Implementation of a high-quality biospecimen program to support molecular medicine.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 200-200
Author(s):  
Susanne Morrill ◽  
Daniza Mandich ◽  
Richard Cartun ◽  
Andrew L. Salner
Keyword(s):  
Cancer Genomics ◽  
Care Program ◽  
High Volume ◽  
The Cancer Genome Atlas ◽  
Molecular Medicine ◽  
Successful Implementation ◽  
High Quality ◽  
Fresh Tissue ◽  
Tissue Samples ◽  
Tissue Handling

200 Background: The successful implementation of a tumor genomics program relies heavily upon the collection of high quality tumor tissue samples. Although there has been an evolution towards utilizing formalin-fixed paraffin embedded (FFPE) tissue, many research centers continue to rely upon frozen fresh tissue for these types of analyses. A comprehensive effort is required to supply high-volume and high-quality tissue for research. Most community hospitals, even with superb pathology departments, are not well suited to deliver consistent tissue samples without a concerted programmatic effort. As part of the NCI Community Cancer Centers Program (NCCCP), we undertook the development of such capability at our hospital. In addition, as a member of H. Lee Moffitt Cancer Center’s Total Cancer Care program, we received grant funding to help support this comprehensive effort. Our patients and clinicians expressed a strong desire to participate in this type of translational research. This project has been funded in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. Methods: We developed a comprehensive staffing model to implement this program, including a program coordinator, consenters, pathology assistant, lab aide, and data manager. We developed superb relationships with surgeons, interventional radiologists, pathologists and staffs to assure appropriate referrals and processes, and implemented quality checks as a standard. We developed relationships with Moffitt, The Cancer Genome Atlas, and other research efforts, which help provide funding. Results: We successfully implemented a program which resulted in high levels of patient and provider satisfaction, high numbers of fresh frozen and FFPE tissues (nearly 3,000 over 3 years), high quality pass rates, low ischemia time, and high satisfaction on the part of our research partners. We have incorporated best practices in our tissue handling protocols. Conclusions: We successfully implemented a comprehensive cancer genomics bio-specimen program utilizing dedicated staff, working with patients and clinicians closely, and assuring careful coordination of all efforts.

scholarly journals Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines and tissue samples

2019 ◽  
Vol 35 (17) ◽  
pp. 3140-3142 ◽  
Author(s):  
Marc Streit ◽  
Samuel Gratzl ◽  
Holger Stitz ◽  
Andreas Wernitznig ◽  
Thomas Zichner ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Genomics ◽  
Cancer Cell Line ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Tissue Samples ◽  
Web Based ◽  
Prioritization Process ◽  
Cancer Genome Atlas

Abstract Summary Ordino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, filter and explore genes, cell lines and tissue samples based on pre-loaded data, including The Cancer Genome Atlas, the Cancer Cell Line Encyclopedia and manually uploaded information. Interactive tabular data visualization that facilitates the user-driven prioritization process forms a core component of Ordino. Detail views of selected items complement the exploration. Findings can be stored, shared and reproduced via the integrated session management. Availability and implementation Ordino is publicly available at https://ordino.caleydoapp.org. The source code is released at https://github.com/Caleydo/ordino under the Mozilla Public License 2.0. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines, and tissue samples

2018 ◽  
Author(s):  
Marc Streit ◽  
Samuel Gratzl ◽  
Holger Stitz ◽  
Andreas Wernitznig ◽  
Thomas Zichner ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Genomics ◽  
Cancer Cell Line ◽  
The Cancer Genome Atlas ◽  
Analysis Tool ◽  
Tissue Samples ◽  
Web Based ◽  
Link Type ◽  
Prioritization Process ◽  
Cancer Genome Atlas

AbstractSummaryOrdino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, filter, and explore genes, cell lines, and tissue samples based on pre-loaded data, including The Cancer Genome Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE), and manually uploaded information. Interactive tabular data visualization that facilitates the user-driven prioritization process forms a core component of Ordino. Detail views of selected items complement the exploration. Findings can be stored, shared, and reproduced via the integrated session management.Availability and ImplementationOrdino is publicly available at https://ordino.caleydoapp.org. The source code is released at https://github.com/Caleydo/ordino under the Mozilla Public License [email protected]

scholarly journals Expression and clinical significance of miR-3615 in hepatocellular carcinoma

2021 ◽  
Vol 49 (1) ◽  
pp. 030006052098154
Author(s):  
Xin Yuan ◽  
Yize Zhang ◽  
Zujiang Yu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Curve ◽  
Clinicopathological Characteristics ◽  
The Cancer Genome Atlas ◽  
Prognostic Information ◽  
Ki 67 ◽  
Tissue Samples ◽  
Expression Levels ◽  
Kaplan Meier ◽  
Serum Alpha Fetoprotein

Objective To investigate the association between microRNA-3615 (miR-3615) expression and the prognosis and clinicopathological features in patients with hepatocellular carcinoma (HCC). Methods We obtained clinicopathological and genomic data and prognostic information on HCC patients from The Cancer Genome Atlas (TCGA) database. We then analyzed differences in miR-3615 expression levels between HCC and adjacent tissues using SPSS software, and examined the relationships between miR-3615 expression levels and clinicopathological characteristics. We also explored the influence of miR-3615 expression levels on the prognosis of HCC patients using Kaplan–Meier survival curve analysis. Results Based on data for 345 HCC and 50 adjacent normal tissue samples, expression levels of miR-3615 were significantly higher in HCC tissues compared with adjacent tissues. MiR-3615 expression levels in HCC patients were negatively correlated with overall survival time and positively correlated with high TNM stage, serum Ki-67 expression level, and serum alpha-fetoprotein level. There were no significant correlations between miR-3615 expression and age, sex, and pathological grade. Conclusion MiR-3615 may be a promising new biomarker and prognostic factor for HCC.

scholarly journals Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yan ◽  
Zuotian Huang ◽  
Tong Mou ◽  
Yunhai Luo ◽  
Yanyao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Competing Endogenous Rna ◽  
Tissue Samples ◽  
Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

Pyometra Management Practices in the High Quality, High Volume Spay-Neuter Environment

2021 ◽  
Vol 42 ◽  
pp. 100499
Author(s):  
Ashlee J. McCallin ◽  
Veronica A. Hough ◽  
Rachael E. Kreisler
Keyword(s):  
Management Practices ◽  
High Volume ◽  
High Quality

57 Precision neoantigen discovery using novel algorithms and expanded HLA-ligandome datasets

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A62-A62
Author(s):  
Dattatreya Mellacheruvu ◽  
Rachel Pyke ◽  
Charles Abbott ◽  
Nick Phillips ◽  
Sejal Desai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Lines ◽  
Antigen Processing ◽  
Large Scale ◽  
Prediction Models ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
High Quality ◽  
Tissue Samples

BackgroundAccurately identified neoantigens can be effective therapeutic agents in both adjuvant and neoadjuvant settings. A key challenge for neoantigen discovery has been the availability of accurate prediction models for MHC peptide presentation. We have shown previously that our proprietary model based on (i) large-scale, in-house mono-allelic data, (ii) custom features that model antigen processing, and (iii) advanced machine learning algorithms has strong performance. We have extended upon our work by systematically integrating large quantities of high-quality, publicly available data, implementing new modelling algorithms, and rigorously testing our models. These extensions lead to substantial improvements in performance and generalizability. Our algorithm, named Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), is integrated into the ImmunoID NeXT Platform®, our immuno-genomics and transcriptomics platform specifically designed to enable the development of immunotherapies.MethodsIn-house immunopeptidomic data was generated using stably transfected HLA-null K562 cells lines that express a single HLA allele of interest, followed by immunoprecipitation using W6/32 antibody and LC-MS/MS. Public immunopeptidomics data was downloaded from repositories such as MassIVE and processed uniformly using in-house pipelines to generate peptide lists filtered at 1% false discovery rate. Other metrics (features) were either extracted from source data or generated internally by re-processing samples utilizing the ImmunoID NeXT Platform.ResultsWe have generated large-scale and high-quality immunopeptidomics data by using approximately 60 mono-allelic cell lines that unambiguously assign peptides to their presenting alleles to create our primary models. Briefly, our primary ‘binding’ algorithm models MHC-peptide binding using peptide and binding pockets while our primary ‘presentation’ model uses additional features to model antigen processing and presentation. Both primary models have significantly higher precision across all recall values in multiple test data sets, including mono-allelic cell lines and multi-allelic tissue samples. To further improve the performance of our model, we expanded the diversity of our training set using high-quality, publicly available mono-allelic immunopeptidomics data. Furthermore, multi-allelic data was integrated by resolving peptide-to-allele mappings using our primary models. We then trained a new model using the expanded training data and a new composite machine learning architecture. The resulting secondary model further improves performance and generalizability across several tissue samples.ConclusionsImproving technologies for neoantigen discovery is critical for many therapeutic applications, including personalized neoantigen vaccines, and neoantigen-based biomarkers for immunotherapies. Our new and improved algorithm (SHERPA) has significantly higher performance compared to a state-of-the-art public algorithm and furthers this objective.

scholarly journals A machine learning approach for somatic mutation discovery

2018 ◽  
Vol 10 (457) ◽  
pp. eaar7939 ◽  
Author(s):  
Derrick E. Wood ◽  
James R. White ◽  
Andrew Georgiadis ◽  
Beth Van Emburgh ◽  
Sonya Parpart-Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Somatic Mutation ◽  
Mutation Detection ◽  
Checkpoint Inhibitors ◽  
False Negative ◽  
The Cancer Genome Atlas ◽  
High Quality ◽  
Lung Cancer Patients ◽  
Mutation Discovery

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load–based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.

scholarly journals Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiwei Mu ◽  
Yi Tang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
Xiaohong Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Future Research ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Sets ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

Dedicated clinical trial tissue tracking database to improve turn-around time at high-volume center.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1543-1543
Author(s):  
Peter Blankenship ◽  
David DeLaRosa ◽  
Marc Burris ◽  
Steven Cusson ◽  
Kayla Hendricks ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Tissue Sample ◽  
High Volume ◽  
Trial Participation ◽  
Fresh Tissue ◽  
Oncology Clinical Trials ◽  
The Status ◽  
Unique Source

1543 Background: Tissue requirements in oncology clinical trials are increasingly complex due to prescreening protocols for patient selection and serial biopsies to understand molecular-level treatment effects. Novel solutions for tissue processing are necessary for timely tissue procurement. Based on these needs, we developed a Tissue Tracker (TT), a comprehensive database for study-related tissue tasks at our high-volume clinical trial center. Methods: In this Microsoft Access database, patients are assigned an ID within the TT that is associated with their name, medical record number, and study that follows their request to external users: pathology departments, clinical trial coordinators and data team members. To complete tasks in the TT, relevant information is required to update the status. Due to the high number of archival tissue requests from unique pathology labs, the TT has a “Follow-Up Dashboard” that organizes information needed to conduct follow-up on all archival samples with the status “Requested”. This results in an autogenerated email and pdf report sent to necessary teams. The TT also includes a kit inventory system and a real-time read only version formatted for interdepartmental communication, metric reporting, and other data-driven efforts. The primary outcome in this study was to evaluate our average turnaround time (ATAT: average time from request to shipment) for archival and fresh tissue samples before and after TT development. Results: Before implementing the TT, between March 2016 and March 2018, we processed 2676 archival requests from 235 unique source labs resulting in 2040 shipments with an ATAT of 19.29 days. We also processed 1099 fresh biopsies resulting in 944 shipments with an ATAT of 7.72 days. After TT implementation, between April 2018 and April 2020, we processed 2664 archival requests from 204 unique source labs resulting in 2506 shipments (+28.0%) with an ATAT of 14.78 days (-23.4%). During that same period, we processed 1795 fresh biopsies (+63.3%) resulting in 2006 shipments (+112.5%) with an ATAT of 6.85 days (-11.3%). Conclusions: Oncology clinical trials continue to evolve toward more extensive tissue requirements for prescreening and scientific exploration of on-treatment molecular profiling. Timely results are required to optimize patient trial participation. During the intervention period, our tissue sample volume and shipments increased, but the development and implementation of an automated tracking system allowed improvement in ATAT of both archival and fresh tissue. This automation not only improves end-user expectations and experiences for patients and trial sponsors but this allows our team to adapt to the increasing interest in tissue exploration.

Sign in / Sign up

Export Citation Format

Share Document