Detecting multiple sub-types of breast cancer in a single patient

Determining the molecular or genomic sub-type of a cancer of a particular organ is important for prognosis and treatment planning. While clinical tests determine the dominant subtype of cancer in a patient, it is believed that some cancer treatments targeting the dominant sub-types ultimately prove ineffective because they ignore the existence of additional sub-types in the same patient. We present a method to detect co-existence of two common sub-types of breast cancer - HER2 and basal-like - in whole slide images of H&E-stained tumors in the same patient. Our main contribution is in formulating this problem in precision medicine and in preparation of its training data. The detectors tested to be highly accurate in classifying patches taken from test cases with well-separated molecular profiles, confirming that image classification was effective in detecting the two sub-types. Interestingly, test patients whose molecular profiles were not very clearly separated appeared to include heterogeneous patches - some HER2 and other basal-like - indicating existence of a secondary sub-type within a single patient. The detection of a secondary sub-type in a single patient may impact the prognosis and treatment of the cancer.