Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Clin Cancer Res. Author manuscript; available in PMC 2017 March 15.
Published in final edited form as:
PMCID: PMC4990782

The Initial Detection and Partial Characterization of Circulating Tumor Cells in Neuroendocrine Prostate Cancer



The transition of prostate adenocarcinoma to a predominantly androgen receptor (AR) signaling independent phenotype can occur in the later stages of the disease and is associated with low AR expression and/or the development of small cell or neuroendocrine tumor characteristics. As metastatic tumor biopsies are not always feasible and are difficult to repeat, we sought to evaluate noninvasive methods to identify patients transitioning towards a neuroendocrine phenotype (NEPC).

Experimental Design

We prospectively studied a metastatic tumor biopsy, serum biomarkers and circulating tumor cells (CTC, Epic Sciences) from patients with castration resistant prostate cancer (CRPC) including those with pure or mixed NEPC histology present on biopsy. CTCs labeled with the patient’s clinical status were used to learn features that discriminate NEPC patients, which was then applied to an independent cohort.


Twenty-seven patients with CRPC including 12 NEPC and 5 with atypical clinical features suggestive of NEPC transition were studied. CTCs from NEPC patients demonstrated frequent clusters, low or absent AR expression, lower cytokeratin expression, and smaller morphology relative to typical CRPC. A multivariate analysis of protein and morphologic variables enabled distinguishing CTCs of NEPC from CRPC. This CTC classifier was applied to an independent prospective cohort of 159 metastatic CRPC patients and identified in 17/159 (10.7%) of cases, enriched in patients with high CTC burden (p<0.01) and visceral metastases (p=0.04).


CTCs from patients with NEPC have unique morphologic characteristics, which were also identified in a subset of CRPC patients with aggressive clinical features potentially undergoing NEPC transition.

Keywords: AR independence, circulating tumor cell, CTC, CRPC, NEPC


Neuroendocrine prostate cancer (NEPC) is an aggressive, androgen receptor (AR) independent subtype of prostate cancer that most commonly becomes manifest in the later stages of castration resistant prostate cancer (CRPC) and is associated with treatment resistance (15). The diagnosis of NEPC remains challenging and currently relies on a combination of pathologic and clinical features suggestive of AR signaling independence. Before NEPC develops, metastatic tumor biopsies often show mixed features with both adenocarcinoma and neuroendocrine carcinoma cells present. There are no reliable serum markers to consistently diagnose patients transforming to the NEPC phenotype and the incidence of circulating tumor cells (CTCs) in these patients is unknown. Detection of NEPC has clinical implications, as NEPC patients would not be expected to respond well to currently approved AR-targeted therapies for CRPC and may be better served by therapies specifically directed to NEPC.

CTCs provide the potential for non-invasive, real-time molecular characterization of cancer in patients with metastatic disease. To date, the only FDA-cleared test for CTC detection and enumeration is the CellSearch® technology, based on immunomagnetic enrichment of CTCs expressing the epithelial cell adhesion molecule (EpCAM). Several other platforms have recently been developed to improve sensitivity of CTC detection, most of which include enrichment and/or other physical selection methods (6, 7). There is mounting evidence that non-traditional populations of CTCs also exist, including EpCAM/cytokeratin (CK)-negative CTCs (8) and/or cells smaller in size than traditional CTCs, some even smaller than neighboring white blood cells (6, 9). The Epic Sciences platform is a non-selection based platform that characterizes all nucleated cells and identifies CTCs based on a multi-parametric digital pathology process identifying abnormal cells among the normal white blood cells utilizing protein expression and cell morphology (1012). This technique has demonstrated the ability to identify distinct CTC populations including traditional (CK+, CD45−), apoptotic, CK-negative, and CTC clusters (12, 13). We aimed to characterize CTCs from patients with CRPC and NEPC utilizing the Epic platform and correlate results with patient-matched tumor biopsy and clinical features.


CTC collection

Under IRB approved protocols at Weill Cornell Medical College and Memorial Sloan Kettering Cancer Center, patients with metastatic CRPC including those with pure or mixed NEPC were prospectively enrolled. NEPC was defined by the presence of either a pure or mixed small cell high-grade neuroendocrine carcinoma histology in a metastatic tumor biopsy and confirmed by at least 20% positive immunohistochemical staining for a neuroendocrine marker (synaptophysin, chromogranin). CRPC was defined clinically, with or without a metastatic biopsy confirming prostate adenocarcinoma. CRPC patients were sub-classified as atypical CRPC if the biopsy showed adenocarcinoma and the patient had clinical features suggestive of an AR independent transition which included radiographic progression in the setting of a low PSA <1 ng/ml, visceral progression in the absence of PSA progression (defined by Prostate Cancer Working Group 2 criteria (14) and/or elevated serum chromogranin A >3× upper limit of normal.

Clinical demographics including prior therapies, sites of metastases, PSA, serum neuroendocrine marker levels, and CTC number (CellSearch®, Raritan, NJ) were collected. Blood (10 mL) from each subject was shipped to Epic Sciences within 48 hours and processed immediately on arrival. Red blood cells were lysed, approximately 3 million nucleated blood cells dispensed onto 10–16 glass slides as previously described (1012){Werner, 2015 #993} and placed at −80°C for long term storage.

CTC identification

Two slides from each patient were evaluated by immunofluorescence (IF) {Marrinucci, 2010 #769;Marrinucci, 2012 #763;Werner, 2015 #993} (Figure 1A) using antibodies targeting cytokeratins (CK), CD45, AR, and 4',6-diamidino-2-phenylindole (DAPI) counterstain. Slides were imaged using a platform that captures all 3 million cells per slide in less than 15 minutes, and analyzed by a proprietary software that characterizes each cell by parameters including cell size, shape, nuclear area, presence of macronucleoli, CK and AR expression, uniformity and cellular localization. CTC candidates were identified in an interactive report, reviewed by trained technicians. CK+/CD45− cells with intact, DAPI+ nuclei exhibiting tumor-associated morphologies were classified as traditional CTCs. CTCs with non-traditional characteristics were recorded, such as CK− /CD45− cells with morphological distinction and/or AR positivity, CK+/CD45− small cells, CTC clusters, CTCs with multiple marconucleoli and apoptotic CTCs (with nuclear or cytoplasmic fragmentation).

Figure 1
(A) Epic platform workflow starting from a single blood tube to the identification and characterization of all nucleated cells. Steps include 1) Blood lysed, nucleated cells from blood sample placed onto slides; 2) Slides stored in −80C biorepository; ...

Pathologic evaluation

Patient-matched metastatic tumor biopsies were reviewed by two anatomic genitourinary pathologists and classified as adenocarcinoma or NEPC based on presence of either pure or mixed small cell high grade neuroendocrine carcinoma histology in a metastatic tumor biopsy and confirmed by at least 20% positive immunohistochemical (IHC) staining for the neuroendocrine marker chromogranin and/or synaptophysin (15). IHC was quantified on scale 0–3 and positive IHC was defined as any staining intensity seen of target cells above background. To assess AURKA amplification, we used a locus specific probe plus reference probe FISH assay as previously described (16).

Statistical Analysis

CTC morphological/molecular data and clinical information were compiled into patient datasets (NEPC, CRPC, atypical CRPC) using KNIME, where cytokeratin expression, AR expression, presence of clusters and various nuclear and cytoplasmic morphological features were analyzed with single cell resolution (Table S1). Kernel density estimates (KDE) of each CTC characteristic were performed to provide univariate distributions across each aggregate subtype. Patient samples were analyzed for frequency of cell types at calculated cell counts per mL of blood, and univariate distributions of CTC biomarkers were compared at the patient level for each diagnostic category. Supervised learning was performed using the Random Forest classifier algorithm (R package ‘randomForest’) built with 1,001 decision trees and configured to provide a probability output (17).

Leave-One-Out Cross-Validation

To evaluate the robustness of the Random Forest classifier, leave-one-out cross-validation was performed; CTCs from patients with atypical CRPC were removed from analysis, while CTCs from NEPC were labeled NEPC+ and CRPC were labeled NEPC−. Leave-one-out cross-validation at the blood sample level with the dataset partitioned into training and test sets is shown in Figures S1–3, where atypical CRPC patients were excluded from the analysis. For each blood tube, CTCs from every other sample were used to train a classifier, and CTCs from the blood tube being evaluated were held-out as a test set. CTCs from the test set were analyzed by the trained classifier, where the output is an estimated probability of class membership to NEPC+ and NEPC− for each CTC belonging to the held-out sample. This cycle was repeated iteratively for each sample, and the classifier output was collected at the end of each iteration. The criteria for patient-level class membership was established as at least 3 CTCs with a p(NEPC) score greater than 0.95.

Atypical CRPC and Contemporary Cohort Analysis

A classifier was first trained on NEPC and CRPC samples, without atypical CRPC samples. This classifier was then used to classify the atypical CRPC sample CTCs, as well as CTCs from a 159 patient validation cohort. In the validation cohort, the same criteria for patient positivity (at least 3 CTCs with p(NEPC) greater than 0.95) was applied to generate patient-level predictions from the classifier’s single-cell output. KDE curves were used to plot the distribution of NEPC+ class membership values for individual CTCs for each patient.


CTCs from 27 patients with metastatic prostate cancer were evaluated. The patients identified either pathologically as NEPC (n=12) or clinically as atypical CRPC (n=5) as defined above demonstrated a higher frequency of liver metastases and lower PSA compared to other CRPC patients (Table 1, Table S2). Overall, bone metastases were present in 24/27 (88.9%) of patients, and liver metastases were present in 8/12 (66.7%) of NEPC and 5/15 (33.3%) of CRPC of whom 4 had atypical clinical features (Table S3). Median serum PSA level was 1.9 ng/ml in NEPC, 2.8 ng/ml in atypical CRPC, and 53.4 ng/ml in other CRPC patients. Serum neuroendocrine marker levels varied considerably within the NEPC subgroup and were also elevated in cases of CRPC.

Table 1
Clinical data derived from each patient sample including diagnosis, site of metastasis, biopsy site, pathological analysis and IHC results for common prostate cancer markers.


Enumeration of CTCs using both the CellSearch and Epic platforms was performed. Of note, 6/13 evaluated NEPC and atypical CRPC patients had CellSearch® CTC count of <5 CTC/7.5 mL (range 0–384, with 5 of these 13 patients having a CellSearch® CTC count of 0). In contrast, all 17 NEPC and atypical CRPC patients had CTCs ≥5 CTC/7.5mL using the Epic platform. Further characterization of the detected CTCs revealed heterogeneity of cytokeratin (CK) and AR expression in both NEPC and CRPC, with a significantly greater proportion of CK-negative and AR-negative CTCs in NEPC compared to CRPC (Figures 12, Table S4). CTCs in NEPC patients overall had lower AR expression, higher cytoplasmic circularity, and higher nuclear to cytoplasmic ratio. The prevalence of CK-negative CTC subpopulations in NEPC patients is potentially consistent with a proposed epithelial-mesenchymal-transition (EMT) (18, 19).

Figure 2
(Top panel) The kernel density estimate (KDE) curves for Cytokeratin Expression (left) and Androgen Receptor Expression (right) for CTCs aggregated from all NEPC (red), CRPC (blue), and atypical CRPC (green) patient samples. (Top Right Panel) \ (Lower ...

Within the NEPC subgroup, there was a greater proportion of small cell CTCs in patients with metastatic biopsy confirming small cell carcinoma highlighting phenotypic similarities between tumor and CTCs. CTCs were tested by IF for the presence of the neuroendocrine marker CD56 (Figure 3A). Of those samples matched with metastatic biopsy showing neuroendocrine features, detectable CTCs, 7/12 (58%) had ≥1 CD56+ CTC and 0/8 (0%) non-neuroendocrine samples with detectable CTCs had ≥1 CD56+ CTC. Of patient samples with small cell carcinoma pathology by tumor biopsy, 5/7 (71%) had ≥1 CD56+ CTC. A confusion matrix demonstrates high specificity for small-cell NEPC patients, demonstrating concordance to tumor tissue (Table S5). Additional molecular characterization of these CTCs using fluorescence in situ hybridization (FISH) for AURKA, a gene commonly amplified in NEPC (20), showed concordance with matched metastatic biopsies in selected cases (an example is highlighted in Figure S4) but was not present in all cases or all cells in positive cases.

Figure 3
(A) Representative images of CTCs from CRPC and NEPC patients evaluated by IF for CD56 expression. All CTCs evaluated from CRPC patients were CD56 negative. Heterogeneous expression of CD56 was observed within and among NEPC patient samples. CTCs including ...

Based on the observed differences in CTCs between groups, we sought to identify CTC characteristics specific to NEPC, as described in the Methods. Cell-level features were utilized to train Random Forest cell-level classifiers in both the LOOCV and for the classification of CTCs in the test cohort are shown in Table S1. KDE analysis of the patient groups’ CTCs in aggregate revealed significant differences in CK, AR and morphological characteristics when compared to CRPC (Figures 23B).

Identification of NEPC CTCs

To demonstrate the diagnostic potential of CTC characteristics in distinguishing NEPC, the observed differences between NEPC and CRPC were used to train a Random Forest classifier. Results from leave-one-out cross-validation of NEPC and CRPC samples are shown in Figures S5–S6, where the output from the classifier is a p(NEPC+) value and a p(NEPC−) value for each CTC, corresponding to the estimated probability of the cell’s class membership as NEPC+ and NEPC−.

From the density curve in Figure S4A, the samples from patients with NEPC demonstrated a spike in the curves near the high end of the p(NEPC+) spectrum, with many curves peaking near a p(NEPC+) score of 95%. In Figure S4B, the number of CTCs/mL with p(NEPC+) scores greater than or equal to 95% are presented in a bar chart for each patient sample, where each column is colored by the actual clinical diagnosis that the classifier is trying to predict.

Obtaining positive signals at the CTC level from samples that the classifier does not encounter during training demonstrates the classifier’s ability to detect NEPC from CRPC in a robust manner that mitigates the risk of over-fitting. These conditions simulate the environment that the classifier would face in practice, in the sense that any future blood sample sent in for NEPC analysis is presented to the algorithm as a series of CTCs that it has not encountered during training, which the classifier will then estimate the probability of class membership for each CTC from the new sample.

Atypical CRPC

The clinical significance of patients with castration resistant adenocarcinoma that develop progressive disease in the setting of low serum PSA <1ng/ml, visceral metastases in the absence of PSA progression, or elevated serum chromogranin is not well established. One hypothesis is that these tumors are less androgen responsive and may be in transition towards an AR negative/low or NEPC phenotype and/or demonstrate intratumoral heterogeneity with both adenocarcinoma and NEPC present within or between metastases. We applied the NEPC classification model trained to distinguish NEPC vs. CRPC CTCs to the 5 atypical CRPC patients and found that atypical CRPC is associated with an increase in heterogeneity of CRPC cells and a higher burden of NEPC-like cells compared to CRPC patients (Figure S4, Table S6).

Patient Case Studies

Atypical CRPC patient 6, for example, harbored CTCs of various morphologies with a predominance of NEPC+ CTCs (Figure 4). Patient 6 is a 64 year old man who presented with metastatic hormone naïve prostate cancer, developed clinical progression within six months on primary hormonal therapy, was not responsive to subsequent abiraterone, radium-223, or docetaxel, and developed progressive bone metastases and new liver metastases in the setting of a stable PSA. Despite his bone biopsy at progression showing adenocarcinoma without neuroendocrine features (Figure 4A), his clinical history and CTC characteristics obtained at the time of bone biopsy supported AR independence.

Figure 4
Metastatic biopsy showing morphologic characteristics and IHC for synaptophysin (SYP) and androgen receptor (AR) of metastatic biopsies from patients 6 (A) and 12 (B). Atypical CRPC Patient 6 tumor was characterized as poorly differentiated adenocarcinoma, ...

Another example of how CTCs can be used to understand disease heterogeneity is illustrated in the case of Patient 12, a 68 year old gentleman with CRPC who had a bone biopsy at the time of castration resistance for research which showed prostate adenocarcinoma. He was treated with abiraterone and prednisone. Despite PSA stability, follow-up imaging at 3 months on abiraterone revealed new liver and lung metastases and his serum chromogranin was markedly elevated at 17,340 ng/ml (ULN 95 ng/ml). Liver biopsy was consistent with NEPC (small cell carcinoma) (Figure 4B). Similar to Patient 6, CTCs at time of liver biopsy showed heterogeneous CTC populations including both NEPC and CRPC cell characteristics, suggesting intra-patient heterogeneity. These cases support CTCs as potentially useful in capturing tumor heterogeneity that might not be assessed on metastatic biopsy.

Validation Cohort

We evaluated baseline CTCs from 159 CRPC patients prospectively enrolled in an independent patient cohort at MSKCC for the presence of NEPC+ CTCs (Figure 5A). NEPC+ CTC subpopulations were identified in 17 of 159 (10.7%) cases. A significantly higher proportion of CRPC patients with visceral metastases harbored NEPC+ CTCs compared to those that were NEPC− (35% versus 15%, respectively; p=0.04). Patients with NEPC+ CTCs also had an overall higher CTC burden (median CTC count 64.6 versus 4.2; p<0.01). To address whether the CTC classifier was a reflection of an overall higher CTC count, linearity was assessed with a Pearson’s coefficient showing a weak relationship between frequency of NEPC CTCs and total cell count (Figure S6). Representative images of NEPC+ CTC characteristics observed in the validation cohort are shown in Figure 5B.

Figure 5
(A) Clinical data of validation cohort (n=159) including sites of metastases, age, serum PSA, and CTC count. NS=not statistically significant at 0.05 level. P-value from two-sided tests comparing NEPC+ to NEPC− are based on Fisher’s Exact ...


Histologic and molecular subtyping of cancer often influences clinical decision making, and tissue confirmation is typically required at cancer diagnosis before treatment recommendations are offered. Prostate cancer is the most common cancer in men in the United States and Europe (21, 22), and in nearly all cases diagnostic biopsies reveal adenocarcinoma upon initial diagnosis. Prostate adenocarcinomas are characterized by AR expression and activation, and therefore hormonal therapies targeting the AR are the mainstay of systemic therapy (23). Small cell neuroendocrine carcinoma of the prostate is a rare histologic subtype at diagnosis, representing less than 1% of all new prostate cancer diagnoses (24). However, in a subset of patients with metastatic prostate adenocarcinoma treated with AR targeted therapies, prostate adenocarcinomas can develop histologic transformation towards a predominantly neuroendocrine carcinoma likely as a mechanism of acquired resistance (15). The NEPC phenotype is associated with aggressive disease, frequent visceral metastases, and low or absent AR expression on metastatic tumor biopsy (4). In this setting, patients are often offered platinum based chemotherapy with regimens similar to small cell neuroendocrine carcinoma of the lung (25, 26). Therefore, identification of advanced prostate cancer patients that have acquired NEPC has potential clinical implications.

However, the diagnosis of NEPC can be complex as there is a spectrum of morphologies seen in advanced prostate cancer with AR-positive adenocarcinoma and AR-negative small cell carcinoma representing the extreme phenotypes. Metastatic biopsies often reveal mixed features of both adenocarcinoma and neuroendocrine carcinoma, with variable AR or neuroendocrine marker protein expression (15). The clinical significance of mixed tumors is less clear and treatment decisions are often individualized based on a combination of pathologic and clinical features. Furthermore, for patients with an atypical clinical presentation such as rapid radiographic progression in setting of a low or modestly elevated PSA, platinum-based therapies are sometimes considered even in the absence of neuroendocrine morphology on biopsy. Another challenge in the diagnosis of NEPC is that metastatic biopsies are not always feasible for patients suffering from advanced prostate cancer, may carry additional risks for the patient including complications from biopsy procedure or delay of initiation of appropriate systemic therapy, and does not always capture disease heterogeneity. Therefore, a noninvasive marker to detect NEPC progression and simultaneously capture intrapatient heterogeneity is an unmet need.

We found that CTCs from metastatic prostate cancer patients are often phenotypically heterogeneous. CTCs from patients with pathologically confirmed NEPC were predominantly of smaller size compared to other CRPC patients and demonstrated lower AR expression and abnormal nuclear and cytoplasmic features. There was also a higher prevalence of low cytokeratin expressing CTCs in NEPC, possibly related to EMT changes that can occur during metastatic transit and treatment resistance (27, 28). When applied to an independent cohort, we found that up to 10% of CRPC patients also harbored similar NEPC+CTC subpopulations and their presence was associated with aggressive clinical features (ie., visceral metastases, high CTC burden). These data support the possible detection of circulating neuroendocrine cancer cells in patients with metastatic CRPC; however, the lack of definitive markers and mixed cellular subpopulations observed reinforce the biologic and clinical complexity underlying disease progression and NEPC transformation. Differences in nuclear size may also be contributed by the presence of visceral metastases as has been recently observed by Chen et al (29). What remains unclear are the dynamics by which these CTCs arise and how the classifier performs as a predictive or prognostic biomarker. Serial monitoring of CTCs in larger cohorts could help elucidate how consistently the classifier emerges during the course of therapy and during the CRPC-to-NEPC transition. Future studies including single cell sequencing of CTCs will also be important to molecularly characterize these heterogeneous populations and may improve our understanding of this complex resistance phenotype.

In this proof of principle study, we demonstrate that CTCs from patients with NEPC have distinct characteristics. The results presented here indicate the feasibility of analyzing CTCs using the Epic platform and support the development of further studies to validate the clinical utility of CTCs for the early detection of patients transforming towards NEPC and the prognostic and potential predictive impact of CTC characteristics in predicting response to AR-directed therapies in CRPC.

Statement of Translational Relevance

NEPC is an aggressive variant of prostate cancer that most commonly arises in later stages of prostate cancer as a mechanism of treatment resistance. Diagnosis of NEPC typically relies on metastatic tumor biopsy since serum markers are unreliable. We show that the CTC populations from patients with NEPC characterized with the Epic platform demonstrate a unique morphology, lower AR expression, and lower cytokeratin expression compared to CTCs from other patients with castration resistant prostate cancer (CRPC). The presence of NEPC-like CTC subpopulations occurs in approximately 10% of unselected patients with CRPC and is associated with aggressive clinical features. CTCs may provide utility for early diagnosis of NEPC-associated acquired resistance and warrants larger clinical studies.

Supplementary Material



Funding: Department of Defense W81XWH-13-1-0275 (H.B.), Damon Runyon- Gordon Family Clinical Investigator Award CI-67-13 (H.B.), Prostate Cancer Foundation (H.B.), NIH/NCI Cancer Center Support Grant P30 CA008748 (N.S.; H.I.S.)


Conflicts of Interest: A.J., M.L., J.L., R.K., R.G., D.M, and R.D are employed by Epic Sciences.


1. Aggarwal R, Zhang T, Small EJ, Armstrong AJ. Neuroendocrine prostate cancer: subtypes, biology, and clinical outcomes. J Natl Compr Canc Netw. 2014;12:719–726. [PubMed]
2. Tagawa ST. Neuroendocrine prostate cancer after hormonal therapy: knowing is half the battle. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2014;32:3360–3364. [PubMed]
3. Beltran H, Tagawa ST, Park K, MacDonald T, Milowsky MI, Mosquera JM, et al. Challenges in recognizing treatment-related neuroendocrine prostate cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2012;30:e386–e389. [PubMed]
4. Beltran H, Tomlins S, Aparicio A, Arora V, Rickman D, Ayala G, et al. Aggressive variants of castration-resistant prostate cancer. Clinical cancer research : an official journal of the American Association for Cancer Research. 2014;20:2846–2850. [PMC free article] [PubMed]
5. Aparicio A, Tzelepi V. Neuroendocrine (Small-Cell) Carcinomas: Why They Teach Us Essential Lessons About Prostate Cancer. Oncology (Williston Park, NY) 2014;28 [PubMed]
6. Ozkumur E, Shah AM, Ciciliano JC, Emmink BL, Miyamoto DT, Brachtel E, et al. Inertial focusing for tumor antigen-dependent and -independent sorting of rare circulating tumor cells. Science translational medicine. 2013;5:179ra47. [PMC free article] [PubMed]
7. Yap TA, Lorente D, Omlin A, Olmos D, de Bono JS. Circulating tumor cells: a multifunctional biomarker. Clinical cancer research : an official journal of the American Association for Cancer Research. 2014;20:2553–2568. [PubMed]
8. Yu M, Bardia A, Wittner BS, Stott SL, Smas ME, Ting DT, et al. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science. 2013;339:580–584. [PMC free article] [PubMed]
9. Phillips KG, Kuhn P, McCarty OJ. Physical biology in cancer. 2. The physical biology of circulating tumor cells. Am J Physiol Cell Physiol. 2014;306:C80–C88. [PubMed]
10. Hsieh HB, Marrinucci D, Bethel K, Curry DN, Humphrey M, Krivacic RT, et al. High speed detection of circulating tumor cells. Biosensors & bioelectronics. 2006;21:1893–1899. [PubMed]
11. Marrinucci D, Bethel K, Bruce RH, Curry DN, Hsieh B, Humphrey M, et al. Case study of the morphologic variation of circulating tumor cells. Human pathology. 2007;38:514–519. [PubMed]
12. Marrinucci D, Bethel K, Kolatkar A, Luttgen MS, Malchiodi M, Baehring F, et al. Fluid biopsy in patients with metastatic prostate, pancreatic and breast cancers. Physical biology. 2012;9:016003. [PMC free article] [PubMed]
13. Werner SGRP, Landers M, Valenta DT, Schroeder M, Greene SB, Bales N, Dittamore R, Marrinucci D. Analytical Validation and Capabilities of the Epic CTC Platform: Enrichment-Free Circulating Tumour Cell Detection and Characterization. Journal of Circulating Biomarkers. 2015;4
14. Scher HI, Halabi S, Tannock I, Morris M, Sternberg CN, Carducci MA, et al. Design and end points of clinical trials for patients with progressive prostate cancer and castrate levels of testosterone: recommendations of the Prostate Cancer Clinical Trials Working Group. J Clin Oncol. 2008;26:1148–1159. [PMC free article] [PubMed]
15. Epstein JI, Amin MB, Beltran H, Lotan TL, Mosquera JM, Reuter VE, et al. Proposed morphologic classification of prostate cancer with neuroendocrine differentiation. Am J Surg Pathol. 2014;38:756–767. [PMC free article] [PubMed]
16. Mosquera JM, Beltran H, Park K, MacDonald TY, Robinson BD, Tagawa ST, et al. Concurrent AURKA and MYCN gene amplifications are harbingers of lethal treatment-related neuroendocrine prostate cancer. Neoplasia. 2013;15:1–10. [PMC free article] [PubMed]
17. Breiman L. Random Forests. Machine Learning. 2001;45:5–32.
18. Tam WL, Weinberg RA. The epigenetics of epithelial-mesenchymal plasticity in cancer. Nature medicine. 2013;19:1438–1449. [PMC free article] [PubMed]
19. Scheel C, Weinberg RA. Cancer stem cells and epithelial-mesenchymal transition: concepts and molecular links. Seminars in cancer biology. 2012;22:396–403. [PubMed]
20. Beltran H, Rickman DS, Park K, Chae SS, Sboner A, MacDonald TY, et al. Molecular characterization of neuroendocrine prostate cancer and identification of new drug targets. Cancer Discov. 2011;1:487–495. [PMC free article] [PubMed]
21. Siegel RL, Miller KD, Jemal A. Cancer statistics 2015. CA Cancer J Clin. 2015;65:5–29. [PubMed]
22. Malvezzi M, Bertuccio P, Levi F, La Vecchia C, Negri E. European cancer mortality predictions for the year 2014. Ann Oncol. 2014;25:1650–1656. [PubMed]
23. Cooper CS, Eeles R, Wedge DC, Van Loo P, Gundem G, Alexandrov LB, et al. Corrigendum: Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue. Nat Genet. 2015;47:689. [PubMed]
24. Wang W, Epstein JI. Small cell carcinoma of the prostate. A morphologic and immunohistochemical study of 95 cases. Am J Surg Pathol. 2008;32:65–71. [PubMed]
25. Papandreou CN, Daliani DD, Thall PF, Tu SM, Wang X, Reyes A, et al. Results of a phase II study with doxorubicin, etoposide, and cisplatin in patients with fully characterized small-cell carcinoma of the prostate. J Clin Oncol. 2002;20:3072–3080. [PubMed]
26. Aparicio AM, Harzstark AL, Corn PG, Wen S, Araujo JC, Tu SM, et al. Platinum-based chemotherapy for variant castrate-resistant prostate cancer. Clinical cancer research : an official journal of the American Association for Cancer Research. 2013;19:3621–3630. [PMC free article] [PubMed]
27. Armstrong AJ, Marengo MS, Oltean S, Kemeny G, Bitting RL, Turnbull JD, et al. Circulating tumor cells from patients with advanced prostate and breast cancer display both epithelial and mesenchymal markers. Molecular cancer research : MCR. 2011;9:997–1007. [PMC free article] [PubMed]
28. Tanaka H, Kono E, Tran CP, Miyazaki H, Yamashiro J, Shimomura T, et al. Monoclonal antibody targeting of N-cadherin inhibits prostate cancer growth, metastasis and castration resistance. Nature medicine. 2010;16:1414–1420. [PMC free article] [PubMed]
29. Chen J, Ho H, Lichterman J, Lu Y, Zhang Y, Garcia MA, et al. Subclassification of Prostate Cancer Circulating Tumor Cells by Nuclear Size Reveals Very Small Nuclear Circulating Tumro Cells in Patients with Visceral Metastases. Cancer. 2015;121:3240–3251. [PMC free article] [PubMed]