Kim SW, et al. J Korean Med Sci. 2020 Nov;36(1):e12. https://doi.org/10.3346/jkms.2021.36.e12

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.

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.

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.

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.