Rini, Brian I.

LBA110 Background: There are limited data on COVID-19 in patients with cancer. We characterize the outcomes of patients with cancer and COVID-19 and identify potential prognostic factors. Methods: The COVID-19 and Cancer Consortium (CCC19) cohort study includes patients with active or prior hematologic or invasive solid malignancies reported across academic and community sites. Results: We included 1,018 cases accrued March-April 2020. Median age was 66 years (range, 18-90). Breast (20%) and prostate (16%) cancers were most prevalent; 43% of patients were on active anti-cancer treatment. At time of data analysis, 106 patients (10.4%) have died and 26% met the composite outcome of death, severe illness requiring hospitalization, and/or mechanical ventilation. In multivariable logistic regression analysis, independent factors associated with increased 30-day mortality were age, male sex, former smoking, ECOG performance status (2 versus 0/1: partially adjusted odds ratio (pAOR) 2.74, 95% CI 1.31-5.7; 3/4 versus 0/1: pAOR 5.34, 95% CI 2.44-11.69), active malignancy (stable/responding, pAOR 1.93, 95% CI 1.06-3.5; progressing, pAOR 3.79, 95% CI 1.78-8.08), and receipt of azithromycin and hydroxychloroquine. Tumor type, race/ethnicity, obesity, number of comorbidities, recent surgery, and type of active cancer therapy were not significant factors for mortality. Conclusions: All-cause 30-day mortality and severe illness in this cohort were significantly higher than previously reported for the general population and were associated with general risk factors as well as those unique to patients with cancer. Cancer type and treatment were not independently associated with increased 30-day mortality. Longer follow-up is needed to better understand the impact of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.

5061 Background: The phase 3 JAVELIN Renal 101 trial (NCT02684006) in treatment-naive patients with advanced renal cell carcinoma (aRCC) demonstrated significantly improved progression-free survival (PFS; hazard ratio [HR], 0.69; 95% CI, 0.56, 0.84; P < 0.001) and higher objective response rate (ORR; 51.4% vs 25.7%) with avelumab + axitinib vs sunitinib (Motzer RJ, et al. N Engl J Med. 2019;380:1103-15). NLR has emerged as a potential prognostic biomarker in aRCC; elevated NLR is associated with poorer prognosis. Here, we describe the association of NLR with the efficacy of avelumab + axitinib from JAVELIN Renal 101. Methods: We examined baseline NLR and its association with efficacy outcomes. PFS, best overall response (per blinded independent central review using RECIST 1.1), and overall survival (OS) data from the avelumab + axitinib arm from the first interim analysis of JAVELIN Renal 101 were analyzed (data cutoff, June 20, 2018). Multivariate Cox regression analyses of PFS and OS were also conducted. Results: In the avelumab + axitinib arm, patients with < median NLR (N = 217) had longer observed PFS (stratified HR, 0.85; 95% CI, 0.634, 1.153) and longer observed OS (stratified HR, 0.51; 95% CI, 0.300, 0.871) than patients with ≥ median NLR (N = 217). The ORR was 57.1% in patients with < median NLR vs 47.5% in patients with ≥ median NLR, with complete response in 5.5% vs 1.4%. Multivariate analysis showed that low NLR was associated with longer PFS and OS by treating baseline NLR as either a continuous variable or a binary variable (dichotomized by median). Conclusions: Low NLR was associated with better observed treatment outcomes in patients with aRCC who received avelumab + axitinib. Clinical trial information: NCT02684006 .

Objective To evaluate patient‐reported outcome (PRO) data from the IMmotion150 study. The phase 2 IMmotion150 study showed improved progression‐free survival with atezolizumab plus bevacizumab vs sunitinib in patients with programmed death‐ligand 1 (PD‐L1)+ tumours and suggested activity of atezolizumab monotherapy in previously untreated metastatic renal cell carcinoma (mRCC). Patients and methods Patients with previously untreated mRCC were randomised to atezolizumab 1200 mg intravenously (i.v.) every 3 weeks ( n = 103), the atezolizumab regimen plus bevacizumab 15 mg/kg i.v. every 3 weeks ( n = 101), or sunitinib 50 mg orally daily (4 weeks on, 2 weeks off; n = 101). The MD Anderson Symptom Inventory (MDASI) and Brief Fatigue Inventory (BFI) were administered on days 1 and 22 of each 6‐week cycle. Time to deterioration (TTD), change from baseline in MDASI core and RCC symptom severity, interference with daily life, and BFI fatigue severity and interference scores were reported for all comers. The TTD was the first ≥2‐point score increase over baseline. Absolute effect size ≥0.2 suggested a clinically important difference with checkpoint inhibitor therapy vs sunitinib. Results Completion rates were >90% at baseline and ≥80% at most visits. Delayed TTD in core and RCC symptoms, symptom interference, fatigue, and fatigue‐related interference was observed with atezolizumab (both alone and in combination) vs sunitinib. Improved TTD (hazard ratio [HR], 95% confidence interval [CI]) was more pronounced with atezolizumab monotherapy: core symptoms, 0.39 (0.22–0.71); RCC symptoms, 0.22 (0.12–0.41); and symptom interference, 0.36 (0.22–0.58). Change from baseline by visit, evaluated by the MDASI, also showed a trend favouring atezolizumab monotherapy vs sunitinib. Small sample sizes may have limited the ability to draw definitive conclusions. Conclusion PROs suggested that atezolizumab alone or with bevacizumab maintained daily function compared with sunitinib. Notably, symptoms were least severe with atezolizumab alone vs sunitinib (IMmotion150; ClinicalTrials.gov Identifier: NCT01984242).

4515 Background: The phase 2 IMmotion150 study showed improved PFS with atezo + bev vs sun in patients (pts) whose tumors were PD-L1+ and corroborated the activity of atezo monotherapy in previously untreated mRCC. This study offers an opportunity to evaluate PROs with immunotherapy and VEGF-directed therapy alone and in combination. Methods: Pts randomized to atezo 1200 mg IV q3w alone or with bev 15 mg/kg IV q3w, or sun 50 mg po qd for 4 wk on/2 wk off completed the MD Anderson Symptom Inventory (MDASI) and Brief Fatigue Inventory (BFI) q3w of each 6-wk cycle until progression (RECIST 1.1). Time to deterioration (TTD; first ≥ 2-point score increase over baseline [BL]) and change from BL (effect size [ES] ≥ 0.2 suggests a clinically important difference vs sun) in MDASI symptom severity and interference and BFI fatigue severity and interference scores are reported for all-comers. Results: Completion rates were > 90% at BL and ≥ 80% at most visits across arms. BL PRO scores indicating mild symptoms and interference were similar across arms. Delayed TTD of symptom severity and interference with daily life was seen with atezo vs sun and atezo + bev vs sun and was longest with atezo alone (Table). Pts reported milder symptoms and less interference during the first 6 cycles with atezo vs sun: ES mean (range) was 0.36 (0.07-0.68) for core symptom severity and 0.36 (0.04-0.83) for symptom interference. The 5 worst pt-reported symptoms during tx (dry mouth, fatigue, rash, drowsiness, lack of appetite) were all in the sun arm; all 16 symptoms measured were milder with atezo vs sun. Conclusions: PROs suggest that atezo alone or with bev maintained daily function with minimal symptom interference vs sun. Clinical activity, safety and PRO data for atezo support its investigation in adjuvant tx of high-risk RCC pts (IMmotion010; NCT03024996). Clinical trial information: NCT01984242. [Table: see text]

4505 Background: While inhibiting VEGF improves outcomes in mRCC pts, most develop resistance, often within a year. Here, we report results from a Ph II study of atezo (anti–PD-L1) and bev (anti-VEGF) vs and following atezo or sun (TKI) in mRCC pts. Methods: Pts with untreated mRCC were enrolled in the hypothesis generating IMmotion150 study (NCT01984242) and randomized to atezo 1200 mg IV q3w + bev 15 mg/kg IV q3w, atezo alone or sun 50 mg PO QD 4 wk on/2 wk off. After progression on atezo or sun, crossover to atezo + bev was allowed. PD-L1 status was scored on tumor-infiltrating immune cells (IC, SP142 IHC assay). The primary analysis was modified prior to final analysis to reflect the coprimary endpoints of IRF-assessed PFS (RECIST v1.1) in ITT pts and pts with PD-L1 expression on ≥ 1% of IC (PD-L1+). Results: 54% of pts were PD-L1+. In PD-L1+ pts 1L treatment resulted in a PFS hazard ratio (HR) of 0.64 for atezo + bev vs sun (table). After 1L treatment, 78% of sun and 60% of atezo pts who progressed subsequently received atezo + bev and achieved ORRs of 28% and 24%, respectively (table). Safety was comparable to the known individual profiles of atezo and bev. Additional clinical, safety and biomarker data will be presented. Conclusions: Atezo + bev resulted in encouraging antitumor activity in 1L pts with PD-L1+ mRCC. Preliminary activity in the 2L setting was demonstrated in pts who crossed over to atezo + bev, regardless of prior therapy. 1L atezo + bev vs sun is being evaluated in the ongoing Ph III study IMmotion151 (NCT02420821). Clinical trial information: NCT01984242. [Table: see text]

432 Background: In clinical trials and clinical practice, tumor response assessment with computed tomography (CT) defines critical end points in patients with metastatic disease treated with systemic agents. Methods to reduce errors and improve efficiency in tumor response assessment could improve patient care. Methods: Eleven readers from 10 different institutions independently categorized tumor response according to three different therapeutic response criteria using paired baseline and initial post-therapy CT studies from 20 randomly selected patients with metastatic renal cell carcinoma treated with sunitinib as part of a completed phase III multi-institutional study. Images were evaluated with a manual tumor response evaluation method (standard-of-care) and with computer-assisted response evaluation (CARE) that included stepwise guidance, interactive error-identification and correction methods, automated tumor metric extraction, calculations, response categorization, and data/image archival. A cross-over design, patient randomization, and two-week washout period were used to reduce recall bias. Comparative effectiveness metrics included error rate and mean patient evaluation time. Results: The standard-of-care method was on average associated with one or more errors in 30.5% (6.1/20) of patients while CARE had a 0.0% (0.0/20) error rate (p<0.001). The most common errors were related to data transfer and arithmetic calculation. In patients with errors, the median number of error types was 1 (range 1-3). Mean patient evaluation time with CARE was twice as fast as the standard-of-care method (6.4 vs. 13.1 minutes, p<0.001). Conclusions: Computer-assisted tumor response evaluation reduced errors and time of evaluation, indicating better overall effectiveness than manual tumor response evaluation methods that are the current standard-of-care.

431 Background: While targeting VEGF improves outcomes for mRCC pts, resistance invariably develops, often within the first year. Here, we describe the efficacy and safety of atezo (anti-PD-L1) with bev (anti-VEGF) and atezo monotherapy vs sun (TKI) in first-line mRCC. Methods: Treatment-naïve mRCC pts were enrolled in a hypothesis generating Ph II study (IMmotion150; NCT01984242) and randomized to atezo 1200 mg IV q3w + bev 15 mg/kg IV q3w, atezo alone or sun 50 mg PO QD 4 wk on/2 wk off. Crossover to atezo + bev after disease progression was allowed for pts receiving atezo or sun. PD-L1 expression was scored on tumor-infiltrating immune cells (IC, SP142 IHC assay). Coprimary endpoints were PFS (RECIST v1.1 by independent review [IRF]) in ITT pts and pts with PD-L1 expression on ≥ 1% of IC (PD-L1+). Results: Baseline characteristics were comparable across arms and between ITT and PD-L1+ pts. The majority of sun and atezo pts subsequently received atezo + bev. Median survival follow up was 20.7 mo. The PFS HR in ITT pts for atezo + bev vs sun was 1.00 and 1.19 for atezo vs sun. In PD-L1+ pts, the PFS HR for atezo + bev vs sun was 0.64 and 1.03 for atezo vs sun (table). Tx-related Gr 3-4 AEs were seen in 40%, 16% and 57% of pts in the atezo + bev, atezo and sun arms, respectively. AEs leading to death occurred in 3%, 2% and 2% of pts, respectively. Conclusion: Atezo + bev resulted in encouraging antitumor activity in the PD-L1+ subgroup of first-line RCC pts. Atezo + bev safety is consistent with the known profile of atezo and bev individually. The clinical benefit of atezo + bev vs sun will be evaluated in the ongoing Ph III study IMmotion151 (NCT02420821). Clinical trial information: NCT01984242. [Table: see text]

Abstract Purpose: Little is known about the association between myeloid-derived suppressor cell (MDSC) subsets and various chemokines in patients with renal cell carcinoma (RCC) or the factors that draw MDSC into tumor parenchyma. Experimental Design: We analyzed polymorphonuclear MDSC (PMN-MDSC), monocytic MDSC (M-MDSC), and immature MDSC (I-MDSC) from the parenchyma and peripheral blood of 48 patients with RCC, isolated at nephrectomy. We analyzed levels of IL1β, IL8, CXCL5, Mip-1α, MCP-1, and Rantes. Furthermore, we performed experiments in a Renca murine model to assess therapeutic synergy between CXCR2 and anti-PD1 and to elucidate the impact of IL1β blockade on MDSC. Results: Parenchymal PMN-MDSC have a positive correlation with IL1β, IL8, CXCL5, and Mip-1α, and I-MDSC correlate with IL8 and CXCL5. Furthermore, peripheral PMN-MDSC correlate with tumor grade. Given that PMN-MDSC express CXCR2 and parenchymal PMN-MDSC correlated with IL8 and CXCL5, we assessed the response of CXCR2 blockade with or without anti-PD1. Combination therapy reduced tumor weight and enhanced CD4+ and CD8+ T-cell infiltration. In addition, anti-IL1β decreased PMN-MDSC and M-MDSC in the periphery, PMN-MDSC in the tumor, and peripheral CXCL5 and KC. Anti-IL1β also delayed tumor growth. Conclusions: Parenchymal PMN-MDSC have a positive correlation with IL1β, IL8, CXCL5, and Mip-1α, suggesting they may attract PMN-MDSC into the tumor. Peripheral PMN-MDSC correlate with tumor grade, suggesting prognostic significance. Anti-CXCR2 and anti-PD1 synergized to reduce tumor weight and enhanced CD4+ and CD8+ T-cell infiltration in a Renca murine model, suggesting that CXCR2+ PMN-MDSC are important in reducing activity of anti-PD1 antibody. Finally, anti-IL1β decreases MDSC and delayed tumor growth, suggesting a potential target for MDSC inhibition. Clin Cancer Res; 23(9); 2346–55. ©2016 AACR.

Abstract Purpose: We examined the hypothesis that mutations in mTOR pathway genes are associated with response to rapalogs in metastatic renal cell carcinoma (mRCC). Experimental Design: We studied a cohort of mRCC patients who were treated with mTOR inhibitors with distinct clinical outcomes. Tumor DNA from 79 subjects was successfully analyzed for mutations using targeted next-generation sequencing of 560 cancer genes. Responders were defined as those with partial response (PR) by RECIST v1.0 or stable disease with any tumor shrinkage for 6 months or longer. Nonresponders were defined as those with disease progression during the first 3 months of therapy. Fisher exact test assessed the association between mutation status in mTOR pathway genes and treatment response. Results: Mutations in MTOR, TSC1, or TSC2 were more common in responders, 12 (28%) of 43, than nonresponders, 4 (11%) of 36 (P = 0.06). Mutations in TSC1 or TSC2 alone were also more common in responders, 9 (21%), than nonresponders, 2(6%), (P = 0.05). Furthermore, 5 (42%) of 12 subjects with PR had mutations in MTOR, TSC1, or TSC2 compared with 4 (11%) of 36 nonresponders (P = 0.03). Eight additional non-mTOR pathway genes were found to be mutated in at least 4 of 79 tumors (5%); none were associated positively with response. Conclusions: In this cohort of mRCC patients, mutations in MTOR, TSC1, or TSC2 were more common in patients who experienced clinical benefit from rapalogs than in those who progressed. However, a substantial fraction of responders (24 of 43, 56%) had no mTOR pathway mutation identified. Clin Cancer Res; 22(10); 2445–52. ©2016 AACR. See related commentary by Voss and Hsieh, p. 2320