Chiu, April

Abstract Background: Myelofibrosis (MF) is characterized by progressive bone marrow (BM) fibrosis resulting from aberrant megakaryopoiesis and expression of pro-inflammatory cytokines. These processes, heavily influenced by bromodomain and extraterminal domain (BET) protein-mediated gene regulation, lead to myeloproliferation and cytopenias. There is a high unmet need for a treatment that can potentially delay or reverse BM fibrosis in patients (pts) with MF. Pelabresib (CPI-0610) is a potent, first-in-class, selective, oral small-molecule BET inhibitor, which is able to modify the expression of genes involved in nuclear factor kappa B (NFκB) signaling in pts with MF. Pelabresib is currently being investigated as a monotherapy and in combination with the JAK inhibitor (JAKi) ruxolitinib (RUX), in the ongoing MANIFEST Phase 2 study (NCT02158858). Methods: In arm 1 of MANIFEST, pelabresib is administered as monotherapy to pts with MF who are intolerant/refractory to, or ineligible for RUX; in arm 2, as an 'add-on' to RUX in pts with MF who have suboptimal/lost response; and in arm 3 as combination therapy with RUX in JAKi treatment-naïve pts with MF. To evaluate the effects of pelabresib on BM biology, analyses were conducted using BM biopsy samples obtained pre-treatment and at 24 weeks post-treatment during the MANIFEST trial. Central pathology review of reticulin staining was conducted to evaluate BM fibrosis (BMF) grading and immunohistochemistry staining for changes in erythroids (ERY) and megakaryocytes (MK), respectively. Independent digital images of stained BM slides were also evaluated with a semi-quantitative cell-specific detection algorithm for reticulin density, percentage CD71+ ERY, mean number of CD61+ MK and mean distance between nuclei for CD61+ MK. Results: Exploratory analyses across the three treatment arms showed BM improvements after 24 weeks of treatment. Paired comparison by digital analysis of baseline and 24-week treatment BM biopsies from 29 pts showed reduction (range 67%-15.5%) in reticulin intersections per field in 45% (13/29) pts. Furthermore, digital analysis of CD71+ ERY and CD61+ MK from paired biopsies showed increased ERY progenitors in 50% (16/32) pts, decreased MK density in 57% (15/26) pts and decreased MK clustering in 46% (12/26) pts at 24 weeks. Decreased MK clustering correlated with decreased density of CD61+ MK in 75% pts (9/12). Correlative analysis between observed clinical responses and bone marrow improvements are ongoing and will be available for presentation at the conference. We previously reported (Talpaz M, et al. ASH 2020; Abstract 2163) that in pts with MF, pelabresib can reduce spleen volume, improve symptoms, improve bone marrow fibrosis, demonstrate increase in hemoglobin levels, and reduce transfusion burden. Updated 24-week data reflecting a higher number of evaluable pts and new long-term data at 48 weeks across the MANIFEST trial, will be presented. Conclusions: Treatment with pelabresib as monotherapy or in combination with RUX, in both RUX treatment-naïve and -experienced pts with MF, resulted in increased ERY progenitors, decreased MK density and reduction of BM fibrosis, as assessed by digital pathology. These data suggest possible disease-modifying potential for pelabresib by improving BM histology and function, leading to potential clinical benefits. Disclosures Verstovsek: Roche: Research Funding; Promedior: Research Funding; Blueprint Medicines Corp: Research Funding; Incyte Corporation: Consultancy, Research Funding; Protagonist Therapeutics: Research Funding; Genentech: Research Funding; CTI BioPharma: Research Funding; Celgene: Consultancy, Research Funding; Gilead: Research Funding; Ital Pharma: Research Funding; PharmaEssentia: Research Funding; NS Pharma: Research Funding; AstraZeneca: Research Funding; Novartis: Consultancy, Research Funding; Sierra Oncology: Consultancy, Research Funding; Constellation: Consultancy; Pragmatist: Consultancy. Salama: Mayo Clinic: Current Employment, Other: Mayo Clinic had the contractual work for the central pathology review for this study and I was one of the reviewing pathologists; Constellation Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Mascarenhas: AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kartos: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Constellation: Consultancy, Membership on an entity's Board of Directors or advisory committees; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Geron: Consultancy, Research Funding; Prelude: Consultancy; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; PharmaEssentia: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Galecto: Consultancy; Promedior: Consultancy, Membership on an entity's Board of Directors or advisory committees; Merus: Research Funding; Forbius: Research Funding; CTI Biopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech/Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Geron: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Talpaz: Celgene: Consultancy; Imago: Consultancy; Takeda: Other: Grant/research support ; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Mesa: La Jolla Pharma: Consultancy; Samus: Research Funding; Constellation Pharmaceuticals: Consultancy, Research Funding; Pharma: Consultancy; CTI: Research Funding; Abbvie: Research Funding; CTI: Research Funding; Genentech: Research Funding; Gilead: Research Funding; Promedior: Research Funding; Novartis: Consultancy; Celgene: Research Funding; Incyte Corporation: Consultancy, Research Funding; Sierra Oncology: Consultancy, Research Funding; AOP: Consultancy. Vannucchi: Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees. Rampal: Incyte: Consultancy, Research Funding; Pharmaessentia: Consultancy; Blueprint: Consultancy; Disc Medicine: Consultancy; Stemline: Consultancy, Research Funding; BMS/Celgene: Consultancy; Novartis: Consultancy; Sierra Oncology: Consultancy; CTI: Consultancy; Abbvie: Consultancy; Jazz Pharmaceuticals: Consultancy; Constellation: Research Funding; Memorial Sloan Kettering: Current Employment; Kartos: Consultancy. Oh: Abbvie: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Celgene Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; CTI Biopharma: Membership on an entity's Board of Directors or advisory committees; Disc Medicine: Membership on an entity's Board of Directors or advisory committees; Geron: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Kartos Therapeutics: Membership on an entity's Board of Directors or advisory committees; PharamaEssentia: Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Membership on an entity's Board of Directors or advisory committees. Taverna: Constellation Pharmaceuticals: Current Employment. Cui: Constellation Pharmaceuticals: Current Employment. Zavidij: Constellation Pharmaceuticals: Current Employment. Chen: Constellation Pharmaceuticals: Current Employment. Colak: Constellation Pharmaceuticals: Current Employment. Efuni: Constellation Pharmaceuticals: Current Employment. Keller: Constellation Pharmaceuticals: Current Employment. Trojer: Constellation Pharmaceuticals: Current Employment. Harrison: Galacteo: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte Corporation: Speakers Bureau; Constellation Pharmaceuticals: Research Funding; Sierra Oncology: Honoraria; CTI BioPharma: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Geron: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Keros: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AOP Orphan Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Promedior: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Shire: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

Abstract Introduction: Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology. However, these biological subgroups overlap clinically. While R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard of care for treating patients with DLBCL, predicting which patients will not benefit from such therapy is important so that alternative therapy or clinical trials can be considered. Most of the studies stratifying patients select biomarkers first, then explore how these biomarkers can stratify patients based on outcome. We explored the potential of using machine learning to first group patients with DLBCL based on survival, then isolating the biomarkers necessary for predicting these survival subgroups. Methods: RNA was extracted from tissue paraffin blocks from 379 R-CHOP treated patients with de novo DLBCL, and from 247 patients with extranodal DLBCL. A targeted hybrid capture RNA panel of 1408 genes was used for next generation sequencing (NGS). Sequencing was performed using an Illumina NextSeq 550 System platform. Ten million reads per sample in a single run were required, and the read length was 2 × 150 bp. An expression profile was generated from the sequencing coverage profile of each individual sample using Cufflinks. A machine learning system was developed to classify patients into four groups based on their overall survival. This machine learning approach based on Naïve Bayesian algorithm was also used to discover the relevant subset of genes with which to classify patients into each of the four survival groups. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict the survival group for each patient. Results: Using machine learning, patients were first divided into two groups: short survival (S) and long survival (L). To refine this model, we used the same approach and divided the patients in each group into two subgroups, generating four groups: long survival in the long group (LL), short survival in the long group (LS), long survival in the short group (SL), and short survival in the short group (SS). The hazard ratio for this model was 0.174 (confidence interval: 0.120-0.251), and P-value <0.0001. After defining these four groups, a machine learning algorithm was used to discover the biomarkers from the expression data of the 1408 genes from NGS data. To reduce the effects of noise and avoid overfitting, we employed a 12-step cross validation to obtain a robust measure. For an individual gene, a generalized Naïve Bayesian classifier was constructed on the training of one of the 12 subsets and tested on the other 11 testing subsets. This allowed us to limit the prediction process to 60 genes for each separation step. Using the selected biomarkers, we classified the patients in the original set (379 patients) into LL, LS, SL, and SS groups and then evaluated the survival pattern of these groups. As shown in Fig. 1A, the selected biomarkers predicted survival as expected in the overall survival groups prior to biomarker selection. For additional validation of the system, we used the selected biomarkers to classify a completely new set of 247 samples of patients with extranodal DLBCL. As shown in Fig. 1B, these selected biomarkers successfully predicted the overall survival in this group of patients with an HR of 0.530 (confidence interval: 0.234-1.197, P=0.005). This classification correlated with cell of origin classification, TP53 mutation status, MYC expression, and IRF4 expression. However, in a multivariate analysis, only TP53 mutation was independent in predicting prognosis (P=0.005) and age (below or over 60) (P=0.01) along with the survival grouping (P<0.000001). Conclusions: Using a novel machine learning approach with the expression levels of 180 genes, we developed a model that can reliably stratify patients with DLBCL treated with R-CHOP into four survival subgroups. This model can be used to identify patients who may not respond well to R-CHOP to be considered for alternative therapy and clinical trials. Figure 1 Figure 1. Disclosures Hsi: AbbVie Inc, Eli Lilly: Research Funding. Ferreri: Ospedale San Raffaele srl: Patents & Royalties; BMS: Research Funding; Pfizer: Research Funding; Beigene: Research Funding; Hutchison Medipharma: Research Funding; Amgen: Research Funding; Genmab: Research Funding; ADC Therapeutics: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; PletixaPharm: Membership on an entity's Board of Directors or advisory committees; x Incyte: Membership on an entity's Board of Directors or advisory committees; Adienne: Membership on an entity's Board of Directors or advisory committees. Piris: Millenium/Takeda, EUSA, Jansen, NanoString, Kyowa Kirin, Gilead and Celgene.: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Winter: BMS: Other: Husband: Data and Safety Monitoring Board; Actinium Pharma: Consultancy; Janssen: Other: Husband: Consultancy; Agios: Other: Husband: Consultancy; Gilead: Other: Husband: Consultancy; Epizyme: Other: Husband: Data and Safety Monitoring Board; Ariad/Takeda: Other: Husband: Data and Safety Monitoring Board; Merck: Consultancy, Honoraria, Research Funding; Novartis: Other: Husband: Consultancy, Data and Safety Monitoring Board; Karyopharm (Curio Science): Honoraria.

Abstract Introduction/Objective Amyloidosis encompasses a heterogeneous group of disorders characterized by abnormal deposition of misfolded proteins leading to progressive organ failure. Accurate amyloid typing is essential for proper patient management, as treatment regimens vary dramatically across different types. Bone marrow (BM) biopsy, in conjunction with fat pad aspiration/biopsy, is often the first step in patients with suspected amyloidosis. Although BM involvement by AL amyloid has been previously characterized, little is known about the incidence, morphology and clinical phenotype of non-AL amyloid in BM. Methods/Case Report We retrospectively identified 1469 BM biopsies by querying our reference laboratory database of 19,298 specimens from myriad anatomic sites typed by mass spectrometry-based proteomics (LC-MS/MS). These were reviewed for frequency of amyloid types (N=1469), distribution of amyloid deposits (N=139), and clinical phenotypes (N=345), with particular emphases on cardiac involvement. Results (if a Case Study enter NA) We identified the following amyloid types: AL (N=1172; 79.8%), ATTR (transthyretin) (N=240; 16.3%), AH (immunoglobulin heavy chain) (N=38; 2.6%), AA (serum amyloid A) (N=17; 1.2%), and Aβ2M (β2-microglobulin) (N=2; 0.1%). ATTR deposits showed striking predilection for periosteal soft tissue and/or periosteal vessels, and rarely involved BM stroma and/or interstitial vessels, while AL variably involved these compartments. AA primarily involved interstitial vessels. Both AL and ATTR cases commonly had a monoclonal gammopathy (AL: 92.9%; ATTR: 62.5%) with concomitant cardiac amyloidosis (AL: 91.6%; ATTR: 100%). Compared to AL, ATTR patients had higher stage cardiac amyloidosis and lower overall survival. Conclusion ATTR is common in BM, constituting16.3% of cases in our cohort. Rarer amyloid types, such as AA, AH and AB2M can also occur in BM. ATTR was frequently identified in patients with concomitant monoclonal gammopathy, in whom AL may have been suspected. Although ATTR deposits have distinctive morphologic distribution, primarily involving periosteal soft tissue and/or periosteal vessels and rarely involving BM stroma and/or interstitial vessels, there is considerable morphologic overlap with AL. Therefore, it is imperative to type BM amyloidosis, preferably by LC-MS/MS, to ensure proper patient management. Furthermore, BM involvement by ATTR may be a marker for advanced stage of disease.

Key Points Systemic immune microenvironment signatures in CMML indicate an altered T- and natural killer cell balance. CMML bone marrow dendritic cell aggregates associate with disease progression and systemic regulatory T-cell phenotypic switch.

Abstract Diffuse large B-cell lymphoma (DLBCL) is the major type of aggressive B-cell lymphoma. High-grade B-cell lymphoma (HGBCL) with MYC/BCL2 double-hit (DH) represents a distinct entity with dismal prognosis after standard immunochemotherapy in the current WHO lymphoma classification. However, whether TP53 mutation synergizes with MYC abnormalities (MYC rearrangement and/or Myc protein overexpression) contributing to HGBCL-like biology and prognosis is not well investigated. In this study, patients with DLBCL with MYC/TP53 abnormalities demonstrated poor clinical outcome, high-grade morphology, and distinct gene expression signatures. To identify more effective therapies for this distinctive DLBCL subset, novel MYC/TP53/BCL-2–targeted agents were investigated in DLBCL cells with MYC/TP53 dual alterations or HGBCL-MYC/BCL2-DH. A BET inhibitor INCB057643 effectively inhibited cell viability and induced apoptosis in DLBCL/HGBCL cells regardless of MYC/BCL2/TP53 status. Combining INCB057643 with a MDM2-p53 inhibitor DS3032b significantly enhanced the cytotoxic effects in HGBCL-DH without TP53 mutation, while combining with the BCL-2 inhibitor venetoclax displayed potent therapeutic synergy in DLBCL/HGBCL cells with and without concurrent TP53 mutation. Reverse-phase protein arrays revealed the synergistic molecular actions by INCB057643, DS3032b and venetoclax to induce cell-cycle arrest and apoptosis and to inhibit AKT/MEK/ERK/mTOR pathways, as well as potential drug resistance mechanisms mediated by upregulation of Mcl-1 and RAS/RAF/MEK/ERK pathways. In summary, these findings support subclassification of DLBCL/HGBCL with dual MYC/TP53 alterations, which demonstrates distinct pathobiologic features and dismal survival with standard therapy, therefore requiring additional targeted therapies. Implications: The clinical and pharmacologic studies suggest recognizing DLBCL with concomitant TP53 mutation and MYC abnormalities as a distinctive entity necessary for precision oncology practice. Visual Overview: http://mcr.aacrjournals.org/content/molcanres/19/2/249/F1.large.jpg.

Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expression signatures of normal B cells retained in DLBCL. We hypothesize that classifiers incorporating both genome-wide gene-expression and pathogenetic variables can improve the therapeutic significance of DLBCL classification. To develop such refined classifiers, we performed targeted RNA sequencing (RNA-Seq) with a commercially available next-generation sequencing (NGS) platform in a large cohort of 418 DLBCLs. Genetic and transcriptional data obtained by RNA-Seq in a single run were explored by state-of-the-art artificial intelligence (AI) to develop a NGS-COO classifier for COO assignment and NGS survival models for clinical outcome prediction. The NGS-COO model built through applying AI in the training set was robust, showing high concordance with COO classification by either Affymetrix GeneChip microarray or the NanoString Lymph2Cx assay in 2 validation sets. Although the NGS-COO model was not trained for clinical outcome, the activated B-cell–like compared with the germinal-center B-cell–like subtype had significantly poorer survival. The NGS survival models stratified 30% high-risk patients in the validation set with poor survival as in the training set. These results demonstrate that targeted RNA-Seq coupled with AI deep learning techniques provides reproducible, efficient, and affordable assays for clinical application. The clinical grade assays and NGS models integrating both genetic and transcriptional factors developed in this study may eventually support precision medicine in DLBCL.