“I was not predicting the future, I was trying to prevent it.” -- Ray Bradbury
At Epic Sciences, we are focused on improving the treatment of cancer. This requires a real-time assessment of tumor burden within a patient which is representative to the heterogeneous and evolving state of the tumors within the patient.
Utilizing the Epic CTC platform we aim to identify new therapeutic strategies as alternatives to or in addition to standard of care therapies to extend and improve patients’ lives.
Working with the Institute of Cancer Research, we identified a training model which may help predict the therapeutic benefit from androgen receptor targeting therapies for metastatic castration-resistant prostate cancer (mCRPC) patients. In the model, patients with high mean androgen receptor (AR) protein expression measured in all CTC subpopulations was associated with non-response.
In a separate study, with Memorial Sloan Kettering Cancer Center (MSKCC) we identified an association where patients who showed a durable response to androgen receptor targeted therapy also harbored low mean AR expression in all CTC subpopulations. In the MSKCC study, patients who had progressive disease after failing androgen receptor targeted therapy had high AR expression in all CTC subpopulations.
In addition to measuring AR, the protein cell surface marker PSMA is associated with androgen receptor targeted therapies failure and progressive disease. We assessed patients’ CTCs for the presence of PSMA protein to determine if a possible patient selection device could be developed to maximize therapeutic benefit to a subset of patients who may likely have increased risk of AR targeted therapy failure.
In collaboration with Progenics Pharmaceuticals, we were able to develop an assay to detect and quantify PSMA expression on diverse types of CTCs in patients with mCRPC. The assay was unaffected by the presence of a PSMA-targeted therapeutic agent. Inter- and intra-patient PSMA heterogeneity was also seen with the Epic platform, which was able to resolve PSMA expression with high fidelity.
GU14 Abstract #78: Sequential monitoring and characterization of circulating tumor cells (CTCs) using the Epic Sciences platform in metastatic castration-resistant prostate cancer (mCRPC) patients (pts) treated with recently approved therapeutics.
Figure from Abstract #78 showing a training model for prognosticating responders or non-responders of androgen targeting therapy from the mean AR of all CTC subtypes.
GU14 Abstract #132: Molecular characterization of circulating tumor cells (CTC) and CTC subpopulations in progressive metastatic castration-resistant prostate cancer (mCRPC).
Figure from Abstract #132 showing an association between mean AR of any CTCs between responders and non-responders of AR targeting therapies.
GU14 Abstract #198: Heterogeneity of prostate-specific membrane antigen (PSMA) expression in classic and apoptotic circulating tumor cells (CTC) in metastatic castration-resistant prostate cancer (mCRPC).
Figure from Abstract #198 showing PSMA protein characterization on traditional CTCs (blue dots), and apoptotic CTCs (red dots). Many patients harbored CTC heterogeneity within a blood sample which may provide clues to patients who receive partial benefit from targeted therapies.
GU14 Abstract #266: Expression of prostate-specific membrane antigen (PSMA) on circulating tumor cells (CTCs) in castration-resistant prostate cancer.
Figure from Abstract #266 showing PSMA protein expression on traditional CTC and CTC clusters. Further highlighting CTC protein heterogeneity.
Summary:
While still early, these data suggest that through the analysis of Epic CTCs, we may be able to identify patients who will fail androgen receptor targeted therapy a priori. These patients could then be assessed for patient selection and compatibility with a novel anti-PSMA antibody drug conjugate currently in clinical trials.
While we are optimistic of these early data, the road to developing clinical utility requires more studies to refine the training model. In addition, we would need to validate the model in prospective studies before a tool could be employed in clinical practice.
In combining these models with studies of CTC heterogeneity we hope to unlock the phenomenon of partial therapeutic response. The Epic platform is ideally suited to assess this heterogeneity, thus enabling clinical studies that will improve predictive medicine strategies for patient treatment.