Unsupervised self-organising map of prostate cell Raman spectra shows
  disease-state subclustering
Daniel West Susan Stepney Y. Hancock
Prostate cancer is a disease which poses an interesting clinical question:
should it be treated? A small subset of prostate cancers are aggressive and
require removal and treatment to prevent metastatic spread. However,
conventional diagnostics remain challenged to risk-stratify such patients,
hence, new methods of approach to biomolecularly subclassify the disease are
needed. Here we use an unsupervised, self-organising map approach to analyse
live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is
to test the feasibility of this method to differentiate, at the
single-cell-level, cancer from normal using high-dimensional datasets with
minimal preprocessing. The results demonstrate not only successful separation
of normal prostate and cancer cells, but also a new subclustering of the
prostate cancer cell-line into two groups. Initial analysis of the spectra from
each of the cancer subclusters demonstrates a differential expression of
lipids, which, against the normal control, may be linked to disease-related
changes in cellular signalling.