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The molecular origin and taxonomy of mucinous ovarian carcinoma.

Dane CheasleyMatthew J WakefieldGeorgina L RylandPrue E AllanKathryn AlsopKaushalya C AmarasingheSumitra AnandaMichael S AnglesioGeorge Au-YeungMaret BöhmDavid D L BowtellAlison BrandGeorgia Chenevix-TrenchMichael ChristieYoke-Eng ChiewMichael ChurchmanAnna De FazioRenee DemeoRhiannon DudleyNicole FairweatherClare G FedeleSian FeredayStephen B FoxC Blake GilksCharlie GourleyNeville F HackerAlison M HadleyJoy HendleyGwo-Yaw HoSiobhan HughesDavid G HunstmanSally M HunterTom W JoblingKimberly R KalliScott H KaufmannCatherine J KennedyMartin KoebelCécile LepageJason LiRichard LupatOrla M McNallyJessica Nell McAlpineAnne-Marie Mes-MassonLinda MileshkinDiane M ProvencherJan PymanKurosh RahimiSimone M RowleyCarolina SalazarGoli SamimiHugo SaundersTimothy SempleRagwha SharmaAlice J SharpeAndrew N StephensNiko ThioMichelle C TorresNadia TraficanteZhongyue XingMagnus ZethovenYoland C AntillClare L ScottIan G CampbellKylie L Gorringe
Published in: Nature communications (2019)
Mucinous ovarian carcinoma (MOC) is a unique subtype of ovarian cancer with an uncertain etiology, including whether it genuinely arises at the ovary or is metastatic disease from other organs. In addition, the molecular drivers of invasive progression, high-grade and metastatic disease are poorly defined. We perform genetic analysis of MOC across all histological grades, including benign and borderline mucinous ovarian tumors, and compare these to tumors from other potential extra-ovarian sites of origin. Here we show that MOC is distinct from tumors from other sites and supports a progressive model of evolution from borderline precursors to high-grade invasive MOC. Key drivers of progression identified are TP53 mutation and copy number aberrations, including a notable amplicon on 9p13. High copy number aberration burden is associated with worse prognosis in MOC. Our data conclusively demonstrate that MOC arise from benign and borderline precursors at the ovary and are not extra-ovarian metastases.
Keyphrases
  • copy number
  • high grade
  • low grade
  • mitochondrial dna
  • genome wide
  • small cell lung cancer
  • dna methylation
  • multiple sclerosis
  • gene expression
  • single molecule
  • machine learning
  • deep learning
  • data analysis