Total and free PSA kinetics in patients without prostate cancer undergoing radical cystoprostatectomy

Relationship between PSA kinetics and Tc-99m HYNIC PSMA SPECT/CT detection rates of biochemical recurrence in patients with prostate cancer after radical prostatectomy

BACKGROUND

Prostate-specific antigen (PSA) levels should reflect or be proportional to the size and the metabolic activity of prostatic metastases. Moreover, a rapid change in PSA kinetics, either before or after treatment, is an indicator of poor prognosis after radical prostatectomy. Therefore, the purpose of this study was to investigate the effect of total PSA at the time of Tc-99m HYNIC PSMA SPECT/CT (trigger PSA), PSA velocity (PSAvel), and PSA doubling time (PSAdt) on the Tc-99m HYNIC PSMA SPECT/CT detection rate in prostate cancer patients who showed biochemical recurrence after radical prostatectomy during follow-up.

METHODS

In total, 208 patients who showed an increase in PSA were evaluable for this retrospective analysis covering November 2015 to March 2017. Data were available for calculation of PSAvel in 112 patients and for PSAdt in 157 patients. Logistic regression analysis was used to determine whether there was a relationship between the PSA levels and PSA kinetics and the rate of detection of relapse using Tc-99m HYNIC PSMA SPECT/CT.

RESULTS

Tc-99m HYNIC PSMA SPECT/CT detected disease relapse in 151 of 208 patients (72.6%). The PSA level (P < 0.0001) and PSAdt (P = 0.0036) were significantly different between SPECT-positive patients (higher PSA level, shorter PSAdt) and SPECT-negative patients (lower PSA, longer PSAdt). ROC analysis showed that a PSA level of 1.30 ng/mL and a PSAdt of 2.9 months were optimal cut-off values. Patients with purely local recurrence had lower PSAvel and longer PSAdt values (P < 0.001). According to the multivariate analysis, a pathological positive SPECT/CT scan was associated with the PSA level (P < 0.001), PSAdt <6 months (P < 0.05), and Gleason scores (GSC) >7 (P < 0.05).

CONCLUSION

The Tc-99m HYNIC PSMA SPECT/CT detection rate is influenced by trigger PSA, PSAdt, and PSAvel. Like PSA, PSAdt is an independent predictor of Tc-99m HYNIC PSMA SPECT/CT. PSAdt should be taken into account by physicians especially when PSA <1 ng/mL.

Characterization of humoral responses of ovarian cancer patients: antibody subclasses and antigenic components

OBJECTIVES

Current antigen-based diagnostic assays for ovarian cancers rely on intravasation of specific aberrantly expressed proteins and their achieving detectable steady-state concentrations, resulting in their inability to truly detect small early lesions. In contrast, tumor antigen immunorecognition is observed following initial transformation events. Our objective was to characterize humoral antitumor responses in terms of IgG subclasses generated and tumor antigens recognized.

METHODS

For patients with benign and malignant ovarian disease, tumor-reactive IgG subclasses were characterized by Western immunoblotting. Antigen recognition patterns were analyzed by 2-dimensional electrophoresis and proteins exhibiting shared or stage-specific recognition were defined by mass spectrometry (MS) sequencing.

RESULTS

Sera from ovarian cancer patients exhibited significantly greater immunoreactivities than either controls or women with benign disease. While late-stage patients recognized more proteins at greater intensity, stage-specific differential recognition patterns were observed in the IgG subclasses, with the greatest recognition appearing in IgG2 subclasses. Immunoreactivity in IgG2 and IgG3 from stage I and II patients appears to be most intense with nuclear antigens >40 kDa, while, in stage III patients, additional immunoreactivity was present in the <40 kDa components. Stage III patients also exhibited similar reaction with membrane antigens <40 kDa. Two-dimensional electrophoresis revealed 32 stage-linked antigenic differences with 11 in early-stage and 21 in late-stage ovarian cancer.

CONCLUSIONS

Owing to the timing and stability of humoral responses, quantitation of IgG subclasses recognizing specific tumor antigens provides superior biomarkers for early cancer identification and allows for differentiation of benign versus malignant ovarian masses and early- and late-stage cancers.

Cancer screening: a mathematical model relating secreted blood biomarker levels to tumor sizes

BACKGROUND

Increasing efforts and financial resources are being invested in early cancer detection research. Blood assays detecting tumor biomarkers promise noninvasive and financially reasonable screening for early cancer with high potential of positive impact on patients' survival and quality of life. For novel tumor biomarkers, the actual tumor detection limits are usually unknown and there have been no studies exploring the tumor burden detection limits of blood tumor biomarkers using mathematical models. Therefore, the purpose of this study was to develop a mathematical model relating blood biomarker levels to tumor burden.

METHODS AND FINDINGS

Using a linear one-compartment model, the steady state between tumor biomarker secretion into and removal out of the intravascular space was calculated. Two conditions were assumed: (1) the compartment (plasma) is well-mixed and kinetically homogenous; (2) the tumor biomarker consists of a protein that is secreted by tumor cells into the extracellular fluid compartment, and a certain percentage of the secreted protein enters the intravascular space at a continuous rate. The model was applied to two pathophysiologic conditions: tumor biomarker is secreted (1) exclusively by the tumor cells or (2) by both tumor cells and healthy normal cells. To test the model, a sensitivity analysis was performed assuming variable conditions of the model parameters. The model parameters were primed on the basis of literature data for two established and well-studied tumor biomarkers (CA125 and prostate-specific antigen [PSA]). Assuming biomarker secretion by tumor cells only and 10% of the secreted tumor biomarker reaching the plasma, the calculated minimally detectable tumor sizes ranged between 0.11 mm(3) and 3,610.14 mm(3) for CA125 and between 0.21 mm(3) and 131.51 mm(3) for PSA. When biomarker secretion by healthy cells and tumor cells was assumed, the calculated tumor sizes leading to positive test results ranged between 116.7 mm(3) and 1.52 x 10(6) mm(3) for CA125 and between 27 mm(3) and 3.45 x 10(5) mm(3) for PSA. One of the limitations of the study is the absence of quantitative data available in the literature on the secreted tumor biomarker amount per cancer cell in intact whole body animal tumor models or in cancer patients. Additionally, the fraction of secreted tumor biomarkers actually reaching the plasma is unknown. Therefore, we used data from published cell culture experiments to estimate tumor cell biomarker secretion rates and assumed a wide range of secretion rates to account for their potential changes due to field effects of the tumor environment.

CONCLUSIONS

This study introduced a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays. Assuming physiological data on CA125 and PSA from the literature, the model predicted detection limits of tumors that were in qualitative agreement with the actual clinical performance of both biomarkers. The model may be helpful in future estimation of minimal detectable tumor sizes for novel proteomic biomarker assays if sufficient physiologic data for the biomarker are available. The model may address the potential and limitations of tumor biomarkers, help prioritize biomarkers, and guide investments into early cancer detection research efforts.