Intrahepatic IgA complex induces polarization of cancer-associated fibroblasts to matrix phenotypes in the tumor microenvironment of HCC

BACKGROUND AND AIMS

Cancer-associated fibroblasts (CAFs) play key roles in the tumor microenvironment. IgA contributes to inflammation and dismantling antitumor immunity in the human liver. In this study, we aimed to elucidate the effects of the IgA complex on CAFs in Pil Soo Sung the tumor microenvironment of HCC.

APPROACH AND RESULTS

CAF dynamics in HCC tumor microenvironment were analyzed through single-cell RNA sequencing of HCC samples. CAFs isolated from 50 HCC samples were treated with mock or serum-derived IgA dimers in vitro. Progression-free survival of patients with advanced HCC treated with atezolizumab and bevacizumab was significantly longer in those with low serum IgA levels ( p <0.05). Single-cell analysis showed that subcluster proportions in the CAF-fibroblast activation protein-α matrix were significantly increased in patients with high serum IgA levels. Flow cytometry revealed a significant increase in the mean fluorescence intensity of fibroblast activation protein in the CD68 + cells from patients with high serum IgA levels ( p <0.001). We confirmed CD71 (IgA receptor) expression in CAFs, and IgA-treated CAFs exhibited higher programmed death-ligand 1 expression levels than those in mock-treated CAFs ( p <0.05). Coculture with CAFs attenuated the cytotoxic function of activated CD8 + T cells. Interestingly, activated CD8 + T cells cocultured with IgA-treated CAFs exhibited increased programmed death-1 expression levels than those cocultured with mock-treated CAFs ( p <0.05).

CONCLUSIONS

Intrahepatic IgA induced polarization of HCC-CAFs into more malignant matrix phenotypes and attenuates cytotoxic T-cell function. Our study highlighted their potential roles in tumor progression and immune suppression.

Effect of driving pressure-guided positive end-expiratory pressure on postoperative pulmonary complications in patients undergoing laparoscopic or robotic surgery: a randomised controlled trial

BACKGROUND

Individualised positive end-expiratory pressure (PEEP) improves respiratory mechanics. However, whether PEEP reduces postoperative pulmonary complications (PPCs) remains unclear. We investigated whether driving pressure-guided PEEP reduces PPCs after laparoscopic/robotic abdominal surgery.

METHODS

This single-centre, randomised controlled trial enrolled patients at risk for PPCs undergoing laparoscopic or robotic lower abdominal surgery. The individualised group received driving pressure-guided PEEP, whereas the comparator group received 5 cm H2O fixed PEEP during surgery. Both groups received a tidal volume of 8 ml kg-1 ideal body weight. The primary outcome analysed per protocol was a composite of pulmonary complications (defined by pre-specified clinical and radiological criteria) within 7 postoperative days after surgery.

RESULTS

Some 384 patients (median age: 67 yr [inter-quartile range: 61-73]; 66 [18%] female) were randomised. Mean (standard deviation) PEEP in patients randomised to individualised PEEP (n=178) was 13.6 cm H2O (2.1). Individualised PEEP resulted in lower mean driving pressures (14.7 cm H2O [2.6]), compared with 185 patients randomised to standard PEEP (18.4 cm H2O [3.2]; mean difference: -3.7 cm H2O [95% confidence interval (CI): -4.3 to -3.1 cm H2O]; P<0.001). There was no difference in the incidence of pulmonary complications between individualised (25/178 [14.0%]) vs standard PEEP (36/185 [19.5%]; risk ratio [95% CI], 0.72 [0.45-1.15]; P=0.215). Pulmonary complications as a result of desaturation were less frequent in patients randomised to individualised PEEP (8/178 [4.5%], compared with standard PEEP (30/185 [16.2%], risk ratio [95% CI], 0.28 [0.13-0.59]; P=0.001).

CONCLUSIONS

Driving pressure-guided PEEP did not decrease the incidence of pulmonary complications within 7 days of laparoscopic or robotic lower abdominal surgery, although uncertainty remains given the lower than anticipated event rate for the primary outcome.

CLINICAL TRIAL REGISTRATION

KCT0004888 (http://cris.nih.go.kr, registration date: April 6, 2020).

Conization before radical hysterectomy in patients with early-stage cervical cancer: A Korean multicenter study (COBRA-R)

OBJECTIVE

To investigate the impact of conization on survival outcomes and to identify a specific population that might benefit from conization before radical hysterectomy (RH) in patients with early-stage cervical cancer.

METHODS

From six institutions in Korea, we identified node-negative, margin-negative, parametria-negative, 2009 FIGO stage IB1 cervical cancer patients who underwent primary type C RH between 2006 and 2021. The patients were divided into multiple groups based on tumor size, surgical approach, and histology. We performed a series of independent 1:1 propensity score matching and compared the survival outcomes between the conization and non-conization groups.

RESULTS

In total, 1254 patients were included: conization (n = 355) and non-conization (n = 899). Among the matched patients with a tumor size of >2 cm, the conization group showed a significantly better 3-year disease-free survival (DFS) rate compared with the non-conization group when RH was conducted via minimally invasive surgery (MIS), in those with squamous cell carcinoma (96.3% vs. 87.4%, P = 0.007) and non-squamous cell carcinoma (97.0% vs. 74.8%, P = 0.021). However, no difference in DFS was observed between the two groups among the matched patients with a tumor size of ≤2 cm, regardless of surgical approach or histological type. In patients who underwent MIS RH, DFS significantly worsened as the residual tumor size increased (P < 0.001).

CONCLUSION

Cervical conization was associated with a lower recurrence rate in patients with early-stage cervical cancer with a tumor size of >2 cm who underwent primary MIS RH. Cervical conization may be performed prior to MIS RH to minimize the uterine residual tumor.

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry-based proteomics methods. We conducted label-free liquid chromatography-tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073-2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.

Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record

Background

As the worldwide spread of coronavirus disease 2019 (COVID-19) continues for a long time, early prediction of the maximum severity is required for effective treatment of each patient.

Objective

This study aimed to develop predictive models for the maximum severity of hospitalized COVID-19 patients using artificial intelligence (AI)/machine learning (ML) algorithms.

Methods

The medical records of 2,263 COVID-19 patients admitted to 10 hospitals in Daegu, Korea, from February 18, 2020, to May 19, 2020, were comprehensively reviewed. The maximum severity during hospitalization was divided into four groups according to the severity level: mild, moderate, severe, and critical. The patient's initial hospitalization records were used as predictors. The total dataset was randomly split into a training set and a testing set in a 2:1 ratio, taking into account the four maximum severity groups. Predictive models were developed using the training set and were evaluated using the testing set. Two approaches were performed: using four groups based on original severity levels groups (i.e., 4-group classification) and using two groups after regrouping the four severity level into two (i.e., binary classification). Three variable selection methods including randomForestSRC were performed. As AI/ML algorithms for 4-group classification, GUIDE and proportional odds model were used. For binary classification, we used five AI/ML algorithms, including deep neural network and GUIDE.

Results

Of the four maximum severity groups, the moderate group had the highest percentage (1,115 patients; 49.5%). As factors contributing to exacerbation of maximum severity, there were 25 statistically significant predictors through simple analysis of linear trends. As a result of model development, the following three models based on binary classification showed high predictive performance: (1) Mild vs. Above Moderate, (2) Below Moderate vs. Above Severe, and (3) Below Severe vs. Critical. The performance of these three binary models was evaluated using AUC values 0.883, 0.879, and, 0.887, respectively. Based on results for each of the three predictive models, we developed web-based nomograms for clinical use (http://statgen.snu.ac.kr/software/nomogramDaeguCovid/).

Conclusions

We successfully developed web-based nomograms predicting the maximum severity. These nomograms are expected to help plan an effective treatment for each patient in the clinical field.

Kernel-based hierarchical structural component models for pathway analysis

MOTIVATION

Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex.

RESULTS

To model complex effects including nonlinear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models nonlinear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies.

AVAILABILITY AND IMPLEMENTATION

Freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clinicopathologic and protein markers distinguishing the “polymerase epsilon exonuclease” from the “copy number low” subtype of endometrial cancer

Objective

The need to perform genetic sequencing to diagnose the polymerase epsilon exonuclease (POLE) subtype of endometrial cancer (EC) hinders the adoption of molecular classification. We investigated clinicopathologic and protein markers that distinguish the POLE from the copy number (CN)-low subtype in EC.

Methods

Ninety-one samples (15 POLE, 76 CN-low) were selected from The Cancer Genome Atlas EC dataset. Clinicopathologic and normalized reverse phase protein array expression data were analyzed for associations with the subtypes. A logistic model including selected markers was constructed by stepwise selection using area under the curve (AUC) from 5-fold cross-validation (CV). The selected markers were validated using immunohistochemistry (IHC) in a separate cohort.

Results

Body mass index (BMI) and tumor grade were significantly associated with the POLE subtype. With BMI and tumor grade as covariates, 5 proteins were associated with the EC subtypes. The stepwise selection method identified BMI, cyclin B1, caspase 8, and X-box binding protein 1 (XBP1) as markers distinguishing the POLE from the CN-low subtype. The mean of CV AUC, sensitivity, specificity, and balanced accuracy of the selected model were 0.97, 0.91, 0.87, and 0.89, respectively. IHC validation showed that cyclin B1 expression was significantly higher in the POLE than in the CN-low subtype and receiver operating characteristic curve of cyclin B1 expression in IHC revealed AUC of 0.683.

Conclusion

BMI and expression of cyclin B1, caspase 8, and XBP1 are candidate markers distinguishing the POLE from the CN-low subtype. Cyclin B1 IHC may replace POLE sequencing in molecular classification of EC.

Body mass index and cyclin B1, caspase 8, and X-box binding protein 1 are candidate markers distinguishing between the polymerase epsilon exonuclease (POLE) and copy number (CN)-low subtypes of endometrial cancer. Cyclin B1 immunohistochemistry expression was significantly higher in the POLE than in the CN-low subtype and may substitute POLE sequencing.

Effects of hydrogenated fat-spray-coated β-carotene supplement on plasma β-carotene concentration and conception rate after embryo transfer in Hanwoo beef cows

We hypothesised that hydrogenated fat (HF)-spray-coated β-carotene (βC) supplement could be used to increase plasma βC concentration and conception rates after embryo transfer (ET) in Hanwoo beef cows. In Experiment 1, 12 multiparous Hanwoo cows were fed one of four experimental diets in a triplicate 4 × 4 Latin square design for a 28-day period. Treatments included no βC addition (control), HF-uncoated βC (HFuβC), HF-spray-coated βC (HFβC), and HF-spray-coated βC and vitamin A (HFβCA). The cows under βC-supplemented treatments were fed 400 mg/day of βC, and a daily intake for vitamin A of HFβCA treatment was 30 000 IU/day as retinyl acetate. Blood was collected on days 0, 26, 27, and 28 to analyse βC and other metabolite concentrations. In Experiment 2, 199 Hanwoo cows with low fertility were randomly assigned to either control (n = 99) or HFβC treatments (n = 100) based on the results of Experiment 1. The oestrus of the cows was synchronised for ET. The HFβC group was fed from 4 weeks before to 4 weeks after ET with a daily intake of 400 mg βC. Pregnancy for conception rates was diagnosed on day 60 after ET, and blood was collected for βC concentrations on the day before ET. Supplementing βC resulted in a high plasma βC concentration (P < 0.001). Supplementing HFβC or HFβCA resulted in higher βC concentrations than HFuβC (P < 0.001); however, there was no difference between HFβC and HFβCA groups. Plasma retinol concentration was lower in the HFβCA treatment than in the control and HFβC groups (P < 0.05). Blood metabolites were unaffected by the treatments. The retinol:βC ratio was lower in the βC-supplemented treatments than in the controls, and was lower in HFβC and HFβCA than in HFuβC groups (P < 0.001). Plasma βC concentration was positively correlated with plasma high-density lipoprotein, low-density lipoprotein, and total cholesterol (P < 0.05). Plasma retinol concentration was negatively associated with plasma protein (P < 0.01), but positively associated with plasma creatinine (P < 0.001) and urea (P < 0.01). Supplementing HFβC to low-fertility cows resulted in higher plasma βC concentration (P < 0.001) and conception rates (P = 0.024) than those in the controls. In conclusion, HFβC had a better bioavailability than HFuβC, and an increase in conception rates by supplementing HFβC may be beneficial for producing more calves given the low pregnancy rates of bovine ET in Korea.

Comparisons of survival outcomes between bevacizumab and olaparib in BRCA-mutated, platinum-sensitive relapsed ovarian cancer: a Korean Gynecologic Oncology Group study (KGOG 3052)

OBJECTIVE

To compare survival outcomes between bevacizumab (BEV) and olaparib (OLA) maintenance therapy in BRCA-mutated, platinum-sensitive relapsed (PSR) high-grade serous ovarian carcinoma (HGSOC).

METHODS

From 10 institutions, we identified HGSOC patients with germline and/or somatic BRCA1/2 mutations, who experienced platinum-sensitive recurrence between 2013 and 2019, and received second-line platinum-based chemotherapy. Patients were divided into BEV (n=29), OLA (n=83), and non-BEV/non-OLA users (n=36). The OLA and non-BEV/non-OLA users were grouped as the OLA intent group. We conducted 1:2 nearest neighbor-matching between the BEV and OLA intent groups, setting the proportion of OLA users in the OLA intent group from 65% to 100% at 5% intervals, and compared survival outcomes among the matched groups.

RESULTS

Overall, OLA users showed significantly better progression-free survival (PFS) than BEV users (median, 23.8 vs. 17.4 months; p=0.004). Before matching, PFS improved in the OLA intent group but marginal statistical significance (p=0.057). After matching, multivariate analyses adjusting confounders identified intention-to-treat OLA as an independent favorable prognostic factor for PFS in the OLA 65P (adjusted hazard ratio [aHR]=0.505; 95% confidence interval [CI]=0.280-0.911; p=0.023) to OLA 100P (aHR=0.348; 95% CI=0.184-0.658; p=0.001) datasets. The aHR of intention-to-treat OLA for recurrence decreased with increasing proportions of OLA users. No differences in overall survival were observed between the BEV and OLA intent groups, and between the BEV and OLA users.

CONCLUSION

Compared to BEV, intention-to-treat OLA and actual use of OLA maintenance therapy were significantly associated with decreased disease recurrence risk in patients with BRCA-mutated, PSR HGSOC.

Nonalcoholic fatty liver disease and early prediction of gestational diabetes using machine learning methods

Background/Aims

To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model.

Methods

This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10–14 weeks and screened them for GDM at 24–28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks.

Results

Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1–4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563–0.697 in settings 1–3 vs. 0.740–0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719–0.819 in setting 5, P=not significant between settings 4 and 5).

Conclusions

We developed an early prediction model for GDM using machine learning. The inclusion of NAFLD-associated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144)

Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study

BACKGROUND

Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness.

OBJECTIVE

This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission.

METHODS

This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical.

RESULTS

Out of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models.

CONCLUSIONS

Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.

Development of Machine Learning Models to Predict Platinum Sensitivity of High-Grade Serous Ovarian Carcinoma

To support the implementation of individualized disease management, we aimed to develop machine learning models predicting platinum sensitivity in patients with high-grade serous ovarian carcinoma (HGSOC). We reviewed the medical records of 1002 eligible patients. Patients' clinicopathologic characteristics, surgical findings, details of chemotherapy, treatment response, and survival outcomes were collected. Using the stepwise selection method, based on the area under the receiver operating characteristic curve (AUC) values, six variables associated with platinum sensitivity were selected: age, initial serum CA-125 levels, neoadjuvant chemotherapy, pelvic lymph node status, involvement of pelvic tissue other than the uterus and tubes, and involvement of the small bowel and mesentery. Based on these variables, predictive models were constructed using four machine learning algorithms, logistic regression (LR), random forest, support vector machine, and deep neural network; the model performance was evaluated with the five-fold cross-validation method. The LR-based model performed best at identifying platinum-resistant cases with an AUC of 0.741. Adding the FIGO stage and residual tumor size after debulking surgery did not improve model performance. Based on the six-variable LR model, we also developed a web-based nomogram. The presented models may be useful in clinical practice and research.

Clinical Characteristics and Outcomes of COVID-19 Cohort Patients in Daegu Metropolitan City Outbreak in 2020

BACKGROUND

A coronavirus disease 2019 (COVID-19) outbreak started in February 2020 and was controlled at the end of March 2020 in Daegu, the epicenter of the coronavirus outbreak in Korea. The aim of this study was to describe the clinical course and outcomes of patients with COVID-19 in Daegu.

METHODS

In collaboration with Daegu Metropolitan City and Korean Center for Diseases Control, we conducted a retrospective, multicenter cohort study. Demographic, clinical, treatment, and laboratory data, including viral RNA detection, were obtained from the electronic medical records and cohort database and compared between survivors and non-survivors. We used univariate and multi-variable logistic regression methods and Cox regression model and performed Kaplan-Meier analysis to determine the risk factors associated with the 28-day mortality and release from isolation among the patients.

RESULTS

In this study, 7,057 laboratory-confirmed patients with COVID-19 (total cohort) who had been diagnosed from February 18 to July 10, 2020 were included. Of the total cohort, 5,467 were asymptomatic to mild patients (77.4%) (asymptomatic 30.6% and mild 46.8%), 985 moderate (14.0%), 380 severe (5.4%), and 225 critical (3.2%). The mortality of the patients was 2.5% (179/7,057). The Cox regression hazard model for the patients with available clinical information (core cohort) (n = 2,254) showed the risk factors for 28-day mortality: age > 70 (hazard ratio [HR], 4.219, P = 0.002), need for O₂ supply at admission (HR, 2.995; P = 0.001), fever (> 37.5°C) (HR, 2.808; P = 0.001), diabetes (HR, 2.119; P = 0.008), cancer (HR, 3.043; P = 0.011), dementia (HR, 5.252; P = 0.008), neurological disease (HR, 2.084; P = 0.039), heart failure (HR, 3.234; P = 0.012), and hypertension (HR, 2.160; P = 0.017). The median duration for release from isolation was 33 days (interquartile range, 24.0-46.0) in survivors. The Cox proportional hazard model for the long duration of isolation included severity, age > 70, and dementia.

CONCLUSION

Overall, asymptomatic to mild patients were approximately 77% of the total cohort (asymptomatic, 30.6%). The case fatality rate was 2.5%. Risk factors, including older age, need for O₂ supply, dementia, and neurological disorder at admission, could help clinicians to identify COVID-19 patients with poor prognosis at an early stage.

Clinicopathologic and protein markers distinguishing POLE and copy-number low group in endometrial cancer

e18110

Background: Requirement of DNA sequencing to diagnose POLE group is a barrier for adoption of molecular subtyping in endometrial cancer. We aimed to identify clinicopathologic and protein markers distinguishing POLE and copy-number (CN) low group in endometrial cancer. Methods: Ninety-one samples (POLE: 15, CN low 76) classified as POLE or CN low groups by integrative clustering were selected from The Cancer Genome Atlas endometrial cancer dataset. Clinicopathologic variables and normalized reverse phase protein array expression data were extracted. We first selected clinicopathologic variables associated with group (POLE vs CN low) via univariate analysis. Then, we identified protein makers using the logistic regression model with significant clinicopathologic variables as adjusting covariates. The differentially expressed proteins were selected based on q-value of the false discovery rate for multiple comparison. With various q-value cut-off ( < 5%, 10%, 20%), several logistic models including differentially expressed markers were constructed by stepwise selection method using the area under curve (AUC) from 5-fold cross-validation (CV). Results: Among clinicopathologic variables, body mass index (BMI) and tumor grade were associated with group (p = 0.02 and p < 0.01, respectively). Being adjusted for BMI and tumor grade, 5 proteins were associated with group in q-value cut-off of < 5%. The model including clinicopathologic variables and 5 proteins identified BMI, Cyclin B1, Caspase 8, and XBP1 as markers distinguishing POLE and CN low groups. The mean of CV AUC, sensitivity and specificity of the selected model were 0.97, 0.97, and 0.60, respectively. Conclusions: BMI and expression levels of Cyclin B1, Caspase 8, XBP1 are candidate markers distinguishing POLE and CN low group. A further validation study using immunohistochemical staining is necessary to facilitate the adoption of molecular subtyping as daily practice.

Development of Web-Based Nomograms to Predict Treatment Response and Prognosis of Epithelial Ovarian Cancer

Purpose

Discovery of models predicting the exact prognosis of epithelial ovarian cancer (EOC) is necessary as the first step of implementation of individualized treatment. This study aimed to develop nomograms predicting treatment response and prognosis in EOC.

Materials and Methods

We comprehensively reviewed medical records of 866 patients diagnosed with and treated for EOC at two tertiary institutional hospitals between 2007 and 2016. Patients’ clinico-pathologic characteristics, details of primary treatment, intra-operative surgical findings, and survival outcomes were collected. To construct predictive nomograms for platinum sensitivity, 3-year progression-free survival (PFS), and 5-year overall survival (OS), we performed stepwise variable selection by measuring the area under the receiver operating characteristic curve (AUC) with leave-one-out cross-validation. For model validation, 10-fold cross-validation was applied.

Results

The median length of observation was 42.4 months (interquartile range, 25.7 to 69.9 months), during which 441 patients (50.9%) experienced disease recurrence. The median value of PFS was 32.6 months and 3-year PFS rate was 47.8% while 5-year OS rate was 68.4%. The AUCs of the newly developed nomograms predicting platinum sensitivity, 3-year PFS, and 5-year OS were 0.758, 0.841, and 0.805, respectively. We also developed predictive nomograms confined to the patients who underwent primary debulking surgery. The AUCs for platinum sensitivity, 3-year PFS, and 5-year OS were 0.713, 0.839, and 0.803, respectively.

Conclusion

We successfully developed nomograms predicting treatment response and prognosis of patients with EOC. These nomograms are expected to be useful in clinical practice and designing clinical trials.

Photodynamic therapy by conjugation of cell-penetrating peptide with fluorochrome

Photodynamic therapy (PDT) is a promising alternative therapy that could be used as an adjunct to chemotherapy and surgery for cancer, and works by destroying tissue with visible light in the presence of a photosensitizer (PS) and oxygen. The PS should restrict tissue destruction only to the tumor and be activated by light of a specific wavelength; both of these properties are required. Arginine-rich peptides, such as cell-penetrating peptides, have membrane-translocating and nuclear-localizing activities, which have led to their application in various drug delivery modalities. Protamine (Pro) is an arginine-rich peptide with membrane-translocating and nuclear-localizing properties. The reaction of an N-hydroxysuccinimide (NHS) ester of rhodamine (Rho) and clinical Pro was carried out in this study to yield RhoPro, and a demonstration of its phototoxicity, wherein clinical Pro improved the effect of PDT, was performed. The reaction between Pro and the NHS ester of Rho is a solution-phase reaction that results in the complete modification of the Pro peptides, which feature a single reactive amine at the N-terminal proline and a single carboxyl group at the C-terminal arginine. This study aimed to identify a new type of PS for PDT by in vitro and in vivo experiments and to assess the antitumor effects of PDT, using the Pro-conjugated PS, on a cancer cell line. Photodynamic cell death studies showed that the RhoPro produced has more efficient photodynamic activities than Rho alone, causing rapid light-induced cell death. The attachment of clinical Pro to Rho, yielding RhoPro, confers the membrane-internalizing activity of its arginine-rich content on the fluorochrome Rho and can induce rapid photodynamic cell death, presumably owing to light-induced cell membrane rupture. PDT using RhoPro for HT-29 cells was very effective and these findings suggest that RhoPro is a suitable candidate as a PS for solid tumors.

Purification and characterization of novel whey glycoprotein WGP-88 which binds to a monoclonal antibody to PAS-4 glycoprotein in the bovine milk fat globule membrane

A monoclonal antibody to the PAS-4 glycoprotein (78 kDa) of the bovine milk fat globule membrane (MFGM) specifically recognized PAS-4, and was named KAS4. A component recognized by KAS4 was found in whey protein, this being a glycoprotein of 88 kDa by SDS-PAGE and named WGP-88. WGP-88 was purified and characterized in comparison with PAS-4. WGP-88 had apparent pI values of 6.45 and 6.39, while those of PAS-4 were 7.39 and 7.35. Neuraminidase digestion shifted the pI values of WGP-88 to 6.57 and of PAS-4 to 7.52. WGP-88 was rich in polar amino residues (44.9 mol%), while PAS-4 was abundant in nonpolar amino acid residues (48.7 mol%). WGP-88 contained 17.1% of carbohydrate and PAS-4 had 7.2%. The results of reductive hydrolysis, N-glycanase digestion, and a lectin blot analysis suggested that N- and O-linked sugar chains were contained in both glycoproteins. WGP-88 and PAS-4 had a different N-terminal amino acid sequence. WGP-88 and PAS-4 respectively inhibited competitively the binding of KAS4 to PAS-4 and WGP-88. Our studies revealed WGP-88 recognized by KAS4 mAb to be a novel whey protein and to have different biochemical properties from those of PAS-4.

Rapid and simple procedure for purifying PAS-4 glycoprotein from bovine milk fat globule membrane

The isolation and partial characterization of PAS-4 glycoprotein (78 kDa) from bovine milk fat globule membrane (MFGM) is described. PAS-4 was selectively extracted with Triton X-114 nonionic detergent and then fractionated on DEAE-Sepharose at pH 7.5. The PAS-4 fraction that was not bound on DEAE-Sepharose gave a single band by SDS-PAGE. The recovery of PAS-4 was 57.4% from MFGM. An amino acid analysis found a high percentage of nonpolar residues. Approximately 7.2% of carbohydrate from PAS-4 was composed of mannose, galactose (Gal), N-acetylglucosamine, N-acetylgalactosamine (GalNAc), and sialic acid, most of the Gal and GalNAc in PAS-4 being released after mild alkaline hydrolysis. This indicated that PAS-4 contained both N- and O-linked sugar chains in concordance with the results of lectin affinity. PAS-4 had apparent isoelectric points of 7.45, 7.41, and 7.32, but these were shifted to pI 7.47 by a neuraminidase treatment. The apparent molecular weight of PAS-4 after deglycosylation with N-glycanase was approximately 57,000 by SDS-PAGE.