Phenotypic Heterogeneity of Circulating Tumor Cells Informs Clinical Decisions between AR Signaling Inhibitors and Taxanes in Metastatic Prostate Cancer

Abstract

The heterogeneity of an individual patient's tumor has been linked to treatment resistance, but quantitative biomarkers to rapidly and reproducibly evaluate heterogeneity in a clinical setting are currently lacking. Using established tools available in a College of American Pathologists-accredited and Clinical Laboratory Improvement Amendments-certified clinical laboratory, we quantified digital pathology features on 9,225 individual circulating tumor cells (CTC) from 179 unique metastatic castration-resistant prostate cancer (mCRPC) patients to define phenotypically distinct cell types. Heterogeneity was quantified on the basis of the diversity of cell types in individual patient samples using the Shannon index and associated with overall survival (OS) in the 145 specimens collected prior to initiation of the second or later lines of therapy. Low CTC phenotypic heterogeneity was associated with better OS in patients treated with androgen receptor signaling inhibitors (ARSI), whereas high heterogeneity was associated with better OS in patients treated with taxane chemotherapy. Overall, the results show that quantifying CTC phenotypic heterogeneity can help inform the choice between ARSI and taxanes in mCRPC patients. Cancer Res; 77(20); 5687-98. ©2017 AACR.

©2017 American Association for Cancer Research.

Figures

Figure 1. The Distribution of Patient Samples in the CTC Contributing Cohort and Clinical Association Cohort

CONSORT diagram showing the breakdown of patient samples analyzed for this study. Displayed from top to bottom is the total number of samples collected, samples included for CTC digital pathology for unsupervised clustering and phenotypic assessment (CTC Contributing Cohort), and the subset of samples used for the clinical association cohort, by therapy class administered and line of therapy.

Figure 2. CTC and Clinical Association Analysis Overview

Shown are schematics for (A) CTC detection and digital pathology analysis on single cells, as well as generation of patient-level quantification of phenotypic heterogeneity by (B) Shannon index.

Figure 3. Phenotypic cell types resulting from unsupervised clustering

(A) Shorthand description of each cell type’s distinguishing features. (B) Example images of each cell type. Blue = DAPI (DNA), Red = cytokeratins, green = CD45, white = AR. Note that AR signal indicates AR protein overexpression.

Figure 4. CTC Phenotypic Features, Cell Subtype Classifications, and Shannon Index of CTC Phenotypic Entropy in Patient Samples

(A) CTC phenotypic features included in this analysis and unsupervised clustering of the CTC phenotypic features identified across all CTCs in the CTC Contributing Cohort (n=9225) was used to categorize CTCs into 15 phenotypic subtypes (‘A’-’O’). Shown is a heat map of mean individual cell features per phenotypic subtype. High = red, low = blue. (B) Heat map of CTC phenotype densities detected per patient sample, organized by line of therapy. The bar plot above shows the resulting Shannon index by samples from the observed intra-sample diversity of CTC phenotypes. (C) An example of CTCs from a low Shannon index sample. (D) An example of CTCs from a high Shannon index sample.

Figure 5. The Degree of Inter-Sample Shannon Index is Related to Overall Survival of ARSI, but not Taxanes

(A) The relationship between degree of heterogeneity (Shannon index, x-axis) and overall survival (y-axis) is shown, along with nonparametric kernel estimates of median survival. Colors represent treatment received after pre-therapy draw. “O” = patient alive at last observation, “X” = patient died at time indicated. Overall Survival is alternately visualized with Kaplan-Meier plots from patients starting (B) ARSI and (C) Taxanes with survival curves dichotomized with the survival crossover point from (A), indicated with an arrow. Individual covariates were tested for additive power to predict overall survival using a Cox proportional hazards (PH) model. (D) The resulting p-values, hazard ratios, and 95% confidence intervals. (E) The interaction of therapy and heterogeneity integrated into the multivariate Cox PH model. The forest plot shows hazard ratios and 95% confidence intervals.

Figure 6. Heterogeneous Genomic Profiles Observed in High Phenotypic Heterogeneity Samples

Copy number variation plots are displayed in parts A and B, where chromosomes are shown left to right, 1 to 22, X and Y, odd as red, even as blue. Log2 normalized copy number ratio (sample/reference) is indicated on the y-axis for two patient samples that exhibited high CTC phenotypic heterogeneity (by both Shannon index and Pleomorphism index). (A) Red circles indicate chromosome Y loss and a chr5q deletion that were observed in sub-patterns of genomic pattern II. All cells identified for this patient are visualized here. (B) The four dominant profiles found across 53 of the 62 individual cells sequenced are illustrated. (C) Heatmap of degree of subclonality of genomic alterations present in high phenotypic heterogeneity samples, with tile darkness indicating the proportion of single-cell whole genome sequenced CTCs with the indicated genomic alteration per sample. Black tiles indicate complete clonality of a given alteration, and white indicate complete absence. Gray tiles indicate degrees of sub-clonality.