Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival

Abstract

The expanding targeted therapy landscape requires combinatorial biomarkers for patient stratification and treatment selection. This requires simultaneous exploration of multiple genes of relevant networks to account for the complexity of mechanisms that govern drug sensitivity and predict clinical outcomes. We present the algorithm, Digital Display Precision Predictor (DDPP), aiming to identify transcriptomic predictors of treatment outcome. For example, 17 and 13 key genes were derived from the literature by their association with MTOR and angiogenesis pathways, respectively, and their expression in tumor versus normal tissues was associated with the progression-free survival (PFS) of patients treated with everolimus or axitinib (respectively) using DDPP. A specific eight-gene set best correlated with PFS in six patients treated with everolimus: AKT2, TSC1, FKB-12, TSC2, RPTOR, RHEB, PIK3CA, and PIK3CB (r = 0.99, p = 5.67E−05). A two-gene set best correlated with PFS in five patients treated with axitinib: KIT and KITLG (r = 0.99, p = 4.68E−04). Leave-one-out experiments demonstrated significant concordance between observed and DDPP-predicted PFS (r = 0.9, p = 0.015) for patients treated with everolimus. Notwithstanding the small cohort and pending further prospective validation, the prototype of DDPP offers the potential to transform patients’ treatment selection with a tumor- and treatment-agnostic predictor of outcomes (duration of PFS).

Transcriptomics in Tumor and Normal Lung Tissues Identify Patients With Early-Stage Non–Small-Cell Lung Cancer With High Risk of Postsurgery Recurrence Who May Benefit From Adjuvant Therapies

“…Using the DDPP methodology, 19 we identified a transcriptomic biomarker signature and defined a DDPP low versus high score that significantly correlates with DFS. The cornerstone of the methodology was the exploration of transcription profiles of paired tumor and normal lung tissues from the same patient.…”

“…Multiple comparison correction was performed on the P values using false discovery rate 25 , and genes with a false discovery rate < 0.05 were considered significant. Patients were classified on the basis of the DDPP score 19 or expression levels (high v low) of particular immune checkpoint genes using k-mean clustering (k = 2). Survival analysis was carried out in R on the 120 patients with NSCLC who had full clinical data available using univariate and multivariate Cox regression models (packages survival and survminer).…”

“…DDPP is a novel tool that relates the differential expression of genes in tumor versus normal tissue to clinical outcome of patients with cancer of any histology. 19 The full transcriptome (approximately 19,500 genes) was analyzed for tumor and normal bronchial tissue of each patient and correlated (Pearson correlation) with the disease-free survival (DFS). The most significant set of genes (on the basis of P value and correlation coefficient) were selected as the correlator for each histology and chemotherapy group: AC_no-chemotherapy (NC), AC_chemotherapy, SCC_NC, SCC_chemotherapy, LCC_NC, and LCC_chemotherapy resulting in six different linear regression correlators, one for each group.…”

“…We hypothesized that two key biological factors could explain the risk of recurrence of patients with early-stage NSCLC after curative-intent surgery: (1) differential gene expression between tumor and normal lung tissues from the same patient that indicate activated molecular pathways, using the Digital Display Precision Predictor (DDPP) method 19 and (2) immune-competent versus immune-tolerant status of the host assessed by the level of activation of the immune regulatory genes programmed death-ligand 1 (PD-L1) and cytotoxic T-cell lymphocyte (CTLA)-4 in normal lung that may limit the immune response to tumor cells. 20 An immunecompetent profile would enable elimination of circulating tumor cells released during surgery and of established micrometastases by activated immune cells.…”

“…to clinical outcome of patients with cancer of any histology. 19 The full transcriptome (approximately 1).…”