Evidence map›Paper›PMID 37384294›Full record

ArticleFrontiers in oncology2023

Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatments and receptor subtypes.

Katharine Yu, Amrita Basu, Christina Yau, Denise M Wolf, Hani Goodarzi, Sourav Bandyopadhyay, James E Korkola, Gillian L Hirst, Smita Asare, Angela DeMichele and 5 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 5 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 5 institutions in 1 country.

Katharine YuBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.
Amrita BasuDepartment of Surgery, University of California, San Francisco, San Francisco, CA, United States.
Christina YauDepartment of Surgery, University of California, San Francisco, San Francisco, CA, United States.
Denise M WolfDepartment of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, United States.
Hani GoodarziUniversity of California, San Francisco, San Francisco, CA, United States.
Sourav BandyopadhyayUniversity of California, San Francisco, San Francisco, CA, United States.
James E KorkolaOregon Health and Science University, Portland, OR, United States.
Gillian L HirstDepartment of Surgery, University of California, San Francisco, San Francisco, CA, United States.
Smita AsareBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.
Angela DeMicheleUniversity of Pennsylvania, Philadelphia, PA, United States.
Nola HyltonUniversity of California, San Francisco, San Francisco, CA, United States.
Douglas YeeUniversity of Minnesota, Minneapolis, MN, United States.
Laura EssermanDepartment of Surgery, University of California, San Francisco, San Francisco, CA, United States.
Laura van 't VeerDepartment of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, United States.
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.
University of California, San Francisco · USOregon Health & Science University · USQuantum Leap Healthcare Collaborative · USUniversity of Minnesota · USUniversity of Pennsylvania · US

Funding

Translational InformaticsP30CA082103 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Kate Taylor Shumate · 1999 to 2026
$209.7M
The I SPY 2.2 TRIAL: Evolving to Imaging and Molecular Biomarker Response Directed Adaptive Sequential Treatment to Optimize Breast Cancer OutcomesP01CA210961 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Nola M. Hylton-Watson · 2017 to 2026
$22.9M
The Cancer Cell Map Initiative v2.0U54CA274502 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Trey Ideker, Nevan J Krogan · 2022 to 2026
$14.2M
NCI NIH HHS P01 CA210961NCI NIH HHS P30 CA082103NCI NIH HHS U54 CA274502
6 · The paper itself

Abstract

Introduction: Drug resistance is a major obstacle in cancer treatment and can involve a variety of different factors. Identifying effective therapies for drug resistant tumors is integral for improving patient outcomes. Methods: In this study, we applied a computational drug repositioning approach to identify potential agents to sensitize primary drug resistant breast cancers. We extracted drug resistance profiles from the I-SPY 2 TRIAL, a neoadjuvant trial for early stage breast cancer, by comparing gene expression profiles of responder and non-responder patients stratified into treatments within HR/HER2 receptor subtypes, yielding 17 treatment-subtype pairs. We then used a rank-based pattern-matching strategy to identify compounds in the Connectivity Map, a database of cell line derived drug perturbation profiles, that can reverse these signatures in a breast cancer cell line. We hypothesize that reversing these drug resistance signatures will sensitize tumors to treatment and prolong survival. Results: We found that few individual genes are shared among the drug resistance profiles of different agents. At the pathway level, however, we found enrichment of immune pathways in the responders in 8 treatments within the HR+HER2+, HR+HER2-, and HR-HER2- receptor subtypes. We also found enrichment of estrogen response pathways in the non-responders in 10 treatments primarily within the hormone receptor positive subtypes. Although most of our drug predictions are unique to treatment arms and receptor subtypes, our drug repositioning pipeline identified the estrogen receptor antagonist fulvestrant as a compound that can potentially reverse resistance across 13/17 of the treatments and receptor subtypes including HR+ and triple negative. While fulvestrant showed limited efficacy when tested in a panel of 5 paclitaxel resistant breast cancer cell lines, it did increase drug response in combination with paclitaxel in HCC-1937, a triple negative breast cancer cell line. Conclusion: We applied a computational drug repurposing approach to identify potential agents to sensitize drug resistant breast cancers in the I-SPY 2 TRIAL. We identified fulvestrant as a potential drug hit and showed that it increased response in a paclitaxel-resistant triple negative breast cancer cell line, HCC-1937, when treated in combination with paclitaxel.

Indexed as

breast cancerdrug repositioningdrug repurposingdrug resistanceprimary drug resistance

Identifiers

PMID37384294
PMCPMC10294228
OpenAlexW4380632905

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.