Accuracy of pleural biopsy for the diagnosis of histologic subtype of malignant pleural mesothelioma: Necropsy-based study of 134 cases.

BACKGROUND: Definition of histologic subtype of malignant pleural mesothelioma (MPM) is important for management of patients, because surgical treatment improves prognosis for patients with epithelioid but not biphasic or sarcomatoid MPM. In a series of necropsies performed in a hospital specialized for MPM diagnosis, we retrospectively investigated the accuracy of histologic diagnosis performed on pathologic specimens collected through pleural biopsies obtained at video-assisted thoracoscopic surgery (VATS) or surgery. METHODS: We reviewed histologic records of an unselected series of autopsies performed in patients with MPM employed in the Monfalcone shipyards (Northeast Italy) or living with shipyard workers from 1999 through 2017. Using necropsy results as a gold standard, we calculated sensitivity, specificity, and positive and negative predictive values of histology from VATS or surgery after combining nonepithelioid subtypes. RESULTS: We retrieved necropsy records for 134 patients: 62 (46.3%) with epithelioid, 51 (38.1%) with biphasic, and 21 (15.7%) with sarcomatoid MPM. We observed good sensitivity of VATS (0.94) and surgery (0.89) in diagnosing epithelioid MPM. Conversely, specificity was low (VATS: 0.46; surgery: 0.32). Therefore, positive predictive values were also low (VATS: 0.58; surgery: 0.60). Misclassification was particularly high for biphasic MPM (three-fourths of biphasic MPM at necropsy had been classified as epithelioid at VATS or surgery). CONCLUSIONS: We observed a substantial degree of misclassification between epithelioid and biphasic MPM for pleural biopsies performed during VATS. Our results suggest caution should be taken in using histologic subtype obtained from VATS in selecting patients with MPM for surgical treatment. We also observed substantial misclassification of biospecimens collected during MPM surgery.

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.

Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations.

Sarcomatoid mesothelioma is an aggressive malignancy that can be challenging to distinguish from benign spindle cell mesothelial proliferations based on biopsy, and this distinction is crucial to patient treatment and prognosis. A novel deep learning based classifier may be able to aid pathologists in making this critical diagnostic distinction. SpindleMesoNET was trained on cases of malignant sarcomatoid mesothelioma and benign spindle cell mesothelial proliferations. Performance was assessed through cross-validation on the training set, on an independent set of challenging cases referred for expert opinion ('referral' test set), and on an externally stained set from outside institutions ('externally stained' test set). SpindleMesoNET predicted the benign or malignant status of cases with AUC's of 0.932, 0.925, and 0.989 on the cross-validation, referral and external test sets, respectively. The accuracy of SpindleMesoNET on the referral set cases (92.5%) was comparable to the average accuracy of 3 experienced pathologists on the same slide set (91.7%). We conclude that SpindleMesoNET can accurately distinguish sarcomatoid mesothelioma from benign spindle cell mesothelial proliferations. A deep learning system of this type holds potential for future use as an ancillary test in diagnostic pathology.

Histopathological typing in diffuse malignant epithelioid mesothelioma: implication for prognosis and molecular basis.

The prognostic impact of tumour grading, cytological and architectural patterns and stromal features in diffuse pleural malignant epithelioid mesothelioma (MEM) has been partly studied but not correlated to molecular features. We performed a retrospective study on 92 MEM in our department in order to assess the prognostic role of architectural and stromal patterns, especially tumour to stroma ratio. Secondly, based on The Cancer Genome Atlas (TCGA) database, we analysed the differentially expressed genes in prognostic groups of interest. Our results showed that tumour grading, tumour to stroma ratio and predominant pattern were related to overall survival, p≤0.001, p=0.01 and p=0.001, respectively. In univariate analysis, for high grade tumours hazard ratio (HR) was 4.75 (2.47-9.16), for stroma poor tumours HR=0.016, for predominant tubular or tubulopapillary pattern HR=0.044. In multivariate analysis, high grade tumours were related to overall survival [HR=3.09 (1.50-6.35), p=0.002] and predominant tubular or tubulopapillary pattern [HR=0.56 (0.32-0.99), p=0.045]. In TCGA analysis, after grading of diagnostic slides, we showed that KRTDAP and CXRCR1 expression was higher in low grade tumours, unlike PDZD7 and GPR176 expression which was higher in high grade tumours. FAM81B had a higher expression in stroma poor tumours. We did not find any differentially expressed genes in the architectural patterns group. Our work suggests that tumour grading is an important parameter in MEM with an underlying genomic basis. The role of tumour to stroma ratio needs to be investigated and might also have a genomic basis.

DNA Methylation Profiling Discriminates between Malignant Pleural Mesothelioma and Neoplastic or Reactive Histologic Mimics.

The diagnosis of malignant pleural mesothelioma (MPM) is challenging because of its potential overlap with other neoplasms or even with reactive conditions. DNA methylation analysis is effective in diagnosing tumors. In the present study, this approach was tested for use in MPM diagnosis. The DNA methylation patterns of a discovery cohort and an independent-validation cohort of MPMs were compared to those of 202 cases representing malignant and benign diagnostic mimics (angiosarcoma, desmoid-type fibromatosis, epithelioid sarcoma, leiomyosarcoma, lung adenocarcinoma, lung squamous cell carcinoma, melanoma, nodular fasciitis, reactive mesothelial hyperplasia, sclerosing fibrous pleuritis, solitary fibrous tumor, and synovial sarcoma). By both unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding analysis, MPM samples in the discovery cohort exhibited a DNA methylation profile different from those of other neoplastic and reactive mimics. These results were confirmed in the independent validation cohort and by in silico analysis of the MPM-The Cancer Genome Atlas data set. Copy number variation profiles were also inferred to identify molecular hallmarks of MPM, including CDKN2A and NF2 deletions. Methylation profiling was effective in the diagnosis of MPM, although caution is advised in samples with low tumor cell content.

The concept of mesothelioma in situ, with consideration of its potential impact on cytology diagnosis.

Diffuse malignant mesothelioma (MM) is an incurable tumour of the serosal membranes, which is often caused by exposure to asbestos and commonly diagnosed at advanced stage. Malignant mesothelioma in situ (MMIS) is now included as diagnostic category by the World Health Organization (WHO). However, our international survey of 34 pulmonary pathologists with an interest in MM diagnosis highlights inconsistency regarding how the diagnosis is being made by experts, despite published guidelines. Whilst the WHO restricts the diagnosis to surgical samples, the very concept has implication for cytological diagnosis, which is already regarded as controversial in itself by some. MMIS is currently only applicable as precursor to MM with an epithelioid component, and raises the possibility for different molecular pathways for different histological MM subtypes. The clinical implications of MMIS at this stage are uncertain, but aggressive therapies are being initiated in some instances. Based on the results of the survey we here present a critical appraisal of the concept, its clinical and conceptual implications and provide practice suggestions for diagnosis. A low threshold for ancillary testing is suggested. The designations of 'malignant mesothelioma, cannot exclude MMIS' or 'atypical mesothelial proliferation with molecular indicators of malignancy, so-called MMIS' could be used on cytology samples, adding 'no evidence of invasion in sample provided' for surgical samples. Clinical and radiological correlation are integral to diagnosis and best done at multidisciplinary meetings. Finally, collaborative studies are required to improve our understanding of MMIS.

The Staging of Malignant Pleural Mesothelioma.

Staging of malignant pleural mesothelioma has been challenging because of a paucity of cases and poor survival. At least 5 staging systems were proposed before 1990 until the first consensus system was published in 1995. This system used tumor, node, metastasis designations and borrowed heavily from parenchymal lung cancer descriptors. With the establishment of a database to collect cases from 1995 to 2013, evidence-based revisions to the 1995 staging classification were published in 2016. With improving imaging technology, clinical staging will become more refined and, it is hoped, more useful for prognostication even without operative resection.

Updates in Staging and Management of Malignant Pleural Mesothelioma.

While without treatment, malignant pleural mesothelioma (MPM) confers poor survival, cancer-directed surgery as part of multimodality treatment is associated with a 15% 5-year survival. Extrapleural pneumonectomy (EPP) and radical or extended pleurectomy/decortication (P/D) are the 2 types of resection performed in this context. Preoperative staging is critical to patient selection for surgery; P/D is recommended over EPP in most cases. Adjuvant therapy with intraoperative platforms, traditional chemotherapy, hemithoracic radiotherapy resection, and new immunotherapy agents are instrumental in achieving durable long-term results. We outline the latest understanding of disease staging and describe the current state of literature and practice.

Development of a Novel Prognostic Risk Classification System for Malignant Pleural Mesothelioma.

INTRODUCTION: This study aimed to assess prognostic factors to better understand malignant pleural mesothelioma (MPM) and to develop a new classification protocol beyond the standard tumor node metastasis (TNM) staging system. MATERIALS AND METHODS: We retrospectively reviewed the data of 188 patients with MPM who had not undergone surgical resection. For each patient, we calculated the maximum standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis (TLG) on pretreatment 18F-fluoro-2-deoxyglucose-positron emission tomography/computed tomography. Using the Cox proportional hazards model, we evaluated the relationships among potential MPM predictors, including age, gender, performance status, histologic type, stage, possible serum markers, and volume-based positron emission tomography parameters, as well as overall survival. RESULTS: The median survival was 461 days, and the 1- and 2-year overall survival rates were 60.70% and 31.10%, respectively. Univariate and multivariate analyses revealed that the significant independent predictors of poor survival outcomes were the non-epithelioid histologic type, elevated serum lactate dehydrogenase levels, a neutrophil-to-lymphocyte ratio of ≥ 5.0, and a TLG of ≥ 525 g. We then used these results to develop a prognostic risk classification system. From the resulting survival curve, we found a significant difference among the 3 risk groups of independent variables. Moreover, there were significant differences between all pairs of 2 separated risk groups. CONCLUSIONS: Pathologic subtypes, serum lactate dehydrogenase, neutrophil-to-lymphocyte ratio, and TLG in 18F-fluoro-2-deoxyglucose-positron emission tomography/computed tomography were independent and significant prognostic factors of MPM. Using this model, we created a new risk classification system that supplants the standard TNM staging protocol.