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C.W. Seder
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MINI 32 - Topics in Localized Lung Cancer (ID 166)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Treatment of Localized Disease - NSCLC
- Presentations: 1
- Moderators:D. Boffa, T. D'Amico
- Coordinates: 9/09/2015, 18:30 - 20:00, 201+203
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MINI32.08 - Identification of a Meta-Gene Network Associated with Metformin Sensitivity and Recurrence in Stage I Non-Small Cell Lung Cancer (ID 1727)
18:30 - 20:00 | Author(s): C.W. Seder
- Abstract
- Presentation
Background:
We recently reported an association between progression-free survival and metformin exposure in patients with early stage non-small cell lung cancer (NSCLC). Local recurrence in stage I disease is estimated to be as high as 50% in US populations. Therefore, a method to identify NSCLC patients who are most likely to benefit from metformin treatment has potential clinical relevance.
Methods:
Three previously published, publically available gene expression array data sets documenting the effects of metformin treatment on transcriptional activity in human cell lines were used for the initial stages of the present study. These data sets were evaluated individually for enrichment of differentially expressed genes with a gene set analysis related to biological processes also performed. Differentially expressed genes common to all three studies were then used to form a metformin meta-gene. This combined meta-gene was evaluated topologically using a protein-protein interaction database to determine if any gene products had previously observed direct interactions. The metformin meta-gene network was then examined in expression array data sets from stage I NSCLC patients (n=293) assembled from multiple published studies.
Results:
We identified several biological themes resulting from metformin treatment, including: immune cell differentiation, response to hypoxia, steroid receptor signaling, alternate splicing, and changes in cellular metabolism. Intersecting the differentially expressed genes from each data set, we identified 105 genes consistently up-regulated and 30 genes consistently down-regulated by metformin treatment, forming a tissue-independent meta-gene for metformin effects. Two networks of interacting genes were identified in this analysis; the first network consisting of 27 genes (22 up-regulated and 5 down-regulated) and the second consisting of three up-regulated genes.This meta-gene was then examined in two independent cohorts of stage I adenocarcinoma. In the first cohort (n=125), patients clustered into two groups when k-means analysis was performed with respect to the 30 genes in the metformin meta-gene network. These patients had a significantly (p=0.014) different incidence of recurrence between the two clusters. This result was independently validated in the second data set (n=168) where patients clustered into two groups and also demonstrated significant stratification of recurrence (HR=1.21; p=0.001).
Conclusion:
We have identified a meta-gene of interacting proteins associated with both metformin therapy and recurrence-free survival in early stage lung cancer patients. This suggests a potential method for identifying NSCLC patients most likely to benefit from metformin therapy, and furthermore, identifies mechanistic avenues by which metformin treatment may benefit early stage lung cancer patients.
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P2.06 - Poster Session/ Screening and Early Detection (ID 219)
- Event: WCLC 2015
- Type: Poster
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P2.06-012 - A Model Incorporating Clinical, Radiographic, and Biomarker Characteristics Predicts Malignancy in Indeterminate Pulmonary Nodules (ID 2890)
09:30 - 17:00 | Author(s): C.W. Seder
- Abstract
Background:
The high false-positive rate associated with low-dose computed tomography (CT) lung cancer screening results in unnecessary testing, cost, and patient anxiety. We hypothesized that an algorithm incorporating clinical, radiographic, and serum biomarker data would be capable of differentiating benign from malignant pulmonary nodules.
Methods:
An institutional biorepository was used to identify 84 patients with ≤ 2 cm indeterminate pulmonary nodules identified on CT scan, including 50 patients with biopsy-proven, node-negative, non-small cell lung cancer (NSCLC) and 34 patients with benign, non-calcified, solitary pulmonary nodules. Clinical and radiographic data were collected from patient charts and imaging studies. Serum specimens were evaluated in a blinded manner for 55 biomarkers using multiplex immunoassays. Random forest analyses were used to generate a multivariate cross-validation prediction model incorporating clinical, radiographic, and serum biomarker data.
Results:
A total of 84 patients were identified with a median nodule size of 5 mm for benign nodules and 15 mm for NSCLC. Median smoking histories were 21 and 28 pack-years and patient age was 62 and 70 years, respectively. An algorithm incorporating serum biomarker profile (IGFBP-4, IGFBP-5, IL-10, IL-1ra, IL-6, SDF-1alpha, IGF-2), age, sex, BMI, COPD, smoking history, hemoptysis, previous cancer, nodule size, nodule location, spiculation, nodule type, and nodule count provided the optimal performance with a sensitivity 92%, specificity 65%, NPV 85%, and PPV 79%. This model performed with an overall accuracy of 81% with a cross-validated AUC=0.904.
Conclusion:
An algorithm incorporating clinical, radiographic, and serum biomarker characteristics may help differentiate benign from malignant pulmonary nodules. This model is currently being externally validated in a second-site patient cohort.
