Analysis of Non-Relapsed and Relapsed Adult Type Granulosa Cell Tumors Suggests Stable Transcriptomes during Tumor Progression

Noora Andersson, Ulla Maija Haltia, Anniina Färkkilä, Swee Chong Wong, Katja Eloranta, David B. Wilson, Leila Unkila-Kallio, Marjut Pihlajoki, Antti Kyrönlahti, Markku Heikinheimo

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4 Scopus citations


Adult-type granulosa cell tumor (AGCT) is a rare ovarian malignancy characterized by slow growth and hormonal activity. The prognosis of AGCT is generally favorable, but one-third of patients with low-stage disease experience a late relapse, and over half of them die of AGCT. To identify markers that would distinguish patients at risk for relapse, we performed Lexogen QuantSeq 3 mRNA sequencing on formalin-fixed paraffin-embedded, archival AGCT tissue samples tested positive for the pathognomonic Forkhead Box L2 (FOXL2) mutation. We compared the transcriptomic profiles of 14 non-relapsed archival primary AGCTs (follow-up time 17–26 years after diagnosis) with 13 relapsed primary AGCTs (follow-up time 1.7–18 years) and eight relapsed tumors (follow-up time 2.8–18.9 years). Non-relapsed and relapsed primary AGCTs had similar transcriptomic profiles. In relapsed tumors three genes were differentially expressed: plasmalemma vesicle associated protein (PLVAP) was upregulated (p = 0.01), whereas argininosuccinate synthase 1 (ASS1) (p = 0.01) and perilipin 4 (PLIN4) (p = 0.02) were downregulated. PLVAP upregulation was validated using tissue microarray RNA in situ hybridization. In our patient cohort with extremely long follow-up, we observed similar gene expression patterns in both primary AGCT groups, suggesting that relapse is not driven by transcriptomic changes. These results reinforce earlier findings that molecular markers do not predict AGCT behavior or risk of relapse.

Original languageEnglish
Pages (from-to)686-698
Number of pages13
JournalCurrent Issues in Molecular Biology
Issue number2
StatePublished - Feb 2022


  • Adult-type granulosa cell tumor
  • Archival FFPE samples
  • Transcriptomic profiling
  • Tumor evolution
  • Tumor heterogeneity


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