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EP1.11 - Screening and Early Detection (ID 201)
- Event: WCLC 2019
- Type: E-Poster Viewing in the Exhibit Hall
- Track: Screening and Early Detection
- Presentations: 1
- Now Available
- Coordinates: 9/08/2019, 08:00 - 18:00, Exhibit Hall
EP1.11-24 - The TREAT Model 2.0: Predicting Lung Cancer in Patients Seeking Care in High-Risk Clinics (Now Available) (ID 2713)
08:00 - 18:00 | Author(s): Shawn Regis
Appropriate risk-stratification of indeterminate pulmonary nodules (IPNs) is necessary to estimate the best diagnostic strategy. Validated models for patients with high-risk IPNs are poorly calibrated. We sought to expand our previous Thoracic Research Evaluation And Treatment (TREAT) model into a more generalized, robust model for lung cancer prediction, the TREAT 2.0.Method
A total of 1402 patients with known or suspected lung cancer were used to recalibrate the TREAT 1.0 model. Clinical data and patient demographics were retrospectively collected from six clinics located in four U.S. states. Six datasets were divided into 3 clinical groups: patients who presented to a pulmonary nodule clinic (n=375), patients who presented to an outpatient thoracic surgery clinic (n=553) and patients who presented for surgical resection (n=474). A logistic regression model using multiple imputation was developed and validated. Model variables included age, body mass index, gender, smoking pack-years, size of nodule, spiculation, growth over time, location in upper lobe, prior cancer history, pre-operative FEV1, pre-operative symptoms, FDG-PET positivity, and clinical group. The discrimination and calibration of the TREAT 2.0 model was estimated and compared to two other common models for lung nodules, the Mayo Clinic and Herder models.Result
Lung cancer prevalence was as follows: pulmonary nodule clinic 42%, thoracic surgery clinic 73%, and surgical resection cohort 90%. The strongest predictors of cancer were clinical group, age, nodule growth, PET positivity, and smoking pack-years. The median TREAT 2.0 area under the receiver operating curve (AUC) for the imputed dataset was 0.86 (95% confidence interval (CI), 0.86-0.87) and the Brier score was 0.13. The TREAT 2.0 model had better accuracy (p < 0.001) (Figure 1) and calibration than the Mayo Clinic (AUC =0.74 95% CI: 0.74-0.75; Brier score=0.21) or Herder models (AUC=0.75; 95%CI: 0.74-0.75 and Brier score=0.19).Conclusion
The TREAT 2.0 model is more accurate and better calibrated than the Mayo Clinic or Herder models in patients presenting with nodules at high risk for lung cancer. Nodule calculators such as the TREAT 2.0 that account for variation in lung cancer prevalence with a variable for clinical group may improve generalizability and increase use in clinical practice.
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MA15 - Usage of Computer and Molecular Analysis in Treatment Selection and Disease Prognostication (ID 141)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Pathology
- Presentations: 1
- Now Available
MA15.06 - Stage I Lung Adenocarcinoma Gene Expression Associated with Aggressive Histologic Features for Guiding Precision Surgery and Therapy (Now Available) (ID 1124)
15:45 - 17:15 | Author(s): Shawn Regis
Stage I lung adenocarcinomas (LUADs) show heterogeneity in histologic patterns which correlate with malignant behavior. Solid, micropapillary and cribriform patterns are associated with worse survival whereas lepidic (in situ) predominance has the best prognosis. In this study, we sought to characterize histologic pattern specific gene expression in resected clinical stage I LUADs. We also aimed to train and validate a genomic biomarker predictive of histologic aggressive patterns with the ultimate goal of being able to impact surgical and therapeutic decision making for post-biopsy management.Method
A training cohort of 56 tumors from patients meeting NCCN high-risk screening criteria with stage I LUAD was included for pathologic annotation and whole exome RNA sequencing. Histologic pattern subtyping in 5% increments including all diagnostic slides was performed. A single representative FFPE block was chosen for RNA library-prep with Illumina TruSeq Access Kit and sequencing. Negative binomial models were used to identify gene expression differences associated with percent solid, cribriform, or micropapillary histology, and EnrichR was used for gene pathway enrichment analysis. Ss-GSEA was used to predict tumor infiltration of 20 immune cell types. A random-forest classifier for predicting aggressive histologic patterns was trained using 5-fold cross validation. A set of tumors from 16 independent patients with ≤2.0 cm clinical stage I LUAD was macro-dissected into 32 paired components (lepidic + non-lepidic regions) and subjected to RNAseq. Six tumors were defined as non-aggressive (lepidic + acinar/papillary) and ten tumors were defined as aggressive (lepidic + solid/micropapillary/cribriform). Four aggressive tumors were upstaged after surgical resection.Result
In the training cohort, we identified 1322 genes associated with tumor histologic composition(FDR q <0.05 and fold-change > 2). Genes whose expression differs with solid histology% are enriched for involvement in DNA replication, cell cycle regulation and inflammation (FDR q<0.001). Genes whose expression is associated with micropapillary% are enriched for involvement in tRNA-aminoacylation and decrease of T-cell activity (FDR q<0.001). The functional enrichment of genes whose expression is associated with cribiform% was less informative. LUADs with micropapillary patterns exhibited gene expression consistent with decreased antigen presentation and low T-cell infiltration, and solid patterns exhibited gene expression consistent with increased infiltration of T-regulatory and Th2 cells (FDR q<0.05).
A gene expression classifier was trained to predict the presence of aggressive histologic patterns. We validated this classifier on a set of 16 tumor specimens from which we macro-dissected and analyzed tissue from the most aggressive histologic pattern (AUC = 1.00). We also found that this classifier could differentiate lepidic regions isolated from aggressive tumors from lepidic regions isolated from non-aggressive tumors (AUC = 0.74).Conclusion
We identified solid-, micropapillary- and cribriform-specific gene expression and associated immune response among clinical stage I LUADs, and developed a classifier predictive of aggressive histologic features using either lepidic (in situ) or non-lepidic components. As such, this biomarker has the potential to predict histologic aggressiveness even from pre-surgical tumor biopsies where all histologic patterns may not be represented. Such a biomarker may be useful in guiding clinical decision making including extent of surgical resection.