Dataset Information

Apolipoproteins as Differentiating and Predictive Markers for Assessing Clinical Outcomes in Patients with Small Cell Lung Cancer.

ABSTRACT:

Purpose

The present study aimed to investigate the value of apolipoproteins, including ApoA-1, ApoC-III, and ApoE, in patients with small cell lung cancer (SCLC) as potential biomarkers for diagnosis, prognosis, and cancer progression.

Materials and methods

Lung samples were collected from 89 patients with SCLC. Nineteen lung samples from non-small cell lung cancer (NSCLC) patients and 12 normal lung tissues were used as controls. Expression profiles of ApoA-1, ApoC-III, and ApoE in different samples were examined using immunohistochemical methods, and the expression levels were correlated with cancer types, treatment, and outcomes using chi-square and Mann-Whitney tests.

Results

Expression of ApoA-1 and ApoC-III in SCLC was significantly different, compared with that in NSCLC and normal lung tissues, and was correlated with recurrence of SCLC. Patients undergoing neoadjuvant chemotherapy before surgery showed significantly reduced expression of ApoA-1 and increased expression of ApoC-III and ApoE. Nevertheless, the expression levels of ApoA-1, ApoC-III, and ApoE were not correlated with SCLC staging.

Conclusion

ApoA-1 and ApoC-III may be used as differentiating and predictive markers for SCLC. ApoA-1, ApoC-III, and ApoE may be used to monitor the efficacy of chemotherapy.

SUBMITTER: Shi J

PROVIDER: S-EPMC4800341 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Publications

Apolipoproteins as Differentiating and Predictive Markers for Assessing Clinical Outcomes in Patients with Small Cell Lung Cancer.

Shi Jian J Yang Huichai H Duan Xiaoyang X Li Lihua L Sun Lulu L Li Qian Q Zhang Junjun J

Yonsei medical journal 20160501 3

<h4>Purpose</h4>The present study aimed to investigate the value of apolipoproteins, including ApoA-1, ApoC-III, and ApoE, in patients with small cell lung cancer (SCLC) as potential biomarkers for diagnosis, prognosis, and cancer progression.<h4>Materials and methods</h4>Lung samples were collected from 89 patients with SCLC. Nineteen lung samples from non-small cell lung cancer (NSCLC) patients and 12 normal lung tissues were used as controls. Expression profiles of ApoA-1, ApoC-III, and ApoE ...[more]

PMID: 26996551

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Project description:Background: A pressing challenge in treating non-small cell lung cancer (NSCLC) lies in the significant variability of patient responses to immunotherapy. This issue underscores the critical need for innovative predictive models that can navigate beyond conventional biomarkers to enhance patient outcomes, particularly by addressing immunotherapy resistance or responsiveness. Hypothesis: We hypothesize that an integrated multi-omics approach will uncover interactions within the NSCLC tumor immune microenvironment (TIME) and identify novel biomarkers that are predictive of individual immunotherapy responses, thus aiding in the development of a robust personalized treatment planning model. Objective: To develop a predictive model for NSCLC immunotherapy response/resistance by identifying new biomarkers using co-detection by indexing (CODEX) and Digital Spatial Profiling of whole transcriptome atlas (DSP-WTA). Methods: We utilized a multi-omics approach, combining CODEX for spatial mapping of protein expression at the single-cell level and DSP-WTA for comprehensive transcriptomic insights from the cell types identified by CODEX. This methodology facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first-line immunotherapy. Results: Our analysis identified three cell types, proliferating tumor cells, granulocytes, and vessels, that are associated with resistance to immunotherapy. The high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the training cohort (N = 33) and 1.8 (p = 0.05) in the validation cohort (N = 35). In the response cell type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells returned a HR of 0.4 (p = 0.019) in the training set and 0.49 (p = 0.036) in the validation set. Gene signatures related to these cell types also predicted outcomes with high accuracy. The resistant gene model, which included 8 genes associated with epithelial-mesenchymal transition (EMT) and cell migration showed a HR of 5.3 (p < 0.001) in the training set and 2.2 (p = 0.036) in the validation set. The response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the training set and 0.38 (p = 0.034) in the validation set. Conclusion: This research highlights the potential of a multi-omics strategy in advancing NSCLC treatment toward precision oncology. By offering insights into the TIME and unveiling novel biomarkers, our model seeks to define resistance and to improve prediction of response to treatment.

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Dataset Information

Apolipoproteins as Differentiating and Predictive Markers for Assessing Clinical Outcomes in Patients with Small Cell Lung Cancer.

Purpose

Materials and methods

Results

Conclusion

Publications

Apolipoproteins as Differentiating and Predictive Markers for Assessing Clinical Outcomes in Patients with Small Cell Lung Cancer.

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