An Omentum-inspired 3D PEG Hydrogel for Identifying ECM-drivers of Drug Resistant Ovarian Cancer

Brooks, Elizabeth; Gencoglu, Maria F.; Corbett, Daniel C.; Stevens, Kelly R.; Peyton, Shelly R.

doi:10.1101/560482

Cited by 6 publications

(6 citation statements)

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“…2G). Previous studies have shown that spheroid formation from single cancer cells occurs within ~2 weeks (9,12,37), indicating that the PA-VH/KN hydrogels are sufficiently stable to support spheroid formation. The discrepancy between the quantified weight loss and the unchanged hydrogel structure may be attributed to swelling effects of the hydrogels, with >90% water content (22).…”

Section: Hydrogel Stability Characterizationmentioning

confidence: 98%

Peptide-protein coassembling matrices as a biomimetic 3D model of ovarian cancer

et al. 2020

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Bioengineered three-dimensional (3D) matrices expand our experimental repertoire to study tumor growth and progression in a biologically relevant, yet controlled, manner. Here, we used peptide amphiphiles (PAs) to coassemble with and organize extracellular matrix (ECM) proteins producing tunable 3D models of the tumor microenvironment. The matrix was designed to mimic physical and biomolecular features of tumors present in patients. We included specific epitopes, PA nanofibers, and ECM macromolecules for the 3D culture of human ovarian cancer, endothelial, and mesenchymal stem cells. The multicellular constructs supported the formation of tumor spheroids with extensive F-actin networks surrounding the spheroids, enabling cell-cell communication, and comparative cell-matrix interactions and encapsulation response to those observed in Matrigel. We conducted a proof-of-concept study with clinically used chemotherapeutics to validate the functionality of the multicellular constructs. Our study demonstrates that peptide-protein coassembling matrices serve as a defined model of the multicellular tumor microenvironment of primary ovarian tumors.

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Section: Hydrogel Stability Characterizationmentioning

confidence: 98%

Peptide-protein coassembling matrices as a biomimetic 3D model of ovarian cancer

et al. 2020

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“…The disparity in performance between the training and testing data sets may be related to differences in the expression patterns in different tissue types, or batch effects. IC 50 measurements for the same cell line and drug are known to vary significantly between studies, especially when the cell line is drug insensitive, 92 which may contribute to the poor correlation between results of both data sets.…”

Section: Discussionmentioning

confidence: 99%

Pathway‐extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors

Bagchee-Clark

Mucaki

Whitehead

et al. 2020

MedComm

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Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi‐gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology‐based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway‐extended SVMs predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway‐extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer aetiology. Ensemble machine learning‐based averaging of multiple pathway‐extended models derived for an individual drug increased accuracy to >70% for erlotinib, gefitinib, lapatinib and sorafenib. Through incorporation of novel cancer biomarkers, machine learning‐based pathway‐extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy.

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Section: Discussionmentioning

confidence: 99%

Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors

Bagchee-Clark

Mucaki

Whitehead

2020

Preprint

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Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi-gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support Vector Machine learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology-based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway-extended support vector machines predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway-extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer etiology. Ensemble machine learning-based averaging of multiple pathway-extended models derived for an individual drug increased accuracy to >70% for erlotinib, gefitinib, lapatinib, and sorafenib. Through incorporation of novel cancer biomarkers, machine learning-based pathway-extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy.

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An Omentum-inspired 3D PEG Hydrogel for Identifying ECM-drivers of Drug Resistant Ovarian Cancer

Cited by 6 publications

References 50 publications

Peptide-protein coassembling matrices as a biomimetic 3D model of ovarian cancer

Peptide-protein coassembling matrices as a biomimetic 3D model of ovarian cancer

Pathway‐extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors

Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors

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