A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation

Abstract

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

Keywords: bioinformatic modeling; cancer; intracellular signaling pathway; personalized medicine; response to target drug therapy.

Figure 1. Scatter plot showing the percent of patients with a particular cancer type responding to a particular treatment (x-axis) in a clinical trial versus the percent of patients with a particular cancer type having the Drug Score for the particular drug above an arbitrary chosen cut-off value (250) (y-axis)

ccRCC stands for Clear Cell Renal Cell Carcinoma, nHLymphoma for non-Hodgkin Lymphoma, lung AC for lung adenocarcinoma.

Figure 2. Cohort of tumors with BRAF V600E mutation (left bar) had significantly higher proportion of patients for whom Vemurafenib was predicted to be beneficial compared to a cohort with wild-type BRAF (right bar)

Red bars show predicted non-responders and green bars show predicted responders (having non-zero DS for Vemurafenib).