Developing Novel Algorithms to Quantify
Order & Disorder in Tissues and Tumors
Methods for Quantifying Hidden Spatial Information in Tumor Architecture
Why Tumor Biologists Should Never See Randomness But Only Degrees of Disorder
Research Goals
Develop Quantitative Algorithms for the Following:
A. Measuring cellular and tissue phenotypes in health and disease for predicting behaviors in cell biology, development, degenerative diseases, and cancer.
B. Cracking the "tissue spatial code" that governs development, wound healing, and limb regeneration.
C. Developing the concept of Heritable Non-Genetic Information (HNI, "honey") and cracking it's code. HNI are types of heritable code that are NOT stored in DNA, but work in parallel with DNA, to govern how cells behave in living things.
D. Measure subtle regions of order and disorder in tumors. These algorithms will allow pathologists and scientists to find sub-groups of cancers that may benefit from more specific types of therapy, which reduces the need for unnecessary treatment and side effects.
The TSGL seeks to establish the foundational algorithms for measuring order and disorder in tissues and tumors.
Mission
Cells within a tumor are arranged in various patterns that seem random, except for large-scale morphological features that pathologists can clearly see and subjectively score. These obvious patterns of cell shape and nuclear shape are the reason why a pathologist can say that this tumor is a "Grade 1," while that tumor is a "Grade 3" and more aggressive. Likewise, these large-scale patterns allow pathologists to categorize one tumor as an "adenoid cystic carcinoma," while a different tumor as "spindle cell carcinoma." Beyond these large-scale morphological features, the human eye is unable to measure subtle regions of order and disorder within a tumor. In biology, this problem is referred to as "heterogeneity": in other words, what we see is too complicated for us to pick out any patterns, so we assume that it's random. The answer to the problem of heterogeneity is multi-fold. Traditionally, it has worked well to turn features into numbers in order to apply statistics. However, in order to extract more useful information from shapes in cells and tissues, we need more insightful spatial algorithms that measure what traditional methods -- such as, area, volume, surface area, signal intensity, etc. -- cannot.