Haploinsufficiency networks identify targetable patterns of allelic deficiency in low mutation ovarian cancer

Area of Science:

• Oncology
• Genomics
• Molecular Biology

Background:

• Targeted cancer therapies rely on identifying specific oncogenic gene changes.
• High-grade serous ovarian cancer (OV) is characterized by somatic copy-number alterations (SCNAs), not just point mutations.
• The biological impact of SCNAs in OV remains largely unclear.

Purpose of the Study:

• To identify SCNA patterns most disruptive to OV tumor biology using haploinsufficiency network analyses.
• To understand the role of autophagy and proteostasis pathways in OV.
• To identify key genes within disrupted pathways that could be therapeutic targets.

Main Methods:

• Haploinsufficiency network analyses were performed on 187 KEGG pathways.
• Comparative analysis of SCNA-driven pathway disruption across 21 cancer types.
• Gene knockdown experiments targeting prioritized genes (MAP1LC3B/LC3 and BECN1).

Main Results:

• Autophagy was the most significantly disrupted KEGG pathway by coincident gene deletions in OV.
• OV exhibits the most severe disruption in autophagy and compensatory proteostasis pathways compared to other cancers.
• MAP1LC3B (LC3) and BECN1 were identified as the most impactful genes in these disrupted pathways.
• Knockdown of LC3 and BECN1 expression sensitized cells to autophagic stress, irrespective of platinum resistance.

Conclusions:

• Pathway network analysis is a valuable tool for interpreting the impact of SCNA landscapes in tumors.
• Autophagy pathway disruption is a key feature of high-grade serous ovarian cancer.
• Targeting core autophagy genes like LC3 and BECN1 presents a potential therapeutic avenue for OV, independent of platinum sensitivity.