Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features.

Goal: The early diagnosis and treatment of hepatitis is essential to reduce hepatitis-related liver function deterioration and mortality. One component of the widely-used Ishak grading system for the grading of periportal interface hepatitis is based on the percentage of portal borders infiltrated by lymphocytes. Thus, the accurate detection of lymphocyte-infiltrated periportal regions is critical in the diagnosis of hepatitis. However, the infiltrating lymphocytes usually result in the formation of ambiguous and highly-irregular portal boundaries, and thus identifying the infiltrated portal boundary regions precisely using automated methods is challenging. This study aims to develop a deep-learning-based automatic detection framework to assist diagnosis. Methods: The present study proposes a framework consisting of a Structurally-REfined Deep Portal Segmentation module and an Infiltrated Periportal Region Detection module based on heterogeneous infiltration features to accurately identify the infiltrated periportal regions in liver Whole Slide Images. Results: The proposed method achieves 0.725 in F1-score of lymphocyte-infiltrated periportal region detection. Moreover, the statistics of the ratio of the detected infiltrated portal boundary have high correlation to the Ishak grade (Spearman's correlations more than 0.87 with p-values less than 0.001) and medium correlation to the liver function index aspartate aminotransferase and alanine aminotransferase (Spearman's correlations more than 0.63 and 0.57 with p-values less than 0.001). Conclusions: The study shows the statistics of the ratio of infiltrated portal boundary have correlation to the Ishak grade and liver function index. The proposed framework provides pathologists with a useful and reliable tool for hepatitis diagnosis.

Clinimetric properties of the "FIND-NEEDS" to screen geriatric conditions.

INTRODUCTION: Comprehensive geriatric assessment (CGA) is used to thoroughly assess and identify complex healthcare problems among older adults. However, administration of CGA is time-consuming and labor intensive. A simple screening tool with the mnemonic "FIND-NEEDS" was developed to quickly identify common geriatric conditions. The present study was to evaluate the clinimetric properties of the FIND-NEEDS. METHODS: First-visiting older adults aged 65 years and above (and who were able to communicate by themselves or with the help of a caregiver) were assessed (October to December, 2021) using the FIND-NEEDS and CGA at geriatric outpatient clinics of a tertiary, referred medical center. The FIND-NEEDS was examined for its criterion-related validity and compared with the CGA results. Two types of scoring (summed score and binary score) of FIND-NEEDS and CGA were analyzed using Spearman correlation, sensitivity and specificity, and area under receiver operating characteristic curve (AUC). RESULTS: The mean age of the 114 outpatients was 78.3±7.6 years, and 79(69.3%) were female. The internal consistency was excellent when using all FIND-NEEDS items, and was acceptable when using domain scores. Exploratory factor analysis showed that most of the FIND-NEEDS domain scores had factor loadings higher than 0.3. Intercorrelations of binary scores between domains of FIND-NEEDS and CGA showed most domains were moderately correlated. The overall correlation of summed scores between FIND-NEEDS and CGA was high. The FIND-NEEDS summed score was moderately correlated with CGA score (r=0.494; p<0.001), and the binary score showed excellent correlation (r=0.944; p<0.001). When using the CGA score as the gold standard, the FIND-NEEDS showed excellent AUC (0.950), sensitivity (1.00), and specificity (0.90). DISCUSSION/CONCLUSION: The present study demonstrated that the FIND-NEEDS had acceptable clinimetric properties to screen for geriatric problems among older adults. Further in-depth assessment and care plan can then be conducted afterwards.

Germline MLH1 and MSH6 mutations from two Lynch syndrome families identified in a patient with early-onset of endometrial cancer: A case report.

Introduction: Lynch syndrome is caused by a germline mutation in mismatch repair (MMR) genes, leading to the loss of expression of MMR heterodimers, either MLH1/PMS2 or MSH2/MSH6, or isolated loss of PMS2 or MSH6. Concurrent loss of both heterodimers is uncommon, and patients carrying pathogenic variants affecting different MMR genes are rare, leading to the lack of cancer screening recommendation for these patients.Case presentation:Here, we reported a female with a family history of Lynch syndrome with MLH1 c.676C > T mutation. She developed endometrial cancer at 37 years old, with loss of MLH1/PMS2 expression. Immunohistochemical staining on tumor samples incidentally detected the additional loss of MSH6 expression. Whole exome sequencing on genomic DNA from peripheral blood revealed MSH6 c.2731C > T mutation, which was confirmed to be inherited from her mother, who had an early-onset ascending colon cancer without cancer family history. Conclusion: This is a rare case of the Lynch syndrome harboring germline mutations simultaneously in two different MMR genes inherited from two families with Lynch syndrome. The diagnosis of endometrial cancer at the age less than 40 years is uncommon for Lynch syndrome-related endometrial cancer. This suggests an earlier cancer screening for patients carrying two MMR mutations.

Tight junction protein cingulin variant is associated with cancer susceptibility by overexpressed IQGAP1 and Rac1-dependent epithelial-mesenchymal transition.

BACKGROUND: Cingulin (CGN) is a pivotal cytoskeletal adaptor protein located at tight junctions. This study investigates the link between CGN mutation and increased cancer susceptibility through genetic and mechanistic analyses and proposes a potential targeted therapeutic approach. METHODS: In a high-cancer-density family without known pathogenic variants, we performed tumor-targeted and germline whole-genome sequencing to identify novel cancer-associated variants. Subsequently, these variants were validated in a 222 cancer patient cohort, and CGN c.3560C > T was identified as a potential cancer-risk allele. Both wild-type (WT) (c.3560C > C) and variant (c.3560C > T) were transfected into cancer cell lines and incorporated into orthotopic xenograft mice model for evaluating their effects on cancer progression. Western blot, immunofluorescence analysis, migration and invasion assays, two-dimensional gel electrophoresis with mass spectrometry, immunoprecipitation assays, and siRNA applications were used to explore the biological consequence of CGN c.3560C > T. RESULTS: In cancer cell lines and orthotopic animal models, CGN c.3560C > T enhanced tumor progression with reduced sensitivity to oxaliplatin compared to the CGN WT. The variant induced downregulation of epithelial marker, upregulation of mesenchymal marker and transcription factor, which converged to initiate epithelial-mesenchymal transition (EMT). Proteomic analysis was conducted to investigate the elements driving EMT in CGN c.3560C > T. This exploration unveiled overexpression of IQGAP1 induced by the variant, contrasting the levels observed in CGN WT. Immunoprecipitation assay confirmed a direct interaction between CGN and IQGAP1. IQGAP1 functions as a regulator of multiple GTPases, particularly the Rho family. This overexpressed IQGAP1 was consistently associated with the activation of Rac1, as evidenced by the analysis of the cancer cell line and clinical sample harboring CGN c.3560C > T. Notably, activated Rac1 was suppressed following the downregulation of IQGAP1 by siRNA. Treatment with NSC23766, a selective inhibitor for Rac1-GEF interaction, resulted in the inactivation of Rac1. This intervention mitigated the EMT program in cancer cells carrying CGN c.3560C > T. Consistently, xenograft tumors with WT CGN showed no sensitivity to NSC23766 treatment, but NSC23766 demonstrated the capacity to attenuate tumor growth harboring c.3560C > T. CONCLUSIONS: CGN c.3560C > T leads to IQGAP1 overexpression, subsequently triggering Rac1-dependent EMT. Targeting activated Rac1 is a strategy to impede the advancement of cancers carrying this specific variant.

Setting up a specialized maternity unit in a tertiary hospital: An oasis for pregnant women with COVID-19 during the pandemic.

OBJECTIVE: The COVID-19 pandemic has had an enormous impact on society and the medical environment in Taiwan in 2022. As pregnant women with COVID-19 are at higher risk for multiple complications, Taiwan needs a COVID-19 specialized maternity unit to improve the quality of maternal and neonatal care. MATERIALS AND METHODS: We share our experience with specialized maternity unit for pregnant women with COVID-19 at the National Cheng Kung University Hospital, where we can have careful evaluation, safe birth, and comprehensive postpartum care. RESULTS: Our COVID-19 specialized maternity unit enrolled 253 pregnant women with COVID-19, 90 (35.6%) pregnant women were admitted to the specialized maternity unit, and 71 (28.1%) pregnant women gave birth during hospitalization in two months. All pregnant women recovery well and real-time polymerase chain reaction tests on all infants were negative for COVID-19. CONCLUSION: A specialized maternity unit can provide pregnant women with a safe birth environment, immediate maternity care, and high medical quality. It can also help health workers in non-specialized maternity units deal with COVID-19-related psychological stress. Therefore, setting up one specialized maternity unit in the city during the pandemic should be guardedly considered.

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images.

BACKGROUND: Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models. With gigapixel WSIs exhibiting diverse histological appearances, obtaining clinically prognostic and explainable features remains challenging. Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival. METHODS: The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset was used in this investigation. A deep convolutional survival model (DeepConvSurv) extracted histopathological information from the image patches of nine different tissue types, including tumors, lymphocytes, stroma, and mucus. The tissue map of the WSIs was segmented using image processing techniques that involved localizing and quantifying the tissue region. Six survival models with the concordance index (C-index) were used as the evaluation metrics. RESULTS: We extracted 128 histopathological features from four histological types and five tissue area features from WSIs to predict colorectal cancer survival. Our method performed better in six distinct survival models than the Whole Slide Histopathological Images Survival Analysis framework (WSISA), which adaptively sampled patches using K-means from WSIs. The best performance using histopathological features was 0.679 using LASSO-Cox. Compared to histopathological features alone, tissue area features increased the C-index by 2.5%. Based on histopathological features and tissue area features, our approach achieved performance of 0.704 with RIDGE-Cox. CONCLUSIONS: A deep learning-based algorithm combining histopathological features with tissue area proved clinically relevant and effective for predicting cancer survival.

Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN.

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models.

Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.

BACKGROUND: The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS: In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS: The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS: We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

Electrical Sympathetic Neuromodulation Protects Bone Marrow Niche and Drives Hematopoietic Regeneration during Chemotherapy.

The sympathetic nervous system (SNS) of the bone marrow regulates the regeneration and mobilization of hematopoietic stem cells. Chemotherapy can damage bone marrow SNS, which impairs hematopoietic regeneration and aggravates hematologic toxicities. This leads to long-term bone marrow niche damage and increases mortality in patients undergoing chemotherapy. Electrical neuromodulation has been used to improve functional recovery after peripheral nerve injury. This study demonstrates that electrical sympathetic neuromodulation (ESN) of bone marrow can protect the bone marrow niche from chemotherapy-induced injury. Using carboplatin-treated rats, the SNS via the sciatic nerve innervating the femoral marrow with the effective protocol for bone marrow sympathetic activation is electrically stimulated. ESN can mediate several hematopoietic stem cells maintenance factors and promote hematopoietic regeneration after chemotherapy. It also activates adrenergic signals and reduces the release of pro-inflammatory cytokines, particularly interleukin-1 ß, which contribute to chemotherapy-related nerve injury. Consequently, the severity of chemotherapy-related leukopenia, thrombocytopenia, and mortality can be reduced by ESN. As a result, in contrast to current drug-based treatment, such as granulocyte colony-stimulating factor, ESN can be a disruptive adjuvant treatment by protecting and modulating bone marrow function to reduce hematologic toxicity during chemotherapy.

Pathogenicity Prediction of Single Amino Acid Variants With Machine Learning Model Based on Protein Structural Energies.

The most popular tools for predicting pathogenicity of single amino acid variants (SAVs) were developed based on sequence-based techniques. SAVs may change protein structure and function. In the context of van der Waals force and disulfide bridge calculations, no method directly predicts the impact of mutations on the energies of the protein structure. Here, we combined machine learning methods and energy scores of protein structures calculated by Rosetta Energy Function 2015 to predict SAV pathogenicity. The accuracy level of our model (0.76) is higher than that of six prediction tools. Further analyses revealed that the differential reference energies, attractive energies, and solvation of polar atoms between wildtype and mutant side-chains played essential roles in distinguishing benign from pathogenic variants. These features indicated the physicochemical properties of amino acids, which were observed in 3D structures instead of sequences. We added 16 features to Rhapsody (the prediction tool we used for our data set) and consequently improved its performance. The results indicated that these energy scores were more appropriate and more detailed representations of the pathogenicity of SAVs.

PD-L1 Expression in High-Risk Early-Stage Colorectal Cancer-Its Clinical and Biological Significance in Immune Microenvironment.

Programmed death-ligand 1 (PD-L1) is an immune checkpoint molecule that can regulate immune responses in the tumor microenvironment (TME); however, the clinical applications of PD-L1 in early-stage colorectal cancer (CRC) remain unclear. In this study, we aimed to investigate the relationship between PD-L1 expression and survival outcome and explore its relevant immune responses in CRC. PD-L1 expression was evaluated by immunohistochemical staining to determine the tumor proportion score and combined positive score (CPS) in a Taiwanese CRC cohort. The oncomine immune response research assay was conducted for immune gene expression analyses. CRC datasets from the TCGA database were reappraised for PD-L1-associated gene enrichment analyses using GSEA. The high expression of PD-L1 (CPS ≥ 5) was associated with longer recurrence-free survival (p = 0.031) and was an independent prognostic factor as revealed by multivariate analysis. High PD-L1 expression was related to six immune-related gene signatures, and CXCL9 is the most significant overexpressed gene in differential analyses. High CXCL9 expression correlated with increased infiltration levels of immune cells in the TME, including CD8+ T lymphocytes and M1 macrophages. These findings suggest that high PD-L1 expression is a prognostic factor of early-stage CRC, and CXCL9 may play a key role in regulating PD-L1 expression.

Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care.

To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets.

Low expression of cytosolic NOTCH1 predicts poor prognosis of breast cancer patients.

The incidence of breast cancer is increasing, and is one of the leading causes of cancer death worldwide. Dysregulation of NOTCH1 signaling is reported in breast cancer. In present study, bioinformatics was utilized to study the expression of NOTCH1 gene in breast cancer from public databases, including the Kaplan-Meier Plotter, PrognoScan, Human Protein Atlas, and cBioPortal. The relationship between NOTCH1 mRNA expression and survival of patients was inconsistent in public databases. In addition, we performed immunohistochemistry (IHC) staining of 135 specimens from our hospital. Lower cytoplasmic staining of NOTCH1 protein was correlated with cancer recurrence, bone metastasis, and a worse disease-free survival of patients, especially those with estrogen receptor-positive and human epidermal growth factor receptor 2-positive (HER2+) cancers. In TCGA breast cancer dataset, lower expression of NOTCH1 in breast cancer specimens was correlated with higher level of CCND1 (protein: cyclin D1). Decreased expression of NOTCH1 was correlated with lower level of CCNA1 (protein: cyclin A1), CCND2 (protein: cyclin D2), CCNE1 (protein: cyclin E1), CDK6 (protein: CDK6), and CDKN2C (protein: p18). In conclusion, NOTCH1 mRNA expression is not consistently correlated with clinical outcomes of breast cancer patients. Low cytoplasmic expression of NOTCH1 in IHC study is correlated with poor prognosis of breast cancer patients. Cytoplasmic localization of NOTCH1 protein failed to initial oncogenic signaling in present study. Expression of NOTCH1 mRNA was discordant with cell cycle-related genes. Regulation of NOTCH1 in breast cancer involves gene expression, protein localization and downstream signaling.

Integration of an Intensive Care Unit Visualization Dashboard (i-Dashboard) as a Platform to Facilitate Multidisciplinary Rounds: Cluster-Randomized Controlled Trial.

BACKGROUND: Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU). OBJECTIVE: i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touch screens during MDRs. This study aimed to evaluate the performance, including the efficiency of prerounding data gathering, communication accuracy, and information exchange, and clinical satisfaction of integrating i-Dashboard as a platform to facilitate MDRs. METHODS: A cluster-randomized controlled trial was performed in 2 surgical ICUs at a university hospital. Study participants included all multidisciplinary care team members. The performance and clinical satisfaction of i-Dashboard during MDRs were compared with those of the established electronic medical record (EMR) through direct observation and questionnaire surveys. RESULTS: Between April 26 and July 18, 2021, a total of 78 and 91 MDRs were performed with the established EMR and i-Dashboard, respectively. For prerounding data gathering, the median time was 10.4 (IQR 9.1-11.8) and 4.6 (IQR 3.5-5.8) minutes using the established EMR and i-Dashboard (P<.001), respectively. During MDRs, data misrepresentations were significantly less frequent with i-Dashboard (median 0, IQR 0-0) than with the established EMR (4, IQR 3-5; P<.001). Further, effective recommendations were significantly more frequent with i-Dashboard than with the established EMR (P<.001). The questionnaire results revealed that participants favored using i-Dashboard in association with the enhancement of care plan development and team participation during MDRs. CONCLUSIONS: i-Dashboard increases efficiency in data gathering. Displaying i-Dashboard on large touch screens in MDRs may enhance communication accuracy, information exchange, and clinical satisfaction. The design concepts of i-Dashboard may help develop visualization dashboards that are more applicable for ICU MDRs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04845698; https://clinicaltrials.gov/ct2/show/NCT04845698.

Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer.

To evaluate whether adjusted computed tomography (CT) scan image-based radiomics combined with immune genomic expression can achieve accurate stratification of cancer recurrence and identify potential therapeutic targets in stage III colorectal cancer (CRC), this cohort study enrolled 71 patients with postoperative stage III CRC. Based on preoperative CT scans, radiomic features were extracted and selected to build pixel image data using covariate-adjusted tensor classification in the high-dimension (CATCH) model. The differentially expressed RNA genes, as radiomic covariates, were identified by cancer recurrence. Predictive models were built using the pixel image and immune genomic expression factors, and the area under the curve (AUC) and F1 score were used to evaluate their performance. Significantly adjusted radiomic features were selected to predict recurrence. The association between the significantly adjusted radiomic features and immune gene expression was also investigated. Overall, 1037 radiomic features were converted into 33 × 32-pixel image data. Thirty differentially expressed genes were identified. We performed 100 iterations of 3-fold cross-validation to evaluate the performance of the CATCH model, which showed a high sensitivity of 0.66 and an F1 score of 0.69. The area under the curve (AUC) was 0.56. Overall, ten adjusted radiomic features were significantly associated with cancer recurrence in the CATCH model. All of these methods are texture-associated radiomics. Compared with non-adjusted radiomics, 7 out of 10 adjusted radiomic features influenced recurrence-free survival. The adjusted radiomic features were positively associated with PECAM1, PRDM1, AIF1, IL10, ISG20, and TLR8 expression. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. Adjusted CT scan image-based radiomics with immune genomic expression covariates using the CATCH model can efficiently predict cancer recurrence. The correlation between adjusted radiomic features and immune genomic expression can provide biological relevance and individualized therapeutic targets.

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer.

Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.

Discovery of Potential Neuroprotective Agents against Paclitaxel-Induced Peripheral Neuropathy.

Chemotherapy-induced neurotoxicity is a common adverse effect of cancer treatment. No medication has been shown to be effective in the prevention or treatment of chemotherapy-induced neurotoxicity. Using minoxidil as an initial template for structural modifications in conjunction with an in vitro neurite outgrowth assay, an image-based high-content screening platform, and mouse behavior models, an effective neuroprotective agent CN016 was discovered. Our results showed that CN016 could inhibit paclitaxel-induced inflammatory responses and infiltration of immune cells into sensory neurons significantly. Thus, the suppression of proinflammatory factors elucidates, in part, the mechanism of action of CN016 on alleviating paclitaxel-induced peripheral neuropathy. Based on excellent efficacy in improving behavioral functions, high safety profiles (MTD > 500 mg/kg), and a large therapeutic window (MTD/MED > 50) in mice, CN016 might have great potential to become a peripherally neuroprotective agent to prevent neurotoxicity caused by chemotherapeutics as typified by paclitaxel.

The Expression Quantitative Trait Loci in Immune Response Genes Impact the Characteristics and Survival of Colorectal Cancer.

The impact of germline variants on the regulation of the expression of tumor microenvironment (TME)-based immune response genes remains unclear. Expression quantitative trait loci (eQTL) provide insight into the effect of downstream target genes (eGenes) regulated by germline-associated variants (eVariants). Through eQTL analyses, we illustrated the relationships between germline eVariants, TME-based immune response eGenes, and clinical outcomes. In this study, both RNA sequencing data from primary tumor and germline whole-genome sequencing data were collected from patients with stage III colorectal cancer (CRC). Ninety-nine high-risk subjects were subjected to immune response gene expression analyses. Seventy-seven subjects remained for further analysis after quality control, of which twenty-two patients (28.5%) experienced tumor recurrence. We found that 65 eQTL, including 60 germline eVariants and 22 TME-based eGenes, impacted the survival of cancer patients. For the recurrence prediction model, 41 differentially expressed genes (DEGs) achieved the best area under the receiver operating characteristic curve of 0.93. In total, 19 survival-associated eGenes were identified among the DEGs. Most of these genes were related to the regulation of lymphocytes and cytokines. A high expression of HGF, CCR5, IL18, FCER1G, TDO2, IFITM2, and LAPTM5 was significantly associated with a poor prognosis. In addition, the FCER1G eGene was associated with tumor invasion, tumor nodal stage, and tumor site. The eVariants that regulate the TME-based expression of FCER1G, including rs2118867 and rs12124509, were determined to influence survival and chromatin binding preferences. We also demonstrated that FCER1G and co-expressed genes in TME were related to the aggregation of leukocytes via pathway analysis. By analyzing the eQTL from the cancer genome using germline variants and TME-based RNA sequencing, we identified the eQTL in immune response genes that impact colorectal cancer characteristics and survival.

Association between Altered Oncogenic Signaling Pathways and Overall Survival of Patients with Metastatic Colorectal Cancer.

Systemic characterization of genomic alterations into signaling pathways helps to understand the molecular pathogenies of colorectal cancer; however, their clinical implications remain unclear. Here, 128 patients with metastatic colorectal cancer (mCRC) receiving targeted next generation sequencing were retrospectively enrolled to analyze the impact of altered oncogenic pathways on clinical outcome. The datasets from Memorial Sloan Kettering Cancer Center were used for validation. In 123 patients with non-MSI-high tumor, the most common mutated gene was TP53 (84.6%), followed by APC (78.0%), KRAS (49.6%), and SMAD4 (22.8%). When mutated genes were allocated into signaling pathways defined as The Cancer Genome Atlas Pan-Cancer Analysis Project, alterations of cell cycle, Wnt, p53, RTK-RAS, PI3K, TGF-ß, Notch, and Myc pathways were identified in 88%, 87%, 85%, 75%, 28%, 26%, 17%, and 10% of mCRC tissues, respectively. The survival analyses revealed that Myc and TGF-ß pathway alterations were associated with a shorter overall survival (OS) (hazard ratio [HR]: 2.412; 95% confidence interval [CI]: 1.139-5.109; p = 0.018 and HR: 2.754; 95% CI: 1.044-7.265; p = 0.033, respectively). The negative prognostic impact of altered TGF-ß pathway was maintained in patients receiving an anti-EGFR antibody. The OS of patients with mCRC carrying MYC and BRAF mutation was shorter than those with either MYC or BRAF mutation (HR: 4.981, 95% CI: 0.296-83.92; p = 0.02). These findings have clinical implications, such as prognosis prediction, treatment guidance, and molecular-targeted therapy development.

Super-Resolution Microscopy Reveals That Stromal Interaction Molecule 1 Trafficking Depends on Microtubule Dynamics.

Store-operated Ca2+ entry (SOCE) is an essential pathway for Ca2+ signaling, and regulates various vital cellular functions. It is triggered by the endoplasmic reticulum Ca2+ sensor stromal interaction molecule 1 (STIM1). Illustration of STIM1 spatiotemporal structure at the nanometer scale during SOCE activation provides structural and functional insights into the fundamental Ca2+ homeostasis. In this study, we used direct stochastic optical reconstruction microscopy (dSTORM) to revisit the dynamic process of the interaction between STIM1, end-binding protein (EB), and microtubules to the ER-plasma membrane. Using dSTORM, we found that"powder-like"STIM1 aggregates into "trabecular-like" architectures toward the cell periphery during SOCE, and that an intact microtubule network and EB1 are essential for STIM1 trafficking. After thapsigargin treatment, STIM1 can interact with EB1 regardless of undergoing aggregation. We generated STIM1 variants adapted from a real-world database and introduced them into SiHa cells to clarify the impact of STIM1 mutations on cancer cell behavior. The p.D76G and p.D84Y variants locating on the Ca2+ binding domain of STIM1 result in inhibition of focal adhesion turnover, Ca2+ influx during SOCE and subsequent cell migration. Inversely, the p.R643C variant on the microtubule interacting domain of STIM1 leads to dissimilar consequence and aggravates cell migration. These findings imply that STIM1 mutational patterns have an impact on cancer metastasis, and therefore could be either a prognostic marker or a novel therapeutic target to inhibit the malignant behavior of STIM1-mediated cancer cells. Altogether, we generated novel insight into the role of STIM1 during SOCE activation, and uncovered the impact of real-world STIM1 variants on cancer cells.