LiverOMICs

LiverOMICs

Present work is concentrating on the development of in silico models to improve outcome in primary liver cancer with the aim to use multi-omic biological data to facilitate diagnostics, management stratification and therapeutic design.

Projects in progress:

1. Multi-OMIC data and Machine Learning in Primary Liver Cancer

In this work we are exploring the ability of a machine learning algorithm to rank drug therapies in both Hepatocellular Carcinoma (HCC) and Cholangiocarcinoma (CCA). So far the algorithm has been trained on Mass Spec phosphoproteomic data. Initial assessment of the algorithm performance in a simple in vivo model of a single cell monolayer is promising. The plan is to further test algorithm performance in more complex 3D cancer systems. With, the ultimate aim to develop a system, where patient tumor tissue is maintained in vitro and the therapeutic efficacy of an algorithm recommended high ranked drug therapy can be validated before clinical use.  

2. ExomiR resetting of the energy profile in Hepatocellular Cancer (HCC) via the mitochondrial proteome.

Dysregulation of energy is a key aspect of cancer biology and the site of cellular energy production are mitochondria. This work is analysing extracellular vesicle biology and their non coding RNA (long and short) cargo influence on mitochondrial bioenergetics.

3. Stratification of clinical risk in Primary Sclerosing Cholangitis (PSC)

In this work we are undertaking transcriptomic profiling of cholangiocarcinoma in PSC to identify key molecular events to improve diagnostics and recommendations for therapy.

 

Head of Group:

Miss Shirin Elizabeth Khorsandi

Key collaborators:

Professor Pedro Cutillas, Professor of Cell Signalling and Proteomics at Cancer Research UK Barts Centre and Queen Mary Turing Fellow, London, UK; Dr Sam Das, Assistant Professor, Department of Cardiovascular Pathology, Ross Research Building, Johns Hopkins University, Baltimore, Maryland, USA; Kings College Hospital Charity; Kings Liver Biobank

Key publications:

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Gerdes H, Casado P, Dokal A, Hijazi M, Akhtar N, Osuntola R, Rajeeve V, Fitzgibbon J, Travers J, Britton D, Khorsandi S & Cutillas PR. Nat Commun. 2021 Mar 25;12(1):1850

Effects of thyroid hormone on mitochondria and metabolism of human preimplantation embryos. Noli L, Khorsandi SE, Pyle A, Giritharan G, Fogarty N, Capalbo A, Devito L, Jovanovic VM, Khurana P, Rosa H, Kolundzic N, Cvoro A, Niakan KK, Malik A, Foulk R, Heaton N, Ardawi MS, Chinnery PF, Ogilvie C, Khalaf Y, Ilic D. Stem Cells. 2020 Mar;38(3):369-381

Modern Outcomes Following Treatment of Hepatocellular Carcinoma in Hereditary Hemochromatosis: A Matched Cohort Study. McPhail MJW, Khorsandi SE, Abbott L, Al-Kadhimi G, Kane P, Karani J, O'Grady J, Heaton N, Bomford A, Suddle A. Am J Clin Oncol. 2019 Dec;42(12):918-923.

An in silico argument for mitochondrial microRNA as a determinant of primary non function in liver transplantation. Khorsandi SE, Salehi S, Cortes M, Vilca-Melendez H, Menon K, Srinivasan P, Prachalias A, Jassem W, Heaton N. Sci Rep. 2018 Feb 15;8(1):3105.

Subunit composition of respiratory chain complex 1 and its responses to oxygen in mitochondria from human donor livers. Khorsandi SE, Taanman JW, Heaton N. BMC Res Notes. 2017 Nov 2;10(1):547. doi

The microRNA Expression Profile in Donation after Cardiac Death (DCD) Livers and Its Ability to Identify Primary Non Function. Khorsandi SE, Quaglia A, Salehi S, Jassem W, Vilca-Melendez H, Prachalias A, Srinivasan P, Heaton N. PLoS One. 2015 May 15;10(5):e0127073.

 

Published by: Foundation for Liver Research

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