Detecting HRD in ovarian cancers using a deep learning solution based on low pass whole-genome sequencing

Deficiency in the homologous recombination repair system represents up to 50% of ovarian, breast, prostate, and pancreatic cancers. While Poly ADP-ribose polymerase inhibitors (PARPi) treatment revolutionized the management of patients, inducing synthetic lethality in cells with homologous recombination deficiency (HRD), detection of HRD is challenging.

Indeed, homologous recombination repair (HRR) genetic testing does not suffice to determine with certainty HRD status, and researchers use the indirect genomic instability score to evaluate genomic scars or mutational signatures resulting from HRD.

Implementing methods able to detect HRD is crucial for identifying patients who could benefit from treatment with PARPi.

In this webinar, Prof. Alexandre Harlé will share his experience in HRD detection of ovarian cancers using SOPHiA GENETICS deep learning-based solution. Therefore, you will have the opportunity to discover:
- Current challenges for the implementation of HRD testing
- The analytical performance evaluation of the Genomic Integrity Index (GII) provided by the SOPHiA DDM™ HRD Solution
- The concordance of the HRD status obtained by GII with that obtained from the standard method

Disclaimer notice:
The term SOPHIA used by the speaker refers to SOPHiA GENETICS and its products.
The opinions expressed during this presentation are these of the speaker and may not represent the opinions of SOPHiA GENETICS.
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