Classification of non-small cell lung cancer according to the WHO 2021: from morphology to personalized therapy

Incorporating Genetic Biomarkers in WHO Classification of Lung Cancer

The WHO 2015 classification of lung tumours: Evolution of tumour classification in lung cancer

Background: Epidermal growth factor receptor (EGFR) mutations have been known to be associated with adenocarcinoma, women, non- smokers and East-Asian ethnicity. This study was aimed to characterize the frequency of EGFR mutations and their association with histologic subtypes in primary lung adenocarcinoma in an Indian cohort. Methods: Two seventy-four cases were categorized using 2015 WHO classification of lung tumors. The frequency of each histologic subtype and cell type was correlated with EGFR exon sequences in a subset of 120 cases using polymerase chain reaction (PCR) gene sequencing. Results: The predominant biopsy categories in 274 cases were acinar 167(61%), solid 63(23%), mucinous 19(7%), lepidic 11(4%) and others 14(5%). EGFR mutations were detected in 49/120 (40.8%) including 3/5(60%) lepidic, 4/9(44.4%) papillary, 29/68(42.7%) acinar, 10/24(41.7%) solid and 1/13(7.7%) mucinous subtypes and were significantly associated with the cuboidal cell type (p=0.01). These mutations were common in women and non-smokers, although not statistically significant. Exon 19 mutations predominated in 36/49(73.4%). The majority, 213/263 (81%), were thyroid transcription factor 1(TTF-1) positive. The polygonal cell type and the solid subtype were frequent amongst stage IV tumors and smokers. Conclusions : EGFR mutations were most frequently seen with the lepidic and papillary subtypes, not associated with the mucinous subtype, more common in women and non-smokers and significantly associated with the cuboidal cell type. DOI: 10.21276/APALM.1372

Adenosquamous carcinomas of the lung carry a poor prognosis compared to other non-small cell lung cancers (NSCLC) (Tian. 2017, Baine et al. 2018, Wood et al. 2017, 2015 WHO Classification of Lung Tumors). Adenosquamous carcinoma (ASC) has features of both Adenocarcinoma (ADC) and Squamous cell carcinoma (SCC) in the same tumor. The incidence of ASC varies between studies but is estimated to account for 0.4 to 4% of all lung cancers (2015 WHO Classification of Lung Tumors). Diagnosis of these cancers depends on several factors including adequate sampling of the tumor, careful review and objective interpretation of histologic criteria. An automated method for light microscopy review could help standardize and speed up correct identification of this important subtype and lead to better targeted therapies. In addition, an automated technique to quantify the relative contributions of histologies in heterogeneous tumors would lead to a better understanding of tumor biology. 476 tissue samples from NSCLC patients were scanned at 0.5pixel/um resolution, and the tumor area was selected by a pathologist. Each slide tumor area was divided into smaller image ‘patches’ of 512 × 512 pixels. We trained a Convolution Neural Network based on the Inception V3 and Resnet18 architecture using transfer learning and weakly-supervised learning, to classify tissue patches using the whole-slide level labels. Once the network was trained, a patch-level prediction was performed on 20 unseen test slides. A whole-slide diagnosis was performed by choosing the most common histology predicted among all patches extracted for each slide. In addition, a patch-based heatmap was created to identify the heterogeneity of histologies within a single tissue sample. The model’s whole slide prediction of ADC or SCC histologies on the test set, matched the pathologist’s whole-slide diagnosis, with an F1-score of 0.91 at the individual tile level and 1.0 (perfect) in slide level. The trained model also identified a few likely adenosquamous carcinomas, where slides were found to have patches with features from both ADC and SCC at high proportions. We are currently examining whether samples identified as heterogeneous can be confirmed by pathologists’ visual inspection, and whether we can visualize the network features that contribute to the patch-level classification decision. The development of an algorithm to identify mixed histologic subtypes may enable better selection of patients responsive to drug treatment and elucidate the biological mechanism leading to tumor heterogeneity. Citation Format: Reheman Baikejiang, Jennifer Giltnane, Eloisa Fuentes, Cleopatra Kozlowski. A deep-learning based approach to assess heterogeneity of histologies in non-small cell lung cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-088.

The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015

Lung Cancer in the United Kingdom

P40.02 Pemetrexed in Advanced-stage Lymphoepithelioma Carcinoma of Lung

IA01.04 Does Cytological Material Fit All - Lessons from EBUS/Bronchoscopy

Background: Lung cancer is one of the most frequently occurring neoplasms and usually has poor prognosis in the world as well as in Vietnam. Clinical signs and symptoms, chest X-ray, CT-Scanner only have a role to guide the diagnosis. Histopathology helps to diagnose and classify some of histological types of lung cancer. Objectives: 1. To describe some clinical features and radiographic pulmonary lesions in patients with primary malignant lung tumors. 2. To diagnose and classify histopathological types of lung cancer and to initially determine immunohistochemical markers exposure in the 2015 WHO classification of lung tumors. Materials and Methods: Cross-sectional research on 80 patients diagnosed by chest X-ray, CT Scanner and histopathology at the Hospital of Hue University of Medicine and Pharmacy and at the Hue Central Hospital from 4.2018 to 4.2019. Results: Chronic coughing was the most common chief complain (58.8%). Frequent respiratory symptoms were chronic coughing (81.3%), chest pain (60%). The most frequent mediastinal symptom was apnea (28.8%). Majority of patients presented general symptoms such as malaise (80%), rapid weight loss (43.8%). The percentage of bone metastasis was 20% and that of peripheral lymph node metastasis was 17.5%. Most cases (90%) had a single tumor and 57.5% of total cases have tumors that were 3 to 6 cm in size. Lesions in left and right lung had similar proportions which were evenly distributed throughout the lung lobes. Regarding to clinical stages, most of cases were in stage IIIA and above (93.7%). Epithelial carcinoma accounted for the highest number (67.5%), squamous carcinoma contributed 22.5% and others were responsible for low rates. Epithelial carcinoma had high positive rates for CK AE1/3, CK7, CK19, TTF1, CEA. Squamous carcinoma had high positive rates for CK AE1/2, P63, Ki67. There was a statistically moderate correlation between the results of histopathology and the conclusions of immunohistochemistry. Conclusions: majority of hospitalized patients were not in early stages and presented combinations of symptoms which were not only manifested in lung but in other locations, including distant metastases. Computerized tomography is almost the main test helping in diagnosis and staging. There was a statistically moderate correlation between the results of histopathology and the conclusions of immunohistochemistry. Key words: lung cancer, pulmonary carcinoma, X-ray, computerized tomography, histopathology, immunohistochemistry

Introduction to 2021 WHO Classification of Thoracic Tumors

BackgroundA specialized classification for small biopsies was added to the 2015 WHO classification of lung tumors. The purpose of this study is to explore and summarize the experience of applying the newly proposed classifications and criteria to clinical practice.MethodsWe used the 2015 WHO criteria to sort out 5032 small lung biopsies from a group of Chinese patients, and demonstrated their clinicopathological features, mutational status and the relationship between these factors.ResultsThe most common diagnosis was primary lung carcinoma (3130, 62.2%), among which adenocarcinoma (1421, 28.2%) was the most frequent histological type. The mutational assays using ARMS-PCR technology demonstrated that EGFR was positive in 56.1% cases(499/889, from adenocarcinoma and NSCC, favor adenocarcinoma), ALK in 5.7% cases(12/211, from NSCC, which comprised all the primary lung carcinomas except small cell carcinomas), and ROS1 in 0.9% cases(2/211, from NSCC). Another 898 NSCC specimens went through an immunohistochemical (IHC) examination for ALK (D5F3) and 38 of them were positive (4.2%). The overall mutation rate of ALK was 4.5% (50/1119). There was no significant difference between ARMS-PCR and immunohistochemistry in the positive rate of ALK mutation detection (P = 0.359). EGFR mutations (P = 0.02) and ALK mutations (P < 0.001) both decreased with an increasing patient age. Furthermore, the amount of EGFR mutations was higher in adenocarcinoma (64.1% vs 34.1%, P < 0.001) than in NSCC, favor adenocarcinoma. In contrast, ALK mutations were more common in NSCC, favor adenocarcinoma (4.2% vs 8.4%, P = 0.021).ConclusionsThis single-center study exhibited a large subset of small lung biopsies from a Chinese institution and demonstrated that applying the 2015 WHO classification for small lung biopsies can help predict the mutational status of primary lung carcinomas.

Classification histomoléculaire des cancers pulmonaires : quoi de neuf en 2017 ?

Using Molecular Diagnostics for Inherited Retinal Dystrophies: The 6 "I"s That Are Necessary to Diagnose 2 Eyes Genetically.

The new, interdisciplinary IASLC/ATS/ERS classification of lung adenocarcinoma has achieved a considerable impact since its publication in the year 2011. It separates tumours into preinvasive, minimally invasive and invasive subtypes. The preinvasive lesions atypical, adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) together with the minimally invasive adenocarcinoma (MIA), have an excellent prognosis after complete resection with 100 % survival. It enables a reproducible tumour grading by the determination of the predominant histological growth pattern which could be confirmed in several follow-up studies. Thereby the mixed subtype was eliminated which formerly represented about 80 % of all adenocarcinomas. Similarly, the terms bronchioloalveolar adenocarcinoma and bronchioloalveolar tumour growth were eliminated because they represented several distinct entities, specifically the in-situ lesions AAH and ACIS as well as the non-in-situ/invasive tumours like minimally invasive adenocarcinoma, lepidic predominant adenocarcinoma (LPA) and invasive mucinous adenocarcinoma (IMA). Although the classification is based on data from tumour resections it accommodates the fact that most tumours are diagnosed on biopsies and cytological specimens and includes recommendations for an efficient work-up to preserve tissue for molecular testing. Furthermore, the morphological analysis may provide hints for molecular changes including mutations with therapeutic relevance that may enable targeted molecular diagnostics. This review presents essentials facts of the new classification that will be part of the next WHO classification of lung tumors and its follow-up publications.

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