Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study

Federica Ferrarini; Elisabetta Zulato; Massimo Moro; Paola Del Bianco; Cristina Borzi; Giovanni Esposito; Tiziana Zanin; Gabriella Sozzi; Stefano Indraccolo

doi:10.3389/fonc.2023.1070505

Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study

Front Oncol. 2023 Feb 28:13:1070505. doi: 10.3389/fonc.2023.1070505. eCollection 2023.

Authors

Federica Ferrarini¹, Elisabetta Zulato², Massimo Moro³, Paola Del Bianco⁴, Cristina Borzi³, Giovanni Esposito¹, Tiziana Zanin², Gabriella Sozzi³, Stefano Indraccolo^{2

5}

Affiliations

¹ Immunology and Molecular Oncology Diagnostics Unit, Istituto Oncologico Veneto IOV IRCCS, Padova, Italy.
² Basic and Translational Oncology Unit, Istituto Oncologico Veneto IOV IRCCS, Padova, Italy.
³ Tumor Genomics Unit Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
⁴ Clinical Research Unit, Istituto Oncologico Veneto IOV IRCCS, Padova, Italy.
⁵ Department of Surgery, Oncology and Gastroenterology, Università degli Studi di Padova, Padova, Italy.

Abstract

Introduction: Genetically characterized patient-derived tumor xenografts (PDX) are a valuable resource to understand the biological complexity of cancer and to investigate new therapeutic approaches. Previous studies, however, lack information about metabolic features of PDXs, which may limit testing of metabolism targeting drugs.

Methods: In this pilot study, we investigated by immunohistochemistry (IHC) expression of five essential metabolism-associated markers in a set of lung adenocarcinoma PDX samples previously established and characterized. We exploited digital pathology to quantify expression of the markers and correlated results with tumor cell proliferation, angiogenesis and time of PDX growth in mice.

Results: Our results indicate that the majority of the analyzed PDX models rely on oxidative phosphorylation (OXPHOS) metabolism, either alone or in combination with glucose metabolism. Double IHC enabled us to describe spatial expression of the glycolysis-associated monocarboxylate transporter 4 (MCT4) marker and the OXPHOS-associated glutaminase (GLS) marker. GLS expression was associated with cell proliferation and with expression of liver-kinase B1 (LKB1), a tumor suppressor involved in the regulation of multiple metabolic pathways. Acetyl CoA carboxylase (ACC) was associated with the kinetics of PDX growth.

Conclusion: Albeit limited by the small number of samples and markers analyzed, metabolic classification of existing collections of PDX by this mini panel will be useful to inform pre-clinical testing of metabolism-targeting drugs.

Keywords: IHC; NSCLC; OXPHOS metabolism; digital pathology; metabolic classification.

Grants and funding

The research leading to these results has received funding from AIRC under IG 2020 - ID. 25179 project – P.I. Stefano Indraccolo and AIRC IG 2019 – ID. 23244 – P.I. Gabriella Sozzi and from IOV Intramural Grant 5x1000 (year 2018), PI Stefano Indraccolo.