Early-stage treatment improves prognosis of lung cancer and two large randomized controlled trials have shown that early detection with low-dose computed tomography (LDCT) reduces mortality. Despite this, lung cancer screening (LCS) remains challenging. In the context of a global shortage of radiologists, the high rate of false-positive LDCT results in overloading of existing lung cancer clinics and multidisciplinary teams. Thus, to provide patients with earlier access to life-saving surgical interventions, there is an urgent need to improve LDCT-based LCS and especially to reduce the false-positive rate that plagues the current detection technology. In this context, LCS can be improved in three ways: (1) by refining selection criteria (risk factor assessment), (2) by using Computer Aided Diagnosis (CAD) to make it easier to interpret chest CTs, and (3) by using biological blood signatures for early cancer detection, to both spot the optimal target population and help classify lung nodules. These three main ways of improving LCS are discussed in this review.

The number of genomic alterations required for targeted therapy of non-squamous non-small cell lung cancer (NS-NSCLC) patients has increased and become more complex these last few years. These molecular abnormalities lead to treatment that provides improvement in overall survival for certain patients. However, these treated tumors inexorably develop mechanisms of resistance, some of which can be targeted with new therapies. The characterization of the genomic alterations needs to be performed in a short turnaround time (TAT), as indicated by the international guidelines. The origin of the tissue biopsies used for the analyses is diverse, but their size is progressively decreasing due to the development of less invasive methods. In this respect, the pathologists are facing a number of different challenges requiring them to set up efficient molecular technologies while maintaining a strategy that allows rapid diagnosis. We report here our experience concerning the development of an optimal workflow for genomic alteration assessment as reflex testing in routine clinical practice at diagnosis for NS-NSCLC patients by using an ultra-fast-next generation sequencing approach (Ion Torrent Genexus Sequencer, Thermo Fisher Scientific). We show that the molecular targets currently available to personalized medicine in thoracic oncology can be identified using this system in an appropriate TAT, notably when only a small amount of nucleic acids is available. We discuss the new challenges and the perspectives of using such an ultra-fast NGS in daily practice.

Tumor mutational burden (TMB) has emerged as an important potential biomarker for prediction of response to immune-checkpoint inhibitors (ICIs), notably in non-small cell lung cancer (NSCLC). However, its in-house assessment in routine clinical practice is currently challenging and validation is urgently needed. We have analyzed sixty NSCLC and thirty-six melanoma patients with ICI treatment, using the FoundationOne test (FO) in addition to in-house testing using the Oncomine TML (OTML) panel and evaluated the durable clinical benefit (DCB), defined by >6 months without progressive disease. Comparison of TMB values obtained by both tests demonstrated a high correlation in NSCLC (R² = 0.73) and melanoma (R² = 0.94). The association of TMB with DCB was comparable between OTML (area-under the curve (AUC) = 0.67) and FO (AUC = 0.71) in NSCLC. Median TMB was higher in the DCB cohort and progression-free survival (PFS) was prolonged in patients with high TMB (OTML HR = 0.35; FO HR = 0.45). In contrast, we detected no differences in PFS and median TMB in our melanoma cohort. Combining TMB with PD-L1 and CD8-expression by immunohistochemistry improved the predictive value. We conclude that in our cohort both approaches are equally able to assess TMB and to predict DCB in NSCLC.

Introduction Lung cancer (LC) is the leading cause of death from cancer. Early diagnosis of LC is of paramount importance in terms of prognosis. The health authorities of most countries do not accept screening programmes based on low-dose chest CT (LDCT), especially in Europe, because they are flawed by a high rate of false-positive results, leading to a large number of invasive diagnostic procedures. These authorities advocated further research, including companion biological tests that could enhance the effectiveness of LC screening. The present project aims to validate early diagnosis of LC by detection and characterisation of circulating tumour cells (CTCs) in a peripheral blood sample taken from a prospective cohort of persons at high-risk of LC. Methods and analysis The AIR Project is a prospective, multicentre, double-blinded, cohort study conducted by a consortium of 21 French university centres. The primary objective is to determine the operational values of CTCs for the early detection of LC in a cohort of asymptomatic participants at high risk for LC, that is, smokers and ex-smokers (≥30 pack-years, quitted ≤15 years), aged ≥55 years, with chronic obstructive pulmonary disease (COPD). The study participants will undergo yearly screening rounds for 3 years plus a 1-year follow-up. Each round will include LDCT plus peripheral blood sampling for CTC detection. Assuming 5% prevalence of LC in the studied population and a 10% dropout rate, a total of at least 600 volunteers will be enrolled. Ethics and dissemination The study sponsor is the University Hospital of Nice. The study was approved for France by the ethical committee CPP Sud-Méditerranée V and the ANSM (Ministry of Health) in July 2015. The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations. Trial registration number NCT02500693.

The histological distinction of lung neuroendocrine carcinoma, including small cell lung carcinoma (SCLC), large cell neuroendocrine carcinoma (LCNEC) and atypical carcinoid (AC), can be challenging in some cases, while bearing prognostic and therapeutic significance. To assist pathologists with the differentiation of histologic subtyping, we applied a deep learning classifier equipped with a convolutional neural network (CNN) to recognize lung neuroendocrine neoplasms. Slides of primary lung SCLC, LCNEC and AC were obtained from the Laboratory of Clinical and Experimental Pathology (University Hospital Nice, France). Three thoracic pathologists blindly established gold standard diagnoses. The HALO-AI module (Indica Labs, UK) trained with 18,752 image tiles extracted from 60 slides (SCLC = 20, LCNEC = 20, AC = 20 cases) was then tested on 90 slides (SCLC = 26, LCNEC = 22, AC = 13 and combined SCLC with LCNEC = 4 cases; NSCLC = 25 cases) by F1-score and accuracy. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The tumor maps were false colored and displayed side by side to original hematoxylin and eosin slides with superimposed pathologist annotations. The trained HALO-AI yielded a mean F1-score of 0.99 (95% CI, 0.939–0.999) on the testing set. Our CNN model, providing further larger validation, has the potential to work side by side with the pathologist to accurately differentiate between the different lung neuroendocrine carcinoma in challenging cases.