Evaluation of the Idylla system to detect the EGFRT790M mutation using extracted DNA

International audienceIntroduction: During the last few years, detection of epidermal growth-factor-receptor (EGFR)-activating mutations has become a routine part of clinical practice because of their importance in choosing the optimal treatment strategy for non-small-cell lung cancers (NSCLCs). The emergence of third-generation EGFR-tyrosine-kinase inhibitors required the implementation of sensitive methods to detect the subclonal EGFRT790M mutation. Clinical implications make it essential to rapidly search for the T790M mutation, which is a real challenge for laboratories. The aim of this study was to compare performances of next-generation sequencing (NGS), one of the most frequently used molecular biology methods, and Idylla EGFR-Mutation Assay (henceforth Idylla), a fully automated real-time polymerase chain reaction (PCR) that is increasingly used in pathology laboratories, to detect the EGFRT790M mutation using DNA.Methods: This retrospective study used 47 DNA samples extracted from NSCLC biopsies that previous NGS identified as: 29 harboring EGFR and T790M resistance mutations, 11 EGFR-activating mutation without T790 M and 7 wild-type EGFR. EGFRT790M limit-of-detection (LOD) experiments used a commercial DNA known to harbor that mutation.Results: Idylla detected primary EGFR-activating mutations and the T790 M mutation in 97.5 % and 65.5 % of the cases, respectively. The results of this retrospective analysis and LOD experiments showed that the Idylla should only be used to detect EGFR mutations in samples with > 25 ng of DNA and > 10 % tumor cells.Conclusions: Idylla was able to rapidly detect EGFR-activating mutations but detecting subclone mutations, like T790M, with < 25 ng of good-quality DNA or < 10 % tumor cells (variant allele frequency below the assay's validated LOD) was not always reliable

New human in vitro co‐culture model of keratinocytes and sensory neurons like cells releasing substance P with an evaluation of the expression of ZIKV entry receptors: A potent opportunity to test Zika virus entry and to study Zika virus' infection in neurons?

Abstract During the course of acute ZIKV infection, pruritus is a cardinal symptom widely documented in the literature. Its frequent association with dysesthesia and several dysautonomic manifestations, suggests a pathophysiological mechanism involving the peripheral nervous system. The aim of this study was to develop a functional human model to potentially able to be infected by ZIKV: by demonstrating the functionality on a new human model of co‐culture of keratinocyte and sensory neuron derived from induced pluripotent stem cells using a classical method of capsaicin induction and SP release, and verify the presence of ZIKV entry receptor in these cells. Depending of cellular type, receptors of the TAMs family, TIMs (TIM1, TIM3 and TIM4) and DC‐SIGN and RIG1 were present/detected. The cells incubations with capsaicin resulted in an increase of the substance P. Hence, this study demonstrated the possibility to obtain co‐cultures of human keratinocytes and human sensory neurons that release substance P in the same way than previously published in animal models which can be used as a model of neurogenic skin inflammation. The demonstration of the expression of ZIKV entry receptors in these cells allows to considerate the potent possibility that ZIKV is able to infect cells

Multicenter automatic detection of invasive carcinoma on breast whole slide images.

Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as "test reference dataset" and "test target dataset") and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92.1% and 96.3%, 95% and 87.8%, and 73.9% and 70.6%, respectively. At slide level, accuracy, recall, and precision were 97.6% and 92.0%, 90.9% and 100%, and 100% and 70.8% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center. This methodology allows the implementation of AI diagnostic tools to help in routine pathology practice

AI-Augmented Pathology for Head and Neck Squamous Lesions Improves Non-HN Pathologist Agreement to Expert Level

Abstract Importance Diagnosis of head and neck squamous dysplasias and carcinomas is challenging, with a moderate inter-rater agreement. Nowadays, new artificial intelligence (AI) models are developed to automatically detect and grade lesions, but their contribution to the performance of pathologists hasn’t been assessed. Objective To evaluate the contribution of our AI tool in assisting pathologists in diagnosing squamous dysplasia and carcinoma in the head and neck region. Design, Setting, and Participants We evaluated the effectiveness of our previously described AI model, which combines an automatic classification of laryngeal and pharyngeal squamous lesions with a confidence score, on a panel of eight pathologists coming from different backgrounds and with different levels of experience on a subset of 115 slides. Main Outcomes and Measures The main outcome was the inter-rater agreement, measured by the weighted linear kappa. Other outcomes on diagnostic efficiency were assessed using paired t tests. Results AI-Assistance significantly improved the inter-rater agreement (linear kappa 0.73, 95%CI [0.711-0.748] with assistance versus 0.675, 95%CI [0.579-0.765] without assistance, p < 0.001). The agreement was even better on high confidence predictions (mean linear kappa 0.809, 95%CI [0.784-0.834] for assisted review, versus 0.731, 95%CI [0.681-0.781] non-assisted, p = 0.018). These improvements were particularly strong for non-specialized and younger pathologists. Hence, the AI-Assistance enabled the panel to perform on par with the expert panel described in the literature. Conclusions and Relevance Our AI-Assistance is of great value for helping pathologists in the difficult task of diagnosing squamous dysplasias and carcinomas, improving for the first time the inter-rater agreement. It demonstrates the possibility of a truly Augmented Pathology in complex tasks such as the classification of head and neck squamous lesions