Kothadia, Sejal

Abstract Introduction: In the ASPIRE and ENDEAVOR trials, multiple myeloma (MM) patients treated with carfilzomib (K) had significantly improved progression‐free survival and overall survival compared with standard of care. The incidence of all-grade adverse cardiovascular events (ACVE) was 26.6% and 24.5% in the K treated groups in ASPIRE and ENDEAVOR respectively. The atherosclerotic cardiovascular disease (ASCVD) score is a risk stratification tool used by cardiologists to classify patients with a score ≥7.5% as high risk for the incidence of ASCVD events. To date, there are no clinically relevant models that predict the likelihood of ACVE in MM patients treated with K nor are we aware of any protective factors against ACVE. Our study aims to identify factors which can predict and mitigate the incidence of ACVE. Methods: A retrospective chart review of 372 MM patients who were treated with K between 2011 and 2018 at our institution was performed.Data were summarized using descriptive statistics; Chi-square tests of association and Kruskal-Wallis tests were employed to test for differences across groups, where appropriate. Logistic regression was performed to examine the risk factors associated with ACVE. Results: Of the 372 patients, 243 (65%) were Durie-Salmon Stage (DSS) III, 70 (19%) were International Staging System (ISS) III; 186 (50%) were men. There were a total of 102 (27%) ACVE and 33 (9%) ≥grade 3 events. The most common ACVE were congestive heart failure (29%), asymptomatic elevations in pulmonary arterial systolic pressure (PASP) (27%), and hypertension (HTN) (16%). Sixty-eight (66%) Caucasians, 14 (14%) African Americans and 17 (17%) Hispanics developed ACVE. Aspirin (ASA) (64%), beta blockers (BB) (51%), calcium channel blockers (CCB) (43%) and statins (37%) were the most common cardiac medications patients were taking at the time of K initiation. Regardless of cardiac history, patients who were taking a CCB had higher odds of experiencing an ACVE compared to patients who were not taking a CCB (CCB + Cardiac history: OR=1.9; CI=1.0-3.7; CCB, no Cardiac history: OR=6.3; CI= 2.1-18.4) (Table 1). Patients with an ASCVD score ≥7.5% did not have an increased incidence of ACVE but did have an increased incidence of ≥grade 3 vs. grade 1-2 ACVE (100% vs. 78%, p=0.092). Prior cardiac history, including history of HTN, was common (59%) but not associated with incidence or severity of ACVE. Those with type 2 diabetes had a higher rate of ≥grade 3 vs. grade 1-2 ACVE (33% vs. 10%, p=0.004). Patients on certain CV medications while receiving K-based treatment had a higher incidence of ACVE compared to those not on these medications, including BB (51% vs. 34%, p=0.002), CCB (43% vs. 26%, p=0.002), aldosterone antagonists (AA) (8% vs. 3%, p=0.024) and statins (37% vs. 25%, p=0.022). Patients on ASA were more likely to experience ≥grade 3 compared to grade 1-2 ACVE (78% vs. 53%, p=0.016). ACE inhibitors or angiotensin receptor blockers (ACE/ARB) were not associated with increased incidence or severity of ACVE. The use of K in relapsed, as opposed to induction setting, was associated with a higher incidence of ACVE (31% vs. 20%, p=0.035). Maximum dose and number of K doses were not associated with ACVE. There was no relationship between PASP elevation and age >75, use of BB, CCB, AA, ACE/ARB, number of K treatments or maximum dose of K. Factors that were associated with increased odds of ACVE in multivariate analysis included patients who received K in the relapsed setting (OR= 1.7; CI=0.94-3.0) and use of an AA (OR= 4.5; CI=1.4-14.1) (Table 1). Conclusion: In line with prior studies, 28% of MM patients treated with K at our institution developed an ACVE, with 9% grade 3 or above. Use of CCB and AA were particularly associated with increased risk of ACVE, while use of ACE/ARB were not. ASCVD score ≥7.5% was associated with an increased incidence of ≥grade 3 ACVE in K-treated patients and should perhaps be evaluated as a predictive tool in prospective studies. Disclosures Siegel: Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau; Karyopharm: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Speakers Bureau. Biran:Merck: Research Funding; Amgen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Honoraria, Speakers Bureau; BMS: Research Funding.

Abstract Background Early phase clinical trials evaluate the safety and efficacy of new treatments. The exclusion/inclusion criteria in these trials are usually rigorous and may exclude many patients seen in clinical practice. Our objective was to study the comorbidities limiting the participation of patients with breast, colorectal, or lung cancer in clinical trials. Materials and Methods We queried ClinicalTrials.gov on December 31, 2016. We reviewed the eligibility criteria of 1,103 trials. Logistic regression analyses were completed, and exclusion was studied as a binary variable. Results Out of 1,103 trials, 70 trials (6%) excluded patients >75 years of age, and 45% made no reference to age. Eighty-six percent of trials placed restrictions on patients with history of prior malignancies. Regarding central nervous system (CNS) metastasis, 416 trials (38%) excluded all patients with CNS metastasis, and 373 (34%) only allowed asymptomatic CNS metastasis. Regarding chronic viral infections, 347 trials (31%) excluded all patients with human immunodeficiency virus, and 228 trials (21%) excluded all patients with hepatitis B or C infection. On univariate analysis, chemotherapy trials were more likely to exclude patients with CNS metastasis and history of other malignancies than targeted therapy trials. Multivariate analysis demonstrated that industry-sponsored trials had higher odds of excluding patients with compromised liver function. Conclusion Many clinical trials excluded large segments of the population of patients with cancer. Frequent exclusion criteria included patients with CNS metastasis, history of prior malignancies, and chronic viral infections. The criteria for participation in some clinical trials may be overly restrictive and limit enrollment. Implications for Practice The results of this study revealed that most early phase clinic trials contain strict exclusion criteria, potentially excluding the patients who may be more likely to represent the population treated in clinical settings, leaving patients susceptible to unintended harm from inappropriate generalization of trial results. Careful liberalization of the inclusion/exclusion criteria in clinical trials will allow investigators to understand the benefits and drawbacks of the experimental drug for a broader population, and possibly improve recruitment of patients with cancer into clinical trials.

93 Background: Tumor mutational load (TML) has been proposed as a biomarker of responsiveness to novel immune checkpoint inhibitors (ICIs) due its association with increased neoantigen formation and tumor immunogenicity. Gastrointestinal cancers (GICs) are generally insensitive to ICIs; therefore the aim of this study was to evaluate the TML, clinically relevant genomic alterations (CRGAs), and PDL-1 status of GIC patients (pts). Methods: We analyzed samples of pts with GICs using a multiplatform profiling tool (Caris Life Sciences, Phoenix, AZ). TML and CRGAs were calculated based on next generation sequencing and mutational load was stratified as high (≥17 mut/Mb), intermediate (8-16 mut/Mb), and low (≤ 7 mut/Mb). PD-L1 status was determined by IHC. Descriptive statistics and simple linear regression were used for analysis. Results: A total of 85 pts with GICs were analyzed. The median pt age was 65. 53% were males and 47% were females. Tumors were colorectal 51%, pancreatic 13%, esophageal 13%, gastric 9%, hepatobiliary 11%, anal canal 4%, and small intestine 1%. 80 cases (94%) had an adequate sample for profiling and TML was available in 59 cases (74%). Cases were 5% TML-high, 56% TML-intermediate, and 39% TML-low. The median TML was 9 mut/Mb and median CRGAs was 3. The most commonly detected CRGAs were TP53 (56%), APC (44%), KRAS (40%), ATM (18%) and PTEN (6%). A positive but very weak correlation was found between TML and number of CRGAs (R2 = 0.3). PD-L1 status was available in 74 cases (93%) with 86% being PD-L1 negative and 14% PD-L1 positive. PD-L1 expression was most commonly seen in esophageal (50%) and anal canal (30%) cancers. Among 57 tumors tested for both biomarkers, no correlation was found between a high TML and PD-L1 expression. Conclusions: TML varies among GICs, and higher TML scores were not associated with PD-L1 expression, but there was a weak correlation between TML and the number of CRGAs. Further analysis by stratification of GIC type and prognostic analysis to assess the correlation between TMB and response to ICIs is warranted.

803 Background: The routine use of comprehensive genomic profiling (CGP) has led to better genomic characterization, more personalized treatments and higher enrollment in clinical trials for many solid cancers. The aim of this study was to evaluate the clinical and genomic prolife of patients (pts) with gastrointestinal cancers (GIC) with the use of a CGP assay and their potential therapeutic benefit at a large comprehensive cancer center. Methods: We retrospectively analyzed 83 consecutive pts with GIC that had CGP at the JTCC between 01/2014-09/2016. Demographics, CGP results, and clinical characteristics were studied. Clinically relevant genomic alterations (CRGAs) were identified using the FoundationOne assay (Foundation Medicine, Cambridge, MA), and were defined as alterations linked to approved therapies and those under evaluation in genotype-driven clinical trials. Results: 83 pts with GIC were analyzed. 46.9% males and 53.0% females. The median age at diagnosis was 58.5 years. 73 (88%) patients were stage III/IV at diagnosis. Tumors consisted of colorectal (36%), gastric (24%), pancreatic (12%) and cholangiocarcinoma (6%). All the pts were found to have at least one genomic alteration (GA). CRGAs were identified in 63 (76%) of cases, with an average of 2 per pt. The most frequently identified GAs were TP53 43 (29.9%), APC 24 (16.7%), KRAS 31 (21.5%), ARID1A 12 (8.3%), PIK3CA 11 (7.6%), and SMAD4 11 (7.6%). Of the 63 pts with CRGAs, 23 (36%) received one of the therapies suggested by the NGS assay, and 9 (14%) were enrolled in a clinical trial. Conclusions: The use of CGP assay identified a high frequency of CRGAs in GIC pts. The assay provided information for genomic-directed therapy in 36% of the patients. The routine use of CGP in GIC may lead to pts receive more personalized treatments. The potential benefit of selected targeted therapies in individual cases suggests that larger studies of treatment guided by routine CGP in gastrointestinal malignancies are warranted.

791 Background: With the surge of drug development in the past decade, early phase clinical trials (EPCT) have gained value evaluating the potential benefits of new therapies. The inclusion/exclusion criteria in EPCT are usually rigorous and may exclude many patients (pts) commonly seen in clinical practice. Our objective was to identify the most common comorbidities excluded in EPCT for CRC. Methods: ClinicalTrials.gov was queried on December 1stof 2015. We reviewed the characteristics and eligibility criteria of 369 phase I/II interventional drug trials including: experimental arm therapy, location, and exclusion/inclusion criteria. Logistic regressions were completed and exclusion was studied as a binary variable. Results: Of the 369 trials, 68% were phase II and 32% phase I. 46% were conducted in the United States, 30% in Europe, 15% in Asia and 9% in other locations. 74 (20%) trials excluded pts > 70 years of age. 142 (39%) trials required creatinine levels < 1.5 mg/dl, liver enzymes (AST/ALT) < 2.5 and bilirubin < 1.5 of the upper limit of normal. Cytopenia was a significant exclusion factor: 147 (47%) trials required Hgb > 9 g/dl and 218 (59%) excluded pts with platelets < 100,000/dl. In terms of comorbidities, 98 (27%) trials excluded pts with heart failure (NYHA class 3/4), 74 (20%) with atrial fibrillation, 112 (31%) with any anticoagulation therapy and 155 (42%) with positive HIV. Trials located in the US were more likely to exclude pts with Hgb < 9g/dl (OR: 1.5, 95%CI: 1.1-2.3, p < 0.05), immunotherapy trials were more likely to exclude pts on any anticoagulation (OR:1.8, 95%CI: 1.2-2.8, p < 0.007) and targeted therapy trials were more likely to exclude pts with history of DVT/PE or cardiovascular diseases (OR: 3.4, 95%CI: 1.9-5.8, p < 0.0001; OR: 2.3, 95%CI: 1.3-3.8, p < 0.002, respectively). Conclusions: 20% of EPCTs on CRC excluded pts with advanced age, organ dysfunction and common comorbidities. Many of the EPCT reviewed were not inclusive of our aging oncology population who are more likely to have multiple comorbidities. Investigators should review whether sufficient justification exists for every exclusion criterion before their incorporation in future trial protocols.