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Cited by 201Gene expression and cancer classificationComputational Drug Discovery MethodsBreast Cancer Treatment Studies

Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning

Open access
Mucaki, Eliseos J., Zhao, Jonathan Z. L., Lizotte, Daniel J., Rogan, Peter K.·2019·Signal Transduction and Targeted Therapy
DOI 10.1038/s41392-018-0034-5PMC PMC6329797

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Abstract

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI 50 values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI 50 thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI 50 values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: BARD1 , BCL2 , BCL2L1 , CDKN2C , FAAP24 , FEN1 , MAP3K1 , MAPK13 , MAPK3 , NFKB1 , NFKB2 , SLC22A5 , SLC31A2 , TLR4 , and TWIST1 ; carboplatin: AKT1 , EIF3K , ERCC1 , GNGT1 , GSR , MTHFR , NEDD4L , NLRP1 , NRAS , RAF1 , SGK1 , TIGD1 , TP53 , VEGFB , and VEGFC; and oxaliplatin: BRAF , FCGR2A , IGF1 , MSH2 , NAGK , NFE2L2 , NQO1 , PANK3 , SLC47A1 , SLCO1B1 , and UGT1A1 . Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free; N = 19), and 79% recurrence in smokers (62% disease-free; N = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.

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  • Prior work has assessed the utility of mutational and transcriptomic signatures in predicting response to cisplatin, but none of these advancements have been translated into routine clinical care.

    “Given its widespread use, it comes as no surprise that prior work has assessed the utility of mutational and transcriptomic signatures in predicting response to this drug; yet, to the best knowledge of these authors, none of these advancements have been translated into routine clinical care15–19.”

    From Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature (2023) · cites this paper as [16]

    The Introduction and Results describe gene-expression signatures developed to predict cisplatin response and tested against patient outcomes. Our copy does not establish that mutational signatures were assessed or that none of these approaches entered routine clinical care; searches for mutations and clinical implementation did not find those points. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The cisplatin response signature produced by Mucaki et al. from breast cancer cell lines in the GDSC dataset does not contain any CisSig genes.

    “In a 2019 manuscript by Mucaki et al., the authors produce a cisplatin response signature from breast cancer cell lines in the GDSC dataset; however, their approach only includes genes with a known relationship to cisplatin response (none of which overlap with the genes in CisSig)16. Therefore, their final cisplatin response signature does not contain any CisSig genes.”

    From Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature (2023) · cites this paper as [16]

    The Results (Selection of platin drug-related genes; page unavailable) and Materials and methods (Data and preprocessing; page unavailable) support that Mucaki et al. developed cisplatin signatures from breast cancer cell lines, starting with genes previously associated with drug response. The paper identifies its GDSC cell lines as bladder, not breast, and does not discuss CisSig or establish that its signatures have no CisSig genes. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. [41]Airley, R. Cancer Chemotherapy (Wiley-Blackwell, Hoboken, New Jersey 2009).

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Authors
Mucaki, E. J., Zhao, J. Z. L., Lizotte, D. J., & Rogan, P. K.
Affiliations
Western University (CA)
Corresponding author
Rogan, Peter K.
ORCID
Venue
Signal Transduction and Targeted Therapy
Publisher
Springer Science and Business Media LLC
Published
2019-01-11
Volume
4
Issue
1
Pages
1-1
ISSN
2059-3635 (ISSN-L)
DOI
10.1038/s41392-018-0034-5
PMC
PMC6329797
OpenAlex
W2950457484
Cited by
200
References
57
Citations by year
2026: 8 · 2025: 15 · 2024: 21 · 2023: 44 · 2022: 32 · 2021: 38 · 2020: 31 · 2019: 9 · 2018: 2
FWCI
9.10
Type
journal article
Language
en
Open access
Licence
Locations
  • Signal Transduction and Targeted Therapy (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Scholarship@Western (Western University) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Scholarship@Western (Western University) (submittedVersion)
Funders
  • Compute Canada
  • Canada Research Chairs
  • Natural Sciences and Engineering Research Council of Canada (RGPIN-2015, RGPIN-2015-06290)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
cisplatin, carboplatin, oxaliplatin, drug response prediction, gene expression signatures, drug resistance, support vector machine, feature selection, bladder cancer, ovarian cancer, colorectal cancer, disease recurrence prediction, remission prediction

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