• New platform optimizes selection of comb

    From ScienceDaily@1:317/3 to All on Tue Apr 12 22:30:44 2022
    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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