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S. Ghosh



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    MA 14 - Diagnostic Radiology, Staging and Screening for Lung Cancer I (ID 672)

    • Event: WCLC 2017
    • Type: Mini Oral
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      MA 14.03 - Lung Cancer Risk Score Analysis Using Plasma microRNA Profiles (ID 8335)

      15:55 - 16:00  |  Author(s): S. Ghosh

      • Abstract
      • Presentation
      • Slides

      Background:
      There is a need for more accurate and minimally invasive methods to screen high risk populations and diagnose lung cancer during its asymptomatic early stages. microRNAs (miRNAs) are small, non-coding strands of RNA that are shown to lead to carcinogenesis when dysregulated. miRNAs, expressed in a tissue specific manner, are stable and detectable in small quantities, thus are promising candidates for biomarkers. Through the use of previous miRNA profiling done in our group, we aim to validate this panel in a large sample size of non-small cell lung cancers (NSCLC) using miRNAs 21, 155, 210, and 223 in blood plasma to determine if this miRNA panel is able to differentiate lung cancer cases from controls.

      Method:
      A nested case-control study of 64 patients with stage I/II NSCLC and 110 healthy controls with similar age, gender, and smoking history was performed. Plasma was provided by Conservant Bio, Lung Cancer Biospecimen Resource Network, and Alberta’s Tomorrow Project. miRNA was isolated using the Qiagen miRNeasy serum/plasma kit. miR-21, 155, 210, and 223 were quantified via RT-PCR using C. elegans miR-39 as a spiked-in endogenous control. Binary logistic regression (SPSS version 15) was performed to develop a combined risk score of the patients’ risk of having lung cancer. Receiver operating curve (ROC) analysis was used to determine risk category cut-off values based on the sensitivity and specificity. Plasma samples were taken 4-7 months post resection and also analyzed and compared to pre-operative samples and controls.

      Result:
      The cases and controls showed similar age ranges (mean=61.97, SD=7.76; mean=61.38, SD=7.95) respectively. Smoking history was higher in the cases (mean=51.5, SD=29.46) than controls (mean=30.98, SD=10.84). The combined score was dichotomized at -0.4169 into high and low risk categories (sensitivity=81%, specificity=41%, AUC=72.3%), the cases pre-operative samples compared to healthy controls was significantly different (odds ratio=3, p-value=0.003, 95% C.I.=[1.440,6.249]). For the cases post-operative samples compared to healthy controls, the combined score was dichotomized at -0.3255 (sensitivity=77%, specificity=41%, AUC=67%), also showing a significant difference (odds ratio=2.3, p-value=0.023, 95% C.I.=[1.120,4.621]). There is no significant difference in the combined risk score when comparing the pre-operative and post-operative NSCLC samples.

      Conclusion:
      Through binary logistic regression miRNA profiling has the potential to assist in screening the high-risk population for lung cancer. Used in conjunction with radiologic screening, this approach could allow early detection and treatment of disease while sparing patients unnecessary investigations and biopsies.

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