Droin, Nathalie

Abstract Introduction Blast transformation (BT) in CMML occurs in 15-30% of patients (pts) with a median overall survival (OS) <6 months (mo). While clonal evolution can be observed, biological mechanisms of BT remain unknown for many pts. Using paired samples from chronic phase (CP) and at BT, this study defines the cytogenetic and molecular landscape of BT-CMML. Methods BT-CMML pts from Mayo Clinic (n=153, 31%), MD Anderson (n=195, 40%), and 7 centers of the Groupe Francophone des Myélodysplasies (n=145, 29%) were included after IRB approval. ICC 2022 criteria defined CMML and AML. Pt data were collected at initial diagnosis or referral at CP and again at BT. Clonal dominance was inferred from a natural log-fold difference of mutation variant allele fractions (VAF). OS was censored at allogeneic stem cell transplant (alloSCT). Single-cell multiomic analyses used the Tapestri platform (Mission Bio). Statistics used R. Results Among 1,883 cases of CMML with a median follow up of 96 mo, 493 (26%) underwent BT. The median age at CP and BT was 69 and 71 years, respectively. BT-CMML pts were predominantly male (69%) and had proliferative CMML (53%) or CMML-1 (69%) in CP; 39% and 33% were considered high-risk by CPSS-Mol and BLAST-Mol criteria, respectively. The median time to BT was 16 mo (interquartile range 7-35 mo) and was not different in males vs females (p=0.60). Compared to their CP parameters (p<0.001 each), pts at BT had lower median hemoglobin (10.8 vs 9.2 g/dL) and platelets (100 vs 44 x109/L) but higher leukocytes (13.9 vs 16.3 x109/L), peripheral blasts (0% vs 14%), and bone marrow blasts (6% vs 32%). Blasts exhibited mostly monocytic (57%) or immature myeloid (40%) differentiation. Abnormal karyotypes (AK) were more prevalent at BT (25% vs 62%, p<0.001). Among 318 pts with paired karyotype data, 221 (69%) had a normal karyotype (NK) in CP, of which 123 (56%) remained normal at BT and 98 (44%) acquired new abnormalities. Conversely, 97 (31%) pts had an AK in CP, of which 67 (69%) acquired abnormalities or increased clonal metaphases (6%), 23 (24%) remained stable, and 7 (7%) reverted to a NK at BT. Overall, 46% acquired and 8% lost a clonal cytogenetic abnormality, while 46% remained unchanged. Numeric (37%) and structural (30%) changes were most common. Complex (12% vs 3%) and monosomal (5% vs 2%) karyotypes were more frequent at BT than in CP. Only 6 (2%) pts acquired an AML-defining rearrangement at BT. Mutational data were available for 248 paired cases. The number of somatic mutations was higher at BT (median 3 vs 4, p<0.001). Mutations in RUNX1 (27% vs 39%), NRAS (23% vs 33%), KRAS (11% vs 17%), FLT3 (6% vs 15%), NPM1 (0% vs 11%), IDH2 (5% vs 11%), and CEBPA (3% vs 10%) were more frequent at BT (all p<0.05). At BT, the average VAF of the dominant clone increased in 32%, decreased in 35%, and remained unchanged (delta ≤5%) in 33%. The most frequent dominant clones both in CP and at BT had mutations in TET2 (8% vs 4%) or co-dominant mutations involving SRSF2-TET2 (7% vs 3%). Mutational clonal evolution was observed in 72%, with mutations gained in 45% and lost from the dominant clone in 19%. The most gained mutations at BT were in NRAS (15%) and RUNX1 (13%). Single cell proteogenomic analyses in 4 selected CP and BT pts shed light on the impact of NRAS and RUNX1 mutations on clonal hierarchies and differentiation. Among 196 fully evaluable cases, a new cytogenetic and/or molecular aberration was uniquely gained in 106 (54%) cases and lost in 9 (5%), with 40 (20%) both gaining and losing a genetic alteration. Forty-one (21%) patients had no identifiable genetic change between CP and BT, suggesting driver alterations outside of that detected by routine analyses. Median OS was 31 mo from CMML diagnosis and 5.6 mo from BT. Compared to supportive care, treatment in CP did not prolong median time to BT (14 vs 17 mo, p=0.91) or OS (35 vs 29 mo, p=0.05), regardless of intensity. After BT, median OS was 1.2 mo with supportive care (n=83), 8.7 mo with a hypomethylating agent (HMA, n=39), 10 mo with HMA plus venetoclax (n=37), 7.8 mo with chemotherapy (n=91), and 31 mo in pts who underwent alloSCT (n=57). Conclusions In this large BT-CMML cohort, cytogenetic (46% gained, 8% lost) or mutational (45% gained, 19% lost) driver events were identified in 79% of pts, with 21% having no identifiable changes by conventional testing. Whole genome/transcriptome analyses are underway in this subgroup to identify novel drivers of BT.

Abstract Background The expansion of hematopoietic stem and progenitor cells (HSPCs) in the bone marrow is universally associated with adverse outcomes and disease progression in all chronic myeloid neoplasms, to include chronic myelomonocytic leukemia (CMML). However, HSPCs are a heterogeneous cellular compartment and the impact of its composition on clinical outcomes remains poorly defined. Single cell RNA-sequencing (scRNA-seq) represents an optimal strategy to annotate cellular composition but no large clinically annotated scRNA-seq datasets are currently available. To address this, we have embarked to generate a clinically annotated 500 patient CMML scRNA-seq atlas from CD34+ enriched BMNCs to establish among the largest clinically and genetically annotated single cell references for any chronic myeloid malignancy. Here we report the interim analysis of 113 clinically annotated cases that demonstrates massive reshaping of HSPC composition that is independently a predictor of overall survival. Methods Single-cell RNA expression profiling was performed on 113 CD34-enriched CMML BMNC samples using 10x Genomics GEM-X Universal 3' Gene Expression v4 (4-plex) chemistry. Libraries were sequenced on NovaSeq S4 flow cells in two runs, and data were processed using Cell Ranger to generate gene expression matrices. A total of 502,659 single cells from 113 samples were processed using Seurat. The upper threshold for gene count was defined as the mean number of detected genes plus two standard deviations data normalization was performed using the log-normalization method. Batch correction and integration were conducted with Harmony to mitigate technical variation across pools. Descriptive statistics were used to tabulate clinical and genetic parameters. KM curves, log-rank test, and cox regression were used to calculate survival, test for statistical differences, and perform multivariate analysis, respectively. Results To derive high resolution cell annotations, we employed the SingleR algorithm using a large hematopoietic reference of over 300,000 single cells (Zhang, X. Nature Immunology, 2024). Using this approach a diverse set of cell types were identified that included HSCs, 23 myeloid progenitors, 18 erythroid progenitors, and 18 lymphoid progenitors. This diverse cellular repertoire displayed massive reshaping relative to the normal CD34 enriched reference including a >53x expansion of multilineage GMPs and a >100x depletion of B-cell progenitors consistent with the known myeloid skewing seen in this disease. To understand whether expansion of specific progenitors was associated with outcomes we iteratively evaluated key progenitor populations and observed that expansions of HSC (HR 0.468 p<0.005) and MEP (HR 0.485, p<0.005) were associated with improved survival while GMP (HR 1.729, p=0.029) expansions were associated with inferior survival when using a cut point derived with a maximally selected ranks statistics approach. To achieve a more comprehensive metric of HSPC composition we explored GMP ratios to include GMP/HSC ratios and GMP/MEP ratios and observed that both were associated with inferior survival in univariate and multivariate when considering clinical and genetic features such as the CPSS-molecular prognostic score and FAB classification. When considering both GMP/HSC and GMP/MEP ratios in a multivariate model only GMP/MEP ratios and CPSS molecular remained independently prognostic. As an orthogonal approach, pseudotime projections were carried out using a reference map from Setty et al., 2019 to calculate differentiation potential and branch probabilities. This approach enabled both cell identity assignment and placement of cells along the hematopoietic differentiation trajectory. Samples were classified as monocyte-biased, MEP-biased, or normal-like, as previously described (Ferrall-Fairbanks et al. 2022) and demonstrated that increased GMP ratios are associated with a monocytic biased trajectory and that this trajectory is also associated with overall survival. Conclusion This study represents an interim analysis of among the largest scRNA-seq cohorts generated in chronic myeloid malignancies. This enabled us, for the first time, to demonstrate that HSPC composition is independently prognostic even when considering modern CMML prognostic models. As this cohort continues to increase, validation of these findings and examining the impact of therapy on HPSC composition in serial samples will be prioritized.

Chronic myelomonocytic leukemia (CMML) is a severe myeloid malignancy with limited therapeutic options. Single-cell analysis of clonal architecture demonstrates early clonal dominance with few residual WT hematopoietic stem cells. Circulating myeloid cells of the leukemic clone and the cytokines they produce generate a deleterious inflammatory climate. Our hypothesis is that therapeutic control of the inflammatory component in CMML could contribute to stepping down disease progression. The present study explored the contribution of immature granulocytes (iGRANs) to CMML progression. iGRANs were detected and quantified in the peripheral blood of patients by spectral and conventional flow cytometry. Their accumulation was a potent and independent poor prognostic factor. These cells belong to the leukemic clone and behaved as myeloid-derived suppressor cells. Bulk and single-cell RNA-Seq revealed a proinflammatory status of iGRAN that secreted multiple cytokines of which CXCL8 was at the highest level. This cytokine inhibited the proliferation of WT but not CMML hematopoietic stem and progenitor cells (HSPCs) in which CXCL8 receptors were downregulated. CXCL8 receptor inhibitors and CXCL8 blockade restored WT HSPC proliferation, suggesting that relieving CXCL8 selective pressure on WT HSPCs is a potential strategy to slow CMML progression and restore some healthy hematopoiesis.

Abstract TET2 mutations (mTET2) are common genetic events in myeloid malignancies and clonal hematopoiesis. These mutations arise in the founding clone and are implicated in many clinical sequelae associated with oncogenic feedforward inflammatory circuits. However, the direct downstream effector of mTET2 responsible for the potentiation of these inflammatory circuits is unknown. To address this, we performed scRNA-seq and scATAC-seq in patients with COVID-19 with and without TET2-mutated clonal hematopoiesis reasoning that inflammation from COVID-19 may highlight critical downstream transcriptional targets of mTET2. Using this approach, we identified metastasis-associated lung adenoma transcript 1 (MALAT1), a therapeutically tractable lncRNA, as a central downstream effector of mTET2 that is both necessary and sufficient to induce the oncogenic proinflammatory features of mTET2 in vivo. We also elucidate the mechanism by which mTET2 upregulate MALAT1 and describe an interaction between MALAT1 and p65, which leads to RNA “shielding” from protein phosphatase 2A dephosphorylation, thus preventing resolution of inflammatory signaling. Significance: This work identifies MALAT1 as a requisite downstream effector of oncogenic feedforward inflammatory circuits necessary for the development of TET2-mutated CH and fulminant myeloid malignancy. We elucidate a novel mechanism by which MALAT1 “shields” p65 from dephosphorylation to potentiate this circuit and nominate MALAT1 inhibition as a future therapeutic strategy.

Abstract Background: COVID-19 causes significant morbidity and mortality, albeit with considerable heterogeneity among affected individuals. It remains unclear which host factors determine disease severity and survival. Given the propensity of clonal hematopoiesis (CH) to promote inflammation in healthy individuals, we investigated its effect on COVID-19 outcomes. Methods: We performed a multi-omics interrogation of the genome, epigenome, transcriptome, and proteome of peripheral blood mononuclear cells from COVID-19 patients (n=227). We obtained clinical data, laboratory studies, and survival outcomes. We determined CH status and TET2-related DNA methylation. We performed single-cell proteogenomics to understand clonal composition in relation to cell phenotype. We interrogated single-cell gene expression in isolation and in conjunction with DNA accessibility. We integrated these multi-omics data to understand the effect of CH on clonal composition, gene expression, methylation of cis-regulatory elements, and lineage commitment in COVID-19 patients. We performed shRNA knockdowns to validate the effect of one candidate transcription factor in myeloid cell lines. Results: The presence of CH was strongly associated with COVID-19 severity and all-cause mortality, independent of age (HR 3.48, 95% CI 1.45-8.36, p=0.005). Differential methylation of promoters and enhancers was prevalent in TET2-mutant, but not DNMT3A-mutant CH. TET2-mutant CH was associated with enhanced classical/intermediate monocytosis and single-cell proteogenomics confirmed an enrichment of TET2 mutations in these cell types. We identified cell-type specific gene expression changes associated with TET2 mutations in 102,072 single cells (n=34). Single-cell RNA-seq confirmed the skewing of hematopoiesis towards classical and intermediate monocytes and demonstrated the downregulation of EGR1 (a transcription factor important for monocyte differentiation) along with up-regulation of the lncRNA MALAT1 in monocytes. Combined scRNA-/scATAC-seq in 43,160 single cells (n=18) confirmed the skewing of hematopoiesis and up-regulation of MALAT1 in monocytes along with decreased accessibility of EGR1 motifs in known cis-regulatory elements. Using myeloid cell lines for functional validation, shRNA knockdowns of EGR1 confirmed the up-regulation of MALAT1 (in comparison to wildtype controls). Conclusions: CH is an independent prognostic factor in COVID-19 and skews hematopoiesis towards monocytosis. TET2-mutant CH is characterized by differential methylation and accessibility of enhancers binding myeloid transcriptions factors including EGR1. The ensuing loss of EGR1 expression in monocytes causes MALAT1 overexpression, a factor known to promote monocyte differentiation and inflammation. These data provide a mechanistic insight to the adverse prognostic impact of CH in COVID-19. Citation Format: Moritz Binder, Terra L. Lasho, Wazim Mohammed Ismail, Nana A. Ben-Crentsil, Jenna A. Fernandez, Minsuk Kim, Susan M. Geyer, Amelia Mazzone, Christy M. Finke, Abhishek A. Mangaonkar, Jeong-Heon Lee, Kwan Hyun Kim, Vernadette A. Simon, Fariborz Rakhshan Rohakthar, Amik Munankarmy, Susan M. Schwager, Jonathan J. Harrington, Melissa R. Snyder, Nathalie M. Droin, Eric Solary, Keith D. Robertson, Eric D. Wieben, Eric Padron, Nicholas Chia, Alexandre Gaspar-Maia, Mrinal M. Patnaik. Enhancer deregulation inTET2-mutant clonal hematopoiesis is associated with increased COVID-19 severity and mortality [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 806.

Abstract Mutations in chromatin regulator ASXL1 are frequently identified in myeloid malignancies, in particular ∼40% of patients with chronic myelomonocytic leukemia (CMML). ASXL1 mutations are associated with poor prognosis in CMML and significantly co-occur with NRAS mutations. Here, we show that concurrent ASXL1 and NRAS mutations defined a population of CMML patients who had shorter leukemia-free survival than those with ASXL1 mutation only. Corroborating this human data, Asxl1−/− accelerated CMML progression and promoted CMML transformation to acute myeloid leukemia (AML) in NrasG12D/+ mice. NrasG12D/+;Asxl1−/− (NA) leukemia cells displayed hyperactivation of MEK/ERK signaling, increased global levels of H3K27ac, upregulation of Flt3. Moreover, we find that NA-AML cells overexpressed all the major inhibitory immune checkpoint ligands: programmed death-ligand 1 (PD-L1)/PD-L2, CD155, and CD80/CD86. Among them, overexpression of PD-L1 and CD86 correlated with upregulation of AP-1 transcription factors (TFs) in NA-AML cells. An AP-1 inhibitor or short hairpin RNAs against AP-1 TF Jun decreased PD-L1 and CD86 expression in NA-AML cells. Once NA-AML cells were transplanted into syngeneic recipients, NA-derived T cells were not detectable. Host-derived wild-type T cells overexpressed programmed cell death protein 1 (PD-1) and T-cell immunoreceptor with immunoglobulin and ITIM domains (TIGIT) receptors, leading to a predominant exhausted T-cell phenotype. Combined inhibition of MEK and BET resulted in downregulation of Flt3 and AP-1 expression, partial restoration of the immune microenvironment, enhancement of CD8 T-cell cytotoxicity, and prolonged survival in NA-AML mice. Our study suggests that combined targeted therapy and immunotherapy may be beneficial for treating secondary AML with concurrent ASXL1 and NRAS mutations.

Abstract Background : The 2016 revision to the World Health Organization classification of myeloid neoplasms has recommended distinction between "proliferative" (WBC ≥ 13 x 10(9)/L) and "dysplastic" (WBC < 13 X 10(9)/L) subtypes of chronic myelomonocytic leukemia (CMML). In addition, CMML is characterized by a spectrum of cytogenetic and molecular abnormalities (Patnaik Leukemia 2014). In the current study, we looked for correlations between gene expression profiles (GEP) and morphologic or molecular categories of previously untreated patients with CMML. Methods : 35 patients with CMML were included in the study. Targeted capture assays were carried out on BM DNA specimens obtained at diagnosis for 28 myeloid-relevant genes (Patnaik Blood C. J.). RNA sequencing (Stranded Total RNA- Illumina, San Diego, CA), was performed on 9 normal BM controls, and 35 CMML samples (25 peripheral blood (PB) and 10 BM). The RNA-Seq data was analyzed using MAPRSeq v.2.0. Normalization and differential expression analysis was performed using edgeR 2.6.2. We selected genes that were differentially expressed with a False Discovery Rate (FDR) <0.1 and a fold change bigger than 2. Pathway enrichment analysis was performed using Ingenuity Pathway Analysis (IPA). Results: Among the 35 study patients, 75% were males and median age was 71 years (range, 55-87). 21 (60%), 9 (25%) and 5 (15%) patients were classified as CMML-0, 1 and 2, respectively. 16 (45%) had a dysplastic while 19 (55%) had a proliferative phenotype. At a median follow-up of 16 months, 5 (14%) deaths and 4 (14%) leukemic transformations were documented. Mutational frequencies included; TET2 45%, ASXL1 45%, SRSF2 40%, NRAS 14%, SETBP1 13%, CBL 10%, JAK2 7%, RUNX1 6%, DNMT3A 6%, U2AF1 6%, SF3B1 5%, ZRSR2 4%, Tp53 4%, and IDH2 4%. Abnormal cytogenetics (n=9, 35%) included; -Y 8%, trisomy 8 6%, monosomy 7 3%, del(13q) 3% and monosomal karyotype 3%. i) Differential GEP in PB samples: 25 PB CMML samples were analyzed. Unsupervised GEP demonstrated two unique clusters (figure one); cluster one was enriched with dysplastic CMML (90%) and cluster two with proliferative CMML (64%). Consistent with the difference in the distribution of morphologic categories, in comparison to cluster one, patients in cluster two had a higher white blood count (p=0.003), higher monocyte count (p=0.02) and higher risk prognostication by the Mayo Model (p=0.016); there was no significant difference between the two clusters in terms of age and gender distribution, hemoglobin level, neutrophil and platelet counts, PB or BM blast content, cytogenetic risk stratification, ASXL1, TET2, RAS, CBL and SETBP1 mutational status. Genes significantly upregulated (FDR<1e-5 and fold change ≥ 16x); in cluster two (proliferative CMML) in comparison to cluster one included; PRG2, CTSG, BEX1, CD34, CRISP2, while those significantly down-regulated included (FDR<1e-15 and fold change ≥ 5x); IL2RB, PRF1, GNLY, PDGFRB. IPA analysis identified the following pathways preferentially expressed in cluster two (proliferative CMML) 1) mitotic roles of polo-like kinase (KIF23, WEE-1, PLK1, PLK4), 2) Cell cycle: G2/M checkpoint regulation (CDC25C, WEE-1, AURKA, CDK1), 3) Cell cycle: chromosomal replication (CDC45, RPA3, MCM2, CDC7), and 4) BRCA1 in DNA damage response (BARD1, RBBP8, PLK1, RAD51). Whereas in the cluster one (dysplastic CMML), preferentially expressed pathways included; 1) T-cell receptor signalling (CD247 PRKCQ, CD4, PLCG1, CD28) and 2) STAT3 signalling (SOCS3, MAP3K9). ii) Differential GEP in BM samples: 10 BM CMML samples were analyzed along with 10 normal controls. Samples and controls clustered separately and within the CMML samples two clusters were identified (figure two). These clusters had equal representations of dysplastic (n=5) and proliferative (n=5) CMML sub-types. Additional samples are being processed. Conclusions: Differential gene expression profiling in patients with CMML identifies two unique clusters, clinically demarcated, at least in PB samples, by "proliferative" and "dysplastic" CMML subtypes. The proliferative cluster is enriched in genes involved in cell cycle regulation and DNA damage response; whereas the dysplastic cluster is enriched in T-cell receptor and STAT3 signalling. The prognostic and therapeutic impact of these gene clusters remains to be assessed in a larger group of informative patients. Figure 1. Figure 1. Figure 2. Figure 2. Disclosures Al-Kali: Novartis: Research Funding; Celgene: Research Funding.

Introduction: Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic neoplasm characterized by sustained peripheral blood (PB) monocytosis and an inherent risk for leukemic transformation. Clonal origins of the disease can be detected in hematopoietic progenitor cells (CD34+/CD38-), while the complete spectrum of mutational evolution can be seen in circulating monocytes (CD14+). Cell sorting strategies have been employed to select cells in CMML, and while there are adequate monocyte numbers in the PB, there are very few circulating progenitor cells. In addition, attrition related to the selection process significantly depletes primary cells available for biological experiments and multiomics studies such as RNA-seq, ChIP-seq, ATAC-seq, and DIP-seq. While single-cell methods may be able to overcome this challenge, bulk sequencing methods remain a robust and cost-effective approach. We hypothesized that, secondary to the stem cell origin of this disease and significant myeloproliferation, PB mononuclear cells (MNC) would provide comparable results with regards to transcriptomic analysis, in comparison to cell selection procedures. Methods: Peripheral blood obtained from 15 molecularly annotated patients with WHO-defined CMML was ACK-lysed and subjected to a Ficoll procedure for collection of MNC. MNC were left unsorted (n=5) or further selected for CD34+/CD38- (n=5) and CD14+ (n=5) using a fully automated RoboSep-S (StemCell Technologies) protocol. All samples were then subjected to bulk whole transcriptome shotgun sequencing (using Illumina TruSeq and an Illumina HiSeq 4000). After data quality control, counts of detectable transcripts were log2-normalized and Pearson's product-moment correlation coefficients were calculated to evaluate the correlation between the two cell-sorting strategies and unsorted cells in terms of detectable transcripts. To visualize sample differences log2-normalized transcripts counts were centered and scaled per gene for a select number of genes relevant to myeloid biology as well as a number of housekeeping genes. Results: Fifteen patients with WHO-defined CMML, median age 69 years (55-73 years), 66% male, were included. Next generation sequencing for somatic mutations was performed on PB MNC obtained at CMML diagnosis (Figure 1, top heatmap). Considering the small sample size, mutations were evenly distributed among groups with the exception of ASXL1 (higher frequency in CD14+ and CD34+/CD38- cells), ZRSR2 (higher frequency in unsorted cells), and TET2 (lower frequency in CD14+ cells). The three groups were also well matched with regards to other CMML-related variables such as WHO and FAB morphological subtypes, cytogenetic abnormalities, and risk stratification by the Mayo Molecular Model. Transcriptomic analysis revealed a strong positive correlation between the median number of log2-normalized detectable transcripts in unsorted cells and CD34+/CD38- cells (ρ = 0.96, p < 0.001, top scatterplot). Likewise, there was a strong positive correlation between the median number of log2-normalized detectable transcripts in unsorted cells and CD14+ cells (ρ = 0.91, p < 0.001, bottom scatterplot). The latter correlation was marginally lower, which was explained by increased global gene expression in 3 of the 5 CD14+ samples (bottom heatmap). Increased gene expression in these 3 samples involved key myeloid genes and housekeeping genes known to have stable expression across human tissues alike. In comparison to PB MNC, both cell sorting strategies resulted in significant depletion of primary cells required for other experiments, and for procedures such as ChIP-seq, DIP-seq and ATAC-seq (CD34+/CD38- had greater depletion than CD14+). Additional experiments to assess this strategy for the above mentioned epigenetic studies are currently being planned. Conclusions: Accounting for sample differences, different cell sorting strategies (unsorted, CD34+/CD38- selection, and CD14+ selection) yielded similar results when performing bulk transcriptomic assessments on PB MNC from patients with CMML. For the purpose of gene expression profiling there was no clear advantage with CD34+/CD38- or CD14+ selection. These results support the use of unsorted cells for bulk transcriptomic analysis in CMML. Figure 1 Disclosures Patnaik: Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.