Mechanomics Biomarker for Cancer Cells Unidentifiable through Morphology and Elastic Modulus

Cellular mechanical properties are potential cancer biomarkers used for objective cytology to replace the current subjective method relying on cytomorphology. However, heterogeneity among intra/intercellular mechanics and the interplay between cytoskeletal prestress and elastic modulus obscured the difference detectable between malignant and benign cells. In this work, we collected high density nanoscale prestress and elastic modulus data from a single cell by AFM indentation to generate a cellular mechanome. Such high dimensional mechanome data was used to train a malignancy classifier through machine learning. The classifier was tested on 340 single cells of various origins, malignancy, and degrees of similarity in morphology and elastic modulus. The classifier showed instrument-independent robustness and classification accuracy of 89% with an AUC-ROC value of 93%. A signal-to-noise ratio 8 times that of the human-cytologist-based morphological method was also demonstrated, in differentiating precancerous hyperplasia cells from normal cells derived from the same lung cancer patient.

Automated summary

Cellular mechanical properties can serve as potential cancer biomarkers for objective cytology, with a classifier trained through high dimensional mechanome data achieving a classification accuracy of 89% and instrument-independent robustness. This approach also demonstrated a signal-to-noise ratio 8 times higher than traditional morphology-based methods in differentiating precancerous cells from normal cells.