Complex in vitro 3D models of digestive system tumors to advance precision medicine and drug testing: Progress, challenges, and trends

Digestive system cancers account for nearly half of all cancers around the world and have a high mortality rate. Cell culture and animal models represent cornerstones of digestive cancer research. However, their ability to en- able cancer precision medicine is limited. Cell culture models cannot retain the genetic and phenotypic heteroge- neity of tumors and lack tumor microenvironment (TME). Patient-derived xenograft mouse models are not suitable for immune-oncology research. While humanized mouse models are time- and cost-consuming. Suitable preclinical models, which can facilitate the understanding of mechanisms of tumor progression and develop new therapeutic strategies, are in high demand. This review article summarizes the recent progress on the establish- ment of TME by using tumor organoid models and microfluidic systems. The main challenges regarding the translation of organoid models from bench to bedside are discussed. The integration of organoids and a microflu- idic platform is the emerging trend in drug screening and precision medicine. A future prospective on this field is also provided.This study was supported by the National Natural Science Foundation of China (Grant No.82073148), the Guangdong Provincial Key Laboratory of Digestive Cancer Research (No. 2021B1212040006), the Sanming Project of Medicine in Shenzhen (SZSM201911010), the Shenzhen Key Medical Discipline Construction Fund (SZXK016), the Shenzhen Sustainable Project (KCXFZ202002011010593), and the Shenzhen-Hong Kong-Macau Technology Research Programme (Type C) (Grant No. SGDX2020110309260100)

The highest realm of precision treatment of cancer – Extending from micro-level precision to holistic personalization

BackgroundPrecision medicine opened the era of precision cancer treatment based on genetic variation, abnormal protein and other personalized factors. However, control of cancer cell proliferation and disease diffusion is not all we can do in cancer treatment. The strength of the human immune system, human basic physiological functions and normal metabolic activity, as well as the elimination of disease complications, these are the important aspects closely related to cancer lesions and therapeutic effect. Precision medicine is rapidly starting to build a micro-level personalized human pathological condition description system by the current results of genes, proteins, metabolites, and other genomics research. Chinese medicine (CM), which has a history of more than 2,000 years in China, is a mature medical system which describe and adjust-control the state of the human body from the overall level personally. The future precise treatment of cancer requires micro-level precision, and personalized holistic human status control. Thus, the introduction of the CM's system of syndrome differentiation and treatment, making micro-level precision treatment gradually extended to personalized holistic human status control of the patient, will be the highest attainment of precision medicine in the treatment of cancer.AimsFrom the perspectives of theoretical and clinical practice, in personalised cancer treatment, it’s need target therapy for cancer lesions as well as proliferation control. It’s also requiring symptomatic treatment for different dysfunctions and complications in organs and systems based on holistic status. By achieving an organic integration of the micro-level precision of treating local lesions and the holistic personalised recuperation, the highest realm of precision treatment of cancer can be achieved

Personalized Estimate of Chemotherapy-Induced Nausea and Vomiting: Development and External Validation of a Nomogram in Cancer Patients Receiving Highly/Moderately Emetogenic Chemotherapy.

Chemotherapy-induced nausea and vomiting (CINV) is presented in over 30% of cancer patients receiving highly/moderately emetogenic chemotherapy (HEC/MEC). The currently recommended antiemetic therapy is merely based on the emetogenic level of chemotherapy, regardless of patient's individual risk factors. It is, therefore, critical to develop an approach for personalized management of CINV in the era of precision medicine.A number of variables were involved in the development of CINV. In the present study, we pooled the data from 2 multi-institutional investigations of CINV due to HEC/MEC treatment in Asian countries. Demographic and clinical variables of 881 patients were prospectively collected as defined previously, and 862 of them had full documentation of variables of interest. The data of 548 patients from Chinese institutions were used to identify variables associated with CINV using multivariate logistic regression model, and then construct a personalized prediction model of nomogram; while the remaining 314 patients out of China (Singapore, South Korea, and Taiwan) entered the external validation set. C-index was used to measure the discrimination ability of the model.The predictors in the final model included sex, age, alcohol consumption, history of vomiting pregnancy, history of motion sickness, body surface area, emetogenicity of chemotherapy, and antiemetic regimens. The C-index was 0.67 (95% CI, 0.62-0.72) for the training set and 0.65 (95% CI, 0.58-0.72) for the validation set. The C-index was higher than that of any single predictor, including the emetogenic level of chemotherapy according to current antiemetic guidelines. Calibration curves showed good agreement between prediction and actual occurrence of CINV.This easy-to-use prediction model was based on chemotherapeutic regimens as well as patient's individual risk factors. The prediction accuracy of CINV occurrence in this nomogram was well validated by an independent data set. It could facilitate the assessment of individual risk, and thus improve the personalized management of CINV

Combined burden and functional impact tests for cancer driver discovery using DriverPower

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery

Changing the approach to anticoagulant therapy in older patients with multimorbidity using a precision medicine approach

The ageing of the world population has resulted in an increase in the number of older patients with multimorbid conditions receiving multiple therapies. This emerging clinical scenario poses new challenges, which are mostly related to the increased incidence of adverse effects. This translates into poor clinical care, reduced cost-effectiveness of drug therapies, and social isolation of multimorbid patients due to reduced autonomy. A strategy to address these emerging challenges could involve the personalization of therapies based on the clinical, molecular, and genetic characterization of multimorbid patients. Anticoagulation therapy is a feasible model to implement personalized medicine since it generally involves older multimorbid patients receiving multiple drugs. In this study, in patients with atrial fibrillation, the use of the new generation of anticoagulation therapy, i.e., direct oral anti-coagulants (DOACs), is based on a preliminary assessment of the molecular targets of DOACS and any possible drug–drug interactions. Then, the genetic polymorphism of enzymes metabolizing DOACs is studied. After DOAC prescription, its circulating levels are measured. Clinical data are being collected to assess whether this personalized approach improves the safety and efficacy profiles of anticoagulation therapy using DOACs, thereby reducing the costs of healthcare for ageing multimorbid patients

Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning

Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider disease manifestations and provides physicians with treatment solutions from similar previous cases for reference. The proposed DSS supports natural language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease classification with the help of the ontology

Report of the third Asian Prostate Cancer study meeting.

The Asian Prostate Cancer (A-CaP) study is an Asia-wide initiative that was launched in December 2015 in Tokyo, Japan, with the objective of surveying information about patients who have received a histopathological diagnosis of prostate cancer (PCa) and are undergoing treatment and clarifying distribution of staging, the actual status of treatment choices, and treatment outcomes. The study aims to clarify the clinical situation for PCa in Asia and use the outcomes for the purposes of international comparison. Following the first meeting in Tokyo in December 2015, the second A-CaP meeting was held in Seoul, Korea, in September 2016. This, the third A-CaP meeting, was held on October 14, 2017, in Chiang Mai, Thailand, with the participation of members and collaborators from 12 countries and regions. In the meeting, participating countries and regions presented the current status of data collection, and the A-CaP office presented a preliminary analysis of the registered cases received from each country and region. Participants discussed ongoing challenges relating to data input and collection, institutional, and legislative issues that may present barriers to data sharing, and the outlook for further patient registrations through to the end of the registration period in December 2018. In addition to A-CaP-specific discussions, a series of special lectures were also delivered on the situation for health insurance in the United States, the correlation between insurance coverage and PCa outcomes, and the outlook for robotic surgery in the Asia-Pacific region. Members also confirmed the principles of authorship in collaborative studies, with a view to publishing original articles based on A-CaP data in the future

Longitudinal Monitoring of SARS-CoV-2 IgM and IgG Seropositivity to Detect COVID-19.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a novel beta-coronavirus that has recently emerged as the cause of the 2019 coronavirus pandemic (COVID-19). Polymerase chain reaction (PCR) based tests are optimal and recommended for the diagnosis of an acute SARS-CoV-2 infection. Serology tests for viral antibodies provide an important tool to diagnose previous exposure to the virus. Here we evaluate the analytical performance parameters of the Diazyme SARS-CoV-2 IgM/IgG serology assays and describe the kinetics of IgM and IgG seroconversion observed in patients with PCR-confirmed COVID-19 who were admitted to our hospital.MethodsWe validated the performance of the Diazyme assay in 235 presumed SARS-CoV-2 negative subjects to determine specificity. Subsequently, we evaluated the SARS-CoV-2 IgM and IgG seroconversion of 54 PCR-confirmed COVID-19 patients and determined sensitivity of the assay at three different timeframes.ResultSensitivity and specificity for detecting seropositivity at ≥15 days following a positive SARS-CoV-2 PCR result, was 100.0% and 98.7% when assaying for the panel of IgM and IgG. The median time to seropositivity observed for a reactive IgM and IgG result from the date of a positive PCR was 5 days (IQR: 2.75-9 days) and 4 days (IQR: 2.75-6.75 days), respectively.ConclusionsOur data demonstrate that the Diazyme IgM/IgG assays are suited for the purpose of detecting SARS-CoV-2 IgG and IgM in patients with suspected SARS-CoV-2 infections. For the first time, we report longitudinal data showing the evolution of seroconversion for both IgG and IgM in a cohort of acutely ill patients in the United States. We also demonstrate a low false positive rate in patients who were presumed to be disease free