SOURCE-LINKED INTELLIGENCE

Convolutional neural networks to reveal resistant phenotypes behind the complex genotypes of ovarian cancer

CORDIS · observation · Publication date unknown

ypes of cancer. Here, I will explore the impact of the CNVs on the treatment response of HGSOC patients and their distal effects on the transcriptome using convolutional neural networks. This complex machine learning model will be trained with the largest available longitudinal cohort of HGSOC samples at the single-cell resolution and will reveal which CNVs, and their specific combinations, have a relevant role in shaping the HGSOC tumours upon treatment. Employing a systems biology approach I will identify convergent phenotypes within these relevant CNV profiles and then validate their effects on treatment using data from both external cohorts and drug-treated patient derived organoid models. This novel approach enables revealing resistance mechanisms driven by complex genotypes, and thus allows finding specific vulnerabilities to combat emerging resistance in HGSOC. Copy number variation, ovarian cancer, single-cell transcriptomics, machine learning

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recordType

award

status

CLOSED

region

EU

value

215534.4

unit

EUR