assessment-resistance-to-tyrosine-kinase-inhibitors-an-interrogation

Assessment of Resistance to Tyrosine Kinase Inhibitors by an Interrogation of Signal Transduction Pathways by Antibody Arrays

Cancer patients with an aberrant regulation of the protein phosphorylation networks are often treated with the tyrosine kinase inhibitors. Response rates ...

University of Southern California

Proteomic-based biomarker discovery approaches broadly attempt to identify proteins whose basal abundance, or change in abundance in response to a perturbation (e.g., a therapeutic intervention) is able to discriminate between populations of patients. Up until recently, the majority of approaches for discovering circulating biomarkers have focused on directly profiling serum or plasma to identify such proteins. However, the complexity and dynamic range of protein abundance in serum and plasma create a significant challenge for proteomics methods. To overcome these barriers, diverse approaches to simplify or to fractionate serum and plasma have been developed. For some diseases, such as those related to specific organs, there may be useful marker proteins that originate in the organ. Here, we describe an approach for marker discovery that focuses on the profiling of either primary tissue or cell culture models thereof.

Model-based discovery of circulating biomarkers.

Tumor-derived, circulating proteins are potentially useful as biomarkers for detection of cancer, for monitoring of disease progression, regression and recurrence, and for assessment of therapeutic response. Here we interrogated how a protein's stability, cellular localization, and abundance affect its observability in blood by mass-spectrometry-based proteomics techniques. We performed proteomic profiling on tumors and plasma from two different xenograft mouse models. A statistical analysis of this data revealed protein properties indicative of the detection level in plasma. Though 20% of the proteins identified in plasma were tumor-derived, only 5% of the proteins observed in the tumor tissue were found in plasma. Both intracellular and extracellular tumor proteins were observed in plasma; however, after normalizing for tumor abundance, extracellular proteins were seven times more likely to be detected. Although proteins that were more abundant in the tumor were also more likely to be observed in plasma, the relationship was nonlinear: Doubling the spectral count increased detection rate by only 50%. Many secreted proteins, even those with relatively low spectral count, were observed in plasma, but few low abundance intracellular proteins were observed. Proteins predicted to be stable by dipeptide composition were significantly more likely to be identified in plasma than less stable proteins. The number of tryptic peptides in a protein was not significantly related to the chance of a protein being observed in plasma. Quantitative comparison of large versus small tumors revealed that the abundance of proteins in plasma as measured by spectral count was associated with the tumor size, but the relationship was not one-to-one; a 3-fold decrease in tumor size resulted in a 16-fold decrease in protein abundance in plasma. This study provides quantitative support for a tumor-derived marker prioritization strategy that favors secreted and stable proteins over all but the most abundant intracellular proteins.

Impact of protein stability, cellular localization, and abundance on proteomic detection of tumor-derived proteins in plasma.

Clinical tools to accurately describe, evaluate, and predict an individual's response to cancer therapy are a field-wide priority; in many advanced cancers, only 10-20% of individuals will have a clinical benefit from therapy, yet we treat the entire population. Furthermore, many therapies are initially effective, but lose effectiveness over time. Here we describe methods to derive in vitro models of resistance to EGFR tyrosine kinase inhibitors. We additionally describe approaches to characterize possible mechanisms of resistance by genomic and transcriptomic approaches.

Investigation of acquired resistance to EGFR-targeted therapies in lung cancer using cDNA microarrays.

Clinical oncology is hampered by lack of tools to accurately assess a patient's response to pathway-targeted therapies. Serum and tumor cell surface proteins whose abundance, or change in abundance in response to therapy, differentiates patients responding to a therapy from patients not responding to a therapy could be usefully incorporated into tools for monitoring response. Here, we posit and then verify that proteomic discovery in in vitro tissue culture models can identify proteins with concordant in vivo behavior and further, can be a valuable approach for identifying tumor-derived serum proteins. In this study, we use stable isotope labeling of amino acids in culture (SILAC) with proteomic technologies to quantitatively analyze the gefitinib-related protein changes in a model system for sensitivity to EGF receptor (EGFR)-targeted tyrosine kinase inhibitors. We identified 3,707 intracellular proteins, 1,276 cell surface proteins, and 879 shed proteins. More than 75% of the proteins identified had quantitative information, and a subset consisting of 400 proteins showed a statistically significant change in abundance following gefitinib treatment. We validated the change in expression profile in vitro and screened our panel of response markers in an in vivo isogenic resistant model and showed that these were markers of gefitinib response and not simply markers of phospho-EGFR downregulation. In doing so, we also were able to identify which proteins might be useful as markers for monitoring response and which proteins might be useful as markers for a priori prediction of response.

Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition.

Anterior gradient 2 (AGR2) is associated with metastatic progression in prostate cancer cells as well as other normal and malignant tissues. We investigated AGR2 expression in patients with metastatic prostate cancer.

Anterior gradient 2 (AGR2): blood-based biomarker elevated in metastatic prostate cancer associated with the neuroendocrine phenotype.

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Un toolkit multipiattaforma per la spettrometria di massa e proteomica.

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Un toolkit multiplataforma para espectrometr&iacute;a de masas y prote&oacute;mica.

Circulating biomarkers are of significant interest for cancer detection and treatment personalization. However, the biophysical processes that determine how proteins are shed from cancer cells or their microenvironment, diffuse through tissue, enter blood vasculature, and persist in circulation remain poorly understood. Since approaches primarily focused on experimental evaluation are incapable of measuring the shedding and persistence for every possible marker candidate, we propose an interdisciplinary computational/experimental approach that includes computational modeling of tumor tissue heterogeneity. The model implements protein production, transport, and shedding based on tumor vascularization, cell proliferation, hypoxia, and necrosis, thus quantitatively relating the tumor and circulating proteomes. The results highlight the dynamics of shedding as a function of protein diffusivity and production. Linking the simulated tumor parameters to clinical tumor and vascularization measurements could potentially enable this approach to reveal the tumor-specific conditions based on the protein detected in circulation and thus help to more accurately manage cancer diagnosis and treatment. 

Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor.

The ability to enumerate all of the proteins in a cell is quickly becoming a reality. Quantitative proteomics adds an extra dimension to proteome-wide discovery experiments by enabling differential measurements of protein concentrations, characterization of protein turnover, increased stringency of co-immunoprecipitation reactions, as well as many other intriguing applications. One of the most widely used techniques that enable relative protein quantitation is stable isotope labeling by amino acids in cell culture (SILAC) (Ong et al., Mol Cell Proteomics 1(5):376-386, 2002). Over the past decade, SILAC has become the preferred approach for proteome-wide quantitation by mass spectrometry. This approach relies on the metabolic incorporation of isotopically enriched amino acids into the proteome of cells-the proteome of "light" (H, C, N) cells can then be compared to "heavy" (H, C, N) cells as the isotopically labeled proteins and peptides are easily distinguished in a mass spectrometer. Since cellular uptake and response to isotopically different amino acid(s) is naïve, it is without impact on cell physiology. We provide a detailed step-by-step procedure for performing SILAC-based experiment for proteome-wide quantitation in this chapter.

Quantitative Proteomics Using SILAC.

Androgen receptor (AR) regulation pathways are essential for supporting the growth and survival of prostate cancer cells. Recently, sub-populations of prostate cancer cells have been identified with stem cell features and are associated with the emergence of treatment-resistant prostate cancer. Here, we explored the function of AR in prostate cancer-associated fibroblasts (CAFs) relative to growth and stem cell-associated characteristics. CAFs were isolated from the murine cPtenL prostate cancer model and cultured with human prostate cancer epithelial (hPCa) cells. A murine-specific AR antisense oligonucleotide (ASO) was used to suppress the expression of AR in the CAF cells. CAFs express low, but significant levels of AR relative to fibroblasts derived from non-malignant tissue. CAFs promoted growth and colony formation of hPCa cells, which was attenuated by the suppression of AR expression. Surprisingly, AR-depleted CAFs promoted increased stem cell marker expression in hPCa cells. Interferon gamma (IFN-γ) and macrophage colony-stimulating factor (M-CSF) were increased in AR-depleted CAF cells and exhibited similar effects on stem cell marker expression as seen in the CAF co-culture systems. Clinically, elevated IFN-γ expression was found to correlate with histologic grade in primary prostate cancer samples. In summary, AR and androgen-dependent signaling are active in CAFs and exert significant effects on prostate cancer cells. IFN-γ and M-CSF are AR-regulated factors secreted by CAF cells, which promote the expression of stem cell markers in prostate cancer epithelial cells. Understanding how CAFs and other constituents of stromal tissue react to anti-cancer therapies may provide insight into the development and progression of prostate cancer.

Androgen receptor in cancer-associated fibroblasts influences stemness in cancer cells.

Mutations or deletions in exons 18-21 in the ) are present in approximately 15% of tumors in patients with non-small cell lung cancer (NSCLC). They lead to activation of the EGFR kinase domain and sensitivity to molecularly targeted therapeutics aimed at this domain (gefitinib or erlotinib). These drugs have demonstrated objective clinical response in many of these patients; however, invariably, all patients acquire resistance. To examine the molecular origins of resistance, we derived a set of gefitinib-resistant cells by exposing lung adenocarcinoma cell line, HCC827, with an activating mutation in the EGFR tyrosine kinase domain, to increasing gefitinib concentrations. Gefitinib-resistant cells acquired an increased expression and activation of JUN, a known oncogene involved in cancer progression. Ectopic overexpression of JUN in HCC827 cells increased gefitinib IC from 49 nmol/L to 8 μmol/L ( < 0.001). Downregulation of JUN expression through shRNA resensitized HCC827 cells to gefitinib (IC from 49 nmol/L to 2 nmol/L;  < 0.01). Inhibitors targeting JUN were 3-fold more effective in the gefitinib-resistant cells than in the parental cell line ( < 0.01). Analysis of gene expression in patient tumors with EGFR-activating mutations and poor response to erlotinib revealed a similar pattern as the top 260 differentially expressed genes in the gefitinib-resistant cells (Spearman correlation coefficient of 0.78,  < 0.01). These findings suggest that increased JUN expression and activity may contribute to gefitinib resistance in NSCLC and that JUN pathway therapeutics merit investigation as an alternate treatment strategy. .

JUN-Mediated Downregulation of EGFR Signaling Is Associated with Resistance to Gefitinib in EGFR-mutant NSCLC Cell Lines.

Monocyte chemoattractant protein-1 (MCP-1) stimulates the migration of monocytes to inflammatory sites, leading to the progression of many diseases. Recently, we described a monocyte-targeting peptide amphiphile micelle (MCP-1 PAM) incorporated with the chemokine receptor CCR2 binding motif of MCP-1, which has a high affinity for monocytes in atherosclerotic plaques. We further report here the biomimetic components of MCP-1 PAMs and the influence of the nanoparticle upon binding to monocytes. We report that MCP-1 PAMs have enhanced secondary structure compared to the MCP-1 peptide. As a result, MCP-1 PAMs displayed improved binding and chemoattractant properties to monocytes, which upregulated the inflammatory signaling pathways responsible for monocyte migration. Interestingly, when MCP-1 PAMs were incubated in the presence of prostate cancer cells in vitro, the particle displayed anticancer efficacy by reducing CCR2 expression. Given that monocytes play an important role in tumor cell migration and invasion, our results demonstrate that PAMs can improve the native biofunctional properties of the peptide and may be used as an effective inhibitor to prevent chemokine-receptor interactions that promote disease progression.

Protein Mimetic and Anticancer Properties of Monocyte-Targeting Peptide Amphiphile Micelles.

To monitor therapies targeted to epidermal growth factor receptors (EGFR) in non-small cell lung cancer (NSCLC), we investigated Peroxiredoxin 6 (PRDX6) as a biomarker of response to anti-EGFR agents.

A blood biomarker for monitoring response to anti-EGFR therapy.

The cancer-associated protein Anterior Gradient 2 (AGR2) has been described, predominantly in adenocarcinomas. Increased levels of extracellular AGR2 (eAGR2) have been correlated with poor prognosis in cancer patients, making it a potential biomarker. Additionally, neutralizing AGR2 antibodies showed preclinical effectiveness in murine cancer models suggesting eAGR2 may be a therapeutic target. We set out to identify a peptide by mRNA display that would serve as a theranostic tool targeting AGR2. This method enables the selection of peptides from a complex (>10) library and incorporates a protease incubation step that filters the selection for serum stable peptides. We performed six successive rounds of enrichment using a 10-amino acid mRNA display library and identified several AGR2 binding peptides. One of these peptides (H10), demonstrated high affinity binding to AGR2 with a binding constant (K) of 6.4 nM. We developed an AGR2 ELISA with the H10 peptide as the capture reagent. Our H10-based ELISA detected eAGR2 from cancer cell spent media with a detection limit of (20-50 ng/ml). Furthermore, we investigated the therapeutic utility of H10 and discovered that it inhibited cell viability at IC (9-12 μmoles/L) in cancer cell lines. We also determined that 10 μg/ml of H10 was sufficient to inhibit cancer cell migration in breast and prostate cancer cell lines. A control peptide did not show any appreciable activity in these cells. The H10 peptide showed promise as both a novel diagnostic and a potential therapeutic peptide.

Article	Year
Model-based discovery of circulating biomarkers. Methods in molecular biology (Clifton, N.J.)\| PubMed ID: 21468942	2011
Impact of protein stability, cellular localization, and abundance on proteomic detection of tumor-derived proteins in plasma. PloS one\| PubMed ID: 21829587	2011
Investigation of acquired resistance to EGFR-targeted therapies in lung cancer using cDNA microarrays. Methods in molecular biology (Clifton, N.J.)\| PubMed ID: 21960227	2012
Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition. Molecular cancer therapeutics\| PubMed ID: 22411897	2012
Anterior gradient 2 (AGR2): blood-based biomarker elevated in metastatic prostate cancer associated with the neuroendocrine phenotype. The Prostate\| PubMed ID: 22911164	2013
A cross-platform toolkit for mass spectrometry and proteomics. Nature biotechnology\| PubMed ID: 23051804	2012
Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor. Cancer informatics\| PubMed ID: 26715830	2015
Quantitative Proteomics Using SILAC. Methods in molecular biology (Clifton, N.J.)\| PubMed ID: 28188530	2017
Androgen receptor in cancer-associated fibroblasts influences stemness in cancer cells. Endocrine-related cancer\| PubMed ID: 28264911	2017
JUN-Mediated Downregulation of EGFR Signaling Is Associated with Resistance to Gefitinib in EGFR-mutant NSCLC Cell Lines. Molecular cancer therapeutics\| PubMed ID: 28566434	2017
Protein Mimetic and Anticancer Properties of Monocyte-Targeting Peptide Amphiphile Micelles. ACS biomaterials science & engineering\| PubMed ID: 29302619	2017
A blood biomarker for monitoring response to anti-EGFR therapy. Cancer biomarkers : section A of Disease markers\| PubMed ID: 29689709	2018
Identification, characterization and application of a new peptide against anterior gradient homolog 2 (AGR2). Oncotarget\| PubMed ID: 29937991	2018

Kian Kani

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