Defining principles of combination drug mechanisms of action

Abstract

Combination chemotherapies have been a mainstay in the treatment of disseminated malignancies for almost 60 y, yet even successful regimens fail to cure many patients. Although their singledrug components are well studied, the mechanisms by which drugs work together in clinical combination regimens are poorly understood. Here, we combine RNAi-based functional signatures with complementary informatics tools to examine drug combinations. This approach seeks to bring to combination therapy what the knowledge of biochemical targets has brought to single-drug therapy and creates a statistical and experimental definition of "combination drug mechanisms of action." We show that certain synergistic drug combinations may act as a more potent version of a single drug. Conversely, unlike these highly synergistic combinations, most drugs average extant single-drug variations in therapeutic response. When combined to form multidrug regimens, averaging combinations form averaging regimens that homogenize genetic variation in mouse models of cancer and in clinical genomics datasets. We suggest surprisingly simple and predictable combination mechanisms of action that are independent of biochemical mechanism and have implications for biomarker discovery as well as for the development of regimens with defined genetic dependencies.chemotherapy | lymphoma | RNAi signature | systems biology C urrent rationales for the design of combination chemotherapy regimens were developed in the 1940s and 1950s (1-5) and, remarkably, were concurrent with the identification of DNA as the genetic material. The development of these regimens in the absence of any knowledge of cancer genomics was a remarkable achievement. However, although our knowledge of the genetic drivers of cancer and the mechanisms of drug action has increased dramatically over the past 30 y, this information has been difficult to adapt to clinical practice. As such, even very successful combination regimens often fail to cure many patients (6, 7). We hypothesize that part of this failure is due to the absence of mechanistic information about how drugs in regimens interact to promote combination effects (we term these effects "combination mechanisms of action"). We sought to address this gap by investigating specific hypotheses concerning the "mechanisms of action" of combination therapy.The classic term "drug mechanism of action" refers to the description of a specific biochemical event, which is often the activation or inhibition of an enzymatic effect. However, in recent years, "signature"-based prediction has provided a powerful new strategy for examining drug mechanism. In signature-based approaches, a series of drug-induced molecular/phenotypic measurements are made in an experimental system. Collections of measurements from many small molecules form multivariate signatures that aim to fingerprint drugs based on their relative signature similarity (8-13). In several landmark studies using Saccharomyces cerevisiae; gene expression compendia (8); and, later, barcoded loss o...

Intratumor heterogeneity alters most effective drugs in designed combinations

“…1B). These compositions and drugs come from previous experimental information (26,29). The tumor subpopulations are taken from a battery of shRNA knockdowns of genes in the DNA damage repair and apoptosis pathways in the murine Eμ-myc; p19…”

“…Arf−/− lymphoma cells partially infected with a specific shRNA and treated with a broad range of chemotherapies (26,29). The toxicity dataset was a binary matrix of known dose-limiting toxicities for each drug.…”

“…As the first manifestation of our analysis, we used our empirical efficacy dataset (26,29) in concert with a symmetric toxicity profile. In this circumstance, we expect the algorithm to produce as optimal solutions drug combinations with the most efficacious drug plus additional drugs incorporated To examine the effects of intratumor heterogeneity on optimal drug combinations, Monte Carlo sampling was applied to sample 10,000 heterogeneous tumor compositions.…”

The use of transcriptomics to predict drug efficacy and treatment outcome in tuberculosis

“…Conversely, drugs that do not elicit a transcriptional response in combination therapy may not be enhancing bacterial killing in the specific model evaluated. A cancer study of lymphoma and tumour cells treated with a variety of anticancer drugs individually and in combination revealed, using RNAi technology, that targeting the expression of eight genes was sufficient to predict the mode of action of single drugs alone as well as their combinatory effect [26]. The same methodology was also able to recognize whether a drug combination represented an average of single drug effects, a signature dominated by one of the drugs, or a completely new profile uncovering a combinatory mode of action [26].…”

Precision Oncology: The Promise of Big Data and the Legacy of Small Data

“…Concerning sub-clonality, the utility of sequential approaches to precision oncology is acquiring relevance, as from the recent interest in temporal collateral sensitivity (Pritchard et al, 2013;Zhao et al, 2016). In particular, as sub-clones compete for dominance, some may prevail when they are resistant to treatment, thus becoming drug-induced sub-clones.…”