Stratifying breast cancer for metabolic treatment
Mariia Yuneva (Crick), Gyorgy Szabadkai (UCL/Crick), Marco Novelli (UCL) and Rob Stein (UCL)
Apply to: UCL
Summary:
Remodelling of cellular metabolism is one of the well-known hallmarks of cancer (1). Initially
cellular transformation was believed to be associated with decreased overall mitochondrial
activity, but accumulated data have proved that mitochondria are a hub of multiple processes
essential for different stages of tumorigenesis (2). Being shaped by genetic, epigenetic and
environmental cues, tumour metabolism including mitochondrial metabolism is affected by
and contributes to tumour heterogeneity, which is one of the main obstacles in designing
specific and efficient anti-cancer therapies.
The overarching aim of our research programme is to understand how transcriptional and
translational regulation of the mitochondrial proteome (termed as mitochondrial biogenesis) is
converted into bioenergetic and signaling responses of the organelle and metabolic
requirements of cancer cells. In particular, we focus on how coordinated regulation of
mitochondrial gene expression provides adaptation to metabolic/mitochondrial stress in
cancer development and progression (3). We use (i) unbiased approaches, including
bioinformatic analysis of genome sequencing and gene expression data as well as (ii) high
throughput functional imaging of metabolic function (4) and evaluation of metabolic fluxes by
metabolomics approaches (5) in order to identify components of adaptive mitochondrial
biogenesis and metabolic pathways which will represent therapeutic targets to develop novel
treatment strategies.
We have developed a novel bi-clustering algorithm able to handle a large volume of data to
recognize a subset of genes (co-regulated group) in a subset of samples (different tumour
tissues or subtypes). Analysis of the expression pattern of ~1K mitochondrial genes gave a
robust quantitative output, which was able to distinguish tumours with specific mitochondrial
gene expression patterns originating from different tissues (CCLE dataset) and from different
tumour subtypes (BRCA datasets, ICGC and TCGA databases). Based on the mitochondrial and
genome wide expression patterns we have developed a scoring system centred on the
quantification of a geneset (120 genes) by nanostring technology. Cellular models of breast
cancer, selected by the scoring system were used to verify the metabolic phenotype associated
with the different mitochondrial patterns. We have shown that in the two main groups high
and low mitochondrial oxidative activity determined different substrate preferences and
metabolic fluxes.
The current project will utilise patient samples to characterise mitochondrial subtypes of
breast cancer. (i) patient samples (needle biopsies) will be available from the clinical practice of
R. Stein and collaborating surgeons (UCLH). These will include both freshly collected samples
and 300 very well characterised samples from patients with luminal breast cancer from the
OPTIMA prelim clinical trial (6). (ii) pathological classification will be performed with the
supervision of M. Novelli and the breast cancer pathology specialist Elaine Borg (UCLH). (iii)
Samples will be used for nanostring based gene expression patterning to determine their
mitochondrial gene expression identity. In addition, mitochondrial function will be
characterised by live cell imaging approaches supervised by G. Szabadkai’s group (UCL-Crick).
(iv) Metabolite concentrations and metabolic fluxes in primary samples and cells originating
from the samples will be determined by metabolomics approaches in M. Yuneva’s group
(Crick).
We expect that we will stratify breast cancers using their mitochondrial status and clarify their
relationship to existing classifications. The project will help the student to develop basic skills
in translational research and provide preliminary data for Clinical Research Training Fellowship
application.
References:
1. Hanahan D., and Weinberg R.A. 2011. Hallmarks of cancer: the next generation. Cell, 144,
646-674.
2. Gasparre G, Porcelli AM, Lenaz G, Romeo G. 2013. Relevance of mitochondrial genetics
and metabolism in cancer development. Cold Spring Harb Perspect Biol. 5(2).
3. Yao Z, Jones a WE, Fassone E, Sweeney MG, Lebiedzinska M, Suski JM, Wieckowski MR,
Tajeddine N, Hargreaves IP, Yasukawa T, Tufo G, Brenner C, Kroemer G, Rahman S,
Szabadkai G. 2013. PGC-1β mediates adaptive chemoresistance associated with
mitochondrial DNA mutations. Oncogene, 32:2592–600.
4. Blacker TS, Mann ZF, Gale JE, Ziegler M, Bain AJ, Szabadkai G*, Duchen MR* 2014.
Separating NADH and NADPH fluorescence in live cells and tissues using FLIM. Nat
Commun, 5:3936.
5. Yuneva MO, Fan TW, Allen TD, Higashi RM, Ferraris DV, Tsukamoto T, Matés JM, Alonso
FJ, Wang C, Seo Y, Chen X, Bishop JM. 2012 The metabolic profile of tumors depends on
both the responsible genetic lesion and tissue type. Cell Metab. 15(2):157-70.
6. Stein, RC, Dunn, JA, Bartlett, JM, Campbell, AF, Marshall, A, Hall, P, Rooshenas, L, Morgan,
A, Poole, C, Pinder, SE, Cameron, DA, Stallard, N, Donovan, JL, Mccabe, C, Hughes-Davies,
L & Makris, A. 2016. OPTIMA prelim: a randomised feasibility study of personalised care in
the treatment of women with early breast cancer. Health Technol Assess; 20(10): 1-202.