  Multiplex immunofluorescence and immunohistochemistry benefit patients by
allowing cancer pathologists to identify several proteins expressed on the
surface of cells, enabling cell classification, better understanding of the
tumour micro-environment, more accurate diagnoses, prognoses, and tailored
immunotherapy based on the immune status of individual patients. However, they
are expensive and time consuming processes which require complex staining and
imaging techniques by expert technicians. Hoechst staining is much cheaper and
easier to perform, but is not typically used in this case as it binds to DNA
rather than to the proteins targeted by immunofluorescent techniques, and it
was not previously thought possible to differentiate cells expressing these
proteins based only on DNA morphology. In this work we show otherwise, training
a deep convolutional neural network to identify cells expressing three proteins
(T lymphocyte markers CD3 and CD8, and the B lymphocyte marker CD20) with
greater than 90% precision and recall, from Hoechst 33342 stained tissue only.
Our model learns previously unknown morphological features associated with
expression of these proteins which can be used to accurately differentiate
lymphocyte subtypes for use in key prognostic metrics such as assessment of
immune cell infiltration,and thereby predict and improve patient outcomes
without the need for costly multiplex immunofluorescence.
