Development and initial clinical testing of a multiplexed circulating tumor cell assay in patients with clear cell renal cell carcinoma

Abstract

Although therapeutic options for patients with advanced renal cell carcinoma (RCC) have increased in the past decade, no biomarkers are yet available for patient stratification or evaluation of therapy resistance. Given the dynamic and heterogeneous nature of clear cell RCC (ccRCC), tumor biopsies provide limited clinical utility, but liquid biopsies could overcome these limitations. Prior liquid biopsy approaches have lacked clinically relevant detection rates for patients with ccRCC. This study employed ccRCC-specific markers, CAIX and CAXII, to identify circulating tumor cells (CTC) from patients with metastatic ccRCC. Distinct subtypes of ccRCC CTCs were evaluated for PD-L1 and HLA-I expression and correlated with patient response to therapy. CTC enumeration and expression of PD-L1 and HLA-I correlated with disease progression and treatment response, respectively. Longitudinal evaluation of a subset of patients demonstrated potential for CTC enumeration to serve as a pharmacodynamic biomarker. Further evaluation of phenotypic heterogeneity among CTCs is needed to better understand the clinical utility of this new biomarker.

Trial registration: ClinicalTrials.gov NCT03203473 NCT04071223.

Keywords: biomarkers; circulating tumor cells; clear cell renal cell carcinoma; exclusion-based sample preparation; pharmacodynamic; prognostic.

Conflict of interest statement

David J. Beebe and Joshua M. Lang hold equity in Salus Discovery LLC, which has licensed technology utilized in the manuscript. D. J. Beebe holds equity in Tasso, Inc., Stacks for the Future, LLC, and Bellbrook Labs, LLC. Rana R. McKay is a consultant/Advisory Board Member for Bristol‐Myers Squibb, Dendreon, Exelixis, Janssen, Pfizer, Novartis, and Tempus; receives institutional research funding from Bayer and Pfizer. Xiao X. Wei receives institutional research funding from Bristol‐Myers Squibb.

© 2021 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

Figures

Fig. 1

Heterogeneity of RCC biomarkers on cells in circulation. Flow cytometric evaluation of the frequency of expression of different biomarkers on CTCs from n = 3 different patients (A, B, or C). The first row of pie graphs represents the distribution of CK‐positive CTCs (gray) vs. CK‐negative / exclusion channel negative cells (purple). The second row of Euler diagrams portrays the frequency of expression of other markers of renal cancer origin (CAIX, CAXII, and EpCAM) within the CK‐positive vs. CK‐negative / exclusion negative cell fractions. Overlapping circles indicate the co‐expression of different biomarkers on the same cells. Subpopulation frequencies are rounded to the nearest whole percent, with percentages 

Fig. 2

Representative Images and Clinical Correlations of CTC Enumeration. (A) Schematic overview of the method used to isolate and identify CTCs from whole blood. (B) Three representative CTCs and one WBC from a patient blood sample. Examples of CTCs with intact nuclei that were CAXII S+ (top), Double+ (middle), CK S+ (bottom), all Exclusion. Scale bars represent 5 microns. (C) Enumeration of all CTCs for n = 20 patients and n = 2 healthy donors where each symbol represents the number of CTCs from one patient. Significance was determined by a one tailed Mann‐Whitney test p < 0.05 (*). Error bars represent standard error. ROC curve showing sensitivity and specificity of total CTC number to differentiate between patients whose disease was progressing vs responding. (D) CTC enumeration separated by phenotype for the same n = 20 patients: CAXII single+ (green), double+ (blue), and CK single+ (gray). Percentages of different CTC subpopulations are listed in supplementary table 2. (E) two‐tailed Mann‐Whitney tests and (F) ROC evaluation of the ability of the different populations of CTCs to differentiate between therapeutic progression or response for the same n = 20 patients. ROC curve parameters are tabulated in table below ROC curves.

Fig. 3

Quantification of Immunotherapy Biomarkers on CTCs. (A) Positive and negative expression of PD‐L1 and HLA‐I expression on CTCs is shown with scale bars representing 5 microns. (B) Biomarker evaluation was performed as a single ‘All CTCs’ (black) population as well as by each subpopulation, CK single+ (gray), double+ (blue), and CAXII single+ (green), where each dot represents a single CTC. Average CTC expression is represented by a black bar through each population, and % positive is defined as the frequency of CTCs with biomarker expression above the red dotted line cutoff for positivity. Representative dataset shown from one patient sample. C) The % positive and average expression of CTCs from each patient were determined for PD‐L1 and HLA‐I for cohort = 20 patients. Samples without CTCs are indicated with an ‘X’.

Fig. 4

Clinical Utility of Biomarker Evaluation with Different CTC Populations. Scatter plots showing the average expression of either PD‐L1 or HLA‐I on either CK + or CAXII S + CTCs in patients either responding or stable (Res) vs. progressing (Prog) on either ICIs or TKIs. AUC values annotated on graphs are a measure of the ability of the assay to identify patients who are progressing. Data represent 42 total samples from 24 unique patients.

Fig. 5

Longitudinal Sampling of CTCs. CTC biomarker evaluation of 4 patients (#21‐24) over time were compared to therapeutic history (colored bars) and radiographic assessment of response (numbers on graphs and corresponding radiographic scan images) for each patient. Graphs represent enumeration for each CTC population. Dashed line represents the optimal cutoff for CK + CTC number (2.6) identified in Figure 2.