Synergistic Effect of Venetoclax and Bendamustine in Early T-cell Precursor Acute Lymphoblastic Leukemia.

BACKGROUND/AIM: To date, therapeutic options for T-cell acute lymphoblastic leukemia (T-ALL) remain very limited. This study evaluated the efficacy of monotherapies and combination therapies including a selective BCL-2 inhibitor for T-ALL cell lines, namely Jurkat, CCRF-CEM, and Loucy. MATERIALS AND METHODS: Loucy is an early T-precursor ALL (ETP-ALL) cell line characterized by an immature phenotype, whereas Jurkat and CCRF-CEM are late T-cell progenitor ALL (LTP-ALL) cell lines. Monotherapy was conducted with venetoclax, cytarabine, bendamustine, or azacytidine, whereas combination therapy was performed with venetoclax plus cytarabine, venetoclax plus bendamustine, or venetoclax plus azacytidine. Cell viability assay was conducted after 48 h using Trypan blue and the 3-(4, 5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS). Statistical analysis for evaluating synergistic interactions between anticancer drugs was performed by using the SynergyFinder Plus and drc R package. RESULTS: Adding venetoclax to cytarabine, bendamustine, or azacitidine achieved an additive effect, with Loewe synergic scores ranging from -10 to 10 in Jurkat and CCRF-CEM. Conversely, the combination of venetoclax and cytarabine displayed an additive effect (Loewe synergic score: 8.45 and 5.82 with MTS and Trypan blue assays, respectively), whereas venetoclax plus bendamustine or azacitidine exhibited a synergistic effect (Loewe synergic score >10 with MTS assay) in Loucy. Remarkably, the Bliss/Loewe score revealed that the combination of venetoclax and bendamustine was the most synergistic, yielding a score of 13.832±0.55. CONCLUSION: The combination of venetoclax and bendamustine demonstrated the greatest synergistic effect in suppressing ETP-ALL cell proliferation. Further studies are warranted to determine the mechanisms for the synergism between venetoclax and bendamustine in high-risk T-ALL.

Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI.

Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer (LACC), but its responsiveness varies among patients. A reliable tool for predicting CRT responses is necessary for personalized cancer treatment. In this study, we constructed prediction models using handcrafted radiomics (HCR) and deep learning radiomics (DLR) based on pretreatment MRI data to predict CRT response in LACC. Furthermore, we investigated the potential improvement in prediction performance by incorporating clinical factors. A total of 252 LACC patients undergoing curative chemoradiotherapy are included. The patients are randomly divided into two independent groups for the training (167 patients) and test datasets (85 patients). Contrast-enhanced T1- and T2-weighted MR scans are obtained. For HCR analysis, 1890 imaging features are extracted and a support vector machine classifier with a five-fold cross-validation is trained on training dataset to predict CRT response and subsequently validated on test dataset. For DLR analysis, a 3-dimensional convolutional neural network was trained on training dataset and validated on test dataset. In conclusion, both HCR and DLR models could predict CRT responses in patients with LACC. The integration of clinical factors into radiomics prediction models tended to improve performance in HCR analysis. Our findings may contribute to the development of personalized treatment strategies for LACC patients.

Circulating-tumor DNA Assessment in Diffuse Large B-cell Lymphoma to Determine Up-front Stem Cell Transplantation: A Pilot Study.

BACKGROUND/AIM: This study evaluated the possibility of clinical use of circulating-tumor DNA (ctDNA) as a biomarker to determine up-front autologous stem cell transplantation (auto-SCT) for patients with high-risk diffuse large B-cell lymphoma (DLBCL) in practice. PATIENTS AND METHODS: To explore the dynamics of ctDNA in DLBCL, blood samples were collected sequentially before and after treatment from patients with newly diagnosed DLBCL who received rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy. To conduct ctDNA genotyping and ctDNA monitoring simultaneously, targeted sequencing by cancer personalized profiling using deep sequencing was used. RESULTS: Ten patients between the ages of 50 and 60 years were enrolled. Based on the international prognostic index (IPI), seven patients were classified as high-IPI-risk group, and three patients were classified as low-IPI-risk group. The IPI risk group correlated with total metabolic tumor volume. All patients completed six cycles of R-CHOP chemotherapy, and seven patients achieved complete response. Changes in ctDNA mutation numbers did not correlate with changes in PET scan images and treatment response. In most high-risk patients, new mutations appeared in ctDNA after completion of chemotherapy that conceivably marked resistant clones. Notably, disease relapse did not occur in high-risk patients with poor prognostic mutations who underwent autologous SCT. CONCLUSION: ctDNA monitoring was meaningful in high-risk patients. Moreover, ctDNA and well-known prognostic factors should be considered in the decision making for auto-SCT. If a new genetic mutation in ctDNA with a negative prognosis would emerge during treatment, high-risk patients should consider auto-SCT.

Relationship between Human Papillomavirus Status and the Cervicovaginal Microbiome in Cervical Cancer.

Uterine cervical cancer (CC) is a complex, multistep disease primarily linked to persistent infection with high-risk human papillomavirus (HR-HPV). However, it is widely acknowledged that HR-HPV infection alone cannot account for the formation and progression of CC. Emerging evidence suggests that the cervicovaginal microbiome (CVM) also plays a significant role in HPV-related CC. Certain bacteria, such as Fusobacterium spp., Porphyromonas, Prevotella, and Campylobacter, are currently being considered as potential microbiomarkers for HPV-positive CC. However, the composition of the CVM in CC is inconsistent; thus, further studies are needed. This review comprehensively discusses the complex interplay between HPV and the CVM in cervical carcinogenesis. It is postulated that the dynamic interaction between HPV and the CVM creates an imbalanced cervicovaginal microenvironment that triggers dysbiosis, enhances HPV persistence, and promotes cervical carcinogenesis. Moreover, this review aims to provide updated evidence on the potential role of bacteriotherapy, particularly probiotics, in the treatment of CC.

Transcriptomic Network Analysis Using Exfoliative Cervical Cells Could Discriminate a Potential Risk of Progression to Cancer in HPV-related Cervical Lesions: A Pilot Study.

BACKGROUND/AIM: Cervical cancer is the fourth most common type of cancer in women worldwide and it is a major cause of cancer-related deaths in developing countries. Despite the marked reduction observed in the rates of the disease as a result of screening programs, it is necessary to develop robust biomarkers that can detect the neoplastic progression early in HPV-related cervical lesions. MATERIALS AND METHODS: We performed comparative mRNA sequencing from exfoliative cervical cytology samples from nine Korean women using the Illumina NovaSeq6000 platform. Each pathological tissue was matched to the corresponding cytological sample. The pathologic diagnosis was scrutinized with ancillary immunohistochemistry and was considered a confirmative (endpoint) diagnosis. The pathological diagnoses consisted of three cases of chronic cervicitis, 2 high-grade squamous intraepithelial lesions (HSILs), 2 squamous cell carcinomas in situ (CIS), and 2 invasive squamous cell carcinomas (SQCCs), respectively. Using bioinformatic analyses, differentially expressed genes (DEGs; fold change ≥1.5; p<0.05) were applied for Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and protein-protein interaction (PPI) networks. RESULTS: From a total of 55,882 genes, 438 DEGs were pinpointed; 282 genes were up-regulated and 156 genes down-regulated. These transcriptomic profiles were clearly divided into neoplastic (HSIL, CIS, and SQCC; ≥HSILs) and non-neoplastic lesions. The up-regulated DEGs were HIF-1a, EDN1, PIK3R3, PPP1CA and AKR1C1. GO, GSEA, and PPI network analyses showed marked associations with metabolism, proteolysis, or proteoglycan process pathways in cervical carcinogenesis. CONCLUSION: The transcriptomic analysis using exfoliative cervical cells was more likely representative of its corresponding histopathological diagnosis, thus emphasizing its potential utility in clinical practice. This study provides comprehensive transcriptomic network analyses for robust biomarkers that might present a high potential risk of progression to cancer in the exfoliative cervical cytology; our findings support their clinical utility for improved cervical cancer screening.

Axonal Regeneration: Underlying Molecular Mechanisms and Potential Therapeutic Targets.

Axons in the peripheral nervous system have the ability to repair themselves after damage, whereas axons in the central nervous system are unable to do so. A common and important characteristic of damage to the spinal cord, brain, and peripheral nerves is the disruption of axonal regrowth. Interestingly, intrinsic growth factors play a significant role in the axonal regeneration of injured nerves. Various factors such as proteomic profile, microtubule stability, ribosomal location, and signalling pathways mark a line between the central and peripheral axons' capacity for self-renewal. Unfortunately, glial scar development, myelin-associated inhibitor molecules, lack of neurotrophic factors, and inflammatory reactions are among the factors that restrict axonal regeneration. Molecular pathways such as cAMP, MAPK, JAK/STAT, ATF3/CREB, BMP/SMAD, AKT/mTORC1/p70S6K, PI3K/AKT, GSK-3ß/CLASP, BDNF/Trk, Ras/ERK, integrin/FAK, RhoA/ROCK/LIMK, and POSTN/integrin are activated after nerve injury and are considered significant players in axonal regeneration. In addition to the aforementioned pathways, growth factors, microRNAs, and astrocytes are also commendable participants in regeneration. In this review, we discuss the detailed mechanism of each pathway along with key players that can be potentially valuable targets to help achieve quick axonal healing. We also identify the prospective targets that could help close knowledge gaps in the molecular pathways underlying regeneration and shed light on the creation of more powerful strategies to encourage axonal regeneration after nervous system injury.

Immune Pathway and Gene Database (IMPAGT) Revealed the Immune Dysregulation Dynamics and Overactivation of the PI3K/Akt Pathway in Tumor Buddings of Cervical Cancer.

Tumor budding (TB) is a small cluster of malignant cells at the invasive front of a tumor. Despite being an adverse prognosis marker, little research has been conducted on the tumor immune microenvironment of tumor buddings, especially in cervical cancer. Therefore, RNA sequencing was performed using 21 formalin-fixed, paraffin-embedded slides of cervical tissues, and differentially expressed genes (DEGs) were analyzed. Immune Pathway and Gene Database (IMPAGT) was generated for immune profiling. "Pathway in Cancer" was identified as the most enriched pathway for both up- and downregulated DEGs. Kyoto Encyclopedia of Genes and Genomes Mapper and Gene Ontology further revealed the activation of the PI3K/Akt signaling pathway. An IMPAGT analysis revealed immune dysregulation even at the tumor budding stage, especially in the PI3K/Akt/mTOR axis, with a high efficiency and integrity. These findings emphasized the clinical significance of tumor buddings and the necessity of blocking the overactivation of the PI3K/Akt/mTOR pathway to improve targeted therapy in cervical cancer.

Transcriptomic Immune Profiles Can Represent the Tumor Immune Microenvironment Related to the Tumor Budding Histology in Uterine Cervical Cancer.

Tumor budding (TB) histology has become a critical biomarker for several solid cancers. Despite the accumulating evidence for the association of TB histology with poor prognosis, the biological characteristics of TB are little known about in the context related to the tumor immune microenvironment (TIME) in uterine cervical cancer (CC). Therefore, this study aimed to identify the transcriptomic immune profiles related to TB status and further provide robust medical evidence for clinical application. In our study, total RNA was extracted and sequenced from 21 CC tissue specimens. As such, 1494 differentially expressed genes (DEGs) between the high- and low-TB groups were identified by DESeq2. After intersecting the list of DEGs and public immune genes, we selected 106 immune-related DEGs. Then, hub genes were obtained using Least Absolute Shrinkage and Selection Operator regression. Finally, the correlation between the hub genes and immune cell types was analyzed and four candidate genes were identified (one upregulated (FCGR3B) and three downregulated (ROBO2, OPRL1, and NR4A2) genes). These gene expression levels were highly accurate in predicting TB status (area under the curve >80%). Interestingly, FCGR3B is a hub gene of several innate immune pathways; its expression significantly differed in the overall survival analysis (p = 0.0016). In conclusion, FCGR3B, ROBO2, OPRL1, and NR4A2 expression can strongly interfere with TB growth and replace TB to stratify CC patients.

Predicting Tumor Budding Status in Cervical Cancer Using MRI Radiomics: Linking Imaging Biomarkers to Histologic Characteristics.

BACKGROUND: Our previous study demonstrated that tumor budding (TB) status was associated with inferior overall survival in cervical cancer. The purpose of this study is to evaluate whether radiomic features can predict TB status in cervical cancer patients. METHODS: Seventy-four patients with cervical cancer who underwent preoperative MRI and radical hysterectomy from 2011 to 2015 at our institution were enrolled. The patients were randomly allocated to the training dataset (n = 48) and test dataset (n = 26). Tumors were segmented on axial gadolinium-enhanced T1- and T2-weighted images. A total of 2074 radiomic features were extracted. Four machine learning classifiers, including logistic regression (LR), random forest (RF), support vector machine (SVM), and neural network (NN), were used. The trained models were validated on the test dataset. RESULTS: Twenty radiomic features were selected; all were features from filtered-images and 85% were texture-related features. The area under the curve values and accuracy of the models by LR, RF, SVM and NN were 0.742 and 0.769, 0.782 and 0.731, 0.849 and 0.885, and 0.891 and 0.731, respectively, in the test dataset. CONCLUSION: MRI-based radiomic features could predict TB status in patients with cervical cancer.

Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer.

OBJECTIVE: To compare the radiomic features of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and intratumoral heterogeneity according to tumor budding (TB) status and to develop a prediction model for the TB status using the radiomic feature of 18F-FDG PET/CT in patients with cervical cancer. MATERIALS AND METHODS: Seventy-six patients with cervical cancer who underwent radical hysterectomy and preoperative 18F-FDG PET/CT were included. We assessed the status of intratumoral budding (ITP) and peritumoral budding (PTB) in all available hematoxylin and eosin-stained specimens. Three conventional metabolic parameters and fifty-nine features were extracted and analyzed. Univariate analysis was used to identify significant metabolic parameters and radiomic findings for TB status. The prediction model for TB status was built using 3 machine learning classifiers (random forest, support vector machine, and neural network). RESULTS: Univariate analysis led to the identification of 2 significant metabolic parameters and 12 significant radiomic features according to intratumoral budding (ITB) status. Among these parameters, following multivariate analysis for the ITB status, only compacity remained significant (odds ratio, 5.0047; 95% confidence interval, 1.1636-21.5253; p = 0.0305). Two conventional metabolic parameters and 25 radiomic features were selected by the Lasso regularization, and the prediction model for the ITB status had a mean area under the curve of 0.762 in the test dataset. CONCLUSION: Radiomic features of 18F-FDG PET/CT were associated with the ITB status. The prediction model using radiomic features successfully predicted the TB status in patients with cervical cancer. The prediction models for the ITB status may contribute to personalized medicine in the management of patients with cervical cancer.

Intratumoral budding: A novel prognostic biomarker for tumor recurrence and a potential predictor of nodal metastasis in uterine cervical cancer.

OBJECTIVE: To evaluate the prognostic and predictive significance of lymphovascular invasion (LVI) and lymph node metastasis of intratumoral budding (ITB) and its correlation with clinicopathological parameters in patients with cervical cancer. METHODS: Total 151 patients with cervical cancer who underwent radical hysterectomy with pelvic and/or paraaortic lymphadenectomy were included. We assessed the status of ITB and peritumoral budding (PTB) in all available hematoxylin and eosin-stained specimens. Univariate and multivariate analyses were performed for ITB, PTB, and other clincopathological parameters as predictors of recurrence. RESULTS: ITBhigh (≥3TB/HPF) was significantly associated with large tumor size, deep stromal invasion, LVI, parametrial invasion, and lymph node metastasis. The numbers of ITBs and PTBs were positively correlated (r2 = 0.754, p < 0.0001). ITBhigh was more frequently observed in squamous cell carcinoma compared with adenocarcinoma and adenosquamous cell carcinoma (p = 0.010). ITBhigh was found to be an independent prognostic factor for tumor recurrence by multivariate analysis (hazard ratio, 1.92; 95% confidence interval [CI], 1.37-9.90; p = 0.026). Multiple logistic regression showed association of LVI (odds ratio [OR], 1.85; 95% CI, 1.11-3.06; p = 0.017) and lymph node metastasis (OR, 1.96; 95% CI, 1.26-4.66; p = 0.019). CONCLUSION: ITBhigh is an independent prognostic factor for tumor recurrence. ITB is a surrogate marker for predicting LVI in cervical cancers. The evaluation of ITB may be readily applied in the clinical setting for improved prognosis and to guide the clinical management of patients with cervical cancer.

Potential Association between Vaginal Microbiota and Cervical Carcinogenesis in Korean Women: A Cohort Study.

Convincing studies demonstrated that vaginal flora is one of the most impactful key components for the well-being of the genital tract in women. Nevertheless, the potential capability of vaginal-derived bacterial communities as biomarkers to monitor cervical carcinogenesis (CC) has yet to be studied actively compared to those of bacterial vaginosis (BV). We hypothesized that vaginal microbiota might be associated with the progression of CC. In this study, we enrolled 23 participants, including healthy controls (HC group; n = 7), patients with cervical intraepithelial neoplasia (CIN) 2 and 3 (CIN group, n = 8), and patients with invasive cervical cancer (CAN group; n = 8). Amplicon sequencing was performed using the Ion Torrent PGM to characterize the vaginal microbiota. Patients with CIN and CAN presented vaginal microbiota dysbiosis compared with HC. The alpha diversity analysis revealed that CC has a trend to be increased in terms of diversity indexes. Moreover, CC was associated with the abundance of specific microbes, of which Lactobacillus and Gardnerella were the most significantly different between HC and CIN, whereas Streptococcus was differentially abundant in CAN compared with CIN. We then evaluated their diagnostic abilities. Testing in terms of diagnostic ability using the three genera revealed considerably high performance with an area under the receiver-operating characteristic curve of 0.982, 0.953, and 0.922. The current study suggests that the presence of Gardnerella and Streptococcus may be involved in the advancment of CC.

Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis.

The fecal microbiota is being increasingly implicated in the diagnosis of various diseases. However, evidence on changes in the fecal microbiota in invasive cervical cancer (ICC) remains scarce. Here, we aimed to investigate the fecal microbiota of our cohorts, develop a diagnostic model for predicting early ICC, and identify potential fecal microbiota-derived biomarkers using amplicon sequencing data. We obtained fecal samples from 29 healthy women (HC) and 17 women with clinically confirmed early ICC (CAN). Although Shannon's diversity index was not reached at statistical significance, the Chao1 and Observed operational taxonomic units (OTUs) in fecal microbiota was significantly different between CAN and HC group. Furthermore, there were significant differences in the taxonomic profiles between HC and CAN; Prevotella was significantly more abundant in the CAN group and Clostridium in the HC group. Linear discriminant analysis effect size (LEfSe) analysis was applied to validate the taxonomic differences at the genus level. Furthermore, we identified a set of seven bacterial genera that were used to construct a machine learning (ML)-based classifier model to distinguish CAN from patients with HC. The model had high diagnostic utility (area under the curve [AUC] = 0.913) for predicting early ICC. Our study provides an initial step toward exploring the fecal microbiota and helps clinicians diagnose.

Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia.

Although emerging evidence revealed that the gut microbiome served as a tool and as biomarkers for predicting and detecting specific cancer or illness, it is yet unknown if vaginal microbiome-derived bacterial markers can be used as a predictive model to predict the severity of CIN. In this study, we sequenced V3 region of 16S rRNA gene on vaginal swab samples from 66 participants (24 CIN 1-, 42 CIN 2+ patients) and investigated the taxonomic composition. The vaginal microbial diversity was not significantly different between the CIN 1- and CIN 2+ groups. However, we observed Lactobacillus amylovorus dominant type (16.7%), which does not belong to conventional community state type (CST). Moreover, a minimal set of 33 bacterial species was identified to maximally differentiate CIN 2+ from CIN 1- in a random forest model, which can distinguish CIN 2+ from CIN 1- (area under the curve (AUC) = 0.952). Among the 33 bacterial species, Lactobacillus iners was selected as the most impactful predictor in our model. This finding suggests that the random forest model is able to predict the severity of CIN and vaginal microbiome may play a role as biomarker.

Tumor budding in cervical cancer as a prognostic factor and its possible role as an additional intermediate-risk factor.

OBJECTIVE: To evaluate the prognostic value and its possible role as an additional intermediate-risk factor of tumor budding (TB) in cervical cancer following radical hysterectomy. METHODS: In total, 136 patients with cervical cancer who underwent radical hysterectomy with pelvic and/or paraaortic lymphadenectomy were included. We assessed the status of TB in available hematoxylin and eosin-stained specimens. Univariate and multivariate analyses for predicting tumor recurrence and death were performed using TB and other clinicopathologic parameters. To evaluate additional intermediate-risk factors of TB, patients who had at least one high-risk factor were excluded, and a total of 81 patients were included. We added TB to three conventional intermediate-risk models and compared their performance with new and conventional models using the log-rank test and receiver operating characteristic analysis. RESULTS: High TB was defined as ≥5 per high-power field for disease-free survival and ≥ 8 per high-power field for overall survival. Multivariate analysis revealed that high TB was an independent prognostic factor for predicting overall survival (hazard ratio, 4.96; 95% confidence intervals, 1.06-23.29; p = .0423). The addition of TB to the conventional intermediate-risk models improved the accuracy of recurrence prediction. Among the risk models, the new model using at least two risk factors, including tumor size (≥ 4 cm), deep stromal invasion (outer one-third of entire cervical thickness), lymphovascular invasion, and high TB, was the most accurate for predicting tumor recurrence (area under the curve, 0.708, hazard ratio, 4.25; p = .0231). CONCLUSION: High TB may be a prognostic biomarker of cervical cancer. Moreover, the addition of TB to the conventional intermediate-risk models improves the stratification of tumor recurrence.

Improvement in RNA quantity and quality in cervico-vaginal cytology.

The separation of exfoliated cells from the brushes used during cervico-vaginal smears is difficult, a problem which may affect the quality of ribonucleic acid (RNA) extracted. We compared the results of RNA extraction from cervico-vaginal cytology samples according to the type of tubes, preservative solutions, and storage temperature. The samples included exfoliated cervico-vaginal cytological specimens from patients with human papilloma virus 16, positive for cervical intraepithelial neoplasia or cervical cancer. Exfoliated cells were obtained by shaking a brush in a conventional rigid vial tube or squeezing the brush in a soft vial tube. RNA quantity and quality were compared between the two tubes. The concentration and purity of RNA (A260/A280 and A260/A230 ratios) was compared amongst five groups: Group 1, standard frozen storage; Group 2-4, RNA stabilization reagents with room temperature [RNAlater RNA Stabilization Reagent, RNAprotect cell Reagent and AllProtect Tissue Reagent]; and Group 5, Surepath Preservative fluid. To demonstrate the utility of the extracted RNA for PCR-based cDNA synthesis, GAPDH and E6 were targeted and gel band densities of GAPDH and E6 were measured. The median RNA concentration was significantly higher in the soft tubes compared with the rigid tubes (100.2 vs. 7.1 ng/µL, p = 0.0209). The purity of the RNA was higher in soft vial tubes than in rigid vials, as measured by A260/280 and A260/230 ratios. The RNA concentration, purity, and GAPDH density of groups 1, 2 and 3 were significantly higher than those of groups 4 and 5. Moreover, E6 density of group 1 and 2 was significantly higher than that of group 3, 4 and 5. The use of soft tubes enhanced the mRNA quantity and quality in cervico-vaginal cytology. The products of mRNA extraction using RNAlater RNA Stabilization Reagent and RNAprotect Cell Reagent at room temperature were comparable to those obtained by conventional frozen storage. Our protocol improved the yield and quality of RNA and might produce better results for molecular analysis in cervico-vaginal cytology.

The ability of whole-body SUVmax in F-18 FDG PET/CT to predict suboptimal cytoreduction during primary debulking surgery for advanced ovarian cancer.

BACKGROUND: The aim of this study was to (1) evaluate the ability of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) parameters to predict suboptimal cytoreduction and (2) to create a risk model for predicting suboptimal cytoreduction in advanced ovarian cancer. From 2011 to 2015, 51 patients underwent primary cytoreductive surgery for advanced ovarian cancer were enrolled. A residual disease with maximal diameter >  1 cm was considered a suboptimal surgical result. The SUVmax values for nine abdominal regions, the sum of 9 regional SUVmax (WB1SUVmax) and WB2SUVmax (WB1SUVmax plus SUVmax of lymph nodes) were used for PET parameter. Multiple logistic regression analysis was used to determine the predictive value of PET and clinical parameters for risk model. In addition, assessments of disease-free survival (DFS) and overall survival (OS) were performed. RESULTS: Seventeen of the 51 patients (33.3%) underwent suboptimal cytoreduction. The ECOG status (OR, 4.091), SUVmax of central (OR, 5.250), right upper (OR, 4.148), left upper (OR, 5.921) and WB2SUVmax (OR, 4.148) were associated with suboptimal cytoreduction. The risk model can divide the risk groups of suboptimal cytoreduction (area under the curve (AUC), 0.775; p = 0.0001). The DFS and OS in the high-risk group were significantly worse than those in the low-risk group (p = 0.0379 for DFS; p = 0.0211 for OS). CONCLUSIONS: The presence of hypermetabolic lesions in the central, right upper, and left upper regions showed predictive value for suboptimal cytoreduction. Our risk model may be helpful for selecting patients who may show suboptimal cytoreduction.

Prevalence, survival outcomes, and clinicopathologic factors associated with negative high risk human papillomavirus in surgical specimens of cervical cancer with pretreatment negative DNA genotype test.

OBJECTIVE: The aim of this study was to detect high risk human papillomavirus in cervical cancer with a pretreatment negative high risk human papillomavirus DNA genotype test and to evaluate clinicopathologic characteristics and survival outcomes according to high risk human papillomavirus status. METHODS: We investigated high risk human papillomavirus status in surgical specimens from 30 cases of cervical cancer using polymerase chain reaction. Polymerase chain reaction primers were set to detect the presence of the common L1 and E7 regions of human papillomavirus types 16, 18, 31, 33, 45, 52, and 58. We analyzed the following clinicopathologic parameters to evaluate their relationships with high risk human papillomavirus status: age, histology, stage, tumor size, invasion depth, lymphovascular invasion, and recurrent status. RESULTS: Among 30 cases with a pretreatment negative DNA genotype test, high risk human papillomavirus was detected in 12 (40.0%), whereas 18 (60.0%) were negatives. Of 12 high risk human papillomavirus positive cases, 10 (33.3%) were positive for the L1 region, 6 (20.0%) of the 7 types were positive for the E7 region, and 4 (13.1%) were positive for both L1 and E7 regions. According to a multiple logistic regression model, tumor size (odds ratio 7.80; 95% confidence interval 1.476 to 41.216; P=0.0097) and stage (odds ratio 7.00; 95% confidence interval 1.293 to 37.910; P=0.0173) were associated with negative high risk human papillomavirus DNA status. Kaplan-Meier survival plots showed that negative high risk human papillomavirus status was associated with worse disease free survival in contrast with positive high risk human papillomavirus status (P=0.0392). CONCLUSIONS: Negative high risk human papillomavirus was found in 60% of cervical cancers with a pretreatment negative DNA genotype test. Moreover, the negative high risk human papillomavirus group was associated with worse survival outcome.

Prognostic value of pre-treatment human papilloma virus DNA status in cervical cancer.

OBJECTIVE: Although the relationship between human papilloma virus (HPV) and cervical cancer is well established, the prognostic value of HPV status has not been determined, largely because previous studies have yielded conflicting results. This study aimed to investigate the prognostic value of pre-treatment HPV DNA for predicting tumor recurrence in cervical cancer. METHODS: The study included 248 eligible patients who provided cervical cell specimens for HPV genotyping before surgery or concurrent chemoradiotherapy (CCRT). Of these 248 patients, 108 were treated with radical hysterectomy for International Federation of Gynecology and Obstetrics (FIGO) stage IB1-IIA cervical cancer, and 140 were treated with CCRT for FIGO stage IB2-IV cervical cancer. RESULTS: HPV 16 and 18 were the two most common HPV types detected, with prevalence rates of 52.4% and 12.5%, respectively. The pre-treatment HPV DNA test showed that 18.5% of cervical cancers were HPV negative. Multivariate analysis showed that HPV negativity was associated with poorer disease-free survival (DFS) than HPV-positive status (hazard ratio [HR], 3.97; 95% confidence interval [CI], 1.84-8.58; p=0.0005), and patients with HPV 16-positive cancers had better DFS (HR, 0.41; 95% CI, 0.23-0.72; p=0.0019). In the surgery group, only HPV 16 positivity was significantly correlated with DFS (HR, 0.34; 95% CI, 0.12-0.96; p=0.0416). In the CCRT group, only HPV negativity was significantly correlated with DFS (HR, 3.75; 95% CI, 1.78-7.90; p=0.0005). CONCLUSIONS: Pre-treatment HPV DNA status may be a useful prognostic biomarker in cervical cancer. The presence of HPV 16 DNA was associated with better DFS, and HPV negativity was associated with worse DFS. However, larger sample sizes and more comprehensive studies are required to verify our findings.