Feasibility of Ultra-High-Throughput Functional Screening of Melanoma Biopsies for Discovery of Novel Cancer Drug Combinations

Abstract

Purpose Successful development of targeted therapy combinations for cancer patients depends on first discovering such combinations in predictive preclinical models. Stable cell lines and mouse xenograft models can have genetic and phenotypic drift and may take too long to generate to be useful as a personalized medicine tool. Experimental Design To overcome these limitations, we have used a platform of ultra-high-throughput functional screening of primary biopsies preserving both cancer and stroma cell populations from melanoma patients to nominate such novel combinations from a library of thousands of drug combinations in a patient-specific manner within days of biopsy. In parallel, patient-derived xenograft (PDX) mouse models were created and novel combinations tested for their ability to shrink matched patient-derived tumors. Results The screening method identifies specific drug combinations in tumor cells with patterns that are distinct from those obtained from stable cell lines. Screening results were highly specific to individual patients. For patients with matched PDX models, we confirmed that individualized novel targeted therapy combinations could inhibit tumor growth. In particular, a combination of multi-kinase and PI3K/Akt inhibitors was effective in some BRAF-wild-type melanomas, and the addition of cediranib to the BRAF inhibitor PLX4720 was effective in a PDX model with BRAF mutation. Conclusions This proof-of-concept study demonstrates the feasibility of using primary biopsies directly for combinatorial drug discovery, complementing stable cell lines and xenografts, but with much greater speed and efficiency. This process could potentially be used in a clinical setting to rapidly identify therapeutic strategies for individual patients.

Drug responses are conserved across patient-derived xenograft models of melanoma leading to identification of novel drug combination therapies

“…A few studies have suggested that highly robust targeted therapy drug responses observed in a PDXC can translate to some measure of response in a PDX. 9 , 10 However, to date, no studies have demonstrated a statistical correlation between the magnitudes of targeted therapy drug response in HTDS utilising PDXC, when compared with the response in vivo in the corresponding PDX. We therefore investigated the correlation between the response to the BRAF inhibitor vemurafenib, the MEK inhibitor cobimetinib or the combination of both drugs, across eight PDXCs and corresponding PDX models (Fig.…”

“…Past studies have shown that a robust single agent or combination therapy drug response in culture (PDXCs) led to some measure of inhibitory activity in vivo in the corresponding PDXs. 9 , 20 However, we are not aware of any published studies to date that determined whether there is a direct statistical correlation in the magnitude of drug responses, at all response levels, when single and combination therapies are compared across multiple PDXCs and corresponding PDXs.…”

“…Prior studies have shown that a robust drug response in PDXCs leads to some measure of significant activity in vivo in the corresponding PDXs. 9 , 10 However, detailed studies demonstrating a direct statistical correlation in the magnitude of targeted therapy drug response between PDXCs and corresponding PDXs are needed. The existence of a strong correlation would increase support for the utility of PDXCs as a model for discovery of drug combination therapies targeting resistant cancers.…”

Evolution of the Experimental Models of Cholangiocarcinoma

“…In this approach, tumor fragments derived from biopsies or surgical specimens are directly implanted into the dorsal region (subcutaneous implantation) or target organs (orthotopic implantation) of immunodeficient mice [ 209 ]. PDX retain the main traits of tumor bulk, including the surrounding stroma, 3D architecture, and tumor heterogeneity ( Figure 2 D), thus providing a powerful and reliable tool for predicting the therapeutic response in different types of solid cancers [ 210 , 211 , 212 ].…”

Ex Vivo Analysis of Primary Tumor Specimens for Evaluation of Cancer Therapeutics

“…In addition to the use of two-dimensional cultures as a tool to understand drug sensitivity at the level of individual patient samples and drugs, many studies have taken a population perspective to understand the patterns of sensitivity across broad panels of drugs and the manner by which these sensitivity patterns correlate with genetic, transcriptomic, and clinical features in hematologic malignancies (Dietrich et al 2018;Frismantas et al 2017;Lee et al 2018;Pemovska et al 2013;Tyner et al 2013Tyner et al , 2018. Studies of a similar nature in solid tumors have also incorporated a combination of two-dimensional, three-dimensional, and in vivo patient-derived models to integrate with genomics across a variety of solid tumor types (Brodin et al 2019;Friedman et al 2017;Pauli et al 2017;Saeed et al 2017Saeed et al , 2019, and in some cases sensitivity to RNA interference-mediated gene knockdown has been included in the analyses to help understand and prioritize drug sensitivity patterns (Moser et al 2014, Tyner et al 2009, Xu et al 2018. Analytical tools to assist with such studies, such as the open source Breeze platform for quality control and analysis of high-throughput drug sensitivity data, will assuredly help with these large-scale analyses in future studies (Potdar et al 2020).…”