Update on Circulating Tumor Cells in Genitourinary Tumors with Focus on Prostate Cancer

Adapting to hormone-therapy resistance for adopting the right therapeutic strategy in advanced prostate cancer

INTRODUCTION

The androgen/androgen receptor (AR) axis represents a key driver of treatment resistance in prostate cancer (PCa) patients receiving androgen deprivation therapy (ADT) and targeted agents, and a deeper comprehension of resistance mechanisms is fundamental to adopt effective therapeutic strategies.

AREAS COVERED

We review the mechanisms of primary or secondary resistance to hormone therapy (HT) in PCa, especially focusing on available data and emerging evidence.

EXPERT OPINION

First- and second-generation HT resistance has been associated with several AR-dependent and AR-independent mechanisms, ranging from the amplification of the AR gene locus to somatic AR mutations and the intratumoral synthesis of androgens from adrenal steroids and cholesterol. As reported in the current review, the development of novel and effective treatments is needed to personalize anticancer therapies in this setting and to finally improve clinical outcomes in patients with HT resistant disease.

Cell-free DNA in the management of prostate cancer: Current status and future prospective

Objective

With the escalating prevalence of prostate cancer (PCa) in China, there is an urgent demand for novel diagnostic and therapeutic approaches. Extensive investigations have been conducted on the clinical implementation of circulating free DNA (cfDNA) in PCa. This review aims to provide a comprehensive overview of the present state of cfDNA as a biomarker for PCa and to examine its merits and obstacles for future clinical utilization.

Methods

Relevant peer-reviewed manuscripts on cfDNA as a PCa marker were evaluated by PubMed search (2010-2022) to evaluate the roles of cfDNA in PCa diagnosis, prognosis, and prediction, respectively.

Results

cfDNA is primarily released from cells undergoing necrosis and apoptosis, allowing for non-invasive insight into the genomic, transcriptomic, and epigenomic alterations within various PCa disease states. Next-generation sequencing, among other detection methods, enables the assessment of cfDNA abundance, mutation status, fragment characteristics, and epigenetic modifications. Multidimensional analysis based on cfDNA can facilitate early detection of PCa, risk stratification, and treatment monitoring. However, standardization of cfDNA detection methods is still required to expedite its clinical application.

Conclusion

cfDNA provides a non-invasive, rapid, and repeatable means of acquiring multidimensional information from PCa patients, which can aid in guiding clinical decisions and enhancing patient management. Overcoming the application barriers of cfDNA necessitates increased data sharing and international collaboration.

Peptide-Functionalized Nanoemulsions as a Promising Tool for Isolation and Ex Vivo Culture of Circulating Tumor Cells

Circulating Tumor Cells (CTCs) are shed from primary tumors and travel through the blood, generating metastases. CTCs represents a useful tool to understand the biology of metastasis in cancer disease. However, there is a lack of standardized protocols to isolate and culture them. In our previous work, we presented oil-in-water nanoemulsions (NEs) composed of lipids and fatty acids, which showed a benefit in supporting CTC cultures from metastatic breast cancer patients. Here, we present Peptide-Functionalized Nanoemulsions (Pept-NEs), with the aim of using them as a tool for CTC isolation and culture in situ. Therefore, NEs from our previous work were surface-decorated with the peptides Pep10 and GE11, which act as ligands towards the specific cell membrane proteins EpCAM and EGFR, respectively. We selected the best surface to deposit a layer of these Pept-NEs through a Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) method. Next, we validated the specific recognition of Pept-NEs for their protein targets EpCAM and EGFR by QCM-D and fluorescence microscopy. Finally, a layer of Pept-NEs was deposited in a culture well-plate, and cells were cultured on for 9 days in order to confirm the feasibility of the Pept-NEs as a cell growth support. This work presents peptide-functionalized nanoemulsions as a basis for the development of devices for the isolation and culture of CTCs in situ due to their ability to specifically interact with membrane proteins expressed in CTCs, and because cells are capable of growing on top of them.

Prostate Health Index Density Outperforms Prostate Health Index in Clinically Significant Prostate Cancer Detection

Background

Prostate-specific antigen (PSA) is considered neither sensitive nor specific for prostate cancer (PCa). We aimed to compare total PSA (tPSA), percentage of free PSA (%fPSA), the PSA density (PSAD), Prostate Health Index (PHI), and the PHI density (PHID) to see which one could best predict clinically significant prostate cancer (csPCa): a potentially lethal disease.

Methods

A total of 412 men with PSA of 2-20 ng/mL were prospectively included. Serum biomarkers for PCa was collected before transrectal ultrasound guided prostate biopsy. PHI was calculated by the formula: (p2PSA/fPSA) x √tPSA. PHID was calculated as PHI divided by prostate volume measured by transrectal ultrasound.

Results

Of the 412 men, 134 (32.5%) and 94(22.8%) were diagnosed with PCa and csPCa, respectively. We used the area under the receiver operating characteristic curve (AUC) and decision curve analyses (DCA) to compare the performance of PSA related parameters, PHI and PHID in diagnosing csPCa. AUC for tPSA, %fPSA, %p2PSA, PSAD, PHI and PHID were 0.56、0.63、0.76、0.74、0.77 and 0.82 respectively for csPCa detection. In the univariate analysis, the prostate volume, tPSA, %fPSA, %p2PSA, PHI, PSAD, and PHID were all significantly associated with csPCa, and PHID was the most important predictor (OR 1.41, 95% CI 1.15-1.72). Besides, The AUC of PHID was significantly larger than PHI in csPCa diagnosis (p=0.004). At 90% sensitivity, PHID had the highest specificity (54.1%) for csPCa and could reduce the most unnecessary biopsies (43.7%) and miss the fewest csPCa (8.5%) when PHID ≥ 0.67. In addition to AUC, DCA re-confirmed the clinical benefit of PHID over all PSA-related parameters and PHI in csPCa diagnosis. The PHID cut-off value was positively correlated with the csPCa ratio in the PHID risk table, which is useful for evaluating csPCa risk in a clinical setting.

Conclusion

The PHID is an excellent predictor of csPCa. The PHID risk table may be used in standard clinical practice to pre-select men at the highest risk of harboring csPCa.

Prostate Cancer in 2021: Novelties in Prognostic and Therapeutic Biomarker Evaluation

Simple Summary

In 2021, the identification of effective biomarkers became a major focus of prostate cancer (PCa) in order to improve outcomes and select potentially responsive patients. The aim of this contribution is to review the main 2021 novelties in prognostic and therapeutic markers in PCa, with special reference to PCa grading, aggressive variant PCa and molecular markers predicting significant disease or response to therapy.

Abstract

The 2021 novelties in prognostic and therapeutic tissue markers in patients with prostate cancer (PCa) can be subdivided into two major groups. The first group is related to prognostic markers based on morphological and immunohistochemical evaluations. The novelties in this group can then be subdivided into two subgroups, one involving morphologic evaluation only, i.e., PCa grading, and the other involving both morphologic and immunohistochemical evaluations, i.e., aggressive variant PCa (AVPCa). Grading concerns androgen-dependent PCa, while AVPCa represents a late phase in its natural history, when it becomes androgen-independent. The novelties of the other major group are related to molecular markers predicting significant disease or response to therapy. This group mainly includes novelties in the molecular evaluation of PCa in tissue material and liquid biopsies.

Circulating Tumor DNA Testing for Homology Recombination Repair Genes in Prostate Cancer: From the Lab to the Clinic

Approximately 23% of metastatic castration-resistant prostate cancers (mCRPC) harbor deleterious aberrations in DNA repair genes. Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) therapy has shown improvements in overall survival in patients with mCRPC who harbor somatic and/or germline alterations of homology recombination repair (HRR) genes. Peripheral blood samples are typically used for the germline mutation analysis test using the DNA extracted from peripheral blood leucocytes. Somatic alterations can be assessed by extracting DNA from a tumor tissue sample or using circulating tumor DNA (ctDNA) extracted from a plasma sample. Each of these genetic tests has its own benefits and limitations. The main advantages compared to the tissue test are that liquid biopsy is a non-invasive and easily repeatable test with the value of better representing tumor heterogeneity than primary biopsy and of capturing changes and/or resistance mutations in the genetic tumor profile during disease progression. Furthermore, ctDNA can inform about mutation status and guide treatment options in patients with mCRPC. Clinical validation and test implementation into routine clinical practice are currently very limited. In this review, we discuss the state of the art of the ctDNA test in prostate cancer compared to blood and tissue testing. We also illustrate the ctDNA testing workflow, the available techniques for ctDNA extraction, sequencing, and analysis, describing advantages and limits of each techniques.

Enhancing Prediction Performance by Add-On Combining Circulating Tumor Cell Count, CD45neg EpCAMneg Cell Count on Colorectal Cancer, Advance, and Metastasis

Simple Summary

Information describing circulating tumor cells (CTCs) holds promise for clinical applications. However, conventional CTCs enumeration could ignore the CTCs more relevant to cancer metastasis. Thus, negative selection CTC enumeration was proposed, by which information on the numbers of CTCs and CD45^neg EpCAM^neg cells can be obtained. By combining this approach with the conventional biomarker carcinoembryonic antigen (CEA), this study aimed to explore whether any combination of these biomarkers could improve the predictive performance for colorectal cancer (CRC) or its status. Results revealed that a combination of the two cell populations showed improved performance (AUROC: 0.893) for CRC prediction over the use of only one population. Compared with CEA alone, the combination of the three biomarkers increased the performance (AUROC) for advanced CRC prediction from 0.643 to 0.727. Compared with that of CEA alone for metastatic CRC prediction, the AUROC was increased from 0.780 to 0.837 when the CTC count was included.

Abstract

Conventional circulating tumor cell (CTC) enumeration could ignore the CTCs more relevant to cancer metastasis. Thus, negative selection CTC enumeration was proposed, by which information on two cellular biomarkers (numbers of CTCs and CD45^neg EpCAM^neg cells) can be obtained. By combining this approach with the conventional biomarker carcinoembryonic antigen (CEA), this study aimed to explore whether any combination of these biomarkers could improve the predictive performance for colorectal cancer (CRC) or its status. In this work, these two cell populations in healthy donors and CRC patients were quantified. Results revealed that enumeration of these two cell populations was able to discriminate healthy donors from CRC patients, even patients with non-advanced CRC. Moreover, the combination of the two cell populations showed improved performance (AUROC: 0.893) for CRC prediction over the use of only one population. Compared with CEA alone, the combination of the three biomarkers increased the performance (AUROC) for advanced CRC prediction from 0.643 to 0.727. Compared with that of CEA alone for metastatic CRC prediction, the AUROC was increased from 0.780 to 0.837 when the CTC count was included. Overall, this study demonstrated that the combination of these two cellular biomarkers with CEA improved the predictive performance for CRC and its status.