Differential early diagnosis of benign versus malignant lung cancer using systematic pathway flux analysis of peripheral blood leukocytes.

Early diagnosis of lung cancer is critically important to reduce disease severity and improve overall survival. Newer, minimally invasive biopsy procedures often fail to provide adequate specimens for accurate tumor subtyping or staging which is necessary to inform appropriate use of molecular targeted therapies and immune checkpoint inhibitors. Thus newer approaches to diagnosis and staging in early lung cancer are needed. This exploratory pilot study obtained peripheral blood samples from 139 individuals with clinically evident pulmonary nodules (benign and malignant), as well as ten healthy persons. They were divided into three cohorts: original cohort (n = 99), control cohort (n = 10), and validation cohort (n = 40). Average RNAseq sequencing of leukocytes in these samples were conducted. Subsequently, data was integrated into artificial intelligence (AI)-based computational approach with system-wide gene expression technology to develop a rapid, effective, non-invasive immune index for early diagnosis of lung cancer. An immune-related index system, IM-Index, was defined and validated for the diagnostic application. IM-Index was applied to assess the malignancies of pulmonary nodules of 109 participants (original + control cohorts) with high accuracy (AUC: 0.822 [95% CI: 0.75-0.91, p < 0.001]), and to differentiate between phases of cancer immunoediting concept (odds ratio: 1.17 [95% CI: 1.1-1.25, p < 0.001]). The predictive ability of IM-Index was validated in a validation cohort with a AUC: 0.883 (95% CI: 0.73-1.00, p < 0.001). The difference between molecular mechanisms of adenocarcinoma and squamous carcinoma histology was also determined via the IM-Index (OR: 1.2 [95% CI 1.14-1.35, p = 0.019]). In addition, a structural metabolic behavior pattern and signaling property in host immunity were found (bonferroni correction, p = 1.32e - 16). Taken together our findings indicate that this AI-based approach may be used for "Super Early" cancer diagnosis and amend the current immunotherpay for lung cancer.

Comparing Patient-Derived Xenograft and Computational Response Prediction for Targeted Therapy in Patients of Early-Stage Large Cell Lung Cancer.

PURPOSE: Targeted therapy (TT) provides highly effective cancer treatment for appropriately selected individuals. A major challenge of TT is to select patients who would benefit most. EXPERIMENTAL DESIGN: The study uses cancer material from 25 patients primarily diagnosed with non-small cell lung cancer (NSCLC). Patient-derived xenografts (PDXs) are treated with cetuximab and erlotinib. Treatment response is measured by tumor shrinkage comparing tumor volume at day 25 (V25) with tumor volume at baseline (V0). Shrinkage below 40% is considered as treatment response: V25/V0 < 0.4 (<40%). Furthermore, RNA-seq data from each tumor sample are used to predict tumor response to either treatment using an in silico molecular signaling map (MSM) approach. RESULTS: PDX response was 40% (10/25; 95% CI [21.13%, 61.34%]) under cetuximab and 20% (5/25; 95% CI [6.83%, 40.70%]) under erlotinib. MSM predicted response was 48% (12/25; 95% CI [27.8%, 68.7%]) under cetuximab and 40% (10/25; 95% CI [21.13%, 61.34%]) under erlotinib. Agreement between PDX and MSM response prediction is substantial under cetuximab and erlotinib: 84% (21/25, P = 0.001) and 80% (20/25, P = 0.003). A total of 5 from the 25 patients have been treated with cetuximab showing a clinical response identical to both predictions. CONCLUSIONS: For NSCLC patients, this proof-of-concept study shows a considerable agreement in response prediction from MSM and PDX approaches, but MSM saves time and laboratory resources. Our result indicates the potential of MSM-based approach for clinical decision making when selecting cancer TTs. Clin Cancer Res; 22(9); 2167-76. ©2015 AACR.