Cancer Genome Study Using Samples From Patients Treated on Clinical Trial SHR1020-SHR-1210-II-OS

2019 ◽  
Author(s):  
Keyword(s):  
Clinical Trial ◽  
Cancer Genome ◽  
Genome Study

scholarly journals Landscape Analysis of Breast Cancer and Acute Myeloid Leukemia Trials Using the My Cancer Genome Clinical Trial Data Model

2021 ◽  
pp. 975-984
Author(s):  
Neha M. Jain ◽  
Marilyn Holt ◽  
Christine Micheel ◽  
Mia Levy
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Acute Myeloid Leukemia ◽  
Data Model ◽  
Myeloid Leukemia ◽  
Clinical Trial Data ◽  
Cancer Genome ◽  
Landscape Analysis ◽  
Drug Classes ◽  
Acute Myeloid

PURPOSE The field of oncology is expanding rapidly. New trials are opening as an increasing number of therapeutic agents are being investigated before they can become approved therapies. Aggregate views of these data, particularly data associated with diseases, biomarkers, and drugs, can be helpful in understanding the trends in current research as well as existing gaps in cancer care. METHODS In this paper, we performed a landscape analysis for breast cancer and acute myeloid leukemia related trials with structured, curated data from clinical trials using the My Cancer Genome clinical trial knowledgebase. RESULTS We have performed detailed analytics on breast cancer (N = 1,128) and acute myeloid leukemia trial sets (N = 483) to highlight the top biomarkers, drug classes, and drugs—thereby supporting a full view of biomarkers, biomarker groups, and drugs that are currently being explored in these respective diseases. CONCLUSION Analysis and data visualization of the cancer clinical trial landscape can inform strategic planning for new trial designs and trial activation at a particular site.

The Nationwide Cancer Genome Screening Project in Japan, SCRUM-Japan GI-screen: Efficient identification of cancer genome alterations in advanced esophageal cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4062-4062
Author(s):  
Yuichiro Nakashima ◽  
Takashi Kojima ◽  
Hiroki Hara ◽  
Ken Kato ◽  
Takeshi Kajiwara ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Esophageal Cancer ◽  
Copy Number Variant ◽  
Cancer Genome ◽  
Loss Of Function ◽  
Advanced Esophageal Cancer ◽  
Surgical Specimen ◽  
Dna And Rna ◽  
Ffpe Tumor ◽  
Genome Screening

4062 Background: We have conducted the Nationwide Cancer Genome Screening Project in Japan since April 2015 using Next Generation Sequencing in advanced non-colorectal gastrointestinal (GI) cancer (aNon-CRC), called as the SCRUM-Japan GI-SCREEN. The objective is to evaluate the frequency of cancer genome alterations in aNon-CRC and to identify patients who are candidate for clinical trial for corresponding targeting agents. Methods: This study is ongoing with the participation of 20 major cancer centers. Patients with aNon-CRC, including advanced esophageal cancer (aEC), who plan to or receive chemotherapy were eligible. DNA and RNA were extracted from FFPE tumor samples and were analyzed by the Oncomine Cancer Research Panel (OCP) which allows to detect gene mutation, copy number variant (CNV) and fusions across 143 genes in a CLIA certified CAP accredited laboratory. The detected genomic variant data were classified according to genetic drivers of cancer including gain- and loss-of-function or single nucleotide variant based on the Oncomine Knowledgebase. In this presentation, we show the results of aEC cohort. Results: As of October 31st in 2016, a total of 180 aEC samples were analyzed. The sequence with the OCP was successfully performed in 121 (67.2%). Out of 157 patients except for the 23 patients in which precise data is not collected, the proportion of sample and histology type is followed; surgical specimen 58.0%, squamous cell carcinoma 92.4%. The frequently detected mutations in 114 samples of which results were available were TP53 (77.2%), NFE2L2 (23.7%), CDKN2A (9.6%), PIK3CA (7.0%), RB1 (6.1%), and CNVs were CCND1 (37.7%), EGFR (7.9%), MYC (7.9%), SOX2 (6.1%), ATP11B (5.3%), NKX2-1 (5.3%). ERBB2 amplification was identified in 3 cases (2.6%) and FGFR3-TACC3fusion was identified in one case (0.9%). Conclusions: This nationwide screening system is efficient to detect rare gene alterations in aEC. This novel knowledge provides an intriguing background to investigate new target approaches and represents a progress toward more precision medicine. Clinical trial information: UMIN000016344.

scholarly journals My Cancer Genome: Coevolution of Precision Oncology and a Molecular Oncology Knowledgebase

2021 ◽  
pp. 995-1004
Author(s):  
Marilyn E. Holt ◽  
Kathleen F. Mittendorf ◽  
Michele LeNoue-Newton ◽  
Neha M. Jain ◽  
Ingrid Anderson ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Building Blocks ◽  
Cancer Genome ◽  
Primary Sources ◽  
Precision Oncology ◽  
Patients With Cancer ◽  
Time Investment ◽  
Molecular Oncology ◽  
Manual Curation ◽  
New Biomarkers

PURPOSE The My Cancer Genome (MCG) knowledgebase and resulting website were launched in 2011 with the purpose of guiding clinicians in the application of genomic testing results for treatment of patients with cancer. Both knowledgebase and website were originally developed using a wiki-style approach that relied on manual evidence curation and synthesis of that evidence into cancer-related biomarker, disease, and pathway pages on the website that summarized the literature for a clinical audience. This approach required significant time investment for each page, which limited website scalability as the field advanced. To address this challenge, we designed and used an assertion-based data model that allows the knowledgebase and website to expand with the field of precision oncology. METHODS Assertions, or computationally accessible cause and effect statements, are both manually curated from primary sources and imported from external databases and stored in a knowledge management system. To generate pages for the MCG website, reusable templates transform assertions into reconfigurable text and visualizations that form the building blocks for automatically updating disease, biomarker, drug, and clinical trial pages. RESULTS Combining text and graph templates with assertions in our knowledgebase allows generation of web pages that automatically update with our knowledgebase. Automated page generation empowers rapid scaling of the website as assertions with new biomarkers and drugs are added to the knowledgebase. This process has generated more than 9,100 clinical trial pages, 18,100 gene and alteration pages, 900 disease pages, and 2,700 drug pages to date. CONCLUSION Leveraging both computational and manual curation processes in combination with reusable templates empowers automation and scalability for both the MCG knowledgebase and MCG website.

Designing Transformative Clinical Trials in the Cancer Genome Era

2013 ◽  
Vol 31 (15) ◽  
pp. 1834-1841 ◽  
Author(s):  
Stefan Sleijfer ◽  
Jan Bogaerts ◽  
Lillian L. Siu
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Point Of Care ◽  
Growth Factor Receptor ◽  
Molecular Profiling ◽  
Cancer Genome ◽  
Histopathologic Diagnosis ◽  
Epidermal Growth ◽  
Clinical Trial Designs ◽  
Tumor Types

The incorporation of molecular profiling into routine clinical practice has already been adopted in some tumor types, such as human epidermal growth factor receptor 2 (HER2) testing in breast cancer and KRAS genotyping in colorectal cancer, providing a guide to treatment selection that is not afforded by histopathologic diagnosis alone. It is inevitable that over time, with rapid advances in scientific knowledge, bioinformatics, and technology to identify oncogenic drivers, molecular profiling will complement histopathologic data to influence management decisions. Emerging technologies such as multiplexed somatic mutation genotyping and massive parallel genomic sequencing have become increasingly feasible at point-of-care locations to classify cancers into molecular subsets. Because these molecular subsets may differ substantially between each other in terms of sensitivity or resistance to systemic agents, there is consensus that clinical trials should be more stratified for or be performed only in such molecularly defined subsets. This approach, however, poses challenges for clinical trial designs because smaller numbers of patients would be eligible for such trials, while the number of novel anticancer drugs warranting further clinical exploration is rapidly increasing. This article provides an overview of the emerging methodologic challenges in the cancer genome era and offers some potential solutions for transforming clinical trial designs so they can identify new active anticancer regimens in molecularly defined subgroups as efficiently as possible.

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