Biomarker For Prostate Cancer Research Articles

Ultrasensitive prostate cancer marker PCA3 detection with impedimetric biosensor based on specific label-free aptamers

Prostate cancer (PCa) appears among the most frequently diagnosed types of malignancies in males. Because of the high demand and increasing detection rate of early PCa, alongside the specificity limitations of the gold standard clinical tools available for the diagnosis and prognosis of prostate cancer, there is an urgent need for more reliable PCa markers and highly sensitive diagnostic tools to avoid under-treatment and over-diagnosis. PCA3, or prostate cancer antigen 3, is a potential prostate cancer biomarker that is more specific and useful for preventing unnecessary repeat biopsies, particularly in men with persistently high prostate-specific antigen indices after a negative biopsy. Additionally, an electrochemically based biosensor would prove to be a powerful diagnostic tool for PCA3 detection in urine because of its simplicity, sensitivity, and cost-effectiveness, in contrast to the more traditional PCa diagnostics that depend on blood testing. This paper aimed to design a novel and simple electrochemical impedimetric biosensor based on a label-free RNA-aptamer (CG3-PCA3) as the molecular recognition element for detecting PCA3. The proposed aptasensor for the detection of PCA3 has been developed using a screen-printed carbon electrode (SPCE) modified by gold nanoparticles (AuNPs), further improving sensitivity and allowing the immobilisation of thiolate aptamers on its surface. The findings presented here demonstrated a high sensitivity to PCA3, with a detection limit of 20 fM in artificial urine and 1 fM in buffer. These results indicate that the PCA3 aptasensor could be a promising tool for routine PCa diagnosis due to its high sensitivity and cost-effectiveness.

1,25-Dihydroxyvitamin D3 Suppresses Prognostic Survival Biomarkers Associated with Cell Cycle and Actin Organization in a Non-Malignant African American Prostate Cell Line.

Vitamin D3 is a steroid hormone that confers anti-tumorigenic properties in prostate cells. Serum vitamin D3 deficiency has been associated with advanced prostate cancer (PCa), particularly affecting African American (AA) men. Therefore, elucidating the pleiotropic effects of vitamin D on signaling pathways, essential to maintaining non-malignancy, may provide additional drug targets to mitigate disparate outcomes for men with PCa, especially AA men. We conducted RNA sequencing on an AA non-malignant prostate cell line, RC-77N/E, comparing untreated cells to those treated with 10 nM of vitamin D3 metabolite, 1α,25(OH)2D3, at 24 h. Differential gene expression analysis revealed 1601 significant genes affected by 1α,25(OH)2D3 treatment. Pathway enrichment analysis predicted 1α,25(OH)2D3- mediated repression of prostate cancer, cell proliferation, actin cytoskeletal, and actin-related signaling pathways (p < 0.05). Prioritizing genes with vitamin D response elements and associating expression levels with overall survival (OS) in The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA PRAD) cohort, we identified ANLN (Anillin) and ECT2 (Epithelial Cell Transforming 2) as potential prognostic PCa biomarkers. Both genes were strongly correlated and significantly downregulated by 1α,25(OH)2D3 treatment, where low expression was statistically associated with better overall survival outcomes in the TCGA PRAD public cohort. Increased ANLN and ECT2 mRNA gene expression was significantly associated with PCa, and Gleason scores using both the TCGA cohort (p < 0.05) and an AA non-malignant/tumor-matched cohort. Our findings suggest 1α,25(OH)2D3 regulation of these biomarkers may be significant for PCa prevention. In addition, 1α,25(OH)2D3 could be used as an adjuvant treatment targeting actin cytoskeleton signaling and actin cytoskeleton-related signaling pathways, particularly among AA men.

Prostate cancer biomarkers: from early diagnosis to precision treatment.

Prostate cancer (PCa) is the second most prevalent cancer in men. In 2020, approximately 1,414,259 new cases were reported that accounted for 3,75,324 deaths (Sung et al. in CA 71:209-249, 2021). PCa is often asymptomatic at early stages; hence, routine screening and monitoring based on reliable biomarkers is crucial for early detection and assessment of cancer progression. Early diagnosis of disease is key step in reducing PCa-induced mortality. Biomarkers such as PSA have played vital role in reducing recent PCa deaths. Recent research has identified many other biomarkers and also refined PSA-based tests for non-invasive diagnosis of PCa in patients. Despite progress in screening methods, an important issue that influences treatment is heterogeneity of the cancer in different individuals, necessitating personalized treatment. Currently, focus is to identify biomarkers that can accurately diagnose PCa at early stage, indicate the stage of the disease, metastatic nature and chances of survival based on individual patient profile (Fig.1). Fig.1 Graphical abstract.

Increased α2,3-sialyl N-glycosylated prostate-specific membrane antigen (PSMA) in post-DRE urine is associated with high grade group prostate cancer.

Aberrant glycosylation of proteins is an important hallmark in multiple cancers. Prostate-specific membrane antigen (PSMA), a highly glycosylated protein with 10 N-linked glycosylation sites, is an Food and Drug Administration approved theranostic for prostate cancer. However, glycosylation changes in PSMA that are associated with prostate cancer disease progression have not been fully characterized. We investigated whether urinary PSMA sialylation correlate with high-grade prostate cancer. Urine samples were collected from men after digital rectal examination (DRE) before prostate biopsy. Lectin-antibody enzyme-linked immunoassay was used to quantify α2,3-sialyl PSMA in post-DRE urine samples from subjects with benign prostate tumors, Grade Group 1 prostate cancer and those with Grade Group ≥2 disease. There are significant increases in α2,3-sialylated PSMA in patients with Grade Group ≥2 disease compared to benign (p = 0.0009) and those with Grade Group 1 disease (p = 0.0063). There were no significant differences in α2,3-sialyl PSMA levels between Grade Group 1 and benign prostate tumors (p = 0.7947). Our study shows that there are significant differences in the abundance of α2,3-sialylated PSMA in post-DRE urines from disease stratified prostate cancer patients, and the increase is correlated with progression and disease severity. The detection of increased PSMA sialyation in post-DRE urines from patients with higher Grade Group ≥2 disease states provides novel untapped potential for the development of prognostic biomarkers for prostate cancer. Specifically, quantitation of α2,3-sialylated PSMA shows potential for discriminating between benign to intermediate grade disease, which is a significant clinical challenge in staging and risk stratification of prostate cancer.

Liquid biomarkers in prostate cancer: recent advancements and future directions.

Traditional diagnostic approaches of prostate cancer like PSA are limited by high false-positive rates and insufficient capture of tumour heterogeneity, necessitating the development of more precise tools. This review examines the latest advancements in liquid biomarkers for prostate cancer, focusing on their potential to refine diagnostic accuracy and monitor disease progression. Liquid biomarkers have gained prominence because of their minimally invasive nature and ability to reflect the molecular characteristics of prostate cancer. Circulating tumour cells provide insight into tumour cell dissemination and are indicative of aggressive disease phenotypes, with single-cell analyses revealing genomic instability and treatment resistance. Circulating tumour DNA offers real-time tumour genomic information, aiding in treatment decision-making in advanced prostate cancer, where it has been associated with clinical progression. MicroRNAs act as oncogenes or tumour suppressors and exhibit diagnostic and prognostic potential; however, their clinical utility is constrained by the lack of consistent validation. Extracellular vesicles contain tumour-derived biomolecules, with specific proteins demonstrating prognostic relevance. Applications of these markers to urinary testing have been demonstrated. Liquid biomarkers show potential in refining prostate cancer management. Future research should aim to integrate these biomarkers into a cohesive framework in line with precision medicine principles.

Evaluation of miR-148a-3p and miR-106a-5p as Biomarkers for Prostate Cancer: Pilot Study.

MicroRNAs (miRNAs) are a class of small non-coding RNAs that may function as tumor suppressors or oncogenes. Alteration of their expression levels has been linked to a range of human malignancies, including cancer. The objective of this investigation is to assess the relative expression levels of certain miRNAs to distinguish between prostate cancer (PCa) from benign prostatic hyperplasia (BPH). Blood plasma was collected from 66 patients diagnosed with BPH and 58 patients with PCa. Real-time PCR technology was used to evaluate the relative expression among the two groups for miR-106a-5p and miR-148a-3p. The significant downregulation of both miRNAs in plasma from PCa versus BPH patients suggests their potential utility as diagnostic biomarkers for distinguishing between these conditions. The concurrent utilization of these two miRNAs slightly enhanced the sensitivity for discrimination among the two analyzed groups, as shown in ROC curve analysis. Further validation of these miRNAs in larger patient cohorts and across different stages of PCa may strengthen their candidacy as clinically relevant biomarkers for diagnosis and prognosis.

Diagnostic and prognostic biomarkers in prostate cancer

Diagnostic and prognostic biomarkers in prostate cancer

Advances in prostate cancer biomarkers and probes

Advances in prostate cancer biomarkers and probes

Multiplexed Opto-Microfluidic Biosensing: Advanced Platform for Prostate Cancer Detection.

Cancer stands as a prominent global cause of mortality, necessitating early detection to augment survival rates and alleviate economic burdens on healthcare systems. In particular, prostate cancer (PCa), impacting 1.41 million men globally in 2020, accentuates the demand for sensitive and cost-effective detection methods beyond traditional prostate-specific antigen (PSA) testing. While clinical techniques exhibit limitations, biosensors emerge as compact, user-friendly alternatives to traditional laboratory approaches. However, existing biosensors predominantly concentrate on PSA detection, prompting the necessity for advancing toward multiplex sensing platforms. This study introduces a compact opto-microfluidic sensor featuring a substrate of gold nanospikes, fabricated via electrodeposition, for enhanced sensitivity. Embedded within a microfluidic chip, this nanomaterial enables the precise and concurrent measurement of PSA, alongside two complementary PCa biomarkers, matrix metalloproteinase-2 (MMP-2) and anti-α-methylacyl-CoA racemase (anti-AMACR) in diluted human plasma, offering a comprehensive approach to PSA analysis. Taking advantage of the localized surface plasmon resonance principle, this biosensor offers robustness and sensitivity in real sample analysis without the need for labeling agents. With the limit of detection at 0.22, 0.37, and 0.18 ng/mL for PSA, MMP-2, and anti-AMACR, respectively, this biosensing platform holds promise for point-of-care analysis, underscoring its potential impact on medical diagnostics.

Genomic Frequencies of Dynamic DNA Sequences and Mammalian Lifespan.

Dynamic DNA sequences (i.e. sequences capable of forming hairpins, G-quadruplexes, i-motifs, and triple helices) can cause replication stress and associated mutations. One example of such a sequence occurs in the RACK7 gene in human DNA. Since this sequence forms i-motif structures at neutral pH that cause replication stress and result in spontaneous deletions in prostate cancer cells, our initial aim was to determine its potential utility as a biomarker of prostate cancer. We cloned and sequenced the region in RACK7 where i-motif deletions often occur in DNA obtained from eight individuals. Expressed prostatic secretions were obtained from three individuals with a positive biopsy for prostate cancer and two with individuals with a negative biopsy for prostate cancer. Peripheral blood specimens were obtained from two control healthy bone marrow donors and a marrow specimen was obtained from a third healthy marrow donor. Follow-up computer searches of the genomes of 74 mammalian species available at the NCBI ftp site or frequencies of 6 dynamic sequences known to produce mutations or replication stress using a program written in Mathematica were subsequently performed. Deletions were found in RACK7 in specimens from both older normal adults, as well as specimens from older patients with cancer, but not in the youngest normal adult. The deletions appeared to show a weak trend to increasing frequency with patient age. This suggested that endogenous mutations associated with dynamic sequences might accumulate during aging and might serve as biomarkers of biological age rather than direct biomarkers of cancer. To test that hypothesis, we asked whether or not the genomic frequencies of several dynamic sequences known to produce replication stress or mutations in human DNA were inversely correlated with maximum lifespan in mammals. Our results confirm this correlation for six dynamic sequences in 74 mammalian genomes studied, thereby suggesting that spontaneously induced replication stress and mutations linked to dynamic sequence frequency may limit lifespan by limiting genome stability.

Endoplasmic reticulum stress-related genes as prognostic and immunogenic biomarkers in prostate cancer

BackgroundThe metastasis and aggressive nature of prostate cancer (PCa) has become a major malignancy related threat that concerns men’s health. The efficacy of immune monotherapy against PCa is questionable due to its lymphocyte-suppressive nature.MethodEndoplasmic reticulum stress- (ERS-) and PCa-prognosis-related genes were obtained from the Molecular Signatures Database and the Cancer Genome Atlas database. The expression, prognosis and immune infiltration values of key genes were explored by “survival R package”, “rms”, “xCELL algorithm”, and univariate–multivariate Cox and LASSO regression analyses. The “consensus cluster plus R package” was used for cluster analysis.ResultAs ERS-related genes, ERLIN2 and CDK5RAP3 showed significant expressional, prognostic and clinic-pathologic values. They were defined as the key genes significantly correlated with immune infiltration and response. The nomogram was constructed with T-stage and primary treatment outcome, and the risk-prognostic model was constructed in the following way: Riskscore = (− 0.1918) * ERLIN2 + (0.5254) * CDK5RAP3. Subsequently, prognostic subgroups based on key genes classified the high-risk group as a pro-cancer subgroup that had lower mutation rates of critical genes (SPOP and MUC16), multiple low-expression immune-relevant molecules, and differences in macrophages (M1 and M2) expressions. Finally, ERLIN2 as an anti-oncogene and CDK5RAP3 as a pro-oncogene were further confirmed by cell phenotype assays and immunohistochemistry.ConclusionWe identified ERLIN2 and CDK5RAP3 as ERS-related genes with important prognostic and immunologic values, and classified patients between high- and low-risk subgroups, which provided new prognostic markers, immunotherapeutic targets, and basis for prognostic assessments.

Development and Validation of an 18-Gene Urine Test for High-Grade Prostate Cancer

Benefits of prostate cancer (PCa) screening with prostate-specific antigen (PSA) alone are largely offset by excess negative biopsies and overdetection of indolent cancers resulting from the poor specificity of PSA for high-grade PCa (ie, grade group [GG] 2 or greater). To develop a multiplex urinary panel for high-grade PCa and validate its external performance relative to current guideline-endorsed biomarkers. RNA sequencing analysis of 58 724 genes identified 54 markers of PCa, including 17 markers uniquely overexpressed by high-grade cancers. Gene expression and clinical factors were modeled in a new urinary test for high-grade PCa (MyProstateScore 2.0 [MPS2]). Optimal models were developed in parallel without prostate volume (MPS2) and with prostate volume (MPS2+). The locked models underwent blinded external validation in a prospective National Cancer Institute trial cohort. Data were collected from January 2008 to December 2020, and data were analyzed from November 2022 to November 2023. Protocolized blood and urine collection and transrectal ultrasound-guided systematic prostate biopsy. Multiple biomarker tests were assessed in the validation cohort, including serum PSA alone, the Prostate Cancer Prevention Trial risk calculator, and the Prostate Health Index (PHI) as well as derived multiplex 2-gene and 3-gene models, the original 2-gene MPS test, and the 18-gene MPS2 models. Under a testing approach with 95% sensitivity for PCa of GG 2 or greater, measures of diagnostic accuracy and clinical consequences of testing were calculated. Cancers of GG 3 or greater were assessed secondarily. Of 761 men included in the development cohort, the median (IQR) age was 63 (58-68) years, and the median (IQR) PSA level was 5.6 (4.6-7.2) ng/mL; of 743 men included in the validation cohort, the median (IQR) age was 62 (57-68) years, and the median (IQR) PSA level was 5.6 (4.1-8.0) ng/mL. In the validation cohort, 151 (20.3%) had high-grade PCa on biopsy. Area under the receiver operating characteristic curve values were 0.60 using PSA alone, 0.66 using the risk calculator, 0.77 using PHI, 0.76 using the derived multiplex 2-gene model, 0.72 using the derived multiplex 3-gene model, and 0.74 using the original MPS model compared with 0.81 using the MPS2 model and 0.82 using the MPS2+ model. At 95% sensitivity, the MPS2 model would have reduced unnecessary biopsies performed in the initial biopsy population (range for other tests, 15% to 30%; range for MPS2, 35% to 42%) and repeat biopsy population (range for other tests, 9% to 21%; range for MPS2, 46% to 51%). Across pertinent subgroups, the MPS2 models had negative predictive values of 95% to 99% for cancers of GG 2 or greater and of 99% for cancers of GG 3 or greater. In this study, a new 18-gene PCa test had higher diagnostic accuracy for high-grade PCa relative to existing biomarker tests. Clinically, use of this test would have meaningfully reduced unnecessary biopsies performed while maintaining highly sensitive detection of high-grade cancers. These data support use of this new PCa biomarker test in patients with elevated PSA levels to reduce the potential harms of PCa screening while preserving its long-term benefits.

Unveiling the potential of SLURP1 protein as a biomarker for prostate cancer screening.

Prostate cancer (PCa) develops slowly and lacks obvious symptoms in the early stage, which makes early screening and diagnosis difficult. Urine collection is simple and is an ideal source of biomarkers. In this study, we performed urinary proteomic studies in PCa patients to screen proteins and apply them to the non-invasive early diagnosis of PCa. Urine samples from PCa patients, benign prostatic hyperplasia (BPH) patients and normal control group were collected. Mass spectrometry was used for proteomic analysis and screening target proteins. Western blot and enzyme-linked immunosorbent assay (ELISA) were used to verify the results. Correlations with clinical indicators were explored, and receiver operating characteristic (ROC) curves were drawn to evaluate the value of target proteins in PCa. A total of 1065 proteins were identified. Urinary SLURP1 protein was significantly elevated in patients with PCa compared with normal controls and patients with BPH patients. Western blot and ELISA further verified the expression changes of SLURP1. The immunohistochemical staining results revealed a substantial increase in positive SLURP1 expression within PCa tumor tissue. Correlation analysis showed a positive correlation between the expression level of urine SLURP1 protein and serum PSA. ROC curve analysis of the SLURP1 protein in the urine of both normal individuals and PCa patients is determined to be 0.853 (95% CI=0.754 to 0.954). The concentration of SLURP1 protein in urine of PCa patients is increased, which can serve as a biomarker for screening PCa.

The Suppression of the Epithelial to Mesenchymal Transition in Prostate Cancer through the Targeting of MYO6 Using MiR-145-5p.

Aberrant expression of miR-145-5p has been observed in prostate cancer where is has been suggested to play a tumor suppressor role. In other cancers, miR-145-5p acts as an inhibitor of epithelial-to-mesenchymal transition (EMT), a key molecular process for tumor progression. However, the interaction between miR-145-5p and EMT remains to be elucidated in prostate cancer. In this paper the link between miR-145-5p and EMT in prostate cancer was investigated using a combination of in silico and in vitro analyses. miR-145-5p expression was significantly lower in prostate cancer cell lines compared to normal prostate cells. Bioinformatic analysis of The Cancer Genome Atlas prostate adenocarcinoma (TCGA PRAD) data showed significant downregulation of miR-145-5p in prostate cancer, correlating with disease progression. Functional enrichment analysis significantly associated miR-145-5p and its target genes with EMT. MYO6, an EMT-associated gene, was identified and validated as a novel target of miR-145-5p in prostate cancer cells. In vitro manipulation of miR-145-5p levels significantly altered cell proliferation, clonogenicity, migration and expression of EMT-associated markers. Additional TCGA PRAD analysis suggested miR-145-5p tumor expression may be useful predictor of disease recurrence. In summary, this is the first study to report that miR-145-5p may inhibit EMT by targeting MYO6 in prostate cancer cells. The findings suggest miR-145-5p could be a useful diagnostic and prognostic biomarker for prostate cancer.

Fiber-laser based on D-shaped fiber biosensor for prostate cancer biomarker detection

This work describes the development of a fiber laser-based biosensor using a D-shaped fiber for detecting prostate cancer biomarkers. The biosensor probe was fabricated by coating the D-shaped fiber with gold using sputter coater with deposition time of 6 min and current of 40 mA. Then, the gold-coated D-shaped fiber was inserted inside the single ring cavity for biosensing. The antibody-antigen interaction was conducted using layer-by-layer (LbL) immobilization method. The antibodies were then immobilized on the biosensor, followed by the immunoassay, which involves detecting various concentrations of human chorionic gonadotropin (hCG) antigen. The performance of the biosensor was evaluated in terms of sensitivity, which are determined to be 0.2171 nm/(µg/mL). As a results, this biosensor able to improve the prostate cancer monitoring, diagnosis and early identification. It also suggests that this study paves the way for personalized health strategies and advancements in prostate cancer diagnostics.

Evaluating Leukocyte Telomere Length and Myeloid-Derived Suppressor Cells as Biomarkers for Prostate Cancer.

Leukocyte telomere length (LTL) and myeloid-derived suppressor cells (MDSC) are associated with aging and the development and progression of cancer. However, the exact nature of this relationship remains unclear. Our study aimed to investigate the potential of LTL and MDSC as diagnostic biomarkers for prostate cancer while also seeking to deepen our understanding of the relationship of these potential biomarkers to each other. Our study involved patients undergoing a prostate biopsy. We analyzed the relative LTL in genomic DNA obtained from peripheral blood leukocytes as well as the percentage of MDSC and their subtypes in peripheral blood mononuclear cells (PBMC). Our evaluation focused on examining the relationship between LTL and MDSC and pathological diagnoses as well as investigating the correlation between LTL and MDSC levels. In our study of 102 participants, 56 were pathologically diagnosed with localized prostate cancer (cancer group), while 46 tested negative (control group). The cancer group exhibited significantly shorter LTL in comparison to the control group (p = 0.024). Additionally, the cancer group showed a tendency towards a higher percentage of monocytic MDSC (M-MDSC), although this difference did not reach statistical significance (p = 0.056). Our multivariate logistic regression analysis revealed that patients with shorter LTL and higher percentages of M-MDSC had a 2.98-fold (95% CI = 1.001-8.869, p = 0.049) and 3.03-fold (95% CI = 1.152-7.977, p = 0.025) increased risk of prostate cancer diagnosis, respectively. There was also a significant negative correlation between LTL and M-MDSC. (r = -0.347, p < 0.001). Our research has established a correlation between LTL and MDSC in patients undergoing biopsy for prostate cancer. Notably, we observed that individuals with localized prostate cancer tend to have shorter LTL and a higher percentage of M-MDSC prior to their diagnosis. These findings suggest that LTL and M-MDSC could potentially serve as adjunctive biomarkers for the early diagnosis of prostate cancer.

Abstract 1033: Evaluation of autoantibodies to proteins encoded by cancer driver genes as biomarkers for prostate cancer

Abstract Prostate cancer (PCa) is a complex disease driven by genomic alterations, and understanding the immune response to these alterations can provide valuable insights into its pathogenesis. This study aimed at exploring the immunogenicity of proteins encoded by PCa driver genes and identifying corresponding autoantibodies as potential PCa biomarkers. Bioinformatics analysis, enzyme-linked immunosorbent assay (ELISA), and immunohistochemistry (IHC) were conducted in this study. A comprehensive screening process involving bioinformatics analysis and published studies identified a set of PCa driver genes for further investigation. Three strategies were employed to identify candidate driver genes: (1) utilizing the IntOGen database (Martínez-Jiménez et.al, Nature Reviews Cancer 2020), nine cohorts comprising 1503 primary PCa patient samples were analyzed, resulting in the identification of 63 PCa driver genes, with the top 20 genes exhibiting high mutation rates and frequencies in these cohorts; (2) employing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, 16 driver genes that were enriched in prostate cancer pathways were identified from the initial 63 genes; (3) referring to a published article (Dietlein et.al, Nature Genetics 2020), the top 17 driver genes that highly related to prostate cancer across 28 tumor types were selected. A final set of 15 PCa driver genes, which appeared in at least two of the screening strategies, were included in subsequent analyses. ELISA was then performed to assess the presence of autoantibodies targeting proteins encoded by these 15 PCa driver genes. Autoantibodies were evaluated using serum samples from 203 PCa patients and 93 normal controls. The results revealed that seven out of the 15 autoantibodies showed significantly elevated levels in the sera of PCa patients compared to normal controls. Notably, the autoantibody targeting SPOP (speckled-type POZ protein) exhibited the highest frequency of 42.4%, followed by the autoantibody to PTEN (phosphatase and tensin homolog) at 37.4% in the PCa group. Finally, the SPOP protein was analyzed by IHC on the prostate tissue microarray containing PCa tissues, prostate simple hyperplasia tissues, cancer adjacent prostate tissues, and normal prostate tissues further to explore the consistency of SPOP expression in antigen-antibody reactions. High expression of SPOP protein was found in the PCa tissues. These findings suggest that proteins encoded by PCa driver genes may possess immunogenic properties due to neoepitopes possibly generated by mutations, leading to the production of corresponding autoantibodies. Identifying specific autoantibodies associated with PCa driver genes could offer novel opportunities for discovering autoantibody-based biomarkers in PCa. Further validation of these autoantibodies is warranted to evaluate their diagnostic potential in PCa. Citation Format: Cuipeng Qiu, Xiao Wang, Carlos A. Casiano, Jian-Ying Zhang. Evaluation of autoantibodies to proteins encoded by cancer driver genes as biomarkers for prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1033.

Abstract 4570: Prostate-specific membrane antigen targeted chemo-photodynamic therapy for the treatment of prostate cancer

Abstract Introduction: Prostate cancer is the most prevalent cancer among men in the United States and is their second leading cause of cancer death. During initial screen, about 17-31% of patients are diagnosed with non-metastatic high-risk localized prostate cancer. These patients have an increased risk of treatment failure and oncological progression. The contemporary treatment paradigm of high risk localized prostate cancer is moving toward a multidisciplinary integrated approach of systemic and local therapy. In addition, incorporation of molecular biomarkers can also improve the risk stratification and treatment of prostate cancer. Among the biomarkers for prostate cancer, prostate specific membrane antigen (PSMA) has been proven to be an excellent target for diagnosis and treatment of prostate cancer. We have generated a PSMA-targeted photodynamic therapy (PDT) agent, PSMA-1-Pc413, and a PSMA-targeted chemotherapeutic agent, PSMA-1-MMAE. The objective of this study is to generate a new molecule that combines PDT and chemotherapy to achieve improved antitumor activity. Methods: Using a bifunctional PSMA ligand, a novel theranostic PSMA-targeting conjugate was synthesized, which integrated both the photosensitizer Pc413 and the microtubular inhibitor monomethyl auristatin E (MMAE) in one simple molecule, PSMA-1-MMAE-Pc413. This molecule was first tested for in vitro uptake and cytotoxicity studies using PSMA-positive PC3pip and PSMA-negative PC3flu cells. The conjugate was then tested for in vivo fluorescence imaging using mice bearing both PC3pip and PC3flu tumors. Finally, the antitumor activity of PSMA-1-MMAE-Pc413 was tested in mice with PC3pip tumors with and without light irradiation. Results: In in vitro uptake studies, the uptake of PSMA-1-MMAE-Pc413 in PC3pip tumor was about 3-fold higher than that in PC3flu cells. In in vitro cytotoxicity studies, when exposed to 5 J/cm2 of 672nm light, PSMA-1-MMAE-Pc413 demonstrated a synergistic effect leading to greater cytotoxicity for PC3pip cells (IC50=2.77nM) when compared to PSMA-1-Pc413 with light irradiation (IC50=372nM) or PSMA-1-MMAE-Pc413 without light irradiation (IC50=12.6nM). In in vivo imaging studies, PSMA-1-MMAE-Pc413 demonstrated selective uptake in PC3pip tumors. Further, antitumor activity studies showed that PSMA-1-MMAE-Pc413 with light irradiation significantly inhibited PC3pip tumor growth and prolonged survival time as compared to mice receiving equimolar amounts of PSMA-1-Pc413 with light irradiation or PSMA-1-MMAE-Pc413 without light irradiation. Conclusion: We have synthesized a new theranostic agent PSMA-1-MMAE-Pc413 that combines imaging, chemotherapy and PDT. The new molecule dramatically improves the treatment effect as compared to each single treatment alone. (The work is supported by NIH 1RO1CA255925-01A.) Citation Format: Xinning Wang, Aditi Shirke, Sriprada Chavali, Lifang Zhang, Ethan Walker, Gopolakrishnan Ramamurthy, James Basilion. Prostate-specific membrane antigen targeted chemo-photodynamic therapy for the treatment of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4570.

Abstract 1839: Proteomic and glycoproteomic differences associated with prostate cancer in urine

Abstract Background and Aims: Prostate cancer is one of the most common cancers affecting men in the United States, with ~288,300 new cases and 34,700 deaths in 2023 alone. Prostate-specific antigen (PSA), the most widely used prostate cancer biomarker, lacks specificity and sensitivity, leading to unnecessary biopsies, overdiagnosis, and overtreatment. There is an urgent unmet clinical need to find new biomarkers that can distinguish patients with benign disease from those with prostate cancer. Urine is an attractive source for these biomarkers as prostate-derived proteins are more highly concentrated in urine than in blood. To better understand both global proteomic and glycoproteomic molecular signatures that could lead to improved diagnostics for prostate cancer, we analyzed urine samples from unaffected men, men with benign prostatic hyperplasia (BPH), and men with prostate cancer. Methods: Urine samples (N = 58 total) were collected from unaffected men (N = 17), men with BPH (N = 24), and men with prostate cancer (N = 17). Samples were digested with trypsin and analyzed with high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) for shotgun proteomics analysis. Association with cancer was determined through supervised analysis using Pearson correlation. For glycoproteomics analysis, intact glycopeptides were enriched using strong anion exchange-electrostatic repulsion hydrophilic interaction chromatography, fractionated using high pH reversed-phase chromatography, and analyzed with LC-MS/MS. Association with the three conditions was determined through several rounds of supervised analysis using Pearson correlation. Results: In total, 3542 human proteins were identified, and 78 proteins (p &amp;lt; 0.01) were significantly associated with prostate cancer. The glycolysis pathway and lysosome pathway were enriched in samples from men with prostate cancer. In the glycoproteomics analysis, 36 proteins (p &amp;lt; 0.05) had altered glycan diversity in one of the three conditions, and the complement and coagulation cascade pathway and the lysosome pathway were highly enriched. Six proteins (p &amp;lt; 0.05) had significantly increased glycan diversity in the cancerous samples than in the BPH and normal samples. Conclusions: This study identifies a set of interesting proteomic and glycoproteomic differences in urine from men with prostate cancer compared to BPH and non-cancer samples, which warrant further investigation in additional sample cohorts as potential novel biomarkers. Citation Format: Nikhiya Shamsher, Fernando Garcia-Marques, Abel Bermudez, Mark R. Flory, Sharon J. Pitteri. Proteomic and glycoproteomic differences associated with prostate cancer in urine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1839.

Abstract 7492: Automated identification and enumeration of CELLSEARCH Circulating Tumor Cells (CTC) with a deep learning algorithm

Abstract Introduction: CTC enumeration in blood samples with CELLSEARCH® is a prognostic biomarker in metastatic breast, prostate and colorectal cancer (full intended use documents.cellsearchctc.com). Currently, CTC identification is performed by visual assessment, which is time-consuming and potentially affected by subjective interpretations. Recently, we developed a Deep Learning (DL) algorithm for automated CTC identification in CELLSEARCH images (CellFind®, Menarini Silicon Biosystems, research use only). This study aims to assess the CTC identification performance of CellFind on 2 different datasets: the first for comparison with human reviewers, and the second for validation with much higher number of images. Methods: CellFind is composed of an image segmentation followed by a classification network, which was trained using 13067 CTC and 52890 non-CTC images from 215 breast, 123 prostate and 180 colorectal cancer samples. The performance of CellFind was tested on 2 separate datasets. 1) The first dataset was made of CELLSEARCH gallery images from 26 breast cancer, 68 prostate cancer, and 40 benign samples. 8 human reviewers, qualified for CTC image analysis, performed blind labeling. The Ground Truth (GT) was generated by the classification of the 3 most experienced reviewers by majority voting (1621 CTCs out of 17080 images). Accuracy and F1 metrics were used to rank the performance of DL and 5 reviewers against the GT. 2) The second dataset was made of CELLSEARCH gallery images from 63 breast, 66 prostate and 32 colorectal cancer samples. 3 experienced reviewers created the GT classification by majority voting, identifying 20052 CTCs out of 117447 images.Finally, each patient was assigned to the favorable or unfavorable group by applying the validated cutoff on CTC enumeration per sample (CTC≥5 for breast and prostate cancer, CTC≥3 for colorectal cancer). Results: 1) On 17080 gallery images from 134 samples, CellFind reached top human-level performance, as ranked both by accuracy (97.8% for DL vs. 96.8-98.0%) and F1 (88.6% for DL vs. 83.3-89.9% for reviewers). On the 94 cancer samples, DL accuracy on favorable/unfavorable prognosis was 98.9% vs. 95.8-97.9% for reviewers. 2) On the bigger dataset with 117447 gallery images from 161 cancer samples, CellFind obtained accuracy = 96.0% and F1 = 87.8% for CTC identification (TP = 17039, FP = 1735, FN = 3013, TN = 95660). For favorable/unfavorable prognosis the DL accuracy was 95.4%. Conclusion: Automated identification and enumeration of CTCs in CELLSEARCH images with CellFind can remove human subjectivity from the review process and maximize standardization among different research centers. CellFind performed better than most operators and reduced the data processing time required for each blood sample by the operator. Citation Format: Luca Biasiolli, Pietro Ansaloni, Nicolò Gentili, Daniele Giardiello, Francesco Montanari, Ramona Miserendino, Giulio Signorini, Gianni Medoro. Automated identification and enumeration of CELLSEARCH Circulating Tumor Cells (CTC) with a deep learning algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7492.