[A new one in the lung and pleura neoplasms classification (WHO, 2021, 5th edition)]. / Novoe v Klassifikatsii novoobrazovanii legkikh i plevry (VOZ, 2021 g., 5-e izdanie).

The article contains an overview of the new WHO classification of thoracic tumors (2021). As in the previous edition of 2015, considerable attention is paid to neoplasms of the lungs and pleura. The article presents current data on molecular genetic features and morphological manifestations of a number of new lung tumors, with a detailed histological and immunohistochemical data. Thoracis undifferentiated tumor with SMARCA4 deficiency and bronchiolar adenoma are described. Emphasis is placed on the algorithms of morphological diagnostics, including a complete description of the tumor and facilitating the study in the practice of a pathologist. The main morphological criteria of mesothelial tumors of the pleura are given; describes in detail the procedure for assessing the degree of malignancy of diffuse epithelioid pleural mesothelioma and non-mucinous lung adenocarcinomas.

The International Association for the Study of Lung Cancer Thymic Epithelial Tumor Staging Project: Unresolved Issues to be Addressed for the Next Ninth Edition of the TNM Classification of Malignant Tumors.

Thymic epithelial tumors are presently staged using a consistent TNM classification developed by the International Association for the Study of Lung Cancer (IASLC) and approved by the Union for International Cancer Control and the American Joint Committee on Cancer. The stage classification is incorporated in the eight edition of the TNM classification of thoracic malignancies. The IASLC Staging and Prognostic Factors Committee (SPFC)-Thymic Domain (TD) is in charge for the next (ninth) edition expected in 2024. The present article represents the midterm report of the SPFC-TD: in particular, it describes the unresolved issues identified by the group in the current stage classification which are worth being addressed and discussed for the ninth edition of the TNM classification on the basis of the available data collected in the central thymic database which will be managed and analyzed by Cancer Research And Biostatistics. These issues are grouped into issues of general importance and those specifically related to T, N, and M categories. Each issue is described in reference to the most recent reports on the subject, and the priority assigned by the IASLC SPFC-TD for the discussion of the ninth edition is provided.

Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma; ADC, LCNEC, SCC, SCLC, and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We first evaluated the diagnostic performance of each model in the test sets. The Xception model and the CNN built from scratch both achieved the highest performance with a macro average AUC of 0.90. The CNN built from scratch model obtained a macro average AUC of 0.97 on the dataset of four classes excluding LCNEC, and 0.95 on the dataset of three subtypes of lung carcinomas; NSCLC, SCLC, and non-tumor, respectively. Of particular note is that the relatively simple CNN built from scratch may be an approach for pathological image analysis.

Pulmonary "Inflammatory Leiomyosarcomas" Are Indolent Tumors With Diploid Genomes and No Convincing Rhabdomyoblastic Differentiation.

Inflammatory leiomyosarcoma is a rare myogenic tumor with striking inflammatory infiltrates and a specific genomic pattern of near-haploidization despite exception(s). Recent studies demonstrated that inflammatory leiomyosarcoma shares substantially overlapping features with histiocyte-rich rhabdomyoblastic tumor, including expression of rhabdomyoblastic markers such as myogenin, MyoD1, and PAX7 and a high prevalence of genomic near-haploidization, suggesting that they represent a unifying entity, for which the term inflammatory rhabdomyoblastic tumor was coined. In this study, we identified 4 pulmonary tumors histologically typical of inflammatory leiomyosarcomas, all in men (aged 26 to 49), presented as slow-growing well-defined nodules ranging from 1.4 to 3.5 cm, and following uneventful postoperative courses. All tumors were positive for desmin immunostaining, while only 1 and 2 were focally positive for smooth muscle actin and smooth muscle myosin heavy chain, respectively. They showed no expression of myogenin, MyoD1, or PAX7 by immunohistochemistry or RNA sequencing. Copy number analyses by whole-exome sequencing (N=1), OncoScan single-nucleotide polymorphism array (2), and fluorescence in situ hybridization (1) revealed/suggested diploid genomes. Together with a previously reported case, all these pulmonary "inflammatory leiomyosarcomas" seemed clinically, pathologically, and genomically alike. Despite a superficial resemblance to conventional inflammatory leiomyosarcoma in somatic soft tissues (now preferably termed inflammatory rhabdomyoblastic tumor), they differ in the lack of convincing rhabdomyoblastic differentiation and genomic near-haploidization. Therefore, we propose that these pulmonary tumors probably represent a distinct entity, for which the exact line of differentiation, and perhaps the most suitable terminology to better reflect its nature, remains to be determined. The term inflammatory rhabdomyoblastic tumor seems inappropriate for this group of tumors.

Stiffness reduction and collagenase resistance of aging lungs measured using scanning acoustic microscopy.

Lung tissue stiffness is altered with aging. Quantitatively evaluating lung function is difficult using a light microscope (LM) alone. Scanning acoustic microscope (SAM) calculates the speed-of-sound (SOS) using sections to obtain histological images by plotting SOS values on the screen. As SOS is positively correlated with stiffness, SAM has a superior characteristic of simultaneously evaluating tissue stiffness and structure. SOS images of healthy bronchioles, arterioles, and alveoli were compared among young, middle-aged, and old lung sections. Formalin-fixed, paraffin-embedded (FFPE) sections consistently exhibited relatively higher SOS values than fresh-frozen sections, indicating that FFPE became stiffer but retained the relative stiffness reflecting fresh samples. All lung components exhibited gradually declining SOS values with aging and were associated with structural alterations such as loss of smooth muscles, collagen, and elastic fibers. Moreover, reaction to collagenase digestion resulted in decreased SOS values. SOS values of all components were significantly reduced in young and middle-aged groups, whereas no significant reduction was observed in the old group. Protease damage in the absence of regeneration or loss of elastic components was present in old lungs, which exbited dilated bronchioles and alveoli. Aging lungs gradually lose stiffness with decreasing structural components without exposure to specific insults such as inflammation.

The classification of neuroendocrine neoplasms of the lung and digestive system according to WHO, 5th edition: similarities, differences, challenges, and unmet needs.

Neuroendocrine neoplasms (NENs) are a group of disease entities sharing common morphological, ultrastructural and immunophenotypical features, yet with distinct biological behavior and clinical outcome, ranging from benign to frankly malignant. Accordingly, a spectrum of therapeutic options for each single entity is available, including somatostatin analogues (SSA), mTOR-inhibitors, peptide receptor radionuclide therapy (PRRT), non-platinum and platinum chemotherapy. In the last few decades, several attempts have been made to better stratify these lesions refining the pathological classifications, so as to obtain an optimal correspondence between the scientific terminology and, the predictive and prognostic features of each disease subtype, and achieve a global Classification encompassing NENs arising at different anatomical sites. The aim of this review was to analyze, compare and discuss the main features and issues of the latest WHO Classifications of NENs of the lung and the digestive system, in order to point out the strengths and limitations of our current understanding of these complex diseases.

Subtyping non-small cell lung cancer by histology-guided spatial metabolomics.

PURPOSE: Most cancer-related deaths worldwide are associated with lung cancer. Subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (AC) and squamous cell carcinoma (SqCC) is of importance, as therapy regimes differ. However, conventional staining and immunohistochemistry have their limitations. Therefore, a spatial metabolomics approach was aimed to detect differences between subtypes and to discriminate tumor and stroma regions in tissues. METHODS: Fresh-frozen NSCLC tissues (n = 35) were analyzed by matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) of small molecules (< m/z 1000). Measured samples were subsequently stained and histopathologically examined. A differentiation of subtypes and a discrimination of tumor and stroma regions was performed by receiver operating characteristic analysis and machine learning algorithms. RESULTS: Histology-guided spatial metabolomics revealed differences between AC and SqCC and between NSCLC tumor and tumor microenvironment. A diagnostic ability of 0.95 was achieved for the discrimination of AC and SqCC. Metabolomic contrast to the tumor microenvironment was revealed with an area under the curve of 0.96 due to differences in phospholipid profile. Furthermore, the detection of NSCLC with rarely arising mutations of the isocitrate dehydrogenase (IDH) gene was demonstrated through 45 times enhanced oncometabolite levels. CONCLUSION: MALDI-MSI of small molecules can contribute to NSCLC subtyping. Measurements can be performed intraoperatively on a single tissue section to support currently available approaches. Moreover, the technique can be beneficial in screening of IDH-mutants for the characterization of these seldom cases promoting the development of treatment strategies.

Identification of A-to-I RNA editing profiles and their clinical relevance in lung adenocarcinoma.

Adenosine-to-inosine (A-to-I) RNA editing is a widespread posttranscriptional modification that has been shown to play an important role in tumorigenesis. Here, we evaluated a total of 19,316 RNA editing sites in the tissues of 80 lung adenocarcinoma (LUAD) patients from our Nanjing Lung Cancer Cohort (NJLCC) and 486 LUAD patients from the TCGA database. The global RNA editing level was significantly increased in tumor tissues and was highly heterogeneous across patients. The high RNA editing level in tumors was attributed to both RNA (ADAR1 expression) and DNA alterations (mutation load). Consensus clustering on RNA editing sites revealed a new molecular subtype (EC3) that was associated with the poorest prognosis of LUAD patients. Importantly, the new classification was independent of classic molecular subtypes based on gene expression or DNA methylation. We further proposed a simplified model including eight RNA editing sites to accurately distinguish the EC3 subtype in our patients. The model was further validated in the TCGA dataset and had an area under the curve (AUC) of the receiver operating characteristic curve of 0.93 (95%CI: 0.91-0.95). In addition, we found that LUAD cell lines with the EC3 subtype were sensitive to four chemotherapy drugs. These findings highlighted the importance of RNA editing events in the tumorigenesis of LUAD and provided insight into the application of RNA editing in the molecular subtyping and clinical treatment of cancer.

Elastin in pulmonary pathology: relevance in tumours with a lepidic or papillary appearance. A comprehensive understanding from a morphological viewpoint.

Elastin and collagen are the main components of the lung connective tissue network, and together provide the lung with elasticity and tensile strength. In pulmonary pathology, elastin staining is used to variable extents in different countries. These uses include evaluation of the pleura in staging, and the distinction of invasion from collapse of alveoli after surgery (iatrogenic collapse). In the latter, elastin staining is used to highlight distorted but pre-existing alveolar architecture from true invasion. In addition to variable levels of use and experience, the interpretation of elastin staining in some adenocarcinomas leads to interpretative differences between collapsed lepidic patterns and true papillary patterns. This review aims to summarise the existing data on the use of elastin staining in pulmonary pathology, on the basis of literature data and morphological characteristics. The effect of iatrogenic collapse and the interpretation of elastin staining in pulmonary adenocarcinomas is discussed in detail, especially for the distinction between lepidic patterns and papillary carcinoma.

In vitro anti-inflammatory, anti-arthritic and anti- proliferative activity of green synthesized silver nanoparticles - Phoenix dactylifera (Rothan dates)

Abstract Traditionally dates is consumed as a rich source of iron supplement and the current research discuss the synthesis of silver nanoparticles (AgNPs) using methanolic seed extract of Rothan date and its application over in vitro anti-arthritic, anti-inflammatory and antiproliferative activity against lung cancer cell line (A549). FTIR result of synthesised AgNPs reveals the presence of functional group OH as capping agent. XRD pattern confirms the crystalline nature of the AgNPs with peaks at 38º, 44º, 64º and 81º, indexed by (111), (200), (220) and (222) in the 2θ range of 10-90, indicating the face centered cubic (fcc) structure of metallic Ag. HR- TEM results confirm the morphology of AgNPs as almost spherical with high surface areas and average size of 42 ± 9nm. EDX spectra confirmed that Ag is only the major element present and the Dynamic light scattering (DLS) assisted that the Z-average size was 203nm and 1.0 of PdI value. Zeta potential showed − 26.5mv with a single peak. The results of the biological activities of AgNPs exhibited dose dependent activity with 68.44% for arthritic, antiinflammatory with 63.32% inhibition and anti-proliferative activity illustrated IC50 value of 59.66 µg/mL expressing the potential of AgNPs to combat cancer

The histologic phenotype of lung cancers is associated with transcriptomic features rather than genomic characteristics.

Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conduct whole-exome sequencing and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations are shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrate similar subclonal architecture. On the other hand, transcriptomic profiling reveals shared pathways between the same histologic subtypes from different patients, which is supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data derived from mixed histologic subtypes in the setting of identical genetic background and exposure history support that the histologic fate of lung cancer cells is associated with transcriptomic features rather than the genomic profiles in most tumors.

Prognostic value of different N1 lymph node zones in pN1M0 non-small cell lung cancer: a systematic review and meta-analysis.

The IASLC lymph node map grouped the lymph node stations into "zones" for prognostic analyses. In the N1 lymph nodes group, N1 nodes are divided into the Hilar/Interlobar zone (N1h) and Peripheral zone (N1p). There is no consensus on the different prognostic values of N1 lymph nodes in N1h and N1p. Therefore, we conducted a systematic review and meta-analysis to assess the survival difference between N1h and N1p in patients of pN1M0 NSCLC. Medline, the Cochrane Library, Embase, and the Web of science were systematically searched to identify relevant studies published up to April 4th, 2020. A retrospective and prospective cohort study comparing N1h versus N1p to the pN1M0 NSCLC was included. Hazard ratios (HRs) for OS were aggregated according to a fixed or random-effect model. Ten publications for 1946 patients of pN1M0 NSCLC were included for the meta-analysis.The 5-year OS was lower for patients with N1h (HR: 1.67, 95% CI 1.44-1.94; P < 0.001). The pooled 5-year OS in N1h and N1p were 40% and 56%, respectively. The patients in pN1M0 NSCLC have different survival according to different N1 lymph node zones involvement: patients with N1p metastasis have a better prognosis than those with N1h metastasis.

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.

Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subjects from 13 independent centers. SSL (~3150 labeled, ~40,950 unlabeled; ~6300 labeled, ~37,800 unlabeled patches) performs significantly better than the SL. No significant difference is found between SSL (~6300 labeled, ~37,800 unlabeled) and SL (~44,100 labeled) at patch-level diagnoses (area under the curve (AUC): 0.980 ± 0.014 vs. 0.987 ± 0.008, P value = 0.134) and patient-level diagnoses (AUC: 0.974 ± 0.013 vs. 0.980 ± 0.010, P value = 0.117), which is close to human pathologists (average AUC: 0.969). The evaluation on 15,000 lung and 294,912 lymph node images also confirm SSL can achieve similar performance as that of SL with massive annotations. SSL dramatically reduces the annotations, which has great potential to effectively build expert-level pathological artificial intelligence platforms in practice.

Bronchiolar adenomas (BA) - A detailed radio-pathologic analysis of six cases and review of literature.

Bronchiolar adenomas (BAs)/ciliated muco-nodular papillary tumors (CMPTs), are small, peripheral lung nodules arising predominantly in the elderly that follow a benign course. They can be mistaken for adenocarcinomas on frozen section. Immunohistochemistry (IHC) for basal cell markers highlights the continuous layer of basal cells underlying the tumor cells in BAs. BAs are further subdivided into proximal-type and distal type. Six BAs were retrieved from the pathology archives. The cases were classified based on morphology into proximal and distal BAs. The clinical and radiological features were reviewed. Immunohistochemistry and special stains were performed. The most common radiological picture of BA/CMPT was of a solid nodule with SUVmax < 3 as seen in 60% cases. 40% cases showed cavitation on CT. On histological examination, four cases were morphologically classified as proximal BAs and two as distal BAs. In proximal BAs, TTF1 was focally positive only in the basal cells in three of four. The mucin stained acidic. In distal BAs, TTF1 was diffusely positive in both basal and luminal cells. There was scant intracellular neutral mucin. Both the distal BAs had concomitant neuroendocrine tumors in the same lobe. Though the number of cases evaluated in this study is too low to be statistically significant, this study provides additional evidence to the concept of BA classification based on site specific histology and supplementary immunohistochemistry and reiterates the radiological features that may help distinguish it from malignant lesions.

A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning.

OBJECTIVE: To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. METHODS: We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome (n = 163), patients with Yin deficiency syndrome (n = 174), and healthy controls (n = 185) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. RESULTS: Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. CONCLUSIONS: It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.

The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study.

BACKGROUND: Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progress rapidly; for others with advanced sarcomatoid cancers, a poor prognosis can progress slowly. Therefore, we aimed to develop and validate a machine-learning tool-known as OncoCast-MPM-that could create a model for patient prognosis. METHODS: We did a retrospective study looking at malignant pleural mesothelioma tumours using next-generation sequencing from the Memorial Sloan Kettering Cancer Center-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). We collected clinical, pathological, and routine next-generation sequencing data from consecutive patients with malignant pleural mesothelioma treated at the Memorial Sloan Kettering Cancer Center (New York, NY, USA), as well as the MSK-IMPACT data. Together, these data comprised the MSK-IMPACT cohort. Using OncoCast-MPM, an open-source, web-accessible, machine-learning risk-prediction model, we integrated available data to create risk scores that stratified patients into low-risk and high-risk groups. Risk stratification of the MSK-IMPACT cohort was then validated using publicly available malignant pleural mesothelioma data from The Cancer Genome Atlas (ie, the TCGA cohort). FINDINGS: Between Feb 15, 2014, and Jan 28, 2019, we collected MSK-IMPACT data from the tumour tissue of 194 patients in the MSK-IMPACT cohort. The median overall survival was higher in the low-risk group than in the high-risk group as determined by OncoCast-MPM (30·8 months [95% CI 22·7-36·2] vs 13·9 months [10·7-18·0]; hazard ratio [HR] 3·0 [95% CI 2·0-4·5]; p<0·0001). No single factor or gene alteration drove risk differentiation. OncoCast-MPM was validated against the TCGA cohort, which consisted of 74 patients. The median overall survival was higher in the low-risk group than in the high-risk group (23·6 months [95% CI 15·1-28·4] vs 13·6 months [9·8-17·9]; HR 2·3 [95% CI 1·3-3·8]; p=0·0019). Although stage-based risk stratification was unable to differentiate survival among risk groups at 3 years in the MSK-IMPACT cohort (31% for early-stage disease vs 30% for advanced-stage disease; p=0·90), the OncoCast-MPM-derived 3-year survival was significantly higher in the low-risk group than in the high-risk group (40% vs 7%; p=0·0052). INTERPRETATION: OncoCast-MPM generated accurate, individual patient-level risk assessment scores. After prospective validation with the TCGA cohort, OncoCast-MPM might offer new opportunities for enhanced risk stratification of patients with malignant pleural mesothelioma in clinical trials and drug development. FUNDING: US National Institutes of Health/National Cancer Institute.

Characteristic of molecular subtypes in lung adenocarcinoma based on m6A RNA methylation modification and immune microenvironment.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky. METHODS: The gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model. RESULT: Consensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094). CONCLUSION: Our study provides a robust signature for predicting patients' prognosis, which might be helpful for therapeutic strategies discovery of LUAD.

Association of Lung Adenocarcinoma Subtypes According to the IASLC/ATS/ERS Classification and Programmed Cell Death Ligand 1 (PD-L1) Expression in Tumor Cells.

Background: Programmed cell death-ligand 1 (PD-L1) protein expression is one of the most extensively studied biomarkers in patients with non-small cell lung cancer (NSCLC). However, there is scarce information regarding its association with distinct adenocarcinoma subtypes. This study evaluated the frequency of PD-L1 expression according to the IASLC/ATS/ERS classification and other relevant histological and clinical features. Patients and Methods: PD-L1 expression was assessed by immunohistochemistry (IHC). According to its positivity in tumor cells membrane, we stratified patients in three different tumor proportions score (TPS) cut-off points: a) <1% (negative), b) between 1 and 49%, and c) ≥50%; afterward, we analyzed the association among PD-L1 expression and lung adenocarcinoma (LADC) predominant subtypes, as well as other clinical features. As an exploratory outcome we evaluated if a PD-L1 TPS score ≥15% was useful as a biomarker for determining survival. Results: A total of 240 patients were included to our final analysis. Median age at diagnosis was 65 years (range 23-94 years). A PD-L1 TPS ≥1% was observed in 52.5% of the entire cohort; regarding specific predominant histological patterns, a PD-L1 TPS ≥1 was documented in 31.2% of patients with predominant-lepidic pattern, 46.2% of patients with predominant-acinar pattern, 42.8% of patients with a predominant-papillary pattern, and 68.7% of patients with predominant-solid pattern (p = 0.002). On the other hand, proportion of tumors with PD-L1 TPS ≥50% was not significantly different among adenocarcinoma subtypes. At the univariate survival analysis, a PD-L1 TPS cut-off value of ≥15% was associated with a worse PFS and OS. Conclusion: According to IASLC/ATS/ERS lung adenocarcinoma classification, the predominant-solid pattern is associated with a higher proportion of PD-L1 positive samples, no subtype was identified to be associated with a high (≥50%) TPS PD-L1.

The impact of reclassifying cancers of unspecified histology on international differences in survival for small cell and non-small cell lung cancer (ICBP SurvMark-2 project).

Survival from lung cancer remains low, yet is the most common cancer diagnosed worldwide. With survival contrasting between the main histological groupings, small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), it is important to assess the extent that geographical differences could be from varying proportions of cancers with unspecified histology across countries. Lung cancer cases diagnosed 2010-2014, followed until 31 December 2015 were provided by cancer registries from seven countries for the ICBP SURVMARK-2 project. Multiple imputation was used to reassign cases with unspecified histology into SCLC, NSCLC and other. One-year and three-year age-standardised net survival were estimated by histology, sex, age group and country. In all, 404 617 lung cancer cases were included, of which 47 533 (11.7%) and 262 040 (64.8%) were SCLC and NSCLC. The proportion of unspecified cases varied, from 11.2% (Denmark) to 29.0% (The United Kingdom). After imputation with unspecified histology, survival variations remained: 1-year SCLC survival ranged from 28.0% (New Zealand) to 35.6% (Australia) NSCLC survival from 39.4% (The United Kingdom) to 49.5% (Australia). The largest survival change after imputation was for 1-year NSCLC (4.9 percentage point decrease). Similar variations were observed for 3-year survival. The oldest age group had lowest survival and largest decline after imputation. International variations in SCLC and NSCLC survival are only partially attributable to differences in the distribution of unspecified histology. While it is important that registries and clinicians aim to improve completeness in classifying cancers, it is likely that other factors play a larger role, including underlying risk factors, stage, comorbidity and care management which warrants investigation.