Kommalapati, Anuhya

150 Background: Pancreatic neuroendocrine tumors (pNETs) comprise less than 5% of all pancreatic tumors and 7% of all neuroendocrine tumors (NETs). There has been considerable progress in understanding the biology of pNETs and numerous therapeutic options emerged, especially with the collaboration of various specialties in the multidisciplinary setting. In this this study, we sought to analyze the association of facility volume, treatment modalities offered, and different risk adjusted outcomes in pNETs. Methods: We extracted data from National Cancer Data Base (NCDB) that covers 70% of all newly diagnosed cancer cases in the U.S and Puerto Rico. Patients with pNETs diagnosed between 2004 & 2017 were included and classified into tertiles based on hospital volume. Volume–outcome relationship was determined by using Cox regression adjusting for patient demographics, comorbidities, tumor characteristics, insurance type and therapy received. Kaplan Meier estimates of OS were compared with log-rank test. The primary predictor of interest was the facility volume defined as number of pNETs treated/year. A total of 7202 patients with pathologically confirmed pNETS were treated at 840 facilities. The median annual facility volume was 5patients/year. Facilities were classified into (T:mean cases/year) T1: < 3; T2:4-8; T3:≥9 cases/year. Results: A total of 7202 pNET patients were treated at 840 facilities. The median annual facility volume was 5 patients/year. Facilities were classified into (T: mean cases/year) T1:<3; T2:4-8; T3:≥9 cases/year. The unadjusted median OS by facility volume was: T1: 71 months (m), T2: 136 m, and T3: not-reached (p < 0.001). On multivariable analysis, compared with patients treated at T3 facilities, patients treated at lower-tertile facilities had higher risk of death [T2 hazard ratio (HR), 1.17 (95% CI, 1.03-1.33); T1 HR, 1.45 (CI, 1.30-1.67), p < 0.0001] and such a difference in OS was more pronounced in stage IV disease (Table). Patients at T3 facilities (vs T1) were more likely to receive surgical resection of the primary tumor (75 vs 49%), lymph node dissection performed at the time of surgery (70 vs 42%), and a better R0 resection (72 vs 46%) (p < 0.01). Conclusions: Patients who were treated for pNETs at high-volume centers (> 9cases/year) had significantly higher OS and were more likely to receive surgical resection along with lymph node dissection and R0 resection. There was a 45% increased risk of death in patients treated at low volume centers (< 3/year) as compared to high-volume centers (> 9/year). While OS difference was noticed in all stages, the difference was more pronounced in stage IV disease demonstrating the importance of multi-disciplinary approach in pNETs management and incorporating such quality measures in low-volume facilities.[Table: see text]

125 Background: The advent of immunotherapy (IO) has led to significant improvement in outcomes for patients (pts) with advanced non-small cell lung cancer (adv NSCLC). The rate of crossovers and receipt of post-protocol IO in pivotal trials leading to FDA approvals of IO in NSCLC has not been systematically evaluated. Here, we evaluate crossover rates and post protocol therapies for pts with adv NSCLC across multiple first-line (1L) IO monotherapy and chemotherapy/IO (chemo/IO) combination trials. Methods: We utilized the publicly available data from pivotal clinical trials leading to approvals of IO or chemo/IO regimens in 1L treatment of adv NSCLC. We extracted data on outcomes, rate of crossover from control arm to IO, proportion of pts in control arm receiving IO and the start dates of these clinical trials. The primary outcomes were the rates of crossover and the proportion of patients in control arms who received post-control IO. Results: The study included 4 trials with IO monotherapy and 12 trials with chemo/IO combinations in 1L adv NSCLC. The primary endpoints for these trials were PFS (25%), OS (19%), and both PFS and OS (56%). The crossover rate from control arm to experimental arm (with IO) ranged from 0-74% in IO monotherapy trials and 0-49% in chemo/IO trials. Two IO monotherapy trials and five chemo/IO trials did not allow crossover; among them, 3 trials had a PFS/OS co-primary endpoint, while others had OS as primary endpoint. Ten of 16 trials provided explicit information on use of subsequent post-protocol therapies in their publications. Among the two IO monotherapy trials which did not allow crossover, post-protocol IO was administered in only 20-30% of patients. Among the six chemo/IO trials with information on post-protocol therapies, 30%-59% patients on the control arm subsequently received some form of IO on progression. Nine of 12 trials started accrual after 10/2015, when nivolumab was approved in the United States as second-line (2L) therapy for adv NSCLC regardless of tumor PD-L1 expression. Conclusions: Despite the highly significant OS benefit from 2L IO, which was the standard of care (SOC) in the United States, the rates of crossover and post-protocol IO administration was distressingly low in 1L IO monotherapy and chemo/IO trials for 1L adv NSCLC. This low rate of 2L treatment with IO may have been due to limited global availability prior to widespread regulatory approval during the conduct of these trials. There is an increased need for consistency in reporting of crossover treatment and post-protocol treatments to allow adequate assessment of the true 1L benefit with IO. Control arms in pivotal trials require scrutiny to ensure confirmation with SOC to provide access to optimal treatments for patients and prevent magnification of observed benefits in experimental arms. The difficulty lies in the global conduct of large randomized clinical trials with differing regulatory approvals.

Hepatocellular carcinoma (HCC) is an aggressive malignancy accounting for 90% of primary liver malignancies. Therapeutic options for HCC are primarily based on the baseline functional status, the extent of disease at presentation and the underlying liver function that is clinically evaluated by the Barcelona-Clinic Liver Cancer system and Child–Pugh score. In patients with advanced HCC, the United States Food and Drug Administration (US-FDA) approved systemic therapies include the combination of atezolizumab–bevacizumab, sorafenib, and lenvatinib in the first line setting while cabozantinib, regorafenib, ramucirumab (in patients with alfa-fetoprotein [AFP] > 400 ng/mL), pembrolizumab, nivolumab, and nivolumab-ipilimumab combination are reserved for patients who progressed on sorafenib. European Medical Agency (EMA) approved the use of atezolizumab–bevacizumab, sorafenib, and lenvatinib in the first line setting, while cabozantinib, regorafenib, and ramucirumab (in patients with alfa-fetoprotein [AFP] > 400 ng/mL) are approved for use in patients that progressed on first-line therapy. In the first line setting, sorafenib demonstrated a median overall survival (OS) benefit of 3 months as compared to that of best supportive care in randomized phase III trials, while lenvatinib was shown to be non-inferior to sorafenib. Recently, phase 3 studies with immunotherapeutic agents including atezolizumab plus a bevacizumab combination and tremelimumab plus durvalumab combination demonstrated a better OS and progression free survival (PFS) compared to sorafenib in the first-line setting, making them attractive first-line options in advanced HCC. In this review, we outlined the tumorigenesis and immune landscape of HCC in brief and discussed the role and rationale of combining immunotherapy and anti-VEGF therapy. We further expanded on potential limitations and the future directions of immunotherapy in combination with targeted agents in the management of advanced HCC.

PURPOSE: Precision oncology promises improved outcomes but the cost-effectiveness and accessibility of targeted therapies is debatable. We report price change patterns from 2015 to 2019 for several oral anticancer medications for common solid tumor malignancies. METHODS: We collected provider utilization and payment data from the public Medicare Part D database and extracted drug price information for commonly prescribed targeted oral anticancer agents for lung, breast, and prostate cancer. We then calculated median Pearson correlation coefficient values for various drugs (containing more than two data points) within each therapeutic class. We also calculated compound annual growth rates (CAGRs) for medication costs within each class and compared them with the consumer price index (CPI). RESULTS: Our study included six epidermal growth factor receptor inhibitors (EGFRi; one generic), five anaplastic lymphoma kinase inhibitors (ALKi), two B-Raf inhibitors (BRAFi), three hormonal agents (one generic), three cyclin-dependent kinases 4 and 6 inhibitors (CDK4/6i), two poly-ADP-ribose inhibitors (PARPi), and seven antiandrogen agents (two generic). The median (range) Pearson correlation coefficient values for cost of drugs within each therapeutic class were 0.967 (0.915-0.978) for EGFRi, 0.981 (0.966-0.989) for ALKi, 0.996 for BRAFi, 0.994 (0.992-0.999) for CDK4/6i, 0.855 for PARPi, and 0.442 (–0.522 to 0.962) for antiandrogens. Therapies with two or fewer data points (generic erlotinib, dacomitinib, abiraterone, apalutamide, and darolutamide) were excluded. The median CAGRs in costs over the 5-year period were 4.56% (EGFRi), 6.40% (ALKi), 2.58% (BRAFi), 5.48% (hormonal agents), 5.21% (CDK4/6i), 27.29% (PARPi), and 34.8% (antiandrogens). The CPI over 5 years was 2.26%/year, and the average inflation rate was 1.90%/year. CONCLUSION: The median CAGR in costs for modern oral precision-driven cancer therapeutic classes mostly outpaced CPI and the average inflation. Increase in cost within the same class should be weighed against incremental clinical benefit for the patients to ensure that rising costs do not limit access to targeted therapies.

248 Background: Esophageal carcinoma is the 6th most common cause of death worldwide. While squamous cell carcinoma (SCC) was the most common among the histologic subtypes in the 20th century, recent reports have demonstrated an increased incidence of adenocarcinoma in both Eastern and Western world. With this changing paradigm of esophageal carcinoma, it is important to estimate the future burden of esophageal carcinoma by histology, age, sex, and race, that could help plan prevention, control, and treatment strategies for this cancer. Methods: We used Surveillance, Epidemiology, and End Results (SEER) 14 registries to obtain incidence data from 2000 to 2016. We applied age-period-cohort models to estimate future esophageal carcinoma incidence rates and the estimated disease burden by multiplying incidence forecasts by corresponding US Census population projections. Results: Our estimate suggests that the incidence (per 100,000 persons) of esophageal adenocarcinoma is increasing in people within the age group of 40-65 years. Our prediction showed that the incidence will increase from 2.12 in 2021 to 2.82 in 2030 (3.2% per year, 95% CI –2.3% to 9.1%, p=0.26). Notably, we noticed a similar marginal increase in incidence among Asians, Black, and Whites in the 40-65 years age group (3.1%, 3.2%, and 4.2%, per year, respectively). White males aged >65 years had the highest incidence of esophageal adenocarcinoma (17.34 in 2021), and incidence decreased 1.3% per year. We noticed a marginally decreased incidence in Black male (-1.1% per year, p=0.71) and Asian male (-3.9% per year, p=0.17) population aged >65 years. Interestingly, we found a decrease in the incidence of SCC in the current age groups 40-65 (-2.7% per year, p=0.31) and >65 years (-4.6% per year, p=0.081). We observed that Asians had the greatest decrease in incidence (-4.3% per year) as compared to that of Blacks (-4.2% per year) and Whites (-2.9% per year), although there was no statistically significant difference in the annual decrease among races. Conclusions: In our study of esophageal cancer, we predict an increased incidence of adenocarcinoma histology subtype in the younger age group (40-65 years) while the SCC subtype will have a decreased trend. Strategies to prevention esophageal adenocarcinoma, including dietary changes, should preferentially target patients <65 years of age.[Table: see text]

Abstract Introduction: Mantle cell lymphoma (MCL) is an uncommon hematological malignancy with an estimated incidence of 1 per 100,000 persons per year in the United States and represents only about 5% of all non-Hodgkin lymphomas. Several studies have shown that treatment at academic centers and a higher hospital case volume are associated with improved outcomes for uncommon hematological malignancies, probably due to increased provider expertise and access to novel therapies. Treatment of MCL can be complex given the heterogenous nature of the disease and a frequent need for autologous stem cell transplantation in eligible patients. However, the impact of treatment at an academic center and facility patient volume on the survival of patients with MCL has not been well studied in large cohorts. In this study, we utilized the National Cancer Database (NCDB) to investigate the impact of treatment at an academic center and treatment facility volume on the overall survival (OS) of patients with MCL. Methods: The NCDB was used to identify adult patients (≥ 18 years) with newly diagnosed MCL from 2004 through 2017. For facility patient volume analysis, patients were divided into groups based on the average number of new MCL patients seen annually: Tercile 1 [T1] (1-3 patients/year), Tercile 2 [T2] (4-5 patients/year) and Tercile 3 [T3] (≥6 patients/year). Treating centers were divided into Academic and Non-academic using the NCDB definitions. Academic centers were defined as centers that accessions more than 500 newly diagnosed cancer cases per year, participate in postgraduate medical education in at least four program areas including internal medicine and surgery and participates in cancer-related clinical trials. The primary endpoint was overall survival (OS). Survival analysis was performed using the Kaplan-Meier method and Cox hazards proportional model. Statistical analysis was performed using SPSS version 25. Results: We identified 22,752 patients with MCL during the study period. 9,484 (42%) patients were treated at academic centers and 13,070 (57%) were treated at non-academic centers. In terms of facility patient volume 10,948 patients (48%) were in the T1 group, 4,637 (20%) were in the T2 group and 7,166 (31%) were in the T3 group. No significant differences were found in baseline demographics (age, gender, race/ethnicity, comorbidity scores), socioeconomical variables (insurance type, median income, area of residence) and disease-related factors (B-symptoms, Ann Arbor stage) between patients treated academic vs nonacademic centers, or between patients in T1 vs T2 vs T3 groups. Notably, compared to lower volume facilities, T3 facilities were more likely to be academic centers (T3: 81% vs T2: 42% vs T1: 16%, p&lt;0.001) . After a median follow-up of 3.4 years, the median overall survival (OS) was 5.6 years for the entire cohort. The median OS was inferior for patients treated at lower volume facilities (4.1 years for T1, 5.1 years for T2 and 9.0 years for T3, p&lt;0.001) (Figure 1A). Similarly, the median OS was shorter for patients treated at non-academic centers vs academic centers (4.3 years vs 7.5 years respectively, p&lt;0.001) (Figure 1B). In a multivariate analysis, treatment at a lower patient volume facility (Hazard ratio [HR] Q1= 1.26 [95%CI = 1.18-1.34]) and treatment at a non-academic center (HR = 1.1, 95%CI = 1.01-1.12) were both independent prognostic factors of inferior OS, after adjusting for demographics (age, gender, ethnicity, area of residence) and socioeconomic variables (income and insurance status). Conclusion: Patients with MCL treated at academic and higher volume facilities had a higher OS compared to patients treated at non-academic and lower volume facilities.. Additional research is needed to fully understand the mechanisms behind these differences. Patients with MCL may benefit from an early referral to academic and high-volume centers. Figure 1 Figure 1. Disclosures Munoz: Merck: Research Funding; Portola: Research Funding; Genentech: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Pharmacyclics/Abbvie, Bayer, Gilead/Kite Pharma, Pfizer, Janssen, Juno/Celgene, BMS, Kyowa, Alexion, Beigene, Fosunkite, Innovent, Seattle Genetics, Debiopharm, Karyopharm, Genmab, ADC Therapeutics, Epizyme, Beigene, Servier: Consultancy; Gilead/Kite Pharma, Kyowa, Bayer, Pharmacyclics/Janssen, Seattle Genetics, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Millennium: Research Funding; Pharmacyclics: Research Funding; Celgene: Research Funding; Physicians' Education Resource: Honoraria; Gilead/Kite Pharma: Research Funding; Kyowa: Honoraria; Bayer: Research Funding; Seattle Genetics: Honoraria; OncView: Honoraria; Targeted Oncology: Honoraria. Paludo: Karyopharm: Research Funding. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Nowakowski: Daiichi Sankyo: Consultancy; Zai Labolatory: Consultancy; TG Therapeutics: Consultancy; Blueprint Medicines: Consultancy; Nanostrings: Research Funding; MorphoSys: Consultancy; Kymera Therapeutics: Consultancy; Incyte: Consultancy; Ryvu Therapeutics: Consultancy; Kyte Pharma: Consultancy; Genentech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/Bristol Myers Squibb: Consultancy, Research Funding; Selvita: Consultancy; Curis: Consultancy; Karyopharm Therapeutics: Consultancy; Bantham Pharmaceutical: Consultancy. Wang: Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; InnoCare: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding.

7 Background: Despite the promise of precision oncology, the cost-effectiveness of targeted treatments is debated. Until now, the increase in price of oral targeted anti-cancer treatments used in common malignancies has not been evaluated. Here, we report patterns in price changes from 2015-2019 for multiple oral anti-cancer medications for common solid tumor malignancies. Methods: We utilized the publicly available Medicare Part D provider utilization and payment database from 2015 to 2019. We extracted drug prices (using generic names) for commonly used targeted oral anticancer agents for lung, breast, and prostate cancer. The primary outcome was the correlation of average change in Medicare spending per dosage unit among the multiple brand-name medications within each class available. We additionally calculated compound annual growth rates (CAGRs) [i.e. mean annual growth rates over a specified period of time] for medication costs within each class, and compared it with the consumer price index (a measure of the average change over time in the prices of consumer items). Results: The study included 6 EGFR inhibitors (1 generic), 5 ALK inhibitors, 2 BRAF inhibitors, 3 hormonal agents (1 generic), 3 CDK4/6 inhibitors, 2 PARP inhibitors, and 7 anti-androgen agents (2 generic). The median (range) Pearson correlation coefficient values for drugs within each class were 0.967 (0.915-0.978) for EGFRi, 0.981 (0.966-0.989) for ALKi, 0.996 for BRAFi, 0.994 (0.992-0.999) for CDK4/6i, 0.855 for PARPi, and 0.442 (-0.522-0.962) for anti-androgens. Except for anti-androgens, all other drug classes showed strong linear association in price increase between two drugs within the same-class. A coefficient could not be calculated for therapies with 2 or fewer data points ( i.e., generic erlotinib, dacomitinib, generic abiraterone, apalutamide, and darolutamide). There was no significant correlation between expenditure for anti-estrogen agents. The median (range) CAGRs in costs over this 5-year period were: were 4.56% for EGFRi, 6.40% for ALKi, 2.58% for BRAFi, 5.48% for hormonal agents, 5.21% for CDK4/6i, 27.29% for PARPi, and 34.8% for anti-androgen agents. Conclusions: The median CAGR in costs for modern oral precision driven cancer therapeutic classes mostly outpaced CPI (2.26%/year), and the average inflation rate (1.90%/year). Increase in cost within the same class should be weighed against incremental clinical benefit for the patients. For most classes despite there being multiple agents, the rise in drug expenditures correlated closely, calling into question the true value of within class competition. There is an urgent need for drug pricing reform given the average expenditure of Medicare part D, and ultimately out of pocket costs for our patients with cancer continues to trend upwards. Increased advocacy efforts are needed to ensure precision therapeutics remains an attainable and sustainable goal.

PURPOSE: The benefit of routine pre-emptive screening for severe acute respiratory syndrome coronavirus 2 infections in patients with cancer before cancer-directed therapies is unclear. Herein, we characterize the outcomes of a cohort of patients with cancer who were diagnosed with COVID-19 by routine screening (RS) in comparison with those diagnosed on the basis of clinical suspicion or exposure history (nonroutine screening [NRS]). METHODS: A multisite prospective observational study was conducted at three major and five satellite campuses of the Mayo Clinic Cancer Center between March 18 and July 31, 2020. The primary outcome was COVID-19–related hospital admission. Secondary outcomes included intensive care unit admissions and all-cause mortality. RESULTS: Five thousand four hundred fifty-two patients underwent RS in the outpatient setting only, and 44 (0.81%) were diagnosed with COVID-19. RS detected 19 additional patients from the scheduled inpatient admissions for surgical or interventional procedures or inpatient chemotherapy. One hundred sixty-one patients were diagnosed with COVID-19 on the basis of NRS. COVID-19–related hospitalization rate (17.5% v 26.7%; P = .14), intensive care unit admission (1.6% v 5.6%; P = .19), and mortality (4.8% v 3.7%; P = .72) were not significantly different between the RS and NRS groups. In the multivariable analysis, age ≥ 60 years (odds ratio, 4.4; P = .023) and an absolute lymphocyte count ≤ 1.4 × 109/L (odds ratio, 9.2; P = .002) were independent predictors of COVID-19–related hospital admission. CONCLUSION: The COVID-19 positivity rate was low on the basis of RS. Comparing the hospital admission and mortality outcomes with the NRS cohort, there were no significant differences. The value of routine pre-emptive screening of asymptomatic patients with cancer for COVID-19 remains low.