New platform optimizes selection of combination cancer therapies
Bioinformatics approach predicts combinations with improved outcomes in pre-clinical and clinical studies
Date:
April 12, 2022
Source:
University of Texas M. D. Anderson Cancer Center
Summary:
Researchers have developed a new computational tool to select
optimal combination therapies for patients with cancer based on
the co-occurring alterations in a given tumor.
FULL STORY ========================================================================== Researchers at The University of Texas MD Anderson Cancer Center have
developed a new bioinformatics platform that predicts optimal treatment combinations for a given group of patients based on co-occurring tumor alterations. In retrospective validation studies, the tool selected combinations that resulted in improved patient outcomes across both pre-clinical and clinical studies.
==========================================================================
The findings were presented today at the American Association for Cancer Research (AACR) Annual Meeting 2022 by principal investigator Anil
Korkut, Ph.D., assistant professor of Bioinformatics and Computational
Biology. The study results also were published today in Cancer Discovery.
The platform, called REcurrent Features LEveraged for Combination Therapy (REFLECT), integrates machine learning and cancer informatics algorithms
to analyze biological tumor features -- including genetic mutations,
copy number changes, gene expression and protein expression aberrations
-- and identify frequent co-occurring alterations that could be targeted
by multiple drugs.
"Our ultimate goal is to make precision oncology more effective and create meaningful patient benefit," Korkut said. "We believe REFLECT may be
one of the tools that can help overcome some of the current challenges
in the field by facilitating both the discovery and the selection of combination therapies matched to the molecular composition of tumors."
Targeted therapies have improved clinical outcomes for many patients
with cancer, but monotherapies against a single target often lead
to treatment resistance. Cancer cells frequently rely on co-occurring alterations, such as mutations in two signaling pathways, to drive tumor progression. Increasing evidence suggests that identifying and targeting
both alterations simultaneously could increase durable responses,
Korkut explained.
Led by Korkut and postdoctoral fellow Xubin Li, Ph.D., the researchers
built and used the REFLECT tool to develop a systematic and unbiased
approach to match patients with optimal combination therapies.
========================================================================== Using REFLECT, they analyzed pan-cancer datasets from both MD Anderson
and publicly available sources, including pre-treatment patient tumor
samples, cell lines and patient-derived xenografts (PDXs), representing
more than 10,000 patients and 33 cancer types. This generated 201 patient cohorts, each defined by a single therapeutically actionable biomarker,
such as EGFR mutation or PD- L1 overexpression.
Within each cohort, the team generated REFLECT signatures of additional alterations that may be actionable therapeutic targets, thus pointing to
sub- cohorts that may benefit from specific combination therapies. Across
all cohorts, the researchers identified a total of 2,166 combinations,
with at least one Food and Drug Administration-approved agent, matched
to co-occurring alterations. In total, 45% of the patients included in
the initial analysis were matched to at least one combination therapy.
The researchers validated the REFLECT approach through retrospective
analysis of publicly available pre-clinical and clinical studies,
comparing REFLECT- matched combinations used in those trials to
combinations not matched by the tool.
In pre-clinical trials with PDX models, REFLECT-matched combinations had
a 34.5% decrease in median tumor volume, while non-matched combinations
had a 5.1% increase. Similarly, progression-free survival (PFS) was
higher with matched combinations. The researchers also demonstrated
a higher synergy score in REFLECT combinations relative to others,
defined using the highest single agent (HSA) model.
The researchers also retrospectively validated the approach in the
clinical setting through available data from the I-PREDICT trials, which evaluated many combination therapies across diverse cancer types. Patients
in this trial that received combinations predicted by REFLECT to be most beneficial had significantly longer PFS and overall survival compared
to other combinations.
==========================================================================
In this study, the team also developed a detailed map of oncogenic
alterations that co-exist with specific immune features. This map revealed
many common alterations that frequently co-occur with immunotherapy
response markers, such as defects in DNA damage repair and changes in
the levels of specific epigenetic regulators. The findings suggest that therapies targeting these pathways should be further studied as options
to improve immunotherapy responses.
"While REFLECT is still a concept that requires additional validation,
we anticipate a great opportunity to translate this work into real
clinical benefits," Korkut said. "In the future, multi-omic profiles from pre-treatment patient samples could be loaded to the REFLECT pipeline
to generate co- alteration signatures, allowing physicians to consider precision combination therapies tailored to molecular profiles of those patients." In the future, this approach will benefit from improved
informatics resources to better match therapies to alterations at the
RNA and protein level, Korkut explained. Additionally, the researchers
plan to expand their study to better address and predict toxicity from
matched drug combinations. Finally, future studies also will seek to
address the significant heterogeneity within tumors, which can affect
response to targeted therapies.
The research was supported by the National Institutes of Health/National
Cancer Institute (P30 CA016672, U01 CA253472-01A1, 5UL1TR003167-02, 7R01CA206025-06), the Ovarian Cancer Research Alliance Collaborative
Research Award, the Innovation in Cancer Informatics Fund, the Cancer Prevention and Research Institute of Texas (CPRIT) (RP170640), MD
Anderson's Colorectal Cancer Moon Shot(R), the Department of Defense (W81WH-18-1-0678), the National Resource for Network Biology, and the
National Institute of General Medical Sciences (GM103504) In addition
to Korkut and Li, additional MD Anderson authors include: Gonghong
Yan, Ph.D., Zeynep Dereli, Ph.D., and Behnaz Bozorgui, Ph.D., all of Bioinformatics and Computational Biology; Parisa Imanirad, Ph.D., of
Systems Biology; David Menter, Ph.D., and Scott Kopetz, M.D., Ph.D.,
both of Gastrointestinal Medical Oncology; Patrick G. Pilie', M.D.,
of Genitourinary Medical Oncology; and Timothy Yap, M.B.B.S., Ph.D.,
of Investigational Cancer Therapeutics. Collaborating authors include: Elisabeth Dowling, Ph.D., of Rice University, Houston; Jacob Elnaggar, of Louisiana State University Health Sciences Center, New Orleans; Augustin
Luna, Ph.D., of Dana-Farber Cancer Institute, Boston; and Chris Sander,
of Dana-Farber Cancer Institute and Harvard Medical School, Boston.
========================================================================== Story Source: Materials provided by University_of_Texas_M._D._Anderson_Cancer_Center. Note: Content may be
edited for style and length.
========================================================================== Journal Reference:
1. Xubin Li, Elisabeth K. Dowling, Gonghong Yan, Zeynep Dereli, Behnaz
Bozorgui, Parisa Imanirad, Jacob H. Elnaggar, Augustin Luna,
David G.
Menter, Patrick G. Pilie, Timothy A. Yap, Scott Kopetz, Chris
Sander, Anil Korkut. Precision combination therapies based on
recurrent oncogenic co-alterations. Cancer Discovery, 2022; DOI:
10.1158/2159-8290.CD-21-0832 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/04/220412141041.htm
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