Medical Biology - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 3 of 3
  • Item
    Thumbnail Image
    Glucagonoma Masquerading as a Mucinous Cancer of the Ovary: Lessons from Cell Biology
    Ho, GY ; Ananda, S ; Vandenberg, CJ ; McNally, O ; Tie, J ; Gorringe, K ; Bowtell, D ; Pyman, J ; Wakefield, MJ ; Scott, CL ; Ho, GY ; Frentzas, S (IntechOpen, 2020-06-17)
    High-grade mucinous ovarian cancer (HGMOC) is often a misnomer as the majority of cases are metastatic disease with a gastro-intestinal origin. The standard platinum-based ovarian cancer (OC) chemotherapy regimens are often ineffective, and there are insufficient data to support the use of colorectal cancer (CRC) chemotherapy regimens due to the rarity of HGMOC. We described a cohort of four consecutive suspected HGMOC cases treated at the Royal Women’s Hospital, Melbourne in 2012. Two cases were treated as primary MOC, whereas the other two were considered to be metastatic CRC based on histopathological and clinical evidence. From the RNAseq analysis, we identified two cases of HGMOC whose gene expression profiles were consistent with mucinous epithelial OC, one case that was treated as metastatic CRC with gene expression profile correlated with CRC and one case with neuroendocrine (NET) gene expression features. Interestingly, glucagon was over-expressed in this tumor that was subsequently confirmed by immunohistochemistry. These findings suggest a rare glucagonoma-like NET appendiceal tumor that had metastasized to the surface of ovary and were unresponsive to CRC chemotherapy regimens. In summary, a carefully curated panel of expression markers and selected functional genomics could provide diagnosis and treatment guidance for patients with possible HGMOC.
  • Item
    No Preview Available
    Clinical MDR1 inhibitors enhance Smac-mimetic bioavailability to kill murine LSCs and improve survival in AML models
    Morrish, E ; Copeland, A ; Moujalled, DM ; Powell, JA ; Silke, N ; Lin, A ; Jarman, KE ; Sandow, JJ ; Ebert, G ; Mackiewicz, L ; Beach, JA ; Christie, EL ; Lewis, AC ; Pomilio, G ; Fischer, KC ; MacPherson, L ; Bowtell, DDL ; Webb, A ; Pellegrini, M ; Dawson, MA ; Pitson, SM ; Wei, AH ; Silke, J ; Brumatti, G (AMER SOC HEMATOLOGY, 2020-10-27)
    The specific targeting of inhibitor of apoptosis (IAP) proteins by Smac-mimetic (SM) drugs, such as birinapant, has been tested in clinical trials of acute myeloid leukemia (AML) and certain solid cancers. Despite their promising safety profile, SMs have had variable and limited success. Using a library of more than 5700 bioactive compounds, we screened for approaches that could sensitize AML cells to birinapant and identified multidrug resistance protein 1 inhibitors (MDR1i) as a class of clinically approved drugs that can enhance the efficacy of SM therapy. Genetic or pharmacological inhibition of MDR1 increased intracellular levels of birinapant and sensitized AML cells from leukemia murine models, human leukemia cell lines, and primary AML samples to killing by birinapant. The combination of clinical MDR1 and IAP inhibitors was well tolerated in vivo and more effective against leukemic cells, compared with normal hematopoietic progenitors. Importantly, birinapant combined with third-generation MDR1i effectively killed murine leukemic stem cells (LSCs) and prolonged survival of AML-burdened mice, suggesting a therapeutic opportunity for AML. This study identified a drug combination strategy that, by efficiently killing LSCs, may have the potential to improve outcomes in patients with AML.
  • Item
    Thumbnail Image
    Inferring structural variant cancer cell fraction
    Cmero, M ; Yuan, K ; Ong, CS ; Schröder, J ; PCAWG Evolution and Heterogeneity Working Group, ; Corcoran, NM ; Papenfuss, T ; Hovens, CM ; Markowetz, F ; Macintyre, G ; PCAWG Consortium, (Nature Research (part of Springer Nature), 2020-02-05)
    We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone's performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.