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

cancer heterogeneity cell motility drug screening dynamic feature selection machine learning metastatic cancer cell detection peer prediction

Journal

Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867

Informations de publication

Date de publication:
2020
Historique:
received: 06 07 2020
accepted: 08 09 2020
entrez: 16 11 2020
pubmed: 17 11 2020
medline: 17 11 2020
Statut: epublish

Résumé

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

Identifiants

pubmed: 33194709
doi: 10.3389/fonc.2020.580698
pmc: PMC7606946
doi:

Types de publication

Journal Article

Langues

eng

Pagination

580698

Informations de copyright

Copyright © 2020 D'Orazio, Corsi, Mencattini, Di Giuseppe, Colomba Comes, Casti, Filippi, Di Natale, Ghibelli and Martinelli.

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Auteurs

Michele D'Orazio (M)

Department of Electronic Engineering, University of Rome "Tor Vergata", Rome, Italy.

Francesca Corsi (F)

Department of Chemical Science and Technologies, University of Rome "Tor Vergata", Rome, Italy.
Department of Biology, University of Rome "Tor Vergata", Rome, Italy.

Arianna Mencattini (A)

Department of Electronic Engineering, University of Rome "Tor Vergata", Rome, Italy.

Davide Di Giuseppe (D)

Department of Electronic Engineering, University of Rome "Tor Vergata", Rome, Italy.

Maria Colomba Comes (M)

Department of Electronic Engineering, University of Rome "Tor Vergata", Rome, Italy.

Paola Casti (P)

Department of Electronic Engineering, University of Rome "Tor Vergata", Rome, Italy.

Joanna Filippi (J)

Department of Electronic Engineering, University of Rome "Tor Vergata", Rome, Italy.

Corrado Di Natale (C)

Department of Electronic Engineering, University of Rome "Tor Vergata", Rome, Italy.

Lina Ghibelli (L)

Department of Biology, University of Rome "Tor Vergata", Rome, Italy.

Eugenio Martinelli (E)

Department of Electronic Engineering, University of Rome "Tor Vergata", Rome, Italy.

Classifications MeSH