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Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data.

ABSTRACT: Small cell lung cancer (SCLC) is an exceptionally lethal malignancy for which more effective therapies are urgently needed. Several lines of evidence, from SCLC primary human tumours, patient-derived xenografts, cancer cell lines and genetically engineered mouse models, appear to be converging on a new model of SCLC subtypes defined by differential expression of four key transcription regulators: achaete-scute homologue 1 (ASCL1; also known as ASH1), neurogenic differentiation factor 1 (NeuroD1), yes-associated protein 1 (YAP1) and POU class 2 homeobox 3 (POU2F3). In this Perspectives article, we review and synthesize these recent lines of evidence and propose a working nomenclature for SCLC subtypes defined by relative expression of these four factors. Defining the unique therapeutic vulnerabilities of these subtypes of SCLC should help to focus and accelerate therapeutic research, leading to rationally targeted approaches that may ultimately improve clinical outcomes for patients with this disease.

SUBMITTER: Rudin CM

PROVIDER: S-EPMC6538259 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data.

Rudin Charles M CM Poirier John T JT Byers Lauren Averett LA Dive Caroline C Dowlati Afshin A George Julie J Heymach John V JV Johnson Jane E JE Lehman Jonathan M JM MacPherson David D Massion Pierre P PP Minna John D JD Oliver Trudy G TG Quaranta Vito V Sage Julien J Thomas Roman K RK Vakoc Christopher R CR Gazdar Adi F AF

Nature reviews. Cancer 20190501 5

Small cell lung cancer (SCLC) is an exceptionally lethal malignancy for which more effective therapies are urgently needed. Several lines of evidence, from SCLC primary human tumours, patient-derived xenografts, cancer cell lines and genetically engineered mouse models, appear to be converging on a new model of SCLC subtypes defined by differential expression of four key transcription regulators: achaete-scute homologue 1 (ASCL1; also known as ASH1), neurogenic differentiation factor 1 (NeuroD1) ...[more]

PMID: 30926931

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Project description:BackgroundNon-small cell lung cancer (NSCLC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable biomarkers, especially immunotherapy-associated biomarkers, that can predict outcomes of these patients.MethodsGene expression profiles of 1026 NSCLC patients were collected from The Cancer Genome Atlas (TCGA) datasets with their corresponding clinical and somatic mutation data. Based on immune infiltration scores, molecular clustering classification was performed to identify immune subtypes in NSCLC. After the functional enrichment analysis of subtypes, hub genes were further screened using univariate Cox, Lasso, and multivariate Cox regression analysis, and the risk score was defined to construct the prognostic model. Other microarray data and corresponding clinical information of 603 NSCLC patients from the GEO datasets were applied to conduct random forest models for the prognosis of NSCLC with 100 runs of cross-validation. Finally, external datasets with immunotherapy and chemotherapy were further applied to explore the significance of risk-scores in clinical immunotherapy response for NSCLC patients.ResultsCompared with Subtype-B, the Subtype-A, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration scores and up-regulation of immunotherapy markers. In addition, we found and validated an eleven -gene signatures for better application of distinguishing high- and low-risk NSCLC patients and predict patients' prognosis and therapeutical response to immunotherapy. Furthermore, combined with other clinical characteristics based on multivariate Cox regression analysis, we successfully constructed and validated a nomogram to effectively predict the survival rate of NSCLC patients. External immunotherapy and chemotherapy cohorts validated the patients with higher risk-scores exhibited significant therapeutic response and clinical benefits.ConclusionThese results demonstrated the immunological and prognostic heterogeneity within NSCLC and provided a new clinical application in predicting the prognosis and benefits of immunotherapy for the disease.

Project description:PurposeMachine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features.MethodsOne hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters-four clinical factors, four laboratory indicators, and 91 [18F]F-FDG PET/CT radiomic features-were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance.ResultsBoruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model.ConclusionsThe proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails.

Project description:IntroductionLung cancer remains the leading cause of tumor-related deaths, despite advances in the understanding of the disease pathogenesis and in its clinical treatment. It is crucial to develop novel technologies to discover disease biomarkers and predict individual therapy response.Materials and methodsWe established 48 patients-derived tumor xenografts (PDTXs) implanted in the subrenal capsule of immunodeficient mice using thin, precision-cut tumor tissue slices, derived from five patients affected by non-small cell lung cancer. Twenty-six tissue slices were immediately processed and implanted at sample recovery [patients-derived tumor xenografts derived from fresh tissue (dPDTX)], whereas the remaining sections were cultured on specific organotypic supports at 37°C and 5% CO2 for 24 h before grafting [patients-derived tumor xenografts derived from cultured tissue (cPDTX)]. At sacrifice, xenografts tissue morphology, proliferation (Ki67), and histotype markers were analyzed. Oncogenic miRNAs profiles were assessed in PDTXs, human tumors, and serum from one patient.ResultsXenografts retained the original cancer features and there were no differences between dPDTXs and cPDTXs. Squamous cell carcinoma (SCC) xenografts showed a higher engraftment rate than adenocarcinoma (AC)-derived tumors. At basal time, Ki67 levels were higher in SCCs than in ACs, and the expression levels of genes associated to a stem cell-like phenotype were also more expressed in SCC samples. The analysis of oncogenic miRNAs showed that circulating miR-19b, -21, and -210 levels were correlated with higher Ki67 expression in xenografts.ConclusionOur study implemented the PDTX model with thin, precision-cut tumor slices from small tumors, which could be useful for clinical applications and predictive purposes. The different engraftment success is likely determined by tumor histotype, high proliferation index, and the expression of genes essential for cancer stem cells maintenance. Our PDTXs model could be a valid tool to expand primary tumors for the discovery of new biomarkers and explore therapeutic options.

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Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data.

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Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data.

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