Cell motility varies according to intrinsic features and microenvironmental stimuli, being a signature of underlying biological phenomena. The heterogeneity in cell response, due to multilevel cell diversity especially relevant in cancer, poses a challenge in identifying the biological scenario from cell trajectories. We propose here a novel peer prediction strategy among cell trajectories, deciphering cell state (tumor vs. nontumor), tumor stage, and response to the anticancer drug etoposide, based on morphology and motility features, solving the strong heterogeneity of individual cell properties. The proposed approach first barcodes cell trajectories, then automatically selects the good ones for optimal model construction (good teacher and test sample selection), and finally extracts a collective response from the heterogeneous populations via cooperative learning approaches, discriminating with high accuracy prostate noncancer vs. cancer cells of high vs. low malignancy. Comparison with standard classification methods validates our approach, which therefore represents a promising tool for addressing clinically relevant issues in cancer diagnosis and therapy, e.g., detection of potentially metastatic cells and anticancer drug screening.

D'Orazio, M., Corsi, F., Mencattini, A., Di Giuseppe, D., Colomba Comes, M., Casti, P., et al. (2020). Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy. FRONTIERS IN ONCOLOGY, 10, 580698 [10.3389/fonc.2020.580698].

Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy

Mencattini A.;Casti P.;Di Natale C.;Ghibelli L.;Martinelli E.
2020-01-01

Abstract

Cell motility varies according to intrinsic features and microenvironmental stimuli, being a signature of underlying biological phenomena. The heterogeneity in cell response, due to multilevel cell diversity especially relevant in cancer, poses a challenge in identifying the biological scenario from cell trajectories. We propose here a novel peer prediction strategy among cell trajectories, deciphering cell state (tumor vs. nontumor), tumor stage, and response to the anticancer drug etoposide, based on morphology and motility features, solving the strong heterogeneity of individual cell properties. The proposed approach first barcodes cell trajectories, then automatically selects the good ones for optimal model construction (good teacher and test sample selection), and finally extracts a collective response from the heterogeneous populations via cooperative learning approaches, discriminating with high accuracy prostate noncancer vs. cancer cells of high vs. low malignancy. Comparison with standard classification methods validates our approach, which therefore represents a promising tool for addressing clinically relevant issues in cancer diagnosis and therapy, e.g., detection of potentially metastatic cells and anticancer drug screening.
2020
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/07 - MISURE ELETTRICHE ED ELETTRONICHE
English
cancer heterogeneity
cell motility
drug screening
dynamic feature selection
machine learning
metastatic cancer cell detection
peer prediction
D'Orazio, M., Corsi, F., Mencattini, A., Di Giuseppe, D., Colomba Comes, M., Casti, P., et al. (2020). Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy. FRONTIERS IN ONCOLOGY, 10, 580698 [10.3389/fonc.2020.580698].
D'Orazio, M; Corsi, F; Mencattini, A; Di Giuseppe, D; Colomba Comes, M; Casti, P; Filippi, J; Di Natale, C; Ghibelli, L; Martinelli, E
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/289521
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