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Small-cell lung cancer.

ABSTRACT: Small-cell lung cancer (SCLC) represents about 15% of all lung cancers and is marked by an exceptionally high proliferative rate, strong predilection for early metastasis and poor prognosis. SCLC is strongly associated with exposure to tobacco carcinogens. Most patients have metastatic disease at diagnosis, with only one-third having earlier-stage disease that is amenable to potentially curative multimodality therapy. Genomic profiling of SCLC reveals extensive chromosomal rearrangements and a high mutation burden, almost always including functional inactivation of the tumour suppressor genes TP53 and RB1. Analyses of both human SCLC and murine models have defined subtypes of disease based on the relative expression of dominant transcriptional regulators and have also revealed substantial intratumoural heterogeneity. Aspects of this heterogeneity have been implicated in tumour evolution, metastasis and acquired therapeutic resistance. Although clinical progress in SCLC treatment has been notoriously slow, a better understanding of the biology of disease has uncovered novel vulnerabilities that might be amenable to targeted therapeutic approaches. The recent introduction of immune checkpoint blockade into the treatment of patients with SCLC is offering new hope, with a small subset of patients deriving prolonged benefit. Strategies to direct targeted therapies to those patients who are most likely to respond and to extend the durable benefit of effective antitumour immunity to a greater fraction of patients are urgently needed and are now being actively explored.

SUBMITTER: Rudin CM

PROVIDER: S-EPMC8177722 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Blood-based biomarkers including systemic inflammation (SI) indicators or circulating factors (cytokines, chemokines, or growth factors) are associated with a poor prognosis for lung cancer patients. Collectively these biomarkers can predict the immune state of a patient. We wanted to define and compare the immune states of small cell and non-small cell lung cancer patients, in the hopes that the information gained could lead to overall improvements in patient care and outcomes. Specimens and data from 235 patients was utilized, 49 surgically resected non-small cell lung cancer (NSCLC) patients with no evidence of disease (DF), 135 advanced non-small cell lung cancer (NSCLC), 51 small cell lung cancer (SCLC). SI markers neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), systemic inflammation index (SII), and systemic inflammation response index (SIRI) were determined from blood counts. Forty-seven plasma cytokines were measured using a multiplex bead-based assay. Progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier and Cox Proportional Hazards models. NSCLC patients had significantly high levels of SI markers than SCLC and DF patients, while NLR, PLR and SII were also higher in SCLC than DF patients. SI optimized marker values to differentiate SI value were; 6.04 (NLR), 320 (PLR), 1615 (SII), and 7.3 (SIRI). Elevated levels NLR (p<0.001), PLR (p<0.001), and SII (p = 0.018) were associated with a worse PFS and OS in NSCLC, while none of the markers were associated with PFS in SCLC patients. NSCLC patients with a poor outcome displayed heterogeneous immune states relative to systemic inflammation and circulating IL-6 markers. These groups could be distinguished based on the cytokines IL-8, TNFα, and IL-27. We identified heterogeneity of immune states in SCLC and NSCLC patients and in NSCLC patients with the poorest prognosis. This heterogeneity could be exploited to improve outcomes for these patients.

Project description:Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.

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Small-cell lung cancer.

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