Biospecimen Digital Twins: Moving from a "High Quality" to a "Fit-for-Purpose" Concept in the Era of Omics Sciences.

The growing demand for personalized medicine we are currently witnessing has given rise to more in-depth research in the field of biomarker discovery and, thus, in biological banks that hold the ability to process, collect, store, and distribute "high-quality" biological specimens. However, the notion of "specimen quality" is subject to change with technological advancements. In this perspective, we propose that the notion of sample quality should shift from a broad definition of "high-quality" to a "fit-for-purpose" concept more suitable for precision medicine studies. Digital twins are a digital replica of real entities. These are largely adopted in any digitalized domain and are currently finding applications in biomedicine. The adoption of digital twins for biosamples, proposed in this paper, can provide prompt information about the whole lifecycle of the physical twin (i.e., the biosample) and substantially extend the possible matching criteria between the available samples and the researchers' and physicians' requests. This fine-tuning matching could greatly contribute to improving the "fit-for-purpose" quality, not only for studies based on current needs, but also to improve the identification of the best available samples in future situations, determined by the evolution of technologies and biosciences. Assuming and exploiting a data-science view in our biobank perspective, the more (accurate) data there are available, the more information can be extrapolated from them, the more opportunities there are for matching future, currently unknown, needs. This should be a mandatory principle that the 'time machines' called biobanks should follow.

Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients.

OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended model (Khorana score). DESIGN: Multiple kernel learning (MKL) based on support vector machines and random optimization (RO) models were used to produce VTE risk predictors (referred to as machine learning [ML]-RO) yielding the best classification performance over a training (3-fold cross-validation) and testing set. RESULTS: Attributes of the patient data set ( n = 1179) were clustered into 9 groups according to clinical significance. Our analysis produced 6 ML-RO models in the training set, which yielded better likelihood ratios (LRs) than baseline models. Of interest, the most significant LRs were observed in 2 ML-RO approaches not including the Khorana score (ML-RO-2: positive likelihood ratio [+LR] = 1.68, negative likelihood ratio [-LR] = 0.24; ML-RO-3: +LR = 1.64, -LR = 0.37). The enhanced performance of ML-RO approaches over the Khorana score was further confirmed by the analysis of the areas under the Precision-Recall curve (AUCPR), and the approaches were superior in the ML-RO approaches (best performances: ML-RO-2: AUCPR = 0.212; ML-RO-3-K: AUCPR = 0.146) compared with the Khorana score (AUCPR = 0.096). Of interest, the best-fitting model was ML-RO-2, in which blood lipids and body mass index/performance status retained the strongest weights, with a weaker association with tumor site/stage and drugs. CONCLUSIONS: Although the monocentric validation of the presented predictors might represent a limitation, these results demonstrate that a model based on MKL and RO may represent a novel methodological approach to derive VTE risk classifiers. Moreover, this study highlights the advantages of optimizing the relative importance of groups of clinical attributes in the selection of VTE risk predictors.

Ensuring Sample Quality for Biomarker Discovery Studies - Use of ICT Tools to Trace Biosample Life-cycle.

The growing demand of personalized medicine marked the transition from an empirical medicine to a molecular one, aimed at predicting safer and more effective medical treatment for every patient, while minimizing adverse effects. This passage has emphasized the importance of biomarker discovery studies, and has led sample availability to assume a crucial role in biomedical research. Accordingly, a great interest in Biological Bank science has grown concomitantly. In biobanks, biological material and its accompanying data are collected, handled and stored in accordance with standard operating procedures (SOPs) and existing legislation. Sample quality is ensured by adherence to SOPs and sample whole life-cycle can be recorded by innovative tracking systems employing information technology (IT) tools for monitoring storage conditions and characterization of vast amount of data. All the above will ensure proper sample exchangeability among research facilities and will represent the starting point of all future personalized medicine-based clinical trials.

SPRECware: software tools for Standard PREanalytical Code (SPREC) labeling - effective exchange and search of stored biospecimens.

Biobanks provide stored material to basic, translational, and epidemiological research and this material should be transferred without institute-dependent intrinsic bias. The ISBER Biospecimen Science Working Group has released a "Standard PREanalytical Code" (SPREC), which is a proposal for a standard coding of the preanalytical options that have been adopted in order to track and make explicit the preanalytical variations in the collection, preparation, and storage of specimens. In this paper we address 2 issues arising in any biobank or biolaboratory aiming at adopting SPREC: (i) reducing the burden required to adopt this standard coding, and (ii) maximize the immediate benefits of this adoption by providing a free, dedicated software tool. We propose SPRECware, a vision encompassing tools and solutions for the best exploitation of SPREC based on information technology (www.sprecware.org). As a first step, we make available SPRECbase, a software tool useful for generating, storing, managing, and exchanging SPREC-related information associated to specimens. Adopting SPREC is useful both for internal purposes (such as finding the samples having some given preanalytical features), and for exchanging the preanalytical information associated to biological samples between Laboratory Information Systems. In case of a common adoption of this coding, it would be easy to find out whether and where, among the participating Biological Resource Centers, the specimens for a given study are available in order to carry out a planned experiment.

Standard preanalytical coding for biospecimens: review and implementation of the Sample PREanalytical Code (SPREC).

The first version of the Standard PREanalytical Code (SPREC) was developed in 2009 by the International Society for Biological and Environmental Repositories (ISBER) Biospecimen Science Working Group to facilitate documentation and communication of the most important preanalytical quality parameters of different types of biospecimens used for research. This same Working Group has now updated the SPREC to version 2.0, presented here, so that it contains more options to allow for recent technological developments. Existing elements have been fine tuned. An interface to the Biospecimen Reporting for Improved Study Quality (BRISQ) has been defined, and informatics solutions for SPREC implementation have been developed. A glossary with SPREC-related definitions has also been added.

RFID as a new ICT tool to monitor specimen life cycle and quality control in a biobank.

BACKGROUND: Biospecimen quality is crucial for clinical and translational research and its loss is one of the main obstacles to experimental activities. Beside the quality of samples, preanalytical variations render the results derived from specimens of different biobanks or even within the same biobank incomparable. Specimens collected along the years should be managed with a heterogeneous life cycle. Hence, we propose to collect detailed data concerning the whole life cycle of stored samples employing radio-frequency identification (RFID) technology.? METHODS: We describe the processing chain of blood biosamples that is operative at the biobank of IRCSS San Raffaele, Rome, Italy (BioBIM). We focus on the problem of tracing the stages following automated preanalytical processing: we collected the time stamps of all events that could affect the biological quality of the specimens by means of RFID tags and readers.? RESULTS: We developed a pilot study on a fragment of the life cycle, namely the storage between the end of the preanalytics and the beginning of the analytics, which is usually not traced by automated tools because it typically includes manual handling. By adopting RFID devices we identified the possible critical time delays. At 1, 3 and 6 months RFID-tagged specimens cryopreserved at -80°C were successfully read.? CONCLUSIONS: We were able to record detailed information about the storage phases and a fully documented specimen life cycle. This will allow us to promote and tune up the best practices in biobanking because i) it will be possible to classify sample features with a sharper resolution, which allows future utilization of stored material; ii) cost-effective policies can be adopted in processing, storing and selecting specimens; iii) after using each aliquot, we can study the life cycle of the specimen with a possible feedback on the procedures.

Standard preanalytical coding for biospecimens: defining the sample PREanalytical code.

BACKGROUND: Management and traceability of biospecimen preanalytical variations are necessary to provide effective and efficient interconnectivity and interoperability between Biobanks. METHODS: Therefore, the International Society for Biological and Environmental Repositories Biospecimen Science Working Group developed a "Standard PREanalytical Code" (SPREC) that identifies the main preanalytical factors of clinical fluid and solid biospecimens and their simple derivatives. RESULTS: The SPREC is easy to implement and can be integrated into Biobank quality management systems and databases. It can also be extended to nonhuman biorepository areas. Its flexibility allows integration of new novel technological developments in future versions. SPREC version 01 is presented in this article. CONCLUSIONS AND IMPACT: Implementation of the SPREC is expected to facilitate and consolidate international multicenter biomarker identification research and biospecimen research in the clinical Biobank environment.