Transforming clinical data into wisdom

There is an increasing body of evidence supporting a more flexible approach in clinical data requirements for the approval of more complex biosimilar substances such as monoclonal antibodies (mAbs). The aim of this paper is to further analyse the role of quality/chemistry, manufacturing and controls (CMC) and clinical data for the conclusion on biosimilarity and the decision on marketing authorisation (MA). In the present study, we analysed the MA applications (MAAs) of all 33 mAbs and three fusion proteins evaluated by the European Medicines Agency (EMA) between July 2012 and November 2022 with special emphasis on all submitted rituximab (four products) and trastuzumab (seven products) biosimilar candidates, including withdrawn applications. For the two withdrawn applications, the comparative efficacy trials suggested biosimilarity, but the quality/CMC package was not accepted by EMA. We therefore investigated whether a negative MAA outcome could have been predicted based on the evidence generated in the quality/CMC packages, regardless of clinical trial data. For this purpose, we reviewed the respective European Public Assessment Reports (EPARs) or withdrawal assessment reports, and the first regulatory assessments for all these 36 MAAs (i.e. day 120 of the centralized procedure), which are not publicly available. During EMA review, where significant issues are identified which would preclude a marketing authorisation, these issues are raised as questions to the applicant and are classified as major objections (MO). In 67% of cases, the outcome of the quality and clinical assessment was the same, i.e. both the quality and clinical assessments either supported approval or did not support approval. In 11% of cases, MO were identified in the quality part of the submission but not in the clinical data. In 22% of cases, MO were raised on the clinical data package but not on the quality data. However, we found no instance where seemingly negative clinical data, including failed efficacy trials, led to a negative overall decision. In each instance, the failure to confirm similar clinical performance in all investigated aspects was eventually viewed as not being related to the biosimilar per se but as being due to imbalances in the trial arms, immaturity of secondary endpoint results, change in the reference product, or even chance findings. Furthermore, when performing an in-depth analysis of the quality and clinical packages of trastuzumab and rituximab biosimilars, we found that in no case were clinical trial data necessary to resolve residual uncertainties regarding the quality part. The results further support the argument that sufficient evidence for biosimilarity can be obtained from a combination of analytical and functional testing and pharmacokinetic studies which may also generate immunogenicity data. This calls into question the usefulness of comparative efficacy studies for the purposes of regulatory decision-making when approving biosimilar mAbs and fusion proteins.

Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. In this study, we aimed to develop predictive models using clinical and neuroimaging data from the multicenter Enhancing Neuro-Imaging and Genetics through Meta-Analysis (ENIGMA)-OCD consortium. Baseline clinical and resting-state functional magnetic imaging (rs-fMRI) data from 159 adult patients aged 18-60years (88 female) with OCD who received CBT at four treatment/neuroimaging sites were included. Fractional amplitude of low frequency fluctuations, regional homogeneity and atlas-based functional connectivity were computed. Clinical CBT response and remission were predicted using support vector machine and random forest classifiers on clinical data only, rs-fMRI data only, and the combination of both clinical and rs-fMRI data. The use of only clinical data yielded an area under the ROC curve (AUC) of 0.69 for predicting remission (p=0.001). Lower baseline symptom severity, younger age, an absence of cleaning obsessions, unmedicated status, and higher education had the highest model impact in predicting remission. The best predictive performance using only rs-fMRI was obtained with regional homogeneity for remission (AUC=0.59). Predicting response with rs-fMRI generally did not exceed chance level. Machine learning models based on clinical data may thus hold promise in predicting remission after CBT for OCD, but the predictive power of multicenter rs-fMRI data is limited.

BackgroundAccurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for stroke focused mostly on clinical characteristics and radiological scores available at baseline. Radiological images are composed of millions of voxels, and a lot of information can be lost when representing this information by a single value. Therefore, in this study we aimed at developing prediction models that take into account the whole imaging data combined with clinical data available at baseline.MethodsWe included 3,279 patients from the MR CLEAN Registry; a prospective, observational, multicenter registry of patients with ischemic stroke treated with EVT. We developed two approaches to combine the imaging data with the clinical data. The first approach was based on radiomics features, extracted from 70 atlas regions combined with the clinical data to train machine learning models. For the second approach, we trained 3D deep learning models using the whole images and the clinical data. Models trained with the clinical data only were compared with models trained with the combination of clinical and image data. Finally, we explored feature importance plots for the best models and identified many known variables and image features/brain regions that were relevant in the model decision process.ResultsFrom 3,279 patients included, 1,241 (37%) patients had a good functional outcome [modified Rankin Scale (mRS) ≤ 2] and 1,954 (60%) patients had good reperfusion [modified Thrombolysis in Cerebral Infarction (eTICI) ≥ 2b]. There was no significant improvement by combining the image data to the clinical data for mRS prediction [mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.81 vs. 0.80] above using the clinical data only, regardless of the approach used. Regarding predicting reperfusion, there was a significant improvement when image and clinical features were combined (mean AUC of 0.54 vs. 0.61), with the highest AUC obtained by the deep learning approach.ConclusionsThe combination of radiomics and deep learning image features with clinical data significantly improved the prediction of good reperfusion. The visualization of prediction feature importance showed both known and novel clinical and imaging features with predictive values.

Various machine learning methods have been proposed for predicting progression of patients with mild cognitive impairment (MCI) to Alzheimer's disease (AD) using neuroimaging data. Even though the vast majority of these works use the public dataset ADNI, reproducing their results is complicated because they often do not make available elements that are essential for reproducibility, such as selected participants and input data, image preprocessing and cross-validation procedures. Comparability is also an issue. Specially, the influence of different components like preprocessing, feature extraction or classification algorithms on the performance is difficult to evaluate. Finally, these studies rarely compare their results to models built from clinical data only, a critical aspect to demonstrate the utility of neuroimaging. In our previous work, 1, 2 we presented a framework for reproducible and objective classification experiments in AD, that included automatic conversion of ADNI database into the BIDS community standard, image preprocessing pipelines and machine learning evaluation. We applied this framework to perform unimodal classifications of T1 MRI and FDG-PET images. In the present paper, we extend this work to the combination of multimodal clinical and neuroimaging data. All experiments are based on standard approaches (namely SVM and random forests). In particular, we assess the added value of neuroimaging over using only clinical data. We first demonstrate that using only demographic and clinical data (gender, education level, MMSE, CDR sum of boxes, ADASCog) results in a balanced accuracy of 75% (AUC of 0.84). This performance is higher than that of standard neuroimaging-based classifiers. We then propose a simple trick to improve the performance of neuroimaging-based classifiers: training from AD patients and controls (rather than from MCI patients) improves the performance of FDG-PET classification by 5 percent points, reaching the level of the clinical classifier. Finally, combining clinical and neuroimaging data, prediction results further improved to 80% balanced accuracy and an AUC of 0.88). These prediction accuracies, obtained in a reproducible way, provide a base to develop on top of it and, to compare against, more sophisticated methods. All the code of the framework and the experiments is publicly available at https://github.com/aramis-lab/AD-ML

Improve clinical feature-based bladder cancer survival prediction models through integration with gene expression profiles and machine learning techniques

On Warfarin Use in Kidney Disease: A Therapeutic Window of Opportunity?

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.

BackgroundCancer survivors can face significant physical and psychosocial challenges; there is a need to identify and predict which survivors experience what sorts of difficulties. As highlighted in the UK National Cancer Survivorship Initiative, routine post-diagnostic collection of patient reported outcome measures (PROMs) is required; to be most informative, PROMs must be linked and analysed with patients' diagnostic and treatment information. We have designed and built a potentially cost-efficient UK-scalable electronic system for collecting PROMs via the internet, at regular post-diagnostic time-points, for linking these data with patients' clinical data in cancer registries, and for electronically managing the associated patient monitoring and communications; the electronic Patient-reported Outcomes from Cancer Survivors (ePOCS) system. This study aims to test the feasibility of the ePOCS system, by running it for 2 years in two Yorkshire NHS Trusts, and using the Northern and Yorkshire Cancer Registry and Information Service.Methods/DesignNon-metastatic breast, colorectal and prostate cancer patients (largest survivor groups), within 6 months post-diagnosis, will be recruited from hospitals in the Yorkshire Cancer Network. Participants will be asked to complete PROMS, assessing a range of health-related quality-of-life outcomes, at three time-points up to 15 months post-diagnosis, and subsequently to provide opinion on the ePOCS system via a feedback questionnaire. Feasibility will be examined primarily in terms of patient recruitment and retention rates, the representativeness of participating patients, the quantity and quality of collected PROMs data, patients' feedback, the success and reliability of the underpinning informatics, and the system running costs. If sufficient data are generated during system testing, these will be analysed to assess the health-related quality-of-life outcomes reported by patients, and to explore if and how they relate to disease, treatment and/or individual differences characteristics.DiscussionThere is currently no system in the UK for collecting PROMs online and linking these with patients' clinical data in cancer registries. If feasible, ePOCS has potential to provide an affordable UK-scalable technical platform to facilitate and support longitudinal cohort research, and improve understanding of cancer survivors' experiences. Comprehensive understanding of survivorship difficulties is vital to inform the development and provision of supportive services and interventions.

P.45 Adult SMA REACH: an integrated model to facilitate transition of data and longitudinal data collection of clinician and patient entered data

To find a more objective method of assessing which patients should be screened for a vestibular schwannoma (VS) with magnetic resonance imaging (MRI) using a deep-learning algorithm to assess clinical and audiometric data. Clinical and audiometric data were collected for 592 patients who received an audiogram between January 2015 and 2020 at Duke University Health Center with and without VS confirmed by MRI. These data were analyzed using a deep learning-based analysis to determine if the need for MRI screening could be assessed more objectively with adequate sensitivity and specificity. Patients with VS showed slightly elevated, but not statistically significant, mean thresholds compared to those without. Tinnitus, gradual hearing loss, and aural fullness were more common in patients with VS. Of these, only the presence of tinnitus was statistically significant. Several machine learning algorithms were used to incorporate and model the collected clinical and audiometric data, but none were able to distinguish ears with and without confirmed VS. When tumor size was taken into account the analysis was still unable to distinguish a difference. Using audiometric and clinical data, deep learning-based analyses failed to produce an adequately sensitive and specific model for the detection of patients with VS. This suggests that a specific pattern of audiometric asymmetry and clinical symptoms may not necessarily be predictive of the presence/absence of VS to a level that clinicians would be comfortable forgoing an MRI.

Machine Learning-Based Prediction of Survival Outcome in Lower Grade Gliomas With Combined Clinical and DNA Methylation Data

St. Louis, Missouri, sits astride the confluence of the Mississippi and Missouri Rivers, a position that made the city the “Gateway to the West.” Its most famous monument, the Gateway Arch, memorializes the millions of pioneers who traveled to the city seeking transportation to the frontier lands of the West, and who were outfitted with the tools and knowledge they needed on the journey to these new lands of opportunity. The Clinical & Translational Science Awards (CTSA) program has enabled Washington University (WU) and its regional partners to create a gateway of another sort, aimed at helping researchers toward new opportunities and discoveries in clinical and translational research. The Washington University Institute of Clinical and Translational Sciences (ICTS) was founded in September 2007 to serve as the intellectual and physical home for clinical and translational research and clinical research training at WU and its partner institutions. ICTS was founded on WU's 4 prior clinical research and training grants—GCRC, K12, K30, and T32—and on an existing university-wide strategic initiative in multidisciplinary collaborative research emphasizing genome sciences, biological imaging, and clinical investigation. The formation of the ICTS resulted in a new level of regional partnership with other academic, health care, and industrial and community partners. In its second year of existence, the ICTS is increasingly able to integrate the different strengths of its partners to reach its goals of reinventing and reinvigorating CTS research and training. In designing the ICTS, we used a conceptual model that identified important phases in the progress of a clinical or translational research project (Figure 1). For each phase, we identifed needed resources and potential barriers to completion, and have structured the ICTS in a way that attempts to provide needed resources and expertise for each phase of research from development and refinement of research ideas through translation of results to clinical practice and community implementation. Education, training, and career development are at the heart of our ICTS, and are linked to all research phases and programmatic activities. Our ICTS has 15 key Program Functions, each linked to 1 or more research phases, and each with specific goals, milestones, and evaluation criteria for various services, cores, and programs. We will highlight 2 new and important program functions below: our community-based research efforts and our programs in biomedical informatics. Phases in the evolution of clinical research projects. The 3 CCBR arms serve as a model for community participation to improve research relevant to public health. A central transformative element offour ICTS was the establishment of the Center for Community-Based Research (CCBR), which has initiated community engagement ef orts in 3 important groups: community organizations, community-based health care providers, and individuals from medically underserved populations. Each of the 3 arms is designed to solicit community input to increase the relevance of research to community needs, and to encourage partnerships between academic researchers and community groups. Our Community Advisory Board, established by the 3 arms, meets quarterly, to ensure relevance of the CCBR programs and the ICTS programs as a whole. The Community Organizations program has focused on cultivating partnerships with community organizations and conducting community needs assessments. This new model has spearheaded a series of conferences with our Regional Health Commission to address how our academic medical center can better serve the needs of our local community and improve the public health. In the fall of 2009, we will begin a series of shared conferences with community organizations highlighting successful community-based participatory research projects. These conferences will include joint presentations by academics and community partners and will strive to facilitate the formation of new partnerships. To further stimulate new community-based participatory research projects, we will soon initiate a research grants program, supported by a regional coalition of universities and health care institutions, to fund research projects that include a high degree of cooperation between community organizations and academic researchers. The program has also initiated a traveling poster presentation forum that translates data in a community relevant fashion. The Community Health Care Practitioners program seeks to expand community networks of practitioners to increase opportunities for clinical and translational (T2 and T3) research. One such effort is the Washington University Pediatric and Adolescent Ambulatory Research Consortium (WU PAARC). Currently comprised of approximately 60 community pediatricians and 5 pediatric nurse practitioners, the network provides a formal structure to develop research ef orts that identify best practices and translate research findings into practice. Community pediatricians participate in the consortium in different ways, from contributing ideas and receiving study updates to helping develop study protocols and recruit patients from their practices. Some studies are initiated by WU PAARC physicians, such as a recent study on a Telephone Asthma Program that resulted from physicians’ frustration with patients not using their asthma maintenance medications. For this randomized controlled trial, 362 families with asthmatic children were recruited from 95 community pediatric offices over 13 months. The 12-month telephone asthma coaching intervention helped parents better manage their child's asthma care and improved children's asthma control and parents’ quality-of-life scores. An upcoming study will compare 2 corticosteroid treatments for croup, a disease commonly seen in community practice but never before studied in this setting. In addition to WU PAARC-sponsored studies, community pediatricians have opportunities to participate in other studies developed by ICTS researchers including participating in study development and implementation. An example is a prevalence study for community-acquired methicillin-resistant Staph aureu s for which 1,300 children were recruited from 11 WU PAARC practices over an 8-month period. WU PAARC serves as a model for cooperative research between academic and community physicians, and these examples show how those involved have a real opportunity to improve the health of children and adolescents. As our ICTS matures, we plan to expand our practice-based research network activities to include new patient and provider populations. The CCBR has also started HealthStreet, a project linking populations that have been underrepresented in research to health information, referrals to social, medical, and psychiatric services, as well as opportunities to participate in research studies. HealthStreet utilizes Community Health Workers and a comprehensive outreach approach to establish trust between the lay and medical communities. The hub of CCBR activity is a new storefront facility in an underserved neighborhood near our medical center where community members can receive free health screenings, health referrals, and information about WU clinical studies. Community Health Workers also reach people in sites such as beauty shops, laundromats, bus stops, and community agencies to spread the word about services offered, and opportunities for research. The 3 CCBR arms serve as a model for community participation to improve research relevant to public health. CCBR investigators are actively training and engaging other ICTS investigators to collaborate on community-based projects with the goal of addressing “outcomes that matter.” Major institutional goals for improving CTS research are the creation of efficient communication and coordination across key function activities; the creation of an informatics infrastructure capable of integrating ICTS program components for data sharing; and the development of electronic resources of existing clinical and biospecimen data that may be used to develop new clinical studies, identify potential research participants, and execute novel molecular correlative studies. The ICTS Center for Biomedical Informatics (CBMI) is addressing these complex core issues through industry-standard hardware infrastructure and software tools to store, integrate, query, analyze, and visualize complex clinical and molecular data sets. Among the several activities of CBMI are the integration and implementation of the research management tool ClinPortal, development and management of biospecimen tracking tools, and creation of the Clinical Investigation Data Exploration Repository (CIDER), a research repository of medical records. ClinPortal is a research management tool that allows investigators and study coordinators to integrate the data collection and management needs of a clinical study with critical regulatory compliance requirements. Investigators create case report forms using data elements (or variables) that are defined using controlled vocabularies, and ClinPortal then automatically generates the backend database schema as well as the forms to enter the data and conduct queries. Use of ClinPortal offers a number of advantages to the institution and to the investigator. By default, data stored in ClinPortal is available for sharing while following all HIPAA regulations, although the investigator that owns the data may turn of sharing if so desired. As a result, once an investigator has established his or her study within the ClinPortal system, there is potential for sharing relevant data with and from other studies. Data access is controlled both by investigator permissions and by regulatory requirements. Future phases include automated access to clinical data from our hospital and outpatient systems. Assuming appropriate release by the patient and access rights for the researcher, investigators using ClinPortal will eventually have access to clinical data from hospital and clinic sources for their study participants. Tissue Suite, a Web-based biospecimen inventory, tracking, and annotation tool, may be used for study-based biospecimen collection efforts and by other non-study investigators interested in identifying existing biospecimens throughout the medical campus that may be available for translational research. The ICTS Translational Pathology and Molecular Phenotyping (TPMP) Core supports the physical storage of biospecimens, while Tissue Suite is the application supported by both the CBMI and the TPMP to provide the informatics to manage data about those biospecimens. Investigators who are managing their own biospecimen collection efforts outside of the TPMP Core may still make use of the Tissue Suite application to manage their inventory in compliance with caBIG standards. Web accessibility, the ability to share biospecimens and biospecimen-related data, advanced search functionality, and capture of clinical, pathological, and quality assurance data for collected biospecimens are key features of Tissue Suite. Together, ClinPortal and Tissue Suite facilitate collaboration between investigators and data integration across studies. As clinical data becomes available through the ClinPortal tool, biospecimens referenced in the complementary Tissue Suite tool will gain increasing clinical annotation, making them even more valuable for translational research studies. Another major focus of the CBMI has been the development of a comprehensive research patient data warehouse. CIDER contains a wealth of data from the electronic medical records of most inpatients and outpatients seen at the WU medical center and affiliated hospitals in the BJC HealthCare system. This system will become available to ICTS investigators in 2009 and will initially contain data on patient encounters, laboratory testing, and medications. We sought community input in designing the patient notification and opt-out procedures and in strictly designating access and use of this information by qualified investigators. We expect that CIDER will become a very useful tool for many clinical and translational researchers to perform a review preparatory to research, identify potential participants to recruit to active clinical studies both by querying CIDER and as they are encountered in the health care system, and implement quality assurance and improvement initiatives. In addition to these highlighted functions, the CBMI is working on other ways to simplify communication and tracking within the ICTS. One new project is the creation of an electronic Research Concierge function to serve as the focal point to help clinical and translational researchers navigate through the study planning, design, review, and execution processes. This Research Concierge function will be added to WU's existing site that currently provides single sign-on access to the resources necessary to propose, manage, and close research projects and satisfy compliance requirements for research. In a nod to St. Louis history, these resources can be found at the WU Research Gateway Web site (http://research.wustl.edu). More information about the WU ICTS can be found at our main site http://icts.wustl.edu.

Discovering and developing new drugs/ medicines is very crucial for the pharmaceutical industry. The increasing number of drugs approved in recent years demonstrates the impact of modern drug discovery approaches, digital technologies, and automated drug development methodologies. Drug development is a systematic and methodological process of developing a new pharmaceutical drug once the process of Drug discovery has identified the prime pharmacological component. The structured sequence of steps followed for drug development aims to ensure the safety and efficacy of the drug being developed. It includes pre-clinical research on microorganisms and animals, preparation of detailed data with respect to pharmacology, pharmacokinetics and toxicology details, application and approval by regulatory authorities and conduction of clinical trials. The conduction of clinical trials is an expensive affair as it needs a collaborative effort by multiple stakeholders along with a high level of monitoring and regulation. The data generated during the lifecycle of clinical trials is very critical for pharmacological scientific publications, regulatory approval for the target drug and post-marketing surveillance that ultimately leads to the development of better decision support systems for drug development. Hence, the data integrity of such data is of prime importance. Several Clinical data management (CDM) systems have been developed to ensure seamless collection and management of clinical trial data. These CDM systems enable useful analysis and decisions supported by authentic data. However, such systems face several security challenges with respect to privacy, integrity and authenticity of the clinical data. Another major challenge in conducting the clinical trials is finding the appropriate willing candidate who is physically and clinically suitable for the study. In view of the above, it is highly desirable to have a technology component that can address the above-mentioned issues. In this chapter, the technologies like blockchain and cloud computing have been introduced to address the challenges posed by clinical trial data management. The paper also proposes a blockchain based secure clinical data management system. The proposed system intends to help the data security issues like data integrity, privacy, ease and quick access to immutable clinical trial data with thorough access control enabling greater transparency and accountability.

The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository is a collaboration established in 2024 by seven stroke centres in Europe. MAGIC consolidates clinical and radiological data from acute ischemic stroke (AIS) patients who underwent endovascular therapy, intravenous thrombolysis, a combination of both, or conservative management. All centres ensure accuracy and completeness of the data. Only patients who did not refuse use of their routine data collected during or after their hospital stay are included in the repository. Approvals or waivers are obtained from the responsible ethics committees before data exchange. A formal data transfer agreement (DTA) is signed by all contributing centres. The centres then share their data, and files are stored centrally on a safe server at the University Hospital Zurich. There, patient identifiers are removed and images are algorithmically de-faced. De-identified structured clinical data are connected to the imaging data by a new identifier. Data are made available to participating centres which have entered into a DTA for stroke research projects. Initially, MAGIC is set to comprise initial and first follow-up imaging of 2,500 AIS patients. Clinical data consist of a comprehensive set of patient characteristics and routine prehospital metrics, treatment and laboratory variables. Our repository will support research by leveraging the entire range of routinely collected imaging and clinical data. This dataset reflects the current state of practice in stroke patient evaluation and management and will enable researchers to retrospectively study clinically relevant questions outside the scope of randomized controlled clinical trials. New centres are invited to join MAGIC if they meet the requirements outlined here. We aim to reach approximately 10,000 cases by 2026.

Resources currently available to the scientific community that may be of interest for endocrinology research are described briefly here. More information is available through The Endocrine Society Home Page (http://www.endo-society.org) or the information provided below. HUMAN TISSUE AND BIOLOGIC SPECIMEN RESOURCES NCI - Cooperative Human Tissue Network (CHTN) The NCI Cooperative Human Tissue Network (CHTN) provides normal, benign, precancerous, and cancerous human tissue to the scientific community for biomedical research. Specimens are collected according to the investigator’s individual protocol. Information provided with the specimens includes routine histopathologic and demographic data. The CHTN can also provide a variety of tissue microarrays. Contact the CHTN Web site at http://www-chtn.ims.nci.nih.gov, or 1-866-GO2-CHTN (1-866-462-2486). NCI - Cooperative Breast Cancer Tissue Resource (CBCTR) The NCI Cooperative Breast Cancer Tissue Resource (CBCTR) can provide researchers with access to formalin-fixed, paraffin-embedded primary breast cancer specimens, with associated pathologic, clinical, and outcome data. All specimens are evaluated for pathologic diagnosis by CBCTR pathologists using standard diagnostic criteria. The collection is particularly well suited for validation studies of diagnostic and prognostic markers. The CBCTR also makes available breast cancer tissue microarrays designed by NCI statisticians to provide high statistical power for studies of stage-specific markers of breast cancer. Contact CBCTR’s Web site at http://cbctr.nci.nih.gov, or contact Steve Marroulis at Information Management Services, Inc.: telephone: (301) 680-9770; e-mail: marrouliss@imsweb.com. NCI - Cooperative Prostate Cancer Tissue Resource (CPCTR) The NCI Cooperative Prostate Cancer Tissue Resource (CPCTR) can provide access to over 4,000 cases of formalin-fixed, paraffin-embedded primary prostate cancer specimens, with associated pathology and clinical data. Fresh-frozen tissue is also available with limited clinical follow-up information. In addition, slides from prostate cancer tissue microarrays with associated pathology and clinical data are now available. Contact the CPCTR Web site at http://www.prostatetissues.org, or contact Steve Marroulis at Information Management Services, Inc.: telephone: (301) 680-9770; e-mail: marrouliss@imsweb.com. NCI - AIDS and Cancer Specimen Resource (ACSR) The AIDS and Cancer Specimen Resource (ACSR) provides qualified researchers with tissue, cell, blood, and fluid specimens, as well as clinical data from patients with AIDS and cancer. The specimens and clinical data are available for research studies, particularly those that translate basic research findings to clinical application. Contact the ACSR Web site (http://acsr.ucsf.edu/) or Dr. Kishor Bhatia, (301) 496-7147; e-mail: bhatiak@mail.nih.gov. NCI - Breast and Ovarian Cancer Family Registries (CFRs) The Breast and Ovarian CFRs facilitate and support interdisciplinary and population-based research on the identification and characterization of breast and ovarian cancer susceptibility genes, with particular emphasis on gene-gene and gene-environment interaction research. Available from the registries are: a) family history, epidemiologic and clinical data, b) updates on cancer recurrence, morbidity and mortality in participating families, and c) biospecimens, including plasma, lymphocytes, serum, DNA, Guthrie cards or buccal smears, and paraffin blocks of tumor tissue. For further information on these registries, contact the CFR Web site (http://epi.grants.cancer.gov/BCFR) or (301) 496-9600. NCI - Specimen Resource Locator The NCI Specimen Resource Locator (http://cancer.gov/specimens) is a database that helps researchers locate specimens for research. The database includes resources such as tissue banks and tissue procurement systems with access to normal, benign, precancerous, and/or cancerous human tissue covering a wide variety of organ sites. Researchers specify the types of specimens, number of cases, preservation methods, and associated data they require. The Locator will search the database and return a list of tissue resources most likely to meet their requirements. When no match is obtained, the researcher is referred to the NCI Tissue Expediter [(301) 496-7147; e-mail: tissexp@mail.nih.gov]. The Tissue Expediter is a scientist who can help match researchers with appropriate resources or identify appropriate collaborators when those are necessary. NIDDK - Biologic Samples from Diabetic Study Foundation A portion (1/3) of all stored nonrenewable samples (plasma, serum, urine) from subjects enrolled in the Diabetes Control and Complications Trial (DCCT) is available for use by the scientific community to address questions for which these samples may be invaluable. Announcements for using this resource appear in the NIH Guide for Grants and Contracts periodically. Inquiries may be addressed to: Catherine C. Cowie, Ph.D., Director, Diabetes Epidemiology Program, NIDDK, 6707 Democracy Blvd., Room 691, MSC 5460, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892-5460. Phone: (301) 594-8804; fax: (301) 480-3503; e-mail: cowiec@extra.niddk.nih.gov. NIDDK - NIDDK Central Repositories (Diabetes Prevention Study) The NIDDK Central Repositories have selected biosamples from the DPT-1 (The Diabetes Prevention Type 1) study that are available to qualified investigators through an application process. These samples are supplied for research purposes only, not for therapeutic, diagnostic, or commercial uses. Information about how to apply for these materials can be obtained from the NIDDK Central Repositories by contacting Ms. Helen Ray of RTI, 1-919-316-3418, or hmp@rti.org. Direct scientific-technical inquiry to the Project Officer of the NIDDK Central Repositories, Dr. Rebekah Rasooly, at phone: (301) 594-6007; e-mail: rr185i@nih.gov. Visit the Repositories Web site at http://www.niddkrepository.org. NICHD - Brain and Tissue Bank for Developmental Disorders The purpose of the Bank is to collect, preserve, and distribute human tissues to investigators interested in autism and developmental disorders; normal tissues may be available for other research purposes. Further information can be obtained at www.btbank.org. The contact persons are H. Ron Zielke or Sally Wisniewsky, University of Maryland (1-800-847-1539), and Carol Petito or Stephanie Lojko, University of Miami (1-800-592-7246). NICHD - Reproductive Tissue Sample Repository (RTSaR) The Reproductive Tissue Sample Repository (RTSaR) is a virtual repository with online tissue sample acquisition capabilities. The RTSaR provides investigators with real-time access to human and nonhuman primate tissue and fluid inventories from four tissue bank facilities that are supported through the Specialized Cooperative Centers Program in Reproduction Research. The tissue banks are located at the University of California, San Diego (human ovary bank), Stanford University (human endometrium and DNA bank), Johns Hopkins University (male reproductive tissues and fluids), and the Oregon National Primate Research Center (nonhuman primate tissues). The web site for the RTSaR is https://rtsar.nichd.nih.gov/rtsar/login. If you wish to access the RTSaR, you can request an id and password to access the system by contacting the network administrator at RTSaR@mail.nih.gov. Once you access the system, contact information for each bank is provided. Access is open to all investigators living in North America who are supported by research and research training grants from the NIH. One id and password will be provided to each principal investigator that can be utilized by any person working in the P.I.’s laboratory, or, in the case of institutional training grants (T32) and institutional career development award programs (K12), any person supported by the aforementioned awards. NCRR - Human Tissues and Organs Resource (HTOR) The Human Tissues and Organs Resource (HTOR) cooperative agreement supports a procurement network developed by the National Disease Research Interchange (NDRI), a not-for-profit organization. By collaborating with various medical centers, hospitals, pathology services, eye banks, tissue banks, and organ procurement organizations, HTOR provides a wide variety of