WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy.

Purpose: Immune checkpoint inhibitors (ICIs) are now one of the standards of care for patients with lung cancer and have greatly improved both progression-free and overall survival, although <20% of the patients respond to the treatment, and some face acute adverse events. Although a few predictive biomarkers have integrated the clinical workflow, they require additional modalities on top of whole-slide images and lack efficiency or robustness. In this work, we propose a biomarker of immunotherapy outcome derived solely from the analysis of histology slides. Approach: We develop a three-step framework, combining contrastive learning and nonparametric clustering to distinguish tissue patterns within the slides, before exploiting the adjacencies of previously defined regions to derive features and train a proportional hazards model for survival analysis. We test our approach on an in-house dataset of 193 patients from 5 medical centers and compare it with the gold standard tumor proportion score (TPS) biomarker. Results: On a fivefold cross-validation (CV) of the entire dataset, the whole-slide image-based survival analysis for patients treated with immunotherapy (WhARIO) features are able to separate a low- and a high-risk group of patients with a hazard ratio (HR) of 2.29 (CI95=1.48 to 3.56), whereas the TPS 1% reference threshold only reaches a HR of 1.81 (CI95=1.21 to 2.69). Combining the two yields a higher HR of 2.60 (CI95=1.72 to 3.94). Additional experiments on the same dataset, where one out of five centers is excluded from the CV and used as a test set, confirm these trends. Conclusions: Our uniquely designed WhARIO features are an efficient predictor of survival for lung cancer patients who received ICI treatment. We achieve similar performance to the current gold standard biomarker, without the need to access other imaging modalities, and show that both can be used together to reach even better results.

Ultrafast Gene Fusion Assessment for Nonsquamous NSCLC.

Introduction: Gene fusion testing of ALK, ROS1, RET, NTRK, and MET exon 14 skipping mutations is guideline recommended in nonsquamous NSCLC (NS-NSCLC). Nevertheless, assessment is often hindered by the limited availability of tissue and prolonged next-generation sequencing (NGS) testing, which can protract the initiation of a targeted therapy. Therefore, the development of faster gene fusion assessment is critical for optimal clinical decision-making. Here, we compared two ultrafast gene fusion assays (UFGFAs) using NGS (Genexus, Oncomine Precision Assay, Thermo Fisher Scientific) and a multiplex reverse-transcriptase polymerase chain reaction (Idylla, GeneFusion Assay, Biocartis) approach at diagnosis in a retrospective series of 195 NS-NSCLC cases and five extrapulmonary tumors with a known NTRK fusion. Methods: A total of 195 NS-NSCLC cases (113 known gene fusions and 82 wild-type tumors) were included retrospectively. To validate the detection of a NTRK fusion, we added five NTRK-positive extrathoracic tumors. The diagnostic performance of the two UFGFAs and standard procedures was compared. Results: The accuracy was 92.3% and 93.1% for Idylla and Genexus, respectively. Both systems improved the sensitivity for detection by including a 5'-3' imbalance analysis. Although detection of ROS1, MET exon 14 skipping, and RET was excellent with both systems, ALK fusion detection was reduced with sensitivities of 87% and 88%, respectively. Idylla had a limited sensitivity of 67% for NTRK fusions, in which only an imbalance assessment was used. Conclusions: UFGFA using NGS and reverse-transcriptase polymerase chain reaction approaches had an equal level of detection of gene fusion but with some technique-specific limitations. Nevertheless, UFGFA detection in routine clinical care is feasible with both systems allowing faster initiation of therapy and a broad degree of screening.

MIAmS: microsatellite instability detection on NGS amplicons data.

MOTIVATION: Microsatellite instability (MSI) is a molecular marker of DNA mismatch repair deficiency frequently at play in oncogenesis. MSI testing is used for diagnostic, prognostic and therapeutic purposes in several cancers. The current gold standard analysis for microsatellite status is based on length distribution analysis of multiplex-PCR generated DNA fragments from tumor samples which is a laborious and time consuming method. Next generation sequencing (NGS) using amplicon panels is an easy, accurate and scalable technique to determine both the microsatellite status and tumor genome mutations at the same time. Here, we describe MIAmS, an application designed to tag microsatellite status from amplicon NGS of tumor samples. Interestingly, this tool does not require paired normal tissue for comparison. In addition, this scalable application provides a user-friendly report for the interpretation of the results by biologists and exhibits a strong accuracy and robustness for determination of the MSI status. AVAILABILITY: https://github.com/bialimed/miams. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online, evaluation data are available at http://www.ebi.ac.uk/ena/data/view/PRJEB31725.