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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.

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.

PURPOSEOur goal was to identify the opportunities and challenges in analyzing data from the American Association of Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE), a multi-institutional database derived from clinically driven genomic testing, at both the inter- and the intra-institutional level. Inter-institutionally, we identified genotypic differences between primary and metastatic tumors across the 3 most represented cancers in GENIE. Intra-institutionally, we analyzed the clinical characteristics of the Vanderbilt-Ingram Cancer Center (VICC) subset of GENIE to inform the interpretation of GENIE as a whole.METHODSWe performed overall cohort matching on the basis of age, ethnicity, and sex of 13,208 patients stratified by cancer type (breast, colon, or lung) and sample site (primary or metastatic). We then determined whether detected variants, at the gene level, were associated with primary or metastatic tumors. We extracted clinical data for the VICC subset from VICC’s clinical data warehouse. Treatment exposures were mapped to a 13-class schema derived from the HemOnc ontology.RESULTSAcross 756 genes, there were significant differences in all cancer types. In breast cancer, ESR1 variants were over-represented in metastatic samples (odds ratio, 5.91; q < 10−6). TP53 mutations were over-represented in metastatic samples across all cancers. VICC had a significantly different cancer type distribution than that of GENIE but patients were well matched with respect to age, sex, and sample type. Treatment data from VICC was used for a bipartite network analysis, demonstrating clusters with a mix of histologies and others being more histology specific.CONCLUSIONThis article demonstrates the feasibility of deriving meaningful insights from GENIE at the inter- and intra-institutional level and illuminates the opportunities and challenges of the data GENIE contains. The results should help guide future development of GENIE, with the goal of fully realizing its potential for accelerating precision medicine.

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 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.

331 Background: Personalized breast cancer risk assessment (CRA) and genetic counseling/testing for women have been shown to improve interventional and clinical outcomes. However, despite CRA being routinely offered to all women at our institution, not all women choose to opt into the program. This study evaluated the demographic distribution of patients that opted out of the CRA to identify potential components influencing this substantial decision. Methods: Our medical center, located in four urban and suburban locations, piloted a clinical framework to provide CRA to all women between the ages of 25 and 75 with a qualifying mammogram. In this single institution retrospective study, we analyzed differences among patients who opted out of the program between July 20, 2020and July 19, 2021. Data elements extracted from the electronic medical record include race, primary language, location of care, and decision to partake in CRA. Overall Chi-square tests and all pairwise comparisons with Bonferroni correction were used to statistically determine the impact of various demographics on opting out. Results: 18726 women met criteria for inclusion and 2717 (14.5%) declined CRA. Within their respective racial groups, 122 (17.6%) Asian, 1175 (16.3%) Black, 455 (14.2%) Hispanic, 112 (15%) Other, and 853 (12.4%) White opted out. Within their identified primary language, 2370 (13.7%) English, 231 (23.1%) Spanish, 35 (19.7%) Bilingual, and 81 (35.2%) Other opted out. Based on their setting of care, 837 (16.8%) Location A, 1436 (14.2%) Location B, 270 (13.8%) Location C, and 174 (10.6%) Location D opted out. The differences between demographics and opting out were statistically significant for all analyses (p

Patients with breast cancer spend significant time1, effort, and financial resources2 to combat the disease for years after their diagnosis. The large volume of healthcare tasks can cause patients to become overburdened, leading to reduced adherence with care plans and worse outcomes3. On the other hand, certain patient characteristics such as physical resilience, financial well-being, and supportive family environments increase patients' capacity to manage care4. Assessing treatment burden and capacity when prescribing care has been applied to populations such as diabetes patients5. We are investigating this paradigm in treatment of patients with breast cancer. The goal of this preliminary study is to identify significant factors that contribute to treatment burden, capacity to manage care, and outcomes of overburden for patients with breast cancer. Examples of treatment burden, capacity to manage care, and outcomes of overburden in patients with breast cancerTreatment burdenCapacity to manage careOutcomes of overburdenTraveling long distances for careAccess to reliable transportationReduced spending on food, utilities, or other necessitiesPaying for child care during chemotherapyFlexibility in informal caregivers' schedulesMissed appointment with medical oncologistRemembering to take medications with mealsMedical understanding or knowledgeWorse than expected side effectsReporting adverse eventsProficiency with mobile deviceTrip to emergency room Through literature review, interviews with survivors, and expert panels of navigators and providers, we will develop a survey instrument given to patients at the time of diagnosis. The survey will assess patient capacity and help providers give treatment options based on attributes of the patient. Additionally, we will attempt to correlate survey results with treatment burden measures derived from electronic health record data at a population level1. With treatment personalized for patient capacity, patients should be better able to adhere to care plans leading to improved quality of life during treatment and beyond. Acknowledgements: The authors would like to thank Cheryl Jernigan, our patient advocate mentor, for her guidance in this project. We would also like to thank the Susan G. Komen Foundation for their support of this research. References: 1. Cheng, A. C. & Levy, M. A. Data Driven Approach to Burden of Treatment Measurement: A Study of Patients with Breast Cancer. AMIA Annu. Symp. proceedings. AMIA Symp. 2016, 1756–1763 (2016). 2. Zafar, S. Y. et al. The financial toxicity of cancer treatment: a pilot study assessing out-of-pocket expenses and the insured cancer patient's experience. Oncologist. 18, 381–90 (2013). 3. Mair, F. S. & May, C. R. Thinking about the burden of treatment. BMJ. 349, g6680–g6680 (2014). 4. Boehmer, K. R., Shippee, N. D., Beebe, T. J. & Montori, V. M. Pursuing Minimally Disruptive Medicine: Correlation of patient capacity with disruption from illness and healthcare-related demands. J. Clin. Epidemiol. (2016). 5. Ishii, H. et al. Reproducibility and Validity of a Questionnaire Measuring Treatment Burden on Patients with Type 2 Diabetes: Diabetic Treatment Burden Questionnaire (DTBQ). Diabetes Ther. 9, 1001–1019 (2018). Citation Format: Cheng A, Levy M. Treatment burden and capacity to manage care among patients with breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr OT2-10-01.

5063 Background: NEPC is a high-grade aggressive form of prostate cancer. We queried whether RB1 mutation status would impact the genomic features of NEPC in RB1 mutated vs non-mutated cases. Methods: From a series of 13,496 cases of clinically advanced PC, we identified 415 cases (3.1%) with a diagnosis of small cell PC or NEPC as determined by the submitting physician. They were sequenced using a hybrid capture-based FDA-approved clinical genomic profiling (CGP) assay to detect all classes of genomic alterations (GA). Tumor mutational burden (TMB) was determined on 0.8 Mbp of sequenced DNA and microsatellite instability (MSI) was determined on 95 loci. PD-L1 expression was determined by IHC (Dako 22C3) with low tumor cell positive staining 1-49% and high staining ≥50% expression. Results: 253 (61%) of NEPC feature GA in RB1 (RB1 mut+). This contrasts with a 5.8% frequency of RB1 GA in the non-NEPC (P

3535 Background: Approximately 20% of colorectal cancer (CRC) has an activating mutation in the PIK3CA oncogene. PIK3CA codes for the catalytic subunit of phosphoinositide 3-kinase alpha (PI3Kα), which ultimately activates the AKT and mammalian target of rapamycin (mTOR) pathway. The PI3Kα inhibitor alpelisib has been approved for breast cancer where a single PIK3CA activating mutation is sufficient for response. However, two activating mutations (multi-hit) in the PIK3CA allele substantially increases PI3Kα signaling compared to single hot spot mutations and results in exceptional response to PI3Kα inhibition. We aimed to identify the prevalence of PIK3CA multi-hit mutations in CRC to identify patients potentially susceptible to PI3K inhibitors. Methods: Tissue-based comprehensive genomic profiling (CGP) was performed in a Clinical Laboratory Improvement Amendments (CLIA)-certified, CAP (College of American Pathologists)-accredited laboratory (Foundation Medicine Inc., Cambridge, MA, USA) on all-comers during the course of routine clinical care from 2013-2021. Approval was obtained from the Western Institutional Review Board (Protocol No. 20152817). Hybrid capture was carried out for at least 324 cancer-related genes, including PIK3CA. Results: We identified 48,836 patients with advanced CRC who underwent Foundation Medicine testing and 846 (1.7%) patients with multi-hit PIK3CA mutations. Additional clinical and molecular data was available for 41,154 of these patients of which 710 (1.7%) had multi-hit PIK3CA and 7627 (19%) had any deleterious PIK3CA mutation . The local colon tumor was used for sequencing in 70% of cases and a separate site in 30% of cases. Patients with PIK3CA multi-hit mutations were 53% male with a median age of 60 (interquartile range 50-70). The microsatellite status was available for 697 of 710 patients with multi-hit PIK3CA and 123/697 (18%) were microsatellite instability-high. The Table outlines the genes with cooccurring mutations of >10% prevalence for multi-hit PIK3CA CRC, including the clinically relevant mutations in KRAS (65%) and BRAF (13%). The four most common PIK3CA variants were H1047R (9.8%), E545K (9.2%), E542K (9.0%) and R88Q (7.1%). The most common variant pair was E542K with E545K in 4.7% of multi-hit cases. Conclusions: Double-hit mutations in PIK3CA are seen in 1.7% of advanced CRC patients and may represent a subset of patients that may have enhanced sensitivity to PI3K inhibitors. Given the high prevalence of CRC in the United States and worldwide this represents a clinically meaningful prevalence of multi-hit PIK3CA. Future investigation on the clinical utility of PI3K inhibitors may be warranted in multi-hit PIK3CA CRC.[Table: see text]

e18772 Background: Dense breast tissue is known to obscure malignancy on mammography and is considered an independent risk factor for breast cancer. Because of its clinical significance, breast density has been incorporated into cancer risk assessment tools, supplemental screening recommendations, and patient notification laws. Changes in density—whether due to intrinsic changes in breast tissue over time or to inter-radiologist variability in interpreting mammographic results—can therefore have significant implications for patients. The purpose of this study was to quantify longitudinal patterns of breast density change. Methods: This retrospective cohort study tracked breast density changes among 37,156 patients who received multiple mammograms at a large academic medical center between October 1, 2015 and June 29, 2021. Breast Imaging-Reporting and Data System (BI-RADS) density categories A (least dense) through D (most dense), visually determined by radiologists at the time of screening, were abstracted from electronic medical records and dichotomized into one of two density statuses: non-dense (categories A and B) and dense (categories C and D). A sequence analysis of longitudinal changes between the two density statuses was performed using SQL. Results: The majority (91.3%, n = 33,920) of patients maintained the same density status (dense or non-dense) throughout the six-year study period, while 8.7% of participants (n = 3,236) experienced between one and six density status changes. Among patients who experienced any density status change, the vast majority (96.7%, n = 3,131) moved exclusively between BI-RADS categories B and C, the intermediate density categories (Table). Frequency of density status change was positively correlated with average number of mammograms performed per patient. Conclusions: Although most patients maintain a consistent breast density status, some will experience multiple status changes even over this relatively short timeframe (< 6 years), with more frequent changes being associated with more frequent mammograms. The overwhelming majority of patients whose density status changed moved exclusively between BI-RADS categories B and C, suggesting a role for inter-radiologist variability in visual assessments of intermediate-density tissue. These findings have implications for how density status changes are incorporated into patient education, risk assessment, and supplemental screening recommendations.[Table: see text]

1025 Background: ER-PR+Her2- breast cancer is a rare subtype occurring at approximately 1% of all breast carcinomas. Most of these cancers behave in an aggressive fashion with limited benefit from anti estrogen therapy, similar to triple negative breast cancer (TNBC). Better characterization of these tumors is needed for predicting clinical behavior, response to endocrine therapy, and eligibility for clinical trials. Here we sought to evaluate the mutational profile of a well curated set of ER-PR+HER2- metastatic breast cancers and compare to other receptor phenotypes. Methods: 2049 consecutive breast cancers submitted to Foundation Medicine for comprehensive genomic profiling (CGP) were included. ER, PR and HER2 expression were abstracted from submitted pathology reports. Cases without complete ER, PR and HER2 information in pathology reports were excluded. CGP was performed as previously described (Frampton, 2013). Results: Patient ages were similar across subgroups. Generally, ER-PR+HER2- tumors were rare (n = 23, 1.1%) and most similar to TNBC in their genomic profiles. These tumors harbored high rates of TP53 and BRCA1 alterations and low rates of PIK3CA, ESR1, and CDH1 alterations. Genomic loss of heterozygosity (gLOH) was similar in the ER-PR+HER2- and ER+PR+HER2-subtypes (8.18% and 8.66% respectively), and lower than TNBC (17.19%). Notably, a high rate of RB1 alterations were identified in the ER-PR+HER2- patients (13%, 3/23), numerically higher than the other subtypes. EGFR, MET, PTEN, CDKN2A and KRAS alterations were also observed at a higher frequency in ER-PR+Her2- cancers (8.7, 4.2, 39.1, 13.0 and 13.0% respectively) relative to the other subtypes. IO drug biomarkers including MSI, TMB and PD-L1 IHC were similar among the groups. Conclusions: The mutational profile for ER-PR+Her2- metastatic breast cancer more closely resembles TNBC than ER+ breast cancer. These data suggest molecular profiling may be a useful adjunct to optimize treatment strategies for this rare subset of cancers. Based on molecular characteristics, we recommend including ER-PR+Her2- patients in clinical trials for TNBC. Finally, genes including RB1, CKDN2A, PTEN, EGFR and MET are mutated at higher frequency in ER-PR+Her2- cancers than other subsets, suggesting unique biology with potential therapeutic implications. [Table: see text]

e18567 Background: Personalized breast cancer risk assessment (CRA) and genetic counseling/testing for women have been shown to improve interventional and clinical outcomes. However, despite CRA being routinely offered to all women at our institution, not all women choose to opt into the program. This study evaluated the demographic distribution of patients that opted out of the CRA to identify potential components influencing this substantial decision. Methods: Our medical center, located in four urban and suburban locations, piloted a clinical framework to provide CRA to all women between the ages of 25 and 75 with a qualifying mammogram. In this single institution retrospective study, we analyzed differences among patients who opted out of the program between July 20, 2020 and July 19, 2021. Data elements extracted from the electronic medical record include race, primary language, location of care, and decision to partake in CRA. Overall Chi-square tests and all pairwise comparisons with Bonferroni correction were used to statistically determine the impact of various demographics on opting out. Results: 18726 women met criteria for inclusion and 2717 (14.5%) declined CRA. Within their respective racial groups, 122 (17.6%) Asian, 1175 (16.3%) Black, 455 (14.2%) Hispanic, 112 (15%) Other, and 853 (12.4%) White opted out. Within their identified primary language, 2370 (13.7%) English, 231 (23.1%) Spanish, 35 (19.7%) Bilingual, and 81 (35.2%) Other opted out. Based on their setting of care, 837 (16.8%) Location A, 1436 (14.2%) Location B, 270 (13.8%) Location C, and 174 (10.6%) Location D opted out. The differences between demographics and opting out were statistically significant for all analyses (p < .0001). Results shown in Table below. Conclusions: The main variables mediating the decision to opt out of CRA are a non-English primary language, non-White race, and location of care, potentially due to inadequate interpretation services, barriers to education, and staffing shortages. Thus, these women are less likely to receive genetic counseling/testing or supplemental screening. Our study is the first to investigate the factors contributing to the uptake of risk assessment. Further study is needed to determine the clinical impact of this striking disparity.[Table: see text]

1027 Background: Advanced breast cancer in women < 40 years is more aggressive, with worse prognosis and disease-free survival, compared to older women with the disease. With increasing availability of targeted and immune therapies, we aimed to compare genomic alterations (GA) using comprehensive genomic profiling (CGP) of tumor tissue. Methods: We analyzed 2,049 breast cancers submitted to Foundation Medicine for CGP. Hybrid-capture based CGP was performed to evaluate all classes of GA. Tumor mutational burden (TMB) was determined on at least 0.8 Mbp of sequenced DNA and microsatellite instability was determined on at least 95 loci. Tumor cell PD-L1 expression (defined as tumor proportion score >/= 1) was determined by IHC (Dako 22C3). We identified 28 (1.37%) patients /= 40 years. Breast tissue was used for CGP in 69.5% of cases and remainder of specimens were lymph node, metastatic, or unspecified. Results: Breast tumors were less likely to be estrogen receptor positive in younger women (54% of those /= 40 years) and more likely to be triple negative (43%, 33%, 26.1% in the same respective groups). There was no clear pattern in HER2+ status by age (0%, 15.1%, 7.2%). Younger women had higher rates of BRCA1 (17.9%, 10.1%, 2.6%), BRCA2 mutations (7.10%, 5.70%, 4.1%), and RB1 mutations (14.3%, 9.4%, 6.1%), and lower rates of CDH1 (7.1%, 5%, 15.4%) and PIK3CA mutations (17.9%, 17.6%, 40.0%). Younger women were more likely to have PD-L1 expression (55.6%, 54.4%, 51.5%) but had lower frequencies of TMB >10 (0.0%, 5.0%, 8.7%). Differences are statistically significant in BRCA1, CDH1, and PIK3CA. Conclusions: These findings confirm that young women with breast cancer have actionable GA. Different mutational profiles may support differential use of targeted and immune therapies. Statistically and clinically significant differences include higher BRCA1 mutations which may lend to PARP inhibitor use and lower PIK3CA mutations which may reduce alpelisib use. Higher RB1 mutations and immunotherapy biomarker differences were not statistically significant. However, these may clinically translate into CDK4/6 resistance and reduced immunotherapy options, respectively. [Table: see text]

Purpose of Review The emergency medicine and critical care needs of the COVID-19 pandemic forced a sudden and dramatic disruption of cancer screening and treatment programs in the USA during the winter and spring of 2020. This review commentary addresses the impact of the pandemic on racial/ethnic minorities such as African Americans and Hispanic-Latina Americans, with a focus on factors related to breast cancer. Recent Findings African Americans and Hispanic-Latina Americans experienced disproportionately higher morbidity and mortality from COVID-19; many of the same socioeconomic and tumor biology/genetic factors that explain breast cancer disparities are likely to account for COVID-19 outcome disparities. Summary The breast cancer clinical and research community should partner with public health experts to ensure participation of diverse patients in COVID-19 treatment trials and vaccine programs and to overcome COVID-19-related breast health management delays that are likely to have been magnified among African Americans and Hispanic-Latina Americans.

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.

547 Background: There is an unmet need to identify metastatic urothelial carcinoma (mUC) patients who might be spared chemotherapy in 1st line. Anti-PD-(L)1 immune checkpoint inhibitors (ICPI) alone without chemotherapy did not show superiority to platinum-based chemotherapy in ITT populations of DANUBE, KEYNOTE-361, and IMvigor130. However, DANUBE and IMvigor130 reported secondary subgroup analyses, both suggesting enhanced benefit for ICPI vs. chemotherapy in patients with tumor mutational burden (TMB) ≥ 10 (mutations/megabase), using same cutoff and assay as pan-tumor CDx for pembrolizumab approved in later lines of therapy. We sought to determine if TMB ≥ 10 identified a group of enhanced relative ICPI benefit (single-agent anti-PD[L]1 w/o chemo) in real-world settings where patients are less eligible for chemotherapy. Methods: Association of genomic data with clinical variables and outcomes in cohort of patients with mUC treated January 2011- April 2021. Longitudinal de-identified clinical data from approximately 280 U.S. academic or community-based cancer clinics were derived from electronic health records, curated via technology-enabled abstraction by Flatiron Health and linked to genomic testing by Foundation Medicine. 849 1st line mUC patients received either ICPI (n = 307) or chemotherapy (n = 542) at physician’s discretion in standard of care settings. All patients underwent genomic testing using Foundation Medicine comprehensive genomic profiling assays (FoundationOne© or FoundationOne©CDx). PFS and OS were assessed unadjusted and adjusted for imbalances using propensity scores. Results: 273 of 849 (32.2%) patients had TMB ≥ 10. Pre-therapy characteristics: patients assigned ICPI vs. chemotherapy had comparable TMB, primary disease site, histology, smoking status, and PD-L1 staining, but were generally older (median years: 72 vs. 67, p < 0.001), higher ECOG scores (p < 0.001), lower CrCl (median ml/min: 49.8 vs. 59.7, p < 0.001), and lower hemoglobin (median: 11.5 vs. 12.1, p < 0.001). Unadjusted, TMB ≥ 10 group showed more favorable PFS (HR: 0.72, 95%CI: 0.52 – 0.99, p = 0.041) and OS (HR: 0.70, 95%CI: 0.49 – 0.1, p = 0.048) for ICPI vs. chemotherapy despite imbalances favoring outcomes on chemotherapy. ICPI vs. chemotherapy outcomes adjusted for imbalances: TMB ≥ 10 group showed more favorable PFS (HR: 0.65, 95%CI: 0.45 – 0.95, p = 0.026) and OS (HR: 0.61, 95%CI: 0.39 – 0.93, p = 0.022), while TMB < 10 had comparable or worse PFS (HR: 1.30, 95%CI: 0.98 – 1.72, p = 0.06) and OS (HR: 1.03; 95%CI: 0.78– 1.34, p = 0.85). Conclusions: In real-world settings, 1st line mUC patients with TMB ≥ 10 have more favorable PFS and OS on single agent ICPI than chemotherapy, adding clinical validity to TMB as a predictive biomarker in patient populations less eligible for chemotherapy than reported trials.

156 Background: NEPC is a high-grade aggressive form of prostate cancer. We queried whether RB1 mutation status would impact the genomic features of NEPC. We hypothesized that there would be differences in GA frequencies in RB1 mutated vs non mutated cases. Methods: From a series of 13,496 cases of clinically advanced PC, 415 (3.1%) histologically defined NEPC were sequenced using a hybrid capture-based FDA-approved clinical genomic profiling (CGP) assay to detect all classes of genomic alterations (GA). Tumor mutational burden (TMB) was determined on 0.8 Mbp of sequenced DNA and microsatellite instability (MSI) was determined on 95 loci. PD-L1 expression was determined by IHC (Dako 22C3) with low tumor cell positive staining 1-49% and high staining ≥50% expression. Results: 253 (61%) of NEPC feature GA in RB1 (RB1 mut+). This contrasts with a 5.8% frequency of RB1 GA in the non-NEPC (P

162 Background: The most useful biomarkers for clinical decision-making identify patients likely to have improved outcomes on one treatment vs. another. To date, no study has compared the treatment class-specific outcomes of patients with metastatic castration-resistant prostate cancer (mCRPC) on Immune Checkpoint Inhibitor (ICPI) vs. taxane chemotherapy by tumor mutational burden (TMB, mutations/megabase). Methods: Association of genomic data with clinical variables and outcomes in cohort of patients with mCRPC treated January 2011- April 2021. Longitudinal de-identified clinical data from ̃280 U.S. academic or community-based cancer clinics were derived from electronic health records, curated via technology-enabled abstraction by Flatiron Health and linked to genomic testing by Foundation Medicine (FoundationOne or FoundationOne CDx assays). 45 patients (14 with TMB ≥ 10, 31 TMB < 10) received single-agent anti-PD1 axis ICPI, 696 (30 with TMB ≥ 10, 666 TMB < 10) received single-agent taxanes, at discretion of physician without randomization. For time to next therapy (TTNT) and overall survival (OS) assessments, imbalances between treatment groups were adjusted with propensity weighting. Results: Overall cohort: Median age: 70 (IQR: 64 – 76), median PSA: 79.4 (IQR: 19.0 – 254), 108 (18.8%) were ECOG 2+, 644 (86.9%) had received prior systemic treatments for mCRPC. Patients receiving ICPI vs. taxanes had comparable pre-therapy age, PSA, hemoglobin, alkaline phosphatase, prior second generation novel hormonal therapy use, and prior opioid use, but higher TMB (median 3.5, IQR: 1.7 – 15 vs. median 2.5, IQR 1.3 – 3.8, p < 0.001), higher ECOG scores (0, 1, 2+ respectively 13.9%, 55.6%, 30.6% vs. 29.4%, 52.6%, 18.8%, p = 0.057), and greater prior taxane use (73.3% vs. 53.7%, p = 0.01). Among patients with evaluable PSA response, no difference was observed on taxanes by TMB level. No patients had PSA decline ≥ 50% on ICPI if TMB < 10, 4 of 9 with TMB ≥ 10 had PSA decline ≥ 50%. TMB < 10 receiving ICPI vs. taxanes had worse TTNT (median 2.4 vs. 4.1 months; HR: 2.7, 95%CI: 1.7 – 4.0, p < 0.001) and numerically worse OS (median 4.2 vs. 6.0 months, HR: 1.08; 95%CI: 0.68– 1.7, p = 0.73). In contrast, for TMB ≥ 10 ICPI vs. taxane use was associated with more favorable TTNT (median 8.0 vs. 2.4 months; HR: 0.37, 95%CI: 0.15 – 0.87, p = 0.022) and OS (median 19.9 vs. 4.2 months; HR: 0.23, 95%CI: 0.10 – 0.57, p = 0.0085). Among all 741 patients, 44 had TMB ≥ 10, 22 had high microsatellite instability (MSI-H), 20 had both. Treatment interactions with TMB ≥ 10 (TTNT: p < 0.001, OS: p = 0.021) were stronger than MSI-H (TTNT: p = 0.0038, OS: p = 0.080). Conclusions: The results suggest ICPI may be a viable alternative to taxane chemotherapy for patients with mCRPC with TMB ≥ 10, adding validity to existing FDA approved platform and pan-tumor TMB score cutoff of 10.

604 Background: MTAP loss is represented across a wide variety of cancer types including PDAC and is an emerging target for synthetic lethality-based cancer therapies. Preclinically, MTAP loss leads to the accumulation of 2-methylthioadenosine, reduced protein arginine N-methyltransferase 5 (PRMT5) methylation activity and increased vulnerability to targeting of the methionine adenosyltransferase IIα (MAT2A)/ PRMT5 axis. In addition, 9p21 loss, homozygous co-deletion of MTAP/CDKN2A or homozygous deletion of either gene have been associated with an immunologically “cold” tumor microenvironment, primary resistance to anti PD(L)1 immunotherapy (IO) and poor prognosis phenotype (Han G, Nat Commun 2021). We investigated concurrent mutations and immune biomarkers in clinical PDAC samples with MTAP-loss versus -intact status. Methods: From a series of 177705 consecutive cases, we performed comprehensive genomic profiling on 9423 cases of PDAC using an FDA-approved assay (F1CDx) to evaluate all classes of genomic alterations (GA). Tumor mutational burden (TMB) was determined on up to 1.1 Mbp of sequenced DNA and microsatellite instability (MSI) was determined on 114 loci. PD-L1 expression was determined by immunohistochemistry (Dako 22C3). Furthermore, we correlated pertinent findings within a database of 16558 cases of clinically advanced cancer with MTAP loss. Results: 2003 (21.3%) of 9423 PDAC demonstrated MTAP-loss. Similar gender, age and number of GA per tumor were observed between MTAP-loss and -intact groups. Frequencies of TP53, CDKN2A/B, SMAD4, PTEN and ARID1A were significantly higher in MTAP-loss PDAC. However, previously-described biomarkers of IO efficacy (MSI, TMB, CD274 amplification and PD-L1 expression) and resistance ( STK11, KEAP1 and MDM2) were infrequent and similar in both groups. The frequencies of other potentially targetable GA including BRCA1/2, ATM, KRAS G12C, ERBB2, BRAF, FGFR1, NF1 and PIK3CA were also infrequent and similar in both groups of PDAC patients. Amongst a database of 16558 cases of clinically advanced cancer with MTAP loss, 1538 (9.3%) featured co-alterations in MTAP and SMAD4. 52% of the MTAP/SMAD4 co-altered cases were PDAC. Conclusions: MTAP loss is associated with a distinctive concurrent genomic profile in PDAC and represents a potential new synthetic lethality-based opportunity for treatment with PRMT5 and MAT2A inhibitors. Furthermore, MTAP loss may represent an independent negative predictive biomarker for immune checkpoint inhibition in PDAC.[Table: see text]

Background: FGFR1 amplification (amp) occurs in ˜15% of breast cancers (BC) and associates with poor prognosis and resistance to endocrine therapy. CCND1 regulates cell cycle progression and is amplified in 15-20% of BC. Co-amp of FGFR1 occurs in 30-40% of CCND1amp tumors, suggesting the possibility of oncogene cooperativity. CDK4/6 inhibitors, which block the action of cyclin D1/CDK4 complexes at the G1-to-S transition, are approved for treatment of ER+ BC and FGFR inhibitors are in early phase clinical trials. Results: Between 11/2013 and 03/2017, 191 BCs from 188 patients with metastatic (M) or refractory locoregional recurrent (RLRR) BC at Vanderbilt (VICC) were profiled by targeted next gen sequencing (Foundation OneTM). These are included within the 2131 publicly available BC sequencing results in GENIE (Foundation OneTM and MSK-IMPACT). Among the GENIE cohort, rates were: FGFR1amp 7% (n=156), CCND1amp 12% (n=261) and CCND1/FGFR1co-amp 3% (n=58). Additional cases showed FGFR1 missense mutations (n=16) and deep deletions (n=5). When the analysis was limited to the VICC cohort allowing restriction to ER+ BC, FGFR1amp (16%) and CCND1amp (23%) rates are similar to rates in primary BC in TCGA (13% FGFR1 [p = 0.44] and 19% CCND1 [p = 0.24]). In GENIE, the most frequent co-mutations in FGFR1amp tumors were TP53 (31%), PIK3CA (21%), GATA3 (13%), CDH1 (11%) and MAP3K1 (10%). However, TP53 and PIK3CA mutations were less common among FGFR1amp tumors than FGFR1non-amp cases (p 0.016). Histopathologic correlation on tumors from our institution show a majority of FGFR1 and/or CCND1 amp BC (64%) were ER+/HER2–; 33% of ER+/FGFR1amp tumors were PR–. Distinctive histologic features associated with FGFR1 and/or CCND1 amp were lobular histology (17%) and neuroendocrine differentiation (14%), 0-10%TILs (94%) and high proliferative rate (46%). Conclusion: FGFR1amp and CCND1amp rates in TCGA are similar to those seen in MBC/LRRBC (GENIE) suggesting FGFR1 can function as both a driver mutation and de novo mechanism of endocrine resistance early in tumorigenesis. Frequent co-amp with CCND1 and lower rates of TP53 and PIK3CAmut also support a driver role for FGFR1amp and FGFR1/CCND1co-amp. The observation of neuroendocrine features in a subset of these tumors suggests lineage plasticity. This may be a consequence of genomic alterations promoting anti-estrogen resistance and is consistent with recently published BC outcome data associating neuroendocrine differentiation with higher grade ER+ tumors, frequent 8p amp, which includes FGFR1, and worse disease-free and overall survival. The frequency of FGFR1amp suggests genotype specific trials with FGFR inhibitors would be highly feasible. Whether FGFR1/CCND1 co-amplified tumors are candidates for treatment with a combination of FGFR and CDK4/6 inhibitors requires further investigation. Citation Format: Gonzalez-Ericsson PI, Estrada MV, Formisano L, Jansen VM, Mayer IA, Rexer BN, Abramson VG, Levy M, Balko JM, Stricker TP, Arteaga CL, Sanders ME. Genenomic landscape of breast cancers with FGFR1 amplification and FGFR1/CCND1 co-amplification revealed by targeted capture next generation sequencing [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD4-07.

e15037 Background: Despite advances in biomarker-directed cancer therapies to predict tumor response to treatment, precision oncology remains an imprecise science. Tumor organoid drug screens present an opportunity to test multiple potentially effective therapies simultaneously before exposing a patient to treatment toxicities. Preliminary studies have established the analytic validity; however, the clinical validity of the tumor organoid response to a drug to predict the tumor response in patients is unclear. Methods: In order to establish the clinical validity of an organoid drug screen, a clinical disease model should have the following features to enable comparison of the tumor’s clinical response to treatment and the organoid’s response to the same treatment. First, a fresh tissue biopsy (non-bone) needs to be obtained for organoid development prior to the start of systemic treatment. Priority should be given to tumors known to successfully grow organoids. Disease models where the standard of care systemic treatment is a single cytotoxic or targeted agent would best assess correlation between the drug screen and patient response, such as metastatic breast, cervical, or prostate cancer. This would often lead to enrolling patients on later line systemic therapies. Studies should avoid drugs whose mechanism of action leverages the patient metabolism or tumor microenvironment (e.g. immunotherapy, aromatase inhibitors, VEGF inhibitors). Patient should have measurable disease that can be measured clinically, radiographically, or pathologically and using standardized response evaluation criteria (e.g. RECIST). Next generation sequencing would assess genomic concordance between the tumor in the patient and organoid. These studies would determine the feasibility and timeliness of prospectively developing tumor organoids that is sufficient to perform a drug screen. Results: We propose two research models to evaluate the clinical validity of tumor organoid drug screens: a metastatic solid tumor and neoadjuvant solid tumor disease model. Metastatic disease models provide an opportunity to assess response across multiple cancer types at the time of progression and initiation of next line of therapy. The efficacy of chemotherapy can be determined using objective data from radiologic response (RECIST). In neoadjuvant models, pathologic (pCR) and radiologic response can provide objective data for organoid response. These study design features will lay the framework for determining the clinical utility of organoid drug screens. Conclusions: We call for clinical studies assessing the clinical validity of tumor organoid drug screens and determining their concordance with patient response to systemic therapy. Advancements in clinically validated tumor organoids have the potential to fundamentally shift clinical paradigms and improve patient outcomes in cancer treatment.

97 Background: The incidence and mortality of early-onset colorectal cancer (EO-CRC) is on the rise. Consequently, there is an urgent unmet need to better understand their unique tumor biology to expand therapeutic options and improve clinical outcomes. Methods: Exploratory Targeted multiplex proteomics (TMP) and targeted 648 gene panel was performed on specimens from 35 patients with resected colon cancer diagnosed at age < = 40 years. TMP panel consisted of 72 proteins involved in differentiation, tumorigenesis, and response to chemotherapy, targeted therapy, and immunotherapy. Clinicopathologic and genomic data were also collected. Results: The median age of diagnosis was 33 years. The cohort included 15 male and 20 female patients. 20 (57%) had left-sided tumors and 6 (17%) had stage IV disease. Notable genomic alterations included mutations in: BRAF V600E (2/35); RAS (15/35); PIK3CA exon 9 or 20 (5/35); and ERBB2 (2/35). One patient exhibited ERBB2 amplification. 9/35 tumors were MSI-H. TMP analysis revealed overexpression of chemotherapy resistance proteins in several patients: ALDH1A1:16/35; ERCC1:1/35; GART:26/35; MDR1:9/35; MGMT:5/35; RRM1:6/35; TUBB3:1/35; TYMS:2/35; XRCC1:11/35. In contrast, some tumors exhibited elevated biomarkers of chemosensitivity: hENT1:8/35; DHFR:12/35; TYMP:15/35; OPRT:8/35; SLFN11:1/35; TLE:11/35; TOPO1:12/35; TOPO2A:1/35. Protein targets of cell signaling pathways were overexpressed in a number of tumors: CAT:16/35; CAV-1: 6/35; CBL:2/35; E-Cadherin: 19/35; HSP90A:16/35; HSP90B:18/35; MET:5/35; NQO1:18/35; paxillin:4/35; SRC:21/35; STAT3:11/35. Regarding EGFR and KRAS, none of the tumors exhibited elevated protein expression level. Furthermore, RAS mutational status did not correlate with the level of EGFR or KRAS protein expression. Antibody drug conjugate biomarkers were observed. HER2 overexpression was noted in one patient who had a confirmed ERBB2 amplification. Regarding immunotherapy targets, PDL-1 protein was not overexpressed in any tumor, whereas MSLN and TROP2 were elevated in 1/35 and 2/35 patients, respectively. Conclusions: TMP analysis of EO-CRC patients revealed marked heterogeneity in the expression of proteins involved in differentiation, tumorigenesis, and response to chemotherapy, targeted therapy, and immunotherapy. Differential protein expression may provide insight into therapeutic vulnerabilities for EO-CRC. Furthermore, the discordance between detected genomic alterations and protein expression levels highlights the complementary nature of genomic sequencing and TMP analysis.

Background: As targeted cancer therapies and molecular profiling become widespread, the era of “precision oncology” is at hand. However, cancer genomes are complex, making mutation-specific outcomes difficult to track. We created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based Querying, to automatically calculate and display mutation-specific survival statistics from electronic health record data.Methods: Patients with cancer genotyping were included, and clinical data was extracted through a variety of algorithms. Results were refreshed regularly and injected into a standard reporting platform. Significant results were highlighted for visual cueing. A subset was additionally stratified by stage, smoking status, and treatment exposure.Results: By August 2015, 4310 patients with a median follow-up of 17 months had sufficient data for survival calculation. As expected, epidermal growth factor receptor (EGFR) mutations in lung cancer were associated with superior overall survival, hazard ratio (HR) = 0.53 (P Interpretation: CUSTOM-SEQ represents a novel rapid learning system for a precision oncology environment. Retrospective studies are often limited by study of specific time periods and can lead to incomplete conclusions. Because data is continuously updated in CUSTOM-SEQ, the evidence base is constantly growing. Future work will allow users to interactively explore populations by demographics and treatment exposure, in order to further investigate significant mutation-specific signals.

This perspective describes the motivation, development, and implementation of pathway-based content for My Cancer Genome, an online precision medicine knowledge resource describing clinical implications of genetic alterations in cancer. As researchers uncover more about cancer pathogenesis, we are learning more not only about the specific genes and proteins involved but also about how those genes and proteins interact with others along cell signaling pathways. This knowledge has led researchers and clinicians to begin to think about cancer therapy using a pathway-based approach. To facilitate this approach, My Cancer Genome used a list of more than 800 cancer-related genes to identify 20 cancer-relevant pathways and then created content focused on demonstrating the therapeutic relevance of these pathways.

Purpose Adjuvant endocrine therapy is a long-term drug therapy prescribed to prevent recurrence of hormone receptor–positive breast cancer. Data on adjuvant endocrine therapy are reported though clinical trials, which may differ from treatment practice and outcomes in the general population of patients with breast cancer. With secondary use of electronic health record (EHR) data, we summarize adjuvant endocrine treatment practice and outcomes in real-world settings. Methods We analyzed treatment data derived from EHR data on 1,587 patients with stage I to III breast cancer at a National Cancer Institute–designated comprehensive cancer center to learn the frequencies of real-world adjuvant endocrine drug switches and discontinuation and to explore the potential cause for drug switches and discontinuation from medical records. We measured rates of drug use, drug switches, early drug discontinuation, adverse events, recurrence, and death. We also measured adverse events and change in menopause status as potential causes for drug switch and discontinuation. Results Within the study population, approximately 49% of patients were lost to follow-up or did not complete adjuvant treatment through 5 years. Fifty-two percent of patients switched to a different endocrine therapy drug during their treatment. We found that age is correlated with drug switches and that adverse events are correlated with drug switches and discontinuation. We also found that patients who switched to an alternative endocrine therapy during treatment were more likely to complete 5 years of treatment. Conclusion This study describes long-term adjuvant endocrine treatment in real-world settings and demonstrates the ability to leverage longitudinal EHR data to characterize oral medication treatment patterns in patients with cancer.

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. [J Med Internet Res 2017;19(7):e265]

Increased understanding of intertumoral heterogeneity at the genomic level has led to significant advancements in the treatment of solid tumors. Functional genomic alterations conferring sensitivity to targeted therapies can take many forms, and appropriate methods and tools are needed to detect these alterations. This review provides an update on genetic variability among solid tumors of similar histologic classification, using non–small cell lung cancer and melanoma as examples. We also discuss relevant technological platforms for discovery and diagnosis of clinically actionable variants and highlight the implications of specific genomic alterations for response to targeted therapy. Clin Cancer Res; 20(9); 2264–75. ©2014 AACR.

Oncology practice increasingly requires the use of molecular profiling of tumors to inform the use of targeted therapeutics. However, many oncologists use third-party laboratories to perform tumor genomic testing, and these laboratories may not have electronic interfaces with the provider’s electronic medical record (EMR) system. The resultant reporting mechanisms, such as plain-paper faxing, can reduce report fidelity, slow down reporting procedures for a physician’s practice, and make reports less accessible. Vanderbilt University Medical Center and its genomic laboratory testing partner have collaborated to create an automated electronic reporting system that incorporates genetic testing results directly into the clinical EMR. This system was iteratively tested, and causes of failure were discovered and addressed. Most errors were attributable to data entry or typographical errors that made reports unable to be linked to the correct patient in the EMR. By providing direct feedback to providers, we were able to significantly decrease the rate of transmission errors (from 6.29% to 3.84%; P < .001). The results and lessons of 1 year of using the system and transmitting 832 tumor genomic testing reports are reported.

Purpose Combining tumor antigens with an immunostimulant can induce the immune system to specifically eliminate cancer cells. Generally, this combination is accomplished in an ex vivo, customized manner. In a preclinical lymphoma model, intratumoral injection of a Toll-like receptor 9 (TLR9) agonist induced systemic antitumor immunity and cured large, disseminated tumors. Patients and Methods We treated 15 patients with low-grade B-cell lymphoma using low-dose radiotherapy to a single tumor site and—at that same site—injected the C-G enriched, synthetic oligodeoxynucleotide (also referred to as CpG) TLR9 agonist PF-3512676. Clinical responses were assessed at distant, untreated tumor sites. Immune responses were evaluated by measuring T-cell activation after in vitro restimulation with autologous tumor cells. Results This in situ vaccination maneuver was well-tolerated with only grade 1 to 2 local or systemic reactions and no treatment-limiting adverse events. One patient had a complete clinical response, three others had partial responses, and two patients had stable but continually regressing disease for periods significantly longer than that achieved with prior therapies. Vaccination induced tumor-reactive memory CD8 T cells. Some patients' tumors were able to induce a suppressive, regulatory phenotype in autologous T cells in vitro; these patients tended to have a shorter time to disease progression. One clinically responding patient received a second course of vaccination after relapse resulting in a second, more rapid clinical response. Conclusion In situ tumor vaccination with a TLR9 agonist induces systemic antilymphoma clinical responses. This maneuver is clinically feasible and does not require the production of a customized vaccine product.

e19008Background: In acute myeloid leukemia (AML) patients, FLT3 mutations (FLT3 mt) are associated with worse prognosis, while NPM1 mutations (NPM1 mt) have better prognosis. There have been confl...

3053Background: Immune checkpoint inhibitors (ICI) are standard of care for many cancer patients (pts). There have been conflicting reports on the effect of the influenza (flu) vaccines (flu-V) on ...

e18572Background: Recurrent and metastatic cancers have been underrepresented in cancer genomics databases until the creation of AACR Project GENIE, a large multi-institutional database derived fro...

The current state-of-the-art assessment of treatment response in breast cancer is based on the response evaluation criteria in solid tumors (RECIST). RECIST reports on changes in gross morphology and divides response into one of four categories. In this paper we highlight how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) may be able to offer earlier, and more precise, information on treatment response in the neoadjuvant setting than RECIST. We then describe how longitudinal registration of breast images and the incorporation of intelligent bioinformatics approaches with imaging data have the potential to increase the sensitivity of assessing treatment response. We conclude with a discussion of the potential benefits of breast MRI at the higher field strength of 3T. For each of these areas, we provide a review, illustrative examples from clinical trials, and offer insights into future research directions.

QUESTION ASKED: Cancer stage, one of the most important prognostic factors for cancer-specific survival, is often documented in narrative form in electronic health records (EHRs), and as such is difficult to abstract by registrars and other secondary data users, including clinicians participating in quality reporting activities. Can the cancer stage be accurately extracted by natural language processing (NLP) of the text from EHRs? SUMMARY ANSWER: In a combined dataset of N = 2,323 patients with lung cancer (training set: n = 1,103; validation set n = 1,220), we analyzed 751,880 documents and discovered at least one stage statement for 98.6% of patients (median of 24 documents with stage statements per patient). Despite a high degree of discordance in patient records (83.6% of patients had conflicting stage statements in their HER; Fig 2 ), algorithmically derived stage accuracy was very high in the validation set, κ = 0.906 (95% CI, 0.873 to 0.939), as compared with the gold standard of tumor registrar–derived stage. METHODS: We developed an NLP algorithm to extract stage statements from machine-readable EHR documents, including automated rules to choose the most likely stage when discordance was present in the EHR; the algorithm was developed on a training set of patients with lung cancer and independently validated on a test set of patients with lung cancer who were seen at our institution. BIAS, CONFOUNDING FACTOR(S), DRAWBACKS: An exact stage (eg, stage I, stage IV) could be calculated for only 72% of the patients; the remainder were assigned an inexact stage (eg, “early stage”). In an exploratory analysis, we were able to distinguish stage IIIA from stage IIIB, but the accuracy was not as good, in the 64% to 79% range. The experiments were carried out only on patients with lung cancer, so it is unknown whether other tumor types would have a similar level of performance. We did not explicitly consider the provenance of the information, (eg, was the stage documented by a medical student, an attending oncologist, etc). Finally, given that this was performed at a single tertiary care institution, there may be significant differences in documentation patterns at other institutions that could affect the reproducibility of the results. REAL-LIFE IMPLICATIONS: This new approach to the determination of summary stage in patients with lung cancer can be applied rapidly and broadly to a patient population with large amounts of EHR data. Despite the presence of significant discordance in documentation, the results were highly accurate. This proof-of-concept suggests that NLP may augment and enhance manual abstraction efforts and may even replace them for certain targeted applications. [Figure: see text]

Background: AACR GENIE is an international data-sharing project that aggregates clinical-grade cancer genomic data. As a demonstration of utility, we evaluated the landscape of ERBB2 mutations in the first 18,486 patients included in this registry and compared it to the first 100 patients enrolled in an ongoing international Phase 2 SUMMIT ‘basket’ study of the pan-HER inhibitor neratinib in ERBB2 mutant solid tumors (NCT01953926). Results: ERBB2 mutations were identified in 2.8% (519/18,486) of patients in the GENIE cohort and observed at all participating centers. In total, there were 482 missense, 66 indels, 19 truncating mutations, and 14 structural variants. A total of 263 unique missense mutations were observed including 12 at previously identified hotspots which accounted for 69.2% of all missense mutations. 35 unique cancer types were represented. The tumor types with the highest proportion of ERBB2 mutations were bladder (12.8%, 82/641), breast (3.9%, 87/2230), colorectal (3.3%, 70/2102), and NSCLC (3%, 90/3006). Among patients with copy number data available (91%) 11% had concurrent ERBB2 amplification, most often in breast cancer. The most frequently observed alterations in ERBB2, adjusted for differing exon coverage between panels, was S310F/Y in 0.46% of the GENIE cohort (12.6% of samples with ERBB2 alterations), Y772_A775dup in 0.21% (6.9%), R678Q in 0.17% (4.5%), L755S in 0.16% (5.2%), V777L in 0.12% (3.8%), and V842I in 0.09% (3.1%). The distribution of specific ERBB2 variants differed significantly by tumor type with exon 20 insertions being most common in NSCLC (44.4%, 40/90), L755S (18.9%, 11/92) in breast, S310F/Y (26.9%, 28/104) in bladder, and V842I (13.9%, 10/72) in colorectal cancer. Structural variants included intragenic deletions (n=4) and fusions involving various partners including GRB7 (n=2), and one each of C1orf87, PPIL6, HEXIM2, THRA, ASIC2, BCA3, WIPF2. The frequencies of ERBB2 mutant cancer types observed in the GENIE cohort were generally comparable to those enrolled to the neratinib basket study including NSCLC (17 vs 22%, respectively), breast (16.4 vs 24%), bladder (15.5 vs 14%), colorectal (13.2 vs 17%), and endometrial (4.2 vs 6%). At the variant level, S310F/Y was less prevalent in GENIE compared to the neratinib study (12.6 vs 24%) while all other mutations were generally similar including L755S (5.2 vs 9%), R678Q (4.5 vs 2%), Y772_A775dup (6.9 vs 13%), V777L (3.8 vs 9%), and V842I (3.1 vs 6%). Conclusion: GENIE confirms that a diversity of ERBB2 mutations are prevalent across a variety of tumor types in patients with advanced cancer. The genomic landscape of ERBB2 mutations was largely similar in the population based GENIE cohort and the neratinib SUMMIT study, providing the first direct evidence that basket study enrollment accurately reflects the true landscape of the target alteration. Citation Format: Alison Schram, Helen H. Won, Fabrice Andre, Monica Arnedos, Funda Meric - Bernstam, Philippe L. Bedard, Kenna R. Shaw, Hugo Horlings, Christine Micheel, Ben Ho Park, Grace Mann, Alshad S. Lalani, Lillian Smyth, David B. Solit, Deborah Schrag, Mia A. Levy, Barrett J. Rollins, Mark Routbort, Charles L. Sawyers, Eva Lepisto, Michael F. Berger, David M. Hyman, on behalf of the AACR Project GENIE Consortium. Landscape of somatic ERBB2 Mutations: Findings from AACR GENIE and comparison to ongoing ERBB2 mutant basket study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-103. doi:10.1158/1538-7445.AM2017-LB-103

AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) is a multi-phase, multi-year, international data-sharing consortium whose goal is to generate an evidence base for precision cancer medicine by integrating and linking clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. The project fulfills an unmet need in oncology by providing the statistical power necessary to identify novel therapeutic targets, to understand genomic determinants of response to therapy, to design new biomarker-driven clinical trials and ultimately, to improve clinical decision-making and the care delivered to patients. Here we describe the goals, structure and data standards of the GENIE consortium and conclusions from a high-level analysis of the first public release of genomic and limited clinical data from approximately 19,000 patients treated at eight cancer centers obtained during this initial phase of the project. We also explore the clinical utility of these genomic data by examining rates of clinical actionability across multiple cancer types and by estimating patient enrollment rates to the NCI MATCH Trial. Based on yearly rates of sequencing at each of the eight founding institutions, together with the planned addition of new members, we estimate the GENIE database could grow to >100,000 samples within five years. Consistent with the goals of the proposed Cancer Moonshot National Cancer Data Ecosystem, GENIE is committed to the principles of generating interoperable, open access data that can be widely shared across the entire scientific community. Citation Format: Ethan Cerami, Alexander S. Baras, Justin Guinney, Eva Lepisto, Trevor J. Pugh, Nikolaus Schultz, Thomas Stricker, Shawn M. Sweeney, Laura J. van't Veer, Gerrit A. Meijer, Fabrice Andre, Victor E. Velculescu, Kenna R. Shaw, Mia A. Levy, Philippe L. Bedard, Barrett J. Rollins, Charles L. Sawyers, on behalf of the AACR Project GENIE Consortium. Landscape analysis of the initial data release from AACR Project GENIE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-102. doi:10.1158/1538-7445.AM2017-LB-102

AACR Project GENIE is an international data-sharing project with the goal of enhancing precision oncology. On January 5, 2017, data from ~19,000 somatic tumor genotype reports and selected clinical information from patients (http://www.aacr.org/RESEARCH/RESEARCH/PAGES/AACR-PROJECT-GENIE-DATA.ASPX) were released to the public. The eight member institutions contributed patient data, which were then made available on a dedicated cBioPortal website and for download by Sage Bionetworks. As part of the analysis of the first data release, member institutions began work to combine and reconcile their clinical actionability knowledgebases (KBs). In this abstract, we present efforts to combine clinical actionability assertions from My Cancer Genome (MCG; https://www.mycancergenome.org; Vanderbilt-Ingram Cancer Center), Personalized Cancer Therapy (https://pct.mdanderson.org; MD Anderson Cancer Center), and OncoKB (http://oncokb.org; Memorial Sloan Kettering Cancer Center). We also present data on matching GENIE patient genotypes to clinical actionability assertions from these KBs and to biomarker-driven clinical trials to begin defining the landscape of clinical actionability across a large cohort of patients at all stages of disease.The three KBs were combined using a modification of OncoKB’s levels of evidence for clinical actionability. Categories included standard of care therapies (e.g., those with FDA labels and in NCCN guidelines) and investigational therapies with strong clinical data, both on and off the recommended diagnosis indications. Diagnoses were mapped to the OncoTree tumor type hierarchy. Initial efforts to combine the KBs resulted in >500 therapeutic assertions. Using the combined KBs, we matched these assertions to GENIE patient diagnoses and genotypes. In our preliminary results, >33% of patient samples match at least one therapeutic assertion. Of these, ~15% match at the standard of care level, and ~8% match at the level of investigational therapies. The remaining matches were exploratory or matched by biomarker but not diagnosis. Most frequently matching diagnoses at the standard-of-care level were non-small cell lung cancer, breast cancer, and melanoma. Further details will be presented.The MCG team curates diagnosis and biomarker eligibility criteria for all recruiting cancer clinical trials reported in ClinicalTrials.gov. As of January 2017, 5,201 recruiting cancer clinical trials from ClinicalTrials.gov have been reviewed, and 1,884 trials were found to have biomarker eligibility criteria. Of these, 352 trials are testing a targeted therapy and have a known driver mutation as an inclusion criterion. Based on preliminary work, ~16% of patient samples match at least one trial in the limited set. When the trial list is expanded to include all biomarker-driven trials, including those exploring the impact of mutations along an entire cell signaling pathway, ~84% of patient samples match at least one trial; matching biomarkers are often exploratory and patient benefit from the trial intervention is not necessarily expected. For the limited trial set, patients with breast cancer, non-small cell lung cancer, glioma, and melanoma were most likely to match a trial. For the expanded trial set, patients with non-small cell lung cancer, colorectal cancer, breast cancer, and glioma were most likely to match a trial. We will show how genetic testing panel size affects trial matching and present clinical trial matching by disease, gene, alteration type, drug class, and cell signaling pathway.In conclusion, the GENIE project has resulted in more than just the shared data. It has fostered collaborations between institutions to reconcile and improve precision cancer medicine KBs and provided a resource for improving cancer genetic testing and the practice of precision cancer medicine. [C. M. and D. C. contributed equally to this work.] Citation Format: Christine M. Micheel, Debayani Chakravarty, Jiaojiong Gao, Ian Maurer, Clinton Miller, Kenna R. Shaw, Mia A. Levy, Nikolaus Schultz, on behalf of the AACR Project GENIE Consortium. Clinical actionability and clinical trial matching for GENIE patient genotypes using My Cancer Genome, Personalized Cancer Therapy, and OncoKB [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-104. doi:10.1158/1538-7445.AM2017-LB-104

e18182 Background: Bioportals that aggregate patient genomic results and diagnosis data elements can be used as a tool for identifying potentially eligible patients for molecularly-driven clinical trials. However, over time, increasing numbers of patients will be deceased, making this process less efficient. Methods: We sought to evaluate the addition of minimal clinical data to a clinical trial prescreening workflow utilizing these bioportals. We selected three molecularly-driven clinical trials currently enrolling patients at Vanderbilt-Ingram Cancer Center and evaluated the incremental contribution of genomic and clinical data to refinement of cohort identification. Utilizing data from the enterprise data warehouse (EDW), we assessed the potentially eligible patient population after addition of gene-level, alteration-level, vital status (known to be deceased), and date of last contact data elements to the data extraction query. Results: Utilizing gene-level and diagnosis data elements only, 68 potentially eligible patients were identified for these trials from a total of 7,200 patients whose NGS data was added to the EDW between 2010 and 2016. Addition of alteration-level detail eliminated 29% of these patients. Of the 53 remaining patients, incorporating vital status resulted in paring the potentially eligible cohorts by an additional 42%. Conclusions: This study demonstrates the added value of querying structured clinical and molecular data stored in the EDW to improve prescreen workflow efficiency and decrease manual review requirements. [Table: see text]

6533 Background: Today’s oncologist is responsible for choosing appropriate cancer genomics tests to inform patient treatment from multiple available platforms, weighing cost, availability, sensitivity and specificity, and clinical actionability. Knowledge-driven clinical decision support tools can assist clinicians in choosing the panel that is most informative in a given clinical space. Methods: Using a queryable knowledgebase of >1800 active clinical trials containing structured eligibility criteria curations for diagnosis and genomic alterations, we compared two CLIA-regulated genomic panels for clinical actionability over the landscape of solid, breast, and lung cancer clinical trials. Results: The larger panel (73 genes) was more actionable than the smaller panel (62 genes) in the breast cancer (10x more trials returned) and solid tumor (2.7x more trials returned) clinical trial space, while the smaller panel returned 1.2x more trials in the lung cancer space (see table). Conclusions: This analysis demonstrates that patient diagnosis has a significant effect on the potential clinical actionability of a given genomic panel. Further, this analysis demonstrates the clinical utility of knowledge-driven clinical decision support tools for test selection, especially given the often-limited tumor sample available, cost of genomic panel testing, and continuously shifting trial landscape. [Table: see text]

Mutations in the PIK3CA gene are common in breast cancer and represent a clinically useful therapeutic target. Several larger, population-based studies have shown a positive prognostic significance associated with these mutations. This study aims to further identify characteristics of patients harboring PIK3CA mutations while evaluating the clinical impact of genomic testing for these mutations. Tumors from 312 patients at Vanderbilt-Ingram Cancer Center were analyzed for PIK3CA mutations using a multiplex screening assay (SNaPshot). Mutation rates, receptor status, histopathologic characteristics, and time to recurrence were assessed. The number of patients participating in clinical trials, specifically trials relating to the PIK3CA mutation, was examined. Statistically significant differences between wild-type and mutated tumors were determined using the Wilcoxon, Pearson, and Fischer exact tests. The PIK3CA mutation was found in 25 % of tumors tested. Patients with PIK3CA mutations were significantly more likely to express hormone receptors, be of lower combined histological grade, and have a reduced time to recurrence. Patients found to have a PIK3CA mutation were significantly more likely to enter a PIK3CA-specific clinical trial. In addition to confirming previously established positive prognostic characteristics of tumors harboring PIK3CA mutations, this study demonstrates the feasibility and utility of mutation profiling in a clinical setting. PIK3CA mutation testing impacted treatment and resulted in more patients entering mutation-specific clinical trials.

195 Background: Breast cancer patients face difficult decisions about their surgical care without a full understanding of their options. The learning health system goal is to use information from the care of prior patients to inform the care of future patients. We aim to apply this concept to generate data-driven surgical paths, develop interactive path visualizations to inform patients, and evaluate their impact. Methods: We used cancer registry and administrative CPT codes for women diagnosed with stage 0-III breast cancer between FY2010-14 at a comprehensive cancer center. We generated surgical event sequences and visualized them using interactive Sankey diagram path visualizations. We will run a prospective educational intervention this winter to evaluate their impact on the shared decision making process. A web-based application will be available to patients prior to, during, and after their surgical clinic visit; we will survey their reaction pre-visit, post-visit, and post-surgery. Results: 1556 patients had 1951 surgical events in the registry and 48% started their surgical care with a breast conserving surgery while 52% began with a mastectomy. Mastectomy paths are presented in Table 1. We have developed interactive visualizations for patients to view, will be conducting our prospective educational intervention this winter, and will be ready to present preliminary results in February. Conclusions: We have been able to develop interactive, data-driven surgical path visualizations for breast cancer patients from cancer registry and administrative data. We will be conducting a prospective educational intervention to evaluate our implementation of this learning health system concept. [Table: see text]

Clinical imaging plays an essential role in cancer care and research for diagnosis, prognosis, and treatment response assessment. Major advances in imaging informatics to support medical imaging have been made during the last several decades. More recent informatics advances focus on the special needs of oncologic imaging, yet gaps still remain. We review the current state, limitations, and future trends in imaging informatics for oncology care including clinical and clinical research systems. We review information systems to support cancer clinical workflows including oncologist ordering of radiology studies, radiologist review and reporting of image findings, and oncologist review and integration of imaging information for clinical decision making. We discuss informatics approaches to oncologic imaging including, but not limited to, controlled terminologies, image annotation, and image-processing algorithms. With the ongoing development of novel imaging modalities and imaging biomarkers, we expect these systems will continue to evolve and mature.

Introduction: Chemotherapy administration is a highly complex and distributed task in both the inpatient and outpatient infusion center settings. The American Society of Clinical Oncology and the Oncology Nursing Society (ASCO/ONS) have developed standards that specify procedures and documentation requirements for safe chemotherapy administration. Yet paperbased approaches to medication administration have several disadvantages and do not provide any decision support for patient safety checks. Electronic medication administration that includes bar coding technology may provide additional safety checks, enable consistent documentation structure, and have additional downstream benefits. Methods: We describe the specialized configuration of clinical informatics systems for electronic chemotherapy medication administration. The system integrates the patient registration system, the inpatient order entry system, the pharmacy information system, the nursing documentation system, and the electronic health record. Results: We describe the process of deploying this infrastructure in the adult and pediatric inpatient oncology, hematology, and bone marrow transplant wards at Vanderbilt University Medical Center. We have successfully adapted the system for the oncology-specific documentation requirements detailed in the ASCO/ONS guidelines for chemotherapy administration. However, several limitations remain with regard to recording the day of treatment and dose number. Conclusion: Overall, the configured systems facilitate compliance with the ASCO/ONS guidelines and improve the consistency of documentation and multidisciplinary team communication. Our success has prompted us to deploy this infrastructure in our outpatient chemotherapy infusion centers, a process that is currently underway and that will require a few unique considerations.

The emergence of high throughput genomic testing of tumors presents both an opportunity and challenge for use in clinical decision making across the continuum of cancer care. A tsunami of molecular data is now reaching the clinic with variable levels of evidence with respect to clinical validity and clinical utility. Not surprising, a large knowledge gap remains for oncologists who try to apply this new data for clinical decisions at the point of care. In order to address this knowledge gap, we have developed My Cancer Genome (MCG), a publically available website with the mission to curate and disseminate knowledge regarding the clinical significance of genomic alterations in cancer. My cancer genome contains content curated by domain experts on the prognostic and therapeutic implications of genomic alterations in cancers, including experimental therapeutics and active clinical trials. In addition to being publically available via MyCancerGenome.org, the content has also been integrated into a decision support tool for interpretation and reporting of results for tumor next generation sequencing via a commercial partner. Opportunities to scale resources like MCG continue to present opportunities and challenges to the cancer community. Data driven approaches where we learn from the experience of all cancer patients will be needed to help realize the promise of precision cancer medicine. Citation Format: Mia Levy. Clinical decision support in the era of genome informed cancer medicine. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr IA04.

e17624 Background: Monitoring quality metrics in near real time can help healthcare providers and organizations improve care delivery, engage patients, and comply with reporting requirements of acc...

e12544 Background: Many innovations in medicine were discovered by astute clinicians recognizing previously unknown patterns in their practice. As the future of cancer treatment increasingly utiliz...