Prostate cancer radiogenomics reveals proliferative gene expression programs associated with distinct MRI-based hypoxia levels

Vilde Eide Skingen; Tord Hompland; Christina Sæten Fjeldbo; Unn Beate Salberg; Hanna Helgeland; Harald Bull Ragnum; Eva-Katrine Aarnes; Ljiljana Vlatkovic; Knut Håkon Hole; Therese Seierstad; Heidi Lyng

doi:10.1016/j.radonc.2023.109875

Prostate cancer radiogenomics reveals proliferative gene expression programs associated with distinct MRI-based hypoxia levels

Radiother Oncol. 2023 Nov:188:109875. doi: 10.1016/j.radonc.2023.109875. Epub 2023 Aug 26.

Affiliations

¹ Department of Radiation Biology, Oslo University Hospital, Oslo, Norway; Department of Physics, University of Oslo, Oslo, Norway.
² Department of Radiation Biology, Oslo University Hospital, Oslo, Norway.
³ Department of Radiation Biology, Oslo University Hospital, Oslo, Norway; Department of Oncology and Hematology, Telemark Hospital Trust, Skien, Norway.
⁴ Department of Pathology, Oslo University Hospital, Oslo, Norway.
⁵ Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
⁶ Department of Radiation Biology, Oslo University Hospital, Oslo, Norway; Department of Physics, University of Oslo, Oslo, Norway. Electronic address: heidi.lyng@rr-research.no.

PMID: 37640161
DOI: 10.1016/j.radonc.2023.109875

Abstract

Background and purpose: The biology behind individual hypoxia levels in patient tumors is poorly understood. Here, we used radiogenomics to identify associations between magnetic resonance imaging (MRI)-based hypoxia levels and biological processes derived from gene expression data in prostate cancer.

Materials and methods: For 85 prostate cancer patients, MRI-based hypoxia images were constructed by combining diffusion-weighted images reflecting oxygen consumption and supply. The ability to differentiate hypoxia levels in these images was verified by comparison with matched biopsy sections stained for the hypoxia marker pimonidazole. For MRI-defined hypoxia levels, corresponding hypoxic fractions were calculated and correlated with biopsy gene expression profiles. Biological processes were predicted by gene set enrichment analysis (GSEA) and validated by immunohistochemistry (Ki67 proliferation marker, reactive stroma grade) and RT-PCR (MYC).

Results: Genes with correlation between expression level and hypoxic fraction were identified for 56 MRI-based hypoxia levels. At all levels, GSEA identified proliferation as the predominant biological process enriched among the correlating genes. Two independent proliferative gene signatures were developed. The Peak1 signature, upregulated at moderate/severe hypoxia, reflected MYC upregulation and high Ki67-proliferation index of cancer cells in pimonidazole-positive regions. The Peak2 signature, upregulated at mild to non-hypoxic levels, was associated with fibroblast gene signature and reactive stroma grade. High scores of both Peak1 and Peak2 indicated elevated risk of biochemical recurrence in multiple cohorts.

Conclusion: Radiogenomics identified two gene expression programs activated at different hypoxia levels, reflecting proliferation of cancer cells and stroma cells. Genes involved in these programs could be candidate targets for intervention.

Keywords: Digital histopathology; Gene expression; Gene signature; Hypoxia; MR-imaging; Prostate cancer; Radiogenomics.