CHAPTER 1: INTRODUCTION
1.6 T HESIS OVERVIEW
erlotinib inhibition plus EGF stimulation. The measured protein abundances are consistent with previous work, and single-cell analysis uniquely reveals single-cell heterogeneity, and different types of strength of protein-protein interactions. This platform helps provide a comprehensive picture of altered signal transduction networks in tumor cells and provides insight into the effect of targeted therapies on protein signaling networks. (Chapter 2 has been taken in part from Proc Natl Acad Sci USA 109, 419-424 (2012)).
Chapter 3 further applies the single-cell microchip to the study the transition of tumor hypoxia. Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling network is less clear. In GBM cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical number of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)––a critical component of hypoxic signaling and a compelling cancer drug target––is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier's principle derived from statistical mechanics, as well as through a steady-state kinetic model of protein interactions, both of which indicate that
hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles. (Chapter 3 has been taken in part from Proc Natl Acad Sci USA 110, E1352-1360 (2013)).
Chapter 4 demonstrates the application of this technology in the preclinical cancer research to study the cancer cell resistance to molecular targeted therapy and corresponding physical approaches to anticipate therapy resistance and identify effective therapy combinations. GBM is an aggressive tumor for which there are no effective surgical or pharmacologic treatments. GBM also serves as a prototype of advanced stage cancer.
While GBM tumors contain druggable targets, resistance to single-agent targeted therapy is rapid and almost universal. Combination therapies that can anticipate resistance may provide a solution, but identifying effective combinations is largely an unmet challenge.
We empirically derived signaling network inferences from quantitative functional proteomic analysis of statistical numbers of single cell separated from the glioblastoma- derived mouse model of mTOR kinase inhibitor resistance. Our approach is based upon elucidating the detailed signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. We assayed for key elements of the phosphoprotein signaling pathways associated with GBM tumor growth and maintenance.
Analysis of how the signaling coordination responses to the targeted inhibitor reveals a rapid adaptation to the presence of the drug, with compensation that occurs via the activation of alternative signaling pathways. The analysis allows us to anticipate resistance,
and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. The analysis also unveils a general and very fast-acting resistance mechanism.
The human-derived GBM model recapitulates the heterogeneity, invasive growth, and a drug response profile reflective of clinical behavior. We sought to elucidate the general mechanism of resistance by considering two resistance mechanisms. The first, Darwinian- like selection, occurs when a drug targeted at the dominant tumor cell population generates an environment suitable for a sub-population of cancer cells to flourish. The second mechanism is one in which the same tumor cells that initially respond to the drug adapt by altering their protein signaling networks. We analyzed the tumor model at 3 stages: control, responding to an mTOR kinase inhibitor, and resistant to that inhibitor. Analysis of the effect of the mTOR inhibitor resolves two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling. This suggested that drugging one target from each mode would provide an effective treatment. We tested 3 therapy combinations expected to be effective, and 4 expected to be ineffective, in mouse tumor models. All predictions were borne out: the effective therapy combinations completely halted tumor growth until the point of drug release, with no apparent side effects. We also identified that cellular adaption, rather than Darwinian evolution, led to resistance. This finding increases the clinical relevance of this work; this resistance mechanism is not readily identified via deep sequencing, but it can be detected via a few- day in vitro analysis using single cell functional proteomics. A retrospective analysis of tumor tissues from all treatment combinations further revealed that the mTOR signaling mode was driving tumor growth, while ERK/Src signaling was the dominant resistance
mechanism. We also show that this type of analysis can be done on a clinically relevant time-scale (Chapter 4 has been taken in part from a manuscript that is currently under review in Nature Medicine).
In Chapter 5, some preliminary results about the clinical translation of single cell proteomic chips will be presented. The hypothesis is that there exists a sufficient pharmacy to treat many GBM patients, and appropriately designed assays can inform, at the individual patient level, how those drugs can be combined for effective therapy. A key challenge is that those diagnostic assays must resolve the functional heterogeneity within a given patient’s tumor. Single cell functional proteomics on statistical numbers of single cells therefore becomes a perfect candidate. Compared to model cell lines, clinical samples always have a low purity and weak functional protein expression. To meet the clinical challenges, the surface chemistries of the SCBCs has been intensively optimized to improve the assay sensitivity. This includes the use of on-chip poly-L-lysine (PLL) treatment and a covalent binding method to immobilize the DNAs to the PLL surface. A protocol on the single cell proteomic analysis of patient biopsy samples has also been developed, tested and standardized to ensure the assay reproducibility and robustness. A case study of a pediatric GBM patient sample will be discussed in detail for demonstrating the process of anticipating potential resistance and identifying the effective therapy combination within a clinical relevant time-scale.