Back

Length of ischemic time is critical for accurate determination of homologous recombination capacity by immunostaining in FFPE tumor samples

Karagöz, E.; Pikkusaari, S.; Tumiati, M.; Virtanen, A.; Salko, M.; Härkönen, A.; Kanerva, A.; Koskela, H.; Tapper, J.; Koivisto-Korander, R.; Joutsiniemi, T.; Haltia, U.-M.; Lassus, H.; Färkkilä, A.; Hynninen, J.; Hietanen, S.; Kauppi, L.

2024-11-29 pathology
10.1101/2024.11.28.24318148 medRxiv
Show abstract

Homologous recombination-deficient (HRD) high-grade serous ovarian cancers (HGSC) are more sensitive to PARP inhibitors compared to their homologous recombination-proficient counterparts. To match the right drug with the right patient the HRD status must be accurately determined. Functional HRD assays, which assess HRD status by quantifying RAD51, a key homologous recombination (HR) protein, are a promising approach for identifying HRD cases. However, these tests are yet to be optimized for pre-analytical variables, specifically HGSC tissue sampling protocols, which can impact RAD51 signal measurement. In this study, we systematically analyzed the impact of ischemic time on formalin-fixed paraffin-embedded HGSC specimens. We demonstrate that the maximum length of ischemic time compatible with accurate HRD calls is 2 hours post-excision. Our findings highlight the importance of properly monitoring and recording sample handling processes, particularly in HGSC, and warrant caution when using archival tumor material where this information is unavailable. Non-optimal pre-analytical factors like ischemic time can cause false HRD calls, thus leading to incorrect patient stratification, which may result in the initiation of treatments with potential side effects without a therapeutic benefit.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.