Prioritizing therapeutics for lung cancer: an integrative meta-analysis of cancer gene signatures and chemogenomic data.

Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain.

Consensus Recommendations to Optimize the Detection and Reporting of NTRK Gene Fusions by RNA-Based Next-Generation Sequencing.

The detection of gene fusions by RNA-based next-generation sequencing (NGS) is an emerging method in clinical genetic laboratories for oncology biomarker testing to direct targeted therapy selections. A recent Canadian study (CANTRK study) comparing the detection of NTRK gene fusions on different NGS assays to determine subjects' eligibility for tyrosine kinase TRK inhibitor therapy identified the need for recommendations for best practices for laboratory testing to optimize RNA-based NGS gene fusion detection. To develop consensus recommendations, representatives from 17 Canadian genetic laboratories participated in working group discussions and the completion of survey questions about RNA-based NGS. Consensus recommendations are presented for pre-analytic, analytic and reporting aspects of gene fusion detection by RNA-based NGS.

PD-L1 Testing for Lung Cancer in 2019: Perspective From the IASLC Pathology Committee.

The recent development of immune checkpoint inhibitors (ICIs) has led to promising advances in the treatment of patients with NSCLC and SCLC with advanced or metastatic disease. Most ICIs target programmed cell death protein 1 (PD-1) or programmed death ligand 1 (PD-L1) axis with the aim of restoring antitumor immunity. Multiple clinical trials for ICIs have evaluated a predictive value of PD-L1 protein expression in tumor cells and tumor-infiltrating immune cells (ICs) by immunohistochemistry (IHC), for which different assays with specific IHC platforms were applied. Of those, some PD-L1 IHC assays have been validated for the prescription of the corresponding agent for first- or second-line treatment. However, not all laboratories are equipped with the dedicated platforms, and many laboratories have set up in-house or laboratory-developed tests that are more affordable than the generally expensive clinical trial-validated assays. Although PD-L1 IHC test is now deployed in most pathology laboratories, its appropriate implementation and interpretation are critical as a predictive biomarker and can be challenging owing to the multiple antibody clones and platforms or assays available and given the typically small size of samples provided. Because many articles have been published since the issue of the IASLC Atlas of PD-L1 Immunohistochemistry Testing in Lung Cancer, this review by the IASLC Pathology Committee provides updates on the indications of ICIs for lung cancer in 2019 and discusses important considerations on preanalytical, analytical, and postanalytical aspects of PD-L1 IHC testing, including specimen type, validation of assays, external quality assurance, and training.

Genes and pathology of non-small cell lung carcinoma.

While histopathology has traditionally been the cornerstone of treatment decisions in the management of lung cancer patients, the complexity and heterogeneity of histological classification has had a limited impact in the routine practice of oncology. This has changed dramatically in the last few years, owing to discoveries of genomic aberrations and results of clinical trials of novel and targeted therapies. These discoveries have resulted in a new way of classifying non-small cell lung cancer (NSCLC), based on the occurrence of putative or proven driver and targetable genomic changes. The rapidity by which the landscape of mutation and genomic changes is being identified also has led to a new paradigm and approaches to pathological diagnosis of NSCLC. In this context, international consortia have proposed new classifications of lung adenocarcinoma and guidelines for molecular testing in lung cancer and have provided concrete recommendations on new ways to practice lung cancer pathology.