Proteomics for cancer biomarker discovery

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 51-54).Background: If we are to successfully treat cancer, we must understand the biologic underpinnings in conjunction with early diagnosis. Genome-wide expression studies have advanced the research of many cancers. Nevertheless, understanding which genes are expressed in a tumor is not equivalent to knowing which proteins are being produced. Proteomics hold great promise for careful examination of the proteins in complex biologic fluids and tissues, and it may be possible to detect disease from a patient's serum, long before it would otherwise be clinically evident. Although there have been steady advances in all the steps of a proteomic analysis, much remains to be standardized. Because of some high-profile problems with the initial analysis of ovarian cancer proteomic data, early exuberance has now been tempered and replaced by a more methodical approach to these studies. Hypothesis: My hypothesis in this thesis is that proteomics is a valuable tool in the diagnosis and study of cancer, as will be demonstrated in several steps. Methods: First, I describe the current field of proteomics, specifically as it applies to early detection of cancer and biomarker discovery.(cont.) I lay out the current state-of-the-art technologies for preparing samples and enumerating the proteins in complex fluids and tissues, giving special treatment to the main threats to validity-chance and bias. I also describe the bioinformatic tools necessary for analyzing the large amounts of data produced. Through the example of a mouse model of colorectal carcinoma, I demonstrate the steps involved in a proteomic study, from procuring samples to peptide and protein determination to bioinformatic analysis. Finally, I discuss these findings in light of the proteomic considerations discussed earlier. Results: From this work, I discovered that proteomic profiling can describe the proteins in serum from mice both with and without colon cancer. Furthermore, I developed a naive Bayes classifier that could distinguish between the serum of mice with colorectal carcinoma and their normal litter-mates. Contributions: Through this work, I have contributed the following. I described the field of proteomics with special emphasis on cancer biomarker discovery and early detection. I enumerated the challenges and pitfalls to developing early detection schemes for cancer based on high-dimensional proteomic analyses.(cont.) I described a set of experiments on mice harboring a gene mutation that predisposes them to colorectal carcinoma. I detailed the bioinformatic analysis of this data, including the development of a naive Bayes classifier to differentiate the cancerous state from the normal state. Finally, I discussed the caveats of the current work, in reference to the initial discussion on the challenges and pitfalls of early detection schemes and cancer biomarker discovery.by Samuel Louis Volchenboum.S.M

An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good

Objective: The Pediatric Cancer Data Commons (PCDC)-a project of Data for the Common Good-houses clinical pediatric oncology data and utilizes the open-source Gen3 platform. To meet the needs of end users, the PCDC development team expanded the out-of-box functionality and developed additional custom features that should be useful to any group developing similar data commons. Materials and methods: Modifications of the PCDC data portal software were implemented to facilitate desired functionality. Results: Newly developed functionality includes updates to authorization methods, expansion of filtering capabilities, and addition of data analysis functions. Discussion: We describe the process by which custom functionalities were developed. Features are open source and available to be implemented and adapted to suit needs of data portals that utilize the Gen3 platform. Conclusion: Data portals are indispensable tools for facilitating data sharing. Open-source infrastructure facilitates a modular and collaborative approach for meeting needs of end users and stakeholders.</p

The Global academic research organization network: Data sharing to cure diseases and enable learning health systems.

Introduction:Global data sharing is essential. This is the premise of the Academic Research Organization (ARO) Council, which was initiated in Japan in 2013 and has since been expanding throughout Asia and into Europe and the United States. The volume of data is growing exponentially, providing not only challenges but also the clear opportunity to understand and treat diseases in ways not previously considered. Harnessing the knowledge within the data in a successful way can provide researchers and clinicians with new ideas for therapies while avoiding repeats of failed experiments. This knowledge transfer from research into clinical care is at the heart of a learning health system. Methods:The ARO Council wishes to form a worldwide complementary system for the benefit of all patients and investigators, catalyzing more efficient and innovative medical research processes. Thus, they have organized Global ARO Network Workshops to bring interested parties together, focusing on the aspects necessary to make such a global effort successful. One such workshop was held in Austin, Texas, in November 2017. Representatives from Japan, Taiwan, Singapore, Europe, and the United States reported on their efforts to encourage data sharing and to use research to inform care through learning health systems. Results:This experience report summarizes presentations and discussions at the Global ARO Network Workshop held in November 2017 in Austin, TX, with representatives from Japan, Korea, Singapore, Taiwan, Europe, and the United States. Themes and recommendations to progress their efforts are explored. Standardization and harmonization are at the heart of these discussions to enable data sharing. In addition, the transformation of clinical research processes through disruptive innovation, while ensuring integrity and ethics, will be key to achieving the ARO Council goal to overcome diseases such that people not only live longer but also are healthier and happier as they age. Conclusions:The achievement of global learning health systems will require further exploration, consensus-building, funding aligned with incentives for data sharing, standardization, harmonization, and actions that support global interests for the benefit of patients

Development of an open-source, flexible framework for complex inter-institutional disparate data sharing and collaboration

Clinical information, “-omic” datasets, and tissue samples are difficult to harmonize and manage for data mining. We have developed a platform for storing clinical research data while providing access to associated data from other information stores. Data on 34 metrics from 11,000 neuroblastoma patients were instantiated into a database. The Django web framework was used to create a model for rapid development of tools and views with a front-end interface for generating complex queries. Working with Nationwide Children’s Hospital, we can now consume their tissue inventory data through an API. The end-user sees the number of patients who both match their search and have tissue available. Since initial implementation, the current tasks revolve around developing a governance structure and the necessary data use agreements. Efforts now are to (1) update the data with 5000 more patients, and (2) link to genomic data stores, facilitating disparate data acquisition for research studies

Advancing clinical and translational research in germ cell tumours (GCT): recommendations from the Malignant Germ Cell International Consortium

YesGerm cell tumours (GCTs) are a heterogeneous group of rare neoplasms that present in different anatomical sites and across a wide spectrum of patient ages from birth through to adulthood. Once these strata are applied, cohort numbers become modest, hindering inferences regarding management and therapeutic advances. Moreover, patients with GCTs are treated by different medical professionals including paediatric oncologists, neuro-oncologists, medical oncologists, neurosurgeons, gynaecological oncologists, surgeons, and urologists. Silos of care have thus formed, further hampering knowledge dissemination between specialists. Dedicated biobank specimen collection is therefore critical to foster continuous growth in our understanding of similarities and differences by age, gender, and site, particularly for rare cancers such as GCTs. Here, the Malignant Germ Cell International Consortium provides a framework to create a sustainable, global research infrastructure that facilitates acquisition of tissue and liquid biopsies together with matched clinical data sets that reflect the diversity of GCTs. Such an effort would create an invaluable repository of clinical and biological data which can underpin international collaborations that span professional boundaries, translate into clinical practice, and ultimately impact patient outcomes.ALF, JFA, and MJM declare funding from St Baldrick’s Foundation; grant reference number 358099

Computer‐assisted Curie scoring for metaiodobenzylguanidine (MIBG) scans in patients with neuroblastoma

BackgroundRadiolabeled metaiodobenzylguanidine (MIBG) is sensitive and specific for detecting neuroblastoma. The extent of MIBG‐avid disease is assessed using Curie scores. Although Curie scoring is prognostic in patients with high‐risk neuroblastoma, there is no standardized method to assess the response of specific sites of disease over time. The goal of this study was to develop approaches for Curie scoring to facilitate the calculation of scores and comparison of specific sites on serial scans.ProcedureWe designed three semiautomated methods for determining Curie scores, each with increasing degrees of computer assistance. Method A was based on visual assessment and tallying of MIBG‐avid lesions. For method B, scores were tabulated from a schematic that associated anatomic regions to MIBG‐positive lesions. For method C, an anatomic mesh was used to mark MIBG‐positive lesions with automatic assignment and tallying of scores. Five imaging physicians experienced in MIBG interpretation scored 38 scans using each method, and the feasibility and utility of the methods were assessed using surveys.ResultsThere was good reliability between methods and observers. The user‐interface methods required 57 to 110 seconds longer than the visual method. Imaging physicians indicated that it was useful that methods B and C enabled tracking of lesions. Imaging physicians preferred method B to method C because of its efficiency.ConclusionsWe demonstrate the feasibility of semiautomated approaches for Curie score calculation. Although more time was needed for strategies B and C, the ability to track and document individual MIBG‐positive lesions over time is a strength of these methods.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146464/1/pbc27417.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146464/2/pbc27417_am.pd

Mapping Pediatric Oncology Clinical Trial Collaborative Groups on the Global Stage

The global pediatric oncology clinical research landscape, particularly in Central and South America, Africa, and Asia, which bear the highest burden of global childhood cancer cases, is less characterized in the literature. Review of how existing pediatric cancer clinical trial groups internationally have been formed and how their research goals have been pursued is critical for building global collaborative research and data-sharing efforts, in line with the WHO Global Initiative for Childhood Cancer. METHODS: A narrative literature review of collaborative groups performing pediatric cancer clinical research in each continent was conducted. An inventory of research groups was assembled and reviewed by current pediatric cancer regional and continental leaders. Each group was narratively described with identification of common structural and research themes among consortia. RESULTS: There is wide variability in the structure, history, and goals of pediatric cancer clinical trial collaborative groups internationally. Several continental regions have longstanding endogenously-formed clinical trial groups that have developed and published numerous adapted treatment regimens to improve outcomes, whereas other regions have consortia focused on developing foundational database registry infrastructure supported by large multinational organizations or twinning relationships. CONCLUSION: There cannot be a one-size-fits-all approach to increasing collaboration between international pediatric cancer clinical trial groups, as this requires a nuanced understanding of local stakeholders and resources necessary to form partnerships. Needs assessments, performed either by local consortia or in conjunction with international partners, have generated productive clinical trial infrastructure. To achieve the goals of the Global Initiative for Childhood Cancer, global partnerships must be sufficiently granular to account for the distinct needs of each collaborating group and should incorporate grassroots approaches, robust twinning relationships, and implementation science