Comprehensive genomic profiling of ESR1, PIK3CA, AKT1, and PTEN in HR(+)HER2(-) metastatic breast cancer: prevalence along treatment course and predictive value for endocrine therapy resistance in real-world practice.

BACKGROUND: The treatment landscape for HR(+)HER2(-) metastatic breast cancer (MBC) is evolving for patients with ESR1 mutations (mut) and PI3K/AKT pathway genomic alterations (GA). We sought to inform clinical utility for comprehensive genomic profiling (CGP) using tissue (TBx) and liquid biopsies (LBx) in HR(+)HER2(-) MBC. METHODS: Records from a de-identified breast cancer clinicogenomic database for patients who underwent TBx/LBx testing at Foundation Medicine during routine clinical care at ~ 280 US cancer clinics between 01/2011 and 09/2023 were assessed. GA prevalence [ESR1mut, PIK3CAmut, AKT1mut, PTENmut, and PTEN homozygous copy loss (PTENloss)] were calculated in TBx and LBx [stratified by ctDNA tumor fraction (TF)] during the first three lines of therapy. Real-world progression-free survival (rwPFS) and overall survival (rwOS) were compared between groups by Cox models adjusted for prognostic factors. RESULTS: ~ 60% of cases harbored 1 + GA in 1st-line TBx (1266/2154) or LBx TF ≥ 1% (80/126) and 26.5% (43/162) in LBx TF < 1%. ESR1mut was found in 8.1% TBx, 17.5% LBx TF ≥ 1%, and 4.9% LBx TF < 1% in 1st line, increasing to 59% in 3rd line (LBx TF ≥ 1%). PTENloss was detected at higher rates in TBx (4.3%) than LBx (1% in TF ≥ 1%). Patients receiving 1st-line aromatase inhibitor + CDK4/6 inhibitor (n = 573) with ESR1mut had less favorable rwPFS and rwOS versus ESR1 wild-type; no differences were observed for fulvestrant + CDK4/6 inhibitor (n = 348). CONCLUSION: Our study suggests obtaining TBx for CGP at time of de novo/recurrent diagnosis, followed by LBx for detecting acquired GA in 2nd + lines. Reflex TBx should be considered when ctDNA TF < 1%.

Methylthioadenosine Phosphorylase Genomic Loss in Advanced Gastrointestinal Cancers.

BACKGROUND: One of the most common sporadic homozygous deletions in cancers is 9p21 loss, which includes the genes methylthioadenosine phosphorylase (MTAP), CDKN2A, and CDKN2B, and has been correlated with worsened outcomes and immunotherapy resistance. MTAP-loss is a developing drug target through synthetic lethality with MAT2A and PMRT5 inhibitors. The purpose of this study is to investigate the prevalence and genomic landscape of MTAP-loss in advanced gastrointestinal (GI) tumors and investigate its role as a prognostic biomarker. MATERIALS AND METHODS: We performed next-generation sequencing and comparative genomic and clinical analysis on an extensive cohort of 64 860 tumors comprising 5 GI cancers. We compared the clinical outcomes of patients with GI cancer harboring MTAP-loss and MTAP-intact tumors in a retrospective study. RESULTS: The prevalence of MTAP-loss in GI cancers is 8.30%. MTAP-loss was most prevalent in pancreatic ductal adenocarcinoma (PDAC) at 21.7% and least in colorectal carcinoma (CRC) at 1.1%. MTAP-loss tumors were more prevalent in East Asian patients with PDAC (4.4% vs 3.2%, P = .005) or intrahepatic cholangiocarcinoma (IHCC; 6.4% vs 4.3%, P = .036). Significant differences in the prevalence of potentially targetable genomic alterations (ATM, BRAF, BRCA2, ERBB2, IDH1, PIK3CA, and PTEN) were observed in MTAP-loss tumors and varied according to tumor type. MTAP-loss PDAC, IHCC, and CRC had a lower prevalence of microsatellite instability or elevated tumor mutational burden. Positive PD-L1 tumor cell expression was less frequent among MTAP-loss versus MTAP-intact IHCC tumors (23.2% vs 31.2%, P = .017). CONCLUSION: In GI cancers, MTAP-loss occurs as part of 9p21 loss and has an overall prevalence of 8%. MTAP-loss occurs in 22% of PDAC, 15% of IHCC, 8.7% of gastroesophageal adenocarcinoma, 2.4% of hepatocellular carcinoma, and 1.1% of CRC and is not mutually exclusive with other targetable mutations.

Tumor Mutational Burden as a Predictor of First-Line Immune Checkpoint Inhibitor Versus Carboplatin Benefit in Cisplatin-Unfit Patients With Urothelial Carcinoma.

PURPOSE: In real-world settings, patients with metastatic urothelial carcinoma (mUC) are often more frail than clinical trials, underscoring an unmet need to identify patients who might be spared first-line chemotherapy. We sought to determine whether tumor mutational burden (TMB) identifies patients with comparable or superior clinical benefit of first-line single-agent immune checkpoint inhibitors (ICPI) in real-world patients deemed cisplatin-unfit. METHODS: Patients with mUC treated in first-line advanced setting (N = 401) received ICPI (n = 245) or carboplatin regiment without ICPI (n = 156) at physician's discretion in standard-of-care settings across approximately 280 US academic or community-based cancer clinics between March 2014 and July 2021. Deidentified data were captured into a real-world clinicogenomic database. All patients underwent testing using Foundation Medicine assays. Progression-free survival (PFS), time to next treatment (TTNT), and overall survival (OS) comparing ICPI versus chemotherapy were adjusted for known treatment assignment imbalances using propensity scores. RESULTS: TMB ≥ 10 was detected in 122 of 401 (30.4%) patients. Among patients receiving ICPI, those with TMB ≥ 10 had more favorable PFS (HR, 0.59; 95% CI, 0.41 to 0.85), TTNT (HR, 0.59; 95% CI, 0.43 to 0.83), and OS (HR, 0.47; 95% CI, 0.32 to 0.68). Comparing ICPI versus carboplatin, adjusting for imbalances, patients with TMB ≥ 10 had more favorable PFS (HR, 0.51; 95% CI, 0.32 to 0.82), TTNT (HR, 0.56; 95% CI, 0.35 to 0.91), and OS (HR, 0.56; 95% CI, 0.29 to 1.08) on ICPI versus chemotherapy, but not TMB < 10. Comparisons unadjusted for imbalances had similar associations. CONCLUSIONS: In real-world settings, mUC patients with TMB ≥ 10 have more favorable outcomes on first-line single-agent ICPI than carboplatin, adding clinical validity to TMB assessed by an existing US Food and Drug Administration-approved platform.

Black Women Are Less Likely to Be Classified as High-Risk for Breast Cancer Using the Tyrer-Cuzick 8 Model.

BACKGROUND: Breast cancer risk assessment is a powerful tool that guides recommendations for supplemental breast cancer screening and genetic counseling. The Tyrer-Cuzick 8 (TC8) model is widely used for calculating breast cancer risk and thus helps determine if women qualify for supplemental screening or genetic counseling. However, the TC8 model may underestimate breast cancer risk in Black women. This study sought to assess this disparity. METHODS: Data on race, breast density, body mass index (BMI), and TC8 scores were retrospectively extracted from the electronic medical record (EMR). Logistic regressions were run to evaluate racial differences in TC8 scores. Summary and correlation statistics determined relationships between BMI, breast density, and race. Rank biserial correlations were employed to explore the impact of breast density and BMI on TC8 scores. RESULTS: Of 15,356 patients, 5796 were White and 5813 were Black. Black patients had higher rates of BMI ≥ 27 compared with White women (79.2% vs. 45.7%), lower rates of breast density (35.1% vs. 56.2%), and lower rates of high-risk TC8 scores (10.7% vs. 17.5%, OR = 1.6646). There was an inverse relationship between TC8 score and BMI (rrb = - 0.04) and a direct relationship between TC8 score and breast density (rrb = 0.37). DISCUSSION: Black women are less likely to have high-risk TC8 scores despite having only marginally lower breast cancer incidence rates and higher breast cancer mortality rates than White women. This suggests that the TC8 model underestimates breast cancer risk in Black women, possibly due to lower rates of breast density and higher BMIs among Black women.

Variable Genomic Landscapes of Advanced Melanomas with Heavy Pigmentation.

BACKGROUND: In the current study, we examined the real-world prevalence of highly pigmented advanced melanomas (HPMel) and the clinicopathologic, genomic, and ICPI biomarker signatures of this class of tumors. MATERIALS AND METHODS: Our case archive of clinical melanoma samples for which the ordering physician requested testing for both PD-L1 immunohistochemistry (IHC) and comprehensive genomic profiling (CGP) was screened for HPMel cases, as well as for non-pigmented or lightly pigmented advanced melanoma cases (LPMel). RESULTS: Of the 1268 consecutive melanoma biopsies in our archive that had been submitted for PD-L1 IHC, 13.0% (165/1268) were HPMel and 87.0% (1103/1268) were LPMel. In the HPMel cohort, we saw a significantly lower tumor mutational burden (TMB, median 8.8 mutations/Mb) than in the LPMel group (11.4 mut/Mb), although there was substantial overlap. In examining characteristic secondary genomic alterations (GA), we found that the frequencies of GA in TERTp, CDKN2A, TP53, and PTEN were significantly lower in the HPMel cases than in LPMel. A higher rate of GA in CTNNB1, APC, PRKAR1A, and KIT was identified in the HPMel cohort compared with LPMel. CONCLUSIONS: In this study, we quantified the failure rates of melanoma samples for PD-L1 testing due to high melanin pigmentation and showed that CGP can be used in these patients to identify biomarkers that can guide treatment decisions for HPMel patients. Using this practical clinical definition for tumor pigmentation, our results indicate that HPMel are frequent at 13% of melanoma samples, and in general appear molecularly less developed, with a lower TMB and less frequent secondary GA of melanoma progression.

Association of CD274 (PD-L1) Copy Number Changes with Immune Checkpoint Inhibitor Clinical Benefit in Non-Squamous Non-Small Cell Lung Cancer.

BACKGROUND: We sought to characterize response to immune checkpoint inhibitor (ICI) in non-squamous non-small cell lung cancer (NSCLC) across various CD274 copy number gain and loss thresholds and identify an optimal cutoff. MATERIALS AND METHODS: A de-identified nationwide (US) real-world clinico-genomic database was leveraged to study 621 non-squamous NSCLC patients treated with ICI. All patients received second-line ICI monotherapy and underwent comprehensive genomic profiling as part of routine clinical care. Overall survival (OS) from start of ICI, for CD274 copy number gain and loss cohorts across varying copy number thresholds, were assessed. RESULTS: Among the 621 patients, patients with a CD274 CN greater than or equal to specimen ploidy +2 (N = 29) had a significantly higher median (m) OS when compared with the rest of the cohort (N = 592; 16.1 [8.9-37.3] vs 8.6 [7.1-10.9] months, hazard ratio (HR) = 0.6 [0.4-1.0], P-value = .05). Patients with a CD274 copy number less than specimen ploidy (N = 299) trended toward a lower mOS when compared to the rest of the cohort (N = 322; 7.5 [5.9-11.3] vs 9.6 [7.9-12.8] months, HR = 0.9 [0.7-1.1], P-value = .3). CONCLUSION: This work shows that CD274 copy number gains at varying thresholds predict different response to ICI blockade in non-squamous NSCLC. Considering these data, prospective clinical trials should further validate these findings, specifically in the context of PD-L1 IHC test results.

Comparative Effectiveness of Immune Checkpoint Inhibitors vs Chemotherapy by Tumor Mutational Burden in Metastatic Castration-Resistant Prostate Cancer.

Importance: The most useful biomarkers for clinical decision-making identify patients likely to have improved outcomes with one treatment vs another. Objective: To evaluate treatment class-specific outcomes of patients receiving immune checkpoint inhibitor (ICI) vs taxane chemotherapy by tumor mutational burden (TMB). Design, Setting, and Participants: This comparative effectiveness analysis of clinical variables and outcomes used prospectively defined biomarker-stratified genomic data from a deidentified clinicogenomic database. Data included men with previously treated metastatic castration-resistant prostate cancer (mCRPC) receiving ICI or single-agent taxane chemotherapy from January 2011 to April 2021 at approximately 280 US academic or community-based cancer clinics (approximately 800 sites of care). Data were analyzed from July to August 2021. Exposures: Single-agent ICI or single-agent taxanes. Treatments were assigned at discretion of physician and patient without randomization. Imbalances of known factors between treatment groups were adjusted with propensity weighting. Main Outcomes and Measures: Prostate-specific antigen (PSA) response, time to next therapy (TTNT), and overall survival (OS). Results: A total of 741 men (median [IQR], 70 [64-76] years) with mCRPC received comprehensive genomic profiling and were treated with ICI or single-agent taxane therapy. At baseline, the median (IQR) PSA level was 79.4 (19.0-254) ng/mL, 108 men (18.8%) had Eastern Cooperative Oncology Group Performance Status scores of 2 or greater, and 644 men (86.9%) had received prior systemic treatments for mCRPC. A total of 45 patients (6.1%) received ICI therapy and 696 patients (93.9%) received taxane therapy. Among patients with TMB of fewer than 10 mutations per megabase (mt/Mb) receiving ICI, compared with those receiving taxanes, had worse TTNT (median [IQR], 2.4 [1.1-3.2] months vs 4.1 [2.2-6.3] months; hazard ratio [HR], 2.65; 95% CI, 1.78-3.95; P < .001). In contrast, for patients with TMB of 10 mt/Mb or greater, use of ICIs, compared with use taxanes, was associated with more favorable TTNT (median [IQR], 8.0 [3.4 to unknown] months vs 2.4 [2.4-7.3] months; HR, 0.37, 95% CI, 0.15-0.87; P = .02) and OS (median 19.9 [8.06 to unknown] months vs 4.2 [2.69 - 6.12] months; HR, 0.23; 95% CI, 0.10-0.57; P = .001). Among all 741 patients, 44 (5.9%) had TMB of 10 mt/Mb or greater, 22 (3.0%) had high microsatellite instability, and 20 (2.7%) had both. Treatment interactions with TMB of 10 mt/Mb or greater (TTNT: HR, 0.10; 95% CI, 0.32-0.31; P < .001; OS: HR, 0.25; 95% CI, 0.076-0.81; P = .02) were stronger than high microsatellite instability alone (TTNT: HR, 0.12; 95% CI, 0.03-0.51; P = .004; OS: HR, 0.38; 95% CI, 0.13-1.12; P = .08). Conclusions and Relevance: In this comparative effectiveness study, ICIs were more effective than taxanes in patients with mCRPC when TMB was 10 mt/Mb or greater but not when TMB was fewer than 10 mt/Mb. The results add validity to the existing TMB cutoff of 10 mt/Mb for ICI use in later lines of therapy, and suggest that ICIs may be a viable alternative to taxane chemotherapy for patients with mCRPC with high TMB.

An Analysis of Electronic Health Record Work to Manage Asynchronous Clinical Messages among Breast Cancer Care Teams.

OBJECTIVE: Asynchronous messaging is an integral aspect of communication in clinical settings, but imposes additional work and potentially leads to inefficiency. The goal of this study was to describe the time spent using the electronic health record (EHR) to manage asynchronous communication to support breast cancer care coordination. METHODS: We analyzed 3 years of audit logs and secure messaging logs from the EHR for care team members involved in breast cancer care at Vanderbilt University Medical Center. To evaluate trends in EHR use, we combined log data into sequences of events that occurred within 15 minutes of any other event by the same employee about the same patient. RESULTS: Our cohort of 9,761 patients were the subject of 430,857 message threads by 7,194 employees over a 3-year period. Breast cancer care team members performed messaging actions in 37.5% of all EHR sessions, averaging 29.8 (standard deviation [SD] = 23.5) messaging sessions per day. Messaging sessions lasted an average of 1.1 (95% confidence interval: 0.99-1.24) minutes longer than nonmessaging sessions. On days when the cancer providers did not otherwise have clinical responsibilities, they still performed messaging actions in an average of 15 (SD = 11.9) sessions per day. CONCLUSION: At our institution, clinical messaging occurred in 35% of all EHR sessions. Clinical messaging, sometimes viewed as a supporting task of clinical work, is important to delivering and coordinating care across roles. Measuring the electronic work of asynchronous communication among care team members affords the opportunity to systematically identify opportunities to improve employee workload.

Learnings From Precision Clinical Trial Matching for Oncology Patients Who Received NGS Testing.

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.

Clinical Application of Computational Methods in Precision Oncology: A Review.

Importance: There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems. Objective: To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology. Evidence Review: The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology. Findings: Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics. Conclusions and Relevance: Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.

Characterizing communication patterns among members of the clinical care team to deliver breast cancer treatment.

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.

Evaluating the Scope of Clinical Electronic Messaging to Coordinate Care in a Breast Cancer Cohort.

Care coordination has received attention as an opportunity to improve healthcare delivery. Current work to quantify provider coordination has primarily relied on identifying shared patients, but neglects to understand communication patterns. We applied social network analysis to electronic health record (EHR) secure messaging data to compare networks of providers who share patients and networks of providers who communicate about patients. We studied 2175 stage I-III breast cancer patients who received outpatient treatment from 1758 providers at a large academic medical center in the southeastern United States. Patients in our cohort were involved in 94324 appointments and were the subject of 307144 message threads. We found that 9.9% of provider-provider pairs that shared patients were mutually involved in electronic communication about their patients. EHR data sources can be used to evaluate provider communication across a clinical enterprise, which can help identify opportunities to improve collaboration and reduce provider burnout.

Measures of Treatment Workload for Patients With Breast Cancer.

PURPOSE: Patients with breast cancer spend a large amount of time and effort receiving treatment. When the number of health care tasks exceeds a patient's ability to manage that workload, they could become overburdened, leading to decreased plan adherence. We used electronic health record data to retrospectively assess dimensions of treatment workload related to outpatient encounters, commuting, and admissions. METHODS: Using tumor registry and scheduling data, we evaluated the sensitivity of treatment workload measures to detect expected differences in breast cancer treatment burden by stage. We evaluated the impact of the on-body pegfilgrastim injector on the treatment workload of patients undergoing a specific chemotherapy protocol. RESULTS: As hypothesized, patients with higher stage cancer experienced higher treatment workload. Over the first 18 months after diagnosis, patients with stage III disease spent a median of 81 hours (interquartile range [IQR], 39 to 113 hours) in outpatient clinics, commuted 61 hours (IQR, 32 to 86 hours), and spent $1,432 (IQR, $690 to $2,552) in commuting costs. In contrast, patients with stage I disease spent a median of 29 hours (IQR, 18 to 46 hours in clinic), commuted for 34 hours (IQR, 19 to 55 hours), and spent $834 (IQR, $389 to $1,649) in commuting costs. In addition, we substantiated claims that the pegfilgrastim on-body injector was effective in reducing some dimensions of workload such as unique appointment days. CONCLUSION: Treatment workload measures capture an important dimension in the experience of patients with cancer. Patients and health care organizations can use workload measures to plan and allocate resources, leading to higher quality and better coordinated care.

Qualitative study of system-level factors related to genomic implementation.

PURPOSE: Research on genomic medicine integration has focused on applications at the individual level, with less attention paid to implementation within clinical settings. Therefore, we conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify system-level factors that played a role in implementation of genomic medicine within Implementing GeNomics In PracTicE (IGNITE) Network projects. METHODS: Up to four study personnel, including principal investigators and study coordinators from each of six IGNITE projects, were interviewed using a semistructured interview guide that asked interviewees to describe study site(s), progress at each site, and factors facilitating or impeding project implementation. Interviews were coded following CFIR inner-setting constructs. RESULTS: Key barriers included (1) limitations in integrating genomic data and clinical decision support tools into electronic health records, (2) physician reluctance toward genomic research participation and clinical implementation due to a limited evidence base, (3) inadequate reimbursement for genomic medicine, (4) communication among and between investigators and clinicians, and (5) lack of clinical and leadership engagement. CONCLUSION: Implementation of genomic medicine is hindered by several system-level barriers to both research and practice. Addressing these barriers may serve as important facilitators for studying and implementing genomics in practice.

American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange: From Inception to First Data Release and Beyond-Lessons Learned and Member Institutions' Perspectives.

The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international data-sharing consortium focused on enabling advances in precision oncology through the gathering and sharing of tumor genetic sequencing data linked with clinical data. The project's history, operational structure, lessons learned, and institutional perspectives on participation in the data-sharing consortium are reviewed. Individuals involved with the inception and execution of AACR Project GENIE from each member institution described their experiences and lessons learned. The consortium was conceived in January 2014 and publicly released its first data set in January 2017, which consisted of 18,804 samples from 18,324 patients contributed by the eight founding institutions. Commitment and contributions from many individuals at AACR and the member institutions were crucial to the consortium's success. These individuals filled leadership, project management, informatics, data curation, contracts, ethics, and security roles. Many lessons were learned during the first 3 years of the consortium, including on how to gather, harmonize, and share data; how to make decisions and foster collaboration; and how to set the stage for continued participation and expansion of the consortium. We hope that the lessons shared here will assist new GENIE members as well as others who embark on the journey of forming a genomic data-sharing consortium.

Internet-Based Assessment of Oncology Health Care Professional Learning Style and Optimization of Materials for Web-Based Learning: Controlled Trial With Concealed Allocation.

BACKGROUND: Precision medicine has resulted in increasing complexity in the treatment of cancer. Web-based educational materials can help address the needs of oncology health care professionals seeking to understand up-to-date treatment strategies. OBJECTIVE: This study aimed to assess learning styles of oncology health care professionals and to determine whether learning style-tailored educational materials lead to enhanced learning. METHODS: In all, 21,465 oncology health care professionals were invited by email to participate in the fully automated, parallel group study. Enrollment and follow-up occurred between July 13 and September 7, 2015. Self-enrolled participants took a learning style survey and were assigned to the intervention or control arm using concealed alternating allocation. Participants in the intervention group viewed educational materials consistent with their preferences for learning (reading, listening, and/or watching); participants in the control group viewed educational materials typical of the My Cancer Genome website. Educational materials covered the topic of treatment of metastatic estrogen receptor-positive (ER+) breast cancer using cyclin-dependent kinases 4/6 (CDK4/6) inhibitors. Participant knowledge was assessed immediately before (pretest), immediately after (posttest), and 2 weeks after (follow-up test) review of the educational materials. Study statisticians were blinded to group assignment. RESULTS: A total of 751 participants enrolled in the study. Of these, 367 (48.9%) were allocated to the intervention arm and 384 (51.1%) were allocated to the control arm. Of those allocated to the intervention arm, 256 (69.8%) completed all assessments. Of those allocated to the control arm, 296 (77.1%) completed all assessments. An additional 12 participants were deemed ineligible and one withdrew. Of the 552 participants, 438 (79.3%) self-identified as multimodal learners. The intervention arm showed greater improvement in posttest score compared to the control group (0.4 points or 4.0% more improvement on average; P=.004) and a higher follow-up test score than the control group (0.3 points or 3.3% more improvement on average; P=.02). CONCLUSIONS: Although the study demonstrated more learning with learning style-tailored educational materials, the magnitude of increased learning and the largely multimodal learning styles preferred by the study participants lead us to conclude that future content-creation efforts should focus on multimodal educational materials rather than learning style-tailored content.

Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network.

BACKGROUND: To realize potential public health benefits from genetic and genomic innovations, understanding how best to implement the innovations into clinical care is important. The objective of this study was to synthesize data on challenges identified by six diverse projects that are part of a National Human Genome Research Institute (NHGRI)-funded network focused on implementing genomics into practice and strategies to overcome these challenges. METHODS: We used a multiple-case study approach with each project considered as a case and qualitative methods to elicit and describe themes related to implementation challenges and strategies. We describe challenges and strategies in an implementation framework and typology to enable consistent definitions and cross-case comparisons. Strategies were linked to challenges based on expert review and shared themes. RESULTS: Three challenges were identified by all six projects, and strategies to address these challenges varied across the projects. One common challenge was to increase the relative priority of integrating genomics within the health system electronic health record (EHR). Four projects used data warehousing techniques to accomplish the integration. The second common challenge was to strengthen clinicians' knowledge and beliefs about genomic medicine. To overcome this challenge, all projects developed educational materials and conducted meetings and outreach focused on genomic education for clinicians. The third challenge was engaging patients in the genomic medicine projects. Strategies to overcome this challenge included use of mass media to spread the word, actively involving patients in implementation (e.g., a patient advisory board), and preparing patients to be active participants in their healthcare decisions. CONCLUSIONS: This is the first collaborative evaluation focusing on the description of genomic medicine innovations implemented in multiple real-world clinical settings. Findings suggest that strategies to facilitate integration of genomic data within existing EHRs and educate stakeholders about the value of genomic services are considered important for effective implementation. Future work could build on these findings to evaluate which strategies are optimal under what conditions. This information will be useful for guiding translation of discoveries to clinical care, which, in turn, can provide data to inform continual improvement of genomic innovations and their applications.

Determining Burden of Commuting for Treatment Using Online Mapping Services - A Study of Breast Cancer Patients.

For patients with breast cancer who must frequent medical centers for care, commuting is a significant burden. This burden could affect their decisions during treatment. We developed a method to use census tracts and zip codes to determine commuting burden for patients with breast cancer with online mapping services, while protecting patient addresses from third parties. We found that patients who lived farther from Vanderbilt had fewer unique appointment days and more appointments scheduled per day. Total burden decreased over time after diagnosis, but advanced stage patients had sustained high levels of commute time until ten months after diagnosis. Additionally, we found that patients who lived far from Vanderbilt were less likely to receive radiotherapy from Vanderbilt. With the amount of work patients put into traveling for care, understanding commuting burden could help healthcare organizations form strategies to improve access to care and compliance with care plans.

Integrating cancer genomic data into electronic health records.

The rise of genomically targeted therapies and immunotherapy has revolutionized the practice of oncology in the last 10-15 years. At the same time, new technologies and the electronic health record (EHR) in particular have permeated the oncology clinic. Initially designed as billing and clinical documentation systems, EHR systems have not anticipated the complexity and variety of genomic information that needs to be reviewed, interpreted, and acted upon on a daily basis. Improved integration of cancer genomic data with EHR systems will help guide clinician decision making, support secondary uses, and ultimately improve patient care within oncology clinics. Some of the key factors relating to the challenge of integrating cancer genomic data into EHRs include: the bioinformatics pipelines that translate raw genomic data into meaningful, actionable results; the role of human curation in the interpretation of variant calls; and the need for consistent standards with regard to genomic and clinical data. Several emerging paradigms for integration are discussed in this review, including: non-standardized efforts between individual institutions and genomic testing laboratories; "middleware" products that portray genomic information, albeit outside of the clinical workflow; and application programming interfaces that have the potential to work within clinical workflow. The critical need for clinical-genomic knowledge bases, which can be independent or integrated into the aforementioned solutions, is also discussed.