Frontiers in Precision Medicine II: Cancer, Big Data and the Public

Precision medicine is being developed within a complex landscape of public policy, science, economics, law, and regulation. In these and other policy areas, the goal of developing individually-tailored therapies poses novel challenges for health care research, delivery and policy. In this symposium, a range of experts in genetics, medicine, bioinformatics, intellectual property, health economics and bioethics identified and discussed many of the pressing questions raised by the development and practice of precision medicine. These and other issues will need to be taken into account as precision medicine moves ahead and becomes the standard of medical practice and care in the United States and around the world

Large-scale 3-dimensional quantitative imaging of tissues: state-of-the-art and translational implications

Recent developments in automated optical sectioning microscope systems have enabled researchers to conduct high resolution, three-dimensional (3D) microscopy at the scale of millimeters in various types of tissues. This powerful technology allows the exploration of tissues at an unprecedented level of detail, while preserving the spatial context. By doing so, such technology will also enable researchers to explore cellular and molecular signatures within tissue and correlate with disease course. This will allow an improved understanding of pathophysiology and facilitate a precision medicine approach to assess the response to treatment. The ability to perform large-scale imaging in 3D cannot be realized without the widespread availability of accessible quantitative analysis. In this review, we will outline recent advances in large-scale 3D imaging and discuss the available methodologies to perform meaningful analysis and potential applications in translational research

Computational pathology in ovarian cancer

Histopathologic evaluations of tissue sections are key to diagnosing and managing ovarian cancer. Pathologists empirically assess and integrate visual information, such as cellular density, nuclear atypia, mitotic figures, architectural growth patterns, and higher-order patterns, to determine the tumor type and grade, which guides oncologists in selecting appropriate treatment options. Latent data embedded in pathology slides can be extracted using computational imaging. Computers can analyze digital slide images to simultaneously quantify thousands of features, some of which are visible with a manual microscope, such as nuclear size and shape, while others, such as entropy, eccentricity, and fractal dimensions, are quantitatively beyond the grasp of the human mind. Applications of artificial intelligence and machine learning tools to interpret digital image data provide new opportunities to explore and quantify the spatial organization of tissues, cells, and subcellular structures. In comparison to genomic, epigenomic, transcriptomic, and proteomic patterns, morphologic and spatial patterns are expected to be more informative as quantitative biomarkers of complex and dynamic tumor biology. As computational pathology is not limited to visual data, nuanced subvisual alterations that occur in the seemingly “normal” pre-cancer microenvironment could facilitate research in early cancer detection and prevention. Currently, efforts to maximize the utility of computational pathology are focused on integrating image data with other -omics platforms that lack spatial information, thereby providing a new way to relate the molecular, spatial, and microenvironmental characteristics of cancer. Despite a dire need for improvements in ovarian cancer prevention, early detection, and treatment, the ovarian cancer field has lagged behind other cancers in the application of computational pathology. The intent of this review is to encourage ovarian cancer research teams to apply existing and/or develop additional tools in computational pathology for ovarian cancer and actively contribute to advancing this important field

Development and application of methodologies and infrastructures for cancer genome analysis within Personalized Medicine

[eng] Next-generation sequencing (NGS) has revolutionized biomedical sciences, especially in the area of cancer. It has nourished genomic research with extensive collections of sequenced genomes that are investigated to untangle the molecular bases of disease, as well as to identify potential targets for the design of new treatments. To exploit all this information, several initiatives have emerged worldwide, among which the Pan-Cancer project of the ICGC (International Cancer Genome Consortium) stands out. This project has jointly analyzed thousands of tumor genomes of different cancer types in order to elucidate the molecular bases of the origin and progression of cancer. To accomplish this task, new emerging technologies, including virtualization systems such as virtual machines or software containers, were used and had to be adapted to various computing centers. The portability of this system to the supercomputing infrastructure of the BSC (Barcelona Supercomputing Center) has been carried out during the first phase of the thesis. In parallel, other projects promote the application of genomics discoveries into the clinics. This is the case of MedPerCan, a national initiative to design a pilot project for the implementation of personalized medicine in oncology in Catalonia. In this context, we have centered our efforts on the methodological side, focusing on the detection and characterization of somatic variants in tumors. This step is a challenging action, due to the heterogeneity of the different methods, and an essential part, as it lays at the basis of all downstream analyses. On top of the methodological section of the thesis, we got into the biological interpretation of the results to study the evolution of chronic lymphocytic leukemia (CLL) in a close collaboration with the group of Dr. Elías Campo from the Hospital Clínic/IDIBAPS. In the first study, we have focused on the Richter transformation (RT), a transformation of CLL into a high-grade lymphoma that leads to a very poor prognosis and with unmet clinical needs. We found that RT has greater genomic, epigenomic and transcriptomic complexity than CLL. Its genome may reflect the imprint of therapies that the patients received prior to RT, indicating the presence of cells exposed to these mutagenic treatments which later expand giving rise to the clinical manifestation of the disease. Multiple NGS- based techniques, including whole-genome sequencing and single-cell DNA and RNA sequencing, among others, confirmed the pre-existence of cells with the RT characteristics years before their manifestation, up to the time of CLL diagnosis. The transcriptomic profile of RT is remarkably different from that of CLL. Of particular importance is the overexpression of the OXPHOS pathway, which could be used as a therapeutic vulnerability. Finally, in a second study, the analysis of a case of CLL in a young adult, based on whole genome and single-cell sequencing at different times of the disease, revealed that the founder clone of CLL did not present any somatic driver mutations and was characterized by germline variants in ATM, suggesting its role in the origin of the disease, and highlighting the possible contribution of germline variants or other non-genetic mechanisms in the initiation of CLL

Development and application of methodologies and infrastructures for cancer genome analysis within Personalized Medicine

Programa de Doctorat en Biomedicina / Tesi realitzada al Barcelona Supercomputing Cener (BSC)[eng] Next-generation sequencing (NGS) has revolutionized biomedical sciences, especially in the area of cancer. It has nourished genomic research with extensive collections of sequenced genomes that are investigated to untangle the molecular bases of disease, as well as to identify potential targets for the design of new treatments. To exploit all this information, several initiatives have emerged worldwide, among which the Pan-Cancer project of the ICGC (International Cancer Genome Consortium) stands out. This project has jointly analyzed thousands of tumor genomes of different cancer types in order to elucidate the molecular bases of the origin and progression of cancer. To accomplish this task, new emerging technologies, including virtualization systems such as virtual machines or software containers, were used and had to be adapted to various computing centers. The portability of this system to the supercomputing infrastructure of the BSC (Barcelona Supercomputing Center) has been carried out during the first phase of the thesis. In parallel, other projects promote the application of genomics discoveries into the clinics. This is the case of MedPerCan, a national initiative to design a pilot project for the implementation of personalized medicine in oncology in Catalonia. In this context, we have centered our efforts on the methodological side, focusing on the detection and characterization of somatic variants in tumors. This step is a challenging action, due to the heterogeneity of the different methods, and an essential part, as it lays at the basis of all downstream analyses. On top of the methodological section of the thesis, we got into the biological interpretation of the results to study the evolution of chronic lymphocytic leukemia (CLL) in a close collaboration with the group of Dr. Elías Campo from the Hospital Clínic/IDIBAPS. In the first study, we have focused on the Richter transformation (RT), a transformation of CLL into a high-grade lymphoma that leads to a very poor prognosis and with unmet clinical needs. We found that RT has greater genomic, epigenomic and transcriptomic complexity than CLL. Its genome may reflect the imprint of therapies that the patients received prior to RT, indicating the presence of cells exposed to these mutagenic treatments which later expand giving rise to the clinical manifestation of the disease. Multiple NGS- based techniques, including whole-genome sequencing and single-cell DNA and RNA sequencing, among others, confirmed the pre-existence of cells with the RT characteristics years before their manifestation, up to the time of CLL diagnosis. The transcriptomic profile of RT is remarkably different from that of CLL. Of particular importance is the overexpression of the OXPHOS pathway, which could be used as a therapeutic vulnerability. Finally, in a second study, the analysis of a case of CLL in a young adult, based on whole genome and single-cell sequencing at different times of the disease, revealed that the founder clone of CLL did not present any somatic driver mutations and was characterized by germline variants in ATM, suggesting its role in the origin of the disease, and highlighting the possible contribution of germline variants or other non-genetic mechanisms in the initiation of CLL

Advancing Precision Medicine through clinical grade whole genome sequencing, return of results and deep brain stimulation

CSHL Meetings and Course: Precision Medicine: Personal Genomes & Pharmacogenomics, November 13th - November 16th 201

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes

Baobab LIMS: An open source biobank laboratory information management system for resource-limited settings

Philosophiae Doctor - PhDA 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 LIMS are available. 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. However, to find a LIMS that incorporates all possible requirements of a biobank may often be a complicated endeavour. The need to implement biobank standard operation procedures as well as stimulate the use of standards for biobank data representation motivated the development 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. Baobab LIMS is based on the Plone web-content management framework, 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. The Baobab LIMS components were tested and evaluated in three human biobanks. The testing of the LIMS modules aided in the mapping of the biobanks requirements to the LIMS functionalities, and furthermore, it helped to reveal new user suggestions, such as the enhancement of the online documentation. The user suggestions are demonstrated to be important for both LIMS strengthen and biobank sustainability. Ultimately, the practical LIMS evaluations showed the ability of Boabab LIMS to be used in the management of human biobanks operations of relatively different biobanking workflows