Researchers have used artificial intelligence to recognise patterns in breast cancer and uncovered five new types of the disease each matched to different personalised treatments.
Their study applied AI and machine learning to gene sequences and molecular data from breast tumours, to reveal crucial differences among cancers that had previously been lumped into one type.
The new study published in the journal 'NPJ Breast Cancer,' researchers found that two of the types were more likely to respond to immunotherapy than others, while one was more likely to relapse on tamoxifen.
The researchers 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 researchers at The Institute of Cancer Research, London previously used AI, in the same way, to uncover five different types of bowel cancer and oncologists are now evaluating their application in clinical trials.
The aim is to apply the AI algorithm to many types of cancer and to provide information for each about their sensitivity to treatment, likely paths of evolution and how to combat drug resistance.
The new research could not only help select treatments for women with breast cancer but also identify new drug targets.
The majority of breast cancers develop in the inner cells that line the mammary ducts and are 'fed' by the hormones estrogen or progesterone. These are classed as 'luminal A' tumours and often have the best cure rates.
However, patients within these groups respond very differently to standard-of-care treatments, such as tamoxifen, or new treatments needed if patients relapse 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, along with 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 a cancer type labeled 'inflammatory' had immune cells present in their tumours and high levels of a protein called PD-L1 suggesting they were likely to respond to immunotherapies.
Another group of patients had 'triple-negative' tumours which don't respond to standard hormone treatments but various indicators suggesting they might also respond to immunotherapy.
The markers identified in this new study do not challenge the overall classification of breast cancer but they do find additional differences within the current sub-divisions of the disease, with important implications for treatment.
The use of AI to understand cancer's complexity and evolution is one of the central strategies the ICR is pursuing as part of a pioneering research program to combat the ability of cancers to adapt and become drug-resistant. The ICR is raising the final PS15 million of a PS75 million investment in a new Centre for Cancer Drug Discovery to house a world-first program of 'anti-evolution' therapies.
"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," said, the study lead author, Dr. Anguraj Sadanandam.
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