Medical imaging markers of cancer initiation, progression and therapeutic response in the breast based on tissue microstructure

Studies of cancer DNA show that breast cancer comprises many different diseases. Some spread quickly and are very dangerous, while others are slow growing and are not life-threatening. Some respond well to chemotherapy or radiotherapy, while others are resistant to treatment. It is important to be able to select the best treatment for a particular patient's cancer, so-called "patient stratification". In this way appropriate treatment can be given whilst potential side-effects from an ineffective treatment are avoided.

Recent discoveries have implicated the stroma, the connective tissue of the breast, in both the aggressiveness of breast cancer and its response to therapy. When small amounts of tissue are removed from a potential breast cancer by needle biopsy, or when a breast cancer is removed by surgery, subtle differences are seen in microscopic imaging (histology) of the stromal tissue around the tumour. These occur at the scale of a few microns and are far below the spatial resolution of X-ray mammography, magnetic resonance imaging (MRI) and ultrasound that radiologists routinely use to detect and assess breast cancer.

A recent innovation, ultrasound shear wave elastography (USSWE), has enabled radiologists in Dundee to show differences in stromal tissue of patients with invasive cancer. USSWE measures the very small displacements of tissue that occur during conventional ultrasound examinations. The MR imaging is specifically designed to (a) assess the microscopic blood supply of the breast tissue, and any cancer present, using a method called dynamic contrast enhancement, DCE-MRI, and (b) assess the size and shape of microstructure by measuring the diffusion of protons, DWI-MRI. Accurate comparison of these with histology of the removed tissue enables the computer to learn the differences in the images between normal tissue and cancer and between normal stroma and stroma associated with cancer. In a further step scientists at the UCL Centre for Medical Image Computing have invented new methods to measure some of these microscopic changes. They generate computer models of these structures and use the subtle changes predicted by these models to adjust the imaging techniques to maximise the differences between normal tissue and the different types of cancer and stromal tissue changes that we see using histology.

We propose a project in which we will develop ways to combine X-ray DBT, DCE-MRI, DWI-MRI and USSWE to best discriminate between normal stroma and stroma near to cancers, and between the different types of cancer: high risk, low risk, and those that do and do not respond to therapy. We will first ask 3 women who have recently been diagnosed with breast cancer to spend about an hour in a MR scanner to allow us to collect a wide range of DW-MRI images so we can optimise MRI acquisition. We will then ask a further 30 women recently diagnosed with cancer for permission to use their radiological images and the histology images of their tissue samples from biopsy and surgery. We will use these images to train our computer methods to best discriminate between the different types of stroma and cancer, and between the different components of the cancer itself. Ten of the patients selected will be undergoing a course of neo-adjuvant therapy (a type of chemotherapy that is given prior to surgery to shrink the tumour).

If successful this project will discover new ways to identify those patients who will respond best to a particular treatment, those patients who do not have life threatening disease and so do not need aggressive therapy, and those patients with types of cancer that require more aggressive treatment. At the end of this project we will translate these findings to a clinical trial.

Breast cancer represents a spectrum of disease, from lesions which are so indolent that they would never present clinically or threaten life, to cancers which - despite aggressive multimodal treatment including surgery, radiotherapy and chemotherapy - will cause death in a short period of time. Although standard prognostic features can sometimes separate breast cancer patients into low and high risk groups, actual prognostication and prediction of treatment responsiveness for many individual patients remains poor. As a result, overtreatment is common, since the consequences of overtreatment are considered to be less adverse than those of under-treatment.

The outcomes of this work, if successful at subsequent clinical trial, will help breast cancer patients and health care professionals' desire for personalised and stratified healthcare to become a reality, with the long term aim of a more accurate disease prognostication and treatment response prediction. This should lead to less overtreatment - with expensive and toxic therapies - of women with low risk disease, and to less risk of under-treatment of women with high risk disease. The further stratification of women at high risk of breast cancer death into those who will be either responsive or resistant to particular therapies will benefit:
a) breast cancer patients who can balance the harms and benefits of treatments offered
b) healthcare providers who will be better able to manage scant resources
c) treatment developers, manufacturers and providers, who may be able to target treatments more effectively to specific subgroups when such treatments may not be cost effective for larger and more heterogeneous patient cohorts.

These benefits will be particularly evident for women with oestrogen receptor negative (ER-) breast cancer, where useful predictive markers are currently lacking. Such cancers tend to be aggressive and occur in younger women, so better management of such disease would have a profound positive impact on society.

Eventually, the integration of multimodal imaging prognostic and predictive information into prognostic and predictive indices could be routinely exploited through the derivation and inclusion of such indices into multimodal imaging workstations. The integration of such imaging parameters with histological, immunohistochemical and genetic information could be developed further into a commercial online prognostic and predictive tool (such as a next generation successor to the currently widely used "Adjuvant Online!" website).

The project partners all have strong links with industry and commercialisation via licensing, consultancy or spin-out company formation is the only really effective way to disseminate the results of new methodological research widely to healthcare providers. We will protect new ideas by patent as appropriate through the commercialisation arms of the two institutions.
We will also disseminate the results of our work through the extensive public engagement and patient awareness initiatives at our disposal and documented in our Pathways to Impact document.