Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer.

PURPOSE: Evaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful but is limited by factors such as intraobserver/interobserver bias and intratumor heterogeneity. We developed an artificial intelligence (AI)-powered analyzer to assess TPS for the prediction of immune checkpoint inhibitor (ICI) response in advanced non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: The AI analyzer was trained with 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSIs) stained by 22C3 pharmDx immunohistochemistry. The clinical performance of the analyzer was validated in an external cohort of 430 WSIs from patients with NSCLC. Three pathologists performed annotations of this external cohort, and their consensus TPS was compared with AI-based TPS. RESULTS: In comparing PD-L1 TPS assessed by AI analyzer and by pathologists, a significant positive correlation was observed (Spearman coefficient = 0.925; P < .001). The concordance of TPS between AI analyzer and pathologists according to TPS ≥50%, 1%-49%, and <1% was 85.7%, 89.3%, and 52.4%, respectively. In median progression-free survival (PFS), AI-based TPS predicted prognosis in the TPS 1%-49% or TPS <1% group better than the pathologist's reading, with the TPS ≥50% group as a reference (hazard ratio [HR], 1.49 [95% CI, 1.19 to 1.86] v HR, 1.36 [95% CI, 1.08 to 1.71] for TPS 1%-49% group, and HR, 2.38 [95% CI, 1.69 to 3.35] v HR, 1.62 [95% CI, 1.23 to 2.13] for TPS <1% group). CONCLUSION: PD-L1 TPS assessed by AI analyzer correlates with that of pathologists, with clinical performance also being comparable when referenced to PFS. The AI model can accurately predict tumor response and PFS of ICI in advanced NSCLC via assessment of PD-L1 TPS.

An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating interobserver and intersite variability.

AIMS: Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD-L1 22C3 expression in UC, but it can vary between pathologists due to the consideration of both immune and tumour cell positivity. METHODS AND RESULTS: An artificial intelligence (AI)-powered PD-L1 CPS analyser was developed using 1,275,907 cells and 6175.42 mm2 of tissue annotated by pathologists, extracted from 400 PD-L1 22C3-stained whole slide images of UC. We validated the AI model on 543 UC PD-L1 22C3 cases collected from three institutions. There were 446 cases (82.1%) where the CPS results (CPS ≥10 or <10) were in complete agreement between three pathologists, and 486 cases (89.5%) where the AI-powered CPS results matched the consensus of two or more pathologists. In the pathologist's assessment of the CPS, statistically significant differences were noted depending on the source hospital (P = 0.003). Three pathologists reevaluated discrepancy cases with AI-powered CPS results. After using the AI as a guide and revising, the complete agreement increased to 93.9%. The AI model contributed to improving the concordance between pathologists across various factors including hospital, specimen type, pathologic T stage, histologic subtypes, and dominant PD-L1-positive cell type. In the revised results, the evaluation discordance among slides from different hospitals was mitigated. CONCLUSION: This study suggests that AI models can help pathologists to reduce discrepancies between pathologists in quantifying immunohistochemistry including PD-L1 22C3 CPS, especially when evaluating data from different institutions, such as in a telepathology setting.

Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases.

BACKGROUND: Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations. METHODS: AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment. RESULTS: Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance. CONCLUSIONS: This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.

Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types.

BACKGROUND: The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types. METHODS: Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions. RESULTS: We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup. CONCLUSION: The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.

The Perceived Meaning of Traumatic Brain Injury for Older Adults: A Longitudinal-Multiple Case Study.

PURPOSE: The aim of this study was to explore the perceived meaning of traumatic brain injury (TBI) over the first-year postinjury among older adults and to explore if and how meaning changes. DESIGN: A longitudinal multiple-case study design was used. METHODS: Semistructured face-to-face interviews were completed at 1 week and 1, 3, 6, and 12 months postinjury. Transcripts were analyzed using inductive thematic analysis. RESULTS: Fifty-five interviews were conducted with 12 participants. Four themes were identified: gratitude, vulnerability and dependence, slowing down and being more careful, and a chance for reflecting on life. Most participants' perceptions of their TBI remained either consistently positive or negative over the first-year postinjury. CLINICAL RELEVANCE: Nurses should elicit and support patients' positive illness perceptions regarding their brain injury, which can contribute to a higher quality of life. For those patients with negative illness perceptions, nurses should provide resources in order to support coping and resilience following brain injury. CONCLUSIONS: This study is the first study to explore individual perceptions over time of the meaning made from experiencing TBI among older adults. Findings can serve as a foundation for tailored supportive interventions among older adults following TBI to maximize quality of life.

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer.

Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.

Kidney Donor Perspectives on Acute Postoperative Pain Management.

This study employed a qualitative descriptive approach to examine living kidney donor's experience of postoperative pain. Thirteen living kidney donors aged 46.5 (±14.4) years participated in this study. Semi-structured interviews were conducted and transcribed. Transcripts were inductively coded and reviewed for trends, patterns, and insights into donor's experience of postoperative pain. Donors experienced postoperative pain from a variety of sources that hindered recovery and created anxiety and fear in some. Donors managed pain with opioid and non-opioid medications, social support, and ambulation. Donor's past experiences with and expectations about pain, relationships with intended recipients, social support, as well as motivations for and meaning of donation informed their experience of postoperative pain. Prompt pharmacologic intervention for pain, as well as further coaching and education about pain management should be emphasized for nurses caring for living kidney donors. Further study of how donor's motivation might mediate their pain experience is needed.

Social integration: A concept analysis.

BACKGROUND: In recent decades, social isolation has been increasingly linked to serious health conditions. However, social integration (SI) is a complex concept that has not been systematically explored or defined in nursing. It is essential for nurses and healthcare providers to have a clearer concept of SI to better provide holistic care to support optimal health. PURPOSE: This concept analysis aimed to clarify the concept of SI in health research and to identify attributes, antecedents, and consequences of the concept of SI to enhance understanding of the concept and its implications for human health. METHODS: Walker and Avant's framework was used as the methodology for the concept analysis of SI. A literature search using PubMed, CINAHL, and Embase databases on SI was conducted with keywords: "integration," "social integration," "social relationships," "social participation," "community integration," "socialization." Studies included in the search were published from 2001 to 2021. RESULTS: SI is affected by multidimensional individual, societal, and environmental factors. Defining attributes are productive activities, social relationships, community engagement, and leisure activities. SI is effective in promoting multiple aspects of health as well as healthy aging and overall well-being. CONCLUSION: The analysis contributes to a comprehensive and fundamental understanding of SI and contributes to helping nurses better understand patients' circumstances that promote or inhibit SI. This knowledge will support the development of interventions that support optimal health and well-being, in assisting patients to remain integrated or reintegrate into society during and following an illness or injury.

Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms.

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.

Artificial intelligence-powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non-small cell lung cancer with better prediction of immunotherapy response.

BACKGROUND: Manual evaluation of programmed death ligand 1 (PD-L1) tumour proportion score (TPS) by pathologists is associated with interobserver bias. OBJECTIVE: This study explored the role of artificial intelligence (AI)-powered TPS analyser in minimisation of interobserver variation and enhancement of therapeutic response prediction. METHODS: A prototype model of an AI-powered TPS analyser was developed with a total of 802 non-small cell lung cancer (NSCLC) whole-slide images. Three independent board-certified pathologists labelled PD-L1 TPS in an external cohort of 479 NSCLC slides. For cases of disagreement between each pathologist and the AI model, the pathologists were asked to revise the TPS grade (<1%, 1%-49% and ≥50%) with AI assistance. The concordance rates among the pathologists with or without AI assistance and the effect of the AI-assisted revision on clinical outcome upon immune checkpoint inhibitor (ICI) treatment were evaluated. RESULTS: Without AI assistance, pathologists concordantly classified TPS in 81.4% of the cases. They revised their initial interpretation by using the AI model for the disagreement cases between the pathologist and the AI model (N = 91, 93 and 107 for each pathologist). The overall concordance rate among the pathologists was increased to 90.2% after the AI assistance (P < 0.001). A reduction in hazard ratio for overall survival and progression-free survival upon ICI treatment was identified in the TPS subgroups after the AI-assisted TPS revision. CONCLUSION: The AI-powered TPS analyser assistance improves the pathologists' consensus of reading and prediction of the therapeutic response, raising a possibility of standardised approach for the accurate interpretation.

Ischemic Stroke Risk Among Adult Brain Tumor Survivors: Evidence to Guide Practice.

ABSTRACT: INTRODUCTION: Primary brain tumors are the leading cause of cancer mortality in the United States affecting approximately 90,000 Americans each year. A major complication for brain tumor survivors is acute ischemic stroke (AIS). Currently, there are limited research to provide guidelines for AIS prevention and management in adult brain tumor survivors. The purpose of this review is to discuss the most common risk factors for AIS in adult brain tumor survivors along with best evidence for assessment, screening, and strategies to prevent AIS in this population. METHODS: Relevant literature was identified by searching CINAHL and PubMed databases using the following keywords: "brain tumor survivors," "adults," "stroke," "risk factors," "guidelines," "prevention," and "management". Articles not pertaining to adult brain tumor survivors and AIS were excluded. RESULTS: The location of the tumor, dose, extent, and type of radiation contribute to the development of vascular injury and subsequent carotid stenosis among brain tumor survivors. Endothelial growth factor inhibitor and chemotherapy drugs induces vascular remodeling. Other symptoms such as neurological impairments and co-morbidities are also present among brain tumor survivors. Furthermore, AIS increases from the time of primary brain tumor diagnosis and incidence further increases among patients who were diagnosed with a brain tumor as a child. CONCLUSION: Nurses play a key role in the assessment, prevention, and identifying individuals who are at risk of AIS during brain tumor survivorship. Engaging patients and their caregivers on minimizing their risks of AIS is crucial in the outpatient setting. Annual surveillance visits that include intracranial artery imaging should be used to identify individuals considered most at risk for developing AIS symptoms.

Predicting pressure injury using nursing assessment phenotypes and machine learning methods.

OBJECTIVE: Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical methods, limiting their accuracy. In this paper, we describe the development of machine learning-based predictive models, using phenotypes derived from nurse-entered direct patient assessment data. METHODS: We utilized rich electronic health record data, including full assessment records entered by nurses, from 5 different hospitals affiliated with a large integrated healthcare organization to develop machine learning-based prediction models for pressure injury. Five-fold cross-validation was conducted to evaluate model performance. RESULTS: Two pressure injury phenotypes were defined for model development: nonhospital acquired pressure injury (N = 4398) and hospital acquired pressure injury (N = 1767), representing 2 distinct clinical scenarios. A total of 28 clinical features were extracted and multiple machine learning predictive models were developed for both pressure injury phenotypes. The random forest model performed best and achieved an AUC of 0.92 and 0.94 in 2 test sets, respectively. The Glasgow coma scale, a nurse-entered level of consciousness measurement, was the most important feature for both groups. CONCLUSIONS: This model accurately predicts pressure injury development and, if validated externally, may be helpful in widespread pressure injury prevention.

High Paip1 Expression as a Potential Prognostic Marker in Hepatocellular Carcinoma.

BACKGROUND/AIM: Translation plays an important role in the carcinogenesis of various human tumors. Paip1 and eIF4A1 are translation-associated proteins that mediate the function of eukaryotic initiation factor 4F complex. This study aimed to analyse the relationship between the expression status of Paip1 and eIF4A1 and clinicopathologic features in hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Immunohistochemical analysis was used to evaluate the expression status of Paip1 and eIF4A1. Two pathologists independently interpreted the immunostained slides. The prognostic value of Paip1 and eIF4A1 was evaluated by the Kaplan-Meier plotter. RESULTS: Among 173 HCC patients, 28 (16.1%) and 46 (26.6%) belonged in the Paip1 and eIF4A1 high-expression groups. High expression of Paip1 and eIF4A1 was associated with advanced TNM stage and more frequent vascular tumor invasion. Univariate analysis indicated that high Paip1 expression was associated with worse five-year overall survival (OS). Public dataset analysis by Kaplan-Meier plotter revealed that high mRNA expression of Paip1, and not of eIF4A1, was significantly associated with worse five-year OS and disease-free survival. CONCLUSION: Paip1 expression has a potential prognostic value in human HCC.

Effects of abutment screw preload in two implant connection systems: A 3D finite element study.

STATEMENT OF PROBLEM: Finite element analysis (FEA) has been used to evaluate the biomechanical behaviors of dental implants. However, in some FEA studies, the influence of the preload condition has been omitted to simplify the analysis. This might affect the results of biomechanical analysis significantly. The preload condition requires analysis. PURPOSE: The purpose of this FEA study was to evaluate and verify the effects of the presence of the preload condition on abutment screws under the occlusal load for external and internal hexagonal connection systems. MATERIAL AND METHODS: The finite element models consisting of bone blocks, 2 different implant systems (Osstem US and GS system; Osstem Implant Co), and crowns were created. With these components, a total of 6 models with different conditions were constructed for FEA: external hexagonal connection system only with preload (EO), external hexagonal connection system with no preload but occlusal load (EN), external hexagonal system with both preload and occlusal load (EP), internal hexagonal system only with preload (IO), internal hexagonal system with no preload but occlusal load (IN), and internal hexagonal system with both preload and occlusal load (IP). An 11.3-degree oblique load (100 N) to the axis of the implant was applied on the occlusal surface of the crown for the models with occlusal load. A preload of 825 N was applied in the abutment screw of the models EO, EP, IO, and IP. The maximum von Mises stress, maximum principal stress, and maximum displacement of the components of the models were evaluated. RESULTS: Both external and internal connection systems resulted in higher maximum von Mises stress and maximum principal stress values in the presence of preload in the abutment screw. The internal connection system showed higher displacement values than the external system with or without occlusal loading, and values tended to increase with the preload condition. CONCLUSIONS: The presence of a preload condition significantly affected the biomechanical behaviors of the components of 2 different connection systems. The preload condition should be included in FEA to achieve more realistic results.

High expression of DEK is associated with poor prognosis in hepatocellular carcinoma.

DEK is an oncogene that has been identified as part of the DEK-CAN fusion gene. DEK plays a role in carcinogenesis through WNT signaling and induces cell proliferation through cyclin-dependent kinase signaling. DEK overexpression has been reported in HCC, but the clinical significance is unclear. This study enrolled 221 cases of HCC. The expression of DEK protein was evaluated by immunohistochemical staining. Cdk4, cyclin D1, Wnt10b, E-cadherin, and ß-catenin were also immunohistochemically stained and analyzed for correlation. The association of clinicopathologic factors with DEK expression was analyzed. DEK expression was observed in 44.8% (99/221) of cases. DEK expression showed a statistical association with clinicopathologic factors, including Edmondson-Steiner grade, presence of vascular emboli, and multiplicity (p<0.05). Among the other IHC markers, the expression of cdk4 was correlated with DEK expression (p<0.05). Patients with high DEK expression showed a significantly lower overall survival rate (p=0.006). However, the disease-free survival rate did not differ significantly. In addition, in a Cox regression model analysis, DEK expression was an independent prognostic factor. In summary, high expression of DEK was observed in HCC and was associated with poor prognostic marker expression and poor prognosis.

Early prediction of neurological outcome after barbiturate coma therapy in patients undergoing brain tumor surgery.

After a difficult brain tumor surgery, refractory intracranial hypertension (RICH) may occur due to residual tumor or post-operative complications such as hemorrhage, infarction, and aggravated brain edema. We investigated which predictors are associated with prognosis when using barbiturate coma therapy (BCT) as a second-tier therapy to control RICH after brain tumor surgery. The study included adult patients who underwent BCT after brain tumor surgery between January 2010 and December 2016. The primary outcome was neurological status upon hospital discharge, which was assessed using the Glasgow Outcome Scale (GOS). In the study period, 4,296 patients underwent brain tumor surgery in total. Of these patients, BCT was performed in 73 patients (1.7%). Among these 73 patients, 56 (76.7%) survived to discharge and 25 (34.2%) showed favorable neurological outcomes (GOS scores of 4 and 5). Invasive monitoring of intracranial pressure (ICP) was performed in 60 (82.2%) patients, and revealed that the maximal ICP within 6 h after BCT was significantly lower in patients with favorable neurological outcome as well as in survivors (p = 0.008 and p = 0.028, respectively). Uncontrolled RICH (ICP ≥ 22 mm Hg within 6 h of BCT) was an important predictor of mortality after BCT (adjusted hazard ratio 12.91, 95% confidence interval [CI] 2.788-59.749), and in particular, ICP ≥ 15 mm Hg within 6 h of BCT was associated with poor neurological outcome (adjusted odds ratio 9.36, 95% CI 1.664-52.614). Therefore, early-controlled ICP after BCT was associated with clinical prognosis. There were no significant differences in the complications associated with BCT between the two neurological outcome groups. No BCT-induced death was observed. The active and timely control of RICH may be beneficial for clinical outcomes in patients with RICH after brain tumor surgery.

Clinical and genomic landscape of gastric cancer with a mesenchymal phenotype.

Gastric cancer is a heterogeneous cancer, making treatment responses difficult to predict. Here we show that we identify two distinct molecular subtypes, mesenchymal phenotype (MP) and epithelial phenotype (EP), by analyzing genomic and proteomic data. Molecularly, MP subtype tumors show high genomic integrity characterized by low mutation rates and microsatellite stability, whereas EP subtype tumors show low genomic integrity. Clinically, the MP subtype is associated with markedly poor survival and resistance to standard chemotherapy, whereas the EP subtype is associated with better survival rates and sensitivity to chemotherapy. Integrative analysis shows that signaling pathways driving epithelial-to-mesenchymal transition and insulin-like growth factor 1 (IGF1)/IGF1 receptor (IGF1R) pathway are highly activated in MP subtype tumors. Importantly, MP subtype cancer cells are more sensitive to inhibition of IGF1/IGF1R pathway than EP subtype. Detailed characterization of these two subtypes could identify novel therapeutic targets and useful biomarkers for prognosis and therapy response.

Expression and prognostic significance of programmed death protein 1 and programmed death ligand-1, and cytotoxic T lymphocyte-associated molecule-4 in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies and causes of death worldwide. In this study, we assessed the correlation between clinicopathologic factors with programmed cell death protein 1 (PD-1) and programmed cell death ligand-1 (PD-L1), and cytotoxic T lymphocyte-associated molecule-4 (CTLA-4) expressions. Furthermore, we analyzed the prognostic significance of these proteins in a subgroup of patients. We retrospectively evaluated the PD-1, PD-L1, and CTLA-4 expressions in 294 HCC tissue microarray samples using immunohistochemistry. PD-1 and PD-L1 expressions were significant related to high CD8+ tumor-infiltrating lymphocytes (TILs) (r = 0.664, p < 0.001 and r = 0.149, p = 0.012). Only high Edmondson-Steiner grade was statistically related to high PD-1 expression. High PD-L1 expression was demonstrated as an independent poor prognostic factor for disease-free survival in addition to previous known factors, size >5 cm and serum albumin ≤3.5 g/dL in high CD8+ TILs group. We have demonstrated that the combined high expression of PD-L1 and CD8+ TIL is an important prognostic factor related to the immune checkpoint pathway in HCC and furthermore, there is a possibility that it could be used as a predictor of therapeutic response. Also, this result would be helpful in evaluating the applicable group of PD-1/PD-L1 blocking agent for HCC patients.

SIRT7, H3K18ac, and ELK4 Immunohistochemical Expression in Hepatocellular Carcinoma.

BACKGROUND: SIRT7 is one of the histone deacetylases and is NAD-dependent. It forms a complex with ETS-like transcription factor 4 (ELK4), which deacetylates H3K18ac and works as a transcriptional suppressor. Overexpression of SIRT7 and deacetylation of H3K18ac have been shown to be associated with aggressive clinical behavior in some cancers, including hepatocellular carcinoma (HCC). The present study investigated the immunohistochemical expression of SIRT7, H3K18ac, and ELK4 in hepatocellular carcinoma. METHODS: A total of 278 HCC patients were enrolled in this study. Tissue microarray blocks were made from existing paraffin-embedded blocks. Immunohistochemical expressions of SIRT7, H3K18ac and ELK4 were scored and analyzed. RESULTS: High SIRT7 (p = .034), high H3K18ac (p = .001), and low ELK4 (p = .021) groups were associated with poor outcomes. Age < 65 years (p = .028), tumor size ≥ 5 cm (p = .001), presence of vascular emboli (p = .003), involvement of surgical margin (p = .001), and high American Joint Committee on Cancer stage (III&V) (p < .001) were correlated with worse prognoses. In multivariate analysis, H3K18ac (p = .001) and ELK4 (p = .015) were the significant independent prognostic factors. CONCLUSIONS: High SIRT7 expression with poor overall survival implies that deacetylation of H3K18ac contributes to progression of HCC. High H3K18ac expression with poor prognosis is predicted due to a compensation mechanism. In addition, high ELK4 expression with good prognosis suggests another role of ELK4 as a tumor suppressor beyond SIRT7's helper. In conclusion, we could assume that the H3K18ac deacetylation pathway is influenced by many other factors.