Purpose:

Circulating tumor cells (CTC) are under investigation as a minimally invasive liquid biopsy that may improve risk stratification and treatment selection. CTCs uniquely allow for digital pathology of individual malignant cell morphology and marker expression. We compared CTC features and T-cell counts with survival endpoints in a cohort of patients with metastatic genitourinary cancer treated with combination immunotherapy.

Experimental Design:

Markers evaluated included pan-CK/CD45/PD-L1/DAPI for CTCs and CD4/CD8/Ki-67/DAPI for T cells. ANOVA was used to compare CTC burden and T-cell populations across timepoints. Differences in survival and disease progression were evaluated using the maximum log-rank test.

Results:

From December 2016 to January 2019, 183 samples from 81 patients were tested. CTCs were found in 75% of patients at baseline. CTC burden was associated with shorter overall survival (OS) at baseline (P = 0.022), but not on-therapy. Five morphologic subtypes were detected, and the presence of two specific subtypes with unique cellular features at baseline and on-therapy was associated with worse OS (0.9–2.3 vs. 28.2 months; P < 0.0001–0.013). Increasing CTC heterogeneity on-therapy had a trend toward worse OS (P = 0.045). PD-L1+ CTCs on-therapy were associated with worse OS (P < 0.01, cycle 2). Low baseline and on-therapy CD4/CD8 counts were also associated with poor OS and response category.

Conclusions:

Shorter survival may be associated with high CTC counts at baseline, presence of specific CTC morphologic subtypes, PD-L1+ CTCs, and low %CD4/8 T cells in patients with metastatic genitourinary cancer. A future study is warranted to validate the prognostic utility of CTC heterogeneity and detection of specific CTC morphologies.

Translational Relevance

There is currently an urgent need for additional risk stratification for patients with genitourinary cancers to inform treatment decisions. Circulating tumor cells represent a promising candidate liquid biopsy that may help to achieve this goal in a minimally invasive fashion. Here, we explore circulating tumor cell morphology and the heterogeneity of the morphologies expressed in an individual patient as prognostic biomarkers for metastatic genitourinary cancer patients on immunotherapy. More work is needed to validate these preliminary findings.

Circulating tumor cells (CTC) are one of many liquid biopsies currently under investigation to improve risk stratification and optimize treatment selection for patients with cancer in a minimally invasive fashion (1). To date, the majority of CTC research has relied on quantitative technology that simply counts the number of epithelial marker–positive cells detected from a peripheral blood sample (1, 2). However, a new generation of CTC technology is emerging that uses a combination of immunofluorescence microscopy and computer vision algorithms to assess CTC morphology and biomarker expression, allowing for direct evaluation of micrometastatic disease at the cellular level. This technology analyzes cell morphology of individual CTCs, similar to how a pathologist examines the cellular features of a solid tumor (3). The ability to describe a heterogeneous population of CTC subtypes seems especially valuable in the setting of primary tumor heterogeneity, whereas other liquid biopsies, such as circulating tumor DNA (ctDNA), can only report an averaged signal.

Ultimately, this work led to the novel biomarker “CTC heterogeneity,” which refers to the degree of uniformity of CTC subtypes expressed by an individual patient. Initial application of CTC subtyping and heterogeneity assessment in metastatic castration-resistant prostate cancer (mCRPC) revealed that patients with CTCs of more uniform size and shape had a better prognosis than those with greater heterogeneity of subtypes (4). Furthermore, patients who displayed multiple CTC subtypes were more likely to respond to taxane chemotherapy versus targeted therapy, suggesting a connection between the mutational burden that allows for resistance and the observation of more varied CTC morphologies. In a follow-up study, patients with mCRPC with high CTC heterogeneity responded better to immunotherapy with ipilimumab and nivolumab (5). These early findings suggest that CTC heterogeneity could potentially function as a predictive biomarker for response to immunotherapy with checkpoint inhibition. One mechanistic hypothesis for this relationship is that the presence of many different cell morphologies might possibly act as a surrogate marker for tumor mutational burden (TMB). TMB has been widely studied as a putative biomarker for immunotherapy response, with measurements taken from tumor specimens, as well as from ctDNA via liquid biopsy (6). Indeed, degree of CTC morphologic heterogeneity may be an ideal candidate for a minimally invasive marker of predicted response to immunotherapy, with the additional advantage over ctDNA that PD-L1 expression can be assessed (7).

Expression of PD-L1 allows cancer cells to inhibit the antitumor response by inducing apoptosis of cytotoxic T cells and increasing regulatory T cells (8). Given these possible tumor-induced changes, in addition to traditional CTC detection, we also applied the Epic Sciences rare cell detection platform to quantify and characterize peripheral blood T-cell populations in a cohort of patients with metastatic genitourinary cancer treated with the combination of cabozantinib and nivolumab with or without ipilimumab (trial ID: NCT02496208). Notably, cabozantinib may have immunomodulatory properties that counteract tumor-induced immunosuppression, providing a rationale for combining this agent with checkpoint inhibitors (9). In this study, we compared T-cell counts and CTC morphologic features at baseline and on-therapy at cycles 2 and 3 with progression-free survival (PFS), overall survival (OS), and response to combination therapy.

Study design

This was a retrospective analysis of two cohorts of patients treated at six centers (NIH, Bethesda, MD; City of Hope, Duarte, CA; University of California, Sacramento, CA; Ohio State University, Columbus, OH; Rutgers University, New Brunswick, NJ; and University of Southern California, Los Angeles, CA). The clinical outcomes of the phase I dose-escalation cohort have been reported previously (10). All patients provided signed informed consent to participate on a protocol approved by each institution's institutional review board/privacy board prior to blood sampling, and studies were conducted in accordance with the Declaration of Helsinki, Belmont Report, and U.S. Common Rule. Blood draws were obtained at baseline [cycle 1 day 1 (–15 days allowed); C1D1], cycle 2 day 1 (C2D1), and cycle 3 day 1 (C3D1).

Patients and treatment

From July 20, 2016 to August 27, 2019, 81 adult patients with metastatic genitourinary cancer were treated (trial ID: NCT02496208) with cabozantinib and nivolumab (n = 60) or cabozantinib, nivolumab, and ipilimumab (n = 21). CTCs were only collected during expansion cohorts with a set dose and schedule. Patients were required to have histologically confirmed metastatic urothelial carcinoma, clear cell renal cell carcinoma, adenocarcinoma of the bladder, squamous cell carcinoma of the penis, squamous cell carcinoma of the bladder, small cell carcinoma of the bladder, renal medullary carcinoma, sarcomatoid bladder and renal cell carcinoma, plasmacytoid carcinoma of the bladder, or other rare bladder/kidney cancer histology (Table 1). Metastatic disease was defined as new or progressive lesions on cross-sectional imaging with at least one site measurable by RECIST or bone disease by sodium fluoride PET-CT. Patients must have progressed on standard therapy or have no existing standard therapy for their disease, including patients with cisplatin-ineligible urothelial cancer.

Table 1.

Patient characteristics.

Patient characteristicsN = 81
Age in years (range) 62 (20–82) 
Male/female (%) 69.1/30.9 
Cancer type, N (%) 
 Urothelial 35 (43.2) 
 Urothelial, renal pelvis 16 (19.8) 
 Bladder adenocarcinoma 9 (11.1) 
 Bladder squamous cell 6 (7.4) 
 Penile 3 (3.7) 
 Bladder small cell 2 (2.5) 
 Urothelial, plasmacytoid variant 1 (1.2) 
 Renal medullary 2 (2.5) 
 Renal clear cell 4 (4.9) 
 Sarcomatoid renal cell 1 (1.2) 
 Chromophobe renal cell 1 (1.2) 
 Urachal adenocarcinoma 1 (1.2) 
Mets type, N (%) 
 Lymph node 20 (24.69) 
 Visceral mets 61 (75.31) 
 Liver mets 27 (33.33) 
 Bone mets 23 (28.40) 
Patient characteristicsN = 81
Age in years (range) 62 (20–82) 
Male/female (%) 69.1/30.9 
Cancer type, N (%) 
 Urothelial 35 (43.2) 
 Urothelial, renal pelvis 16 (19.8) 
 Bladder adenocarcinoma 9 (11.1) 
 Bladder squamous cell 6 (7.4) 
 Penile 3 (3.7) 
 Bladder small cell 2 (2.5) 
 Urothelial, plasmacytoid variant 1 (1.2) 
 Renal medullary 2 (2.5) 
 Renal clear cell 4 (4.9) 
 Sarcomatoid renal cell 1 (1.2) 
 Chromophobe renal cell 1 (1.2) 
 Urachal adenocarcinoma 1 (1.2) 
Mets type, N (%) 
 Lymph node 20 (24.69) 
 Visceral mets 61 (75.31) 
 Liver mets 27 (33.33) 
 Bone mets 23 (28.40) 

Note: Sample counts and percentage by cancer and metastasis subtype.

Sample collection and processing

Blood draws from each study participant were collected in cell-free preservative blood tubes (Streck) and shipped at room temperature to Epic Sciences on the same day of collection. Blood samples taken up to 96 hours previously were processed as described previously (3, 11, 12). Briefly, red blood cells were lysed using an ammonium chloride solution, and approximately 3 × 106 nucleated cells per slide were plated on up to 12 glass microscope slides (25.3 mm × 75.3 mm) and stored at −80°C until analysis. Samples were deemed invalid and were not processed if they arrived outside the 96-hour window, were damaged or drawn in an incorrect tube type, were of insufficient volume (<4 mL), were clotted or hemolyzed, or were shipped at the wrong temperature (e.g., frozen). A total of 183 samples were processed, including 67 at baseline and 116 on-therapy (60 C2D1 and 56 C3D1).

CTC and T-cell detection

Samples were analyzed using the Epic Sciences CTC platform, as described previously (12, 13). The CTC panel included pan-CK/CD45/DAPI/PD-L1 for CTCs and PD-L1–expressing CTC detection (14). Traditional CTCs were defined as cells (CK+ and CD45) with intact nuclei (DAPI), and were generally larger and morphologically distinct from surrounding white blood cells (3, 12). Positivity for the CTC panel was defined as a fluorescence signal above the defined analytic threshold established with cultured cell line control cells spiked into healthy donor blood, as described previously (3, 14). Negativity for CD45 was defined as having intensity below visual detection under the boundary condition that 99% of all cells are detectable globally (12). For PD-L1, the E1L3N clone was used. Cutoffs for positivity were established on the basis of an analytic threshold of fluorescence using cell lines. Of the 67 baseline, 60 C2D1, and 56 C3D1 samples tested with the CTC panel, 63, 55, and 49, respectively, were also analyzed with a T-cell activation panel (all available material was tested). The T-cell activation panel included CD4/CD8/Ki-67/DAPI for analysis of subpopulations of immune cells. Positivity cutoffs for CD4 and CD8 were determined on the basis of the local minimum between positive and negative peaks of cell counts by mean fluorescence intensity (MFI). The specificity of the activation panel was determined by multiple methods, including MFI assessment on positive and negative cells (immunomagnetically purified CD4+ or CD8+ cells) and colocalization with unique T-cell markers, such as CD3. The percentage values were calculated on the basis of the number of CD4+ and CD8+ cells over the total number of DAPI+ cells. For both panels, each slide was imaged through automatic digital pathology pipelines to detect and quantify CTCs and immune cell populations.

CTC heterogeneity

We investigated CTC subtypes using a modification of the digital pathology pipeline previously implemented to quantify variability in CTC morphology of patients with metastatic prostate cancer (4). Briefly, 2,527 CTC images from patients with metastatic genitourinary cancer were analyzed to measure nuclear and cytoplasmic morphologic features on a per-cell basis. Apoptotic CTCs were excluded from the analysis because of the limited ability of digital pathology features to recognize fragmented membrane and nuclei. Unsupervised K-means clustering of the CTCs along their digital pathology features was then used to sort CTCs into phenotypically similar subtypes from all samples. Here, “unsupervised” refers to the idea that the number of morphologies was not dictated at the beginning of the analysis. As CTC morphology is a novel idea, there is minimal published data available addressing whether the number of subtypes and their defining features will remain consistent across different cancer types. For example, Scher and colleagues identified 15 subtypes in metastatic prostate cancer (4). The number of clusters was selected by the “elbow” method to minimize “wide-sense stationary” (15) and the “silhouette” method to maximize the average cluster score (16). All CTCs in the cohort were assigned a phenotypic cell subtype (A–E; Fig. 2A). Next, the patient-level frequency of the defined CTC phenotypic subtypes (categorized as A–E/mL) was determined per sample. CTC heterogeneity was measured using CTC subtype counts by calculating the Shannon index (17).

Statistical analysis

Patient characteristics were evaluated by descriptive statistics. ANOVA was used to compare CTC burden and T-cell populations across timepoints and best response categories. Continuous variables with right-skewed distributions were transformed by log2 (x + 1) to establish a normal distribution. χ2 tests were used to compare CTC subtype enrichment between patients with urothelial cancer and other patients. Time-to-event outcomes, categorized by groups, were evaluated with the Kaplan–Meier method. Differences in survival and disease progression between categorized groups were evaluated using the maximum log-rank test. Because of the exploratory nature of this study, cut-off selection for survival analyses was optimized to obtain the largest differences; in other words, the results shown reflect the greatest effect possible and the P values associated with these analyses would need to be adjusted to account for the optimization because the ones shown are the smallest possible P values that could be obtained without adjustments for the method used to determine the values. However, a cutoff of 4 CTC/mL (Fig. 1) was supported by previous investigations as a possible upper limit for the presence of CTCs in healthy donor blood (12). A cutoff of 1 CTC count per sample (Fig. 2) was supported by the scoring system used in the AR-V7 protein assay (4, 18–20). Cutoffs for CD4+ and C8+ white blood cells are currently being validated with independent study cohorts. Dynamic changes in Shannon index were calculated by subtracting mean-normalized scores between two consecutive timepoints and by association with outcome using a cutoff of >0 or <0. All statistical tests were two-sided and were performed at the 5% significance level. Data consolidation was conducted using KNIME. Statistical analyses utilized the following R packages: survival, stats, vegan, and maxstat. Graphical representations were generated with the following R packages: ggplot2, gridExtra, scales, survminer, and ggthemes. In addition, an identical subanalysis was performed limited solely to the patients with urothelial histology.

Figure 1.

CTC burden and survival analysis. A, CTC count by patient (pts) across three timepoints (baseline, C2D1, and C3D1). Patients are ordered by high to low CTC burden in each timepoint. B, Total CTC burden by timepoint. C, Kaplan–Meier curve at baseline with traditional CTC cutoff of 4/mL. D, Kaplan–Meier curve at C2D1 for PD-L1+ CTCs with a CTC cutoff of 0.

Figure 1.

CTC burden and survival analysis. A, CTC count by patient (pts) across three timepoints (baseline, C2D1, and C3D1). Patients are ordered by high to low CTC burden in each timepoint. B, Total CTC burden by timepoint. C, Kaplan–Meier curve at baseline with traditional CTC cutoff of 4/mL. D, Kaplan–Meier curve at C2D1 for PD-L1+ CTCs with a CTC cutoff of 0.

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Figure 2.

Morphology-based CTC subtypes and survival analysis. A, Unsupervised clustering using digital pathology features clustered CTCs into five subtypes (A–E) with distinct cell morphology (see Materials and Methods). B, Kaplan–Meier curves at baseline and C2D1 with CTC subtypes using a cutoff of 1 cell detected in 1 mL of blood.

Figure 2.

Morphology-based CTC subtypes and survival analysis. A, Unsupervised clustering using digital pathology features clustered CTCs into five subtypes (A–E) with distinct cell morphology (see Materials and Methods). B, Kaplan–Meier curves at baseline and C2D1 with CTC subtypes using a cutoff of 1 cell detected in 1 mL of blood.

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Independent analysis using a different approach

To mitigate bias, an independent statistical analysis was performed at our institution (NIH, Bethesda, MD, author S.M. Steinberg; remainder of analysis was done by author T. Pramparo, Epic Sciences) using a different approach to determine the way in which the patients would be separated into groups. To report findings which were not based on finding an optimal cutoff, but rather based on the distributions of the CTC values, it was decided beforehand that for each of five morphologic subtypes, patients would be separated into subgroups based on the number of detected CTCs of that subtype. Patients with 0 CTCs comprised one group; the other patients either comprised one group if <15 patients were in the group or divided into two more groups of equal size if >15 patients had values >0. This was done for each timepoint (baseline/C1D1, C2D1, and C3D1) using a Kaplan–Meier survival analysis for PFS and OS. Curves began at the time at which data were obtained; for example, CTC values available at cycle 2 were divided into groups and evaluated for association with OS or PFS beginning at cycle 2. These analyses were done using SAS version 9.4 (SAS Institute, Inc.).

Clinical characteristics of study cohort

At baseline, 67 patients who had urothelial carcinoma (n = 46) and other genitourinary cancers (n = 21) were treated. Across all 81 patients, the median age was 63 years (range, 20–82); 35 (69%) were male. A total of 49 patients (75%) had visceral involvement, of which 24 (35%) had liver involvement, 17 (25%) had bone involvement, and 18 (27%) had lymph node involvement (Table 1). Fifty-one (76%) and 44 (66%) baseline patients had C2D1 and C3D1 matched samples, respectively. Forty-nine cycle 2 patients (82%) had cycle 3 matched samples and 42 patients (52%) had matched samples at all three timepoints (Supplementary Fig. S1).

CTC enumeration

CTCs were found in the peripheral blood of 50 patients (75%) at baseline, 46 patients (77%) at C2D1, and 35 patients (62%) at C3D1. Median (range) CTC count/mL was 1.1 (0–216) at baseline, 0.9 (0–91) at C2D1, and 0.4 (0–19) at C3D1 (Fig. 1A). The decrease in total CTC count on-therapy was not statistically significant (P = 0.061; Fig. 1B). Traditional CTC detection with a cutoff >4 CTCs/mL at baseline was associated with worse median OS [2.3 vs. 28.2 months; 95% confidence interval (CI), 0.95–inf vs. 16.28–inf; P = 0.022; Fig. 1C], but not on-therapy. However, exploratory optimization with a higher CTC burden cutoff was indicative of worse survival at C2D1 (Supplementary Fig. S2). CTC burden was not significantly associated with PFS. Results of a subanalysis limited to urothelial-only histology were consistent with the overall analysis (Supplementary Fig. S3).

PD-L1+ CTCs

PD-L1+ CTCs were seen in 10% (n = 7) of patients at baseline, 17% (n = 10) at C2D1, and 4% (n = 2) at C3D1. Median (range) PD-L1+ CTC count/mL was 0 (0–0.9) at baseline, 0 (0–1.4) at C2D1, and 0 (0–0.9) at C3D1. The presence of PD-L1+ CTCs (>0) was associated with worse median PFS at baseline (1.3 vs. 5.1 months; 95% CI, 0.76–inf vs. 3.1–22.1; P = 0.0039; Supplementary Fig. S4) and worse OS on-therapy at C2D1 (4 vs. 28.2 months; 95% CI, 2.8–inf vs. 19.5–inf; P = 0.0091; Fig. 1D).

CTC morphologic subtypes

We identified five distinct morphologic subtypes (Fig. 2A). The characteristics of these five subtypes were as follows: A, medium CK intensity, smallest size, high circularity, low nuclear entropy and speckling, and high nucleus-to-cytoplasm ratio; B, high CK intensity, low CK speckling, high circularity, and low nucleus-to-cytoplasm ratio; C, very elongated cells, medium speckling, and low nucleus-to-cytoplasm ratio; D, large cells, slightly elongated, and high nuclear speckling and entropy; and E, CTC clusters, slightly elongated, and medium CK intensity and speckling. At each timepoint, we saw no significant enrichment in CTC subtypes in patients with urothelial cancer versus other patients in this study, suggesting that these five subtypes may be pan-CTCs, rather than urothelial CTCs specifically (Supplementary Table S1). All subtypes were found in the peripheral blood of patients with similar range of prevalence at all timepoints (from ∼ 10% to 60%; Supplementary Table S2). Subtypes A and E were the most and least represented of all CTCs, respectively (Supplementary Table S3). The presence of subtype B was associated with shorter median OS at baseline (0.8 vs. 28.2 months; 95% CI, 0.62–inf vs. 16.3–inf; P < 0.0001) and at C2D1 (2 vs. 28.2 months; 95% CI, 1.6–inf vs. 19.5–inf; P < 0.00088; Fig. 2B), as well as worse PFS at baseline (0.95 vs. 4.6 months; 95% CI, 0.53–inf vs. 2.76–22.1; P = 0.032; Supplementary Fig. S5). Detection of subtype D at baseline was associated with shorter median OS (2.3 vs. 28.2 months; 95% CI, 0.95–inf vs. 16.3–inf; P = 0.02; Fig. 2B). No subtype attained significance for therapeutic response category at any timepoint. Increasing CTC heterogeneity on-therapy (from baseline to C2D1), as quantified by the Shannon index, showed a trend toward worse OS (5.1 months vs. not reached; 95% CI, 4.4–inf vs. 24.7–inf; P = 0.045; Supplementary Fig. S6). For the subanalysis limited to urothelial-only patients, consistent results were found where the presence of subtype B was associated with shorter median OS at baseline and at C2D1, and detection of subtype D at baseline was associated with shorter median OS (Supplementary Fig. S3B and S3C).

Internal statistical analysis

Results of the NIH statistical analysis were consistent with the Epic Sciences analysis. The results were based on different divisions in the data so they would not be expected to match, but could support the same general conclusions. In these separate analyses, subtype B was associated with a trend toward worse median OS at C2D1 (P = 0.066; Supplementary Fig. S7A), as well as worse PFS at baseline (P = 0.023; Supplementary Fig. S7B) and C3D1 (P = 0.012; Supplementary Fig. S7C). Detection of subtype D at all timepoints was associated with shorter median OS (baseline, P = 0.020; Supplementary Fig. S7D; C2D1, P = 0.048; Supplementary Fig. S7E; and C3D1, P = 0.037; Supplementary Fig. S7F).

T-cell populations

A cutoff of CD4% < 7 at baseline and C2D1 was associated with shorter OS and PFS (baseline OS, 2.3 months vs. not reached; 95% CI, 1.45–inf vs. 24.7–inf; P < 0.0001 and PFS, 1.4 vs. 15.3 months; 95% CI, 1.2–3.6 vs. 5.3–inf; P = 0.00028; Fig. 3A; and C2D1 OS, 3.2 vs. 28.2 months; 95% CI, 2.3–inf vs. 24.7–inf; P = 0.0013 and PFS 1.8 vs. 15.3 months; 95% CI, 1.4–inf vs. 4.5–inf; P = 0.0026; Fig. 3C). A cutoff of CD8% < 3 was associated with shorter survival at baseline (OS, 5.9 vs. 28.2 months; 95% CI, 1.8–inf vs. 24.7–inf; P = 0.019 and PFS, 2.3 vs. 15.3 months; 95% CI, 1.4–inf vs. 4.6–inf; P = 0.029; Fig. 3B), but not at C2D1 or C3D1 (Fig. 3D). CD4% < 7 at baseline and at C2D1 was associated with worse therapeutic response category (ANOVA, P = 0.013 and P = 0.0044; Fig. 3E and H), whereas CD8% < 3 had a trend toward worse therapeutic response (ANOVA, P = 0.39 and P = 0.54; Fig. 3F and G). For the subanalysis limited to urothelial-only patients, consistent results were found where a cutoff of CD4% < 7 at baseline and C2D1 was associated with shorter OS and PFS and a cutoff of CD8% < 3 was associated with shorter survival at baseline, but not at C2D1 (Supplementary Fig. S3D and S3E). Similarly, for the subanalysis, CD4% < 7 at baseline was still associated with worse therapeutic response category (Supplementary Fig. S3F).

Figure 3.

Survival analysis and therapeutic response by T-cell population. A and B, Kaplan–Meier curves for OS and PFS at baseline with white blood cells positive for CD4 and CD8 below cutoff of 7% and 3%, respectively. C and D, Kaplan–Meier curves for OS and PFS at C2D1 with white blood cells positive for CD4 and CD8 below cutoff of 7% and 3%, respectively. Cutoffs are exploratory and were optimized for this cohort (see Materials and Methods). E–H, ANOVA for CD4+ and CD8+ T-cell populations across the different categories of treatment response (E and F at baseline and G and H at cycle 2). CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease.

Figure 3.

Survival analysis and therapeutic response by T-cell population. A and B, Kaplan–Meier curves for OS and PFS at baseline with white blood cells positive for CD4 and CD8 below cutoff of 7% and 3%, respectively. C and D, Kaplan–Meier curves for OS and PFS at C2D1 with white blood cells positive for CD4 and CD8 below cutoff of 7% and 3%, respectively. Cutoffs are exploratory and were optimized for this cohort (see Materials and Methods). E–H, ANOVA for CD4+ and CD8+ T-cell populations across the different categories of treatment response (E and F at baseline and G and H at cycle 2). CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease.

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In this study we examined the prognostic value of CTC enumeration, identification of subtypes, and T-cell counts with outcomes from a prospective clinical trial of patients with metastatic genitourinary cancer, including metastatic urothelial carcinoma, treated with combination immunotherapy. CTCs were present in 75% of patients at baseline, and higher CTC burden (>4) was associated with shorter OS. The percentage of patients with detectable CTCs is in line with previous studies of mUC across multiple detection platforms (7, 21, 22).

We describe five distinct morphologic subtypes of CTCs in this study. Subtypes B and D were associated with worse OS, not specific to urothelial carcinoma. There was no distinct urothelial signature found in the pattern of morphologic subtype expression compared with the other malignancies in our cohort, supporting the pan-cancer theory that certain cellular features are negatively prognostic across cancer types (23). Interestingly, subtype E, which contains CTC clusters, was rarely detected. Notably, the workflow of the CTC detection platform employed herein is by design minimally disruptive to CTC clusters. There is neither an enrichment step, nor size-based microfluidic or affinity capture, and all nucleated cells are directly deposited on a glass slide and fixed for staining. As such, the data suggest that CTC clusters are rare in genitourinary cancers. CTC morphologic subtypes and the heterogeneity of expression of those subtypes may further enhance the performance of this biomarker, with special relevance for patients undergoing immunotherapy, as there may be an association with CTC heterogeneity and TMB.

CTC heterogeneity, as measured by the Shannon index, has been associated with survival and therapeutic response to immunotherapy, chemotherapy, and androgen receptor signaling inhibitor–targeted therapy in patients with mCRPC (4, 5). Here, we found a trend toward worse OS for patients with increasing CTC heterogeneity while on-therapy. A future study is warranted to determine whether CTC heterogeneity will have utility in other genitourinary malignancies, or whether the focus should rest on the detection of specific morphologies, which were significant biomarkers for survival in this study.

Immunotherapy with checkpoint inhibition has led to great interest in predictive markers for success, with candidates such as PD-L1 expression and immune-infiltrating tumor cells. Here, we studied PD-L1 expression on CTCs and applied rare cell detection technology to circulating T-cell populations. We saw that low CD4 and CD8 cell counts were negative prognosticators, presumably representing a suppressed immune response (8). Low counts at baseline and on-therapy were associated with worse OS and, in some cases, with poor therapeutic response category. We also found that the detection of PD-L1+ CTCs was associated with worse OS. Of note, several PD-L1 tumor assays employ different antibody clones, as well as different definitions and cutoffs for positivity (24).

This study was exploratory and hypothesis-generating, and these findings must be confirmed in an independent homogeneous cohort. The results, as shown, reflect the optimal effects and need to be interpreted in that context. The NIH analysis was not optimized, but reflected similar findings. This cohort included a heterogeneous group of genitourinary cancers, including a small number of rare histologies. As such, it was not possible to perform a subanalysis for each histologic type due to small numbers. Each cancer type has a different PFS and OS, thus it is a limitation to evaluate these endpoints for a combined cohort. Future studies would ideally include a greater number of patients, especially to further evaluate PD-L1+ CTCs, as these are known to be rarely expressed and were only exhibited in a small subset of the patients in this cohort (7). T-cell detection was based on CD4/8; however, it is known that there is some minimal expression of these markers on other peripheral blood cells (25).

In conclusion, our analysis found CTCs in patients with metastatic genitourinary cancer at baseline and on-therapy with combination immunotherapy. Patients with CTCs > 4, specific CTC morphologic subtypes, PD-L1+, and low CD4 and CD8 T-cell counts had shorter survival. Future study is warranted to validate the prognostic utility of CTC heterogeneity.

T. Pramparo reports employment with Epic Sciences at the time of this work. A. Mortazavi reports other from NIH during the conduct of the study; Dr. Mortazavi also reports other from Acerta Pharma, Genentech, Roche, Merck, Novartis, Seattle Genetics, Astellas Pharma, Mirati Therapeutics, Bristol Myers Squibb, and Debiopharm Group, as well as personal fees from Seattle Genetics and Pfizer outside the submitted work. S.A. Niglio reports other from Regeneron, Gilead Sciences, and StemCell Technologies outside the submitted work. J.D. Schonhoft reports other from Epic Sciences during the conduct of the study and outside the submitted work. Y. Wang reports personal fees and other from Epic Sciences during the conduct of the study. R. Dittamore reports personal fees from Epic Sciences during the conduct of the study and outside the submitted work. S.K. Pal reports personal fees from Pfizer Inc., Novartis, Aveo, Genentech, Exelixis, Bristol Myers Squibb, Astellas Pharma, Eisai, Roche, Ipsen, and Medivation outside the submitted work. M.N. Stein reports grants from Exelixis, Tmunity, Seattle Genetics, Nektar, Lilly, Bristol Myers Squibb, Harpoon, and Janssen Oncology outside the submitted work. D.I. Quinn reports personal fees from BMS and Exelixis during the conduct of the study. J.B. Trepel reports other from Syndax Pharmaceuticals, EpicentRx Inc., and AstraZeneca outside the submitted work. D.P. Bottaro reports patents 10,035,833 issued, 9,550,818 issued, 8,617,831 issued, 8,569,360 issued, 8,304,199 issued, 7,964,365 issued, and 7,871,981 issued. No disclosures were reported by the other authors.

H.J. Chalfin: Conceptualization, data curation, writing-original draft, writing-review and editing. T. Pramparo: Formal analysis, writing-review and editing. A. Mortazavi: Data curation, writing-review and editing. S.A. Niglio: Data curation, writing-review and editing. J.D. Schonhoft: Data curation, formal analysis, writing-review and editing. A. Jendrisak: Formal analysis, writing-review and editing. Y.-L. Chu: Formal analysis, writing-review and editing. R. Richardson: Data curation. R. Krupa: Data curation. A.K.L. Anderson: Data curation. Y. Wang: Data curation. R. Dittamore: Data curation. S.K. Pal: Resources, data curation, writing-review and editing. P.N. Lara: Resources, data curation, writing-review and editing. M.N. Stein: Resources, data curation, writing-review and editing. D.I. Quinn: Resources, data curation, writing-review and editing. S.M. Steinberg: Conceptualization, formal analysis, writing-review and editing. L.M. Cordes: Resources, data curation, project administration, writing-review and editing. L. Ley: Resources, data curation, project administration, writing-review and editing. M. Mallek: Resources, data curation, project administration, writing-review and editing. O. Sierra Ortiz: Resources, data curation, project administration, writing-review and editing. R. Costello: Project administration, writing-review and editing. J. Cadena: Resources, data curation, project administration, writing-review and editing. C. Diaz: Resources, data curation, project administration. J.L. Gulley: Resources, writing-review and editing. W.L. Dahut: Data curation, writing-review and editing. H. Streicher: Resources, project administration, writing-review and editing. J.J. Wright: Resources, project administration, writing-review and editing. J.B. Trepel: Writing-review and editing. D.P. Bottaro: Resources, writing-review and editing. A.B. Apolo: Conceptualization, resources, data curation, formal analysis, supervision, investigation, methodology, writing-original draft, writing-review and editing.

This project has been funded with federal funds from the NCI, NIH.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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Supplementary data