ALBERT

Description: Introduction The DAWN study (NCT01779791) evaluated efficacy and safety of the Bruton's tyrosine kinase (BTK) inhibitor ibrutinib as monotherapy in relapsed/refractory (R/R) follicular lymphoma (FL) patients (Gopal AK, et al. J Clin Oncol. doi: 10.1200/JCO.2017.76.8853). The overall response rate (ORR) for ibrutinib was 20.9% (95% confidence interval, 13.7-29.7), not meeting the primary end point; however, responders experienced a long duration of response with a median of 19.4 months. We present the results of a biomarker investigation performed on samples from the DAWN study to determine whether somatic mutations could be used to identify FL patients who responded to ibrutinib. Methods DAWN was a multicenter, single-arm, phase 2 study of ibrutinib (560 mg QD) in FL pts with ≥ 2 prior lines of therapy and progressive disease (PD) ≤ 12 months after chemoimmunotherapy (CIT). The primary end point was ORR (complete response [CR] + partial response [PR]). Whole exome sequencing was performed on formalin-fixed, paraffin-embedded tumor samples from 83 patients with available response data - either "responder" (CR + PR; n = 17) or "nonresponder" (stable disease [SD] + PD; n = 66) following ibrutinib treatment. Multiple filters were applied to rule out potential germline variants, and a custom panel of 1216 genes known to be involved in cancer was used for further analysis. Variants enriched in responders or nonresponders were identified using Fisher's exact test. Classifiers were built with variable numbers of genes ranked with a greedy algorithm that selected genes that would, at each iteration, allow the removal of the greatest number of nonresponders from the patient pool, while severely penalizing the removal of responders; classifier performance was assessed using 10-fold cross-validation. Results The overall pattern of variant frequencies identified from the whole exome sequencing in this study was comparable to previously published studies in FL (Krysiak K, et al. Blood. 2017;129:473-483). As there were many more nonresponders than responders in this study, univariate analysis yielded mostly variants significantly enriched in ibrutinib responders but in very low numbers, eg, FANCA, HISTH1B, ANXA6, and PARP10; interestingly, 2 patients with variants in BTG1, which is considered a tumor suppressor, also responded to ibrutinib. Few nonresponder genes were identified in univariate analysis, including NBPF1, ATP6AP1, EP400, and CNOT1; mutations in these genes may activate pathways that bypass BTK, including the mTOR and JAK/STAT pathways. From the selected panel, a 17-gene classifier was developed (Figure) that included variants in ATP6AP1, EP400, ARID1A, SOCS1, TBL1XR1, CNOT1, and KDM2B. Many of these mutated genes have previously been associated with a poor prognosis in cancers, including FL; the biological functions of these genes involve transcription, cell cycle, DNA repair, cell adhesion, protein processing, and transport. Notably, few significant variants emerged that are directly involved in the BTK pathway, unlike previous reports in diffuse large B-cell lymphoma, although, as mentioned above, a few may represent bypass mechanisms to this pathway. Conclusions Mutational analysis of genes in patients from the phase 2 DAWN trial yielded insights into the mechanism of ibrutinib response and resistance in R/R FL. The genes involved in this mechanism demonstrate a large variety of biological functions and show that ibrutinib activity in FL may extend beyond the BTK-NF-kB pathway to gene and protein regulation, DNA repair, adhesion, and other cellular and microenvironmental processes. We have shown previously that its immune activity is also an important mechanism of action in FL (Gopal 2018). A gene-based classifier has been developed that we hypothesize may prove useful in enriching for ibrutinib response in FL; however, this will need to be validated in other data sets. Funding source Sponsored by Janssen Research & Development, LLC. Writing assistance was provided by Jill See of PAREXEL and funded by Janssen Global Services, LLC. Disclosures Balasubramanian: Janssen Research & Development: Employment, Equity Ownership. Hodkinson:Janssen Research & Development: Employment. Schuster:Nordic Nanovector: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Honoraria, Research Funding; Dava Oncology: Consultancy, Honoraria; Novartis Pharmaceuticals Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Consultancy, Honoraria, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees. Fowler:Janssen: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding. Trotman:Takeda: Other: unremunerated advisory role; Celgene: Other: unremunerated advisory role; Roche: Other: unremunerated advisory role; Janssen: Consultancy, Research Funding. Hess:CTI: Research Funding; Celgene: Consultancy, Honoraria, Other: travel expenses, Research Funding; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cheson:AbbVie, Roche/Genentech, Pharmacyclics, Acerta, TG Therapeutics: Consultancy. Schaffer:Janssen Research & Development: Employment, Equity Ownership. Wang:Janssen Research & Development: Employment. Deshpande:Janssen Research & Development: Employment. Vermeulen:Janssen Research & Development: Employment, Equity Ownership. Salles:Gilead: Honoraria, Other: Advisory Board; Amgen: Honoraria; Servier: Honoraria, Other: Advisory Board; Merck: Honoraria; BMS: Honoraria, Other: Advisory Board; Servier: Honoraria; Morphosys: Honoraria; Pfizer: Honoraria; Epizyme: Honoraria; Celgene: Honoraria, Other: Advisory Board, Research Funding; Janssen: Honoraria, Other: Advisory Board; Novartis: Consultancy, Honoraria; Takeda: Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; Abbvie: Honoraria; Acerta: Honoraria. Gopal:Teva: Research Funding; Brim: Consultancy; Asana: Consultancy; Janssen: Consultancy, Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Gilead: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Merck: Research Funding; Aptevo: Consultancy; BMS: Research Funding; Incyte: Consultancy; Spectrum: Research Funding.

Description: Advances in Acute Myeloid Leukemia (AML) research have shown that the bone marrow microenvironment may distinctly vary across disease subtypes, and that this variation is associated with disease risk and response to conventional therapies. Novel therapies aimed at altering the tumor microenvironment, such as T-cell redirection, CAR-T and checkpoint inhibition, are emerging as promising treatment options for AML patients; however, there remains a critical need to determine how response to immune modulation may vary within different subsets of AML. Thus, in collaboration with the Beat AML Consortium, we carried out comprehensive mass cytometry profiling of patient bone marrow samples of nearly 100 Beat AML subjects and characterized their ex vivo response to several immune modulators. As a complement to this study, we leveraged the Beat AML Consortium dataset (including next-generation sequencing, functional cell-based assays, small molecule screening and clinical information) to investigate connections between disease subtype, immune function and clinical outcome. The mass cytometry time of flight (CyTOF) immune profiling, combined with matched genomic, cytogenetic, and outcome data from the same subjects, provided a unique opportunity to investigate features of the immune environment at single-cell resolution and test for their association with clinical covariates in a large treatment-naïve cohort. Interestingly, flow cytometry analysis of T-cells isolated from patient bone marrow showed a distinct subset of AML subjects with highly proliferative T-cells and a group of AML subjects with non-proliferative T-cells. To characterize molecular determinants of T-cell function in the AML microenvironment, we compared the transcriptional profiles of tumor specimens from subjects within these two groups. The data revealed a distinct set of differentially expressed genes associated with T-cell proliferation; pathway enrichment analysis indicated that these genes were involved in leukocyte migration, inflammation and response to hypoxia. Genes related to immune function were also enriched, likely due to the presence of immune cell infiltrates and stromal cells in addition to tumor cells from the AML specimens used for RNA-Seq. To estimate the extent of immune and stromal cells in the AML bone marrow, we next computed the approximate cellularity of the RNA-Seq samples using the xCell algorithm. The results of this analysis indicated enrichment of several types of immune cells in the RNA-Seq specimens from the proliferator group, including monocytes, neutrophils and activated dendritic cells. These observations were validated by preliminary results of the CyTOF immune cell profiling of the same subjects. Ongoing work is focused on the biological interpretation of CyTOF data collected for these subjects, including evaluating the association of functional marker expression on T-cell and myeloid cell populations with T-cell proliferation. Furthermore, we are exploring the functional impact of variation in T-cell fitness and immune cell composition on response to several immune modulators in a series of ex vivo experiments using Beat AML patient samples. Initial findings suggest that for a subset of patients, low baseline levels of T-cell proliferation did not prevent response to immune modulation. We are interrogating the Beat AML dataset for common molecular features of patients in this responder group. Overall, this study evaluates determinants of immune function and variation within the tumor microenvironment of AML patients to advance current knowledge of AML disease biology and to assess the impact of immune fitness on response to immune modulation. These results will contribute to early target identification and development, and importantly shed light on features of the AML bone marrow environment associated with response to therapy. Disclosures Brady: Janssen R&D: Employment. Soong:Janssen R&D: Employment. Lind:Celgene: Research Funding; Monojul: Research Funding; Amgen: Research Funding; Janssen Pharmaceutical R&D: Research Funding; Fluidigm: Honoraria. Schaffer:Janssen Research & Development: Employment, Equity Ownership. Hodkinson:Janssen R&D: Employment. Adams:Janssen Pharmaceutical R&D: Employment. Abraham:Janssen R&D: Employment. Safabakhsh:Janssen R&D: Employment. Tyner:AstraZeneca: Research Funding; Aptose: Research Funding; Array: Research Funding; Genentech: Research Funding; Constellation: Research Funding; Gilead: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Takeda: Research Funding; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees. Druker:Aileron Therapeutics: Consultancy; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; McGraw Hill: Patents & Royalties; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Bristol-Meyers Squibb: Research Funding; ARIAD: Research Funding; Novartis Pharmaceuticals: Research Funding; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; Beta Cat: Membership on an entity's Board of Directors or advisory committees; Millipore: Patents & Royalties; Celgene: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Patient True Talk: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Fred Hutchinson Cancer Research Center: Research Funding; Monojul: Consultancy; Henry Stewart Talks: Patents & Royalties. Huang:Janssen R&D: Employment, Equity Ownership.

Description: Introduction In the phase 3, double-blind, placebo (pbo)-controlled PHOENIX trial (NCT01855750), 838 patients (pts) with non-germinal center B-cell-like (non-GCB) diffuse large B-cell lymphoma (DLBCL) were randomized 1:1 to ibrutinib (IBR; 560 mg/day orally) + rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or pbo + R-CHOP. IBR + R-CHOP did not improve event-free survival (EFS) in the intent-to-treat (ITT) non-GCB population (hazard ratio [HR] 0.934; 95% confidence interval [CI], 0.726-1.200). However, in an exploratory analysis, pts 〈 60 years benefited from the addition of IBR (HR 0.579; 95% CI, 0.380-0.881 for EFS), whereas pts ≥ 60 years did not (HR 1.228; 95% CI, 0.887-1.699 for EFS) due to increased toxicity and reduced R-CHOP exposure. Based on evidence that co-expression of BCL2 and MYC by immunohistochemistry (IHC) have a worse outcome with R-CHOP, we examined their clinical prognostic effect in the 2 arms of the PHOENIX trial. Methods Pretreatment formalin-fixed paraffin-embedded biopsy samples were collected. RNA was extracted and profiled with Illumina RNAseq. Raw sequence reads were aligned to the hs37d5 genome build with STAR v2.5.1b, and gene-level quantification was conducted using RSEM v1.2.23. The median transcript per million mapped reads (TPM) values for BCL2 and MYC gene expression across all pts with RNAseq data (n = 766) were used as the cutoffs between high and low expression for each gene. BCL2 IHC data were available from 184 pts, and based on these, the threshold expression value from RNAseq data that could best approximate positive calls from IHC (≥ 50% lymphoma cells positive) was determined. This threshold value was very close to the median level calculated above and therefore supported the use of the median expression levels as the cutoffs to define high and low expressors in the subsequent analyses. The relationship between expression by RNAseq and survival (EFS, overall survival [OS]) in the 2 study arms was analyzed by Kaplan-Meier estimate with Cox regression to determine HRs and log-rank testing to assess significance. Results Based on a cutoff at the median TPM value, the percentage of non-GCB pts with BCL2-high + MYC-high (n = 234, 30.5%) was consistent with previous literature. In the IBR + R-CHOP (n = 386) and pbo + R-CHOP (n = 380) arms, respectively, 123 (31.9%) and 111 (29.2%) pts were MYC-high + BCL2-high by RNAseq. In the pbo + R-CHOP arm, pts with MYC-high + BCL-2-high had worse EFS (HR 1.820; 95% CI, 1.264-2.620; p = 0.0011) and OS (HR 1.662; 95% CI, 1.014-2.724; p = 0.0415) versus those with low expression of 1 or both markers in the ITT population (Figure), consistent with known poor outcomes associated with these genes. However, there was no difference in outcome for high versus low expression of these genes in the IBR + R-CHOP arm in the ITT population. In the ITT population, pts with MYC-high + BCL2-high had better EFS (HR 0.648; 95% CI, 0.423-0.993; p = 0.045) with IBR + R-CHOP versus pbo + R-CHOP, but there was no significant difference in OS (HR 0.783; 95% CI, 0.446-1.372; p = 0.39; Figure). We also examined the outcome of the 97 (30.6%) pts 〈 60 years of age (n = 317) with MYC-high + BCL2-high and observed an improved EFS and OS with IBR + R-CHOP versus pbo + R-CHOP (HR 0.393; 95% CI, 0.198-0.780; p = 0.0056 for EFS; HR 0.191; 95% CI, 0.055-0.666; p = 0.0037 for OS; Figure). There was no significant difference in EFS or OS in pts 〈 60 years with MYC-low + BCL2-low or in pts ≥ 60 years with MYC-high + BCL2-high in the 2 arms. Conclusions In this exploratory analysis, IBR was associated with improved EFS in combination with R-CHOP compared with pbo + R-CHOP in pts with MYC-high + BCL2-high expression in the ITT (non-GCB) population, without a significant improvement in OS. In pts aged 〈 60 years, both EFS and OS were significantly better with IBR, while there was no significant difference in older pts. These data suggest that IBR + R-CHOP may particularly benefit pts with MYC-high + BCL2-high-expressing lymphomas, a hypothesis warranting further testing in other DLBCL cohorts. Disclosures Johnson: Novartis: Honoraria; Genmab: Honoraria; Kite: Honoraria; Celgene: Honoraria; Takeda: Honoraria; Bristol-Myers Squibb: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Epizyme: Honoraria, Research Funding; Incyte: Honoraria; Boehringer Ingelheim: Honoraria. Balasubramanian:Janssen: Employment; Johnson & Johnson: Equity Ownership; Gilead Sciences: Equity Ownership; Celgene: Equity Ownership; Vertex: Equity Ownership; AbbVie: Equity Ownership. Hodkinson:Janssen: Employment. Schaffer:Johnson & Johnson: Equity Ownership; Janssen: Employment. Parisi:Janssen: Employment. Shreeve:Johnson & Johnson: Equity Ownership; Janssen: Employment. Sun:Janssen: Employment; Johnson & Johnson: Equity Ownership. Vermeulen:Johnson & Johnson: Equity Ownership; Janssen: Employment. Sehn:Abbvie: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; F. Hoffmann-La Roche/Genentech: Consultancy, Honoraria, Research Funding; F. Hoffmann-La Roche/Genentech: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; TEVA Pharmaceuticals Industries: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Janssen-Ortho: Honoraria; TEVA Pharmaceuticals Industries: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria; Kite Pharma: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Kite Pharma: Consultancy, Honoraria; Acerta: Consultancy, Honoraria; Astra Zeneca: Consultancy, Honoraria; Acerta: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Astra Zeneca: Consultancy, Honoraria; Janssen-Ortho: Honoraria; Lundbeck: Consultancy, Honoraria. Staudt:Nanostring: Patents & Royalties. Younes:AstraZeneca: Research Funding; Biopath: Consultancy; Genentech: Research Funding; Pharmacyclics: Research Funding; Syndax: Research Funding; BMS: Research Funding; HCM: Consultancy; Epizyme: Consultancy, Honoraria; Xynomics: Consultancy; Roche: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria; Janssen: Honoraria, Research Funding; Curis: Honoraria, Research Funding; Merck: Honoraria, Research Funding; Abbvie: Honoraria; Takeda: Honoraria.