Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action

Alexander Ling; R. Stephanie Huang

doi:10.1038/s41467-020-19563-6

Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action

Alexander Ling, R. Stephanie Huang

Experimental and Clinical Pharmacology

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

Evidence has recently emerged that many clinical cancer drug combinations may derive their efficacy from independent drug action (IDA), where patients only receive benefit from the single most effective drug in a drug combination. Here we present IDACombo, an IDA based method to predict the efficacy of drug combinations using monotherapy data from high-throughput cancer cell line screens. We show that IDACombo predictions closely agree with measured drug combination efficacies both in vitro (Pearson’s correlation = 0.93 when comparing predicted efficacies to measured efficacies for >5000 combinations) and in a systematically selected set of clinical trials (accuracy > 84% for predicting statistically significant improvements in patient outcomes for 26 first line therapy trials). Finally, we demonstrate how IDACombo can be used to systematically prioritize combinations for development in specific cancer settings, providing a framework for quickly translating existing monotherapy cell line data into clinically meaningful predictions of drug combination efficacy.

Original language	English (US)
Article number	5848
Journal	Nature communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-020-19563-6
State	Published - Nov 17 2020

Bibliographical note

Funding Information:
We thank Gary Oehlert and Emily Kurtz at the University of Minnesota Statistical Consulting Center and Dr. Hae Kyung Im at the University of Chicago for taking the time to provide feedback on our method of estimating uncertainties in the efficacy predictions produced by IDACombo. In particular, Gary made several key contributions to the design of these methods and to our understanding of their limitations. We also thank the Minnesota Supercomputing Institute (MSI, http://www.msi.umn.edu) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. One of the datasets used for the analyses described in this manuscript was contributed by AstraZeneca and the Sanger Institute in collaboration with Sage Bionetworks-DREAM Challenge organizers. It was obtained as part of the AstraZeneca–Sanger Drug Combination Prediction DREAM Challenge through Synapse ID [syn4231880]. This study was supported by NIH/NCI Grants R01CA204856 and R01CA229618. R.S.H. also receives support from a research grant from the Avon Foundation for Women and an OACA Faculty Research Development grant.

Publisher Copyright:
© 2020, The Author(s).

Keywords

Aniline Compounds/administration & dosage
Antineoplastic Agents/administration & dosage
Antineoplastic Combined Chemotherapy Protocols/pharmacology
Cell Line, Tumor
Clinical Trials as Topic
Databases, Pharmaceutical
Dose-Response Relationship, Drug
Drug Screening Assays, Antitumor
ErbB Receptors/metabolism
Humans
Lung Neoplasms/drug therapy
Reproducibility of Results
Sulfonamides/administration & dosage
Taxoids/administration & dosage
Workflow

PubMed: MeSH publication types

Research Support, Non-U.S. Gov't
Journal Article
Research Support, N.I.H., Extramural

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1038/s41467-020-19563-6

Find It

Find It at U of M Libraries

Cite this

@article{bb9d2479858f4f38a695d8f3be0f6227,

title = "Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action",

abstract = "Evidence has recently emerged that many clinical cancer drug combinations may derive their efficacy from independent drug action (IDA), where patients only receive benefit from the single most effective drug in a drug combination. Here we present IDACombo, an IDA based method to predict the efficacy of drug combinations using monotherapy data from high-throughput cancer cell line screens. We show that IDACombo predictions closely agree with measured drug combination efficacies both in vitro (Pearson{\textquoteright}s correlation = 0.93 when comparing predicted efficacies to measured efficacies for >5000 combinations) and in a systematically selected set of clinical trials (accuracy > 84% for predicting statistically significant improvements in patient outcomes for 26 first line therapy trials). Finally, we demonstrate how IDACombo can be used to systematically prioritize combinations for development in specific cancer settings, providing a framework for quickly translating existing monotherapy cell line data into clinically meaningful predictions of drug combination efficacy.",

keywords = "Aniline Compounds/administration & dosage, Antineoplastic Agents/administration & dosage, Antineoplastic Combined Chemotherapy Protocols/pharmacology, Cell Line, Tumor, Clinical Trials as Topic, Databases, Pharmaceutical, Dose-Response Relationship, Drug, Drug Screening Assays, Antitumor, ErbB Receptors/metabolism, Humans, Lung Neoplasms/drug therapy, Reproducibility of Results, Sulfonamides/administration & dosage, Taxoids/administration & dosage, Workflow",

author = "Alexander Ling and Huang, {R. Stephanie}",

note = "Funding Information: We thank Gary Oehlert and Emily Kurtz at the University of Minnesota Statistical Consulting Center and Dr. Hae Kyung Im at the University of Chicago for taking the time to provide feedback on our method of estimating uncertainties in the efficacy predictions produced by IDACombo. In particular, Gary made several key contributions to the design of these methods and to our understanding of their limitations. We also thank the Minnesota Supercomputing Institute (MSI, http://www.msi.umn.edu) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. One of the datasets used for the analyses described in this manuscript was contributed by AstraZeneca and the Sanger Institute in collaboration with Sage Bionetworks-DREAM Challenge organizers. It was obtained as part of the AstraZeneca–Sanger Drug Combination Prediction DREAM Challenge through Synapse ID [syn4231880]. This study was supported by NIH/NCI Grants R01CA204856 and R01CA229618. R.S.H. also receives support from a research grant from the Avon Foundation for Women and an OACA Faculty Research Development grant. Publisher Copyright: {\textcopyright} 2020, The Author(s).",

year = "2020",

month = nov,

day = "17",

doi = "10.1038/s41467-020-19563-6",

language = "English (US)",

volume = "11",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

}

TY - JOUR

T1 - Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action

AU - Ling, Alexander

AU - Huang, R. Stephanie

N1 - Funding Information: We thank Gary Oehlert and Emily Kurtz at the University of Minnesota Statistical Consulting Center and Dr. Hae Kyung Im at the University of Chicago for taking the time to provide feedback on our method of estimating uncertainties in the efficacy predictions produced by IDACombo. In particular, Gary made several key contributions to the design of these methods and to our understanding of their limitations. We also thank the Minnesota Supercomputing Institute (MSI, http://www.msi.umn.edu) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. One of the datasets used for the analyses described in this manuscript was contributed by AstraZeneca and the Sanger Institute in collaboration with Sage Bionetworks-DREAM Challenge organizers. It was obtained as part of the AstraZeneca–Sanger Drug Combination Prediction DREAM Challenge through Synapse ID [syn4231880]. This study was supported by NIH/NCI Grants R01CA204856 and R01CA229618. R.S.H. also receives support from a research grant from the Avon Foundation for Women and an OACA Faculty Research Development grant. Publisher Copyright: © 2020, The Author(s).

PY - 2020/11/17

Y1 - 2020/11/17

N2 - Evidence has recently emerged that many clinical cancer drug combinations may derive their efficacy from independent drug action (IDA), where patients only receive benefit from the single most effective drug in a drug combination. Here we present IDACombo, an IDA based method to predict the efficacy of drug combinations using monotherapy data from high-throughput cancer cell line screens. We show that IDACombo predictions closely agree with measured drug combination efficacies both in vitro (Pearson’s correlation = 0.93 when comparing predicted efficacies to measured efficacies for >5000 combinations) and in a systematically selected set of clinical trials (accuracy > 84% for predicting statistically significant improvements in patient outcomes for 26 first line therapy trials). Finally, we demonstrate how IDACombo can be used to systematically prioritize combinations for development in specific cancer settings, providing a framework for quickly translating existing monotherapy cell line data into clinically meaningful predictions of drug combination efficacy.

AB - Evidence has recently emerged that many clinical cancer drug combinations may derive their efficacy from independent drug action (IDA), where patients only receive benefit from the single most effective drug in a drug combination. Here we present IDACombo, an IDA based method to predict the efficacy of drug combinations using monotherapy data from high-throughput cancer cell line screens. We show that IDACombo predictions closely agree with measured drug combination efficacies both in vitro (Pearson’s correlation = 0.93 when comparing predicted efficacies to measured efficacies for >5000 combinations) and in a systematically selected set of clinical trials (accuracy > 84% for predicting statistically significant improvements in patient outcomes for 26 first line therapy trials). Finally, we demonstrate how IDACombo can be used to systematically prioritize combinations for development in specific cancer settings, providing a framework for quickly translating existing monotherapy cell line data into clinically meaningful predictions of drug combination efficacy.

KW - Aniline Compounds/administration & dosage

KW - Antineoplastic Agents/administration & dosage

KW - Antineoplastic Combined Chemotherapy Protocols/pharmacology

KW - Cell Line, Tumor

KW - Clinical Trials as Topic

KW - Databases, Pharmaceutical

KW - Dose-Response Relationship, Drug

KW - Drug Screening Assays, Antitumor

KW - ErbB Receptors/metabolism

KW - Humans

KW - Lung Neoplasms/drug therapy

KW - Reproducibility of Results

KW - Sulfonamides/administration & dosage

KW - Taxoids/administration & dosage

KW - Workflow

UR - http://www.scopus.com/inward/record.url?scp=85096113206&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85096113206&partnerID=8YFLogxK

U2 - 10.1038/s41467-020-19563-6

DO - 10.1038/s41467-020-19563-6

M3 - Article

C2 - 33203866

AN - SCOPUS:85096113206

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 5848

ER -

Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action

Abstract

Bibliographical note

Keywords

PubMed: MeSH publication types

UN SDGs

Publisher link

Find It

Other files and links

Fingerprint

Cite this