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Challenge on Biomarkers and Treatment for Ovarian Cancer and Cervical...
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Challenge on Biomarkers and Treatment for Ovarian Cancer and Cervical Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Biomarkers".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 13533

Special Issue Editor

Dr. Kevin M. Elias

E-Mail Website
Guest Editor
Director of the Gynecologic Oncology Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
Interests: choriocarcinoma; ovarian cancer; cervical cancer; early detection; biomarkers; targeted therapy

Special Issue Information

Dear Colleagues,

Ovarian cancer is the leading cause of gynecologic cancer death in the United States and Europe, and cervical cancer is the leading cause of gynecologic cancer death worldwide. Biomarkers which can identify women at increased risk for these diseases or that may select individuals for targeted therapies are acutely needed. Tissue, circulating, and imaging markers are needed to triage patients into screening and therapeutic trials. This Special Issue will summarize current data concerning the following topics: early detection, prognostic biomarkers, and targeted therapies for ovarian and cervical cancer.

Dr. Kevin M. Elias
Guest Editor

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Keywords

  • ovarian cancer
  • cervical cancer
  • early detection
  • biomarkers
  • targeted therapy

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Published Papers (6 papers)

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Research

12 pages, 2110 KiB  
Article
Prognostic Impact of Caspase-8, CDK9 and Phospho-CDK9 (Thr 186) Expression in Patients with Uterine Cervical Cancer Treated with Definitive Chemoradiation and Brachytherapy
by Maximilian Fleischmann, Ranadip Mandal, Izabela Kostova, Monika Raab, Mourad Sanhaji, Stephanie Hehlgans, Markus Diefenhardt, Claus Rödel, Emmanouil Fokas, Klaus Strebhardt and Franz Rödel
Cancers 2022, 14(22), 5500; https://doi.org/10.3390/cancers14225500 - 9 Nov 2022
Cited by 3 | Viewed by 1687
Abstract
Introduction: After primary platinum-based chemoradiation of locally advanced uterine cervical cancer, a substantial proportion of women present with persistent, recurrent or metastatic disease, indicating an unmet need for biomarker development. Methods: We evaluated the clinical records of 69 cervical cancer patients (Federation of [...] Read more.
Introduction: After primary platinum-based chemoradiation of locally advanced uterine cervical cancer, a substantial proportion of women present with persistent, recurrent or metastatic disease, indicating an unmet need for biomarker development. Methods: We evaluated the clinical records of 69 cervical cancer patients (Federation of Gynecology and Obstetrics, FIGO Stage > IB3) who were subjected to definitive CRT. Immunohistochemical scoring of caspase-8, cyclin dependent kinase 9 (CDK9) and phosphorylated (phospho-)CDK9 (threonine (Thr) 186) was performed on pretreatment samples and correlated with the histopathological and clinical endpoints, including relapse-free survival (RFS), distant metastasis-free survival (DMFS), cancer-specific survival (CSS) and overall survival (OS). Results: Lower levels of caspase-8 were more prevalent in patients with a higher T-stage (p = 0.002) and a higher FIGO stage (p = 0.003), and were significantly correlated with CDK9 expression (p = 0.018) and inversely with pCDK9 detection (p = 0.014). Increased caspase-8 levels corresponded to improved RFS (p = 0.005), DMFS (p = 0.038) and CSS (p = 0.017) in the univariate analyses. Low CDK9 expression was associated with worse RFS (p = 0.008), CSS (p = 0.015) and OS (p = 0.007), but not DMFS (p = 0.083), and remained a significant prognosticator for RFS (p = 0.003) and CSS (p = 0.009) in the multivariate analyses. Furthermore, low pCDK9 staining was significantly associated with superior RFS (p = 0.004) and DMFS (p = 0.001), and increased CSS (p = 0.022), and remained significant for these endpoints in the multivariate analyses. Conclusion: Increased caspase-8 and CDK9 levels correlate with improved disease-related outcomes in cervical cancer patients treated with CRT, whereas elevated pCDK9 levels predict worse survival in this patient population. Full article
(This article belongs to the Special Issue Challenge on Biomarkers and Treatment for Ovarian Cancer and Cervical Cancer)
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15 pages, 4347 KiB  
Article
Exponential Slope from Absolute Lymphocyte Counts during Radio-Chemotherapy Can Predict an Aggressive Course of Cervical Cancer
by Oyeon Cho, Mison Chun and Suk-Joon Chang
Cancers 2022, 14(20), 5109; https://doi.org/10.3390/cancers14205109 - 18 Oct 2022
Cited by 2 | Viewed by 1809
Abstract
This study aimed to investigate whether the exponential slope α from absolute lymphocyte counts during concurrent radio–chemotherapy (CRT) is associated with aggressive and non-aggressive courses of cervical cancer. We analyzed 362 patients with stage IB–IVB cervical cancer treated with CRT in two groups: [...] Read more.
This study aimed to investigate whether the exponential slope α from absolute lymphocyte counts during concurrent radio–chemotherapy (CRT) is associated with aggressive and non-aggressive courses of cervical cancer. We analyzed 362 patients with stage IB–IVB cervical cancer treated with CRT in two groups: 323 patients without mRNA data (cohort 1) and 39 with mRNA data (cohort 2) from plasma exosomes. We calculated the α of each patient; 69 patients who died of cancer in cohort 1 were divided into 44 who died within 30 months (aggressive group), and 25 who died after more than 30 months (non-aggressive group). The median follow-up periods of cohorts 1 and 2 were 63 and 28 months, respectively. The log2 fold change (log2FC) between read counts of mRNAs before treatment and after the second week of CRT was calculated. Multivariate analyses from cohort 1 showed that neutrophil-to-lymphocyte ratio (NLR) ≥ 2.43 and α < 0.08 were statistically significant predictors of disease-specific survival (DSS) in the aggressive group (DSS-A), whereas α ≥ 0.08 was the only significant predictor of DSS in the non-aggressive group (DSS-NA). The 2.5-year DSS-A and 8-year DSS-NA rates of patients with α ≥ 0.08 and α < 0.08 were 86.7% and 73%, and 78.5% and 94.8% in the high-NLR group, respectively. In cohort 2, patients with both NLR < 2.7 and α ≥ 0.07 had a higher 2.5-year DSS rate than did those with either NLR ≥ 2.72 or α < 0.07. E2F8 and STX6 significantly correlated with ɑ and survival. The 2.5-year DSS rates in patients with E2F8 + STX6 (log2FC) < 0.2429 and ≥0.2429 were 100% and 77.2%, respectively. The exponential slope α can potentially distinguish between aggressive and non-aggressive courses in cervical cancer patients. Full article
(This article belongs to the Special Issue Challenge on Biomarkers and Treatment for Ovarian Cancer and Cervical Cancer)
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14 pages, 1471 KiB  
Article
A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer
by Molly J. Carroll, Katja Kaipio, Johanna Hynninen, Olli Carpen, Sampsa Hautaniemi, David Page and Pamela K. Kreeger
Cancers 2022, 14(17), 4291; https://doi.org/10.3390/cancers14174291 - 1 Sep 2022
Cited by 4 | Viewed by 2468
Abstract
The time between the last cycle of chemotherapy and recurrence, the platinum-free interval (PFI), predicts overall survival in high-grade serous ovarian cancer (HGSOC). To identify secreted proteins associated with a shorter PFI, we utilized machine learning to predict the PFI from ascites composition. [...] Read more.
The time between the last cycle of chemotherapy and recurrence, the platinum-free interval (PFI), predicts overall survival in high-grade serous ovarian cancer (HGSOC). To identify secreted proteins associated with a shorter PFI, we utilized machine learning to predict the PFI from ascites composition. Ascites from stage III/IV HGSOC patients treated with neoadjuvant chemotherapy (NACT) or primary debulking surgery (PDS) were screened for secreted proteins and Lasso regression models were built to predict the PFI. Through regularization techniques, the number of analytes used in each model was reduced; to minimize overfitting, we utilized an analysis of model robustness. This resulted in models with 26 analytes and a root-mean-square error (RMSE) of 19 days for the NACT cohort and 16 analytes and an RMSE of 7 days for the PDS cohort. High concentrations of MMP-2 and EMMPRIN correlated with a shorter PFI in the NACT patients, whereas high concentrations of uPA Urokinase and MMP-3 correlated with a shorter PFI in PDS patients. Our results suggest that the analysis of ascites may be useful for outcome prediction and identified factors in the tumor microenvironment that may lead to worse outcomes. Our approach to tuning for model stability, rather than only model accuracy, may be applicable to other biomarker discovery tasks. Full article
(This article belongs to the Special Issue Challenge on Biomarkers and Treatment for Ovarian Cancer and Cervical Cancer)
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18 pages, 3495 KiB  
Article
Protein Panel of Serum-Derived Small Extracellular Vesicles for the Screening and Diagnosis of Epithelial Ovarian Cancer
by Huiling Lai, Yunyun Guo, Liming Tian, Linxiang Wu, Xiaohui Li, Zunxian Yang, Shuqin Chen, Yufeng Ren, Shasha He, Weipeng He and Guofen Yang
Cancers 2022, 14(15), 3719; https://doi.org/10.3390/cancers14153719 - 30 Jul 2022
Cited by 13 | Viewed by 2379
Abstract
Although ovarian cancer, a gynecological malignancy, has the highest fatality rate, it still lacks highly specific biomarkers, and the differential diagnosis of ovarian masses remains difficult to determine for gynecologists. Our study aimed to obtain ovarian cancer-specific protein candidates from the circulating small [...] Read more.
Although ovarian cancer, a gynecological malignancy, has the highest fatality rate, it still lacks highly specific biomarkers, and the differential diagnosis of ovarian masses remains difficult to determine for gynecologists. Our study aimed to obtain ovarian cancer-specific protein candidates from the circulating small extracellular vesicles (sEVs) and develop a protein panel for ovarian cancer screening and differential diagnosis of ovarian masses. In our study, sEVs derived from the serum of healthy controls and patients with cystadenoma and ovarian cancer were investigated to obtain a cancer-specific proteomic profile. In a discovery cohort, 1119 proteins were identified, and significant differences in the protein profiles of EVs were observed among groups. Then, 23 differentially expressed proteins were assessed using the parallel reaction monitoring in a validation cohort. Through univariate and multivariate logistic regression analyses, a novel model comprising three proteins (fibrinogen gamma gene (FGG), mucin 16 (MUC16), and apolipoprotein (APOA4)) was established to screen patients with ovarian cancer. This model exhibited an area under the receiver operating characteristic curve (AUC) of 0.936 (95% CI, 0.888–0.984) with 92.0% sensitivity and 82.9% specificity. Another panel comprising serum CA125, sEV-APOA4, and sEV-CD5L showed excellent performance (AUC 0.945 (95% CI, 0.890–1.000), sensitivity of 88.0%, specificity of 93.3%, and accuracy of 89.2%) to distinguish malignancy from benign ovarian masses. Altogether, our study provided a proteomic signature of circulating sEVs in ovarian cancer. The diagnostic proteomic panel may complement current clinical diagnostic measures for screening ovarian cancer in the general population and the differential diagnosis of ovarian masses. Full article
(This article belongs to the Special Issue Challenge on Biomarkers and Treatment for Ovarian Cancer and Cervical Cancer)
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15 pages, 4291 KiB  
Article
Defining Models to Classify between Benign and Malignant Adnexal Masses Using Routine Laboratory Parameters
by Elisabeth Reiser, Dietmar Pils, Christoph Grimm, Ines Hoffmann, Stephan Polterauer, Marlene Kranawetter and Stefanie Aust
Cancers 2022, 14(13), 3210; https://doi.org/10.3390/cancers14133210 - 30 Jun 2022
Cited by 2 | Viewed by 1525
Abstract
Discrimination between benign and malignant adnexal masses is essential for optimal treatment planning, but still remains challenging in a routine clinical setting. In this retrospective study, we aimed to compare albumin as a single parameter to calculate models by analyzing laboratory parameters of [...] Read more.
Discrimination between benign and malignant adnexal masses is essential for optimal treatment planning, but still remains challenging in a routine clinical setting. In this retrospective study, we aimed to compare albumin as a single parameter to calculate models by analyzing laboratory parameters of 1552 patients with an adnexal mass (epithelial ovarian cancer (EOC): n= 294; borderline tumor of the ovary (BTO): n = 66; benign adnexal mass: n = 1192) undergoing surgery. Models comprising classical laboratory parameters show better accuracies (AUCs 0.92–0.93; 95% CI 0.90–0.95) compared to the use of single markers, and could easily be implemented in clinical practice by containing only readily available markers. This has been incorporated into a nomogram. Full article
(This article belongs to the Special Issue Challenge on Biomarkers and Treatment for Ovarian Cancer and Cervical Cancer)
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21 pages, 2796 KiB  
Article
Development of a Multiprotein Classifier for the Detection of Early Stage Ovarian Cancer
by Kristin L. M. Boylan, Ashley Petersen, Timothy K. Starr, Xuan Pu, Melissa A. Geller, Robert C. Bast, Jr., Karen H. Lu, Ugo Cavallaro, Denise C. Connolly, Kevin M. Elias, Daniel W. Cramer, Tanja Pejovic and Amy P. N. Skubitz
Cancers 2022, 14(13), 3077; https://doi.org/10.3390/cancers14133077 - 23 Jun 2022
Cited by 6 | Viewed by 2934
Abstract
Background: Individual serum biomarkers are neither adequately sensitive nor specific for use in screening the general population for ovarian cancer. The purpose of this study was to develop a multiprotein classifier to detect the early stages of ovarian cancer, when it is most [...] Read more.
Background: Individual serum biomarkers are neither adequately sensitive nor specific for use in screening the general population for ovarian cancer. The purpose of this study was to develop a multiprotein classifier to detect the early stages of ovarian cancer, when it is most treatable. Methods: The Olink Proseek Multiplex Oncology II panel was used to simultaneously quantify the expression levels of 92 cancer-related proteins in sera. Results: In the discovery phase, we generated a multiprotein classifier that included CA125, HE4, ITGAV, and SEZ6L, based on an analysis of sera from 116 women with early stage ovarian cancer and 336 age-matched healthy women. CA125 alone achieved a sensitivity of 87.9% at a specificity of 95%, while the multiprotein classifier resulted in an increased sensitivity of 91.4%, while holding the specificity fixed at 95%. The performance of the multiprotein classifier was validated in a second cohort comprised of 192 women with early stage ovarian cancer and 467 age-matched healthy women. The sensitivity at 95% specificity increased from 74.5% (CA125 alone) to 79.2% with the multiprotein classifier. In addition, the multiprotein classifier had a sensitivity of 95.1% at 98% specificity for late stage ovarian cancer samples and correctly classified 80.5% of the benign samples using the 98% specificity cutpoint. Conclusions: The inclusion of the proteins HE4, ITGAV, and SEZ6L improved the sensitivity and specificity of CA125 alone for the detection of early stages of ovarian cancer in serum samples. Furthermore, we identified several proteins that may be novel biomarkers of early stage ovarian cancer. Full article
(This article belongs to the Special Issue Challenge on Biomarkers and Treatment for Ovarian Cancer and Cervical Cancer)
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