Molecular deconstruction, detection, and computational prediction of microenvironment-modulated cellular responses to cancer therapeutics

Abstract

The field of bioengineering has pioneered the application of new precision fabrication technologies to model the different geometric, physical or molecular components of tissue microenvironments on solid-state substrata. Tissue engineering approaches building on these advances are used to assemble multicellular mimetic-tissues where cells reside within defined spatial contexts. The functional responses of cells in fabricated microenvironments have revealed a rich interplay between the genome and extracellular effectors in determining cellular phenotypes and in a number of cases have revealed the dominance of microenvironment over genotype. Precision bioengineered substrata are limited to a few aspects, whereas cell/tissue-derived microenvironments have many undefined components. Thus, introducing a computational module may serve to integrate these types of platforms to create reasonable models of drug responses in human tissues. This review discusses how combinatorial microenvironment microarrays and other biomimetic microenvironments have revealed emergent properties of cells in particular microenvironmental contexts, the platforms that can measure phenotypic changes within those contexts, and the computational tools that can unify the microenvironment-imposed functional phenotypes with underlying constellations of proteins and genes. Ultimately we propose that a merger of these technologies will enable more accurate pre-clinical drug discovery.

Keywords: Cancer; Computation; Microenvironment; Therapeutic response.

Figure 1

Deconstructing complex microenvironments into tractable pieces using combinatorial microenvironment microarrays (MEArrays). A) (i) A cartoon a tissue microenvironment in which different cell types interact with each other and with ECM and soluble factors to generate a functional tissue. (ii) Purified ECM, growth factors, and recombinant cell surface receptors are mixed into combinations and printed on substrata that will support cell adhesion. (iii) Live cells are then added and cultured until an endpoint, (iv) when the relevant phenotypic responses are measured. (B) A low resolution scan of a breast cancer cell line on an MEArray that were treated with an anti-cancer agent. Red fluorescence shows the staining of a receptor tyrosine kinase, and green shows nuclei. Inset, shows a higher magnification image in four cells on four distinct microenvironments (a,b,c,d). (image credit: Dr. Tiina Jokela, LBNL) (C) Hypothetically, drug activities (e.g. IC50) (dashed lines) should be shifted in some combinatorial microenvironments.

Figure 2

Three successful approaches to visualizing functional consequences cellular interactions with combinatorial microenvironments in a highly parallel experimental environment. (A) Mixed use of pie charts for detailing microenvironmental composition, together bar graphs depicting functional responses (adapted from [26]). (B) Scatter plots showing single cell functional responses on different ECM combinations (adapted from [28]). (C) Heat maps showing functional consequences of cells interacting with different pair-wise microenvironments (composed of ECM 1–8 with a-p other), statistical significance is on the z-axis (adapted from [27]).

Figure 3

Using microtissues to evaluate emergent properties of higher order tissue organization. Tissues have distinctive architecture, which we usually lose in tissue culture dishes. (A) A cross section of a normal human mammary gland shows many ducts with green keratin 19 expressing luminal epithelial cells surrounded by red keratin 14 myoepithelial cells. (B) Primary human mammary epithelial cells possess the ability to self-organize in rudimentary structures, with luminal cells surrounded by myoepithelial cells, over time with confined in a polymer cylinder (adapted from [48]). (C) Defined microtissues formed through a process of ssDNA-guided assembly. In this case cellular stoichiometry was controlled so that one green cell would be surrounded by about 6 red cells (adapted from [53]).