Beyond Genetics in Personalized Cancer Treatment: Assessing Dynamics and Heterogeneity of Tumor Responses

Tyson, Darren R.; Quaranta, Vito

doi:10.2217/pme.13.6

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Tailoring therapy based on omics has a significant impact in medical oncology that enlarges the number of therapeutic strategies (34–36). Precision medicine using targeted therapy is subjected to two major challenges: biomarker of response (beyond genomic alteration), and acquisition of resistance [reviewed in (37)]. Taking together the recent development of cancer genomic and precision medicine, oncologists face a daily problem to select the optimal and most potent drug to each cancer patient.…”

Section: Discussionmentioning

confidence: 99%

Tumor Tissue Explant Culture of Patient-Derived Xenograft as Potential Prioritization Tool for Targeted Therapy

et al. 2019

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Despite of remarkable progress made in the head and neck cancer (HNC) therapy, the survival rate of this metastatic disease remain low. Tailoring the appropriate therapy to patients is a major challenge and highlights the unmet need to have a good preclinical model that will predict clinical response. Hence, we developed an accurate and time efficient drug screening method of tumor ex vivo analysis (TEVA) system, which can predict patient-specific drug responses. In this study, we generated six patient derived xenografts (PDXs) which were utilized for TEVA. Briefly, PDXs were cut into 2 × 2 × 2 mm3 explants and treated with clinically relevant drugs for 24 h. Tumor cell proliferation and death were evaluated by immunohistochemistry and TEVA score was calculated. Ex vivo and in vivo drug efficacy studies were performed on four PDXs and three drugs side-by-side to explore correlation between TEVA and PDX treatment in vivo. Efficacy of drug combinations was also ventured. Optimization of the culture timings dictated 24 h to be the time frame to detect drug responses and drug penetrates 2 × 2 × 2 mm3 explants as signaling pathways were significantly altered. Tumor responses to drugs in TEVA, significantly corresponds with the drug efficacy in mice. Overall, this low cost, robust, relatively simple and efficient 3D tissue-based method, employing material from one PDX, can bypass the necessity of drug validation in immune-incompetent PDX-bearing mice. Our data provides a potential rationale for utilizing TEVA to predict tumor response to targeted and chemo therapies when multiple targets are proposed.

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Section: Discussionmentioning

confidence: 99%

Tumor Tissue Explant Culture of Patient-Derived Xenograft as Potential Prioritization Tool for Targeted Therapy

et al. 2019

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“…The utility of mathematical models of stochastic molecular pathways is also proposed to be applicable for cancer diagnosis [37] . Mathematical models based on experimental studies are proposed to bridge genetics and tumor behavior, with the potential to provide better personalized cancer treatment [62] , [63] . The development of live imaging-based mathematical models to infer both the heterogeneity and dynamics of cellular behavior will provide further insight into the characteristic features of both tumor and physiological tissue development.…”

Section: Discussionmentioning

confidence: 99%

Live Imaging-Based Model Selection Reveals Periodic Regulation of the Stochastic G1/S Phase Transition in Vertebrate Axial Development

et al. 2014

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In multicellular organism development, a stochastic cellular response is observed, even when a population of cells is exposed to the same environmental conditions. Retrieving the spatiotemporal regulatory mode hidden in the heterogeneous cellular behavior is a challenging task. The G1/S transition observed in cell cycle progression is a highly stochastic process. By taking advantage of a fluorescence cell cycle indicator, Fucci technology, we aimed to unveil a hidden regulatory mode of cell cycle progression in developing zebrafish. Fluorescence live imaging of Cecyil, a zebrafish line genetically expressing Fucci, demonstrated that newly formed notochordal cells from the posterior tip of the embryonic mesoderm exhibited the red (G1) fluorescence signal in the developing notochord. Prior to their initial vacuolation, these cells showed a fluorescence color switch from red to green, indicating G1/S transitions. This G1/S transition did not occur in a synchronous manner, but rather exhibited a stochastic process, since a mixed population of red and green cells was always inserted between newly formed red (G1) notochordal cells and vacuolating green cells. We termed this mixed population of notochordal cells, the G1/S transition window. We first performed quantitative analyses of live imaging data and a numerical estimation of the probability of the G1/S transition, which demonstrated the existence of a posteriorly traveling regulatory wave of the G1/S transition window. To obtain a better understanding of this regulatory mode, we constructed a mathematical model and performed a model selection by comparing the results obtained from the models with those from the experimental data. Our analyses demonstrated that the stochastic G1/S transition window in the notochord travels posteriorly in a periodic fashion, with doubled the periodicity of the neighboring paraxial mesoderm segmentation. This approach may have implications for the characterization of the pathophysiological tissue growth mode.

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“…The burgeoning notion of cellular heterogeneity within a tumor has attracted much attention in cancer research 1,2 . In addition to genetic heterogeneity embodied by the diversity of genomes in each cancer cell due to genome instability [3][4][5] , it is increasingly recognized that the nongenetic variability of cell phenotypes within an isogenic (clonal) cell population contributes to functional heterogeneity of cancer cells 6,7 . Even not considering the variety from stromal cells (endothelium, stromal fibroblasts and immune cells) but just within tumor, the neoplastic cells raise the possibility of functional specialization and cooperation among the cancer cells themselves [8][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Extracting Intercellular Signaling Network of Cancer Tissues using Ligand-Receptor Expression Patterns from Whole-tumor and Singlecell Transcriptomes

Zhou

Taramelli

Pedrini

et al. 2017

Preprint

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The burgeoning notion of cellular heterogeneity within a tumor has attracted much attention in cancer research 1,2 . In addition to genetic heterogeneity embodied by the diversity of genomes in each cancer cell due to genome instability 3-5 , it is increasingly recognized that the nongenetic variability of cell phenotypes within an isogenic (clonal) cell population contributes to functional heterogeneity of cancer cells 6,7 . Even not considering the variety from stromal cells (endothelium, stromal fibroblasts and infiltrating immune cells) but just within the tumor parenchyma, the neoplastic cells raise the possibility of functional specialization and cooperation among the cancer cells themselves [8][9][10][11] . Slow proliferating cancer stem cells vs. fast proliferating vs. migratory cells are examples of (overlapping and dynamic) subsets of cells among the neoplastic cells 12,13 . Cooperation would require cell-cell communication which is mainly mediated by physical cell-cell contact, shared extracellular-matrix and secreted diffusible signaling molecules [14][15][16] . While interaction between distinct genetic clones of cancer cells in a tumor, has received attention in past years 17, 18 the diversification of cell phenotypes within the same clone (non-genetic heterogeneity 7,19,20 ) must also be considered as a source of interaction between distinct types of cells. Here we study crosstalk between cells via secreted (paracrine) soluble or matrix proteins as well as cell surface signaling (juxtacrine) proteins that bind specifically to their cognate receptors. Herein the principle of autocrine stimulation in which a cell secretes a growth factor and also expresses its cognate receptor has long been used to explain the observed co-expression of both ligand and receptor in the tumor cell population which was originally considered to be homogeneous and consisting of only one cell type 21 . But the notion of the diversity of cell subtypes among the tumor parenchyma cells which can differ in their capacity to secrete different sets of mediators and to express distinct receptors for these signaling molecules by necessity entails that networks of

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Beyond Genetics in Personalized Cancer Treatment: Assessing Dynamics and Heterogeneity of Tumor Responses

Cited by 6 publications

References 40 publications

Tumor Tissue Explant Culture of Patient-Derived Xenograft as Potential Prioritization Tool for Targeted Therapy

Tumor Tissue Explant Culture of Patient-Derived Xenograft as Potential Prioritization Tool for Targeted Therapy

Live Imaging-Based Model Selection Reveals Periodic Regulation of the Stochastic G1/S Phase Transition in Vertebrate Axial Development

Extracting Intercellular Signaling Network of Cancer Tissues using Ligand-Receptor Expression Patterns from Whole-tumor and Singlecell Transcriptomes

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