Ip, Andrew

e13559 Background: Recent advances in targeted therapies have increased the survival and quality of life for non-small cell lung cancer (NSCLC) patients. Understanding clinical characteristics and predicting HER2 status may facilitate efficient use of next-generation sequencing (NGS) testing and promote precision medicine/targeted therapies. Methods: NSCLC patients ≤65 years old, diagnosed between 01/2011 and 06/2021 who have NGS test results were identified in from two National Cancer Institute designated cancer centers in the Northeastern region of the United States. Demographics, behavioral, and clinical characteristics were extracted from electronic health record (EHR) data and EHR-linked cancer registry. Prevalence prediction modelling of HER2/ERBB2 mutation was performed using machine learning (ML) methods including logistic regression (LR), random forest (RF), naïve Bayes (NB), extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP). 10- fold cross validation and k-nearest neighbor method were used to impute missing values. Under-sampling, oversampling and feature selection methods were tried. Results: Among 761 NSCLC patients with NGS reports available, 353 (46%) were ≤65 years old. Among 353 (mean age 57, 58% female, 88% non-Hispanic, 69% white, 57% had metastasis, 58% prescribed chemotherapy) 25 (7%) had a HER2 mutation. Based on univariate significance tests with LR on the complete data: cerebrovascular disease, BMI between 27.5-29.9, KRAS, EGFR and MET were significant with odds ratios (ORs) ranging from 0.2-3.9. In our prediction models, BMI, cerebrovascular disease, weight loss, hepatic metastases, diabetes, congestive heart failure, and KRAS, EGFR, MET and RET alteration status were significant predictors in multivariate analyses, suggesting the potential usefulness of these features in predicting HER2 mutation among patients with NSCLC. Area under the ROC curve (AUC) varied across different models from 70.9% (RF), 70% (XGB and MLP), 69.5% (LR) to 68.2% (Naïve Bayes). The best performing model with RF yielding an AUC ~71% can provide relative high specificity and acceptable sensitivity depending on the cut off. Conclusions: Our prediction models yielded AUC around 70% with the best performing model’s AUC being 71% in a subgroup aged ≤65. After further validation, our prediction model may be used to identify patients appropriate for repeat NGS testing who did not have HER2 testing in first molecular profiling. Future work is needed to predict status in repeat testing with previously test negative patients who relapse or have progression of disease. Random forest (RF) Model performance across different cutoff for the predicted probability. [Table: see text]

Abstract Polatuzumab vedotin (PV) is an antibody–drug conjugate targeting CD79b that is approved for patients with relapsed/refractory large B-cell lymphoma (LBCL). Patients who relapse after chimeric antigen receptor (CAR) T-cell therapy were not included in the registration study, and reports of PV use after CAR T cells are limited. This multicenter retrospective analysis included patients with LBCL who relapsed or progressed after CAR T-cell therapy and subsequently received PV with or without rituximab and bendamustine between July 2019 and May 2021. Response to treatment and progression were assessed based on the 2014 Lugano criteria. Fifty-seven patients were included in the study: 18 (32%) patients were primary refractory to CAR T-cell therapy, and 34 (60%) patients received PV-based therapy immediately after CAR T-cell therapy. PV was combined with rituximab in 54 (95%) patients and administered with bendamustine in 35 (61%) patients. A response was achieved in 25 (44%) patients, including complete remission in 8 (14%). No significant association between baseline characteristics and response was observed. After a median follow-up of 47 weeks (95% confidence interval [CI], 40-54), 46 (81%) patients had disease progression or died, and the median progression-free survival was 10 weeks (95% CI, 5-15). On a multivariate analysis, bone marrow involvement (hazard ratio, 5.2; 95% CI, 1.8-15; P = .003) and elevated lactate dehydrogenase levels (hazard ratio, 5.0; 95% CI, 1.4-16; P = .01) were associated with shorter progression-free survival. Studies aimed at better characterizing the intrinsic mechanism of resistance and identifying optimal consolidation strategies for these patients are warranted.

Abstract Introduction. About 60% of patients with large B-cell lymphoma (LBCL) relapse after standard of care (SOC) anti-CD19 autologous chimeric antigen receptor (CAR) T-cell therapy, CD19 downregulation representing a major mechanism of resistance. Therefore, agents able to target B-cell antigens other than CD19 could be clinically effective for these patients. Polatuzumab vedotin (PV) is an antibody-drug conjugate targeting CD79b, and approved by the FDA in combination with bendamustine and rituximab for patients with relapsed or refractory (r/r) LBCL. Patients who relapsed after CAR T-cell therapy were not included in the registration study, and reports of PV use after CAR T-cells in real world practice are very limited. Methods. This is a multi-center retrospective analysis of patients with LBCL who relapsed after SOC CAR T-cell therapy and subsequently received SOC PV with or without rituximab and bendamustine between 07/2019 and 04/2021. PV was given at the standard dose of 1.8 mg/kg IV every 3 weeks in all patients (except for one patient, who received 1.4 mg/Kg). Response to treatment and progression were defined according to 2014 Lugano criteria. Survival curves were calculated using Kaplan-Meier estimates, and were compared between subgroups using the log-rank test. Cox regression was used for multivariate analysis (MVA). Results. Fifty-four patients were included in the study: median age was 59 (range, 22-79 years), 38 (70%) were male, and 30 (56%) had an internal prognostic index score > 3. Median number of systemic therapies before CAR T-cell therapy was 2 (range, 2-6), 16 (30%) patients previously had autologous stem cell transplant (SCT), and 2 (4%) had allogeneic SCT. Sixteen (30%) patients were primary refractory to CAR T-cell therapy, and median time from CAR T-cell therapy to PV was 5 months (range, 1-40 months). CD19 status at time of relapse after CAR T-cell therapy was assessed by immunohistochemistry and/or flow cytometry in 41 patients, and positive in 34 (83%); CD79b status was assessed in 14 patients, and positive in all cases (100%). Thirty-two (59%) patients received PV-based therapy immediately after CAR T cell therapy, while 22 (41%) had intervening treatments (median 1, range 1-5). At time of PV initiation, median absolute neutrophil count was 2.9 (range 0.5-19 X10 9/L), median platelet count was 87 X10 9/L (range 15-437 X10 9/L), median serum creatinine was 0.9 mg/dL (range 0.4-22 mg/dL), and 44 (81%) patients had elevated serum lactate dehydrogenase (LDH). The median number of PV cycles was 2 (range, 1-16): PV was combined with rituximab in 51 (94%) patients, and administered with bendamustine in 33 (61%). A response was achieved in 24 (45%) patients, including complete remission (CR) in 8 (14%) patients and partial remission in 16 (30%)(Figure A), with a median duration of response of 11 weeks (95%CI, 5-17 weeks). No significant association between baseline characteristics and response was observed. To date, 49 (91%) patients stopped PV: 38 (70%) due to progression, 7 (13%) because of CR/patient decision, 3 (6%) to proceed to allogeneic SCT, and 1 (2%) to proceed to an immunotherapy clinical trial (despite absence of progression). No patients stopped therapy because of toxicity. After a median follow up of 45 weeks (95% CI, 20-70 weeks), 44 (81%) patients progressed/died, and median PFS was 9 weeks (95% CI, 4-14 weeks). To date, 34 (63%) died, and median OS was 16 weeks (95% CI, 13-19 weeks)(Figure B). Causes of death included progression in 31 patients and transplant-related complications in 3. On univariate analysis, a shorter median progression-free survival (PFS) was observed for patients with bone marrow (BM) involvement (3 vs 10 weeks, p=0.002), prior central nervous system involvement (4 vs 10 weeks, p=0.02), and elevated LDH (6 months vs not reached, p=0.004). On MVA, the association was maintained only for BM involvement (hazard ratio [HR] 4.8; 95% confidence interval [CI] 1.6-12.5, p=0.004) and elevated LDH (HR 5; 1.4-16.7, p=0.01)(Figure C). Discussion. PV is safe and effective, but has short duration of response in r/r LBCL after anti-CD19 CAR T-cell therapy, except for patients with normal LDH. Studies aimed at better characterizing intrinsic mechanism of resistance, including upregulation of BCL2 family proteins, to favor the development of more effective PV-based combination strategies for these patients, are warranted. Figure 1 Figure 1. Disclosures Crombie: Roche: Research Funding; Merck: Research Funding; Abbvie: Research Funding; Bayer: Research Funding; Karyopharm: Consultancy; Incyte: Consultancy. Kamdar: Celgene (BMS): Consultancy; Adaptive Biotechnologies: Consultancy; Genentech: Research Funding; Kite: Consultancy; AstraZeneca: Consultancy; ADC Therapeutics: Consultancy; Genetech: Other; TG Therapeutics: Research Funding; SeaGen: Speakers Bureau; Celgene: Other; KaryoPharm: Consultancy; AbbVie: Consultancy. Hess: ADC Therapeutics: Consultancy; BMS: Speakers Bureau. Neelapu: Takeda Pharmaceuticals and related to cell therapy: Patents & Royalties; Kite, a Gilead Company, Bristol Myers Squibb, Merck, Poseida, Cellectis, Celgene, Karus Therapeutics, Unum Therapeutics (Cogent Biosciences), Allogene, Precision BioSciences, Acerta and Adicet Bio: Research Funding; Kite, a Gilead Company, Merck, Bristol Myers Squibb, Novartis, Celgene, Pfizer, Allogene Therapeutics, Cell Medica/Kuur, Incyte, Precision Biosciences, Legend Biotech, Adicet Bio, Calibr, Unum Therapeutics and Bluebird Bio: Honoraria; Kite, a Gilead Company, Merck, Bristol Myers Squibb, Novartis, Celgene, Pfizer, Allogene, Kuur, Incyte, Precision BioSciences, Legend, Adicet Bio, Calibr, and Unum Therapeutics: Other: personal fees. Lin: Novartis: Consultancy; Bluebird Bio: Consultancy, Research Funding; Juno: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Gamida Cell: Consultancy; Sorrento: Consultancy; Legend: Consultancy; Takeda: Research Funding; Merck: Research Funding; Vineti: Consultancy. Strati: Astrazeneca-Acerta: Research Funding; Roche-Genentech: Consultancy.

Abstract Introduction: Although increased physical activity (PA) is associated with improved quality of life in cancer survivors, knowledge about the impact of PA on survival in lymphoma patients is limited. We studied the impact of usual adult PA and PA at 3 years after diagnosis on overall (OS) and lymphoma-specific (LSS) survival in lymphoma patients. Methods: Lymphoma patients were prospectively enrolled within 9 months of diagnosis into the Lymphoma SPORE Molecular Epidemiology Resource (MER) cohort. At baseline, patients reported their usual level of mild, moderate and strenuous PA during most of their adult life and (for survivors) again at the 3 year follow-up (FU3), which we used to calculate the Godin Leisure Score Index (LSI). At FU3, patients also self-reported change in PA since diagnosis as increase, decrease or no change. LSI was modeled as a continuous score (per 10-point change) and by tertile. Survival was measured as time from diagnosis (for baseline PA) and time from FU3 (for FU3 PA) until death due to any cause and due to lymphoma. We evaluated the association of PA with outcome using Kaplan-Meier curves as well as hazard ratios (HRs) and 95% confidence intervals (CI) from Cox models stratified by lymphoma subtype and adjusted for age and sex (and baseline PA for change models). Results: Of 4087 patients enrolled in the MER at the Mayo Clinic from 2002-2012, 3129 had baseline PA data (3060 evaluable for LSI), and 1845 (1395 evaluable for LSI) had FU3 PA data. In the baseline cohort, 57.6% of the patients were male. The median age at diagnosis was 62 years (range 18-92 years). The majority had ECOG performance status <2 (95%), no B-symptoms (85.8%) and advanced disease (63% stage III-IV) at diagnosis. The subtypes included were CLL/small lymphocytic (25.9%), follicular (18.1%), diffuse large B-cell (16.8%), marginal zone (8.1%), Hodgkin (6.9%), mantle cell (4.8%), T-cell (5%) and other (14.3%) lymphomas. The median LSI was 28 (range 0-57) at baseline and 25 (range 0-67) at FU3 with a median change in LSI of -2 (range -57 to 61). At a median follow-up of 84 months from diagnosis (IQR 59-119 months) in the cohort with baseline PA, there were 806 total deaths (271 in pts with FU3 PA) and 423 lymphoma deaths (104 in pts with FU3 PA). When analyzed as a continuous variable, baseline LSI was associated with OS (HR 0.95, 95% CI 0.91-0.99) and LSS (HR 0.95, 95% CI 0.90-1.01). LSI by tertiles was associated with OS (p<0.0001, Figure 1a), and patients in the high LSI compared to the low LSI tertile had superior OS (HR 0.81, 95% CI 0.68-0.97) and LSS (HR 0.81, 95% CI 0.63-1.04). For prognosis after 3 years, continuous LSI at FU3 was also associated with superior OS (HR 0.84, 95% CI 0.77-0.92) and LSS (HR 0.76, 95% CI 0.65-0.89). LSI at FU3 by tertiles was associated with OS (p<0.0001, Figure 1b), and patients in the high LSI compared to the low LSI tertile had superior OS (HR 0.51, 95% CI 0.34-0.77) and LSS (HR 0.33, 95% CI 0.16-0.67). A change in LSI from baseline to FU3 PA was associated with superior OS (HR 0.84, 95% CI 0.76-0.92) and LSS (HR 0.74, 95% CI 0.64-0.87) after FU3. Change in LSI by tertiles was associated with OS (p=0.0007, Figure 1c). Compared to patients with stable LSI (change of LSI between -10 and 5 points from baseline, n=429), patients who increased LSI by > 5 (n=464) had improved OS (HR 0.67, 95% CI 0.45-1.00) and LSS (HR 0.47, 95% CI 0.23, 0.94), while patients who decreased LSI by >10 (n=477) had no association with OS or LSS. Self-reported change in PA from baseline to FU3 was associated with OS (p<0.0001, Figure 1d); compared to no change, an increase in PA was not associated with OS (HR 0.79, 95% CI 0.42-1.48) or LSS (OR 0.73, 0.26-2.07), while a reduction in PA was associated with inferior OS (HR 1.94, 95% CI 1.49-2.53) and LSS (HR 2.51, 95% CI 1.62-3.88). After excluding patients with any events through FU3, the associations for the PA change and OS remained significant for change in LSI (HR 0.91, 95%CI 0.83-1.00) and self-reported decrease in PA (HR 1.83, 95%CI 1.32-2.53). Conclusions: Patients with a higher level of usual PA during adult life have significantly better OS and LSS after lymphoma diagnosis compared to those who are less physically active. Higher PA in 3-year survivors is also associated with improved survival, while a reduction in physical activity is associated with worse outcomes. These data support an important role for PA in lymphoma survivorship and also provide a strong rationale for a PA intervention trial in lymphoma patients. Figure 1 Figure 1. Disclosures Cohen: LAM Therapeutics, Inc: Research Funding; Takada: Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bioinvent: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees. Flowers: Spectrum: Consultancy; OptumRx: Consultancy; Acerta: Research Funding; Onyx: Research Funding; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; National Cancer Institute: Research Funding; Burroughs Welcome Fund: Research Funding; Genentech/Roche: Consultancy, Research Funding; Gilead: Consultancy; Research to Practice: Research Funding; Millennium/Takeda: Research Funding; National Institutes Of Health: Research Funding; Prime Oncology: Research Funding; Educational Concepts: Research Funding; Abbvie: Consultancy, Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Seattle Genetics: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Celgene: Consultancy, Research Funding; Infinity: Research Funding; Bayer: Consultancy; Clinical Care Options: Research Funding. Cerhan: Janssen: Other: Scientific Advisory Board (REMICADELYM4001); Janssen: Other: Multiple Myeloma Registry Steering .

AbstractThe impact of physical activity (PA) on lymphoma survival is not known. The association of PA and change in PA with overall (OS), lymphoma‐specific (LSS) and event‐free (EFS) survival was evaluated in a prospective cohort of newly diagnosed lymphoma patients (2002‐2012). We calculated Leisure Score Indexes (mLSI) from the self‐reported usual adult PA (baseline) and at 3‐years post‐diagnosis (FU3), grouping patients by active vs insufficiently active by the American Cancer Society PA guidelines. Associations of PA with survival were assessed using hazard ratios (HRs) and 95% confidence intervals (CI) from Cox models stratified by lymphoma subtype, adjusted for age, sex, baseline BMI, and comorbidity score with change scores further adjusted for baseline PA. Three thousand sixty participants were evaluable at baseline and 1371 at FU3. Active patients had superior survival from baseline [HR (CI): OS 0.82 (0.72‐0.94); LSS 0.74 (0.61‐0.90); EFS 0.92 (0.82‐1.02)] and FU3 [HR (CI): OS 0.64 (0.46‐0.88); LSS 0.32 (0.18‐0.59); EFS 0.82 (0.61‐1.10)] compared to insufficiently active. An increase in mLSI from baseline to FU3 (vs stable mLSI) was associated with superior OS (HR = 0.70, CI 0.49‐1.00) and LSS (HR = 0.49, CI 0.26‐0.94).The continuous change in mLSI at FU3 was significantly associated with OS, LSS and EFS; maintained across subgroups and appeared linear. Higher PA among lymphoma patients at diagnosis and 3 years is significantly associated with OS, LSS, and EFS. Increasing PA after diagnosis is significantly associated with improved OS and LSS supporting an important role for PA in lymphoma survivorship and the need for intervention trials.

95 Background: While PA is known to improve quality of life in lymphoma pts, its impact on OS is not known. We studied the effect of PA on OS in lymphoma pts enrolled at diagnosis in the Iowa/Mayo Molecular Epidemiology Resource. Methods: Godin Leisure Score Index (LSI; Range 0-50) was calculated based on level of PA pre-diagnosis (baseline) and 3 years post diagnosis (FU3), with pts grouped by tertile into high, moderate, or low PA. At FU3, pts also reported perceived change in PA since diagnosis. OS was measured as time from assessment (baseline or FU3) until death from any cause. Associations between LSI tertiles and OS were assessed using Cox models adjusted for lymphoma subtype, age and sex. Results: PA level was available for 3060 pts at baseline and 1371 pts at FU3. 95% had an ECOG performance status < 2, 37% had an aggressive subtype, 29% had BMI ≥ 30, and 54% were age > 60. Indolent lymphoma pts with higher PA at baseline had no difference in OS. Higher PA at FU3 had improved OS (moderate vs low HR 0.63, CI 0.42-0.93; high vs low HR 0.53, CI 0.33-0.86). Pts with BMI < 30 who had higher PA at baseline showed no difference in OS. Higher PA at FU3 had improved OS for pts with BMI < 30 (moderate vs low HR 0.68 CI, 0.48-0.98; high vs low HR = 0.41, CI 0.26-0.64). For pts > 60 years, high vs low baseline PA showed improved OS (HR 0.77, CI 0.63-0.93). Higher PA at FU3 (moderate vs low HR 0.68 CI 0.48-0.96, high vs low HR 0.53 CI 0.34-0.83) was associated with OS. At FU3, compared to no change, perceived reduction of PA was associated with worse OS for indolent lymphoma (HR 2.13, CI 1.56-2.91), BMI < 30 (HR 1.95, CI 1.45-2.62), BMI > 30 (HR 2.00, CI 1.21-3.30), age ≤ 60 (HR 3.06, CI 1.72-5.46), and age > 60 (HR 1.98, CI 1.52-2.58). Perceived increase in PA was not associated with OS. Higher PA in pts with aggressive lymphoma, BMI ≥30, and age < 60 was not significantly associated with OS but trended similarly. All results were similar when analyzing by continuous LSI score. Conclusions: Higher levels of PA, especially at FU3, were associated with superior OS. The effect was more pronounced in pts with indolent lymphoma, BMI < 30, or age > 60. Perceived reduction in PA was associated with worse outcomes. PA should be incorporated into lymphoma survivorship plans.