Expression in 48 Glioblastoma Patients Treated with Concomitant Radiochemotherapy: A Prospective Patient Cohort at a Single Institution

International audienc

Epidermal growth factor receptor in glioblastomas: correlation between gene copy number and protein expression

International audienc

IDH mutation status impact on in vivo hypoxia biomarkers expression: new insights from a clinical, nuclear imaging and immunohistochemical study in 33 glioma patients

International audienc