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AKT inhibitors have promising activity in AKT1E17K-mutant estrogen receptor (ER)–positive metastatic breast cancer, but the natural history of this rare genomic subtype remains unknown. Utilizing AACR Project GENIE, an international clinicogenomic data-sharing consortium, we conducted a comparative analysis of clinical outcomes of patients with matched AKT1E17K-mutant (n = 153) and AKT1–wild-type (n = 302) metastatic breast cancer. AKT1-mutant cases had similar adjusted overall survival (OS) compared with AKT1–wild-type controls (median OS, 24.1 vs. 29.9, respectively; P = 0.98). AKT1-mutant cases enjoyed longer durations on mTOR inhibitor therapy, an observation previously unrecognized in pivotal clinical trials due to the rarity of this alteration. Other baseline clinicopathologic features, as well as durations on other classes of therapy, were broadly similar. In summary, we demonstrate the feasibility of using a novel and publicly accessible clincogenomic registry to define outcomes in a rare genomically defined cancer subtype, an approach with broad applicability to precision oncology.Significance:We delineate the natural history of a rare genomically distinct cancer, AKT1E17K-mutant ER-positive breast cancer, using a publicly accessible registry of real-world patient data, thereby illustrating the potential to inform drug registration through synthetic control data.See related commentary by Castellanos and Baxi, p. 490.

Background: Adjuvant endocrine therapy (AET) significantly improves long-term survival of breast cancer patients with hormone receptor-positive disease. Despite the proven clinical efficacy of AET, many breast cancer survivors either fail to fill their first medication order (primary non-adherence), fail to refill their AET at the prescribed frequency (inadequate adherence), or discontinue therapy early (non-persistence), increasing their risk of death from recurrence. Unfortunately, there is no systematic way whereby oncology providers are notified when their patients do not initially fill or refill their AET prescriptions at the prescribed frequency. EMR integrated pharmacy dispensing data (EIPDD) bridges the gap between pharmacies, clinicians, and patients by providing medication dispensing tracking in the electronic health record. EIPDD could be used to identify and address the documentation and notification gaps with the goal of improving adherence and persistence to AET. This study seeks to evaluate the completeness and timeliness of EIPDD for early detection of primary medication non-adherence events to breast cancer adjuvant endocrine therapy. Materials and Methods: Data was extracted from the Epic EMR of an urban academic medical center for patients with documented Stage 0-III breast cancer with first prescription from a breast oncologist for AET between 2016-2019. Surescripts was used as the EIPDD data source integrated into the local Epic EMR. Patients were classified as having sufficient or insufficient data available based on the pharmacy dispense refresh event occurring within 365 days of AET order. The early detection of primary medication adherence was defined as the first dispense event completed within 90 days of first prescription. Primary non-adherence was defined as the failure to have the prescription for AET dispense event within 90 days of first prescription. Patients whose orders were not sent to an EIPDD contracted pharmacy (non-contract pharmacy) were excluded from the primary adherence evaluation and deemed to have incomplete data. Results: Detailed results are shown in the table, but in summary, 963 patients with stage 0-III breast cancer had 963 first prescription orders for AET between 2016-2019 routed to 646 unique pharmacies of which 634(98%) were contract and 12(2%) were non-contract pharmacies. Among the 948 patients with a first prescription sent to a contract pharmacy, 113(11.9%) had incomplete EIPDD refresh events to assess primary adherence. Among the 835 patients with at least one EIPDD refresh event following their first prescription, 80% of those events occurred within 90 days of the first prescription order, sufficiently timely for early detection of primary adherence. However, among those with primary non-adherence, only 4% had EIPDD refresh events within 90 days. Overall, 33.5% of patients would benefit from an intervention to verify or improve primary adherence to AET. Conclusions: EIPDD presents an opportunity to improve provider awareness of AET primary medication adherence. While the frequency of refreshing data could be improved to support more timely and complete data, EIPDD data represents a promising opportunity to provide clinical decision support to breast cancer survivorship teams with the goal of improving primary adherence to AET. Primary Medication Adherence and Timeliness for Early DetectionCategoryCountPercentageStage 0-III breast cancer 1st prescribed AET by breast provider 2016 -2019963100%Prescription sent to non-contract pharmacy151.5%Prescription sent to contract pharmacy94898.5%Failure to have pharmacy dispense data refresh event within 365 days of AET order11311.9%At least one pharmacy dispense data refresh event within 365 days of AET order83588.1%No AET dispense event within 90 days of prescription order16820.1%Non-primary adherence adequate early detection74.2%Non-primary adherence inadequate early detection16195.8%At least one AET dispense event within 90 days of prescription order (primary medication adherence)66779.9%Primary adherence adequate early detection53279.7%Primary adherence inadequate early detection13520.3% Citation Format: Mia Levy, Shirlene Paul, Jordan Lieberenz. Completeness and timeliness of EMR integrated pharmacy dispensing data for early detection of non-adherence to breast cancer adjuvant endocrine therapy [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-13-05.

Purpose:We wanted to determine the prognosis and the phenotypic characteristics of hormone receptor–positive advanced breast cancer tumors harboring an ERBB2 mutation in the absence of a HER2 amplification.Experimental Design:We retrospectively collected information from the American Association of Cancer Research-Genomics Evidence Neoplasia Information Exchange registry database from patients with hormone receptor–positive, HER2-negative, ERBB2-mutated advanced breast cancer. Phenotypic and co-mutational features, as well as response to treatment and outcome were compared with matched control cases ERBB2 wild type.Results:A total of 45 ERBB2-mutant cases were identified for 90 matched controls. The presence of an ERBB2 mutation was not associated with worse outcome determined by overall survival (OS) from first metastatic relapse. No significant differences were observed in phenotypic characteristics apart from higher lobular infiltrating subtype in the ERBB2-mutated group. ERBB2 mutation did not seem to have an impact in response to treatment or time-to-progression (TTP) to endocrine therapy compared with ERBB2 wild type. In the co-mutational analyses, CDH1 mutation was more frequent in the ERBB2-mutated group (FDR < 1). Although not significant, fewer co-occurring ESR1 mutations and more KRAS mutations were identified in the ERBB2-mutated group.Conclusions:ERBB2-activating mutation was not associated with a worse OS from time of first metastatic relapse, or differences in TTP on treatment as compared with a series of matched controls. Although not significant, differences in coexisting mutations (CDH1, ESR1, and KRAS) were noted between the ERBB2-mutated and the control group.

Supplementary Figure from Natural History and Characteristics of ERBB2-mutated Hormone Receptor–positive Metastatic Breast Cancer: A Multi-institutional Retrospective Case–control Study from AACR Project GENIE

Supplementary Table from Natural History and Characteristics of ERBB2-mutated Hormone Receptor–positive Metastatic Breast Cancer: A Multi-institutional Retrospective Case–control Study from AACR Project GENIE

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., Deep analysis of breast cancer & AML trials using the my cancer genome model

PURPOSE Tumor next-generation sequencing reports typically generate trial recommendations for patients based on their diagnosis and genomic profile. However, these require additional refinement and prescreening, which can add to physician burden. We wanted to use human prescreening efforts to efficiently refine these trial options and also elucidate the high-value parameters that have a major impact on efficient trial matching. METHODS Clinical trial recommendations were generated based on diagnosis and biomarker criteria using an informatics platform and were further refined by manual prescreening. The refined results were then compared with the initial trial recommendations and the reasons for false-positive matches were evaluated. RESULTS Manual prescreening significantly reduced the number of false positives from the informatics generated trial recommendations, as expected. We found that trial-specific criteria, especially recruiting status for individual trial arms, were a high value parameter and led to the largest number of automated false-positive matches. CONCLUSION Reflex clinical trial matching approaches that refine trial recommendations based on the clinical details as well as trial-specific criteria have the potential to help alleviate physician burden for selecting the most appropriate trial for their patient. Investing in publicly available resources that capture the recruiting status of a trial at the cohort or arm level would, therefore, allow us to make meaningful contributions to increase the clinical trial enrollments by eliminating false positives.

Objective As clinical trials evolve in complexity, clinical trial data models that can capture relevant trial data in meaningful, structured annotations and computable forms are needed to support accrual. Material and Methods We have developed a clinical trial information model, curation information system, and a standard operating procedure for consistent and accurate annotation of cancer clinical trials. Clinical trial documents are pulled into the curation system from publicly available sources. Using a web-based interface, a curator creates structured assertions related to disease-biomarker eligibility criteria, therapeutic context, and treatment cohorts by leveraging our data model features. These structured assertions are published on the My Cancer Genome (MCG) website. Results To date, over 5000 oncology trials have been manually curated. All trial assertion data are available for public view on the MCG website. Querying our structured knowledge base, we performed a landscape analysis to assess the top diseases, biomarker alterations, and drugs featured across all cancer trials. Discussion Beyond curating commonly captured elements, such as disease and biomarker eligibility criteria, we have expanded our model to support the curation of trial interventions and therapeutic context (ie, neoadjuvant, metastatic, etc.), and the respective biomarker-disease treatment cohorts. To the best of our knowledge, this is the first effort to capture these fields in a structured format. Conclusion This paper makes a significant contribution to the field of biomedical informatics and knowledge dissemination for precision oncology via the MCG website. Key words knowledge representation, My Cancer Genome, precision oncology, knowledge curation, cancer informatics, clinical trial data model

Breast cancer disparities affect how different populations are impacted by breast cancer incidence, mortality, and survival. We provide an overview of large datasets that scientists can use to study disparities in breast cancer outcomes. Many large datasets are accessible to disparities researchers with a project plan and little or no cost. Yet only two datasets have been significantly used in breast cancer disparities publications. Other datasets combine administrative claim, molecular, electronic health record, patient reported, imaging, and clinical trial data in a way that could benefit disparities research. Many existing datasets lack sufficient diversity or detail in key disparity variables. With this review of the different datasets available and their potential pitfalls, researchers will be better equipped to conduct studies that can identify and solve the problems that lead to health outcome disparities for breast cancer patients.

Digital health advances have transformed many clinical areas including psychiatric and cardiovascular care. However, digital health innovation is relatively nascent in cancer care, which represents the fastest growing area of health-care spending. Opportunities for digital health innovation in oncology include patient-facing technologies that improve patient experience, safety, and patient-clinician interactions; clinician-facing technologies that improve their ability to diagnose pathology and predict adverse events; and quality of care and research infrastructure to improve clinical workflows, documentation, decision support, and clinical trial monitoring. The COVID-19 pandemic and associated shifts of care to the home and community dramatically accelerated the integration of digital health technologies into virtually every aspect of oncology care. However, the pandemic has also exposed potential flaws in the digital health ecosystem, namely in clinical integration strategies; data access, quality, and security; and regulatory oversight and reimbursement for digital health technologies. Stemming from the proceedings of a 2020 workshop convened by the National Cancer Policy Forum of the National Academies of Sciences, Engineering, and Medicine, this article summarizes the current state of digital health technologies in medical practice and strategies to improve clinical utility and integration. These recommendations, with calls to action for clinicians, health systems, technology innovators, and policy makers, will facilitate efficient yet safe integration of digital health technologies into cancer care.

The 2018 ACR recommendations for breast cancer screening in women at higher than average risk include new recommendations for supplemental breast MRI for patients with personal histories of breast cancer (PHBCs) who either carry hereditary cancer gene mutations, have dense breast tissue, or were diagnosed before 50 years of age. In comparison, prior guidelines recommended supplemental MRI only for women with PHBCs who carried hereditary cancer gene mutations. The aim of this study was to quantify the increase in the number of patients with breast cancer for whom supplemental breast MRI would now be recommended.Data were extracted from the electronic health records of patients presenting for screening or diagnostic mammography at an urban academic medical center between July 20, 2020, and July 19, 2021. Data extracted included patient-reported PHBC, age at time of breast cancer diagnosis, and hereditary cancer gene mutation carrier status. Descriptive statistics are reported, evaluating the rate of eligibility for supplemental breast MRI in a retrospective population given the new ACR guidelines.Of the 2,950 patients with self-reported PHBCs who presented for breast cancer screening in the year between July 2020 and July 2021, 1,805 (61%) met the criteria for supplemental breast MRI according to the 2018 guidelines compared with only 3.6% using pre-2018 guidelines.Measuring the impact of the 2018 ACR supplemental MRI recommendations using real-world data at a single urban academic medical center demonstrated a 15-fold increase in potential eligibility for supplemental breast MRI in patients with PHBCs.

In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement.

Integrating different types of data, including electronic health records, imaging data, administrative and claims databases, large data repositories, the Internet of Things, genomics, and other omics data, is both a challenge and an opportunity that must be tackled head on. We explore some of the challenges and opportunities in optimizing data integration to accelerate breast cancer discovery and improve patient outcomes. Susan G. Komen convened three meetings (2015, 2017, and 2018) with various stakeholders to discuss challenges, opportunities, and next steps to enhance the use of big data in the field of breast cancer. Meeting participants agreed that big data approaches can enhance the identification of better therapies, improve outcomes, reduce disparities, and optimize precision medicine. One challenge is that databases must be shared, linked with each other, standardized, and interoperable. Patients want to be active participants in research and their own care, and to control how their data are used. Many patients have privacy concerns and do not understand how sharing their data can help to effectively drive discovery. Public education is essential, and breast cancer researchers who are skilled in using and analyzing big data are needed. Patient advocacy groups can play multiple roles to help maximize and leverage big data to better serve patients. Komen is committed to educating patients on big data issues, encouraging data sharing by all stakeholders, assisting in training the next generation of data science breast cancer researchers, and funding research projects that will use real-life data in real time to revolutionize the way breast cancer is understood and treated.

Objective Research to date focused on quantifying team collaboration has relied on identifying shared patients but does not incorporate the major role of communication patterns. The goal of this study was to describe the patterns and volume of communication among care team members involved in treating breast cancer patients. Materials and Methods We analyzed 4 years of communications data from the electronic health record between care team members at Vanderbilt University Medical Center (VUMC). Our cohort of patients diagnosed with breast cancer was identified using the VUMC tumor registry. We classified each care team member participating in electronic messaging by their institutional role and classified physicians by specialty. To identify collaborative patterns, we modeled the data as a social network. Results Our cohort of 1181 patients was the subject of 322 424 messages sent in 104 210 unique communication threads by 5620 employees. On average, each patient was the subject of 88.2 message threads involving 106.4 employees. Each employee, on average, sent 72.9 messages and was connected to 24.6 collaborators. Nurses and physicians were involved in 98% and 44% of all message threads, respectively. Discussion and Conclusion Our results suggest that many providers in our study may experience a high volume of messaging work. By using data routinely generated through interaction with the electronic health record, we can begin to evaluate how to iteratively implement and assess initiatives to improve the efficiency of care coordination and reduce unnecessary messaging work across all care team roles.

PURPOSE: Clinical notes function as the de facto handoff between providers and assume great importance during unplanned medical encounters. An organized and thorough oncology history is essential in care coordination. We sought to understand reader preferences for oncology history organization by comparing between chronologic and narrative formats. METHODS: A convenience sample of 562 clinicians from 19 National Comprehensive Cancer Network Member Institutions responded to a survey comparing two formats of oncology histories, narrative and chronologic, for the same patient. Both histories were consensus-derived real-world examples. Each history was evaluated using semantic differential attributes ( thorough, useful, organized, comprehensible, and succinct). Respondents choose a preference between the two styles for history gathering and as the basis of a new note. Open-ended responses were also solicited. RESULTS: Respondents preferred the chronologic over the narrative history to prepare for a visit with an unknown patient (66% preference) and as a basis for their own note preparation (77% preference) ( P < .01). The chronologic summary was preferred in four of the five measured attributes ( useful, organized, comprehensible, and succinct); the narrative summary was favored for thoroughness ( P < .01). Open-ended responses reflected the attribute scoring and noted the utility of content describing social determinants of health in the narrative history. CONCLUSION: Respondents of this convenience sample preferred a chronologic oncology history to a concise narrative history. Further studies are needed to determine the optimal structure and content of chronologic documentation for oncology patients and the provider effort to use this format.

Purpose: We wanted to determine the prognosis and the phenotypic characteristics of hormone receptor–positive advanced breast cancer tumors harboring an ERBB2 mutation in the absence of a HER2 amplification. Experimental Design: We retrospectively collected information from the American Association of Cancer Research-Genomics Evidence Neoplasia Information Exchange registry database from patients with hormone receptor–positive, HER2-negative, ERBB2-mutated advanced breast cancer. Phenotypic and co-mutational features, as well as response to treatment and outcome were compared with matched control cases ERBB2 wild type. Results: A total of 45 ERBB2-mutant cases were identified for 90 matched controls. The presence of an ERBB2 mutation was not associated with worse outcome determined by overall survival (OS) from first metastatic relapse. No significant differences were observed in phenotypic characteristics apart from higher lobular infiltrating subtype in the ERBB2-mutated group. ERBB2 mutation did not seem to have an impact in response to treatment or time-to-progression (TTP) to endocrine therapy compared with ERBB2 wild type. In the co-mutational analyses, CDH1 mutation was more frequent in the ERBB2-mutated group (FDR < 1). Although not significant, fewer co-occurring ESR1 mutations and more KRAS mutations were identified in the ERBB2-mutated group. Conclusions: ERBB2-activating mutation was not associated with a worse OS from time of first metastatic relapse, or differences in TTP on treatment as compared with a series of matched controls. Although not significant, differences in coexisting mutations (CDH1, ESR1, and KRAS) were noted between the ERBB2-mutated and the control group.

Colorectal cancer (CRC), a disease of high incidence and mortality, has had few treatment advances owing to a large degree of inter- and intratumoral heterogeneity. Attempts to classify subtypes of colorectal cancer to develop treatment strategies has been attempted by Consensus Molecular Subtypes (CMS) classification. However, the cellular etiology of CMS classification is incompletely understood and controversial. Here, we generated and analyzed a single-cell transcriptome atlas of 49,859 CRC cells from 16 patients, validated with an additional 31,383 cells from an independent CRC patient cohort. We describe subclonal transcriptomic heterogeneity of CRC tumor epithelial cells, as well as discrete stromal populations of cancer-associated fibroblasts (CAFs). Within CRC CAFs, we identify the transcriptional signature of specific subtypes (CAF-S1 and CAF-S4) in more than 1,500 CRC patients using bulk transcriptomic data that significantly stratifies overall survival in multiple independent cohorts. We also uncovered two CAF-S1 subpopulations, ecm-myCAF and TGFß-myCAF, known to be associated with primary resistance to immunotherapies. We demonstrate that scRNA analysis of malignant, stromal, and immune cells exhibit a more complex picture than portrayed by bulk transcriptomic-based Consensus Molecular Subtypes (CMS) classification. By demonstrating an abundant degree of heterogeneity amongst these cell types, our work shows that CRC is best represented in a transcriptomic continuum crossing traditional classification systems boundaries. Overall, this CRC cell map provides a framework to re-evaluate CRC tumor biology with implications for clinical trial design and therapeutic development.

The AACR Project GENIE is an international data-sharing consortium focused on generating an evidence base for precision cancer medicine by integrating clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. In conjunction with the first public data release from approximately 19,000 samples, we describe the goals, structure, and data standards of the consortium and report conclusions from high-level analysis of the initial phase of genomic data. We also provide examples of the clinical utility of GENIE data, such as an estimate of clinical actionability across multiple cancer types (>30%) and prediction of accrual rates to the NCI-MATCH trial that accurately reflect recently reported actual match rates. The GENIE database is expected to grow to >100,000 samples within 5 years and should serve as a powerful tool for precision cancer medicine. Significance: The AACR Project GENIE aims to catalyze sharing of integrated genomic and clinical datasets across multiple institutions worldwide, and thereby enable precision cancer medicine research, including the identification of novel therapeutic targets, design of biomarker-driven clinical trials, and identification of genomic determinants of response to therapy. Cancer Discov; 7(8); 818–31. ©2017 AACR. See related commentary by Litchfield et al., p. 796. This article is highlighted in the In This Issue feature, p. 783

AKT inhibitors have promising activity in

My Cancer Genome (https://www.mycancergenome.org; MCG) recently released a major update to the website, with an ultimate goal of adding of AACR Project GENIE data to demonstrate frequency of genetic variants in cancer, estimate matching to clinical trial arms, and augment the case report database. This abstract presents use of GENIE data 1) as part of a new process to automatically select cancer types and biomarkers for inclusion and 2) to display prevalence data on the website. My Cancer Genome is a publicly available precision cancer medicine knowledgebase managed by the Vanderbilt-Ingram Cancer Center. The mission of MCG is to curate and disseminate knowledge regarding the clinical significance of genomic alterations in cancer. The website was first made available to the public in early 2011 and has since grown into a resource visited more than 7,000 times a week by people from well over 200 countries and territories around the world. The new website was released in June 2019, and it includes several new features: a new clinical trials search using manually curated disease and biomarker eligibility criteria, therapeutic assertions illustrating situations where having or not having a particular biomarker detected supports or does not support use of a particular drug, and the inclusion of data from AACR Project GENIE. Addition of GENIE data to MCG was approved by AACR Project GENIE's Data Use and Publications Committee. First, any disease or biomarker associated with a therapeutic assertion or clinical trial has a page on MCG. In addition, any disease or biomarker that appears five times or more in the GENIE dataset is also given a page. In this way, the website will remain up-to-date with almost all diseases and biomarkers that can be considered relevant as precision oncology knowledge and GENIE grow. Next, GENIE data appears on disease and biomarker pages, in both charts and descriptive text. On disease pages, such as the breast carcinoma page, GENIE data appear in a chart of most commonly altered genes in breast carcinoma and most common alterations in breast carcinoma; in addition, text describing the prevalence of mutations appears under each gene listed in the section on significant genes in breast carcinoma. On biomarker pages, such as the ABL1 page, GENIE data appear in a chart of the most common diseases where ABL1 has been found to be altered and the most common alterations in ABL1 that appear in GENIE cases; in addition, text describing the prevalence of ABL1 mutations appears under each disease listed in the section on the significance of ABL1 in diseases. The addition of data from AACR Project GENIE to My Cancer Genome has improved and enhanced MCG's content, ability to scale, and ability to maintain. Finally, inclusion of data from AACR Project GENIE on My Cancer Genome provides a simple way to select and view subsets of GENIE data for specific diseases and biomarkers in the context of relevant precision oncology therapies and clinical trials. Citation Format: Christine M. Micheel, Michele L. LeNoue-Newton, Marilyn E. Holt, Neha Jain, Mia A. Levy. Augmenting the My Cancer Genome knowledge resource with data from AACR Project GENIE [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2058.

e19328 Background: Patients with breast cancer experience significant disruption to their daily lives when undergoing treatment. When treatment workload exceeds the capacity to manage that treatment, patients are less likely to adhere to care plans and likelier to have worse outcomes. The purpose of this study was to assess treatment workload in patients with operable breast cancer in the SEER-Medicare dataset. Methods: Operable (stage I-III) breast cancer patients diagnosed from 2010-2016, > 65 years old at diagnosis, and with 18 months of continuous Medicare part A, B, and D coverage after diagnosis were included in the cohort. We calculated the number of outpatient appointment days, total cumulative inpatient lengths of stay, and distance traveled for patients in the cohort in the 18 months following diagnosis. We compared treatment workload outcomes between patients with stage I and stage III disease and between patients with HER2+ and HER2-/ER+ disease. Results: 35,071 patients met the inclusion criteria. Compared to stage I patients, stage III patients had more median outpatient appointment days (71 vs 50), more median inpatient days (2 vs 0), and greater median distance traveled (1846 vs 1332 miles). Compared to patients with HER2-/ER+ disease, patients with HER2+ disease had more median outpatient appointment days (70 vs 51), and greater median distance traveled (1775 vs 1350 miles). All comparisons were with Mann Whitney U and were statistically significant with P < .001. Conclusions: Patients with operable breast cancer experience a high amount of treatment workload in the first 18 months after diagnosis. These workload measures derived from SEER-Medicare claims differentiate significantly by stage and subtype, with stage III and HER2+ patients experiencing greater treatment workload than stage I and HER2-/ER+ patients respectively. We suspect that greater treatment workload in stage III is attributed to higher intensity of treatment for later stage cancer and associated complications, while greater treatment workload in HER2+ patients is attributed to the use of trastuzumab. [Table: see text]

e24043 Background: Oncology practices face multiple challenges when trying to implement the Commission on Cancer (CoC) Survivorship Care Plan (SCP) documentation standard. First, it is difficult to systematically identify patients who qualify for a SCP from the electronic medical record (EMR). Second, SCP documentation must be completed within a specific timeframe that does not always align with clinical encounters for routine care. Finally, as practices participate in more regulatory programs, such as the Oncology Care Model (OCM), the burden of meeting the unique but often overlapping documentation standards becomes burdensome. Our goal was to improve adherence to SCP documentation through enhanced clinical documentation workflows and clinical decision support. Methods: To meet CoC SCP standards, SCP templates were built into the EPIC EMR at a single multi-centered institution. These templates allowed the user to indicate on the problem list if the SCP was needed. Likewise, Institute of Medicine (IOM) care plan documentation templates and reminders were built to meet OCM standards. Population dashboards were developed in Tableau to track SCP documentation adherence and provide support regarding eligible patients who required SCP documentation. Patients were eligible who had a cancer diagnosis codes on the problem list, and MC in the EMR. Patients greater than 12 months from being marked curative were considered eligible to complete SCP. Results: Between 2015-2019, 2763 cancer patients were MC by 31 providers, where 806 (30%) were OCM patients. Overall, 1372 (70%) non-OCM patients required an SCP, where 563 (41%) were completed. When examining the OCM patients, 689 (85%) required a SCP, where 552 (80%) were completed. For patients MC in 2018, 163/439 (37%) and 137/190 (72%) of patients received a SCP for the non-OCM and OCM group respectively. For patient MC in 2019 after implementation of the decision support dashboard, 115/145 (79%) and 144/148 (97%) of eligible patients had completed SCP for the non-OCM and OCM group respectively (Table). Conclusions: During the study period, adherence to SCP documentation doubled concordant with CoC standards at that time. Integration of IOM and SCP documentation improved adherence among OMC compared to non-OCM patients. The decision support dashboard helped prioritize cases for SCP documentation for care teams. This is an example of a decision support framework that could be used for other standards driven documentation requirements in cancer.

Tumor heterogeneity, especially intratumoral heterogeneity, is a primary reason for treatment failure. A single biopsy may not reflect the complete genomic architecture of the tumor needed to make therapeutic decisions. Circulating tumor DNA (ctDNA) is believed to overcome these limitations. We analyzed concordance between ctDNA and whole-exome sequencing/whole-genome sequencing (WES/WGS) of tumor samples from patients with breast (n = 12), gastrointestinal (n = 20), lung (n = 19), and other tumor types (n = 13). Correlation in the driver, hotspot, and actionable alterations was studied. Three cases in which more-in-depth genomic analysis was required have been presented. A total 58% (37/64) of patients had at least one concordant mutation. Patients who had received systemic therapy before tissue next-generation sequencing (NGS) and ctDNA analysis showed high concordance (78% (21/27) vs. 43% (12/28) p = 0.01, respectively). Obtaining both NGS and ctDNA increased actionable alterations from 28% (18/64) to 52% (33/64) in our patients. Twenty-one patients had mutually exclusive actionable alterations seen only in either tissue NGS or ctDNA samples. Somatic hotspot mutation analysis showed significant discordance between tissue NGS and ctDNA analysis, denoting significant tumor heterogeneity in these malignancies. Increased tissue tumor mutation burden (TMB) positively correlated with the number of ctDNA mutations in patients who had received systemic therapy, but not in treatment-na&iuml, ve patients. Prior systemic therapy and TMB may affect concordance and should be taken into consideration in future studies. Incorporating driver, actionable, and hotspot analysis may help to further refine the correlation between these two platforms. Tissue NGS and ctDNA are complimentary, and if done in conjunction, may increase the detection rate of actionable alterations and potentially therapeutic targets.

Room for improvement exists regarding recommendations for screening, staging, therapy selection, and frequency of surveillance of gastrointestinal cancers. Screening is costly and invasive, improved staging demands increased sensitivity and specificity to better guide therapy selection. Surveillance requires increased sensitivity for earlier detection and precise management of recurrences. Peripherally collected blood-based liquid biopsies enrich and analyze circulating tumor cells and/or somatic genomic material, including circulating tumor DNA along with various subclasses of RNA. Such assays have the potential to impact clinical practice at multiple stages of management in gastrointestinal cancers. This review summarizes current basic and clinical evidence for the utilization of liquid biopsy in cancers of the esophagus, pancreas, stomach, colon, and rectum. Technical aspects of various liquid biopsy methodologies and targets are reviewed and evidence supporting current commercially available assays is examined. Finally, current clinical applicability, potential future uses, and pitfalls of applying liquid biopsy to the screening, staging and therapeutic management of these diseases are discussed.

10520 Background: Female underrepresentation in academic medicine leadership is well-documented; however, oncology specific data are scarce. This study evaluates female leadership representation in academic medical oncology (MO), radiation oncology (RO) and surgical oncology (SO) programs. Furthermore, we examine the impact of female leadership on overall female faculty representation. Methods: A total of 264 (96%) Accreditation Council for Graduate Medical Education actively accredited MO [144 of 153], RO [93 of 94] and SO [27 of 27] training programs were included. The gender of overall faculty and those in leadership positions (program director and departmental chair/division chief) of each program was determined using hospital websites from 10/01/18 to 01/27/19. The chi-squared goodness-of-fit test was used to examine whether the observed proportion of females in leadership positions deviates significantly from the expected proportion based on the actual proportion of overall female faculty in MO, RO and SO. Two-sample t-tests were used to compare rates of female faculty representation across each program based on the presence/absence of female in a leadership position for MO, RO and SO. Results: Female faculty representation in MO, RO and SO was 37.1% (1,554/4,191), 30.7% (389/1,269) and 38.8% (212/546), respectively. Female representation in leadership positions was 31.5% (82/260), 17.4% (31/178) and 11.1% (5/45), respectively. The observed proportion of females in leadership positions was significantly lower than the expected proportion of females in leadership positions for RO (17.4% vs. 30.7%, p = .0001) and SO (11.1% vs. 38.8%, p = .0001), and demonstrated a trend towards significance for MO (31.5% vs. 37.1%, p = .063). 47.9%, 33% and 18.5% of MO, RO and SO programs had ≥1 female in a leadership position, respectively. Programs that had a female in a leadership position had a higher mean percentage of overall female faculty than those that did not: 41.0% vs 35.0% (p = .0006), 36.0% vs 26.0% (p = .0002) and 39.0% vs 32.0% (p = .348) for MO, RO and SO, respectively. Conclusions: Gender disparity exists in academic MO, RO and SO faculty and is magnified at the leadership level. Programs with a female physician in a leadership position are associated with a higher percentage of female faculty.

Tumor registries are held to a very high standard for identifying and reporting new analytic cancer cases. However, current approaches to new case detection are often inefficient and costly. Efficient and effective detection of new cancer cases has the potential to maintain a high accuracy of reporting while reducing costs, increasing timeliness of reporting, and ultimately advancing cancer research. We describe the development, implementation, and evaluation of an informatics tool that integrates multiple data sources to support the workflow of new case identification at the Vanderbilt University Medical Center (VUMC) tumor registry office. The new system reduced the total number of potential cases to analyze from roughly 13,000 to 2,500 records per month. This resulted in an efficiency gain of roughly 80 man hours per month with a respective annual savings of approximately 50,000 dollars. Further iterative refinement of this approach along with support for case abstraction could result in further efficiencies.

Cancer patients are often treated with multiple sequential chemotherapy protocols ranging in complexity from simple to highly complex patterns of multiple repeating drugs. Clinical documentation procedures that focus on details of single drug events, however, make it difficult for providers and systems to efficiently abstract the sequence and nature of treatment protocols. We have developed a data driven method for cancer treatment plan recognition that takes as input pharmacy chemotherapy dispensing records and produces the sequence of identified chemotherapy protocols. Compared to a manually annotated gold standard, our method was 75% accurate and 80% precise for a breast cancer testing set (110 patients, 2,029 drug events), and 54% accurate and 63% precise for a lung cancer testing set (53 patients, 670 drug events). This method for cancer treatment plan recognition may provide clinicians and systems an abstracted view of the patient’s treatment history.