Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition

Transferrin receptor-based circulating tumor cell enrichment provides a snapshot of the molecular landscape of solid tumors and correlates with clinical outcomes

Circulating tumor cells (CTCs) captured from the bloodstream of patients with solid tumors have the potential to accelerate precision oncology by providing insight into tumor biology, disease progression and response to treatment. However, their potential is hampered by the lack of standardized CTC enrichment platforms across tumor types. EpCAM-based CTC enrichment, the most commonly used platform, is limited by EpCAM downregulation during metastasis and the low EpCAM expression in certain tumor types, including the highly prevalent and lethal NSCLC. In this study we demonstrate that Transferrin Receptor (TfR) is a selective, efficient biomarker for CTC identification and capture in patients with prostate, pancreatic and NSCLC. TfR identifies significantly higher CTC counts than EpCAM, and TfR + -CTC enumeration correlates with disease progression in metastatic prostate and pancreatic cancers, and overall survival and osimetrinib-resistance in non-small cell lung cancer (NSCLC). Profiling of TfR + -CTCs provides a snapshot of the molecular landscape of each respective tumor type and identifies potential mechanisms underlying treatment response to EGFR TKi and immune checkpoint inhibitors in NSCLC.

One sentence summary

Transferrin Receptor identifies circulating tumor cells in solid tumors.

A blood biomarker for monitoring response to anti-EGFR therapy

BACKGROUND AND OBJECTIVE

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.

METHODS

We studied cells that are sensitive (H3255, HCC827) or resistant (H1975, H460) to gefitinib. PRDX6 was examined with either gefitinib or vehicle treatment using enzyme-linked immunosorbent assays. We created xenograft models from one sensitive (HCC827) and one resistant cell line (H1975) and monitored serum PRDX6 levels during treatment.

RESULTS

PRDX6 levels in cell media from sensitive cell lines increased significantly after gefitinib treatment vs. vehicle, whereas there was no significant difference for resistant lines. PRDX6 accumulation over time correlated positively with gefitinib sensitivity. Serum PRDX6 levels in gefitinib-sensitive xenograft models increased markedly during the first 24 hours of treatment and then decreased dramatically during the following 48 hours. Differences in serum PRDX6 levels between vehicle and gefitinib-treated animals could not be explained by differences in tumor burden.

CONCLUSIONS

Our results show that changes in serum PRDX6 during the course of gefitinib treatment of xenograft models provide insight into tumor response and such an approach offers several advantages over imaging-based strategies for monitoring response to anti-EGFR agents.

Quantitative proteomics analysis identifies MUC1 as an effect sensor of EGFR inhibition

Tumor responses to cancer therapeutics are generally monitored every 2-3 months based on changes in tumor size. Dynamic biomarkers that reflect effective engagement of targeted therapeutics to the targeted pathway, so-called "effect sensors", would fulfill a need for non-invasive, drug-specific indicators of early treatment effect. Using a proteomics approach to identify effect sensors, we demonstrated MUC1 upregulation in response to epidermal growth factor receptor (EGFR)-targeting treatments in breast and lung cancer models. To achieve this, using semi-quantitative mass spectrometry, we found MUC1 to be significantly and durably upregulated in response to erlotinib, an EGFR-targeting treatment. MUC1 upregulation was regulated transcriptionally, involving PI3K-signaling and STAT3. We validated these results in erlotinib-sensitive human breast and non-small lung cancer cell lines. Importantly, erlotinib treatment of mice bearing SUM149 xenografts resulted in increased MUC1 shedding into plasma. Analysis of MUC1 using serial blood sampling may therefore be a new, relatively non-invasive tool to monitor early and drug-specific effects of EGFR-targeting therapeutics.

Harnessing Integrative Omics to Facilitate Molecular Imaging of the Human Epidermal Growth Factor Receptor Family for Precision Medicine

Cancer is a growing problem worldwide. The cause of death in cancer patients is often due to treatment-resistant metastatic disease. Many molecularly targeted anticancer drugs have been developed against 'oncogenic driver' pathways. However, these treatments are usually only effective in properly selected patients. Resistance to molecularly targeted drugs through selective pressure on acquired mutations or molecular rewiring can hinder their effectiveness. This review summarizes how molecular imaging techniques can potentially facilitate the optimal implementation of targeted agents. Using the human epidermal growth factor receptor (HER) family as a model in (pre)clinical studies, we illustrate how molecular imaging may be employed to characterize whole body target expression as well as monitor drug effectiveness and the emergence of tumor resistance. We further discuss how an integrative omics discovery platform could guide the selection of 'effect sensors' - new molecular imaging targets - which are dynamic markers that indicate treatment effectiveness or resistance.

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

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.

Broader implications of SILAC-based proteomics for dissecting signaling dynamics in cancer

Large-scale transcriptome and epigenome analyses have been widely utilized to discover gene alterations implicated in cancer development at the genetic level. However, mapping of signaling dynamics at the protein level is likely to be more insightful and needed to complement massive genomic data. Stable isotope labeling with amino acids in cell culture (SILAC)-based proteomic analysis represents one of the most promising comparative quantitative methods that has been extensively employed in proteomic research. This technology allows for global, robust and confident identification and quantification of signal perturbations important for the progress of human diseases, particularly malignancies. The present review summarizes the latest applications of in vitro and in vivo SILAC-based proteomics in identifying global proteome/phosphoproteome and genome-wide protein-protein interactions that contribute to oncogenesis, highlighting the recent advances in dissecting signaling dynamics in cancer.

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

BACKGROUND

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.

METHODS

Blood was collected from 44 patients with metastatic prostate cancer separated as: castration sensitive prostate cancer (CSPC, n = 5); castration resistant prostate cancer (CRPC, n = 36); and neuroendocrine-predominate CRPC defined by PSA ≤ 1 ng/ml in the presence of wide-spread metastatic disease (NE-CRPC, n = 3). AGR2 mRNA levels were measured with RT-PCR in circulating tumor cell (CTC)-enriched peripheral blood. Plasma AGR2 levels were determined via ELISA assay. AGR2 expression was modulated in prostate cancer cell lines using plasmid and viral vectors.

RESULTS

AGR2 mRNA levels are elevated in CTCs and strongly correlated with CTC enumeration. Plasma AGR2 levels are elevated in all sub-groups. AGR2 levels vary independently to PSA and change in some patients in response to androgen-directed and other therapies. Plasma AGR2 levels are highest in the NE-CRPC sub-group. A correlation between AGR2, chromagranin A (CGA), and neuron-specific enolase (NSE) expression is demonstrated in prostate cancer cell lines.

CONCLUSIONS

We conclude that AGR2 expression is elevated at the mRNA and protein level in patients with metastatic prostate cancer. In particular, we find that AGR2 expression is associated features consistent with neuroendocrine, or anaplastic, prostate cancer, exemplified by an aggressive clinical phenotype without elevation in circulating PSA levels. Further studies are warranted to explore the mechanistic and prognostic implications of AGR2 expression in this patient population.