"Sentinel" circulating tumor cells allow early diagnosis of lung cancer in patients with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a risk factor for lung cancer. Migration of circulating tumor cells (CTCs) into the blood stream is an early event that occurs during carcinogenesis. We aimed to examine the presence of CTCs in complement to CT-scan in COPD patients without clinically detectable lung cancer as a first step to identify a new marker for early lung cancer diagnosis. The presence of CTCs was examined by an ISET filtration-enrichment technique, for 245 subjects without cancer, including 168 (68.6%) COPD patients, and 77 subjects without COPD (31.4%), including 42 control smokers and 35 non-smoking healthy individuals. CTCs were identified by cytomorphological analysis and characterized by studying their expression of epithelial and mesenchymal markers. COPD patients were monitored annually by low-dose spiral CT. CTCs were detected in 3% of COPD patients (5 out of 168 patients). The annual surveillance of the CTC-positive COPD patients by CT-scan screening detected lung nodules 1 to 4 years after CTC detection, leading to prompt surgical resection and histopathological diagnosis of early-stage lung cancer. Follow-up of the 5 patients by CT-scan and ISET 12 month after surgery showed no tumor recurrence. CTCs detected in COPD patients had a heterogeneous expression of epithelial and mesenchymal markers, which was similar to the corresponding lung tumor phenotype. No CTCs were detected in control smoking and non-smoking healthy individuals. CTCs can be detected in patients with COPD without clinically detectable lung cancer. Monitoring "sentinel" CTC-positive COPD patients may allow early diagnosis of lung cancer

La pathologie cancéreuse pulmonaire à l’heure de l’intelligence artificielle : entre espoir, désespoir et perspectives

International audienceHistopathology is the fundamental tool of pathology used for more than a century to establish the final diagnosis of lung cancer. In addition, the phenotypic data contained in the histological images reflects the overall effect of molecular alterations on the behavior of cancer cells and provides a practical visual reading of the aggressiveness of the disease. However, the human evaluation of the histological images is sometimes subjective and may lack reproducibility. Therefore, computational analysis of histological imaging using so-called “artificial intelligence” (AI) approaches has recently received considerable attention to improve this diagnostic accuracy. Thus, computational analysis of lung cancer images has recently been evaluated for the optimization of histological or cytological classification, prognostic prediction or genomic profile of patients with lung cancer. This rapidly growing field constantly demonstrates great power in the field of computing medical imaging by producing highly accurate detection, segmentation or recognition tasks. However, there are still several challenges or issues to be addressed in order to successfully succeed the actual transfer into clinical routine. The objective of this review is to emphasize recent applications of AI in pulmonary cancer pathology, but also to clarify the advantages and limitations of this approach, as well as the perspectives to be implemented for a potential transfer into clinical routine.L’histopathologie est l’outil fondamental utilisé en anatomopathologie depuis plus d’un siècle pour établir le diagnostic final d’un carcinome bronchopulmonaire. L’information phénotypique présente sur les images histologiques reflète l’effet global des altérations moléculaires sur le comportement des cellules cancéreuses et fournit une lecture visuelle pratique de l’agressivité de la maladie. Cependant, l’évaluation humaine de l’image histologique peut être parfois subjective et assez peu reproductible selon les cas. Par conséquent, l’analyse computationnelle de l’imagerie histologique via des approches dites « d’intelligence artificielle » (IA) a récemment reçu une attention considérable afin d’améliorer cette précision diagnostique. Ainsi, l’analyse computationnelle d’images de cancer du poumon a récemment été évaluée pour l’optimisation de la classification histologique ou cytologique, la prédiction du pronostic ou du profil génomique des patients atteints d’un cancer pulmonaire. Ce domaine, en pleine croissance, fait constamment preuve d’une grande puissance dans le domaine de l’informatique d’imagerie médicale en produisant des tâches de détection, de segmentation ou de reconnaissance d’une très grande précision. Cependant, il subsiste plusieurs défis ou enjeux majeurs à relever afin de réussir le transfert réel de cette nouvelle approche en routine clinique. L’objectif de cette revue est de faire le point sur les applications récentes de l’IA en pathologie cancéreuse pulmonaire, mais aussi d’apporter des clarifications sur les avantages et les limites de cette approche, ainsi que les perspectives à mettre en œuvre pour un transfert potentiel dans la pratique quotidienne des pathologistes

Comparison of Three Sequencing Panels Used for the Assessment of Tumor Mutational Burden in NSCLC Reveals Low Comparability

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Any Place for Immunohistochemistry within the Predictive Biomarkers of Treatment in Lung Cancer Patients?

The identification of certain genomic alterations (EGFR, ALK, ROS1, BRAF) or immunological markers (PD-L1) in tissues or cells has led to targeted treatment for patients presenting with late stage or metastatic lung cancer. These biomarkers can be detected by immunohistochemistry (IHC) and/or by molecular biology (MB) techniques. These approaches are often complementary but depending on, the quantity and quality of the biological material, the urgency to get the results, the access to technological platforms, the financial resources and the expertise of the team, the choice of the approach can be questioned. The possibility of detecting simultaneously several molecular targets, and of analyzing the degree of tumor mutation burden and of the micro-satellite instability, as well as the recent requirement to quantify the expression of PD-L1 in tumor cells, has led to case by case development of algorithms and international recommendations, which depend on the quality and quantity of biological samples. This review will highlight the different predictive biomarkers detected by IHC for treatment of lung cancer as well as the present advantages and limitations of this approach. A number of perspectives will be considered

BRAFV600E mutation analysis by immunohistochemistry in patients with thoracic metastases from colorectal cancer

International audienceThe BRAFV600E mutation confers worse prognosis to metastatic colorectal cancer (mCRC) patients. In addition, this mutation has a negative predictive value for response to treatment with monoclonal antibodies against EGFR in patients with KRAS wild-type (wt) mCRC. The utility of immunohistochemistry (IHC) as an alternative approach for detection of BRAFV600E in the thoracic metastases of sporadic mCRC patients has not been evaluated until now. The purpose of this study was to compare BRAFV600E IHC staining with molecular biologymethodsandto definethe diagnostic value of the VE1 antibody for the detection of BRAFV600E in this population. BRAF mutations were analysed by two DNA sequencing methods (pyrosequencing and Sanger sequencing) in a Caucasian population of 310 sporadic mCRC with thoracic metastases patients expressing KRAS wt. Detection of the BRAFV600E mutation was performed in the corresponding tumours by IHC using the VE1 antibody and compared to results of the DNA-based assays. Thirty-nine out of 310 (13%) of tumours harboured a BRAF mutation, which corresponded to either a BRAFV600E in 34 of 310 (11%) cases or a non-BRAFV600E mutation in 5 of 310 (2%) cases. IHC with VE1 was strongly positive in 32 of 34 (88%) BRAFV600E mutated tumours and negative in non-BRAFV600E mutated tumours. IHC using the VE1 clone is a specific and sensitive method for the detection of BRAFV600E and may be either a complementary or an alternative method to molecular testing in mCRC patients