Analysis of Gut Microbiome Using Explainable Machine Learning Predicts Risk of Diarrhea Associated With Tyrosine Kinase Inhibitor Neratinib: A Pilot Study

Chi Wah Wong, Susan E Yost, Jin Sun Lee, John D Gillece, Megan Folkerts, Lauren Reining, Sarah K Highlander, Zahra Eftekhari, Joanne Mortimer, Yuan Yuan, Chi Wah Wong, Susan E Yost, Jin Sun Lee, John D Gillece, Megan Folkerts, Lauren Reining, Sarah K Highlander, Zahra Eftekhari, Joanne Mortimer, Yuan Yuan

Abstract

Neratinib has great efficacy in treating HER2+ breast cancer but is associated with significant gastrointestinal toxicity. The objective of this pilot study was to understand the association of gut microbiome and neratinib-induced diarrhea. Twenty-five patients (age ≥ 60) were enrolled in a phase II trial evaluating safety and tolerability of neratinib in older adults with HER2+ breast cancer (NCT02673398). Fifty stool samples were collected from 11 patients at baseline and during treatment. 16S rRNA analysis was performed and relative abundance data were generated. Shannon's diversity was calculated to examine gut microbiome dysbiosis. An explainable tree-based approach was utilized to classify patients who might experience neratinib-related diarrhea (grade ≥ 1) based on pre-treatment baseline microbial relative abundance data. The hold-out Area Under Receiver Operating Characteristic and Area Under Precision-Recall Curves of the model were 0.88 and 0.95, respectively. Model explanations showed that patients with a larger relative abundance of Ruminiclostridium 9 and Bacteroides sp. HPS0048 may have reduced risk of neratinib-related diarrhea and was confirmed by Kruskal-Wallis test (p ≤ 0.05, uncorrected). Our machine learning model identified microbiota associated with reduced risk of neratinib-induced diarrhea and the result from this pilot study will be further verified in a larger study.

Clinical trial registration: ClinicalTrials.gov, identifier NCT02673398.

Keywords: artificial intelligence; breast cancer; diarrhea; explainable machine learning; gut microbiota; neratinib.

Conflict of interest statement

YY has contracted research sponsored by Merck, Eisai, Novartis, Puma, Genentech, Celgene, and Pfizer; is a consultant for Puma, Pfizer, Immunomedics, and is on the Speakers Bureau for Eisai, Novartis, Genentech, AstraZeneca, Daiichi Sankyo, Pfizer, Merck and Immunomedics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Wong, Yost, Lee, Gillece, Folkerts, Reining, Highlander, Eftekhari, Mortimer and Yuan.

Figures

Figure 1
Figure 1
16S rRNA gene sequencing analysis. (A) Relative abundance of top 26 taxa by patient and cycle; (B) Shannon’s alpha diversity by patient and cycle.
Figure 2
Figure 2
Model assessment. (A) Area Under Receiver Operating Characteristic (ROC) Curve; (B) Area Under Precision-Recall Curve (PRC).
Figure 3
Figure 3
Feature importance and local explanation of final model. (A) Bar plot of mean absolute SHAP values of individual features; and (B) Beeswarm plot showing feature values and impact on the model prediction.
Figure 4
Figure 4
Heatmap showing differences in microbiota relative abundance between patients with and without neratinib-induced diarrhea (in log10 scale). *Kruskal-Wallis test with p ≤ 0.05 uncorrected.

References

    1. Saura C, Oliveira M, Feng YH, Dai MS, Chen SW, Hurvitz SA, et al. . Neratinib Plus Capecitabine Versus Lapatinib Plus Capecitabine in HER2-Positive Metastatic Breast Cancer Previously Treated With >/= 2 HER2-Directed Regimens: Phase III NALA Trial. J Clin Oncol (2020) 38(27):3138–49. 10.1200/JCO.20.00147
    1. Singh H, Walker AJ, Amiri-Kordestani L, Cheng J, Tang SH, Balcazar P, et al. . US Food and Drug Administration Approval: Neratinib for the Extended Adjuvant Treatment of Early-Stage HER2-Positive Breast Cancer. Clin Cancer Res (2018) 24(15):3486–91. 10.1158/1078-0432.Ccr-17-3628
    1. Chan A, Delaloge S, Holmes FA, Moy B, Iwata H, Harvey VJ, et al. . Neratinib after trastuzumab-based adjuvant therapy in patients with HER2-positive breast cancer (ExteNET): a multicentre, randomised, double-blind, placebo- controlled, phase 3 trial. Lancet Oncol (2016) 17(3):367–77. 10.1016/s1470-2045(15)00551-3
    1. Rugo HS, Di Palma JA, Tripathy D, Bryce R, Moran S, Olek E, et al. . The characterization, management, and future considerations for ErbB-family TKI-associated diarrhea. Breast Cancer Res Treat (2019) 175(1):5–15. 10.1007/s10549-018-05102-x
    1. Barcenas CH, Hurvitz SA, Di Palma JA, Bose R, Chien AJ, Iannotti N, et al. . Improved tolerability of neratinib in patients with HER2-positive early-stage breast cancer: the CONTROL trial. Ann Oncol (2020) 31(9):1223–30. 10.1016/j.annonc.2020.05.012
    1. Meric-Bernstam F, Johnson AM, Dumbrava EEI, Raghav K, Balaji K, Bhatt M, et al. . Advances in HER2-Targeted Therapy: Novel Agents and Opportunities Beyond Breast and Gastric Cancer. Clin Cancer Res (2019) 25(7):2033–41. 10.1158/1078-0432.Ccr-18-2275
    1. Freedman RA, Gelman RS, Anders CK, Melisko ME, Parsons HA, Cropp AM, et al. . TBCRC 022: A Phase II Trial of Neratinib and Capecitabine for Patients With Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer and Brain Metastases. J Clin Oncol (2019) 37(13):1081–9. 10.1200/jco.18.01511
    1. Murthy RK, Loi S, Okines A, Paplomata E, Hamilton E, Hurvitz SA, et al. . Tucatinib, Trastuzumab, and Capecitabine for HER2-Positive Metastatic Breast Cancer. N Engl J Med (2020) 382(7):597–609. 10.1056/NEJMoa1914609
    1. Rabindran SK, Discafani CM, Rosfjord EC, Baxter M, Floyd MB, Golas J, et al. . Antitumor activity of HKI-272, an orally active, irreversible inhibitor of the HER-2 tyrosine kinase. Cancer Res (2004) 64(11):3958–65. 10.1158/0008-5472.Can-03-2868
    1. Tiwari SR, Mishra P, Abraham J. Neratinib, A Novel HER2-Targeted Tyrosine Kinase Inhibitor. Clin Breast Cancer (2016) 16(5):344–8. 10.1016/j.clbc.2016.05.016
    1. Connell CM, Doherty GJ. Activating HER2 mutations as emerging targets in multiple solid cancers 2017. ESMO Open 2(5):e000279. 10.1136/esmoopen-2017-000279
    1. Ben-Baruch NE, Bose R, Kavuri SM, Ma CX, Ellis MJ. HER2-Mutated Breast Cancer Responds to Treatment With Single-Agent Neratinib, a Second-Generation HER2/EGFR Tyrosine Kinase Inhibitor. J Natl Compr Cancer Network (2015) 13(9):1061–4. 10.6004/jnccn.2015.0131
    1. Mortimer J, Di Palma J, Schmid K, Ye Y, Jahanzeb M. Patterns of occurrence and implications of neratinib-associated diarrhea in patients with HER2-positive breast cancer: analyses from the randomized phase III ExteNET trial. Breast Cancer Res (2019) 21(1):32. 10.1186/s13058-019-1112-5
    1. Aarnoutse R, Ziemons J, Penders J, Rensen SS, de Vos-Geelen J, Smidt ML. The Clinical Link between Human Intestinal Microbiota and Systemic Cancer Therapy. Int J Mol Sci (2019) 20(17). 10.3390/ijms20174145
    1. Khan MAW, Ologun G, Arora R, McQuade JL, Wargo JA. Gut Microbiome Modulates Response to Cancer Immunotherapy. Dig Dis Sci (2020) 65(3):885–96. 10.1007/s10620-020-06111-x
    1. Cheng Y, Ling Z, Li L. The Intestinal Microbiota and Colorectal Cancer. Front Immunol (2020) 11:615056:615056. 10.3389/fimmu.2020.615056
    1. Helmink BA, Khan MAW, Hermann A, Gopalakrishnan V, Wargo JA. The microbiome, cancer, and cancer therapy. Nat Med (2019) 25(3):377–88. 10.1038/s41591-019-0377-7
    1. Elinav E, Garrett WS, Trinchieri G, Wargo J. The cancer microbiome. Nat Rev Cancer (2019) 19(7):371–6. 10.1038/s41568-019-0155-3
    1. Schwabe RF, Jobin C. The microbiome and cancer. Nat Rev Cancer (2013) 13(11):800–12. 10.1038/nrc3610
    1. Osman MA, Neoh HM, Ab Mutalib NS, Chin SF, Jamal R. 16S rRNA Gene Sequencing for Deciphering the Colorectal Cancer Gut Microbiome: Current Protocols and Workflows. Front Microbiol (2018) 9:767. 10.3389/fmicb.2018.00767
    1. Liu CM, Aziz M, Kachur S, Hsueh PR, Huang YT, Keim P, et al. . BactQuant: an enhanced broad-coverage bacterial quantitative real-time PCR assay. BMC Microbiol (2012) 12:56. 10.1186/1471-2180-12-56
    1. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol (2013) 79(17):5112–20. 10.1128/AEM.01043-13
    1. Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G, Parada A, et al. . Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems (2016) 1(1). 10.1128/mSystems.00009-15
    1. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. . QIIME allows analysis of high-throughput community sequencing data. Nat Methods (2010) 7(5):335–6. 10.1038/nmeth.f.303
    1. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. . Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol (2019) 37(8):852–7. 10.1038/s41587-019-0209-9
    1. Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (2016). pp. 785–94. 10.1145/2939672.2939785
    1. Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. . From local explanations to global understanding with explainable AI for trees. Nat Mach Intell (2020) 2(1):56–67. 10.1038/s42256-019-0138-9
    1. Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, et al. . Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat BioMed Eng (2018) 2(10):749–60. 10.1038/s41551-018-0304-0
    1. Shannon CE. The mathematical theory of communication. 1963. MD Comput (1997) 14(4):306–17.
    1. Parada Venegas D, De la Fuente MK, Landskron G, González MJ, Quera R, Dijkstra G, et al. . Short Chain Fatty Acids (SCFAs)-Mediated Gut Epithelial and Immune Regulation and Its Relevance for Inflammatory Bowel Diseases. Front Immunol (2019) 10:277. 10.3389/fimmu.2019.00277
    1. Secombe KR, Van Sebille YZA, Mayo BJ, Coller JK, Gibson RJ, Bowen JM. Diarrhea Induced by Small Molecule Tyrosine Kinase Inhibitors Compared With Chemotherapy: Potential Role of the Microbiome. Integr Cancer Ther (2020) 19:1534735420928493. 10.1177/1534735420928493
    1. Ma W, Mao Q, Xia W, Dong G, Yu C, Jiang F. Gut Microbiota Shapes the Efficiency of Cancer Therapy. Front Microbiol (2019) 10:1050. 10.3389/fmicb.2019.01050
    1. Alexander JL, Wilson ID, Teare J, Marchesi JR, Nicholson JK, Kinross JM. Gut microbiota modulation of chemotherapy efficacy and toxicity. Nat Rev Gastroenterol Hepatol (2017) 14(6):356–65. 10.1038/nrgastro.2017.20
    1. Heshiki Y, Vazquez-Uribe R, Li J, Ni Y, Quainoo S, Imamovic L, et al. . Predictable modulation of cancer treatment outcomes by the gut microbiota. Microbiome (2020) 8(1):28. 10.1186/s40168-020-00811-2
    1. Azuaje F. Artificial intelligence for precision oncology: beyond patient stratification. NPJ Precis Oncol (2019) 3:6. 10.1038/s41698-019-0078-1
    1. Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med (2018) 378(11):981–3. 10.1056/NEJMp1714229
    1. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell (2019) 1(5):206–15. 10.1038/s42256-019-0048-x
    1. Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. In: . Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA: Curran Associates Inc. (2017).
    1. Bach S, Binder A, Montavon G, Klauschen F, Muller KR, Samek W. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PloS One (2015) 10(7):e0130140. 10.1371/journal.pone.0130140
    1. Vabalas A, Gowen E, Poliakoff E, Casson AJ. Machine learning algorithm validation with a limited sample size. PloS One (2019) 14(11):e0224365. 10.1371/journal.pone.0224365

Source: PubMed

赞助者和合作者

医疗条件

药物干预

3
订阅