Attitude towards hereditary cancer risk management among women with cancer in Taiwan.

BACKGROUND: Risk management intentions prior to genetic counseling predict risk management uptake following genetic testing. Limited studies examined the attitude and understanding towards genetic counseling/testing in underserved countries. The purposes of this study were to explore knowledge and attitude towards genetic counseling, testing, and risk management for breast and ovarian cancer, and to understand the factors influencing risk management intentions in women with cancer in Taiwan. METHODS: Cross-sectional with correlational design was used in this study. Participants were enrolled for genetic testing based on clinical criteria suspected of having hereditary cancer. Survey was conducted using a standardized questionnaire including (1) demographics and personal/family history of cancer; (2) prior experience or consideration of genetic testing and reasons for not considering; (3) perception and attitude towards genetic counseling; and (4) intentions for risk management with a hypothetical BRCA1 mutation status. Multinomial logistic regression was used to analyze the predictors of participants' intentions for cancer risk management strategies. RESULTS: A total of 430 women with cancer were analyzed in which 51.6% had family history of cancer in first-degree relatives. Only 30.7% had considered genetic testing and 28.4% had known about genetic counseling prior to the study. When prompted with the services of genetic counseling, the attitude towards genetic counseling was fairly positive (score of 19.8 ± 2.9 out of 25). Given hypothetical BRCA1 mutation status, enhanced breast cancer screening with annual breast MRI was much more accepted than cancer risk reducing interventions. More positive attitude towards genetic counseling (each score point increase) was associated with higher odds of intention for breast MRI (OR 1.20, 95% CI 1.09-1.32) and preventive tamoxifen (OR 1.11, 95% CI 1.02-1.22). Having considered genetic testing prior to the study was associated with higher odds of intention for all four risk management strategies: breast MRI (OR 2.99, 95% CI 1.46-6.11), preventive tamoxifen (OR 1.79, 95% CI 1.00-3.17), risk-reducing mastectomy (OR 2.24, 95% CI 1.13-4.42), and risk-reducing salpingo-oophorectomy (OR 2.69, 95% CI 1.27-6.93). CONCLUSION: Knowledge of genetic testing and positive attitude towards genetic counseling were associated with increased willingness to consider cancer risk management strategies for hereditary breast and ovarian cancer syndrome. Given the limited knowledge on genetic testing and counseling in the studied population, increasing public awareness of these services may increase adoption of the risk management strategies.

Mismatch Repair (MMR) Gene Mutation Carriers Have Favorable Outcome in Colorectal and Endometrial Cancer: A Prospective Cohort Study.

Germline (Lynch syndrome, LS) and somatic deficiencies of mismatch repair proteins (MMRd) are linked to colorectal and endometrial cancer; however, their prognostic impact in Asian populations remains unclear. This prospective cohort study aimed to determine the prevalence and outcome of germline and somatic MMRd in cancer patients suspected of LS. Patients with colorectal or endometrial cancer suspected of LS were enrolled and underwent gene sequencing for germline MMRd (gMMRd) and immunohistochemistry staining of MMR proteins in a subset of the pathological samples (pMMRd). Among the 451 enrolled patients, 36 patients were gMMRd (+). Compared with gMMRd (-) patients, the 10-year relapse-free survival in gMMRd (+) patients was significantly higher (100% vs. 77.9%; p = 0.006), whereas the 10-year overall survival was similar (100% vs. 90.9%; p = 0.12). Among the 102 gMMRd (-) patients with available pMMR status, 13.7% were pMMRd (+). The 5-year relapse-free survival was 62.9% in gMMRd (-) pMMRd (+) patients and 35.0% in gMMRd (-) pMMRd (-) patients, both lower than gMMRd (+) patients (100%; p < 0.001). This study showed that having LS confers a favorable outcome in colorectal and endometrial cancer patients and highlights the importance of germline genetic testing following the detection of somatic MMRd.

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

Antibody CDR amino acids underlying the functionality of antibody repertoires in recognizing diverse protein antigens.

Antibodies recognize protein antigens with exquisite specificity in a complex aqueous environment, where interfacial waters are an integral part of the antibody-protein complex interfaces. In this work, we elucidate, with computational analyses, the principles governing the antibodies' specificity and affinity towards their cognate protein antigens in the presence of explicit interfacial waters. Experimentally, in four model antibody-protein complexes, we compared the contributions of the interaction types in antibody-protein antigen complex interfaces with the antibody variants selected from phage-displayed synthetic antibody libraries. Evidently, the specific interactions involving a subset of aromatic CDR (complementarity determining region) residues largely form the predominant determinant underlying the specificity of the antibody-protein complexes in nature. The interfacial direct/water-mediated hydrogen bonds accompanying the CDR aromatic interactions are optimized locally but contribute little in determining the epitope location. The results provide insights into the phenomenon that natural antibodies with limited sequence and structural variations in an antibody repertoire can recognize seemingly unlimited protein antigens. Our work suggests guidelines in designing functional artificial antibody repertoires with practical applications in developing novel antibody-based therapeutics and diagnostics for treating and preventing human diseases.

Eradicating mesothelin-positive human gastric and pancreatic tumors in xenograft models with optimized anti-mesothelin antibody-drug conjugates from synthetic antibody libraries.

Mesothelin (MSLN) is an attractive candidate of targeted therapy for several cancers, and hence there are increasing needs to develop MSLN-targeting strategies for cancer therapeutics. Antibody-drug conjugates (ADCs) targeting MSLN have been demonstrated to be a viable strategy in treating MSLN-positive cancers. However, developing antibodies as targeting modules in ADCs for toxic payload delivery to the tumor site but not to normal tissues is not a straightforward task with many potential hurdles. In this work, we established a high throughput engineering platform to develop and optimize anti-MSLN ADCs by characterizing more than 300 scFv CDR-variants and more than 50 IgG CDR-variants of a parent anti-MSLN antibody as candidates for ADCs. The results indicate that only a small portion of the complementarity determining region (CDR) residues are indispensable in the MSLN-specific targeting. Also, the enhancement of the hydrophilicity of the rest of the CDR residues could drastically increase the overall solubility of the optimized anti-MSLN antibodies, and thus substantially improve the efficacies of the ADCs in treating human gastric and pancreatic tumor xenograft models in mice. We demonstrated that the in vivo treatments with the optimized ADCs resulted in almost complete eradication of the xenograft tumors at the treatment endpoints, without detectable off-target toxicity because of the ADCs' high specificity targeting the cell surface tumor-associated MSLN. The technological platform can be applied to optimize the antibody sequences for more effective targeting modules of ADCs, even when the candidate antibodies are not necessarily feasible for the ADC development due to the antibodies' inferior solubility or affinity/specificity to the target antigen.

Evaluating BRCA mutation risk predictive models in a Chinese cohort in Taiwan.

Accurate estimation of carrier probabilities of cancer susceptibility gene mutations is an important part of pre-test genetic counselling. Many predictive models are available but their applicability in the Asian population is uncertain. We evaluated the performance of five BRCA mutation risk predictive models in a Chinese cohort of 647 women, who underwent germline DNA sequencing of a cancer susceptibility gene panel. Using areas under the curve (AUCs) on receiver operating characteristics (ROC) curves as performance measures, the models did comparably well as in western cohorts (BOADICEA 0.75, BRCAPRO 0.73, Penn II 0.69, Myriad 0.68). For unaffected women with family history of breast or ovarian cancer (n = 144), BOADICEA, BRCAPRO, and Tyrer-Cuzick models had excellent performance (AUC 0.93, 0.92, and 0.92, respectively). For women with both personal and family history of breast or ovarian cancer (n = 241), all models performed fairly well (BOADICEA 0.79, BRCAPRO 0.79, Penn II 0.75, Myriad 0.70). For women with personal history of breast or ovarian cancer but no family history (n = 262), most models did poorly. Between the two well-performed models, BOADICEA underestimated mutation risks while BRCAPRO overestimated mutation risks (expected/observed ratio 0.67 and 2.34, respectively). Among 424 women with personal history of breast cancer and available tumor ER/PR/HER2 data, the predictive models performed better for women with triple negative breast cancer (AUC 0.74 to 0.80) than for women with luminal or HER2 overexpressed breast cancer (AUC 0.63 to 0.69). However, incorporating ER/PR/HER2 status into the BOADICEA model calculation did not improve its predictive accuracy.

Cancer subtype prediction from a pathway-level perspective by using a support vector machine based on integrated gene expression and protein network.

BACKGROUND AND OBJECTIVE: Distinguishing cancer subtypes is critical for selecting the appropriate treatment strategy. Bioinformatics approaches have gradually taken the place of clinical observations and pathological experiments. However, these approaches are typically only used in gene expression profiling. Previous studies have primarily focused on the gene level or specific diseases, and thus pathway-level factors have not been considered. Therefore, a computational method that integrates gene expression and pathway is necessary. METHODS: This study presented an approach to determine potential fragments of activated pathways around protein networks in different stages of disease. We used a scored equation that integrates genomic and proteomic information and determined the intensity of the pathway link change. A support vector machine (SVM) was used to train and test subtype-predicted models. RESULTS: The performance of the proposed method was evaluated by calculating prediction accuracy. The average prediction accuracy was 67.64% for three subtypes in tumors of neuroepithelial tissues. The results demonstrate that the proposed method applies fewer features than gene expression methods used to obtain similar results CONCLUSIONS: This study suggests a method to implement a cancer subtype classifier based on an SVM from a pathway-level perspective.

Differentiating disease subtypes by using pathway patterns constructed from gene expressions and protein networks.

Gene expression profiles differ in different diseases. Even if diseases are at the same stage, such diseases exhibit different gene expressions, not to mention the different subtypes at a single lesion site. Distinguishing different disease subtypes at a single lesion site is difficult. In early cases, subtypes were initially distinguished by doctors. Subsequently, further differences were found through pathological experiments. For example, a brain tumor can be classified according to its origin, its cell-type origin, or the tumor site. Because of the advancements in bioinformatics and the techniques for accumulating gene expressions, researchers can use gene expression data to classify disease subtypes. Because the operation of a biopathway is closely related to the disease mechanism, the application of gene expression profiles for clustering disease subtypes is insufficient. In this study, we collected gene expression data of healthy and four myelodysplastic syndrome subtypes and applied a method that integrated protein-protein interaction and gene expression data to identify different patterns of disease subtypes. We hope it is efficient for the classification of disease subtypes in adventure.

Revealing pathway maps of renal cell carcinoma by gene expression change.

Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50% and 48.48%. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.

Implementation of an integrated drug information system for inpatients to reduce medication errors in administering stage.

In hospitals, computerized provider order entry (CPOE) with Clinical Decision Support System (CDSS) would decrease respectable amount of medication errors in prescribing stage. However, medication errors occur not only in prescribing stage, but also in administering stage. In this study we constructed an integrated drug information system (IDIS) for inpatients. To reduce medication errors in administering stage, IDIS is constructed on a computerized drug cart and could provide patients' data, drug information with drug images, drug administration routes, drug interactions, and intravenous drug compatibility information. By offering these helpful information, care workers in Taiwan could easily find medication errors as well. IDIS have been constructed and been demonstrated by patients' information from a medical center in Taipei. The primary results showed that 16.3% of inpatients still had drug interaction concerns, i.e. every patient suffered approximately 0.35 drug interaction in average. It seems that except for CPOE with CDSSs, it could be helpful using such a system to prevent medication errors.