Nikita Dahake’s research while affiliated with Philadelphia University and other places

8534 Background: Mutations in the human epidermal growth factor receptor 2 (ERBB2; HER2) are oncogenic in lung adenocarcinoma (LUAD). An FDA-approved drug, trastuzumab deruxtecan, is now available as second line treatment (rx) of patients (pts) with HER2 mutated NSCLC. HER2 activation occurs via gene mutation (mt), gene amplification (amp), or protein overexpression. Further information is needed about HER2 alterations (alt) and the potential association with rx response. The purpose of this study is to describe the genomic landscape of a cohort of pts with HER2 alt LUAD in the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC). Methods: In a cohort of 16,241 NSCLC LUAD pts available in GENIE v.13.0, we analyzed the association of HER2 alt (mt/amp) with alt in other driver genes. Also, we assessed the relationship between HER2 alt and expression levels using TCGA and CPTAC cohorts (566 and 110 pts), and prognosis using GENIE NSCLC BPC cohort (1,846 pts). Results: LUAD pts were classified as having KRASmt (K; 6426 pts; 31.9%), EGFRmt (E; 5256 pts; 26%) or EML4-ALK fusion (A; 102 pts; 0.5%). The remainder were classified as “KRAS/EGFR/ALK other (KEAother).” HER2mt (6.06%) and amp (2.06%) were more frequent in the KEAother cohort(p<0.001). HER2 amp were also found more frequently in the E deletion(del) mt cohort (1.63%), compared to KA cohorts (p<0.001). Additionally, there was significant co-occurrence of HER2 mt and HER2 amp (OR 6.49; p<0.001) in the KEAother cohort. HER2mt/amp had significant odds of co-occurrence with TP53 alt (OR 1.43; p<0.001), CDKN2A del (OR 2.15; p<0.001), TERT amp (OR 2.37; p<0.001), RB1 alt (OR 1.6; p<0.001). Additionally, HER2 amp co-occurred with TP53 alt (OR 4.0; p<0.001), CDKN2A del (OR 3.44; p<0.001), while HER2 mt co-occurred with CDKN2A del (OR 2.03; p<0.001), TERT amp (OR 2.43; p<0.001). BRAF mts were mutually exclusive with HER2 mt/amp (OR 2.31; p<0.001) and HER2 mt (OR 1.93; p<0.001). In the E cohort, there was a significant rate of HER2 mt or amp co-occurring with RB1 alt (OR 2.95; p<0.001). Despite limited survival data in the GENIE database, HER2 mt and amp had a trend toward inferior mOS (HER2mt 29.4 months vs HER2amp 27.8 months vs HER2wt 51.0 months), but this difference was not statistically significant. High HER2 mRNA expression is associated with increased age, while low expression is associated with stage IV disease. Pts with high HER2 expression that never smoked, have a trend toward inferior mOS (p=0.19), but this is based on a limited number of pts (n=18). Conclusions: HER2 mts are found in 1-4% of pts with NSCLC but can be identified in up to 6% of KEAother NSCLC. In contrast to prior reports, there was a significant rate of co-occurring HER2 mt and HER2 amp. BRAF mts were mutually exclusive with both HER2 mt and amp. Further analysis using the Caris database is planned to evaluate clinical outcomes by HER2 alt, co-mts, and prior rx received.

e13516 Background: Temple University Hospital (TUH) created an initiative to increase colorectal cancer (CRC) screening by administering fecal immunochemical tests (FIT) to the underserved population of Philadelphia. We hypothesize that this will increase the percentage of diagnostic colonoscopies performed compared to baseline in our fixed capacity colonoscopy suite and ultimately generate a higher reimbursement per colonoscopy. This potential revenue increase can help fund the infrastructure to administer a large quantity of FIT tests to the community and create a truly equitable population-based approach to CRC screening. Methods: 27,169 colonoscopies and their associated CPT codes from 2017-2022 performed at TUH were analyzed. 16 individual CPT codes were identified and categorized as either screening or diagnostic CPT codes. Reimbursement for each of the CPT codes was determined by the Medicare reimbursement per CPT code and used to determine the average reimbursement per screening CPT code and average reimbursement per diagnostic CPT code. A Monte Carolo simulation model was created using Palisades @Risk software to compare the reimbursements of FIT test prompted vs. non-FIT test prompted colonoscopies over a range of possible distributions of diagnostic CPT codes. Monte Carlo simulations model scenarios that incorporate uncertainties with the objective of providing a distribution of outcomes from the least likely to most likely outcomes. Results: The historical distribution of screening vs. diagnostic CPT codes at TUH is 31% and 69%, respectively, with an average Medicare reimbursement of $2,668 per colonoscopy. We have distributed 292 FIT tests, and 5 FIT test prompted colonoscopies have been completed with 7 associated CPT codes. The preliminary distribution of screening CPT codes vs diagnostic CPT codes is 29$2,691. Lastly, we used a Monte Carlo simulation to run 10 separate simulations with 5000 iterations each to show the most likely reimbursement rates over a possible distribution of diagnostic colonoscopies (Table). Conclusions: Our preliminary data suggests FIT testing can offer three main benefits: 1) Expansion of the number of patients receiving CRC screening; 2) triage optimization of the fixed capacity colonoscopy suite by performing more diagnostic colonoscopies than screening colonoscopies to diagnose more colorectal pathologies; and 3) funding for a population based FIT testing campaign by utilizing the Medicare reimbursement delta from screening to diagnostic colonoscopies. [Table: see text]

e13502 Background: Temple University Hospital has created an initiative to increase colorectal cancer (CRC) screening to a historically underserved population in Northern Philadelphia through the administration of Fecal Immunochemical Tests (FIT). FIT testing possesses the advantage of being inexpensive and easy to use. Moreover, FIT testing serves as an additional opportunity for underserved patients to be integrated into a health network to receive lifesaving care when utilized by a large health system such as Temple Health. This study demonstrates how healthcare institutions, such as Temple Health, can employ probability-based modeling created from empiric data to navigate uncertainty and establish goals for the number of colonoscopies they aim to perform per a specific quantity of FIT tests distributed within the community. By doing so, they can significantly enhance the overall screening efforts in Northern Philadelphia and provide an essential service to those who need it most. Methods: FIT tests were distributed to the participants at community engagement events. FIT response rates, positivity rates, and progression to colonoscopy rates were determined and fit to a binomial distribution in Palisades @Risk software and underwent a Monte Carlo simulation of 10,000 iterations to determine the different probabilities of response rates, positivity rate, and colonoscopies performed per 1000 FIT tests distributed. Results: A total of 292 tests were distributed with a response rate of 56.5% (165 patients) and a positivity rate of 8.9% (26 patients). Out of 292 patients, 2.4% (7 patients) have had a colonoscopy or are scheduled to have a colonoscopy at Temple University Hospital. Probability curves were created to demonstrate the probability of number of responses, number of positive tests, and number of colonoscopies performed per 1000 FIT tests administered to the community. Lastly, we modeled the number of FIT tests that need to be administered to the community to perform at least 100 colonoscopies with different levels of certainty (Table). Conclusions: Here we show how hospital systems, like Temple Health, can use probability distributions based on empiric data to set tangible goals regarding their community engagement efforts to increase access to CRC screening. Through the utilization of probability distributions, healthcare organizations can gain deeper insights into resource allocation needs and strategically direct their initiatives, ultimately fostering a more inclusive and equitable approach to population-based CRC screening. [Table: see text]