Prediction of lung cancer immunotherapy response via machine learning analysis of immune cell lineage and surface markers

Article type: Research Article

Authors: Mueller, Alex N.^a | Morrisey, Samantha^b | Miller, Hunter A.^c | Hu, Xiaoling^b | Kumar, Rohit^{a; d} | Ngo, Phuong T.^{a; d} | Yan, Jun^{b; c; d; e; *} | Frieboes, Hermann B.^{c; d; f; g; *}

Affiliations: [a] School of Medicine, University of Louisville, Louisville, KY, USA | [b] Division of Immunotherapy, Department of Surgery, University of Louisville, Louisville, KY, USA | [c] Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA | [d] UofL Health – Brown Cancer Center, University of Louisville, Louisville, KY, USA | [e] Department of Surgery, University of Louisville, Louisville, KY, USA | [f] Center for Predictive Medicine, University of Louisville, Louisville, KY, USA | [g] Department of Bioengineering, University of Louisville, Louisville, KY, USA

Correspondence: [*] Corresponding authors: Jun Yan, Department of Surgery, CTRB 319, University of Louisville. Louisville, KY 40202, USA. Tel.: +502 852 3628; Fax: +502 852 2123; E-mail: jun.yan@louisville.edu. Hermann B. Frieboes, Department of Bioengineering, Lutz Hall 419, University of Louisville, Louisville, KY 40292, USA. Tel.: +502 852 3302; Fax: +502 852 6806; E-mail: hbfrie01@louisville.edu.

Abstract: BACKGROUND: Although advances have been made in cancer immunotherapy, patient benefits remain elusive. For non-small cell lung cancer (NSCLC), monoclonal antibodies targeting programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1) have shown survival benefit compared to chemotherapy. Personalization of treatment would be facilitated by a priori identification of patients likely to benefit. OBJECTIVE: This pilot study applied a suite of machine learning methods to analyze mass cytometry data of immune cell lineage and surface markers from blood samples of a small cohort (n= 13) treated with Pembrolizumab, Atezolizumab, Durvalumab, or Nivolumab as monotherapy. METHODS: Four different comparisons were evaluated between data collected at an initial visit (baseline), after 12-weeks of immunotherapy, and from healthy (control) samples: healthy vs patients at baseline, Responders vs Non-Responders at baseline, Healthy vs 12-week Responders, and Responders vs Non-Responders at 12-weeks. The algorithms Random Forest, Partial Least Squares Discriminant Analysis, Multi-Layer Perceptron, and Elastic Net were applied to find features differentiating between these groups and provide for the capability to predict outcomes. RESULTS: Particular combinations and proportions of immune cell lineage and surface markers were sufficient to accurately discriminate between the groups without overfitting the data. In particular, markers associated with the B-cell phenotype were identified as key features. CONCLUSIONS: This study illustrates a comprehensive machine learning analysis of circulating immune cell characteristics of NSCLC patients with the potential to predict response to immunotherapy. Upon further evaluation in a larger cohort, the proposed methodology could help guide personalized treatment selection in clinical practice.

Keywords: lung cancer, immunotherapy, machine learning, immune cells, cell markers

DOI: 10.3233/CBM-210529

Journal: Cancer Biomarkers, vol. 34, no. 4, pp. 681-692, 2022