Description
Ovarian cancer is characterized by multiple structural aberrations; most are passenger alterations which do not confer tumor growth. Like many cancers, it is a heterogeneous disease and till date, the histotype-specific copy number landscape has been difficult to elucidate. To dissect the heterogeneity of ovarian cancer and understand the pathogenesis of its various histotypes, we developed an in silico hypothesis-driven workflow to identify histotype-specific copy number aberrations across multiple datasets of epithelial ovarian cancer. In concordance with previous studies on global copy number changes, our study showed similar alterations. However, when the landscape was de-convoluted into histotypes, distinct alterations were observed. We report here a comprehensive histotype-specific copy number landscape of ovarian cancer and showed that there is genomic diversity between the histotypes; some involving well known cancer genes and some novel potential driver genes. Besides preferential occurrence of alterations in some histotypes, opposite trends of alteration were observed; such as ERBB2 amplification in mucinous but deletion in serous tumors. The landscape highlights the need for identifying histotype-specific aberrations in ovarian cancer and present potential to tailor management of ovarian cancer based on molecular signature of histotypes.