Nedved, Adrienne

Background: Two-year follow-up from ZUMA-1 trial for axicabtagene ciloleucel (axi-cel) CD19 chimeric antigen receptor T-cell (CART) in aggressive non-Hodgkin lymphoma (NHL) demonstrated that patients (pts) achieving complete remission (CR) as their best response have the longest progression free survival (PFS), while PFS remained poor for others. Majority of the best response are achieved by month 3 post-infusion. Moreover, a subset of pts who do not achieve CR by month 1 may still achieve CR while others will progress within months. Having a clinically available test to predict for durable response can assist clinicians in selecting pts who can be observed for continued response vs. those who may need intervention earlier. Peak CAR-T expansion has been correlated with best clinical response to axi-cel. However there is no clinically available test to quantify CART cells post treatment. Given the prolonged lymphotoxic effect of the lymphodepletion (LD) chemotherapy administered before CART infusion, we hypothesize that the peak expansion of absolute lymphocyte count (ALC, ALCpeak) after infusion likely corresponds to CART expansion. We examined for correlation between ALCpeak and clinical response at month 3 post-infusion for pts who received axi-cel. Methods: Data was collected from pts who received axi-cel between June 2016 and March 2019 at Mayo Clinic Rochester (NCT02348216, NCT03153462, and standard of care). All infused pts were included in the intention to treat analysis for response and event-free survival (EFS). Lugano 2014 criteria were used for clinical response. EFS was defined as the date of infusion until progression or death due to any cause and evaluated using Kaplan Meier curves with log-rank test. Comparison between subgroups was investigated using Fisher's exact test and Wilcoxon test for categorical and continuous variables, respectively. Statistical analysis performed using JMP Pro 14.1. Results: Of the thirty-six NHL pts treated with axi-cel, the median age was 56 yrs (range 26-65); 78% (28) were male; 44% (16) had DLBCL; the median number of prior therapies was 3 (2-6), 47% (17) had stem cell transplant. Nineteen (53%) pts received bridging therapy after leukapheresis. All pts received fludarabine and cyclophosphamide for LD chemotherapy and achieved ALC nadir by the day of CART infusion (median 0.03 x 10^9/L, range 0.01-0.11). As CART expansion were found to peak within the first two weeks in ZUMA-1 trial, we examined ALC trend over 15 days post infusion and found that ALCpeak generally happened within the same timeframe (median ALCpeak day: 9, range 6-15). At 3 months post infusion, 33.3% (12/36) of pts achieved CR, 8.3% (3/36) pts were in partial remission (PR). No correlation was found between CR response and clinical characteristics: age >60 years, stage III/IV, B symptoms, elevated LDH, IPI score 3-4, number of previous treatments, use of bridging therapy, peak CRP and ferritin. However, the ALCpeak was significantly different between CR and non-CR pts (median, range: 1.09, 0.35-2.55; 0.67, 0.15-1.03; p=0.04). Using receiver operator curve (ROC)-derived area under the curve (AUC) estimates, ALCpeak 1.08 x 10^9/L was identified as the best threshold to discriminate patients at different likelihood to achieve CR at month 3. Pts achieving ALCpeak ≥ 1.08 showed a strong increased likelihood of achieving CR at month 3 (OR 2.8; 95% CI, 1.12-6.99; P=0.02). There was no statistical difference between the ALCpeak high and low groups in terms of use of bridging therapy, occurrence of any grade or higher grade cytokine release syndrome or neurotoxicity, or the use of tocilizumab or steroid. Interestingly, for pts who had changes in clinical response from month 1 to month 3, all 3 pts who converted to CR by month 3 had ALCpeak≥1.08, while the 13 of the 15 pts who had progressed by month 3 had ALCpeak<1.08. In addition, EFS was significantly different between pts who had ALCpeak above and below 1.08 (6 months EFS rate 75% vs. 25%, respectively, Figure 1). Conclusions: We found that the ALC expansion occurred in the first 15 days post axi-cel infusion, consistent with the known time frame for CART expansion. Higher ALCpeak level in this timeframe correlated with higher likelihood of achieving CR, and is associated with conversion to CR. This study is limited by the small sample size and limited follow-up, but the results are promising for further prospective investigation in a larger cohort of patients. Disclosures Paludo: Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding; Celgene: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding. Bennani:Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Karyopharm: Research Funding; Rigel: Consultancy. Kapoor:Celgene: Honoraria; Janssen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Kenderian:Novartis: Patents & Royalties, Research Funding; Tolero: Research Funding; Humanigen: Other: Scientific advisory board , Patents & Royalties, Research Funding; Lentigen: Research Funding; Morphosys: Research Funding; Kite/Gilead: Research Funding. Kumar:Takeda: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

Abstract Background: Chronic myelogenous leukemia (CML) is a BCR-ABL1 driven myeloid neoplasm, with excellent response rates to tyrosine kinase inhibitors (TKI). TKI such as imatinib (IM), dasatinib (DAS), nilotinib (NIL), bosutinib (BOS), and ponatinib (PON) currently US FDA approved, have dramatically changed survival outcomes. That being said, two main challenges exist; suboptimal responses and TKI intolerance. The reason why some patients tolerate/respond to TKI better than others is unknown, but is potentially explainable based on drug metabolism. The field of pharmacogenomics (PGX) is rapidly evolving, with commercial panel's rapidly assessing metabolic pathways such as cytochrome P450 (CYP), UGTA1A, p-glycoprotein/ABCB1. We carried out this study using a 22 gene-PGX panel to assess potential causes of TKI related intolerance and suboptimal responses in compliant patients. Methods: Twenty-nine Mayo Clinic patients with CML were prospectively recruited after informed consent. Buccal swabs were utilized for PGX testing to assess variations/polymorphisms involving CYP3A4/5, 1A2, 2C9, UGT1A1, amongst others (www.Oneome.com). Three groups of patients were recruited, those with i) TKI intolerance, ii) suboptimal responses by ELN criteria, iii) and newly diagnosed cases, to assess if PGX testing could explain TKI intolerance or resistance, or alter choice of therapy in newly diagnosed cases. A pharmacology review was obtained to interpret reports in all cases. Results: 29 patients with CML were prospectively enrolled, median age 64 years, 15 (52%) male. At last follow up (median 21 months), 3 (10%) deaths and no disease progressions were documented. 82% received first line IM, whereas 18% received first line DAS. Major metabolic pathways assessed for IM included, CYP3A4 and 5, while minor pathways included 2C19, 2C9 and 2D6. Majority (83%) of IM patients had normal major pathways with 17% being intermediate-normal. With regards to the minor pathways, 25% were rapid metabolizers for 2C19, whereas 29% were intermediate and 4% poor metabolizers for 2C9 and 50% and 16% were intermediate and poor metabolizers for 2D6. In all IM treated patients PGX testing did not completely explain intolerance or resistance and did not change clinical decision making. Front line DAS was used in 18% and the major pathway assessed was CYP 3A4, with 80% being normal and 20% being intermediate metabolizers. Once again PGX testing did not completely explain intolerance/resistance and no therapeutic changes were based on PGX analysis. Seventeen (59%) patients received second line TKI therapy (50% each for intolerance and resistance), with DAS (58%) and NIL (29%), being the two most common. Once again for DAS, 90% had normal 3A4 metabolism while 10% were intermediate and for NIL 80% were normal and 20% intermediate. Only one of 2 NIL treated patients with UGTA1A polymorphisms developed indirect hyperbilirubinemia (grade 1). In the second line setting, PGX testing did not completely explain or alter treatment decisions. BOS was used as a third line agent in 3 (>10%) patients, 2 who remained refractory and one with intolerance, and all three were normal CYP3A4 metabolizers. None of these patients developed diarrhea a common side effect of BOS. PON was used in one patient with intermediate CYP3A4 metabolism as a 5th line agent and the patient continues to have refractory disease with no thrombotic events. In all cases, no changes were made to concomitant medications based on PGX testing. Conclusion: In conclusion, while PGX testing has rapidly evolved and become commercially available, in the context of TKI therapy for CML, while it offers useful insights on minor metabolic derangements and side effects like NIL induced hyperbilirubinemia, in our study, currently limited by a small sample size, it did not completely explain TKI intolerance or resistance. Recruitment of additional patients (n=100) and correlations with plasma drug levels are currently ongoing. Table. Table. Disclosures Al-Kali: Novartis: Research Funding. Stewart:Amgen Inc., Celgene, Roche, Seattle Genetics: Research Funding; Amgen Inc., BMS, Celgene, Takeda, Roche, Seattle Genetics, Janssen, Ono: Consultancy.

Background: The patient/caregiver experience during CAR-T therapy is stressful, overwhelming, terrifying, and often a patient’s last treatment option. The Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Innovation and Design team has worked with the CAR-T therapy clinical team to develop a patient experience that provides patients with a sense of caring, supportive environment, timely knowledge, and realistic expectations. Using a human-centered design approach, the Innovation and Design team worked with patients and caregivers to understand latent and unspoken needs in order to develop an ideal CAR-T therapy patient journey. Methods: With qualitative interviewing techniques, patient observation, and low fidelity experimentation, 21 patients/caregiver pairs were interviewed throughout their CAR-T therapy experience in 2018. Patients were interviewed at several touch points as well as encouraged to reach out to the Innovation and Design team at any point with reflections on their experiences. Patients were recruited as they began their evaluation phase for CAR-T therapy. The interviews were unscripted to allow for a breadth of discovery by not constraining the conversations to previously developed themes. As themes emerged from patient/caregiver interviews, artifacts and interventions were designed to alleviate pain points and improve the patient/caregiver experience. These artifacts and interventions were integrated into the clinical processes in real time and patient/caregivers were interviewed to understand the impact of these activities. Results: Several themes emerged from qualitative interviews with patients and caregivers. From the themes, interventions were developed. We were able to demonstrate a qualitative improvement in patient/caregiver experience through these interventions (Figure 1). Conclusions: Patients/caregivers undergoing CAR-T therapy have unique issues surrounding the logistics of care, emotional burden, and physical effects of treatment. We implemented processes to address these issues and observed a qualitative improvement via patient interviews/feedback. Ongoing work includes optimizing remote monitoring, digital platforms for patient education, and a quantitative study looking at patient reported outcomes (PROs) in such patients. To our knowledge, this is the first report for care delivery optimization in real-world practice for this new therapy.

Objective Assess the effects of linezolid on hematologic outcomes in newly diagnosed patients with acute myeloid leukemia ( AML ) following induction chemotherapy. Design Single‐center, retrospective, observational, cohort study. Setting Large, tertiary care academic medical center. Patients A total of 225 patients ≥ 18 years admitted between December 2010 and 2013 with newly diagnosed AML were assessed for inclusion. Patients were identified through the use of ICD ‐9 codes and chemotherapy ordered via the computerized physician order entry system. Sixty‐eight patients met inclusion criteria and were grouped into two arms based on antimicrobial treatment: LZD group (linezolid plus gram‐negative antimicrobial, n=21) or control group (vancomycin or daptomycin plus gram‐negative antimicrobial, n=47). Interventions The LZD group received linezolid ≥ 72 hours. The control group received vancomycin or daptomycin ≥ 72 hours. If patients switched extended gram‐positive therapy, they were included in the LZD group as long as they had received ≥ 72 hours of linezolid. Measurements/Results The primary end point of time to neutrophil recovery was not statistically different (28 days for LZD group vs 26 days for control group; p=0.675). The preplanned subgroup analysis of patients who received ≥ 14 days of linezolid demonstrated statistically similar median times to neutrophil recovery (29 days for LZD group vs 26 days for control group; p=0.487). Total duration of extended gram‐positive antimicrobial therapy was significantly longer in the LZD group (27 days vs 16 days; p<0.001). Secondary end points not found to be statistically significant included platelet count at time of neutrophil recovery, duration of neutropenia, and length of hospital stay. Conclusions There were no significant differences in hematologic outcomes in newly diagnosed AML patients who received linezolid for extended gram‐positive antimicrobial coverage following induction chemotherapy. This study provides new insight with a primary focus on the effects of hematologic outcomes when using linezolid in a well‐defined acute leukemia population. Further study is warranted with larger populations to assess the potential adverse effects linezolid may have in patients with acute leukemia.