Artificial Intelligence has been used to recognise patterns in breast cancer, leading to the discovery of five new types of the disease that can be matched to personalised treatments, British researchers say.
The new study, led by The Institute of Cancer Research (ICR), London, found that two types were more likely to respond to immunotherapy than others, while one was more likely to relapse on tamoxifen.
The research team are now developing tests for these types of breast cancer that will be used to select patients for different drugs in clinical trials, with the aim of making personalised therapy a standard part of treatment.
The new research, published in NPJ Breast Cancer, has the potential to help select treatments for women with breast cancer and identify new drug targets, say the authors.
Study leader Dr Anguraj Sadanandam, team leader in systems and precision cancer medicine at The Institute of Cancer Research, London, said: “We are at the cusp of a revolution in healthcare, as we really get to grips with the possibilities AI and machine learning can open up.
“Our new study has shown that AI is able to recognise patterns in breast cancer that are beyond the limit of the human eye, and to point us to new avenues of treatment among those who have stopped responding to standard hormone therapies.
“AI has the capacity to be used much more widely, and we think we will be able to apply this technique across all cancers, even opening up new possibilities for treatment in cancers that are currently without successful options.”
The majority of breast cancers develop in the inner cells that line the mammary ducts and are fed by oestrogen or progesterone. These luminal A tumours usually have the best cure rates.
However, patients within these groups respond differently to standard-of-care treatments, such as tamoxifen, or new treatments such as immunotherapy.
The researchers applied the AI-trained computer software to a vast array of data available on the genetics, molecular and cellular make-up of primary luminal A breast tumours, as well as data on patient survival.
Once trained, the AI was able to identify five different types of disease with particular patterns of response to treatment.
Women with inflammatory cancer type had immune cells present in their tumours and high levels of PD-L1, which suggested they were likely to respond to immunotherapies. AI also suggested that patients with triple negative tumours might also respond to immunotherapy.
Patients with tumours that contained a specific change in chromosome 8 had worse survival than other groups when treated with tamoxifen and tended to relapse after an average of 42 months compared to 83 months in patients who had a different tumour type that contained lots of stem cells. The AI analysis found that those patients may benefit from an additional or new treatment to delay or prevent late relapse.
Dr Maggie Cheang, a pioneer in identifying different types of breast cancer and team leader of the genomic analysis clinical trials team at The Institute of Cancer Research, London, said: “Among the exciting implications of this research is its ability to pick out women who might respond well to immunotherapy, even when the broad classification of their cancer would suggest that these treatments wouldn’t work for them.
“The AI used in our study could also be used to discover new drugs for those most at risk of late relapse, beyond five years, which is common in oestrogen-linked breast cancers and can cause considerable anxiety for patients.”
Heterocellular gene signatures reveal luminal-A breast cancer heterogeneity and differential therapeutic responses. NPJ Breast Cancer 2 August 2019
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