Association of Frailty and the Expanded Operative Stress Score with Preoperative Acute Serious Conditions, Complications, and Mortality in Males Compared to Females: A Retrospective Observational Study.

OBJECTIVE: The aim of this study was to expand Operative Stress Score (OSS) increasing procedural coverage and assessing OSS and frailty association with Preoperative Acute Serious Conditions (PASC), complications and mortality in females versus males. SUMMARY BACKGROUND DATA: Veterans Affairs male-dominated study showed high mortality in frail veterans even after very low stress surgeries (OSS1). METHODS: Retrospective cohort using NSQIP data (2013-2019) merged with 180-day postoperative mortality from multiple hospitals to evaluate PASC, 30-day complications and 30-, 90-, and 180-day mortality. RESULTS: OSS expansion resulted in 98.2% case coverage versus 87.0% using the original. Of 82,269 patients (43.8% male), 7.9% were frail/very frail. Males had higher odds of PASC [adjusted odds ratio (aOR) = 1.31, 95% confidence interval (CI) = 1.21-1.41, P < 0.001] and severe/life-threatening Clavien-Dindo IV (CDIV) complications (aOR = 1.18, 95% CI = 1.09-1.28, P < 0.001). Although mortality rates were higher (all time-points, P < 0.001) in males versus females, mortality was similar after adjusting for frailty, OSS, and case status primarily due to increased male frailty scores. Additional adjustments for PASC and CDIV resulted in a lower odds of mortality in males (30-day, aOR = 0.81, 95% CI = 0.71-0.92, P = 0.002) that was most pronounced for males with PASC compared to females with PASC (30-day, aOR = 0.75, 95% CI = 0.56-0.99, P = 0.04). CONCLUSIONS: Similar to the male-dominated Veteran population, private sector, frail patients have high likelihood of postoperative mortality, even after low-stress surgeries. Preoperative frailty screening should be performed regardless of magnitude of the procedure. Despite males experiencing higher adjusted odds of PASC and CDIV complications, females with PASC had higher odds of mortality compared to males, suggesting differences in the aggressiveness of care provided to men and women.

Patient and Provider Perspectives on Medication Non-adherence Among Patients with Depression and/or Diabetes in Diverse Community Settings - A Qualitative Analysis.

Background: Diabetes and depression affect a significant percentage of the world's total population, and the management of these conditions is critical for reducing the global burden of disease. Medication adherence is crucial for improving diabetes and depression outcomes, and research is needed to elucidate barriers to medication adherence, including the intentionality of non-adherence, to intervene effectively. The purpose of this study was to explore the perspectives of patients and health care providers on intentional and unintentional medication adherence among patients with depression and diabetes through a series of focus groups conducted across clinical settings in a large urban area. Methods: This qualitative study utilized a grounded theory approach to thematically analyze qualitative data using the framework method. Four focus groups in total were conducted, two with patients and two with providers, over a one-year period using a semi-structured facilitation instrument containing open-ended questions about experiences, perceptions and beliefs about medication adherence. Results: Across the focus groups, communication difficulties between patients and providers resulting in medication non-adherence was a primary theme that emerged. Concerns about medication side effects and beliefs about medication effectiveness were identified as perceptual barriers related to intentional medication non-adherence. Practical barriers to medication adherence, including medication costs, forgetting to take medications and polypharmacy, emerged as themes related to unintentional medication non-adherence. Conclusion: The study findings contribute to a growing body of research suggesting health system changes are needed to improve provider education and implement multicomponent interventions to improve medication adherence among patients with depression and/or diabetes, both chronic illnesses accounting for significant disease burden globally.

Closing the loop: automatically identifying abnormal imaging results in scanned documents.

OBJECTIVES: Scanned documents (SDs), while common in electronic health records and potentially rich in clinically relevant information, rarely fit well with clinician workflow. Here, we identify scanned imaging reports requiring follow-up with high recall and practically useful precision. MATERIALS AND METHODS: We focused on identifying imaging findings for 3 common causes of malpractice claims: (1) potentially malignant breast (mammography) and (2) lung (chest computed tomography [CT]) lesions and (3) long-bone fracture (X-ray) reports. We train our ClinicalBERT-based pipeline on existing typed/dictated reports classified manually or using ICD-10 codes, evaluate using a test set of manually classified SDs, and compare against string-matching (baseline approach). RESULTS: A total of 393 mammograms, 305 chest CT, and 683 bone X-ray reports were manually reviewed. The string-matching approach had an F1 of 0.667. For mammograms, chest CTs, and bone X-rays, respectively: models trained on manually classified training data and optimized for F1 reached an F1 of 0.900, 0.905, and 0.817, while separate models optimized for recall achieved a recall of 1.000 with precisions of 0.727, 0.518, and 0.275. Models trained on ICD-10-labelled data and optimized for F1 achieved F1 scores of 0.647, 0.830, and 0.643, while those optimized for recall achieved a recall of 1.0 with precisions of 0.407, 0.683, and 0.358. DISCUSSION: Our pipeline can identify abnormal reports with potentially useful performance and so decrease the manual effort required to screen for abnormal findings that require follow-up. CONCLUSION: It is possible to automatically identify clinically significant abnormalities in SDs with high recall and practically useful precision in a generalizable and minimally laborious way.

Quantitating and assessing interoperability between electronic health records.

OBJECTIVES: Electronic health records (EHRs) contain a large quantity of machine-readable data. However, institutions choose different EHR vendors, and the same product may be implemented differently at different sites. Our goal was to quantify the interoperability of real-world EHR implementations with respect to clinically relevant structured data. MATERIALS AND METHODS: We analyzed de-identified and aggregated data from 68 oncology sites that implemented 1 of 5 EHR vendor products. Using 6 medications and 6 laboratory tests for which well-accepted standards exist, we calculated inter- and intra-EHR vendor interoperability scores. RESULTS: The mean intra-EHR vendor interoperability score was 0.68 as compared to a mean of 0.22 for inter-system interoperability, when weighted by number of systems of each type, and 0.57 and 0.20 when not weighting by number of systems of each type. DISCUSSION: In contrast to data elements required for successful billing, clinically relevant data elements are rarely standardized, even though applicable standards exist. We chose a representative sample of laboratory tests and medications for oncology practices, but our set of data elements should be seen as an example, rather than a definitive list. CONCLUSIONS: We defined and demonstrated a quantitative measure of interoperability between site EHR systems and within/between implemented vendor systems. Two sites that share the same vendor are, on average, more interoperable. However, even for implementation of the same EHR product, interoperability is not guaranteed. Our results can inform institutional EHR selection, analysis, and optimization for interoperability.

Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality.

PURPOSE: The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed. MATERIALS AND METHODS: Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases). RESULTS: Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients. CONCLUSION: Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.

Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database.

In the absence of genome sequencing, two positive molecular tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible reinfection. Using a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results ≥60 days apart, separated by ≥2 consecutive negative test results. The prevalence of chronic medical conditions, symptoms, and severe outcomes related to coronavirus disease 19 (COVID-19) illness were ascertained. The median age of patients was 64.5 years, 40% were Black, and 39% were female. A total of 83% smoked within the prior year, 61% were overweight/obese, 83% had immunocompromising conditions, and 96% had ≥2 comorbidities. The median interval between the two positive tests was 77 days. Among the 19 patients with 60 to 89 days between positive tests, 17 (89%) exhibited symptoms or clinical manifestations consistent with COVID-19 at the time of the second positive test and 14 (74%) were hospitalized at the second positive test. Of the four patients with ≥90 days between two positive tests (patient 2 [PT2], PT8, PT14, and PT19), two had mild or no symptoms at the second positive test and one, an immunocompromised patient, had a brief hospitalization at the first diagnosis, followed by intensive care unit (ICU) admission at the second diagnosis 3 months later. Our study demonstrated a high prevalence of compromised immune systems, comorbidities, obesity, and smoking among patients with repeatedly positive SARS-CoV-2 tests. Despite limitations, including a lack of semiquantitative estimates of viral load, these data may help prioritize suspected cases of reinfection for investigation and continued surveillance. IMPORTANCE The comprehensive characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing and clinical data for patients with repeatedly positive SARS-CoV-2 tests can help prioritize suspected cases of reinfection for investigation in the absence of genome sequencing data and for continued surveillance of the potential long-term health consequences of SARS-CoV-2 infection.

Rate of change in investigational treatment options: An analysis of reports from a large precision oncology decision support effort.

PURPOSE: Genomic analysis of individual patients is now affordable, and therapies targeting specific molecular aberrations are being tested in clinical trials. Genomically-informed therapy is relevant to many clinical domains, but is particularly applicable to cancer treatment. However, even specialized clinicians need help to interpret genomic data, to navigate the complicated space of clinical trials, and to keep up with the rapidly expanding biomedical literature. To quantitate the cognitive load on treating clinicians, we attempt to quantitate the rate of change in potential treatment options for patients considering genomically-relevant and genomically-selected therapy for cancer. MATERIALS AND METHODS: To this end, we analyzed patient-specific reports generated by a precision oncology decision support team (PODS) at a large academic cancer center. Two types of potential treatment options were analyzed: FDA-approved genomically-relevant and genomically-selected therapies and therapies available via clinical trials. We focused on two clinically-actionable alterations: ERBB2 (Her2/neu; amplified vs. non-amplified) and BRAF mutation (V600 vs. non-V600). To determine changes in available treatment options, we grouped patients into similar groups by disease site (ERBB2: breast, gastric and "other"; BRAF: melanoma, non-melanoma). RESULTS: A total of 2927 reports for 2366 unique patients were generated 8/2016-12/2018. Reports included 9902 gene variants and 150 disease classifications. BRAF mutation and ERBB2 amplification were annotated with therapeutic options in 270 reports (225 unique patients). The median survival time of a therapeutic option was nine months. CONCLUSION: When compared to "traditional" clinical practice guideline recommendations, treatment options for personalized cancer therapy change seven times more rapidly; partly due to change in knowledge and partly due to logistics such as clinical trial availability.

Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity.

Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.

OBJECTIVE: There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer. METHODS: We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames (e.g., document classification) and also where very low-level extraction methods were used (e.g. simply identifying clinical concepts). 78 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps. RESULTS: Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 78), with recent work focusing on extracting information related to treatment and breast cancer diagnosis. CONCLUSION: The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.

OCTANE: Oncology Clinical Trial Annotation Engine.

PURPOSE: Many targeted therapies are currently available only via clinical trials. Therefore, routine precision oncology using biomarker-based assignment to drug depends on matching patients to clinical trials. A comprehensive and up-to-date trial database is necessary for optimal patient-trial matching. METHODS: We describe processes for establishing and maintaining a clinical trial database, focusing on genomically informed trials. Furthermore, we present OCTANE (Oncology Clinical Trial Annotation Engine), an informatics framework supporting these processes in a scalable fashion. To illustrate how the framework can be applied at an institution, we describe how we implemented an instance of OCTANE at a large cancer center. OCTANE consists of three modules. The data aggregation module automates retrieval, aggregation, and update of trial information. The annotation module establishes the database schema, implements data integration necessary for automation, and provides an annotation interface. The update module monitors trial change logs, identifies critical change events, and alerts the annotators when manual intervention may be needed. RESULTS: Using OCTANE, we annotated 5,439 oncology clinical trials (4,438 genomically informed trials) that collectively were associated with 1,453 drugs, 779 genes, and 252 cancer types. To date, we have used the database to screen 4,220 patients for trial eligibility. We compared the update module with expert review, and the module achieved 98.5% accuracy, 0% false-negative rate, and 2.3% false-positive rate. CONCLUSION: OCTANE is a general informatics framework that can be helpful for establishing and maintaining a comprehensive database necessary for automating patient-trial matching, which facilitates the successful delivery of personalized cancer care on a routine basis. Several OCTANE components are publically available and may be useful to other precision oncology programs.

Cancer-Related Internet Use and Its Association With Patient Decision Making and Trust in Physicians Among Patients in an Early Drug Development Clinic: A Questionnaire-Based Cross-Sectional Observational Study.

BACKGROUND: The role of cancer-related internet use on the patient-physician relationship has not been adequately explored among patients who are cancer-related internet users (CIUs) in early-phase clinical trial clinics. OBJECTIVE: We examined the association between cancer-related internet use and the patient-physician relationship and decision making among CIUs in an early drug development clinic. METHODS: Of 291 Phase I clinic patients who completed a questionnaire on internet use, 179 were CIUs. Generations were defined by the year of patient's birth: "millennials" (after 1990) and "Generation X/Y" (1965-1990) grouped as "Millennials or Generation X/Y"; "Baby Boomers" (1946-1964); and "Greatest or Silent Generation" (1945 and earlier). Statistical analyses included the Wilcoxon matched-pairs signed-rank test and the Mann-Whitney U test. RESULTS: CIUs were 52% (94/179) female, 44% (78/179) were older than 60 years, and 60% (108/179) had household incomes exceeding US $60,000. The sources of information on cancer and clinical trials included physicians (171/179, 96%), the internet (159/179, 89%), and other clinical trial personnel (121/179, 68%). For the overall sample and each generation, the median values for trust in referring and Phase I clinical trial physicians among early drug development clinic CIUs were 5 on a 0-5 scale, with 5 indicating "complete trust." CIUs' trust in their referring (5) and phase 1 (5) physicians was higher than CIUs' trust in Web-based cancer-related information (3; P<.001 for both). CIUs who reported visiting the National Cancer Institute (NCI) website, NCI.org, to learn about cancer reported higher levels of trust in Web-based cancer-related information than CIUs who did not use the NCI website (P=.02). Approximately half of CIUs discussed internet information with their doctor. Only 14% (23/165) of CIUs had asked their physician to recommend cancer-related websites, and 24% (35/144) of CIUs reported at least occasional conflict between their physician's advice and Web-based information. CONCLUSIONS: Despite the plethora of websites related to cancer and cancer clinical trials, patients in early-phase clinical trial settings trust their physicians more than Web-based information. Cancer-related organizations should provide regularly updated links to trustworthy websites with cancer and clinical trial information for patients and providers and educate providers on reliable cancer websites so that they can better direct their patients to appropriate internet content.

Precision Oncology Decision Support: Current Approaches and Strategies for the Future.

With the increasing availability of genomics, routine analysis of advanced cancers is now feasible. Treatment selection is frequently guided by the molecular characteristics of a patient's tumor, and an increasing number of trials are genomically selected. Furthermore, multiple studies have demonstrated the benefit of therapies that are chosen based upon the molecular profile of a tumor. However, the rapid evolution of genomic testing platforms and emergence of new technologies make interpreting molecular testing reports more challenging. More sophisticated precision oncology decision support services are essential. This review outlines existing tools available for health care providers and precision oncology teams and highlights strategies for optimizing decision support. Specific attention is given to the assays currently available for molecular testing, as well as considerations for interpreting alteration information. This article also discusses strategies for identifying and matching patients to clinical trials, current challenges, and proposals for future development of precision oncology decision support. Clin Cancer Res; 24(12); 2719-31. ©2018 AACR.

Physician interpretation of genomic test results and treatment selection.

BACKGROUND: Genomic testing is increasingly performed in oncology, but concerns remain regarding the clinician's ability to interpret results. In the current study, the authors sought to determine the agreement between physicians and genomic annotators from the Precision Oncology Decision Support (PODS) team at The University of Texas MD Anderson Cancer Center in Houston regarding actionability and the clinical use of test results. METHODS: On a prospective protocol, patients underwent clinical genomic testing for hotspot mutations in 46 or 50 genes. Six months after sequencing, physicians received questionnaires for patients who demonstrated a variant in an actionable gene, investigating their perceptions regarding the actionability of alterations and clinical use of these findings. Genomic annotators independently classified these variants as actionable, potentially actionable, unknown, or not actionable. RESULTS: Physicians completed 250 of 288 questionnaires (87% response rate). Physicians considered 168 of 250 patients (67%) as having an actionable alteration; of these, 165 patients (98%) were considered to have an actionable alteration by the PODS team and 3 were of unknown significance. Physicians were aware of genotype-matched therapy available for 119 patients (71%) and 48 of these 119 patients (40%) received matched therapy. Approximately 46% of patients in whom physicians regarded alterations as not actionable (36 of 79 patients) were classified as having an actionable/potentially actionable mutation by the PODS team. However, many of these were only theoretically actionable due to limited trials and/or therapies (eg, KRAS). CONCLUSIONS: Physicians are aware of recurrent mutations in actionable genes on "hotspot" panels. As larger genomic panels are used, there may be a growing need for annotation of actionability. Decision support to increase awareness of genomically relevant trials and novel treatment options for recurrent mutations (eg, KRAS) also are needed. Cancer 2018;124:966-72. © 2017 American Cancer Society.

Cancer-Related Internet Use and Online Social Networking Among Patients in an Early-Phase Clinical Trials Clinic at a Comprehensive Cancer Center.

PURPOSE: We examined patterns, correlates, and the impact of cancer-related Internet use among patients with advanced cancer in a phase I clinical trials clinic for molecularly targeted oncologic agents. METHODS: An anonymous questionnaire on Internet use for cancer-related purposes that incorporated input from phase I clinical trial oncologists and patients was self-administered by patients age ≥ 18 years in a phase I clinic. Multivariable modeling was used. Data were analyzed for the overall sample and by generation, which was defined by year of birth. RESULTS: Of 291 patients (52% women, 82% non-Hispanic white, 50% age ≤ 60 years), 62% were cancer-related Internet users (CIUs). Cancer-related Internet use was associated with an income of ≥ $60,000 (odds ratio, 2.42; P = .004). CIUs used the Internet to learn about their cancer (85%), treatment adverse effects (65%), clinical trials (52%), new alternative treatments (42%), and symptom management (41%). CIUs most frequently used the hospital Web site (70%) to learn about clinical trials, followed by ClinicalTrials.gov (42%) and search engines (41%). The emotional impact of Internet-derived cancer information on CIUs varied-56% felt empowered, 34% anxious, 29% relieved, and 17% confused. Cancer-related Internet information made 51% of patients from the Millennial (born after 1990) and Generation X/Y (born 1965 to 1990) CIU populations anxious compared with < 29% of CIUs from older generations (born 1964 and before). Most CIUs desired more online information about new experimental drugs (91%) and US Food and Drug Administration-approved drugs for cancer (72%). CONCLUSION: As most phase I patients use the Internet for cancer-related purposes, the Internet overall and hospital Web sites should provide more extensive, pertinent, and helpful information on clinical trials and cancer treatment to phase I patients.

Identifying Health Information Technology Needs of Oncologists to Facilitate the Adoption of Genomic Medicine: Recommendations From the 2016 American Society of Clinical Oncology Omics and Precision Oncology Workshop.

At the ASCO Data Standards and Interoperability Summit held in May 2016, it was unanimously decided that four areas of current oncology clinical practice have serious, unmet health information technology needs. The following areas of need were identified: 1) omics and precision oncology, 2) advancing interoperability, 3) patient engagement, and 4) value-based oncology. To begin to address these issues, ASCO convened two complementary workshops: the Omics and Precision Oncology Workshop in October 2016 and the Advancing Interoperability Workshop in December 2016. A common goal was to address the complexity, enormity, and rapidly changing nature of genomic information, which existing electronic health records are ill equipped to manage. The subject matter experts invited to the Omics and Precision Oncology Workgroup were tasked with the responsibility of determining a specific, limited need that could be addressed by a software application (app) in the short-term future, using currently available genomic knowledge bases. Hence, the scope of this workshop was to determine the basic functionality of one app that could serve as a test case for app development. The goal of the second workshop, described separately, was to identify the specifications for such an app. This approach was chosen both to facilitate the development of a useful app and to help ASCO and oncologists better understand the mechanics, difficulties, and gaps in genomic clinical decision support tool development. In this article, we discuss the key challenges and recommendations identified by the workshop participants. Our hope is to narrow the gap between the practicing oncologist and ongoing national efforts to provide precision oncology and value-based care to cancer patients.

More Medicine, Fewer Clicks: How Informatics Can Actually Help Your Practice.

In the information age, we expect data systems to make us more effective and efficient-not to make our lives more difficult! In this article, we discuss how we are using data systems, such as electronic health records (EHRs), to improve care delivery. We illustrate how US Oncology is beginning to use real-world evidence to facilitate trial accrual by automatic identification of eligible patients and how big data and predictive analytics will transform the field of oncology. Some information systems are already being used at the point of care and are already empowering clinicians to improve the care of their patients in real time. Telehealth platforms are being used to bridge gaps that currently exist in expertise, geography, and technical capability. Optimizing virtual collaboration, such as through virtual tumor boards, is empowering communities that are geographically disparate to coordinate care. Informatics methods can provide solutions to the challenging problems of how to manage the vast amounts of data confronting the practicing oncologist, including information about treatment regimens, side effects, and the influence of genomics on the practice of oncology. We also discuss some of the challenges of clinical documentation in the modern era, and review emerging efforts to engage patients as digital donors of their EHR data.

A feasibility study of returning clinically actionable somatic genomic alterations identified in a research laboratory.

PURPOSE: Molecular profiling performed in the research setting usually does not benefit the patients that donate their tissues. Through a prospective protocol, we sought to determine the feasibility and utility of performing broad genomic testing in the research laboratory for discovery, and the utility of giving treating physicians access to research data, with the option of validating actionable alterations in the CLIA environment. EXPERIMENTAL DESIGN: 1200 patients with advanced cancer underwent characterization of their tumors with high depth hybrid capture sequencing of 201 genes in the research setting. Tumors were also tested in the CLIA laboratory, with a standardized hotspot mutation analysis on an 11, 46 or 50 gene platform. RESULTS: 527 patients (44%) had at least one likely somatic mutation detected in an actionable gene using hotspot testing. With the 201 gene panel, 945 patients (79%) had at least one alteration in a potentially actionable gene that was undetected with the more limited CLIA panel testing. Sixty-four genomic alterations identified on the research panel were subsequently tested using an orthogonal CLIA assay. Of 16 mutations tested in the CLIA environment, 12 (75%) were confirmed. Twenty-five (52%) of 48 copy number alterations were confirmed. Nine (26.5%) of 34 patients with confirmed results received genotype-matched therapy. Seven of these patients were enrolled onto genotype-matched targeted therapy trials. CONCLUSION: Expanded cancer gene sequencing identifies more actionable genomic alterations. The option of CLIA validating research results can provide alternative targets for personalized cancer therapy.

Automated identification of molecular effects of drugs (AIMED).

INTRODUCTION: Genomic profiling information is frequently available to oncologists, enabling targeted cancer therapy. Because clinically relevant information is rapidly emerging in the literature and elsewhere, there is a need for informatics technologies to support targeted therapies. To this end, we have developed a system for Automated Identification of Molecular Effects of Drugs, to help biomedical scientists curate this literature to facilitate decision support. OBJECTIVES: To create an automated system to identify assertions in the literature concerning drugs targeting genes with therapeutic implications and characterize the challenges inherent in automating this process in rapidly evolving domains. METHODS: We used subject-predicate-object triples (semantic predications) and co-occurrence relations generated by applying the SemRep Natural Language Processing system to MEDLINE abstracts and ClinicalTrials.gov descriptions. We applied customized semantic queries to find drugs targeting genes of interest. The results were manually reviewed by a team of experts. RESULTS: Compared to a manually curated set of relationships, recall, precision, and F2 were 0.39, 0.21, and 0.33, respectively, which represents a 3- to 4-fold improvement over a publically available set of predications (SemMedDB) alone. Upon review of ostensibly false positive results, 26% were considered relevant additions to the reference set, and an additional 61% were considered to be relevant for review. Adding co-occurrence data improved results for drugs in early development, but not their better-established counterparts. CONCLUSIONS: Precision medicine poses unique challenges for biomedical informatics systems that help domain experts find answers to their research questions. Further research is required to improve the performance of such systems, particularly for drugs in development.

Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov.

OBJECTIVE: Clinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer therapy. The goal of this project is to create a knowledge base of cancer treatment trials with annotations about genetic alterations from ClinicalTrials.gov. METHODS: We developed a semi-automatic framework that combines advanced text-processing techniques with manual review to curate genetic alteration information in cancer trials. The framework consists of a document classification system to identify cancer treatment trials from ClinicalTrials.gov and an information extraction system to extract gene and alteration pairs from the Title and Eligibility Criteria sections of clinical trials. By applying the framework to trials at ClinicalTrials.gov, we created a knowledge base of cancer treatment trials with genetic alteration annotations. We then evaluated each component of the framework against manually reviewed sets of clinical trials and generated descriptive statistics of the knowledge base. RESULTS AND DISCUSSION: The automated cancer treatment trial identification system achieved a high precision of 0.9944. Together with the manual review process, it identified 20 193 cancer treatment trials from ClinicalTrials.gov. The automated gene-alteration extraction system achieved a precision of 0.8300 and a recall of 0.6803. After validation by manual review, we generated a knowledge base of 2024 cancer trials that are labeled with specific genetic alteration information. Analysis of the knowledge base revealed the trend of increased use of targeted therapy for cancer, as well as top frequent gene-alteration pairs of interest. We expect this knowledge base to be a valuable resource for physicians and patients who are seeking information about personalized cancer therapy.