Consider it a pre-Thanksgiving brain teaser. The image at the right is something called an autostereogram — a three-dimensional picture that can be seen only by staring through and beyond the colored, seemingly random pattern. (You may need to make it bigger, or even to zoom in.) What do you see? Go ahead, I’ll wait.
The picture is of an abstract smiley face in a square frame. Once you see it, it seems crystal clear. But look away and the image is hidden again.
If you were able to make it work, it is because your brain successfully overlapped the two sides of the pattern to visualize a previously hidden image. Former Stanford graduate student Kun-Hsing Yu, MD, PhD and geneticist Michael Snyder, PhD, used a similar layering technique to help researchers and clinicians analyze patches of cancer cells from tissue biopsies. They’ve worked out a way to overlay the visual information on the slide — the shapes and positions of cells — with another layer of information about the cells’ ‘omics’ — a term coined by Snyder to encompass the DNA sequence (the genome), the RNA molecules (transcriptome) and even the proteins (the proteome) present in individual cells.
The research appears in Cell Systems.
As Yu explained to me in an email:
Histopathology evaluation, the visual assessment of microscopic patterns of tumor tissue, is the gold standard for diagnosing many types of cancers. However, the molecular processes underpinning the morphological changes observed in cancer cells are largely unknown. In addition, the clinical utility of integrating histopathology slides and omics data had not been explored. In this study, we successfully revealed the gene and protein expression patterns in lung cancer tissues and showed that dysregulated genes associated with cell de-differentiation [an early step that can lead to cancer] participated in cell-cycle, DNA replication, and p53 signaling pathways.
By layering these multiple types of information, the researchers were able to identify morphological patterns associated with mutation of the p53 protein, which is an important prognostic factor for lung cancer patients. When they integrated this information with other omics data they were able to generate personalized prognoses for individual patients.
Our integrative prediction model can better inform oncologists of their patients’ prognoses than histopathology, clinical history or omics information alone, and could allow clinicians to better formulate treatment plans based on a patient’s predicted survival. It can also help to classify patients into disease subtypes when histopathlogy results are ambiguous. In short, the systematic incorporation of the ‘big data’ of omics research into medical practice can assist in personalized cancer treatment as well as facilitate the development of precision medicine.