Unsupervised self-organising map of prostate cell Raman spectra shows disease-state subclustering
This addresses the clinical challenge of distinguishing aggressive prostate cancers for treatment, though it appears incremental as it applies an existing method to new biomedical data.
The study tackled the problem of risk-stratifying prostate cancer by using an unsupervised self-organising map on Raman spectroscopy data to differentiate normal and cancer cells at the single-cell level, achieving successful separation and identifying two new cancer subclusters with differential lipid expression.
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.