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

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.

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response

“…As extensively studied 55 , 62 , 63 , clusters of cells share common pathways during the drug administering or in response to a given insult. In addition, it is has been also observed an intrinsic heterogeneity in cells response when being in the center or at the boundary of the cluster, due to different motility constraints or different culture media composition 64 .…”

A machine learning model for the prediction of peripherally inserted central catheter-related venous thrombosis among high-risk adult patients

“…Given a set of training instances, each of which is labelled as belonging to one or the other of two categories, SVM creates a model that assigns the new instance to one of the two categories, making it a nonprobabilistic binary nonlinear classi er. A SVM model is a representation of the examples as points in a new prediction space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible [20]. However, when the data distribution is poor or the data sample size is too large, it will be linearly inseparable.…”

Chemical Profiling and Enzyme Inhibitory Activities of Essential Oil Isolated from Pistacia khinjuk Leaves: Insights On GC‐MS Analysis and Skin Aging‐Relevant Enzymes

“…& Buhse. [21,22] It is rich in chemical compounds and possess several properties such as anti-oxidant, anti-microbial, anti-inflammatory, anti-viral and anti-cytotoxic, attributing to the presence of essential oil, terpenoids, alkaloids, tannins and flavonoids. [23][24][25][26] The plant essential oil (EO) was reported to be rich in monoterpene hydrocarbons and monoterpene alcohols namely as α-pinene, β-pinene, phellandrene, sabinene, terpinen-4-ol, dehydro-aromadendrone, β-caryophyllene, germacrene B, and spathulenol.…”

“…This plant is usually found as a dioecious deciduous tree, or as a shrub, the leaves of this species are variable in shape but can be acuminate which may be why a synonym to this species is Pistacia acuminata Boiss. & Buhse [21,22] . It is rich in chemical compounds and possess several properties such as anti‐oxidant, anti‐microbial, anti‐inflammatory, anti‐viral and anti‐cytotoxic, attributing to the presence of essential oil, terpenoids, alkaloids, tannins and flavonoids [23–26] .…”