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Proffered Paper session: Artificial intelligence and machine learning as tools for practice in oncology

4387 - Machine learning-assisted prognostication based on genomic expression in the tumor microenvironment of estrogen receptor positive and HER2 negative breast cancer

Date

28 Sep 2019

Session

Proffered Paper session: Artificial intelligence and machine learning as tools for practice in oncology

Topics

Tumour Immunology;  Cancer Biology;  Pathology/Molecular Biology

Tumour Site

Breast Cancer

Presenters

Yara Abdou

Citation

Annals of Oncology (2019) 30 (suppl_5): v55-v98. 10.1093/annonc/mdz240

Authors

Y. Abdou1, A. Baird2, J. Dolan3, S. Lee4, S. Park5, S. Lee6

Author affiliations

  • 1 Medical Oncology Department, Roswell Park Comprehensive Cancer Center, 14203 - Buffalo/US
  • 2 Department Of Medicine, University of Pittsburgh School of Medicine, Pittsburgh/US
  • 3 Department Of Medicine, University at Buffalo School of Medicine, Buffalo/US
  • 4 Mathematical Sciences, National Institutions for Mathematical Sciences, Daejeon/KR
  • 5 Medical Oncology Department, Roswell Park Comprehensive Cancer Center, 14263 - Buffalo/US
  • 6 Gastrointestinal Medical Oncology, MD Anderson Cancer Center, Houston/US

Resources

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Abstract 4387

Background

Stroma in the tumor microenvironment (TME) is known to impact prognosis and responses to therapy. Few mathematical models exist to prognosticate patients (pts), based on mRNA expressivity in the TME.

Methods

Clinical outcomes data and mRNA-seq of 246 pts with stage 2 estrogen receptor (ER) positive (+) and HER2 negative (-) breast cancer were obtained from TCGA. 26 gene groups composed of 191 genes* enriched in cellular and non-cellular elements of TME, mutational burden (MB), and clinical data were analyzed by Kaplan-Meier (KM) analysis and multivariate nonlinear regression assisted by machine learning to achieve confined optimization with model-data minimization among multiple distribution functions. *Due to character limit, more details about these genes will be shown at actual presentation.

Results

Prognostication was modeled with higher risk score (RS) representing worse prognosis in stage 2 ER+HER2- breast cancer. Six genes (C15orf53, PDGFB, IL10, HS3ST2, GPNMB, PADI4) and seven genes (FCRL3, IFNGR2, ICAM2, CXCR4, HLA-DMB, LGMN, ICOSLG) out of 191 genes associated with poor prognosis were identified (p < 0.05 and 0.050.334) + 0.080 × (P10.528) + 0.156 × (P2-0.116). Based on RS, pts were clustered into 2 groups; high and low RS groups, showing two KM curves with P < 0.001, HR = 3.762 (95% CI 2.914 – 4.939), confirming the validity of RS modeling. Analysis of immune profiles in high and low RS groups shows that expression of genes associated with immunosuppressive factors, T-helper 2 cells, macrophages, neutrophils, co-inhibitory factors of T-cells, and antigen presenting cells are higher in high RS group (p < 0.05). MB did not contribute to survival.

Conclusions

Machine learning-assisted mathematical modeling of RS and gene analysis identified TME-related genes and gene groups that are strongly associated with worse prognosis in stage 2 ER+HER2- breast cancer. RS could potentially prognosticate pts in the clinic with available genomic profiles.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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