Abstracts From the NCCN 2023 Annual Conference

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The following abstracts were accepted for presentation at the NCCN 2023 Annual Conference, March 31 through April 2, 2023. Additional abstracts are available here.


YIA23-001: Targeting NKG2D Ligands Is Therapeutically Effective in NSCLC

Qing Deng, PhD1; MinJae Lee, PhD1; Farjana Fattah, PhD1; Shohei Koyama, MD, PhD2; David Gerber, MD1; Esra Akbay, PhD1

1University of Texas Southwestern Medical Center, Dallas, TX; 2Division of Cancer Immunology, National Cancer Center, Tokyo, Japan

Background: KRAS is the most commonly mutated oncogene in Lung adenocarcinoma (LUAD). Co-occurring mutations in tumor suppressors and a master kinase LKB1/STK11 or TP53 define the major subgroups of KRAS-mutant LUAD with distinct biology and therapeutic vulnerabilities. Inactivating mutations in LKB1 occur in 31% of KRAS mutant LUAD, resulting in approximately 10,000–15,000 deaths annually in the US. KRAS and LKB1 co-mutated (KL) lung tumors represent one of the most aggressive LUAD subtypes and are resistant to immune checkpoint blockade (ICB) therapies. Thus, there is a critical need to identify differential therapeutic targets in KL tumors. Anti-tumor immunity can be mediated in part by cytotoxic T cells and natural killer (NK) cells, which have potent cytotoxic functions. T and NK cells can recognize tumors through expression of stress molecules. KL tumors express activating ligands NK cell activating receptor, The Natural Killer Group 2D (NKG2D). We hypothesized that KL tumors may be directed toward immune cell killing by targeting these surface molecules. Methods: We characterized NKG2DL RNA expression in NSCLC mouse models and patient samples. To validate the protein production we performed ELISA in patient blood samples. Expression in the mouse tumors was confirmed by flow cytometry of tumor tissues. We developed a fusion protein of NKG2D and Igg (antibody) to therapeutically target NKG2DLs in tumors. We then performed therapeutic experiments with the antibody in the genetically engineered mouse KL lung tumor models. We performed analysis of the tumor microenvironment by flow cytometry after treatment. Results: NKG2DL expression is increased in NSCLC cancer tissue as compared to healthy tissues of both in mouse and human tumors. NKG2DL was also detected in the blood of lung cancer patients. We observed significant tumor control and delayed growth in a KL model with an antibody targeting NKG2DLs. Combinations with standard of care further increased therapeutic efficacy of NKG2DL targeting antibody. Conclusions: ICB resistant NSCLCs can be vulnerable to targeting NKG2DLs. We propose that correct scheduling of existing clinically used treatments and NKG2DL targeting antibody treatment can activate innate and adaptive immunity. This therapy can be alternative treatment option in tumors that express NKG2DLs and are resistant to current standard of care.

YIA23-002: Evolutionary Pressures Shape Soft Tissue Sarcoma Development and Response to Radiotherapy

Erik S. Blomain, MD, PhD1; Anish Somani, NA1; Ajay Subramanian, BS1; Shaghayegh Soudi, PhD1; Eniola Oladipo, BS1; Christin New, BA1; Deborah E. Kenney, MS1; Neda Nemat-Gorgani, MS1; Susan M. Hiniker, MD1; Alexander L. Chin, MD, MBA1; Raffi S. Avedian, MD1; Robert J. Steffner, MD1; David G. Mohler, MD1; Matt van de Rijn, MD1; Everett Moding, MD, PhD1

1Stanford University School of Medicine, Stanford, CA

Background: Soft tissue sarcomas (STSs) are rare, mesenchymal tumors that are primarily treated surgically with radiotherapy (RT) added to improve local control. Selective pressures from the tumor microenvironment drive clonal evolution that can substantially impact the response of cancers to therapy. We integrated multi-region sequencing and circulating tumor DNA (ctDNA) analysis to quantify STS clonal selection in the absence and presence of RT. Methods: We retrospectively identified 11 patients with high-grade pleomorphic sarcomas treated with preoperative RT, including 7 patients with paired pre-RT and post-RT samples, and 9 patients treated with upfront surgery. Multi-region whole exome sequencing of 117 spatially distinct tumor regions and matched germline DNA was performed to identify single nucleotide variants (SNVs) and copy number alterations. Personalized hybrid capture panels were designed to enrich for each patient’s SNVs for ultra-deep targeted sequencing of tumor samples and ctDNA analysis. We inferred the subclonal clusters within untreated (no-RT) and irradiated (post-RT) STSs using PyClone. In addition, we evaluated 6 metrics of intratumoral heterogeneity (ITH) using simulated tumors with a similar mutation rate to human STSs and increasing levels of positive selection. We compared ITH metrics for no-RT human STSs with simulated tumors that developed under neutral evolution and post-RT human STSs using two-sided Mann-Whitney U tests. Results: We observed a similar number of mutations and similar mutational signatures between no-RT and post-RT STSs. Analysis of paired tumors samples identified subclonal expansion and/or contraction after RT in all patients analyzed. We identified 3 ITH metrics that significantly increased in simulated STSs with positive selection, and one ITH metric with a dynamic range sufficient to detect increased selection after RT in STSs (Wright’s fixation index: FST). No-RT STSs had significant evidence of positive clonal selection by all 3 ITH metrics in comparison to simulated tumors under neutral evolution. Furthermore, we observed a significant increase in positive selection after RT (median FST: no-RT 0.08 vs. post-RT 0.17, P=0.04). Conclusions: STSs undergo strong positive selection during tumor development, and RT further increases selection for resistant subclones. Characterizing baseline tumor heterogeneity and developing approaches to sensitize resistant subclones could improve responses to RT.

YIA23-002 Figure 1.
YIA23-002 Figure 1.

Mutations, intratumoral heterogeneity metrics, and cluster cellular prevalence in soft tissue sarcomas.

Citation: Journal of the National Comprehensive Cancer Network 21, 5.5; 10.6004/jnccn.2023.5002

YIA23-003: CD19-CAR T Cells Develop Exhaustion Epigenetic Programs During a Clinical Response

Caitlin Zebley, MD, PhD1; Charmaine C Brown, BS1; Tian Mi, BS1; Yiping Fan, PhD1; Shanta Alli, PhD1; Shannon Boi, PhD1; Giovanni Galletti, PhD2; Enrico Lugli, PhD2; Deanna Langfitt, PhD1; Jean-Yves Metais, PhD1; Timothy Lockey, PhD1; MichaelMeagher, PhD1; Brandon Triplett, MD1; Aimee Talleur, MD1; Stephen Gottschalk, MD1; Ben Youngblood, PhD1

1St. Jude Children’s Research Hospital, Memphis, TN; 2Humanitas Clinical and Research Center – IRCCS, Rozzano, Milan, Italy

Background: The goal of this study was to determine the epigenetic landscape of CD19-CAR T cells pre and post infusion in leukemia patients as an initial step to elucidate intrinsic mechanisms that limit CAR T-cell effector functions in humans. Methods: A longitudinal analysis of CD8+ CD19-CAR T cell epigenetic changes was performed by whole-genome DNA methylation profiling of CAR T cells during manufacturing and from peripheral blood mononuclear cells (PBMCs) of 15 patients enrolled on our institutional autologous CD19-CAR T cell therapy study (NCT03573700). CAR T cell expansion and persistence were determined by measuring vector copy numbers in the PBMCs of treated patients. We had previously established novel exhaustion DNA methylation datasets that delineate between progenitor and fully exhausted T cells. These datasets served as a guide for stratifying our post-infusion CAR T cells along the exhaustion developmental trajectory. Results: Our data show that CD19-CAR T cells lose repressive DNA methylation at effector loci (e.g. PRF1, TBET) while gaining methylation at genes associated with memory potential (e.g. LEF1, TCF7). We confirmed these epigenetic changes are coupled to endogenous human T cell effector and memory differentiation by cross-referencing our epigenetic data with publicly available transcriptional profiles for antigen-specific effector and long-lived memory CD8 T cells from individuals vaccinated for yellow fever. Furthermore, we show that CAR T cells were unable to mount an in vivo recall response after relapse of antigen-positive disease or recovery of endogenous B cells. Conclusions: These observations support the conclusion that CD19-CAR T cells acquire stable epigenetic exhaustion programs that limit their protective capacity against tumor.

YIA23-004: Non-Melanoma Skin Cancer (NMSC) in Patients With Chronic Lymphocytic Leukemia (CLL): Biology and Prevention

Deborah M. Stephens, DO1; Kenneth Boucher, PhD1; David A. Wada, MD1; Aaron Atkinson, PhD1; Jack Abbott, MD1; Marianne Bowling, DNP1; Justin Williams, BS1; Anthony D. Pomicter, MS1; Renee Vadeboncouer, MSN, APRN1; Clayton Savage, APRN1; Brynn Parsegov, PA-C1; Lindsey Gilstrap, BS1; Christa Shorter, BS1; Harsh Shah, DO1; Boyu Hu, MD1; Lindsey Fitzgerald, MD1

1Huntsman Cancer Institute, Salt Lake City, UT

Introduction: Patients (pts) with chronic lymphocytic leukemia (CLL) are at least twice as likely to develop a second cancer, which is likely secondary chronic CLL-related immunosuppression (Royle 2011). Non-melanoma skin cancers (NMSC) are the most common type of second cancer and those with CLL are 7X more likely to have recurrent NMSC (Mehrany 2005). In a phase 3 skin cancer prevention study, oral nicotinamide reduced NMSC recurrence by 23% (Chen 2015). Notably, pts with another malignancy (including CLL) or immunosuppression were excluded, so it is unknown whether nicotinamide can reduce NMSC recurrence in CLL patients. We hypothesize that nicotinamide can reverse CLL-related immunosuppression and prevent recurrent NMSC in CLL pts. Methods: At University of Utah, we are enrolling CLL pts with a history of ≥1 NMSC diagnosed ≤5 years to an investigator-initiated, phase 2 double-blind randomized clinical trial (see study schema Figure). Pts are stratified by history of prior OR current CLL therapy AND number of prior NMSC. Pts will be randomized 1:1 to receive oral nicotinamide 500mg twice daily or placebo for 12 mos. Pts will receive skin exams by a dermatologist at Month 6, 12, 18, and 24. Number of NMSC will be counted. Any lesions clinically suspicious for NMSC will be biopsied and reviewed. At Month 12, the # of pts in each group that have developed a subsequent NMSC will be tallied. Primary endpoint will be assessed. Then, un-blinding will occur and pts initially on the placebo arm crossover to nicotinamide therapy. Pts initially on the nicotinamide arm will continue an additional 12 mos of nicotinamide therapy. After an additional 12 mos of therapy, the # of pts in each group that have developed a subsequent NMSC will be tallied. The primary endpoint is the proportion of pts who develop new NMSC during Year 1. With 1:1 randomization, 86 subjects will provide 80% power at alpha = 0.10. Key secondary endpoint is safety assessed according to CTCAE v5 for non-hematologic toxicities and IWCLL (2018) Criteria for hematologic toxicities. Planned correlative studies include evaluation of T-cell exhaustion between treatment arms and analysis of B-human papilloma virus subtypes in CLL cells via whole exome sequencing and skin cancer biopsy samples via polymerase chain reaction. Results: This is an ongoing study with 8 of the planned 86 pts enrolled.

YIA23-004 Figure 1.
YIA23-004 Figure 1.

Nicotinamide study schema.

Citation: Journal of the National Comprehensive Cancer Network 21, 5.5; 10.6004/jnccn.2023.5002

YIA23-005: Early Interim Analysis of a Phase II Randomized Trial of Moderate Versus Ultra-Hypofractionated Post-Prostatectomy Radiation Therapy

William C. Jackson, MD1; Krithika Suresh, PhD1; Daniel E. Spratt, MD2; Robert T. Dess, MD1

1University of Michigan Rogel Cancer Center, Ann Arbor, MI; 2University Hospitals Seidman Cancer Center and Case Western Reserve University, Cleveland, OH

Introduction: Salvage radiation therapy (SRT) is a standard treatment option for men with disease recurrence after radical prostatectomy. The burden of standard conventional or moderately hypofractionated schedules (4–8 weeks) may result in decreased utilization of SRT. We hypothesized that ultra-hypofractionated SRT (UHF-SRT) delivered with 5 treatments over two weeks would be non-inferior to moderately hypofractionated SRT (MHF-SRT) with regards to patient reported quality of life (QOL). Methods: This is an institutional review board approved single center non-inferiority phase II trial comparing two fractionation schemes for men receiving SRT. The primary endpoint is to assess whether UHF-SRT (34 Gy in 5 fractions) is non-inferior to MHF-SRT (55 Gy in 20 fractions) with regards to patient reported changes in genitourinary (GU) and gastrointestinal (GI) QOL at 2-years post-treatment, as assessed by the Expanded Prostate Cancer Index Composite (EPIC-26) questionnaire. Elective nodal coverage was standardized. Total target trial accrual is 136 men. This is an early interim analysis of the first 28 men enrolled from 11/12/21–08/18/22; we report acute physician reported treatment related CTCAE toxicities as well as baseline, end of treatment (EOT), and 3-month post-treatment (3mo) patient reported QOL. Results: Fourteen men were randomized to each arm; all completed protocol specified treatment. Median age was 65 years; 89% received treatment to the prostate bed and pelvic lymph nodes. Median follow-up was 4.9 months, and 86% of men in the MHF-SRT arm experienced acute grade 1 or 2 GU or GI toxicity versus 64% in the UHF-SRT arm. There was a single acute grade 3 toxicity (GI - diarrhea), occurring in the MHF-SRT arm, which resolved with conservative management. Baseline, end of treatment, and 3-month post-treatment QOL scores are in Table 1. Baseline QOL was similar between arms. There was a transient decline in patient reported GI QOL in the UHF-SRT arm at the EOT, but this resolved by 3mo. There were no significant changes in patient reported GU QOL. Conclusions: Early interim analysis results thus far support investigating the hypothesis that UHF-SRT may be non-inferior to MHF-SRT with regards post-treatment patient reported QOL. Completion of trial enrollment and long-term follow-up are needed to establish the non-inferiority of UHF-SRT to MHF-SRT.

YIA23-005 Table 1.

EPIC-26 GU and GI patient reported QOL

YIA23-005 Table 1.

YIA23-006: BE-EPIC: Behavioral Economic Interventions to Embed Palliative Care in Community Oncology

Ravi B. Parikh, MD, MPP1,2; Ramy Sedhom, MD1; William J. Ferrell, MPH1; Katherine Villarin, BS1; Kara Berwanger, BS1; Bethann Scarborough, MD3; Randall Oyer, MD3; Pallavi Kumar, MD, MPH1; Niharika Ganta, MD, MPH; Shanthi Sivendran, MD3; Jinbo Chen, PhD1; Kevin G. Volpp, MD, PhD1; Justin E. Bekelman, MD1

1University of Pennsylvania, Philadelphia, PA; 2Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA; 3The Ann B. Barshinger Cancer Institute, Penn Medicine Lancaster General Health Lancaster, PA

Background: Early palliative care (PC) is an evidence-based practice for patients with advanced cancer, but is underutilized. This cluster randomized trial tests the impact of default, opt-out referrals for patients meeting guideline-based criteria to clinicians on (1) PC referrals and completed visits, and (2) quality of life. Methods: This 2-arm pragmatic randomized trial, conducted in a large, rural community oncology practice, randomizes 4 clinician-pods, consisting of approximately 250 patients with advanced cancer, in a 1:1 fashion into control or intervention arms. Clinicians in the intervention arm receive a nudge consisting of an electronic health record message indicating a patient has a default pended order for PC. For patients whose clinician pods are randomized to the intervention arm, the care team pod receives a message indicating that a default PC referral order has been pended for that patient. Clinicians are given an opportunity to opt out. Clinicians randomized to the control arm receive no intervention beyond usual practice. The primary outcome is presence of a completed PC visit within 24 weeks of enrollment. Secondary outcomes include placement of PC referrals, and absolute and change in quality of life at 9 weeks for patients who receive PC compared to controls. Results/Findings: Study in progress. Differences between arms in baseline characteristics and clinical outcomes will be assessed using two-sided Fisher’s exact tests and chi-square tests for categorical variables and independent samples Student’s t-tests for continuous variables. The unit of analysis will be the patient. The primary study endpoint is a binary patient-level variable indicating completion of a PC visit within 24 weeks of the clinician behavioral nudge. Using a two-sided type-I error rate of 0.05, an expected opt-out rate of 50%, and a baseline PC visit rate of 20%, we expect a sample size of 250 patients will give us 80% power to detect a meaningful 20 percentage-point response in PC visits. In exploratory analyses, we will also evaluate spill-over effects of the nudge on patients who did not meet PC eligibility criteria. Conclusions: This randomized controlled trial tests the impact of opt-out PC referral on rates of completed PC visits and patient quality of life. If successful, we will establish behavioral economic strategies to promote earlier and high-quality PC for patients with advanced cancer.

YIA23-091: Disparities in Lung Cancer Screening Among Black Veterans

Neelima Navuluri, MD, MPH1,2,3; Samantha Morrison, PhD1; Cynthia L. Green, PhD1; Leah L. Zullig, PhD2,4; Sandra L. Woolson, MS; Christopher Cox, MD1; Isaretta Riley, MD, MPH1,2; Scott Shofer, MD1,2

1Duke University School of Medicine, Durham, NC; 2Durham Veterans Affairs Medical Center, Durham, NC; 3Duke Global Health Institute, Duke University, Durham, NC; 4Duke University, Durham, NC

Background: Racial disparities in lung cancer screening (LCS) in the United States have been ascribed to various social determinants of health. Although the Veterans Affairs (VA) system has among the highest rates of LCS completion in the US, it is unclear if similar patterns of racial disparities in LCS exist. We sought to determine whether racial disparities in loss-to-follow up (LTFU) after referral to LCS exist at the Durham VA, and if so, what factors may be associated with LTFU. Methods: The data source was a database of Veterans referred for LCS at the Durham VA between July 2013 and August 2021. The analysis was limited to Veterans who self-identified as White or Black due to small numbers of Veterans in other racial groups. Demographic, clinical, and referral data were supplemented with socioeconomic and co-morbidity data from the VA Corporate Data Warehouse. Logistic regression models were assessed to predict LTFU according to race both unadjusted and adjusted for covariates listed in Table 1 using complete cases (N=4331). We included a significant interaction term between race and age (p=0.024) and transformed variables as needed. A second model was used to determine which covariates were most important for predicting LTFU just within Black Veterans. Results are presented using odds ratio (OR) with 95% confidence interval (CI). Results: Among the 1766 Black Veterans referred for LCS (average age 64.5±5.3; 94.8% male), only 538 (30.5%) completed a screening CT compared to 1154 (41.3%) of 2796 White Veterans (average age 66.0±5.8, 93.8% male). Black Veterans in the cohort had a significantly higher odds of LTFU compared to White Veterans (OR 1.6, 95% CI 1.4–1.8, p<0.001), an observation that still persisted after adjusting for covariates (OR 1.5, 95% CI 1.3–1.9, p<0.001). However, the c-index for the adjusted model was only 0.642, perhaps indicating unmeasured confounders existed. Within Black Veterans, the most important risk factors were age at referral, marital status, combat veteran, 1-year Care Assessment Need score (which reflects likelihood of individual hospitalization or death), current smoker, and pack-year history. Conclusions: Black Veterans had significantly higher loss to follow-up for lung cancer screening, even after adjusting for demographic, socioeconomic, and clinical factors. Further work is needed both to understand the broader mechanistic drivers of this disparity and address risk factors with tailored interventions.

YIA23-091 Table 1.

Patient characteristics by race for veterans eligible and referred to LCS

YIA23-091 Table 1.


BIO23-019: Precision Oncology: Integrating Structured Genomic Data Into the Electronic Health Record via the EPIC® Genomics Module

Joseph Vento, MD1; Travis Osterman, DO, MS1

1Vanderbilt University Medical Center, Nashville, TN

Background: As the number of targeted treatments in oncology continues to grow, the importance of structured genomic data in the electronic health record becomes critical for population-level mutation queries. However, many physician-facing next generation sequencing (NGS) reports consist of free text, a data format that makes these larger analyses time-consuming and inefficient. The EPIC® genomics module allows integration of a variety of NGS results into a unified structured format, granting clinicians an efficient means to identify cohorts of patients with specific mutations that may qualify for new treatments or clinical trials. Methods: Here, we present our institution’s experience with implementation of the EPIC® genomics module from July 2021 to the present. Our genomic data conversion process required mapping mutations from a variety of NGS report formats, including but not limited to Tempus®, FoundationOne®, Invitae®, and an internal platform called SNaPShot, to a Health Level Seven (HL7) standardized format which is interoperable with the electronic health record. We outline an automated workflow so that internally ordered NGS results convert and upload into the genomics module, as well as the process of backloading prior NGS sequencing into the electronic health record. Results: Our genomics module currently contains over 16,000 somatic reports, with over 12,000 of these results backloaded into the module. In Figure 1, we present the results of an example query that identified 57 patients with a KRAS G12C mutation who could be candidates for sotorasib. The described infrastructure supports rapid identification of cohorts of patients with specific mutations with minimal overhead, and this workflow can be used to contact oncologists when new treatments or clinical trials become available that may benefit one of their patients. Conclusions: This project demonstrates the feasibility and utility of a standardized structured format for oncology NGS results in the electronic health record via the EPIC® genomics module. The experience is specific to our institution, but the pipeline is easily translated to other practices, with at least 29 institutions nationwide already implementing similar workflows. This model makes large-scale mutational queries in the electronic health record more efficient, which can translate into more rapid adoption of novel targeted therapies for patients as well as easier patient screening for clinical trials.

BIO23-019 Figure 1.
BIO23-019 Figure 1.

Finding Patients for Tumor Genotype-Informed Treatments

This figure describes the process of identifying 57 potential candidates for sotorasib, a targeted therapy approved in May 2021 for advanced or metastatic non-smallcell lung cancer with a KRAS G12C mutation after one prior systemic treatment. First, open the EPIC dashboard tab (1) and click on the reports tab (2). Choose report library (3) and search for “genomics” (4). Click “New Report” to start a new search (5). Input search criteria, in this case, a KRAS G12C mutation (6). Finally, run the report query (7). Patient information, genomic results, and provider information will populate the report (8).

Citation: Journal of the National Comprehensive Cancer Network 21, 5.5; 10.6004/jnccn.2023.5002


CLO23-029: An APP-Run Lymphadenopathy Clinic: The Cleveland Clinic Experience

Christopher D’Andrea, PA-C1; Stacy Mathews, CNP1; Brian Hill, MD, PhD1

1Cleveland Clinic Taussig Cancer Center, Cleveland, OH

Background: Lymphadenopathy is a specific sign/symptom with variable clinical presentation and etiology. The management and use of biopsy can be highly variable given the associated history and extensive differential diagnosis. For this reason, in 2017 we at The Cleveland Clinic Taussig Cancer Center established an Advanced Practice Provider (APP) staffed lymphadenopathy clinic to specifically evaluate and manage these patients. Methods: We prospectively followed patients referred to our clinic for lymphadenopathy of unknown cause from August 2017 through October 2022. A total of 194 patients were referred during that time. Patients were assessed for lymphadenopathy presentation (e.g., exam, imaging), rate of biopsy referral and diagnostic outcome following biopsy. Results: Of the 194 patients referred, the median age was 50.7 years (range 18–89) with 52% female and 48% male. Lymphadenopathy was most commonly discovered when patients underwent imaging for acute symptom assessment (40%), compared to those where it was found on exam either self or provider (37%), or incidentally identified on imaging performed for other reasons (23%). After the initial consultation, a total of 75 patients (39%) were referred for a tissue assessment (lymph node biopsy n = 59, bone/bone marrow biopsy n = 10, peripheral blood flow cytometry n = 4, skin biopsy n = 2). Of those referred for tissue assessment 48 (64%) were found to have a new malignant diagnosis representing 25% of the 194 total patients referred to our lymphadenopathy clinic. The most common non-malignant diagnoses among patients referred for tissue assessment was a negative/reactive/benign process (12 of 27, 44%), followed by non-caseating granuloma/ill-defined granuloma/granulomatous inflammation/sarcoidosis (6 of 27, 22%), followed by followed by follicular hyperplasia (3 of 27, 11%). The most common malignant diagnosis was lymphoma (87.5% of all malignancies) with follicular lymphoma, grade 1–2 occurring most frequently (14 of 42 lymphoid malignancies, 33%). Conclusion: Our lymphadenopathy clinic provides a valuable referral service for the management of lymphadenopathy of unknown cause. Based on our sampled patient population, 1 in 4 patients referred were found to have a newly diagnosed malignancy. High volume referral centers may consider opening a similar APP-run clinic to systematically evaluate this group of high-risk patients.

CLO23-029 Table 1.

Lymphadenopathy clinical data

CLO23-029 Table 1.


EPR23-078: Analyzing Trends in the Incidence and Mortality of Esophageal Cancer in the United States: A 15-Year Population-Based Study

Neethi Dasu, DO1; Kirti Dasu, BA2; Jason John, DO1; Yaser Khalid, DO3; Brian Blair, DO1; Lucy Joo, DO1; C. Jonathan Foster, DO1

1Jefferson Health New Jersey, NJ; 2Drexel University, PA, NJ; 3Wright Center for GME/Geisinger, Scranton, PA

Introduction: Esophageal cancer accounts for approximately 1% of cancer diagnoses in the US. Few studies have investigated the trend in occurrence and mortality rates of esophageal cancer over time. The objective of this study is to identify temporal trends of esophageal cancer in a national population cohort admitted to U.S. hospitals from the years 2005–2019. Methods: The National Inpatient Sample (NIS) database for the years 2005–2019 was queried to identify adult (age >18 years) patients admitted with the principal procedural codes for esophageal cancer. Data was obtained from all US states that contributed to the NIS. We estimated trends in the total number of patients yearly, prevalence, mortality, and mortality rate for patients admitted for esophageal cancer. Weighted analysis utilizing Stata 17 MP was performed. Results: The study found that a total of 548,111 patients had esophageal cancer, of which 50,312 died. The average yearly mortality rate over this timeframe was 9.5%. Throughout the 15-year period, the prevalence of esophageal cancer has increased from 0.09% in 2005 to 0.11% in 2019 (p<0.0001), mortality rate decreased from 11% in 2005 to 8.0% in 2019 (p<0.0001), hospital length of stay decreased from 7.9 in 2005 to 6.9 days in 2019 (p<0.01), total hospital charges increased from $38477 in 2005 to $84,524 in 2019 (p<0.01), and mean age increased from 66.5 to 67.2 (p<0.01). Discussion/Conclusion: Esophageal cancer historically has been associated with high mortality. As various diagnostic and treatment modalities developed over the decades, there’s been a steady increase in the prevalence of esophageal cancer due to improved disease detection while mortality rates have significantly decreased, which further highlights the utility and efficacy of the advancements made in the interventions employed in the work-up of esophageal cancer. Further investigation with stratification with attention to the subtype of esophageal carcinoma may provide further insight in future studies.


HSR23-094: Uterine Cancer (UC) on the Rise in the United States (US): One of the Largest Racial Disparities in Oncology

Alexandrina Balanean, MPH1; Angelica Falkenstein, PhD1; Yolaine Smith, PhD1; Bruce Feinberg, DO1

1Cardinal Health, Dublin, OH

Introduction: US cancer incidence and mortality are declining, but not for UC; 17% and 36% increases, respectively, may occur by 2040. Despite similar incidence, Black/African American (AA) women more often have aggressive tumors and are half as likely as White/European American (EA) women to be recommended for and have surgery; they are also twice as likely to die, and 5-year survival is 63% versus 84% in EA women. Disparity-sustaining factors are social, biologic, and therapeutic (declining/racially differential use of hysterectomy). We compared sociodemographic, clinical, and treatment characteristics between AA and EA women with UC to determine differences in cancer-specific survival. Methods: Using cohorts of AA and EA women in the 2021 Surveillance, Epidemiology, and End Results (SEER) database, we compared median household income, metropolitan proximity (rural/urban), tumor histology/stage/grade, and hysterectomy recommendation/receipt in women diagnosed with UC. Sociodemographic, clinical, and treatment characteristics were compared using chi-square and Wilcoxon tests, and Monte-Carlo, Kaplan-Meier, and log-rank methods estimated and compared cause of death and survival. Results: In total, 162,562 (18,044 AA and 144,518 EA) women diagnosed with UC from 2000 through 2019 were identified. Median age at diagnosis was 62 years and lower at death in AA than EA women (68 vs 74 years, P<.0001). Median household income was <$65,000/year for more AA than EA women (57% vs 42%), and more AA than EA women lived in metropolitan counties (≥1 million people); 66% vs 59%, all P<.0001. More common in AA than EA women were aggressive tumor histology (non-endometrioid/sarcoma) at 36% versus 15%, late-stage diagnosis (distant/nodal metastasis) at 12% versus 6%, and high tumor grade (III/IV) at 31% versus 18% (all P<.0001). More AA than EA women had hysterectomy status either not recommended or recommended but not performed (17% vs 8%), died (44% vs 29% [UC-specific, 33% vs 18%]), and had shorter survival (8 vs 19 years) (all P<.0001) (Figure). Discussion: Compared with EA women, AA women had higher death rates and shorter survival. This is primarily due to higher rates of late-stage diagnosis, aggressive tumor characteristics, and lower rates of hysterectomy recommendation and receipt in AA women; urban residence and lower income may also contribute. Systemic health care barriers may be mitigated by increasing insurance coverage, research funding, and education.


QIM23-132: Do EHR-Embedded Clinical Decision Support Tools Reduce Variation in Care? A Pre and Post-Implementation Comparison of Regimen Ordering Variation at a Multi-site Community Cancer Clinic

Jonas M. Congelli, RPh1; Heather Lewin, APRN, MSN2; Gregory Calip, PharmD, MPH, PhD2; Marcello Ricottone, PharmD, BCOP2; Shawn Huda, PharmD, BCOP2; Taylor Dias-Foundas, PharmD, BCOP2; Rebecca Maniago, PharmD, BCOP2

1Hematology-Oncology Associates of CNY, East Syracuse, NY; 2Flatiron Health Inc., New York, NY

Background: Clinical decision support (CDS) tools in oncology have been shown to improve care quality, reduce care variation and reduce healthcare costs. Flatiron Assist® (FA) is a customizable CDS tool that is embedded in the electronic health record (EHR) to facilitate selection and documentation of National Comprehensive Cancer Network (NCCN) guideline concordant treatment regimens. Our objective was to compare the distribution of regimens ordered before and after implementation of FA at a community cancer clinic to determine if variation in regimens ordered was impacted by implementing the CDS tool. Methods: This study used the nationwide EHR-derived de-identified Flatiron Health database to analyze treatment regimens ordered for Breast, Colon, Non–Small Cell Lung, Rectal, Prostate and Small Cell Lung Cancers placed at a multi-site community cancer clinic over a 58 month observation period (11/14/2017 to 9/14/2022). FA was implemented on 5/19/20. We compared the total number of unique regimens with respect to the total number of orders placed before and after implementation. We calculated the proportion reduction in the number of regimens ordered with Wald (normal approximation) 95% confidence intervals to describe the relationship between the regimen variation seen before and after implementation of FA. Results: A total of 9,530 orders placed by 57 prescribers were included in the analysis. Before implementation, 5,112 orders were placed consisting of 1,817 unique regimens. After implementation, 4,418 orders were placed consisting of 1,018 unique regimens. We stratified the results to assess reduction in variation by comparing the number of regimens required to reach 75, 80, 90 and 100% of orders respectively. (Table 1). Overall, the number of regimens to reach 100% of orders was reduced by 44.0% (95% confidence interval: 41.7–46.3%, p<0.01) after implementing FA. Conclusions: Following adoption of FA, there was a significant reduction in the number of unique regimens with respect to total regimens ordered. A limitation of this study includes the inability to account for additional factors that may contribute to this reduction in variation. Regardless, these findings suggest that implementing an EHR-embedded CDS tool contributes to a reduction in care variation by standardizing unique regimens ordered at a multi-site community cancer clinic. Future studies will explore other quality metrics that may be impacted by implementing FA.

QIM23-132 Table 1.
QIM23-132 Table 1.
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  • YIA23-002 Figure 1.

    Mutations, intratumoral heterogeneity metrics, and cluster cellular prevalence in soft tissue sarcomas.

  • YIA23-004 Figure 1.

    Nicotinamide study schema.

  • BIO23-019 Figure 1.

    Finding Patients for Tumor Genotype-Informed Treatments

    This figure describes the process of identifying 57 potential candidates for sotorasib, a targeted therapy approved in May 2021 for advanced or metastatic non-smallcell lung cancer with a KRAS G12C mutation after one prior systemic treatment. First, open the EPIC dashboard tab (1) and click on the reports tab (2). Choose report library (3) and search for “genomics” (4). Click “New Report” to start a new search (5). Input search criteria, in this case, a KRAS G12C mutation (6). Finally, run the report query (7). Patient information, genomic results, and provider information will populate the report (8).

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