Acquire: an open-source comprehensive cancer biobanking system

In Vivo Modeling of Human Breast Cancer Using Cell Line and Patient-Derived Xenografts

Historically, human breast cancer has been modeled largely in vitro using long-established cell lines primarily in two-dimensional culture, but also in three-dimensional cultures of varying cellular and molecular complexities. A subset of cell line models has also been used in vivo as cell line-derived xenografts (CDX). While outstanding for conducting detailed molecular analysis of regulatory mechanisms that may function in vivo, results of drug response studies using long-established cell lines have largely failed to translate clinically. In an attempt to address this shortcoming, many laboratories have succeeded in developing clinically annotated patient-derived xenograft (PDX) models of human cancers, including breast, in a variety of host systems. While immunocompromised mice are the predominant host, the immunocompromised rat and pig, zebrafish, as well as the chicken egg chorioallantoic membrane (CAM) have also emerged as potential host platforms to help address perceived shortcomings of immunocompromised mice. With any modeling platform, the two main issues to be resolved are criteria for "credentialing" the models as valid models to represent human cancer, and utility with respect to the ability to generate clinically relevant translational research data. Such data are beginning to emerge, particularly with the activities of PDX consortia such as the NCI PDXNet Program, EuroPDX, and the International Breast Cancer Consortium, as well as a host of pharmaceutical companies and contract research organizations (CRO). This review focuses primarily on these important aspects of PDX-related research, with a focus on breast cancer.

Role of Biobanks for Cancer Research and Precision Medicine in Hepatocellular Carcinoma

INTRODUCTION

Hepatocellular carcinoma (HCC) is a highly complex and deadly cancer. There is an urgent need for new and effective treatment modalities. Since the primary goal in the management of cancer is to cure and improve survival, personalized therapy can increase survival, reduce mortality rates, and improve quality of life. Biobanks hold potential in leading to breakthroughs in biomedical research and precision medicine (PM). They serve as a biorepository, collecting, processing, storing, and supplying specimens and relevant data for basic, translational, and clinical research.

OBJECTIVE

We aimed to highlight the fundamental role of biobanks, harboring high quality, sustainable collections of patient samples in adequate size and variability, for developing diagnostic, prognostic, and predictive biomarkers to develop and PM approaches in the management of HCC.

METHOD

We obtained information from previously published articles and BBMRI directory.

RESULTS AND CONCLUSION

Biobanking of high-quality biospecimens along with patient clinical information provides a fundamental scientific infrastructure for basic, translational, and clinical research. Biobanks that control and eliminate pre-analytical variability of biospecimens, provide a platform to identify reliable biomarkers for the application of PM. We believe, establishing HCC biobanks will empower to underpin molecular mechanisms of HCC and generate strategies for PM. Thus, first, we will review current therapy approaches in HCC care. Then, we will summarize challenges in HCC management. Lastly, we will focus on the best practices for establishing HCC biobanking to support research, translational medicine in the light of new experimental research conducted with the aim of delivering PM for HCC patients.

Informatics Management of Tumor Specimens in the Era of Big Data: Challenges and Solutions

Biomedical data bear the potential to facilitate personalized diagnosis and precision treatment. In the era of Big Data, high-quality annotation of human specimens has become the primary mission of biobankers, especially for tumor biobanks with large amounts of "omics" and clinical data. However, the lack of agreed-upon standardization and the gap among heterogeneous databases make information application and communication a major challenge. International efforts are underway to develop national projects on informatics management. The aim of this review is to provide references in specimen annotation to regulate and take full advantage of biological and biomedical information. First, critical data categories that are vital for specimen applications, including sample attributes, clinical data, preanalytical variations, and analytical records, are systematically listed for subsequent data mining. Second, current standards and guidelines related to biospecimen information are reviewed, and proper standards for tumor biobanks are recommended. In particular, commonly-used approaches and functionalities of data management are summarized and discussed. This review highlights the importance of informatics management of tumor specimens, defines critical data types, recommends data standards, and presents the methodologies of data harmonization for biobankers to reach high quality annotation of biospecimens.

Patient-Derived Xenograft Models in Breast Cancer Research

Patient-derived xenograft (PDX) model can be used as a platform to study the individual patient's sensitivity to targeted agents as well as its ability to guide our understanding in various aspects of tumor biology including the tumor's clonal evolution and interaction with microenvironment. In this chapter, we review the history of PDX models in various tumor types. Additionally, we highlight the key studies that suggested potential value of PDX models in cancer treatment. Specifically, we will briefly introduce several studies on the issue of PDX models for precision medicine. In latter part of this chapter, we focus on the studies that used PDX models to investigate the molecular biology of breast cancer that underlies the process of drug resistance and tumor metastasis. Also, we will address our own experience in developing PDX models using breast cancer tissues from Korean breast cancer patients.

Simultaneous Evaluation of the Preservative Effect of RNAlater on Different Tissues by Biomolecular and Histological Analysis

A major concern in biomedical research is the quality of biological samples. RNAlater is a stabilizer, which was originally developed for RNA preservation in fresh tissues and is important for collection and transportation. However, this reagent lacks a comprehensive and systematic evaluation of its preservative effect on different mammalian tissues under consistent experimental conditions. In this study, we collected liver, kidney, testis, brain, and colon tissues from mice and divided the samples into the following respective groups: fresh, RNAlater preserved, and liquid nitrogen snap frozen. Biomolecules (RNA, DNA, and protein) were extracted from each tissue in each group, and samples were formalin fixed and paraffin embedded for quality assessment. Our results revealed that high-quality (yield, purity, and integrity) nucleic acids could be extracted from all samples. Gene expression determined by quantitative real-time polymerase chain reaction exhibited no major difference among the three groups. Notably, we observed significant protein degradation in brain tissue preserved by RNAlater compared with fresh and snap-frozen tissue. Protein expression of the other four tissues was similar among the three groups. Hematoxylin and eosin staining of all tissue types indicated no apparent difference among the three groups. We concluded that high-quality nucleic acids can be obtained and tissue morphology conserved when tissues are preserved with RNAlater. However, there are tissue-specific differences in protein preservation when using RNAlater, which should be evaluated before extensive storage.

Design and implementation of a mobile system for lung cancer patient follow-up in China and initial report of the ongoing patient registry

Introduction

Management of lung cancer remains a challenge. Although clinical and biological patient data are crucial for cancer research, these data may be missing from registries and clinical trials. Biobanks provide a source of high-quality biological material for clinical research; however, linking these samples to the corresponding patient and clinical data is technically challenging. We describe the mobile Lung Cancer Care system (mLCCare), a novel tool which integrates biological and clinical patient data into a single resource.

Methods

mLCCare was developed as a mobile device application (app) and an internet website. Data storage is hosted on cloud servers, with the mobile app and website acting as a front-end to the system. mLCCare also facilitates communication with patients to remind them to take their medication and attend follow-up appointments.

Results

Between January 2014 and October 2015, 5,080 patients with lung cancer have been registered with mLCCare. Data validation ensures all the patient information is of consistently high-quality. Patient cohorts can be constructed via user-specified criteria and data exported for statistical analysis by authorized investigators and collaborators. mLCCare forms the basis of establishing an ongoing lung cancer registry and could form the basis of a high-quality multisite patient registry. Integration of mLCCare with SMS messaging and WeChat functionality facilitates communication between physicians and patients.

Conclusion

It is hoped that mLCCare will prove to be a powerful and widely used tool that will enhance both research and clinical practice.

Baobab Laboratory Information Management System: Development of an Open-Source Laboratory Information Management System for Biobanking

A laboratory information management system (LIMS) is central to the informatics infrastructure that underlies biobanking activities. To date, a wide range of commercial and open-source LIMSs are available and the decision to opt for one LIMS over another is often influenced by the needs of the biobank clients and researchers, as well as available financial resources. The Baobab LIMS was developed by customizing the Bika LIMS software (www.bikalims.org) to meet the requirements of biobanking best practices. The need to implement biobank standard operation procedures as well as stimulate the use of standards for biobank data representation motivated the implementation of Baobab LIMS, an open-source LIMS for Biobanking. Baobab LIMS comprises modules for biospecimen kit assembly, shipping of biospecimen kits, storage management, analysis requests, reporting, and invoicing. The Baobab LIMS is based on the Plone web-content management framework. All the system requirements for Plone are applicable to Baobab LIMS, including the need for a server with at least 8 GB RAM and 120 GB hard disk space. Baobab LIMS is a server–client-based system, whereby the end user is able to access the system securely through the internet on a standard web browser, thereby eliminating the need for standalone installations on all machines.

Ex vivo tumor culture systems for functional drug testing and therapy response prediction

Optimal patient stratification is of utmost importance in the era of personalized medicine. Prediction of individual treatment responses by functional ex vivo assays requires model systems derived from viable tumor samples, which should closely resemble in vivo tumor characteristics and microenvironment. This review discusses a broad spectrum of model systems, ranging from classic 2D monolayer culture techniques to more experimental ‘cancer-on-chip’ procedures. We mainly focus on organotypic tumor slices that take tumor heterogeneity and tumor–stromal interactions into account. These 3D model systems can be exploited for patient selection as well as for fundamental research. Selection of the right model system for each specific research endeavor is crucial and requires careful balancing of the pros and cons of each technology.

Selection of the right therapy for individual cancer patients is very important with the expanding number of possible treatments. How tumors respond to a therapy can be tested by treating a sample from the tumor outside the body. Various culture methods can be used to maintain this tumor sample. Each of these model systems has its own benefits and disadvantages. In this review, we discuss the advantages and drawbacks of the available model systems and how they can be used to guide personalized medicine.

An open access pilot freely sharing cancer genomic data from participants in Texas

Genomic data sharing in cancer has been restricted to aggregate or controlled-access initiatives to protect the privacy of research participants. By limiting access to these data, it has been argued that the autonomy of individuals who decide to participate in data sharing efforts has been superseded and the utility of the data as research and educational tools reduced. In a pilot Open Access (OA) project from the CPRIT-funded Texas Cancer Research Biobank, many Texas cancer patients were willing to openly share genomic data from tumor and normal matched pair specimens. For the first time, genetic data from 7 human cancer cases with matched normal are freely available without requirement for data use agreements nor any major restriction except that end users cannot attempt to re-identify the participants (http://txcrb.org/open.html).

An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients' tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.