A longitudinal analysis with CA-125 to predict overall survival in patients with ovarian cancer

Dynamic Prediction of Resectability for Patients with Advanced Ovarian Cancer Undergoing Neo-Adjuvant Chemotherapy: Application of Joint Model for Longitudinal CA-125 Levels

In patients with advanced ovarian cancer (AOC) receiving neoadjuvant chemotherapy (NAC), predicting the feasibility of complete interval cytoreductive surgery (ICRS) is helpful and may avoid unnecessary laparotomy. A joint model (JM) is a dynamic individual predictive model. The aim of this study was to develop a predictive JM combining CA-125 kinetics during NAC with patients' and clinical factors to predict resectability after NAC in patients with AOC. A retrospective study included 77 patients with AOC treated with NAC. A linear mixed effect (LME) sub-model was used to describe the evolution of CA-125 during NAC considering factors influencing the biomarker levels. A Cox sub-model screened the covariates associated with resectability. The JM combined the LME sub-model with the Cox sub-model. Using the LME sub-model, we observed that CA-125 levels were influenced by the number of NAC cycles and the performance of paracentesis. In the Cox sub-model, complete resectability was associated with Performance Status (HR = 0.57, [0.34-0.95], p = 0.03) and the presence of peritoneal carcinomatosis in the epigastric region (HR = 0.39, [0.19-0.80], p = 0.01). The JM accuracy to predict complete ICRS was 88% [82-100] with a predictive error of 2.24% [0-2.32]. Using a JM of a longitudinal CA-125 level during NAC could be a reliable predictor of complete ICRS.

The Effect of Delayed Chemotherapy on the Decrease of CA125 in Epithelial Ovarian Cancer During Coronavirus Disease Pandemic in 2020

Objective

To investigate whether patients with epithelial ovarian cancer were affected by delayed chemotherapy during the coronavirus disease pandemic in 2020.

Materials and Methods

A delay of more than 21 days in the planned chemotherapy was defined as "delayed chemotherapy." Forty-five patients with epithelial ovarian, fallopian tube, and peritoneal cancer were delayed between January 1 and March 30, 2020 in the First Affiliated Hospital of Chongqing Medical University. Thirty-two cases were enrolled in this study. Neoadjuvant chemotherapy was used in 8 cases; palliative chemotherapy was used in 5 cases; and maintenance chemotherapy was used in 19 cases. Data included age, pathological type, surgical pathological stage, chemotherapy time and CA125 levels were collected. The half-life of CA125 and the decrease in CA125 levels before and after delayed chemotherapy were calculated.

Results

No patient got coronavirus disease. Compared with patients of ovarian cancer, fallopian tube epithelial cancer and peritoneal epithelial cancer in the same periods in 2019, the half-life of CA125 in neoadjuvant chemotherapy group and recurrence chemotherapy group were more than 20 days, but there was no significant difference. Only when the delayed chemotherapy took place before CA125 turned negative, accompanied by an interval of more than 60 days, the CA125 half-life and the decreased range of CA125 were totally affected.

Conclusion

There was no evidence to support that once chemotherapy was delayed it would influence the decrease of CA125, but whether it would affect the long-term effects such as recurrence and five-year survival rate remains to be further followed up.

CA-125 ELIMination Rate Constant K (KELIM) Is a Marker of Chemosensitivity in Patients with Ovarian Cancer: Results from the Phase II CHIVA Trial

PURPOSE

In patients with ovarian cancer receiving neoadjuvant chemotherapy, the first-line treatment success will depend on both the tumor-primary chemosensitivity and the completeness of interval debulking surgery (IDS). The modeled CA-125 ELIMination rate constant K (KELIM), calculated with the CA-125 longitudinal kinetics during the first 100 chemotherapy days, is a validated early marker of tumor chemosensitivity. The objective was to investigate the role of the chemosensitivity relative to the success of first-line medical-surgical treatment.

EXPERIMENTAL DESIGN

The CA-125 concentrations were prospectively measured in the randomized phase II trial CHIVA (NCT01583322, carboplatin-paclitaxel regimen ± nintedanib, and IDS, n = 188 patients). The KELIM predictive value regarding the tumor response rate, likelihood of complete IDS, risk of subsequent platinum-resistant relapse (PtRR), progression-free survival (PFS), and overall survival (OS) was assessed using univariate and multivariate tests.

RESULTS

The data from 134 patients were analyzed. KELIM was an independent and major predictor of subsequent PtRR risk, and of survivals. The final logistic regression model, including KELIM [OR = 0.13; 95% confidence interval (CI), 0.03-0.49] and complete IDS (no vs. yes, OR = 0.30; 95% CI, 0.11-0.76) highlights the preponderant role of chemosensitivity on the success of the first-line treatment. In patients with highly chemosensitive diseases, the patient prognosis was driven more by the chemotherapy-induced antitumor effects than by the surgery.

CONCLUSIONS

The tumor-primary chemosensitivity, assessed by the modeled CA-125 KELIM calculated during neoadjuvant chemotherapy (http://www.biomarker-kinetics.org/CA-125-neo), may be a major parameter to consider for decision-making regarding IDS attempt, and selecting patients for treatments meant to reverse the primary chemoresistance.See related commentary by May and Oza, p. 4432.

A reactive oxygen species scoring system predicts cisplatin sensitivity and prognosis in ovarian cancer patients

Background

To reveal roles of reactive oxygen species (ROS) status in chemotherapy resistance and to develop a ROS scoring system for prognosis prediction in ovarian cancer.

Methods

We tested the sensitizing effects of ROS elevating drugs to cisplatin (cDDP) in ovarian cancer both in vitro and in vivo. A ROS scoring system was developed using The Cancer Genome Atlas (TCGA) database of ovarian cancer. The associations between ROS scores and overall survival (OS) were analyzed in TCGA, Tothill dataset, and our in-house dataset (TJ dataset).

Results

ROS-inducing drugs increased cisplatin-induced ovarian cancer cell injury in vitro and in vivo. ROS scoring system was established using 25 ROS-related genes. Patients were divided into low (scores 0–12) and high (scores 13–25) score groups. Improved patient survival was associated with higher scores (TCGA dataset hazard ratio (HR) = 0.43, P < 0.001; Tothill dataset HR = 0.65, P = 0.022; TJ dataset HR = 0.40, P = 0.003). The score was also significantly associated with OS in multiple datasets (TCGA dataset r² = 0.574, P = 0.032; Thothill dataset r² = 0.266, P = 0.049; TJ dataset r² = 0.632, P = 0.001) and with cisplatin sensitivity in ovarian cancer cell lines (r² = 0.799, P = 0.016) when used as a continuous variable. The scoring system showed better prognostic performance than other clinical factors by receiver operating characteristic (ROC) curves (TCGA dataset area under the curve (AUC) = 0.71 v.s. 0.65, Tothill dataset AUC = 0.73 v.s. 0.67, TJ dataset AUC = 0.74 v.s. 0.66).

Conclusions

ROS status is associated with chemotherapy resistance. ROS score system might be a prognostic biomarker in predicting the survival benefit from ovarian cancer patients.

Integrated multiomic predictors for ovarian cancer survival

Increasingly affordable high-throughput molecular profiling technologies have made feasible the measurement of omics-wide interindividual variations for the purposes of predicting cancer prognosis. While multiple types of genetic, epigenetic and expression changes have been implicated in ovarian cancer, existing prognostic biomarker strategies are constrained to analyzing a single class of molecular variations. The extra predictive power afforded by the integration of multiple omics types remains largely unexplored. In this study, we performed integrative analysis on tumor-based exome-, transcriptome- and methylome-wide molecular profiles from The Cancer Genome Atlas (TCGA) for variations in cancer-relevant genes to construct robust, cross-validated multiomic predictors for ovarian cancer survival. These integrated polygenic survival scores (PSSs) were able to predict 5-year overall (OS) and progression-free survival in the Caucasian subsample with high accuracy (AUROC = 0.87 and 0.81, respectively). These findings suggest that the PSSs are able to predict long-term OS in TCGA patients with accuracy beyond that of previously proposed protein-based biomarker strategies. Our findings reveal the promise of an integrated omics-based approach in enhancing existing prognostic strategies. Future investigations should be aimed toward prospective external validation, strategies for standardizing application and the integration of germline variants.

Comparative Analysis of Biopsy Upgrading in Four Prostate Cancer Active Surveillance Cohorts

BACKGROUND

Active surveillance (AS) is increasingly accepted for managing low-risk prostate cancer, yet there is no consensus about implementation. This lack of consensus is due in part to uncertainty about risks for disease progression, which have not been systematically compared or integrated across AS studies with variable surveillance protocols and dropout to active treatment.

OBJECTIVE

To compare risks for upgrading from a Gleason score (GS) of 6 or less to 7 or more across AS studies after accounting for differences in surveillance intervals and competing treatments and to evaluate tradeoffs of more versus less frequent biopsies.

DESIGN

Joint statistical model of longitudinal prostate-specific antigen (PSA) levels and risks for biopsy upgrading.

SETTING

Johns Hopkins University (JHU); Canary Prostate Active Surveillance Study (PASS); University of California, San Francisco (UCSF); and University of Toronto (UT) AS studies.

PATIENTS

2576 men aged 40 to 80 years with a GS between 2 and 6 and clinical stage T1 or T2 prostate cancer enrolled between 1995 and 2014.

MEASUREMENTS

PSA levels and biopsy GSs.

RESULTS

After variable surveillance intervals and competing treatments were accounted for, estimated risks for biopsy upgrading were similar in the PASS and UT studies but higher in UCSF and lower in JHU studies. All cohorts had a delay of 3 to 5 months in detecting upgrading with biennial biopsies starting after a first confirmatory biopsy versus annual biopsies.

LIMITATION

The model does not account for possible misclassification of biopsy GS.

CONCLUSION

Men in different AS studies have different risks for biopsy upgrading after variable surveillance protocols and competing treatments are accounted for. Despite these differences, the consequences of more versus less frequent biopsies seem to be similar across cohorts. Biennial biopsies seem to be an acceptable alternative to annual biopsies.

PRIMARY FUNDING SOURCE

National Cancer Institute.

Dynamic Prediction of Outcome for Patients With Ovarian Cancer: Application of a Joint Model for Longitudinal Cancer Antigen 125 Values

Objective

In clinical practice, gynecologic oncologists are interested in predicting the prognosis of patients through information from different sources. Recently, the overall survival (OS) of ovarian cancer patients has been widely and intensively researched, and a large number of risk factors have been determined, including the biomarker of cancer antigen 125 (CA-125). For newly diagnosed patients, it is critical to construct effective prognostic models to predict prognosis dynamically by combining their CA-125 values with adjusted clinical factors.

Methods/Materials

A total of 227 ovarian cancer participants entered this study. A 4-step method was used to construct a joint model to examine the association between longitudinal CA-125 measurements and OS time, to explore time-independent predictive factors influencing OS, and to obtain an accurate and credible dynamic prediction of OS for specific subjects.

Results

We found that CA-125 values were greatly affected by observation time, menarche, Federation International of Gynecology and Obstetrics stage, and ascites at baseline. Similarly, CA-125 values, menopause, Federation International of Gynecology and Obstetrics stage, and surgery state at baseline were selected from the best Cox proportion hazard model and showed a strong correlation with OS. In addition, the analyses presented by the joint model depict that, as time goes by, increasing CA-125 was deemed to be a significant predictor of death.

Conclusions

Together, our results show that a joint model could be highly efficient in clinical consultation and diagnosis for patients newly diagnosed as having ovarian cancer. Longitudinal CA-125 values, which are measured over time, can be used to credibly predict OS after taking all adjusted covariates into account.

A joint model based on longitudinal CA125 in ovarian cancer to predict recurrence

AIMS

To develop a new package of joint model to fit longitudinal CA125 in epithelial ovarian cancer relapse.

PATIENTS & METHODS

Included were 305 epithelial ovarian cancer patients who reached complete remission after cytoreductive surgery and first-line chemotherapy. Univariate and multivariate analysis with a joint model was performed to select independent risk factors, which were subsequently combined to predict recurrence.

RESULTS

Independent factors were longitudinal CA125, age, stage and residual tumor size (p < 0.05). Prediction of recurrence with these factors had an average of 80.7% accuracy, 5.6-10.7% better than kinetic factors.

CONCLUSION

The new package of joint model fits longitudinal CA125 well. Potential application can be extended to other biomarkers.

CA125-related tumor cell kinetics variables after chemotherapy in advanced ovarian cancer: a systematic review

Various kinetic parameters, based on a minimum of two time points, have been built with CA125 determinations. The aim of this study is to review studies about the clinical application of CA125-related tumor cell kinetics variables in patients with advanced ovarian cancer (AOC) receiving chemotherapy. A literature search for studies about CA125-related variables in patients with AOC was undertaken on three databases, by predefined search criteria, and a selection of studies was performed. Sixty-two studies were selected. CA125-related variables were summarized in three groups: response-related, time-to-event, and other CA125-related tumor cell kinetics variables. Even though CA125 changes and half-life after chemotherapy were the most studied, other variables and two models have been well defined, and often showed an interesting power to predict survival. These kinetics variables are related to the CA125 regression curve, pre- and post-chemotherapy kinetics, or are variables inferred from a population model of CA125 kinetics.