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.

Development and Validation of 18F-FDG PET/CT-Based Models for Predicting Successful Complete Cytoreduction During Primary Cytoreductive Surgery for Advanced Ovarian cancer.

PURPOSE: The aim of this study was to develop an 18F-FDG PET/CT-based model to predict complete cytoreduction during primary cytoreductive surgery (CRS) for ovarian cancer (OC). PATIENTS AND METHODS: We retrospectively identified patients with stage III-IV OC who underwent primary CRS between June 2013 and February 2020 at 2 tertiary centers. Patients from each hospital were assigned to training and test sets. The abdominal cavity was divided into 3 sections, and data for the PET/CT-derived parameters were collected through image analysis. Various prediction models were constructed by combining clinicopathologic characteristics and PET/CT-derived parameters. The performance of the model with the highest area under the receiver operating characteristic curve (AUC) was externally validated. RESULTS: The training and test sets included 159 and 166 patients, respectively. The median age of patients in the test set was 55 years; 72.3% of them had stage III tumors, and 65.4% underwent complete cytoreduction. Metabolic tumor volume, total lesion glycolysis, and the number of metastatic lesions above the upper margin of the renal vein (area A) were selected among the PET/CT parameters. The best predictive multivariable model consisted of CA-125 (<750 or ≥750 IU/mL), number of metastatic lesions (<2 or ≥2), and metabolic tumor volume of area A, predicting complete cytoreduction with an AUC of 0.768. The model was validated using a test set. Its predictive performance yielded an AUC of 0.771. CONCLUSIONS: We successfully developed and validated a preoperative model to predict complete cytoreduction in advanced OC. This model can facilitate patient selection for primary CRS in clinical practice.

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 non-linear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models non-linear 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: The HisCoM-Kernel software is freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/. The RNA-seq data underlying this article are available at https://xena.ucsc.edu/, and the others will be shared on reasonable request to the corresponding author. 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.

Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus 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). CONCLUSION: We developed an early prediction model for GDM using machine learning. The inclusion of NAFLDassociated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144).

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.

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.

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.

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.

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.