Entropy of leukemia on multidimensional morphological and molecular landscapes

TL;DR

This paper introduces entropy-based measures on multidimensional landscapes derived from single-cell data to accurately distinguish healthy individuals from AML patients, linking thermodynamics concepts to biomedical diagnostics.

Contribution

It presents a systematic approach to quantify disease states using entropy of cell-population distributions on specific molecular and morphological landscapes, enabling precise AML diagnosis from flow cytometry data.

Findings

Entropy measures effectively differentiate healthy and AML samples.

Method accurately diagnoses AML from flow cytometry data.

Framework links thermodynamics with biomedical data analysis.

Abstract

Leukemia epitomizes the class of highly complex diseases that new technologies aim to tackle by using large sets of single-cell level information. Achieving such goal depends critically not only on experimental techniques but also on approaches to interpret the data. A most pressing issue is to identify the salient quantitative features of the disease from the resulting massive amounts of information. Here, I show that the entropies of cell-population distributions on specific multidimensional molecular and morphological landscapes provide a set of measures for the precise characterization of normal and pathological states, such as those corresponding to healthy individuals and acute myeloid leukemia (AML) patients. I provide a systematic procedure to identify the specific landscapes and illustrate how, applied to cell samples from peripheral blood and bone marrow aspirates, this characterization accurately diagnoses AML from just flow cytometry data. The methodology can generally be applied to other types of cell-populations and establishes a straightforward link between the traditional statistical thermodynamics methodology and biomedical applications.