Central Nervous System Involvement By Mantle Cell Lymphoma

Background: While extranodal involvement by mantle cell lymphoma (MCL) is relatively common, involvement of the central nervous system (CNS) is rare (<5% of cases), with limited treatment options. We report the outcomes of 36 patients (pts) with CNS involvement compared to 72 matched control MCL pts without CNS involvement. Methods: MCL pts with CNS involvement seen at Mayo Clinic between 1/1995-9/2020 were identified using the Mayo Data Explorer tool. CNS involvement was defined by tissue biopsy confirmed CNS MCL, CSF analysis demonstrating lymphoma cells, and/or neuroimaging findings compatible with CNS involvement. A 2:1 control group of MCL pts without CNS involvement, matched by age (+/- 2 years) and year of diagnosis (+/- 1 year), was selected among all MCL cases. Medical records were reviewed for baseline characteristics, treatment modalities, and outcomes. Kaplan-Meier method was used for time to event analysis. Wilcoxon test was used to compare continuous variables and Chi square test was used for categorical variables. Results: Out of 1,753 pts with MCL, 36 (2%) had evidence of CNS involvement, including 4 pts with CNS involvement at initial MCL diagnosis. Baseline characteristics of pts with CNS involvement (CNS MCL group) and those without CNS involvement (control group) are shown in Table 1. At MCL diagnosis, non-CNS extranodal involvement was seen in 30 (83%) pts in the CNS MCL group (24 pts with 1 site and 6 pts with ≥ 2 sites), with bone marrow being the most common extranodal site of involvement (n=24, 67%). For the control group, 54 (75%) pts had extranodal involvement (44 pts with 1 site and 10 pts with ≥ 2 sites), and bone marrow was also the most common extranodal site of involvement (n=50, 69%). Notably, advanced stage disease (stage 3-4) was more commonly seen in the CNS MCL group (n=32, 97%) than in the control group (n=59, 83%) (p=0.04) at MCL diagnosis. Blastoid variant was present in a higher proportion of pts in the CNS MCL group (n=11, 31%) compared to the control group (n=8, 11%) (p=0.02). The CNS MCL group also presented with a higher median serum LDH at diagnosis (239 U/L [range 153-1901] vs. 187 U/L [range 124-588], p=0.02), and higher Ki-67 (40% [range 15-100] vs. 30% [range 10-90], p=0.04) compared to the control group. The most common frontline treatment regimen was anthracycline-based therapies (i.e. R-CHOP, Nordic regimen, R-hyperCVAD) for both groups (58% in CNS MCL group and 56% in control group). 14 (39%) pts in the CNS MCL group underwent autologous stem cell transplant in CR1 vs. 31 pts (43%) in the control group. Similar use of rituximab maintenance was seen in both groups (31% in CNS MCL group and 25% in control group). Median total lines of therapy from initial MCL diagnosis was 3 (range 1-9) in CNS MCL group and 2 (range 1-9) in the control group. The median follow-up from MCL diagnosis was 134 months (95% CI:119-163) for the entire cohort. Median OS from MCL diagnosis was 50.3 months (95% CI: 20.9-71.1) for the CNS MCL group compared to 97.1 months (95% CI: 82.6-192.7; p=<0.001) for the control group (Figure 1). Median time from MCL diagnosis to CNS involvement was 25 months (range 0-167). Median OS from CNS involvement was 4.7 months (95% CI: 2.3-6.7). At last follow up, 31 (86%) pts were deceased from the CNS MCL group, compared to 38 (52%) pts in the control group. For the CNS MCL group, the causes of death were CNS lymphoma in 10 (32%) pts, systemic lymphoma in 9 (29%) pts, treatment-related complication in 7 (23%) pts, and other/unknown in 5 (16%) pts. For the control group, the causes of death were systemic lymphoma in 15 (39%) pts, treatment-related in 2 (5%) pts, and other/unknown in 21 (55%) pts. Conclusion: In pts with MCL, CNS involvement is associated with worse outcomes as evident by a shorter median OS from initial MCL diagnosis (50 months vs. 97 months). Involvement of the CNS by lymphoma is an important contributor for the shorter OS as suggested by the median OS of only 5 months from CNS involvement. Advanced stage, blastoid variant, elevated LDH, and elevated Ki67 at MCL diagnosis were features more commonly seen in the CNS MCL cohort. Validation of risk factors at initial MCL diagnosis associated with CNS involvement and exploring the role of CNS prophylaxis are important topics for further investigation. Figure 1 Figure 1. Disclosures Paludo: Karyopharm: Research Funding. Wang: Novartis: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; InnoCare: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding. Bennani: Purdue Pharma: Other: Advisory Board; Daichii Sankyo Inc: Other: Advisory Board; Kyowa Kirin: Other: Advisory Board; Vividion: Other: Advisory Board; Kymera: Other: Advisory Board; Verastem: Other: Advisory Board. Nowakowski: Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene, NanoString Technologies, MorphoSys: Research Funding. Witzig: Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding. Habermann: Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding.

Safety and Efficacy of Tenalisib Given in Combination with Romidepsin in Patients with Relapsed/Refractory T-Cell Lymphoma: Final Results from a Phase I/II Open Label Multi-Center Study

Background: Tenalisib (RP6530), a highly selective PI3K δ/γ and SIK3 inhibitor has shown promising activity as a single agent in T Cell lymphoma (TCL) and a differentiated safety profile (Huen A et al., Cancers,2020). In vitro studies in TCL cell lines showed synergistic activity when tenalisib was combined with romidepsin. A Phase I/II study of tenalisib in combination with romidepsin was designed to assess safety, pharmacokinetics, and efficacy in patients with relapsed/refractory (R/R) TCL peripheral (PTCL) and cutaneous T cell lymphoma (CTCL) (NCT03770000). Methods: This was a multi-center, open label study. We performed a Phase I, 3+3 dose escalation study to determine the MTD/recommended Phase II dose (RP2D), and a dose expansion study in both the subtypes separately (PTCL and CTCL). Patients received tenalisib at doses ranging from 400-800 mg BID (fasting), orally in combination with romidepsin at doses ranging from 12-14 mg/m 2, intravenously, given on Days 1,8 and 15 of a 28-day cycle. Results: Thirty-three patients (16 PTCL and 17 CTCL) who received more than 1 prior therapy were enrolled in the study; 9 in dose escalation and 24 in dose expansion. Of the 33 patients, 64% were refractory to their last therapy. The median number of prior therapies was 3. Most patients (67%) had stage III/IV disease at time of enrolment. No dose limiting toxicity (DLT) was reported during dose escalation; tenalisib 800 mg BID with romidepsin 14 mg/m 2 (given on Days 1, 8, and 15) was chosen as the RP2D. The most frequent treatment emergent adverse events (TEAEs) were nausea (All: 73% and ≥G3:0%), thrombocytopenia (All:57% and ≥G3:21%), fatigue (All: 54% and ≥G3:6%), AST elevation (All:33% and ≥G3:6%) ALT elevation (All:27% and ≥G3:18%), neutropenia (All: 27% and ≥G3:15%), vomiting (All:27% and ≥G3:0%), decreased appetite (All: 27% and ≥G3:0%). There were no unexpected TEAEs. Among CTCL patients, five related TEAEs led to drug discontinuation were sepsis, ALT elevation, GGT elevation, rash, and dysgeusia. None of the PTCL patients discontinued the study drug due to related TEAEs. Incidences of TEAEs leading to drug interruption (72%) and dose reduction (45%) of any the drugs in the combination were similar in PTCL and CTCL groups. Based on C max and AUC, dose proportional exposure of tenalisib was observed from doses 400-800 mg BID. Co-administration of romidepsin with tenalisib did not significantly alter the PK of either agent. Of the 33 patients enrolled, 27 (12 PTCL and 15 CTCL) who received at least 1 dose of study drug and provided at least 1 post-baseline efficacy assessment were considered evaluable for efficacy as per protocol. The overall response rate (ORR) was of 63%; 7 (26%) patients achieved CR and 10 (37%) patients had PR (Table 1). The median duration of response (DoR) was 5.03 months (range: 2.16 months-Not Reached). In twelve evaluable PTCL patients, the ORR was 75% with 6 CR (50%) and 3 PR (25%). Among 15 evaluable CTCL patients, 8 responded with an ORR of 53.3%, 1 CR (6.7%) and 7 PR (46.7%). The median DoR was 5.03 (range: 2.16 months-Not Reached) for PTCL and 3.8 months (1.9-18.86) for CTCL. Three of the six (50%) PTCL patients with CR were bridged to transplant. Six patients who benefitted with the treatment and completed the protocol were enrolled in an open-label compassionate medication study after Cycle 7 and are being followed up. Conclusions: The combination of tenalisib and romidepsin demonstrates a favorable safety profile and promising anti-tumor activity in patients with R/R TCL. Based on these encouraging results, further development of this combination in PTCL patients in being planned. Figure 1 Figure 1. Disclosures Huen: Rhizen: Research Funding; Elorac: Research Funding; Kyowa Kirin: Research Funding; Tillium: Research Funding; Innate: Research Funding; Galderma: Research Funding; Miragen: Research Funding. Ai: Kymria, Kite, ADC Therapeutics, BeiGene: Consultancy. Feldman: Alexion, AstraZeneca Rare Disease: Honoraria, Other: Study investigator. Alderuccio: ADC Therapeutics: Consultancy, Research Funding; Oncinfo / OncLive: Honoraria; Puma Biotechnology: Other: Family member; Inovio Pharmaceuticals: Other: Family member; Agios Pharmaceuticals: Other: Family member; Forma Therapeutics: Other: Family member. Kuzel: Cardinal Health: Membership on an entity's Board of Directors or advisory committees; Exelixis: Membership on an entity's Board of Directors or advisory committees; Genomic Health: Membership on an entity's Board of Directors or advisory committees; Sanofi-Genzyme Genomic Health Tempus laboratories Bristol Meyers Squibb: Honoraria; Abbvie: Other; Curio Science: Membership on an entity's Board of Directors or advisory committees; AmerisourceBergen Corp: Membership on an entity's Board of Directors or advisory committees; CVS: Membership on an entity's Board of Directors or advisory committees; Tempus Laboratories: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; Merck: Other: Data Monitoring Committee Membership; Amgen: Other: Data Monitoring Committee Membership; SeaGen: Other: Data Monitoring Committee Membership; Medpace: Other: Data Monitoring Committee Membership.

Follicular Lymphoma Tumor-Cell Transcriptional Programs Associate with Distinct Somatic Alterations and Tumor-Immune Microenvironments

Background: Follicular lymphoma (FL) exhibits significant clinical, cellular, molecular, and genetic heterogeneity. Our understanding of FL biology and molecular classifications of FL, to date, has largely been driven by pathologic classification (Grade 1-3b), genetic classification (m7-FLIPI), or gene expression profiling (IR-1/2; Huet-23), along with limited studies on small cohorts or targeted panels. In order to further understand the biological underpinnings and complexity of FL, large-scale and integrated whole exome sequencing (WES) and RNA sequencing (RNAseq) studies are needed. Using a highly-annotated cohort of 93 FL tumors with matched RNAseq, WES, and CyTOF data, we have explored the transcriptomic signature of purified FL B cells and identified unique molecular subsets that are defined by distinct pathway activation, immune content, and genomic signatures. Methods: Frozen cell suspensions from 93 untreated FL (Grade 1-3b) patients' tumor biopsies, enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE, were used for the study. DNA was isolated from whole tumor cell suspensions, and RNA was isolated from both whole tumor and B cell-enriched cell suspensions. RNAseq and WES were performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using a standard pipeline and novel driver genes were identified using Chasm+ driver analysis. Copy number variants were identified from WES data using GISTIC 2.0. NMF clustering and single sample gene set testing for B cell lineage and tumor microenvironment (TME) signatures were performed in R using the NMF and singscore packages. Results: Unsupervised clustering of RNAseq data identified three distinct expression programs which separated patient B cell samples into 3 groups: Group 1 (G1, n=20), Group 2 (G2, n=24), Group 3 (G3, n=43). While no clinical attributes were defined by any single group, G1 consisted of cases that had less aggressive characteristics (63% Stage I-II, 79% FLIPI 0-1). To identify unique transcriptional pathways driving the three expression programs, we scored gene level contributions to NMF expression programs and employed gene set enrichment analysis. This revealed significant pathway association with type-I IFN signaling (G1), DNA repair and stress response (G2), and epigenetic modulation (G3) as differentiating programs between the 3 groups (FDR q<0.001). VIPER master regulator activity inferencing revealed that these pathways were likely being controlled by differential activity in NF-kB, IRFs, STAT1, BCL6, and FOXO1. Each program significantly enriched for, but were not defined by, portions of specific germinal center programs, such as pre-memory (G1), light-zone-to-dark-zone transition (G2), and a pre-light-zone intermediate (G3). We next assessed the connection between B cell programs and the tumor microenvironment (TME) using available paired CyTOF data on 67 cases, which revealed an active TME in G1, with an abundance of CD8 T cell and NK cell populations, a wide variety of immune content in G2 that consisted mostly of Tfh and myeloid cells, and a poorly populated immune compartment in G3 compared to G1 and G2. Finally, somatic driver mutations and copy number alterations from WES were identified and associated with the three clusters. The three groups distinguished themselves by significant enrichment of copy number alterations (TNFAIP3-loss , 1q23-gain, 1q32-gain) in G2, while 10q-loss and mutations in BCL2 and chromatin modifiers (KMT2D and CREBBP) enriched in G3. G1, overall, had lower alteration burden and had weak associations with any specific alterations, suggesting an alternative mechanism for driving the G1 program. Conclusion: In this study, we have identified three unique FL tumor B cell groups, defined by specific transcriptional programs. Pathways such as inflammation, DNA damage response, and chromatin modification contribute most to the differences between B cell samples and group membership. Additionally, each program associated with specific genetic events as well as TME composition, highlighting potential drivers of these B cell states. This study improves the understanding of the mechanisms driving FL tumors and motivates further investigation into transcriptional consequences of genetic events as well as potential tumor intrinsic factors that may influence the TME. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. King: Celgene/BMS: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Chromatin Accessibility Landscapes of Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is the most common malignancy in pediatric patients spanning both B- and T-cell lineages. ALL also occurs less commonly in adults but with cure rates of only around 30%. Past work has characterized ALL into molecular subtypes spanning a range of aberrant chromosomal rearrangements and oncogene chimeric fusions driving malignancy. While transcriptional profiling of these subtypes has been extensively examined, the accompanying chromatin accessibility landscape and corresponding gene regulatory repertoire is not well characterized for many subtypes. To better profile the ALL epigenomic and gene regulatory repertoire we examined chromatin accessibility of 12 distinct molecular subtypes (BCR-ABL1, ETV6-RUNX1, Hyperdiploid, Hypodiploid, KMT2A rearranged, Ph-Like, PAX5, DUX4/ERG, TCF3-PBX1, T-ALL, Early T Precursor and B-other) across 189 primary patient samples of pediatric ALL (n = 106) and adult ALL (n = 83) origin using ATAC-seq. To our knowledge, this represents the largest collection of chromatin accessibility data in primary ALL samples spanning multiple molecular subtypes to date. Collectively, we identified over 600,000 accessible chromatin sites in the ALL genome with over 50,000 regions of differentially accessible chromatin encompassing both common and subtype-specific modalities. Further, transcription factor (TF) footprint profiling of ATAC-seq yielded tens of thousands of candidate TF binding events and identified key TF drivers within distinct molecular subtypes. We additionally performed H3K27ac ChIP-seq in a subset of 12 primary ALL patient samples, with integration of these histone data for select patient samples allowing inferences about candidate super-enhancer drivers of ALL molecular subtypes. Overall, these analyses and data offer a window into the gene regulatory and epigenetic landscape of ALL, and further highlight the complexity and heterogeneity of accessible chromatin landscapes among distinct molecular subtypes of ALL. Disclosures Stock: Pfizer: Consultancy, Honoraria, Research Funding; amgen: Honoraria; agios: Honoraria; jazz: Honoraria; kura: Honoraria; kite: Honoraria; morphosys: Honoraria; servier: Honoraria; syndax: Consultancy, Honoraria; Pluristeem: Consultancy, Honoraria. Pui: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Novartis: Other: Data Monitoring Committee. Evans: Princess Máxima Center for Pediatric Oncology, Scientific Advisory Board, Chair: Membership on an entity's Board of Directors or advisory committees; St. Jude Children's Research Hospital, Emeritus Member (began Jan 2021): Ended employment in the past 24 months; BioSkryb, Inc.: Membership on an entity's Board of Directors or advisory committees.

Characteristics, Management and Outcomes of Patients with Intravascular Lymphoma: A Mayo Clinic Experience

Introduction: Intravascular lymphoma (IVL) is an extra nodal non-Hodgkin lymphoma with tropism for vascular endothelium. It is characterized by growth of large cells within the lumen of small to medium sized blood vessels. Central nervous system (CNS) and skin are predominantly involved. This report represents a retrospective single-institution review of IVL. Methods: We identified patients (pts) with IVL evaluated at Mayo Clinic Cancer Center between January 2003 and December 2018. Demographic, clinical, radiologic, pathologic, and therapeutic data were extracted. Statistical analysis of overall survival (OS) and progression free survival (PFS)] was performed using Kaplan-Meier method. Results: Total number of pts was 55; 22% (12/55) had CNS-only IVL, 14.5% (8/55) had CNS and non-CNS IVL, and 63.6% (35/55) had non-CNS IVL. Eighty seven percent (47/54) pts were B cell type, 11% (6/54) were T cell type, one pt had NK cell type IVL and another was unknown. Four pts were diagnosed by autopsy. Median age at diagnosis was 68 years (range, 40-85). Sixty-four percent were males. ECOG performance status was <2 in 66%. The median follow-up time from diagnosis was 63 months [CI 95%, 9-NR], and 47% (26/55) were alive. The most common diagnostic biopsy sites were bone marrow (BM) 45% (25/55), skin 25% (14/55), and brain 29% (16/55). Twenty-nine patients had a PET scan. Seventy nine percent (23/29) had abnormal PET findings, with mean SUV of 8.6 (range 2.5-19.1). Of the 35 pts with non-CNS IVL, 76% (16/21) had abnormal PET; furthermore, the diagnosis was made with biopsies of the following sites: bone marrow 54% (19/35), skin 40% (14/35), lung 14% (5/35), liver 5.7% (2/35), spleen 2.8% (1/35), and omentum 2.8% (1/35). Forty-six percent (13/28) received CNS prophylaxis and ten percent (3/55) had relapse in CNS. Two out of the three pts who had CNS relapse had received CNS prophylaxis. The median time to CNS relapse in non-CNS IVL was 9 months. The most common first-line regimen was high-dose methotrexate+ rituximab containing regimen 62% (10/16) in IVL with CNS involvement and RCHOP (60%) (17/28) in non-CNS IVL. Seventeen percent of (8/48) pts received autologous stem cell transplant (ASCT) and 63% (5/8) pts were transplanted in first complete remission (CR1), and 3 pts after the first relapse. Median OS (mOS) for the whole cohort was 57 months, [CI 95%, 9-NR], and median PFS was 7 months [CI 95%, 2-NR]. There was no significant difference in mOS between groups; CNS-only IVL- 9 months (CI 95%, 1-NR), non-CNS IVL -62 months (CI 95%, 20-NR) vs combined CNS and non-CNS IVL- 4 months (CI 95%, 3-NR). mOS for those who received ASCT in CR1 was not reached (CI 95%, 10-NR) vs 51 months in non-transplant group (CI 95%, 3-NR) p=0.24. In pts with non-CNS IVL, there was no significant difference in mOS between CNS prophylaxis subgroup (NR: CI 95%, 57-NR) vs no-CNS prophylaxis subgroup (20 months: CI 95%, 0-NR), p=0.12. In those with CNS IVL mOS for early diagnosis (0-30 days from symptom onset to diagnosis was NR (CI 95%, 3-NR) vs mOS for late diagnosis (>30 days {31-14,440})-5months (CI 95% 1-NR), p=0.29]. Conclusion: 1. BM was most frequently involved in our patients. We suggest that BM biopsy should be part of diagnostic testing when IVL is suspected. 2. Most cases are of B-cell linage, consistent with reported literature. All non-B cell cases were in non-CNS locations. 3. PET scans were abnormal in more than 70% of cases indicating that this imaging modality is vital in the diagnosis due to odd location and small size of lesions. 4.Overall prognosis in the literature was poor with most patients surviving <1 year. Our cohort has mOS of 57 months. The reason(s) for better survival in our cohort could not be definitively determined. 5. CNS involvement had an overall trend towards poor prognosis; however, those diagnosed early had better outcomes; this did not reach statistical significance due to small sample size. 6. mOS was not reached for those transplanted CR1. There was a trend towards a better survival associated with CNS prophylaxis versus no prophylaxis in non-CNS IVL. 7. We suggest that CNS-centric therapeutic approach and intensive consolidation with ASCT should be considered in managing IVL. Figure 1 Figure 1. Disclosures Nowakowski: Celgene, NanoString Technologies, MorphoSys: Research Funding; Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees. Habermann: Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Witzig: Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Tun: Mundipharma, Celgene, BMS, Acrotech, TG therapeutics, Curis, DTRM: Research Funding; Gossamer Bio, Acrotech: Consultancy.

Four-Fold Increased Mortality from Sars-Cov-2 Infection in Patients with Hematologic Versus Non-Hematologic Malignancies Treated at the Largest Tertiary COVID-19 Center in Chicago/Rush University Medical Center (March 1, 2020-December 31,2020)

N.K.Y., P.C., & P.R.Y. contributed equally to this study Introduction: Many studies have concluded that active cancer patients infected with SARS-CoV-2 have a more complicated infection course and worse outcomes compared to the general patient population hospitalized with COVID-19. However, little evidence exists whether having a history of cancer plays a significant role in these observations. Patients with hematologic malignancy (HM) might have worse prognosis among all cancer patients but the reason remains unclear. Our objective is to evaluate outcomes and severity of COVID-19 in patients with Hematological Malignancy (HM) versus Solid-tumors (ST) in different clinical settings and also compare these outcomes within the group of patients with hematological malignancies. Methods: This retrospective study examines risk factors and outcomes of COVID-19 in patients with a history of cancer and laboratory-confirmed COVID-19 diagnosis between March 1 st, 2020, and December 31 st, 2020, at Rush University Medical Center, one of the largest COVID-19 tertiary care hospitals in Chicago. Baseline characteristics, malignancy type and types of cancer treatment within the last 30 days were recorded. Measures of COVID-19 severity included hospital admission versus outpatient care, use of oxygen, intensive care unit (ICU) admission, and mechanical ventilation. The primary outcome was death. Statistical analysis was conducted using optimal discriminant analysis, a non-parametric exact machine-learning algorithm which identifies the relationship between independent and dependent variables that maximizes model predictive accuracy adjusted to remove the effect of chance. Analysis was performed separately for each attribute using the entire sample ("training" analysis), then one-sample jackknife analysis was conducted to estimate cross-generalizability of findings using the model to classify an independent random sample. Results: 378 total patients with a history of cancer tested positive for COVID-19 within the time frame of the study. Of these, 294 (78%) patients had ST malignancy and 84 (22%) patients had HM. Characteristics and outcomes are summarized in Table 1. ST patients were marginally older than HM patients (p<0.025). A significantly greater proportion of HM patients were male (p<0.0023). HM and ST patients did not differ with respect to percentage receiving active cancer treatment (p<0.81). Compared to ST patients, more HM patients had received corticosteroids in the 30 days prior to COVID-19 diagnosis (p<0.017), had higher rates of hospitalization (p<0.0013) and ICU requirement (p<0.0001) with a significantly longer length of ICU stay (p<0.0036). Compared to ST patients, HM patients also required oxygen (p<0.002) and mechanical ventilation (p<0.0005) more often and had a 3.88-fold statistically higher death rate (OR 3.88 [95% CI 1.62-9.29] p<0.003). Patients with HM are categorized by disease subtype and summarized in Table 2. The case fatality rate from COVID-19 was 33.3% for patients with myeloproliferative neoplasms/myelodysplastic syndromes (MPN/MDS), 21.4% for patients with chronic lymphocytic leukemia (CLL), 13.6% for patients with non-Hodgkin lymphoma, 10.5% for patients with plasma cell neoplasms, and 4.5% for patients with acute leukemia. When looking at outcomes, CLL had the highest percentage of patients requiring hospital admission, oxygen, and ICU admission, and MPN/MDS had the highest percentage of patients requiring mechanical ventilation. Conclusions: Patients with hematologic malignancies had more severe COVID-19 illness and hospitalization rates and a 3.88-fold higher rate of death than patients with solid tumors. The comparable proportion of patients on anti-cancer therapy despite differences in survival suggests that being on anti-cancer therapy is less important than the underlying diagnosis of HM versus ST as a determinant of poor outcomes. Clinicians should closely monitor and initiate early COVID-19 treatments for all patients with HM and COVID-19. Because HM are highly heterogenous group of cancers, it is important to look at subtypes in greater detail. Numerous patient-level, disease-specific, and therapy-related factors may impact outcomes of COVID-19 among patients with HM, and we are currently analyzing additional data to better understand the factors which make this disease group more susceptible to severe infection. Figure 1 Figure 1. Disclosures Kuzel: Sanofi-Genzyme Genomic Health Tempus laboratories Bristol Meyers Squibb: Honoraria; Genomic Health: Membership on an entity's Board of Directors or advisory committees; Exelixis: Membership on an entity's Board of Directors or advisory committees; Cardinal Health: Membership on an entity's Board of Directors or advisory committees; Abbvie: Other; Curio Science: Membership on an entity's Board of Directors or advisory committees; AmerisourceBergen Corp: Membership on an entity's Board of Directors or advisory committees; CVS: Membership on an entity's Board of Directors or advisory committees; Tempus Laboratories: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; Merck: Other: Data Monitoring Committee Membership; Amgen: Other: Data Monitoring Committee Membership; SeaGen: Other: Data Monitoring Committee Membership; Medpace: Other: Data Monitoring Committee Membership.

Integration of Tumor Transcriptomic, Genomic, and Immune Profiles Reveals Distinct Populations of Low-Grade B-Cell Lymphomas with Poor Outcome

Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

517. Subcutaneous Sarilumab for the Treatment of Hospitalized patients with Moderate to Severe COVID19 Disease: A Pragmatic, Embedded, Multi-Center Randomized Clinical Trial

Background The aim of this pragmatic, embedded adaptive trial was to measure the effectiveness of subcutaneous sarilumab in addition to an evolving standard of care for clinical management of inpatients with moderate to severe COVID-19 disease (NCT04359901). The study is also a real-world demonstration of the realization of a prospective learning healthcare system. Methods Two-arm, randomized, open-label controlled 5-center trial comparing standard care alone to standard care (SOC), which evolved over time, with addition of subcutaneous sarilumab (200 mg or 400 mg anti-IL6R) among hospitalized patients with moderate to severe COVID-19 not requiring mechanical ventilation. The primary outcome was 14-day incidence of intubation or death. The trial used a randomized play-the-winner design and was fully embedded within the EHR system, including the adaptive randomization process. Results Among 417 patients screened, 162 were eligible based on chart review, 53 consented, and 50 were evaluated for the primary endpoint of intubation or death ( >30% of eligible patients enrolled) (Figure 1). After the second interim review, the unblinded Data Monitoring Committee recommended that the study be stopped due to concern for safety: a high probability that rates of intubation or death were higher with addition of sarilumab to SOC (92.6%), and a very low probability (3.4%) that sarilumab would be found to be superior. Figure 1. Key Study Milestones, Outcomes, and Adaptations Conclusion This randomized trial of patients hospitalized with COVID-19 and requiring supplemental oxygen but not mechanical ventilation found no evidence of benefit from subcutaneous sarilumab in addition to an evolving standard-of-care. The numbers of patients and events were too low to allow independent conclusions to be drawn, but this study contributes valuable information about the role of subcutaneous IL-6 inhibition in the treatment of patients hospitalized with COVID-19. The major innovation of this trial was the advancement of embedded, point-of-care clinical trials for FDA-approved drugs; this represents a realization of the learning healthcare system. Methods developed and piloted during the conduct of this trial can be used in future investigations to speed the advancement of clinical science. Disclosures Nishant Shah, MD, General Electric (Shareholder)Pfizer, Inc. (Research Grant or Support) Karen Visnaw, RN, Liquidia (Shareholder) Paul Monach, MD,PhD, Celgene (Consultant)ChemoCentryx (Consultant)Kiniksa (Advisor or Review Panel member)

1185. Oseltamivir Prescribing Patterns for Infants with Influenza and Factors Associated with Guideline Adherence

Background The Centers for Disease Control and Prevention (CDC) recommends oseltamivir be given to children < 2 years old with confirmed or suspected influenza as they are at high risk for complications. We sought to analyze oseltamivir prescribing patterns and to describe factors associated with adherence and non-adherence to CDC guidelines. Methods We used a retrospective cohort of infants ≤ 12 months old born from January 1, 2011 to December 31, 2019 within the University of Pittsburgh Medical Center health system in Southwestern Pennsylvania and who had ≥ 2 well-child visits during their first year. Infants with laboratory-confirmed influenza from January 1, 2011 to April 30, 2020 were included. Electronic health records were reviewed to describe oseltamivir prescriptions and influenza-related characteristics. Factors associated with adherence and non-adherence to CDC influenza treatment guidelines were assessed with univariate logistic regression. Results Of 422 infants with laboratory-confirmed influenza, 86% were prescribed oseltamivir. The proportion of infants prescribed oseltamivir increased from an average of 63% during 2011-2016 to 90% during 2016-2020 (OR:5.2; 95%CI: 2.9-9.5). 96% of prescriptions instructed twice daily dosing, 2% had once daily, and 2% were unknown frequency. 91% of prescriptions were for 5 days, 7% had no duration, and 2% were for > 5 days. Infants ≥ 6 months of age compared to < 6 months were less likely to be prescribed oseltamivir (83.3% vs. 100%; p< 0.001); tested for influenza in the emergency room/urgent care (OR: 0.3; 95%CI: 0.2-0.6), or admitted to the hospital (OR:0.5; 95%CI:0.2-0.9). Infants were more likely to be treated with oseltamivir if they had a known influenza positive contact (OR:2.3; 95%CI:1.0-5.2) or had fever ≥ 38.0C (OR:2.0; 95%CI:1.2-3.5). There was no difference in prescribing practices based on history of prematurity or chronic medical conditions. Conclusion Adherence to CDC influenza treatment guidelines for infants is high and has improved over time. However, targeted education at high-risk contact points may further improve guideline adherence. Disclosures John V. Williams, MD, GlaxoSmithKline (Advisor or Review Panel member, Independent Data Monitoring Committee)Quidel (Advisor or Review Panel member, Scientific Advisory Board) Judith M. Martin, MD, Merck Sharp and Dohme (Consultant)

Review of current controversial issues in clinical trials

In clinical development, adequate and well-controlled randomised clinical trials are usually conducted to evaluate the safety and efficacy of test treatment under investigation. The purpose is to ensure that there is an accurate and reliable assessment of test treatment under study. In practice, however, some controversial issues inevitably appear despite the compliance of good clinical practice. These debatable issues include, but are not limited to, (1) appropriateness of hypotheses for clinical investigation, (2) feasibility of power calculation for sample size requirement, (3) integrity of randomisation/blinding, (4) strategy for clinical endpoint selection, (5) demonstrating effectiveness or ineffectiveness, (6) impact of protocol amendments and (7) independence of independent data monitoring committee. In this article, these controversial issues are discussed. The impact of these issues in evaluating the safety and efficacy of the test treatment under investigation is also assessed. Recommendations regarding possible resolutions to these issues are provided whenever possible.