Citations

Abstract Background Tumor heterogeneity underlies resistance and disease progression in glioblastoma (GBM), and tumors most commonly recur adjacent to the surgical resection margins in contrast non-enhancing (NE) regions. To date, no targeted therapies have meaningfully altered overall patient survival in the up-front setting. The aim of this study was to characterize intratumoral heterogeneity in recurrent GBM using bulk samples from primary resection and recurrent samples taken from contrast-enhancing (EN) and contrast NE regions. Methods Whole exome and RNA sequencing were performed on matched bulk primary and multiple recurrent EN and NE tumor samples from 16 GBM patients who received standard of care treatment alone or in combination with investigational clinical trial regimens. Results Private mutations emerge across multi-region sampling in recurrent tumors. Genomic clonal analysis revealed increased enrichment in gene alterations regulating the G2M checkpoint, Kras signaling, Wnt signaling, and DNA repair in recurrent disease. Subsequent functional studies identified augmented PI3K/AKT transcriptional and protein activity throughout progression, validated by phospho-protein levels. Moreover, a mesenchymal transcriptional signature was observed in recurrent EN regions, which differed from the proneural signature in recurrent NE regions. Conclusions Subclonal populations observed within bulk resected primary GBMs transcriptionally evolve across tumor recurrence (EN and NE regions) and exhibit aberrant gene expression of common signaling pathways that persist despite standard or targeted therapy. Our findings provide evidence that there are both adaptive and clonally mediated dependencies of GBM on key pathways, such as the PI3K/AKT axis, for survival across recurrences.

11511 Background: In the open-label multicenter phase II study, A091401, nivolumab (N) and nivolumab+ipilimumab (N+I) demonstrated a confirmed response rate (RR) of 5% and 16%, respectively in patients (pts) with advanced sarcoma (D’Angelo SP et al Lancet Oncology 2018). Responses occurred in undifferentiated pleomorphic sarcoma (UPS), myxofibrosarcoma, leiomyosarcoma, sarcoma not otherwise specified and alveolar soft part sarcoma. Here, we report efficacy of N and N+I, in each of 3 expansion cohorts [gastrointestinal stromal tumor (GIST), UPS and dedifferentiated liposarcoma (DDLS)]. Methods: Pts refractory to ≥ 1 regimen(s) were randomized (non-comparative) to receive either N [N (3 mg/kg q2W)] or N+I [N (3 mg/kg Q3W x4, then Q2W) plus I (3 mg/kg q3W x4)]. The primary endpoint was 6 month confirmed RR (RECIST v1.1). For UPS and DDLS, 2 confirmed responses in the 1st 12 evaluable pts was needed (85% power, 1-sided alpha=0.15, 5 v 25% RR). For GIST, a confirmed response in the 1st 9 evaluable pts expanded enrollment to 24 (80% power, 1-sided alpha=0.10, 5 v 20% RR). Other endpoints: adverse events (AEs, TRAEs), progression-free and overall survival (PFS, OS), and correlatives. Results: See table. Clinical trial information: NCT02500797 . Conclusions: Neither N or N+I lead to confirmed responses in GIST. In DDLS and UPS, the primary response endpoint was met with N+I but not with N alone (RR 14% for N+I vs. 7% and 8% for N alone). For the GIST cohort TRAE was higher with N+I, holding enrollment as required per protocol. There remains a pressing need to determine genomic and clinical biomarkers of response, resistance and toxicity. Correlative analyses (whole exome sequencing, multiplex IHC and RNAseq) are in progress. Support: U10CA180821, U10CA180882; ClinicalTrials.gov Identifier: NCT02500797. [Table: see text]

8052 Background: A significant proportion of patients with FL experience an early relapse and a subsequent poor outcome. While several prognostic indices have been developed, none were designed to predict early failure. Recently, we established that lack of intrafollicular CD4+ T-cell expression predicted risk of early failure, and integrating this microenvironment biomarker with the Follicular Lymphoma International Prognostic Index, termed BioFLIPI, further improved identification of FL patients at risk of early failure ( Blood 2019;134(suppl1):121). However, the microenvironment may be influenced by the genetic composition of tumor. We investigated whether the CD4 biomarker and BioFLIPI were impacted by genetic features of the tumor as assessed by a 23-gene expression prognostic score ( Lancet Oncol 2018;19:549-61). Methods: Of the 186 cases with FL grade 1-3A treated with immunochemotherapy (IC) in our prior study, 152 had digital expression quantification of 23 selected genes (23-GEP score), which used RNA from formalin-fixed, paraffin-embedded samples. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS at 24 months. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals from logistic regression models. We also used Cox regression to assess associations with continuous EFS and overall survival (OS). Results: 28% of patients failed to achieve EFS24. Lack of CD4+ intrafollicular expression (38% of patients, OR = 2.33, p = 0.024) and high risk 23-GEP score (26% of patients, OR = 3.52, p = 0.001) each predicted early failure, and in a multivariable model that included FLIPI, both CD4+ (OR = 2.26, p = 0.046) and 23-GEP score (OR = 2.26, p = 0.0.057) remained predictors. Similarly, BioFLIPI modeled as a continuous score (1-4, OR per one point increase = 2.31, p < 0.001) predicted early failure, and the association remained (OR = 2.14, p < 0.001) when the high risk 23-GEP score (OR = 2.79, p = 0.013) was included in the model. When stratified on 23-GEP score, BioFLIPI was a stronger predictor of early failure in low risk (74%, OR = 2.51, p = 0.002) relative to high risk (26%, OR = 1.55, p = 0.27) patients. Similar patterns were observed for EFS and OS. Conclusions: CD4+ T-cell infiltrate and tumor gene expression appear to be independently predictive of early failure in newly diagnosed FL patients treated with IC. Future studies should integrate and validate these measures.

Abstract Background: Combination treatments of CDK4 & 6 inhibitors (CDK4 & 6i) with endocrine therapy (ET) have improved outcomes in patients with HR-positive (HR+), HER2-negative (HER2-) advanced breast cancer (ABC). Aside from estrogen receptor positivity (ER+), predictive biomarkers of clinical benefit from CDK4 & 6i in combination with ET remain elusive. We assessed clinical outcomes by genomic alterations detected in baseline circulating tumor DNA (ctDNA) from patients treated with abemaciclib plus a nonsteroidal aromatase inhibitor (NSAI) versus placebo plus NSAI in the Phase 3 study MONARCH 3 (NCT02246621). Methods: MONARCH 3 randomized 493 postmenopausal women with HR+, HER2- ABC who had no prior systemic therapy in the advanced setting to treatment with abemaciclib plus NSAI or placebo plus NSAI. Biomarker analysis of baseline ctDNA was an exploratory objective; plasma was analyzed by the Guardant360 next-generation sequencing (NGS)-based assay to identify potential tumor-related (i.e., somatic) genomic alterations including point mutations (SNV), indels, amplifications, and fusions in over 70 cancer-related genes. Genomic alterations detected in baseline ctDNA were associated with clinical outcomes including progression-free survival (PFS) and objective response rate (ORR). Results: Baseline ctDNA results were available for 295 patients (201 abemaciclib; 94 placebo) with 83% of patients harboring one or more detectable genomic alterations. Commonly altered genes of interest (% frequency) detected in baseline ctDNA included: PIK3CA (38%); TP53 (26%); EGFR (15%); FGFR1 (12%); NF1 (12%); MYC (9%); CCND1 (9%); ESR1 (5%).Overall, patients treated with placebo plus NSAI who harbored detectable ctDNA genomic alterations had shorter median PFS compared to patients without detectable genomic alterations, as shown in the Table (14.9 months [95% CI: 11.0-23.1] vs 19.2 months [95% CI 9.4-NR]). Moreover, genomic alterations in EGFR, FGFR1, MYC, and CCND1 were associated with median PFS &lt;12 months with placebo plus NSAI. However, consistent with the intent-to-treat population, the addition of abemaciclib to NSAI benefitted these genomic subgroups, regardless of ctDNA gene alterations. Additional genomic results, including alterations in cell cycle-associated genes, will be presented. Conclusions: In this exploratory subgroup analysis, the presence of detectable ctDNA genomic alterations was associated with shorter PFS with placebo plus NSAI. Consistently, abemaciclib added to NSAI improved outcomes for genomically identified subgroups. In contrast to prior studies, there was no subgroup (e.g., patients harboring FGFR1 alterations) that did not derive benefit from abemaciclib, supporting the efficacy of abemaciclib, including in difficult to treat tumors. These findings are hypothesis-generating and warrant further investigations in clinical studies of CDK4 & 6i in combination with ET. TableAbemaciclib + NSAIPlacebo + NSAIEvents, n/NMedian PFS (95% CI)Events, n/NMedian PFS (95% CI)Hazard Ratio (95% CI)OVERALLITT population170 / 32828.2 (23.7 - 33.9)123 / 16514.8 (11.2 - 19.2)0.52 (0.42 - 0.66)TR population93 / 20138.7 (31.1 - NR)71 / 9416.5 (11.7 - 23.1)0.45 (0.33 - 0.61)Any Gene AlterationDetected83 / 16634.1 (27.2 - 40.0)62 / 7914.9 (11.0 - 23.1)0.47 (0.34 - 0.66)Not detected10 / 35NR (36.4 - NR)9 / 1519.2 (9.40 - NR)0.31 (0.13 - 0.78)TP53Detected29 / 5327.0 (14.1 - NR)19 / 2315.4 (5.7 - 30.4)0.53 (0.30 - 0.95)Not detected64 / 14839.9 (34.1 - NR)52 / 7117.5 (11.7 - 24.2)0.42 (0.29 - 0.60)EGFRDetected10 / 26NR (32.4 - NR)16 / 1710.9 (2.1 - 24.7)0.20 (0.09 - 0.47)Not detected83 / 17536.4 (27.7 - NR)55 / 7717.6 (14.6 - 25.5)0.52 (0.37 - 0.73)FGFR1Detected15 / 2532.8 (10.1 - NR)10 / 117.6 (1.9 - 15.4)0.37 (0.16 - 0.85)Not detected78 / 17639.9 (31.1 - NR)61 / 8319.2 (14.6 - 26.5)0.45 (0.32 - 0.63)NF1Detected10 / 2435.9 (27 - NR)8 / 1014.6 (1.6 - 33.4)0.33 (0.13 - 0.84)Not detected83 / 17738.7 (30.8 - NR)63 / 8417.5 (11.7 - 24.2)0.47 (0.33 - 0.65)MYCDetected11 / 1720.1 (5.5 - NR)9 / 106.5 (1.2 - 15.7)0.33 (0.13 - 0.86)Not detected82 / 18438.9 (32.9 - NR)62 / 8419.2 (14.6 - 26.5)0.45 (0.32 - 0.63)CCND1Detected8 / 1632.8 (5.5 - NR)10 / 107.2 (3.6 - 9.0)0.28 (0.10 - 0.77)Not detected85 / 18538.7 (31.1 - NR)61 / 8417.6 (14.6 - 25.5)0.48 (0.34 - 0.67)n = number of patients with events; N = total number of patients in the corresponding population and treatment arm; NR = not reached. TR population consists of patients in the ITT population from whom a valid baseline ctDNA result has been obtained. Citation Format: Matthew P Goetz, J. Thaddeus Beck, Mario Campone, Sara Hurvitz, Seock-Ah Im, Stephen Johnston, Antonio Llombart-Cussac, Miguel Martin, Joohyuk Sohn, Masakazu Toi, Lacey M Litchfield, Hillary T Graham, Hong Wang, Sameera R Wijayawardana, Valerie M Jansen, Angelo Di Leo. Efficacy of abemaciclib based on genomic alterations detected in baseline circulating tumor DNA from the MONARCH 3 study of abemaciclib plus nonsteroidal aromatase inhibitor [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD2-06.

Abstract Background: Abemaciclib plus nonsteroidal aromatase inhibitors (AI) demonstrated efficacy with a tolerable safety profile as initial treatment for postmenopausal women with HR+, HER2− advanced breast cancer in the MONARCH 3 trial. In the intent-to-treat (ITT) population, the updated progression-free survival (PFS) with 12 months of additional follow-up was 28.2 versus 14.8 months (HR [95% CI]: 0.525 [0.415, 0.665]; p&lt;.0001) in the abemaciclib plus AI versus placebo plus AI arms, respectively. Here we report data from the additional 12 months of follow-up on tumor response for the ITT population and exploratory subgroups previously identified as significantly prognostic (Di Leo et al. 2018). Methods: In MONARCH 3, a randomized, double-blind, phase III trial, 493 postmenopausal women with HR+, HER2− advanced breast cancer and no prior systemic therapy in the advanced setting, received an AI (anastrozole or letrozole) plus abemaciclib or placebo. From the October 31, 2018 cutoff, secondary endpoints including objective response rate ([ORR], complete response [CR] + partial response [PR]), time to response (TTR), and duration of response ([DoR], time from a confirmed CR or PR until disease progression or death) were assessed in the ITT population and exploratory subgroups previously reported as significantly prognostic. Waterfall plots will be used to illustrate the magnitude of change in tumor size for each subgroup (to be presented). Results: In patients with measurable disease, the ORR was 62.5% (95% CI: 56.7, 68.2) in the abemaciclib plus AI arm and 44.7% (95% CI: 36.2, 53.2) in the placebo plus AI arm (p≤.001). Additional follow-up confirmed that all prognostic subgroups received benefit from the addition of abemaciclib to AI, consistent with the ITT population, as evidenced by change in ORR, with the largest effects observed in subgroups of patients with liver metastases, progesterone receptor-negative tumors, high grade tumors, or treatment-free interval of &lt;36 months (Table). Median TTR was similar between the two treatment arms (abemaciclib plus AI: 3.62 months; placebo plus AI: 3.65 months), and this was generally consistent across subgroups. Responses were more durable in the abemaciclib plus AI arm (median DoR: 32.71 months; 95% CI: 25.74, -) versus the placebo plus AI arm (median DoR: 17.49 months; 95% CI: 11.61, 22.19), including in poor prognostic subgroups. Conclusions: Extended follow-up in the MONARCH 3 trial further confirmed that the addition of abemaciclib to AI is associated with durable tumor responses, including those in patients with clinically poor prognostic characteristics. References: Di Leo A et al. NPJ Breast Cancer. 2018;4:41. Clinical trial information: NCT02246621 This study was funded by Eli Lilly and Company Table: Objective Response RateAbemaciclib + AIPlacebo + AIORR ChangeNn (%)Nn (%)%Measurable disease population269168 (62.5)13259 (44.7)17.8Liver metastasesYes4727 (57.45)306 (20.00)37.45No222141 (63.51)10253 (51.96)11.55Progesterone receptor statusNegative6137 (60.66)298 (27.59)33.07Positive206130 (63.11)10351 (49.51)13.60Treatment-free interval&lt;36 months3117 (54.84)225 (22.73)32.11≥36 months7442 (56.76)3216 (50.00)6.76Tumor gradeHigh5739 (68.42)2811 (39.29)29.13Intermediate/ Low13992 (66.19)7535 (46.67)19.52ECOG PS111275 (66.96)5022 (44.00)22.96015793 (59.24)8237 (45.12)14.12Bone-only diseaseNo256162 (63.28)12253 (43.44)19.84YesNA*NA*NA*NA*NA*N= total number of patients in each subgroup; n= number of responder patients; NA=not applicable*Response rate not reported for bone-only disease because majority of lesions were not measurable Citation Format: Joyce O’Shaughnessy, Stephen Johnston, Miguel Martin, Jens Huober, Masakazu Toi, Valerie AnneMarie Andre, Holly Rene Martin, Molly Catherine Hardebeck, Angelo Di Leo, Matthew Philip Goetz. Updated subgroup tumor response of abemaciclib plus aromatase inhibitor for hormone receptor positive (HR+), HER2 negative advanced breast cancer (MONARCH 3) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-19-09.

TPS10592 Background: Angiosarcomas (ASs) are rare, aggressive, malignant endothelial cell tumors that constitute 1% to 2% of all soft tissue sarcomas with 5-year survival of 35%. Cytotoxic agents have demonstrated response rates of about 15%. Gene-array data from MSKCC has revealed up-regulation of endothelial associated genes, including active members of the angiopoietin system e.g., Tie2 and Angiopoietin 2 (Ang2). Trebananib (AMG386) is a novel agent that targets Ang1 and Ang2. We hypothesize that inhibition of angiopoietin 2 with trebananib will be an effective therapy in patients (pts) with AS. Methods: This CTEP sponsored, investigator initiated, multi-center phase II study uses trebananib, in pts with AS having metastatic/unresectable AS and conducted through the Alliance for Clinical Trials in Oncology (Alliance A091103). Pts must have adequate performance status, organ function, and measurable disease (RECIST v1.1). Patients are treated with trebananib 30mg/kg d1, 8, 15, and 22 every 28 days until either progressive disease or unacceptable toxicity. The primary endpoint is objective overall response rate (ORR) via RECIST v1.1. Secondary endpoints include the evaluation of progression-free survival (PFS) and overall survival (OS). Translational objectives include correlation of ORR, PFS and OS with i. baseline and post-treatment expression of Ang2 and Tie2 by immunohistochemistry (IHC), ii. serum levels of Ang1 and Ang2, iii. baseline and post-treatment phospho-receptor tyrosine kinase (p-RTK) status of Tie2, VEGFR-2, PI3K, MEK and MAPK, and iv. mutational status of VEGFR-2 and amplification of MYC/FLT4. A Simon Optimal two-stage phase II study design is used and one confirmed response in the first 12 pts expands enrollment to 25. Pre- and post-treatment biopsies will be performed in up to 10 pts treated at MSKCC. Enrollment began in July 2012. As of January 2013, 7 pts have been recruited. Clinicaltrials.gov#: NCT01623869. Funded by the Alliance Clinical Trials in Oncology. NIH Grant CA-25224. Clinical trial information: NCT01623869.

8095 Background: Multiple myeloma (MM) remains incurable and invariably relapses after initial therapy. There is no uniform approach to management of relapsed MM and is dictated by type of initial therapy and the response seen, and availability of new drugs. The outcomes associated with current approaches have not been systematically examined. Methods: We enrolled 383 patients (pts) with myeloma who had a documented relapse between Jan 2007 and June 2010, including 220 from Europe, 106 from Asia, and 31 from South America and 26 from North America. Time Zero (T0) was defined as date of treatment after first relapse. Results: Across the study, 61% were male and 49% were over 65 years at T0. ISS stage distribution at diagnosis included 26, 40 and 33% of patients in stages 1, 2 and 3, respectively. Among regimens used at first relapse; bortezomib (Btz) containing regimens were most common (54%), followed by lenalidomide (Len, 25%) and cyclophosphamide (21%). The overall response rate (>=PR) to first therapy after relapse was 58% including 14% with a CR. There was a progressive decrease in the response rates with successive regimen; 45%, 30% and 15% for regimens 2, 3 and 4 respectively. Len use was considerably lower in the Asian cohort, while Btz use was comparable across the regions. The median PFS from T0 was 13 mos and OS was 35 mos, for the entire cohort. The PFS to first salvage regimen was similar across the regions, while OS was shorter for the Asian and South American cohorts. In a univariate analysis, ISS stage 3, presence of cytogenetic abnormalities, history of plasma cell leukemia or extramedullary disease (EMD), bone marrow PC% > 33% and presence of renal insufficiency were all associated with a shorter PFS as well as shorter OS (P<=0.01). In a multivariate model, ISS stage 3 and presence of EMD were most associated with short OS. Conclusions: Median PFS for current second line regimens, typically containing Btz or Len appear to be 1 yr with an OS from first relapse of about 3 yrs. The OS from first relapse is nearly double that seen in a cohort of patients in first relapse prior to introduction of new drugs. Clear-cut regional differences can be seen in terms of patterns of drug use and likely reflect drug availability and healthcare costs.

11015 Background: Immune checkpoint inhibitors have demonstrated activity in multiple tumor types but their activity in soft tissue sarcomas remains limited. In the multicenter phase II study, SARC028, the anti-PD-1 antibody, P demonstrated objective responses that were largely restricted to UPS and LPS subtypes. We now report outcomes from 2 expansion cohorts of SARC 028 in advanced UPS and LPS. Methods: To further confirm the clinical activity of P in UPS and LPS, we enrolled an additional 30 pts in each of 2 expansion cohorts for a total of 40 UPS and 40 LPS pts. Primary endpoint was investigator-assessed response by RECIST v1.1. Secondary endpoints were safety, progression-free survival (PFS), 12-week PFS rate, and overall survival (OS). An ORR of 25% was considered clinically meaningful and < 10% was considered to show lack of efficacy. P was to be considered a success if 8 or more of 40 enrolled patients had a PR or better (1-sided α = 0.042, 82% power). Pts age ≥18 with advanced, refractory UPS or LPS received 200 mg of P IV every 3 weeks until progression or unacceptable toxicity. Results: Preliminary results from the first 10 pts in each of the UPS and LPS cohorts have been reported. We now present summary data after enrolling an additional 30 pts in each cohort. The ORR in the UPS cohort was 23% (9/40), with an additional 5/30 PRs observed in the expansion cohort (total 2 CRs, 7 PRs). In the LPS cohort, the ORR was 10% (4/39 evaluable pts), with an additional 2/30 PRs observed (total 4 PRs). Median PFS for the UPS group was 3 months [95% CI: 2, 5] and 2 months [95% CI: 2, 4] for the LPS group. 12-week PFS rate was 50% in UPS [95% CI: 35, 65] and 44% in LPS [95% CI: 28, 60]. The UPS group had a median OS of 12 months [95% CI: 7, 34] and 13 months [95% CI: 8, NR] for the LPS group. P was well tolerated with no unexpected toxicities. Conclusions: The UPS cohort achieved its primary endpoint, however the activity of P in UPS deserves further evaluation in a randomized study. The activity of P was not confirmed in the LPS cohort. Ongoing biomarker analyses may direct better patient selection and guide future combination strategies. Clinical trial information: NCT02301039.

Introduction. The VES-13 is a simple, self-reported, function based tool originally developed to screen community-dwelling populations to identify persons age 65 years and older at risk of death and functional decline, including death in the next 12 months. The VES-13 items include patient's age, self-rated overall health status, functional limitations in physical activity, and functional disabilities in more complex activities of daily living. As part of the Lymphoma Epidemiology of Outcomes (LEO) cohort study, we collected self-reported VES-13 data at study enrollment on all participants regardless of age, and here we report on the prevalence of vulnerable status defined by the VES-13 and its association with 1-year mortality, overall, stratified at age 65 years, and in the subset of diffuse large B-cell lymphoma (DLBCL) treated with immunochemotherapy. Methods. From 7/2015 to 6/2017, 3253 participants with NHL were enrolled within 6 months of their diagnosis into the LEO cohort. 2004 were evaluable on VES-13, and 1183 (59%) completed it before the initiation of therapy. VES-13 scores range from a minimum of 0 (low risk for decline) to a maximum of 10 (greatest risk for decline), and a score ≥3 was classified as vulnerable. Clinical, pathology and treatment data were abstracted using a standard protocol, and participants were contacted every 6 months for the first three years and then annually thereafter to identify outcomes. Medical records were reviewed by LEO clinicians to classify cause of death according to a standard protocol. Therapy was determined by the treating physician, and this was independent of knowledge of the VES-13 score. The association of VES-13 with 1-year mortality from date of diagnosis was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression models, which also provided model c-statistics. Results. The median age of the 2004 participants in this analysis was 62 years (range 18-94); 57% were male; 54% were ≥65 years; and 28% had a normal body mass index (BMI), 1% were underweight, 35% were overweight, and 36% were obese. Clinically, 59% of participants had an aggressive subtype, 65% were stage III-IV, 24% had B-symptoms, and 11% had a performance status (PS) of ≥2. Overall, 28% of participants were classified as vulnerable (95% CI 26%-30%), with a higher prevalence among those completing the survey after initiation of therapy (38%, 95% 34%-41%) versus before initiation of therapy (22%, 95% CI 20%-24%), and a higher prevalence for those ≥65 years (32%, 95% CI 29%-34%) versus &lt;65 (25%, 95% CI 22%-28%). During the first year of follow-up, there were 147 deaths, with 61% due to lymphoma, 14% therapy-related, 12% due to another malignancy, 3% other causes, and 10% unknown. Significant univariate predictors of 1-year mortality included vulnerable status, older age, PS 2+, stage III-IV, aggressive subtype, lower quality of life score, lower BMI, and 1 or more comorbidities, and were included the backward stepwise modeling. Vulnerable status (OR=3.26, 95% CI 2.15-4.96) was retained in the final model that also included age, PS, stage, and NHL subtype (Table), with a model c-statistic of 0.81. As shown in the Table, these associations were similar whether VES-13 was assessed before (c-statistic 0.77) or after (c-statistic 0.81) initiation of therapy. After adjustment for age, PS, stage and NHL subtype, vulnerable status predicted 1-year mortality in participants ≥65 years (OR=1.96, 95% CI 1.15-3.36; model c-statistic 0.80) as well as &lt;65 years (OR=6.38, 95% CI 3.07-13.3; model c-statistic 0.83). Finally, in the DLBCL patients treated with immunochemotherapy (N=521), vulnerable status (38% of patients) also predicted 1-year mortality (OR=2.11, 95% CI 1.05-4.26) after adjusting for age, PS and stage (model c-statistic 0.78). Conclusion. Based on a self-administered VES-13, 28% of newly diagnosed NHL patients in the LEO cohort were classified as vulnerable, including 25% of those &lt;65 years, and vulnerable status was a strong predictor of 1-year-mortality, even in the subset of DLBCL patients treated with presumed curative intent. These results highlight the potential value of screening for vulnerable status overall and across a wider age spectrum in order to optimize patient management and reduce early mortality. Disclosures Martin: Karyopharm: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Teneobio: Consultancy. Casulo:Gilead: Honoraria, Other: Travel, accommodation, expenses; Celgene: Research Funding; Roche: Other: Travel, accommodation, expenses. Cohen:Takeda Pharmaceuticals North America, Inc.: Research Funding; Bristol-Meyers Squibb Company: Research Funding; Hutchison: Research Funding; Astra Zeneca: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; LAM Therapeutics: Research Funding; UNUM: Research Funding; Gilead/Kite: Consultancy; Janssen Pharmaceuticals: Consultancy; Seattle Genetics, Inc.: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Friedberg:Acerta: Other: Data & Safety Monitoring Committee; Bayer: Honoraria, Other: Data & Safety Monitoring Committee. Kahl:Seattle Genetics: Consultancy; ADC Therapeutics: Consultancy, Research Funding; BeiGene: Consultancy; TG Therapeutics: Consultancy. Lossos:Janssen Scientific: Membership on an entity's Board of Directors or advisory committees; NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Maurer:Nanostring: Research Funding; Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Nastoupil:Spectrum: Honoraria; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Honoraria; TG Therapeutics: Honoraria, Research Funding; Bayer: Honoraria; Celgene: Honoraria, Research Funding. Rutherford:Heron: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Juno Therapeutics Inc: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees. Flowers:National Cancer Institute: Research Funding; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Bayer: Consultancy; Spectrum: Consultancy; Karyopharm: Consultancy; Acerta: Research Funding; TG Therapeutics: Research Funding; Millenium/Takeda: Research Funding; AstraZeneca: Consultancy; Optimum Rx: Consultancy; V Foundation: Research Funding; Celgene: Consultancy, Research Funding; Burroughs Wellcome Fund: Research Funding; Denovo Biopharma: Consultancy; Gilead: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding.

Background: Follicular lymphoma (FL) is the most frequent indolent lymphoma. While immunochemotherapy (IC) treated FL patients who achieve event-free status at 24 months after diagnosis (EFS24) have the subsequent life expectancy of the general population, those who fail to achieve EFS24 have aggressive disease with poor outcomes. Similarly, in FL patients initially observed or treated with rituximab monotherapy, EFS at 12 months (EFS12) is a strong predictor of subsequent outcome. Thus, an unmet patient need is to predict at diagnosis those at greatest risk of early failure in order to identify better treatments. The lymphoma microenvironment may be a key determinant of early failure. Therefore, we aimed to improve risk stratification of newly diagnosed FL patients by using a discovery and validation study design to identify microenvironment determinants of early failure and then integrate them into the Follicular Lymphoma International Prognostic Index (FLIPI). Patients and Methods: We evaluated 496 patients with FL grade 1-3A who were prospectively enrolled into the Molecular Epidemiology Resource (MER) cohort from 2002-2012. In the discovery set of patients (N=166), we stained tissue microarrays for CD4, CD8, FOXP3, CD32b, CD14, CD68, CD70, SIRPa, TIM3, PD-1 and PDL1. Immunohistochemical staining was determined both within and between malignant follicles. Stains were scored to the nearest decile as the percentages of cells positive. Scores for each stain were dichotomized as 0% vs &gt;0% except for CD32b (&lt;20% vs ≥20%), CD70 (&lt;10% vs ≥10%), and SIRPa (&lt;30% vs ≥30%). EFS was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS24 for patients treated with IC at diagnosis and failing to achieve EFS12 for patients who were observed or received other treatments. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals (CI) from logistic regression models. Markers at P≤0.15 were brought forward for validation in a separate set of patients from the MER cohort (N=330), with P&lt;0.005 being declared statistically significant after a Bonferroni correction (0.05/10 tests). In the combined dataset, we also used Cox regression to assess associations with continuous EFS and overall survival (OS). Mass cytometry (CyTOF) was then performed on a subset of patients (N=51) to comprehensively characterize the phenotype of intratumoral immune cells associated with outcome. Results: In the discovery set, CD4, CD8, FOXP3, PD-1, and SIRPα were associated with risk of early failure at P≤0.15 and were subsequently evaluated in the validation set. Only CD4 expression inside the follicle validated. Specifically, lack of CD4 expression inside the follicle was associated with higher risk of early failure in the discovery (OR=1.77, P=0.15) and validation (OR=2.59, P=0.001) sets, with a pooled OR=2.29 (95% CI 1.47-3.58; P&lt;0.001). CyTOF analysis characterized the favorable CD4+ T-cell population associated with achieving EFS12/24 as activated effector memory T cells lacking exhaustion markers (Figure 1A). Adjustment of CD4 expression for FLIPI in the combined dataset did not weaken this association (OR=2.38, 95% CI 1.51-3.79), and lack of CD4 expression was prognostic within each FLIPI group 0-1 (OR=2.65; p=0.036), 2 (OR=2.07; P=0.064) and 3-5 (OR=2.51; P=0.015). We next established a bio-clinical risk model (termed BioFLIPI) that combined CD4 intrafollicular expression and FLIPI into a 1-4 scale, where any expression of intrafollicular CD4 moved a patient up one FLIPI risk group, adding a fourth (high) risk group for FLIPI 3-5 and any CD4 inside expression. The BioFLIPI score was strongly associated with risk of early failure: compared to a BioFLIPI score of 1 (24% of patients), patients with a score of 2 (35% of patients; OR=2.17, 95% CI 1.08-4.69), 3 (31% of patients; OR=3.53, 95% CI 1.78-7.54), and 4 (10% of patients; OR=8.92, 95% CI 4.00-21.1) had increasing risk of early failure. The c-statistic for BioFLIPI was 0.665, which was higher than that for FLIPI alone (0.636). BioFLIPI was also strongly predictive of continuous EFS [Figure 1B], OS [Figure 1C], and for EFS and EFS24 when restricted to IC-treated patients [Figure 1D]. Conclusion: Integration of CD4 expression inside the follicle at diagnosis with FLIPI improves identification of FL patients at risk for early failure. Disclosures Novak: Celgene Coorperation: Research Funding. Nowakowski:Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Ansell:LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment.