Not applicable.
1. Field of the Invention
The present invention relates to combinatorial cellular microarrays, fabrication and materials and methods of using these cellular microarrays, such as for functional analysis of cell and combinatorial microenvironment interactions.
2. Related Art
The interactions between cells and their surrounding microenvironment have functional consequences for cellular behaviour. For instance, on the single cell level, distinct microenvironments can impose specific differentiation, migration, and proliferation of phenotypes, and on the tissue level the microenvironment may control processes as complex as morphogenesis and tumorigenesis (Bissell, M. J. & Labarge, M. A. Context, tissue plasticity, and cancer: are tumor stem cells also regulated by the microenvironment? Cancer Cell 7, 17-23 (2005)). Not only do the cell and molecular contents of microenvironments impact the cells within them, but the elasticity (Engler, A. J., Sen, S., Sweeney, H. L. & Discher, D. E. Matrix elasticity directs stem cell lineage specification. Cell 126, 677-689 (2006)) and geometry (McBeath, R., Pirone, D. M., Nelson, C. M., Bhadriraju, K. & Chen, C. S. Cell shape, cytoskeletal tension, and RhoA regulate stem cell lineage commitment. Dev Cell 6, 483-495 (2004)) of the tissue impact the cells. Defined as the sum total of cell-cell, -ECM, and -soluble factor interactions, in addition to physical characteristics, the microenvironment is highly complex. The phenotypes of cells within a tissue are partially due to their genomic content and partially due to the combinatorial interactions with the molecular and physical components of the microenvironment. A major challenge is to link specific combinations of microenvironmental components with distinctive behaviours. The present invention provides a means for linking the microenvironment with tissue and cell functions and behaviours.
The present invention provides for a combinatorial microenvironment microarray (MEArray) platform and methods. In some embodiments, the MEArray platform may be used for cell-based functional screening of interactions with combinatorial microenvironments.
In other embodiments, the present invention describes methods allowing for simultaneous control of the molecular composition and the elastic modulus, and combines the use of widely available microarray and micropatterning technologies. In some embodiments, MEArray screens require as few as 10,000 cells per array, which facilitate functional studies of cell and microenvironment interactions including rare cell types such as adult progenitor cells.
In one embodiment, the substrate is a planar glass or polymer surface. It is contemplated that the substrate can be any shaped or sized surface including but not limited to beads or particles, or other substrate surfaces.
Monomers can be polymerized on the substrate surface or the surface can be coated with a polymer. In some embodiments, the polymer comprising polydimethylsiloxanes (PDMS), polyacrylamides (PA), polyurethanes, polyethylene glycol, poly(N-isopropylacrylamide), gelatin, or agarose.
In another embodiment, the present invention comprises tuning the elastic modulus of the platform polymers to mimic the stiffnesses of different tissues. For example, the elastic modulus can be tuned by altering the base/cure ratio of polymers such as polydimethylsiloxane (PDMS), or the acrylamide/bis-acrylamide ratio of polyacrylamide (PA). In some embodiments, PDMS can mimic stiffer tissues in the range of 1-10 MPa (e.g., cartilage, cornea, and arterial walls), and PA can mimic softer tissues in the range of 100 Pa-100 kPa (e.g. breast, brain, liver, and prostate).
In some embodiments, the combinatorial microenvironment platform is used to study or detect functional interactions between specific cell or cell types in a specific tissue microenvironment. In further embodiments, the effect of drugs, toxins, analytes or other environmental substances on cells in a particular tissue microenvironment can be studied.
In some embodiments, a method of screening cellular response to a drug comprising the steps of: (a) providing a combinatorial elastic modulus-modified microenvironment microarray (eMEArray) as prepared in claim 10; (b) incubating said eMEArray; (c) contacting a drug with the cells and the eMEArray; (c) detecting any change in the cell.
In other embodiments, modulating or changing a proposed therapeutic regimen based on the drug-cell interaction observed in the eMEArrays. For example, since a sensitive response of cells to Lapatinib in tissues or microenvironments having a similar elastic modulus to 40 kPa was observed and a resistant response was observed in 400 kPa eMEArrays, a therapeutic regimen of using Lapatinib in bone cancers may not be suggested if that would promote a resistant response from cells. Conversely, use of Lapatinib in soft tissues and tumors would likely promote a sensitive response.
In another embodiment, the present MEArrays and methods are used to study the interactions between drugs and cells in an array of microenvironments. Interactions of well-known cancer drugs used effectively for a specific cancer type can be studied in the microenvironment of another tissue to determine the therapeutic effect or any reduction in therapeutic effect that is due to the microenvironment.
Computational and combinatorial chemistry set the course for contemporary drug design and discovery for the last twenty years. In spite of technological advances that dramatically increased compound throughput, the rate of clinically successful therapeutics has not changed significantly. Compounds are identified largely on the behavior of tumor cell lines grown in plastic dishes, ignoring an obvious lack of accurate tissue context —for instance the stiffness of plastic (>3 GigaPa) is many orders of magnitude greater than a soft tissue like breast (˜400 Pa) or even bone (˜1 MegaPa). See Alcaraz, J., et al., Laminin and biomimetic extracellular elasticity enhance functional differentiation in mammary epithelia. EMBO J, 2008. 27(21): p. 2829-38; Levental, K. R., et al., Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell, 2009. 139(5): p. 891-906, both of which are hereby incorporated by reference. Rodent models offer an in vivo microenvironment, but large-scale in vivo screening of combinatorial chemical compound- or gene-libraries remains challenging. Hence, the inability to easily study human cells in their native microenvironment represents a significant challenge in drug discovery, and in cancer research more generally. One of the inventors with others described in Mark A. LaBarge, Celeste M. Nelson et al., “Human mammary progenitor cell fate decisions are products of interactions with combinatorial microenvironments,” Integrative Biology, 2009 January; 1(1):70-9. Epub 2008 Nov. 12, MEArrays and methods of making certain arrays with cells previously, and hereby incorporated by reference for all purposes.
Herein we describe combinatorial mimetic microenvironments fabricated in vitro for cell-based functional screening of interactions with combinatorial microenvironments of various tissues. Herein we further describe compositions and methods based upon our finding that the elastic modulus and molecular composition of the microenvironment will alter therapeutic responses. Drug responses often differ significantly between in vitro and in vivo. Identification of pathways and effectors that modulate drug resistance and sensitivity in vivo is crucial to drug design and therapeutic durability.
It has been shown that microenvironment can affect therapeutic effects via not only chemical components of microenvironment, but also physical properties. For example, myeloma cells have cell adhesion mediated drug resistance via fibronectin-β1 integrin interaction. (Jason S. Damiano, Anne E. Cress et al. “Cell adhesion mediated drug resistance (CAM-DR): role of integrins and resistance to apoptosis in human myeloma cell lines,” Blood 1999 Mar. 1; 93(5):1658-670, hereby incorporated by reference). Another example is that increasing matrix stiffness promotes chemotherapeutic resistance in hepatocellular carcinoma cell lines. (Jörg Schrader, Timothy T. Gordon-Walker et al., “Matrix stiffness modulates proliferation, chemotherapeutic response, and dormancy in hepatocellular carcinoma cells,” Hepatology 2011 April; 53 (4):1192-205. doi:10.1002/hep0.24108, hereby incorporated by reference). Recent work showed that HER2-targeted therapeutic response is different in breast cancer cell lines in 2D and 3D culture microenvironments. See Britta Weigelt, Alvin T. Lo et al. Breast Cancer Research Treat (2010).
Therefore, we sought to quantify what contributions, if any, physical and molecular properties of the microenvironment made to the effect of therapeutics on cells. We found that utilizing bioengineered culture substrata and combinatorial biology we can dissect the role played by microenvironment in drug response, and identify key points of intervention for future combination therapeutic approaches.
In some embodiments, a combinatorial elastic modulus-modified microenvironment microarray (eMEArray, also referred to generally herein as MEArray) platform and methods for cell-based functional screening of interactions with combinatorial microenvironments. In some embodiments, the method allows for simultaneous control of the molecular composition and the elastic modulus, and combines the use of widely available microarray and micropatterning technologies. In some embodiments, eMEArray screens require as few as 10,000 cells per array, which facilitates functional studies of rare cell types such as adult progenitor cells. While entire tissue microenvironments are not completely recapitulated on the present MEArrays, however, comparison of responses in the same cell type to numerous related microenvironments, for instance pairwise combinations of extracellular matrix (ECM) proteins that characterize a given tissue, will provide insights into how microenvironmental components elicit tissue-specific functional phenotypes.
eMEArrays are amenable to time-lapsed analysis, but most often are used for end point analyses of cellular functions that are measureable with fluorescent probes. For instance, DNA synthesis, apoptosis, acquisition of differentiated states, or production of specific gene products are commonly measured.
In some embodiments, the basic flow of an eMEArray experiment is to prepare substrates such as glass or plastic slides coated with the printing substrata and to prepare the master plate of proteins that are to be printed. The arrays are printed with a microarray robot, cells are allowed to attach, grow in culture, and then detected. In some embodiments, the cells are chemically fixed upon reaching the experimental endpoint. Fluorescent or colorimetric assays, imaged with traditional microscopes or microarray scanners, can be used to reveal relevant molecular and cellular phenotypes (
In one embodiment, the platform comprising a substrate wherein the substrate can be a planar glass or polymer surface. It is further contemplated that the substrate can be any shaped- or sized-surface including but not limited to beads or particles, or other substrate surfaces.
Monomers can be polymerized on the substrate surface or the surface can be coated with a polymer to provide a layer of polymer on the substrate surface. In some embodiments, the polymer is polydimethylsiloxanes (PDMS), polyacrylamides (PA), polyurethanes, polyethylene glycol, poly(N-isopropylacrylamide), gelatin, or agarose.
In another embodiment, the elastic modulus of the platform polymer layer is tuned to mimic the stifihesses of different tissues. In some embodiments, the polymers may be thermocurable, UV-curable, thermoplastic or conducting polymers.
In some embodiments, the elastic modulus of the polymers can be tuned, for example, by altering the base/cure ratio of the polymers, or for example, by altering the acrylamide/bis-acrylamide ratio of PA. Various polymers and their elastic moduli are described in Kim, H. N. et al. Patterning Methods for Polymers in Cell and Tissue Engineering. Annals of Biomedical Engineering, doi:10.1007/s10439-012-0510-y (2012 June 19 online) hereby incorporated by reference for all purposes. In other embodiments, polymerization can be controlled in a gradient or variable fashion such as by the methods described in Tse and Engler, Preparation of hydrogel substrates with tunable mechanical properties. Current Protocols in Cell Biology. Chapter 10. 2010, hereby incorporated by reference.
The polymer layer of the eMEArrays can be printed using a wide variety of microenvironment components or elements such as recombinant growth factors, cytokines, and purified ECM proteins, and combinations thereof on to the polymer surface. The platform is limited only by the availability of specific reagents. Examples of some protein components include proteins including, but not limited to, Notch 1 and 3 extracellular domains, E- and P-cadherins, Jagged1, Delta-like ligand 4, Delta serrate-like peptide, sonic hedgehog, TGFβ, EGF, PDGF, FGF, IGF, IL-6, as well as integrin-blocking and -activating antibodies, collagens type I, II, III, IV, and V, laminins I and V, fibronectin, entactin, and collagenase-treated collagen 1 and 4. In some embodiments, the microenvironment components further comprising MATRIGEL.
In some embodiments, the present combinatorial microenvironment technologies are used to mimic the specialized microenvironments in which stem cells reside, called niches, which are essential to stem cell maintenance. In such embodiments, the cells used on the eMEArray are stem cells, or other kinds of progenitor cells from various tissues. In other embodiments, the present combinatorial microenvironment platform is used to study or screen cells such as tumor cells, cell lines, biopsied cells, etc.
The eMEArray method presented here enables functional analysis of cell and combinatorial microenvironment interactions. eMEArray analysis combines use of basic micropatterning technologies, cell biology, and microarray printing robots and analysis devices that are available in many multiuser facilities. eMEArray screens are compatible with most adherent cell types, though serum-free media formulations may need to be adjusted in some cases to include BSA or <1% serum, which can improve attachment. In some embodiments, this method is only limited by the availability of reagents for analyzing a given cellular function. Fluorescence-based assays are compatible with most array-based imaging systems, but colorimetric or other probe detection assays can also work well. Other variations of this method exist and support the general idea that complex microenvironments can be functionally dissected to reveal what roles individual microenvironment molecules and combinations thereof play in a variety of cell functions.
Any microdroplet printer such as a quill printer, sound oscillator printer, or microarray printer can be used to print the polymer with the cellular microenvironments. Known or suitable printers include but are not limited to microdroplet printers by Array-it (Sunnyvale, Calif.) and Shimadzu.
The decision to use polydimethylsiloxane (PDMS)-coated or polyacrylamide (PA)-coated slides depends on the important parameters of the experimental design. The elastic modulus of both polymers can be tuned to mimic the stiffnesses of different tissues by altering the base/cure ratio of PDMS, and the acrylamide/bis-acrylamide ratio of PA. PDMS can mimic stiffer tissues in the range of 1-10 MPa (e.g. cartilage, cornea, and arterial walls), and PA can mimic softer tissues in the range of 100 Pa-100 kPa (e.g. breast, brain, liver, and prostate). See Kim, H. N. et al. Patterning Methods for Polymers in Cell and Tissue Engineering. Annals of biomedical engineering, doi:10.1007/s10439-012-0510-y (2012) hereby incorporated by reference. PDMS is inexpensive, easy to prepare, and the geometry of the printed features will be identical to the head of the printing pins. Thus the size and shape of the features can be precisely controlled using pins with different tip geometries. PDMS is more hydrophobic than PA, which causes some challenges during the cell handling and immunostaining steps, and may be incompatible with some cell lines. Because PA is a hydrogel and a native non-fouling surface, cells will only attach to spots where there are proteins that support cell adhesion. The geometry of the printed features on PA gels do not precisely follow the geometry of the pinhead; usually they become circles, due to diffusion, irrespective of the pinhead geometry that is used. Printing contact time and pin diameter parameters can be empirically determined for optimal feature size on PA gels.
Polydimethylsiloxane (PDMS)
An example of patterned protein deposition on a printed PDMS-coasted eMEArray using a square-tipped silicon pins on a quill pin microarray-printing robot is shown in
An example of an MEArray experiment showing that inverse dilutions of two microenvironment proteins elicited specific keratin expression profiles in a protein concentration-dependent manner in a human multipotent mammary epithelial progenitor cell line (D920 cells), is shown in
An example of an entire scanned MEArray printed on a 40,000 Pa PA gel is shown in
Table 1 of specific reagents and equipment.
Whether developing anti-cancer drugs, improving clinical treatment regimens, or studying human cancer cells, it is important that we are able to manifest the impact of the tissue microenvironment (ME). In this Example, we describe the MEArray platform for the application of determining the functional (e.g. apoptosis, proliferation, differentiation) impact of different tissue-mimetic MEs on drug-cell interactions. We will compare tumor cell drug responses across numerous related ME conditions (differing iteratively by one component). We will develop a representation of how each ME component, and the physical properties of elasticity and shape, work together to elicit specific functional outcomes. Standard-of-care chemotherapeutics and agents that target a specific oncogenic driver (e.g., Her2) will be employed. Context-dependent changes in the antiproliferative effects (IC50 shift) on sensitive cancer cells will be determined on pair-wise combinatorial MEs that serve as mimics of different tissues.
A therapeutically relevant example of ME-modulated drug responsiveness is that HER2-expressing breast cancer cell lines were less responsive to the HER2 kinase inhibitor lapatinib in 3D Matrigel culture compared to 2D growth. This suggested that Matrigel components mediated the resistance response [See Weigelt, B., et al., HER2 signaling pathway activation and response of breast cancer cells to HER2-targeting agents is dependent strongly on the 3D microenvironment. Breast Cancer Res Treat, 2010. 122(1): p. 35-43]. The composition of Matrigel, identified by proteomic methods [Hansen, K. C., et al., An in-solution ultrasonication-assisted digestion method for improved extracellular matrix proteome coverage. Mol Cell Proteomics, 2009. 8(7): p. 1648-57], comprises ˜50 abundant ECM and growth factor proteins. By reducing the 3D ME to predetermined combinations of ME components arrayed on low stiffness substrata (Matrigel is ˜400 Pa), we can measure the responses of breast cancer cells to drugs simultaneously in different ME contexts. An MEArray can contain thousands of unique combinatorial MEs, which can be coupled with engineered and controlled surface stiffness matrices; thus, the elastic modulus (stiffness) and the molecular components used to fabricate the arrays can be chosen to mimic specific tissues. Further, culture conditions (e.g. hypoxia) can add further relevant parameters.
Mammary epithelial cultures and cancer cell lines are available from the LBNL HMEC Bank, and the Breast Cancer Cell Line Bank[Neve, R. M., et al., A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell, 2006. 10(6): p. 515-27]. MEArrays are fabricated by microcontact printing with a quill-pin or a pressure-controlled capillary robot printer onto polyacrylamide (PA) or polydimethylsiloxane (PDMS)-coated glass microscope slides with combinatorial mixtures of ECM and recombinant proteins in an aqueous printing buffer using protocols developed and described above.
More recently we have switched to using PA in favor of PDMS because it is a non-fouling hydrogel with controllable stiffness. Slides are coated with PA prepared at ratios of bis/acrylamide to generate elastic moduli that mimic the stiffness of the target tissue (˜200 Pa-40,000 Pa). See Example 1, and Lin, C., J. K. Lee, and M. A. LaBarge, Fabrication and use of MicroEnvironment microArrays (MEArrays). Journal of Visual Experimentation, 2012, in press.
Initial printed arrays will consist of 2308 printed ME with a total complexity of 192 unique pairwise combinations (thus 12 replicates per ME). The total area covered by one array is approximately 2 cm2 on the microscope slide surface. Examples of some protein components include but are not limited to: Notch 1 and 3 extracellular domains, E- and P-cadherins, Jagged1, Delta-like ligand 4, Delta serrate-like peptide, sonic hedgehog, TGFβ, EGF, PDGF, FGF, IGF, IL-6, as well as integrin-blocking and -activating antibodies, collagens type I, II, III, IV, and V, laminins I and V, fibronectin, entactin, collagenase-treated collagen 1 and 4 and Matrigel.
Pairwise combinations ensure that every ME is related to at least four others by one component. Nine HER2-amplified and three HER2-negative cell lines, which represent three breast cancer subtypes (four each), will be screened on Matrigel-inspired MEArrays to determine how the therapeutic responses vary as a function of microenvironment to HER2 inhibitors (Lapatinib, Trastuzumab), or chemotherapeutics (paclitaxel, doxorubicin) at published IC50 concentrations for each cell line [Konecny, G. E., et al., Activity of the dual kinase inhibitor lapatinib (GW572016) against HER-2-overexpressing and trastuzumab-treated breast cancer cells. Cancer Res, 2006. 66(3): p. 1630-9], after lh and 48 h of exposure. Cells are fixed and stained with antibodies to permit the detection of relevant markers, e.g. EdU, Caspase3, TUNEL, keratin 14/8/19, or function-specific fluorescent probes. Automated image acquisition and image analysis is conducted to quantify morphological and marker fluorescence intensity using the Zeiss 710 LSM and available software packages (e.g. ImageJ, Matlab). Ratiometric profiles will be generated using a standard microarray scanner (e.g. Axon 4200, LBNL) and the subsequent analysis will be performed using GenePix 6.0, Cluster, Treeview, and Matlab software packages.
Comparison of the mean log2 ratio of mean fluorescence for each feature is compared to control (collagen I) to determine whether the ME constituents of that feature impose a phenotype on the cells relative to control. MEs that elicit resistance phenotypes statistically different from the control features are detected by associating a p-value to the control paired with each unique ME by Dunnette's T-test. Variance of the means is confirmed by ANOVA.
Patterns of functional phenotypes that result from the interactions of 192 different microenvironments with 12 genetically diverse cell lines and 4 different drugs at two time points will be generated. Robust evidence of that ME modulates drug responses at early stages of exposure. Genetic diversity, among cell lines, will have a stabilizing impact for identifying molecular markers.
Referring now to
Thus, the eMEArrays and the methods described herein may be used to identify key regulators of an ME-driven drug response phenotype which can later be validated in the 3D matrigel culture model to determine whether the response phenotypes can be predictably modulated.
Recent work showed that HER2-targeted therapeutic response is different in breast cancer cell lines in 2D and 3D culture microenvironments and described in Justin R. Tse, Adam J. Engler et al. Current Protocols in Cell Biology (2010), hereby incorporated by reference. Therefore, we wanted to quantify what contributions, if any, physical and molecular properties of the microenvironment made to the effect of therapeutics on cells. Utilizing bioengineered culture substrata and combinatorial biology we can dissect the role played by microenvironment in drug response, and identify key points of intervention for future combination therapeutic approaches.
Based on our previous years experience with polyacrylamide (PA) based MEArrays we fabricated MEArrays with 160 unique microenvironments meant to represent ECM and growth factor compositions at a variety of putative metastatic sites. The metastatic sites were mimicked still more by printing atop of PA gels tuned to different elastic moduli: 400 Pa, 2500 Pa, 4470 Pa, or 40,000 Pa. A detailed written and video protocol of the MEArray fabrication process is in press at the Journal of Visualized Experimentation (Lin et al., Fabrication and use of microenvironment microarrays (MEArrays), J Vis Exp. 2012 Oct. 11; (68) and hereby incorporated by reference.
During the revamping of the MEArray platform to incorporate tunable elastic modulus, we tested the impact of stiffness (elastic modulus, measured in Pascals (Pa)) alone on responses to lapatinib in HER2+ breast cancer cell line HCC 1569 and in HER2-BT549 cells. We noted that HCC1569 cultured on 2D PA tuned to the physiological stiffness of 400 Pa (Matrigel is ˜400 Pa, normal breast is 200-2400 Pa, whereas TC plastic is >3 GigaPa), crosslinked to type 1 collagen to support cell adhesion, and treated with 1.5 uM lapatinib phenocopied the response of HCC1569 grown in 3D Matrigel (
We previously demonstrated that actinomyosin network inhibitors Y27632 (ROCK1/2 inhibitor), Blebbistatin (myosinII inhibitor), and ML-7 (myosin lightchain kinase inhibitor) altered the modulus-dependent lapatinib response on PA gels. In the case of ML-7, combination of ML-7 with Lapatinib exhibited a synergistic response that caused massive cell death on 2D PA gels. To better understand why changing the elastic modulus of the culture substrata altered sensitivity to lapatinib on PA gels we used phospho-specific intracellular flow cytometry techniques to measure the ratio of phosphorylated HER2 (pHER2, which is considered activated) to total HER2. Cells were first fixed and stained with an antibody that recognized total HER2 on the cell surface, then the cells were permeabilized and stained for pHER2 prior to multicolor analysis on a flow cytometer. HCC1569 cultured on 400 Pa, 2500 Pa, 4470 Pa, 40 KPa gels, or TC plastic while treated with 1.5 uM lapatinib for 4 days showed a higher ratio of pHER2 to total HER2 on more compliant substrata, and that ratio was inversely related to EdU incorporation (
Elastic Modulus of the Culture Substrata Altered HER-2-Targeted Therapeutic (Lapatinib) Response in HER-2+Breast Cancer Cell Lines.
There is a large difference of stiffness between tissue culture dishes and physiological body tissues. By tuning the stiffness of polyacrylamide (PA) gels, we are able to study drug response on different elastic modulus of substrata. Functionalized polyacrylamide (PA) cell culture gels for tunable elastic modulus were made as described above. In some embodiments, the PA gels can be tuned using the methods described in the Examples above, or using the methods known in the art including those described in Justin R. Tse, Adam J. Engler et al. Current Protocols in Cell Biology (2010).
We sought to determine if HER-2 drug response was different between 2D and 3D culture environment and whether that difference is due to different substrata stiffness. Cells were grown on plastic tissue culture dishes (2D), functionalized polyacrylamide cell culture (PA) gels, and in 3D (Matrigel on top, RPMI1640 with 1% FBS and 5% Matrigel 4 days growth then 2 days with 1.5 μM Lapatinib). Referring now to
We next sought to determine if the actinomyosin network plays a role in this different drug response. Cells were grown in 2D, on PA gels, and in 3D Matrigel with 2 days growth, 1 hr w/inhibitors, then 2 days with 1.5 μM lapatinib. Referring now to
To determine whether the actinomyosin network is involved in regulating the modulus-dependent regulation of HER2, HCC 1569 were exposed to Blebbistatin or Y27632 for 24 hours on PA gels of differing compliance. In that short time period, the ratios of pHER2/HER2 exhibited slightly different phenotypes than what was measured in the longer-term 4 day experiment. Nevertheless, modulus-dependent regulation was observed in controls, but was absent in cells treated with the actinomyosin inhibitors (
Abrogation of the modulus-dependent responses on compliant 2D gels by addition of actinomyosin network inhibitors suggested that the mechanosensing network could almost entirely account for results obtained on engineered 2D gel surfaces. To determine whether the 3D Matrigel context response was due only to the physiological modulus, we combined the actinomyosin network inhibitors together with lapatinib and compared the responses of cells in 3D to cells cultured on 2D TC plastic. Adding either Y27632 or ML-7 alone did not have any effect on EdU incorporation on TC plastic or in 3D, and lapatinib alone exhibited the expected context dependent responses (
Different Combinations of ECM Modified Responses to Lapatinib in HCC1569 Cells.
We next sought to determine if the concentration of type I collagen affected HER-2 targeted drug response. Referring now to
Referring now to
We chose for further validation five ME that exhibited strong and reproducible differences compared to type 1 collagen only on the MEArrays, they were: type II collagen, Laminin 1, type 1 Collagen+IL-8, type III collagen, and type 1 +type 4 collagens. To verify the MEArray results, larger PA gels were fabricated with those 5 different ME and HCC1569 were cultured atop of them and exposed to lapatinib (
Referring now to
Therefore, to summarize, HCC 1569 is inhibited more by Lapatinib in 3D culture than in 2D culture, elastic modulus of substrata plays a role in altering drug response to Lapatinib in HCC1569, and different ECM combinations imposed Lapatinib resistant or sensitive states in HCC 1569. Thus, it is contemplated that a cancer cell metastasized to a completely different organ, with a completely different microenvironmental milieu, will exhibit a different therapeutic response in the new microenvironment. Thus, these approaches using the eMEArrays and PA gels, will allow us to better understand the factors that impact drug response.
The above examples are provided to illustrate the invention but not to limit its scope. Other variants of the invention will be readily apparent to one of ordinary skill in the art and are encompassed by the appended claims. All references, publications, databases, and patents listed herein are hereby incorporated by reference for all purposes.
This application is a non-provisional of and claims priority to U.S. Provisional Patent Application No. 61/655,896, filed on Jun. 5, 2012, and to U.S. Provisional Patent Application No. 61/705,727, filed on Sep. 26, 2012, both of which are hereby incorporated by reference.
This invention was made with government support under Grant Numbers AG033176 and AG040081 awarded by the National Institute on Aging and by Laboratory Directed Research and Development and Contract No. DE-ACO2-05CH11231 awarded by the U.S. Department of Energy. The government has certain rights in the invention.
Number | Date | Country | |
---|---|---|---|
61655896 | Jun 2012 | US | |
61705727 | Sep 2012 | US |