bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical evaluation and performance of QIAGEN Clinical Insight Interpret (QCI-I) as a reporting tool for a comprehensive cancer panel (TSO 500).
Authors: Sahajpal Nikhil Shri , Mondal Ashis , Ahluwalia Meenakshi , Kota Vamsi , Njau Allan Njoroge , Okechukwu Nwogbo , Kohle Ravindra 
Published: May 2020

e14048 Background: The rapid adoption of next-generation sequencing (NGS) methods into clinical diagnostic laboratories have revolutionized the field of genetic testing. To interpret the exhaustive NGS data, bioinformatics has become an inevitable component of any laboratory implementing a clinical NGS test. However, there is an unmet need for accurate, efficient and standardized variant interpretation for actionable insights. In this effort, AMP/ASCO/CAP released a joint consensus recommendation proposing a four-tiered system to categorize somatic alterations based on their clinical significance in cancer diagnosis, prognosis, or therapeutics for standardization of interpretation and reporting of results among laboratories. Thus, the aim of the study was to assess and validate QIAGEN Clinical Insight (QCI) Interpret software for clinical laboratory test interpretation. Methods: 67 malignant cases (myeloid neoplasm and lung) were analyzed using the TSO-500 NGS assay and secondary analysis platform (Illumina, San Diego, CA). The data was uploaded and analyzed using QCI Interpret. Patient case draft reports were produced using a TS0-500 customizable workflow with automated variant filtering, and variant classifications. Results: Data analysis on 67 cases resulted in a total of 251 alterations (SNVs and Indels). A total of 77 and 174 alterations were reported as pathogenic/ likely pathogenic into tier based classification by the QCI Interpret for myeloid and lung malignancies, respectively. For myeloid malignancies, the QCI Interpret was able to detect accurately all the clinically relevant variants and listed them into appropriate tier-based classification compared to the expertly reported alterations. For lung malignancies, 16 alteration were classified in tier 1, 96 alteration in tier 2, 61 alteration in tier 3, and 1 alteration in tier 4. The QCI interpret was able to detect 100% of the alterations being validated, and reported them into tier based classification. Conclusions: Systematic evaluation of an automated classification based on AMP/ASCO/CAP using an up to date knowledge base is critical to accurately classify actionable mutations and detect emerging relevant mutations. The QCI Interpret seems to be a critical bioinformatics tool that adheres to the AMP/ASCO/CAP classification system and incorporates TMB and MSI values from the TSO500 assay, which can be used across clinical laboratories for accurate, efficient and standardized variant interpretation for actionable insights.
Development of an algorithm using natural language processing to identify metastatic breast cancer patients from clinical notes.
Authors: Swaminathan Krishna Kumar , Mendonca Emma , Mukherjee Pranay , Thirumalai Karpagavalli , Newsome Rachel , Narayanan Babu 
Published: May 2020

e14056 Background: Determination of the metastatic status of a patient is important for outcomes research and candidacy for clinical trials. Structured data in EMR may not always capture the metastatic status, and it is useful to extract it automatically from physician notes. Contextual understanding of the notes is important to resolve issues such as a) local vs distal metastasis b) statements involving family history of metastasis or physician instructing the patient to look for certain signs of metastasis c) text indicating suspicion of metastasis or absence of metastasis d) indirect utterances, e.g. cancer has spread to the bone. e) corrections to previous findings. Methods: We used a set of 20138 breast cancer patients from Concerto HealthAI real world oncology dataset that includes data from CancerLinQ Discovery to build & validate the set of NLP algorithms. 5300 sentences from 1500 patients were annotated & algorithms manually validated by data abstractors for 500 patients. The algorithms developed were the following: 1) Classification of a sentence into 3 classes: Distal/Local metastasis, Suspicious & Other 2) Classification of a sentence into 2 classes: Distal or Local 3) Classification of a patient into 2 classes: Distal metastasis or not distal metastasis 4) Multi label classification for detecting sites of metastasis. Sentence level algorithms were built using Deep Learning and patient level aggregation of sentence level prediction was done using ML approaches including temporal features. Pretrained ULMFiT model was fine-tuned with Concerto HealthAI’s corpus for sentence classification tasks. Results: At a sentence level, we obtained an accuracy of 0.85 for the distal/local vs suspicious vs irrelevant model and 0.97 for the distal vs not distal metastasis model. Our patient level metrics are shown in the table. The classes used for sites of metastasis are Brain, Bone, Lung, Liver, Distant Lymph nodes & Unknown sites. Subset accuracy (mean fraction of labels which match ) of 0.93 was obtained on the hold out test set at patient level. Conclusions: Metastatic status & site of metastasis can be reliably extracted automatically from clinical notes using deep learning techniques. This information will be valuable for clinical trial matching, outcomes research and other applications. [Table: see text]
A review of gynecological cancers studies of concordance with individual clinicians or multidisciplinary tumor boards for an artificial intelligence-based clinical decision-support system.
Authors: Arriaga Yull Edwin , Hekmat Rezzan , Draulis Karlis , Wang Suwei , Preininger Anita M , Felix Winnie , Dankwa-Mullan Irene , Rhee Kyu , Jackson Gretchen Purcell 
Published: May 2020

e14070 Background: Watson for Oncology (WfO) is an artificial intelligence-based clinical decision-support system that offers potential therapeutic options to cancer-treating physicians. We reviewed studies of concordance between therapeutic options offered by WfO and treatment decisions made by individual clinicians (IC) and multidisciplinary tumor boards (MTB) in practice in gynecological cancers. Methods: We searched PubMed and an internal database to identify peer-reviewed WfO concordance studies of gynecological cancers published between 01/01/2015 and 06/30/2019. Concordance was defined as agreement between therapeutic options recommended or offered for consideration by WfO and treatment decisions made by IC or MTB. Mean concordance was calculated as a weighted average based on the number of patients per study. Statistical significance was evaluated by z-test of two proportions. Results: Our search identified 5 retrospective studies with 635 patients with cervical and ovarian cancers in China and Thailand; 4 compared WfO to MTB and 1 to IC. Overall WfO concordance with MTB and IC for both cancers was 77.2% (SD 11.6%). The concordance between MTB and WfO in cervical and ovarian cancers was 80.5% and 86.2%, respectively ( P = .21); IC concordance with WfO in cervical and ovarian cancers was 65.2% and 73.2%, respectively ( P = .18). MTB concordance with WfO for both cancers combined was 81.5%, significantly higher than the 67.9% IC concordance with WfO for both cancers ( P = .01). Conclusions: Studies of cervical and ovarian cancers demonstrated a statistically significantly higher concordance of MTB and WfO than IC and WFO, suggesting a role for WfO in supporting treatment-decision making in gynecological cancers that aligns with decisions made by MTB. Larger prospective studies are needed to evaluate the technical performance, usability, workflow integration, and clinical impact of WfO in gynecological cancers.[Table: see text]
Machine learning models to predict the response to anti-cancer therapy in metastatic melanoma patients.
Authors: Goussault Romain , Frénard Cécile , Maubec Eve , Muller Philippe , Martin Ludovic , Legoupil Delphine , Aubin Francois , De Quatrebarbes Julie , Jouary Thomas , Hervieu Alice , Machet Laurent , Varey Emilie , Lecerf Philippe , Vrignaud Florence , Khammari Amir , Dréno Brigitte ,   The RIC-Mel Network
Published: May 2020

e14071 Background: Machine learning methods are new artificial intelligence tools with promising applications in healthcare. We developed and validated 4 machine learning models to predict the response to immunotherapy and targeted therapy in stage IIIc or IV melanoma patients. Methods: This work was conducted on data from 10 centers participating in the French network for Research and Clinical Investigation on Melanoma (RIC-Mel), launched in 2012. Thus, 935 patients, corresponding to 1978 systemic treatments have been extracted from RIC-Mel database. The following data were considered: age, sex, Breslow, melanoma type, ulceration, spontaneous regression, mitotic index, number of invaded lymph nodes, extracapsular extension, mutational status, melanoma stage, number of metastasis sites, lines of treatments, and time between first melanoma excision and metastatic relapse. Treatment response: complete response, partial response, stable disease, defined as class 1 and progressive disease as class 2. We split this cohort/database into a training set (80%) and test set (20%). The algorithm performances were evaluated on the test set by the percentage of treatments correctly classified in class 1 or 2. Four machine learning algorithms (linear model, random forest, XGBoost and LightGBM) were compared in terms of performance and interpretation for both types of treatments. Results: The accuracies of the best models for immunotherapy (LightGBM) and targeted therapy (random forest) were respectively 66% and 65%. The most significant variables for building the models were respectively: stage (IIIc or IV), response to previous treatments lines, age, number of metastasis sites and time between first melanoma excision and metastatic relapse. Conclusions: We present here the first machine learning models to predict the response to immunotherapy and targeted therapy in stage IIIc or IV melanoma patients. The most predictive variables are coherent with the literature. Future development will include data from 18FDG-PET/CT imaging and other predictive markers recently identified, as circulating DNA to improve the models performance.
Enhanced co-expression extrapolation (COXEN) gene selection method for building anticancer drug response prediction models.
Authors: Zhu Yitan , Brettin Thomas S. , Xia Fangfang , Shukla Maulik , Partin Alexander , Yoo Hyunseung , Stevens Rick L. 
Published: May 2020

e14073 Background: Accurate prediction of tumor response to a drug treatment is of paramount importance for precision oncology. The co-expression extrapolation (COXEN) gene selection approach has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug. Here, we enhance the original COXEN approach to select genes that are predictive of the efficacies of multiple drugs simultaneously for building general drug response prediction model. Methods: We implemented two methods to select predictive genes. The first method ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs. The second method uses a linear regression model to evaluate the prediction power of a gene for all drugs while the drugs are one-hot encoded in the regression model. Among the predictive genes, we further select genes by evaluating the preservation of co-expression patterns between cancer cases with drug response data available and cancer cases for which drug response needs to be predicted, because the preservation of co-expression patterns indicates the similarity of genomic regulations between cancer cases. Results: To test the enhanced COXEN method, we used a lightGBM regression model to predict drug response based on the selected genes on two benchmark in vitro drug screening datasets. The table below compares the performance of prediction models built based on 200 genes selected by the enhanced COXEN method to that of models built on 200 genes randomly picked from the LINCS gene set, which includes 976 “landmark” genes well-representing cellular transcriptomic changes identified in the Library of Integrated Network-Based Cellular Signatures (LINCS) project. The enhanced COXEN approach selects genes better than random LINCS genes as demonstrated by the increased average coefficient of determination (R2) for predicting the area under the dose response curve through cross-validation. Pair-wise t-test indicates the improvement is statistically significant (p-value ≤ 0.05) on both datasets. Conclusions: Our result demonstrates the benefit of using an enhanced COXEN approach to select genes for building general drug response prediction model. [Table: see text]
Ephesus: A curation workflow tool for the clinical interpretation of genomic variants.
Authors: Li Jian , Niu Lili , Greene Nicole , Douglas Vivian , Schartner Caitlin , Chu Bernie , Kinshuk Fnu , Mu John , Doupnik Austin , Broka Andi , Rao Priyanka , Mohiyuddin Marghoob , Krishna Shuba 
Published: May 2020

e14074 Background: Accurate and comprehensive interpretation of genomic variants has become a bottleneck in clinical sequencing applications due to the accelerated implementation of precision oncology and the rapid growth of relevant biomedical findings. We therefore are motivated to build Ephesus, a framework enabling curation of clinical evidence for biomarkers in somatic cancers. Currently Ephesus is the primary content source for Roche NAVIFY Mutation Profiler (NMP). Methods: Ephesus’ data model ensures adherence to best practice in clinical genomic content curation. Variant classification followed the AMP guidelines for somatic variant interpretation. Approvals and recommendations from regulatory agencies and organizations (FDA, NCCN, etc.) are applied on a regional basis. The data model is mapped to a set of PostgreSQL relational tables. Strategies such as data normalization according to standard ontologies, a submit-review-approval workflow with change logs, and biologically relevant datatype constraints are adopted to enforce data integrity. Ephesus is deployed as a web application, of which the UI allows users to conduct edits, filtering, sorting and bulk operations on entities by attributes. To maximize curation efficiency, a number of inference rules are applied before the content is ingested into NMP. Results: Currently Ephesus is developed to guide data collection, browsing and summarizing related to these primary entities: Biomarkers, Evidence items, Genes, Variants, Variant groups, and Drugs. The content exported on 2020/01/27 contains > 170K biomarker profiles (including combinations), in the context of 13 major cancer types. To evaluate the clinical reporting value of the curated knowledge, more than 60k real clinical cancer patient samples from the AACR GENIE project were queried against Ephesus. We compared the results to the latest content from several major knowledgebases. We see that the performance of Ephesus/NMP excels the others. Conclusions: We have developed Ephesus, a practical content curation framework which provides a highly structured and user-friendly environment for clinically relevant somatic biomarkers. The framework improved the productivity and quality of the manual curation, met clinical interpretation scope and complexity, and assures data integrity and auditability. [Table: see text]
Representing cancer clinical trial criteria and attributes using ontologies: An NLP-assisted approach.
Authors: Lee Kyeryoung , Liu Zhongzhi , Ma Meng , Gilman Chris , Mai Yun , Zhou Xiang , Ghosh Krish , Chen Rong , Schadt Eric , Wang Xiaoyan 
Published: May 2020

e14079 Background: Low patient recruitment is one of the main reasons clinical trials fail. Identifying eligible patients for clinical trials using electric health records (EHRs) can help reach accrual targets. Ontology reasoning implemented in Trial2Patient, a scalable system we developed for matching patient to clinical trials, forms the basis for generating patient cohorts in our system. For efficient cohort definition, an attribute ontology for eligibility criteria and entity categorization is a necessary first step. To meet this requirement, we constructed an ontology platform for lung cancer trials. Methods: We classified 128 non-small cell lung cancer and 38 small cell lung cancer trials into different therapy groups. Among the 166 trials we examined, 110 were immuno-oncology therapy-based, 48 were targeted therapy-based, and 8 were chemotherapy or device trials. We analyzed the eligibility criteria for each trial manually to identify entities from all trials as well as indication specific and further therapy group specific entities. To incorporate a semi-automated, natural language process (NLP)-assisted named entity recognition (NER) into the future cohort definition process, we trained NLP and deep learning models for NER and ontology encoding. Attributes generated from 50 processed NSCLC trials were evaluated with our manually curated attributes. The ontology generated from lung cancer was tested in 74 prostate cancer trials for generalizability. Results: The ontology for lung cancer trials, which is generalizable to prostate cancer and other cancer clinical trials, were constructed. Total 507 attributes were extracted and entities were categorized into 8 groups. Evaluation of attributes generated by NLP and deep learning models compared with manually extracted attributes showed high consistency and accuracy. The average precision, recall and F1 values of 15 most commonly appearing entities (disease, histology, targeted therapy, immunotherapy, radiotherapy, neoadjuvant therapy, age, gender, test, vitals, value, drug, gene, mutation, problem) are 0.873, 0.769, and 0.805, respectively. Conclusions: We contribute to a clinical trial ontology platform for lung cancer and prostate cancer trial recruitment. This ontology platform can be expanded to other solid tumors or hematologic malignancies for clinical trial analysis, and can also be applied to generate synthetic control arm cohorts. We believe NLP-assisted NER can be successfully incorporated for the future work of large scale of clinical trial cohort definition.
Survival among breast cancer patients with a history of previous cancer.
Authors: Pruitt Sandi , Zhu Hong , Gerber David E. , Heitjan Daniel , Maddineni Bhumika , Xiong Danyi , Halm Ethan , Murphy Caitlin C. 
Published: May 2020

e14080 Background: A growing number of women newly diagnosed with breast cancer have survived a previous cancer. Although little is known about their prognosis, this population is frequently excluded from clinical trials. Among women diagnosed with incident breast cancer, we examined the impact of previous cancer on overall and cancer-specific survival. Methods: This population-based cohort study included patients age ≥66 years and diagnosed with breast cancer between 2005-2015 in linked SEER-Medicare data. Separately by breast cancer stage, we estimated overall survival using Cox regression and cause-specific survival using competing risk regression for women with and without previous cancer, adjusting for numerous covariates and competing risk of death from previous cancer, other causes, or the incident breast cancer. Results: Of 138,576 women diagnosed with incident breast cancer, 10,822 (7.8%) had a previous cancer of another organ site and many of these (n = 5,014, 46.3%) were diagnosed ≤5 years of breast cancer. For all breast cancer stages except IV in which there was no significant survival difference, women with vs. without previous cancer had worse overall survival (see Table). This survival disadvantage was driven by deaths due to the previous cancer and other causes. In contrast, while women with previous cancer generally had favorable breast-cancer specific survival, the impact of previous cancer on this outcome varied over time. Conclusions: Many women newly diagnosed with breast cancer are already cancer survivors. These women have generally worse overall survival, worse survival from other causes, but their disease-specific survival varies depending on their breast cancer stage and over time. Future analyses will explore time-varying effect of previous cancer on breast cancer survival. [Table: see text]
A novel approach to quantitatively synthesize subgroup findings from multiple trials.
Authors: Boyne Devon J , Arora Paul , Brenner Darren 
Published: May 2020

e14081 Background: The identification of characteristics that stratify patients according to the likelihood of treatment response is a key goal of precision oncology research. Within randomized trials, effect modification is typically examined through subgroup analyses or the inclusion of an interaction term(s) in a multivariable regression model. To date, systematic reviews of such findings have relied upon qualitative summaries or subgroup-specific meta-analyses. Such approaches are problematic because they do not quantify the magnitude or the degree of uncertainty in the difference in the treatment effects between subgroups. To address this gap, we propose a novel approach that can be used to quantitatively pool subgroup findings from multiple trials. Methods: Our procedure is focused on the estimation of the pooled difference between the subgroup-specific treatment effects within a two-stage meta-analysis. In the first stage, the magnitude and standard error of the difference between the subgroup-specific treatment effects is estimated for each study. When studying relative effect measures, this difference can be estimated on the log-scale, e.g., study-specific difference = log(RRsubgroup A) – log(RRsubgroup B). The standard error of this difference can then be estimated using subgroup-specific 95% CIs. A meta-analysis of the study-specific differences is then conducted. When the differences are estimated on the log-scale, the pooled quantity can be re-expressed as the ratio of the subgroup specific ORs, RRs, or HRs whereby a ratio equal to 1.00 indicates that the subgroup-specific estimates are equal in magnitude. Results: Case-studies and empirical simulations highlighting the application of our approach will be presented. Potential extensions of this methodology will also be discussed. Such extensions include the use of dose-response models to address subgroup categories of a continuous variable and the inclusion of three or more treatments within a network meta-analysis. Conclusions: This method may help to better identify and quantify the degree of heterogeneity of subgroup differences across trials, particularly in settings where the number of patients within each trial is limited.