Breast cancer is the most common cancer in the UK. Approximately 50,000 women will be diagnosed with breast cancer each year. Breast cancers may be broadly classified into distinct subtypes by expression of combinations of oestrogen receptor (ER)-regulated genes, growth factor receptors and basal cytokeratins. A triple negative sub-type (negative for ER, progesterone receptor (PR) and HER2), and its basal-like sub groups with either EGFR or basal cytokeratins CK5/6 positivity, has a high frequency of early metastatic relapse. Unlike ER-positive and HER2-positive tumours, there is currently no specific targeted therapy for this Triple Negative Breast Cancer (TNBC) sub-type.
Our laboratory research aims to identify the biological mechanisms that cause and drive triple negative forms of breast cancer (around 15-20% of all diagnosed breast cancers), based on a molecular pathological characterisation that will lead to the identification and validation of novel therapy targets, prognostic factors and biomarkers for prognosis and treatment.
KIAA0020 as a replication stress tolerance mechanism in Basal-like breast cancer
Genomic instability allows tumours to evolve and adapt to new environments through the acquisition of mutations and genomic aberrations. However, many of the DNA repair defects that drive genomic instability also impair DNA replication, and tumours often acquire mechanisms to maintain high levels of proliferation in the face of this ‘replication stress’. We have identified a gene, KIAA0020, which is amplified and overexpressed in ~25% of basal-like breast cancers. It may be involved in replication stress tolerance in these cancers. In support of this, we have demonstrated that depletion of KIAA0020 in a subset of breast cancer cell line models impairs DNA replication through a mechanism involving PARP1s activity at stalled replication forks. Currently our group is further investigating the role of KIAA0020 in replication stress tolerance and the mechanisms involved in this, as well as the impact KIAA0020 expression has on drug sensitivity in these tumours.
Synthetic sensitivities induced by HORMAD1 expression in breast cancer
We previously demonstrated that expression of the meiotic specific gene HORMAD1, which is overexpressed in approximately 60% of TNBC, drives genomic instability and sensitivity to cisplatin and PARP inhibitors through the inhibition of homologous recombination (PMID: 25770156). To provide further mechanistic insight, and to identify novel drug targets in HORMAD1 overexpressing tumours, we have carried out a synthetic sensitivitiy siRNA screen and identified increased sensitivity to knockdown of > 100 genes upon HORMAD1 expression in an isogenic cell line model. We are currently validating and carrying out mechanistic studies on the most interesting of these candidates including a significant proportion of genes that have roles in DNA damage repair.
Meiotic genes in triple negative breast cancer
We have recently identified additional meiotic specific genes that, like HORMAD1, are expressed in genomically unstable triple negative breast cancers. We are currently characterising the impact these genes have on the biology of TNBC, particularly focussing on their role in Homologous Recombination deficiency and genomic instability.
Genome evolution model for studying genomic instability drivers in TNBC
Genomic instability leaves pathognomonic scars on the genome of cancer cells. Association studies have correlated many of these scars with potential underlying genetic drivers, eg BRCA1 and BRCA2 mutation. However, the scars generated by specific drivers and how they evolve over time have not been so well characterised. Therefore, we have generated a genome evolution model using the genomically stable TNBC cell line, SUM159. We are currently using this model to both understand the genomic consequences of known drivers of genomic instability and discover new ones, including aberrantly regulated meiotic genes.
In vivo & Organoid Liaison Projects
In the Tutt lab at KCL, we have identified targets that drive the malignant phenotype of TNBC for in vitro study. It is now widely recognized that models where you implant patient’s tumour material into a mouse - called patient-derived xenograft models (PDX) - are the best in vivo models of breast cancer, far superior to long-established cancer cell lines grown in petri dishes.
We have developed a bank of xenograft models derived from primary patient material, which encompass the range of phenotypes of human breast cancer. We have used these mouse models, together with our in vitro cell line models and the histopathology of patient material, to validate these targets as “biomarker” targets that could be used in the diagnosis of human disease, e.g. PIM1 kinase.
We are also aiming at using similar material from patients without needing to use mice, by growing them in special conditions of three-dimensional (3D) culture. These are called patient-derived organoids (PDOs). For this purpose, we have now established a state of the art 3D cell culture facility at the ICR, in which we will develop 3D/organoid models of breast cancer from both PDX and patient samples.
For each successful drug that enters the market, 250 have failed in animal-based preclinical testing. Moreover, of the drugs that do enter clinical testing, the majority fails due to lack of effectiveness and the presence of side effects. Clearly, there is an urgent need to improve current laboratory testing to prevent ineffective drugs from entering the costly and undesirable pre-clinical animal testing phase.
This project will directly address this need by generating 3D cultures of organoids (PDO) from already established tumour models (PDX). These laboratory PDO models will reduce the number of animal experiments needed in early phase drug selection processes. To explore if we can also replace the need for PDX models, we will make PDOs directly from patient tumour sample and parallel PDXs.
The two parallel models will be compared and, if successful, support complete replacement of PDX in some situations. We will use the PDO models generated to test a range of established standard-of-care breast cancer treatments, and novel treatments that have already passed strong “proof of concept” in different types of breast cancer.