scholarly journals Molecular Subgroups of T Cell Acute Lymphoblastic Leukemia in Adults Treated According to GMALL Protocols

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 37-38
Author(s):  
Martin Neumann ◽  
Lorenz Bastian ◽  
Sonja Hänzelmann ◽  
Alina Hartmann ◽  
Heiko Trautmann ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Risk Stratification ◽  
Board Of Directors ◽  
Financial Support ◽  
Research Funding ◽  
Good Prognosis ◽  
Molecular Subgroups ◽  
Advisory Committees ◽  
Molecular Subgroup ◽  
Age Dependent

Introduction: In T-ALL, in contrast to BCP-ALL, molecular subgroups are less well defined. Molecular subgroups are based on aberrant expression of oncogenes often resting upon structural aberrations (chromosomal translocation, copy number variations, point mutations). So far, comprehensive studies on molecular subgroups have been predominantly performed in pediatric patients. The clinical relevance of molecular characteristics is not taken into account for risk stratification and targeted therapeutic options for T-ALL patients are limited. Risk stratification is mainly based on immunophenotype and MRD response. Thus, in the German Multicenter Study Group for Adult ALL (GMALL) protocols early and mature T-ALL and molecular failure after first consolidation are high risk features. Whole RNA transcriptome sequencing (RNAseq) enables molecular subgroup allocation profiles with potential prognostic relevance. Herein, we investigated a large cohort of 161 adult T-ALL patients with RNAseq for molecular subgroups and their clinical implication. Patients and methods : RNAseq data (Illumina HiSeq 4000, 100 or 125 bp paired-end, average read count ~30 million reads/sample) were generated from 161 adult T-ALL patients from diagnostic bone marrow samples. For 84 of these T-ALL patients, we additionally investigated DNA methylation using Infinium 450k methylation bead arrays and mutation status using deep targeted DNA sequencing with a gene panel consisting of 206 genes (HiSeq 1500, 100 bp paired-end, average ~800 reads/bp). All patients were treated likewise within pediatric inspired protocols of the GMALL. Clinical follow up and outcome data were available for 129 patients aged between 18 and 55 years. Results: Based on oncogene expression, we could assign molecular subgroups to 160 out of the 161 T-ALL patients: HOXA: n=37 (23%), TLX1: n=37 (23%), TAL/LMO: n=33 (20%), LYL1/LMO2: n=31 (19%), TLX3: n=17 (11%) , NKX2-1: n=4 (2%), TAL2: n=1 (1%). Among others, gene fusion events incorporating TAL1 (n=16), HOXA (n=6), TLX1 (n=14), TLX3 (n=2) and NKX2 (n=2) were detected as underlying mechanisms. Age distribution revealed more TAL/LMO in the younger population (16-25 years: 35% versus >35 years: 3%; p=0.001) and more LYL1/LMO2 and HOXA in the older patients (16-25 years: 23% vs. >35 years: 40%; n.s.). DNA methylation analyses for 84 of the 161 patients support the molecular classification with four distinct subgroups demonstrating a homogenous methylation profile for the TLX1 driven subgroup (methylation cluster M3, n=30). In contrast, methylation cluster M2 (n=21) showed a hypomethylation in CpG islands and comprises the majority of TAL/LMO positive cases (19/21) including all samples with STIL-TAL1 fusions. M1 (n=25) and M4 (n=7) clusters comprised TLX3 (mainly cluster M1, 8/10), HOXA and LYL1/LMO2 cases. Mutation analysis showed a high rate of NOTCH1 (n=71%) mutations in the TLX1 subgroup and an increased rate of mutations in the JAK/STAT pathway (n=29%) and epigenetic regulators (n=50%) in HOXA and LYL1/LMO2 subgroups. Regarding clinical outcome, 126 out of 129 (98%) patients achieved a morphologically complete remission (CR), two patients failed CR, one patient died during induction therapy. Regarding MRD response, 12% of the investigated T-ALL patients showed a molecular failure after consolidation. Noteworthy, we found a molecular CR for 28/30 (93%) patients in the TLX1 subgroup, but only 11/19 (58%) respectively 2/6 (33%) in the HOXA and the LYL1/LMO2 subgroup. This finding results in exceptional five year-overall survival (5y-OS) of 93% in TLX1 patients. Together with the NKX2-1 subgroup (n=4, with 100% 5y-OS), these patients build a prognostically favourable risk group in the overall cohort (94% 5y-OS versus 76% in TAL-LMO patients versus 62% in all other subgroups, p=0.007). This result could not only be found in the overall cohort, but also within the already good risk subgroup of thymic T-ALL patients (93% vs. 75%, p=0.02). Conclusion: This is to our knowledge the largest cohort of adult T-ALL patients characterized by RNAseq on molecular level. Our findings point towards an age dependent distribution of molecular subgroups contributing to age-dependent outcome differences. Patients with TLX1 reveal a molecular subgroup with extraordinary good prognosis. Within thymic T-ALL, the subgroup of patients without TLX1 (~53%) has an inferior but still good prognosis. Disclosures Fiedler: Gilead: Other: support for meeting attendance; Jazz Pharmaceuticals: Honoraria, Other: support for meeting attendance; Abbvie: Membership on an entity's Board of Directors or advisory committees; Morphosys: Consultancy, Honoraria; Celgene: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria; ARIAD/Incyte: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: support for meeting attendance, Patents & Royalties, Research Funding; Daiichi Sankyo: Other: support for meeting attendance. Alakel:Pfizer: Consultancy. Schwartz:AMGEN: Other: personal fees and non-financial support; Novartis: Other: personal fees and non-financial support; Jazz Pharmaceuticals: Other: personal fees and non-financial support; Pfizer: Other: personal fees ; BTG Intl Inc: Other: personal fees ; Gilead Sciences: Other: personal fees and non-financial support ; MSD Sharp & Dohme: Other: personal fees ; Basilea: Other: non-financial suppor. Müller-Tidow:Pfizer: Research Funding, Speakers Bureau; Daiichi Sankyo: Research Funding; Janssen-Cilag GmbH: Speakers Bureau; BiolineRx: Research Funding. Brüggemann:Regeneron: Research Funding; Affimed: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Incyte: Consultancy; Roche: Consultancy; Celgene: Consultancy. Goekbuget:Servier: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Gilead: Consultancy; Erytech: Consultancy; Kite: Consultancy; Jazz: Consultancy, Research Funding.

scholarly journals Age-Related Changes in Human HSC Methylomes Affect Gene Regulatory Elements Critical to HSC Function and Reveal Novel HSC Aging Candidate Genes

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2166-2166
Author(s):  
Abdul-Habib Rahimi ◽  
Reka Toth ◽  
Dieter Weichenhan ◽  
Julia K. Herzig ◽  
Ekaterina Panina ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Board Of Directors ◽  
Candidate Genes ◽  
Research Funding ◽  
Advisory Committees ◽  
Age Related ◽  
Genome Wide ◽  
Age Dependent ◽  
Human Hscs ◽  
Gene Regulatory

Abstract One of the key risk factors for developing acute myeloid leukemia (AML) is advanced age. With a median age of approximately 68 years at diagnosis, AML predominantly affects an elderly population with poor prognosis. Understanding age-related mechanisms preceding AML might foster the development of new therapeutic approaches targeting more specifically pre-malignant genetic or epigenetic changes. Age-related clonal hematopoiesis (ARCH or CHIP) increases the risk of developing leukemia and predominantly affects genes encoding epigenetic modifiers such as DNMT3A or TET2. Changes in the DNA methylome are a characteristic feature of AML and epigenetic therapies with hypomethylating agents are approved for therapy. As demonstrated in murine models, DNA methylation can shape hematopoietic stem cell (HSC) differentiation and aging phenotypes. Here, we aimed to examine genome-wide changes in the DNA methylome and transcriptome of aging human HSCs. Previous studies of aging-related changes in HSC methylomes used murine HSCs, covered only a fraction of the human methylome or were biased towards promoters and CpG islands. Here, we took advantage of tagmentation-based whole-genome bisulfite sequencing (TWGBS) to cover all CpG sites genome-wide using small amounts of input DNA (Wang et al., Nature protocols 2013). We purified HSCs from cord blood (n=3) and bone marrow of young (n=5, defined as age 23-27) and old donors (n=4, defined as age 63-72) using fluorescence-activated cell sorting (FACS) with an 8-color HSC/LSC-panel (Zeijlemaker et al., Leukemia 2016). None of the samples carried CHIP mutations. With low-input RNA-Seq and TWGBS we successfully obtained an integrative data set of the methylome and transcriptome of human HSCs from newborn, young and old individuals. We found that human HSCs show age-specific DNA methylation patterns that progressively change during aging and predominantly clusters with progressive and age-dependent degradation of methylation marks. In addition, we observed an increase in epigenetic heterogeneity in aged HSCs, extending into methylation-based proliferative clocks. We further revealed that wide-spread and progressive degradation of DNA methylation marks during HSC aging largely affected gene regulatory regions such as promoters, enhancers and transcription factor (TF) binding sites. These differentially methylated regions were highly enriched for genes playing a role in T-cell activation, cell adhesion and hematopoietic differentiation. Binding sites for transcription factors associated with AML, as for example the RUNX and GATA family of TFs were highly affected by age-dependent loss of DNA methylation. We further identified regulatory networks with target genes of transcriptional master regulators such as LYL1, regulating HSC pluripotency in combination with GATA2 and RUNX1, or ZNF639, upregulated in leukemic stem cells (LSC), or senescence and cell cycle genes to be upregulated upon aging. Besides known hallmark genes of HSC aging, such as CLU and SELP, we identified candidate genes to be differentially methylated such as HOX genes and other AML-associated genes. We observed deregulated gene expression in aged HSCs affecting HOXA cluster genes, as well as aging and longevity-associated pathways and G2M-checkpoint genes, relevant to DNA damage response. Correlation of epigenome and transcriptome identified a promising set of novel HSC aging-related candidate genes, putatively controlled by DNA methylation and functionally associated with apoptosis and cell adhesion. Furthermore, we discovered age-related epigenetic remodelling of the mTORC1 pathway, a central regulator of aging and cellular senescence. Pharmacological inhibition of mTORC1 using rapamycin was shown to increase lifespan in several model organisms. In summary, we unravel a comprehensive roadmap of the changing epigenome and transcriptome of human HSCs throughout human lifespan. This enables us to precisely pinpoint DNA methylation marks that progressively degrade during aging. Restoring these DNA methylation marks in aged HSCs could potentially ameliorate age-related decline in HSC function and might protect against leukemic transformation. Disclosures Heuser: Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer Pharma AG: Research Funding; Karyopharm: Research Funding; Astellas: Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolremo: Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BergenBio: Research Funding; Pfizer: 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. Buske: Pfizer: Honoraria, Speakers Bureau; Celltrion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; MSD: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bayer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Research Funding.

scholarly journals Metaphase Cytogenetics for Risk Stratification in Newly Diagnosed Multiple Myeloma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4396-4396
Author(s):  
Patrick Mellors ◽  
Moritz Binder ◽  
Rhett P. Ketterling ◽  
Patricia Griepp ◽  
Linda B Baughn ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Risk Stratification ◽  
Board Of Directors ◽  
Research Funding ◽  
Multivariable Analysis ◽  
Risk Modeling ◽  
Newly Diagnosed ◽  
Advisory Committees ◽  
Cytogenetic Abnormalities ◽  
Improve Risk Stratification

Introduction: Abnormal metaphase cytogenetics are associated with inferior survival in newly diagnosed multiple myeloma (MM). These abnormalities are only detected in one third of cases due to the low proliferative rate of plasma cells. It is unknown if metaphase cytogenetics improve risk stratification when using contemporary prognostic models such as the revised international staging system (R-ISS), which incorporates interphase fluorescence in situ hybridization (FISH). Aims: The aims of this study were to 1) characterize the association between abnormalities on metaphase cytogenetics and overall survival (OS) in newly diagnosed MM treated with novel agents and 2) evaluate whether the addition of metaphase cytogenetics to R-ISS, age, and plasma cell labeling index (PCLI) improves model discrimination with respect to OS. Methods: We analyzed a retrospective cohort of 483 newly diagnosed MM patients treated with proteasome inhibitors (PI) and/or immunomodulators (IMID) who had metaphase cytogenetics performed prior to initiation of therapy. Abnormal metaphase cytogenetics were defined as MM specific abnormalities, while normal metaphase cytogenetics included constitutional cytogenetic variants, age-related Y chromosome loss, and normal metaphase karyotypes. Multivariable adjusted proportional hazards regression models were fit for the association between known prognostic factors and OS. Covariates associated with inferior OS on multivariable analysis included R-ISS stage, age ≥ 70, PCLI ≥ 2, and abnormal metaphase cytogenetics. We devised a risk scoring system weighted by their respective hazard ratios (R-ISS II +1, R-ISS III + 2, age ≥ 70 +2, PCLI ≥ 2 +1, metaphase cytogenetic abnormalities + 1). Low (LR), intermediate (IR), and high risk (HR) groups were established based on risk scores of 0-1, 2-3, and 4-5 in modeling without metaphase cytogenetics, and scores of 0-1, 2-3, and 4-6 in modeling incorporating metaphase cytogenetics, respectively. Survival estimates were calculated using the Kaplan-Meier method. Survival analysis was stratified by LR, IR, and HR groups in models 1) excluding metaphase cytogenetics 2) including metaphase cytogenetics and 3) including metaphase cytogenetics, with IR stratified by presence and absence of metaphase cytogenetic abnormalities. Survival estimates were compared between groups using the log-rank test. Harrell's C was used to compare the predictive power of risk modeling with and without metaphase cytogenetics. Results: Median age at diagnosis was 66 (31-95), 281 patients (58%) were men, median follow up was 5.5 years (0.04-14.4), and median OS was 6.4 years (95% CI 5.7-6.8). Ninety-seven patients (20%) were R-ISS stage I, 318 (66%) stage II, and 68 (14%) stage III. One-hundred and fourteen patients (24%) had high-risk abnormalities by FISH, and 115 (24%) had abnormal metaphase cytogenetics. Three-hundred and thirteen patients (65%) received an IMID, 119 (25%) a PI, 51 (10%) received IMID and PI, and 137 (28%) underwent upfront autologous hematopoietic stem cell transplantation (ASCT). On multivariable analysis, R-ISS (HR 1.59, 95% CI 1.29-1.97, p < 0.001), age ≥ 70 (HR 2.32, 95% CI 1.83-2.93, p < 0.001), PCLI ≥ 2, (HR 1.52, 95% CI 1.16-2.00, p=0.002) and abnormalities on metaphase cytogenetics (HR 1.35, 95% CI 1.05-1.75, p=0.019) were associated with inferior OS. IR and HR groups experienced significantly worse survival compared to LR groups in models excluding (Figure 1A) and including (Figure 1B) the effect of metaphase cytogenetics (p < 0.001 for all comparisons). However, the inclusion of metaphase cytogenetics did not improve discrimination. Likewise, subgroup analysis of IR patients by the presence or absence of metaphase cytogenetic abnormalities did not improve risk stratification (Figure 1C) (p < 0.001). The addition of metaphase cytogenetics to risk modeling with R-ISS stage, age ≥ 70, and PCLI ≥ 2 did not improve prognostic performance when evaluated by Harrell's C (c=0.636 without cytogenetics, c=0.642 with cytogenetics, absolute difference 0.005, 95% CI 0.002-0.012, p=0.142). Conclusions: Abnormalities on metaphase cytogenetics at diagnosis are associated with inferior OS in MM when accounting for the effects of R-ISS, age, and PCLI. However, the addition of metaphase cytogenetics to prognostic modeling incorporating these covariates did not significantly improve risk stratification. Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Alnylam: Research Funding; Celgene: Research Funding; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding. Kapoor:Celgene: Honoraria; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Cellectar: Consultancy; Takeda: Honoraria, Research Funding; Amgen: Research Funding; Glaxo Smith Kline: Research Funding. Leung:Prothena: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Omeros: Research Funding; Aduro: Membership on an entity's Board of Directors or advisory committees. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Global DNA Methylation Analysis Identifies Key Pathway Differences Between Poor (Low OCT-1 Activity) and Standard Risk CP-CML Patients At Diagnosis

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3730-3730
Author(s):  
Dale B. Watkins ◽  
Chung Hoow Kok ◽  
Richard J. D'Andrea ◽  
Timothy P. Hughes ◽  
Deborah L. White
Keyword(s):  
Dna Methylation ◽  
Board Of Directors ◽  
Research Funding ◽  
Global Dna Methylation ◽  
Advisory Committees ◽  
Poor Risk ◽  
Normal Individuals ◽  
Risk Patients ◽  
Dna Methylation Analysis ◽  
Standard Risk

Abstract Abstract 3730 Background: DNA methylation, specifically CpG methylation, is an essential mediator of epigenetic gene expression which is of vital importance to many biological processes and human malignancies. DNA hypermethylation has been previously described in a small number of genes in chronic myeloid leukemia (CML); however, current published studies have only examined the methylation status of selected genes, often based on the results of studies in other malignancies. Therefore, the global DNA methylation profile of chronic phase-CML (CP-CML) remains poorly understood, as does the impact of the epigenome on patient response to tyrosine kinase inhibitors (TKIs) including imatinib. The organic cation transport-1 (OCT-1) protein is the major active protein involved in imatinib transport, and we have previously demonstrated that measuring its function in leukemic mononuclear cells, expressed as OCT-1 activity (OA), in patient cells prior to imatinib therapy, provides a strong prognostic indicator. Notably, very low OA (poor risk cohort) is associated with patients at significant risk for poor molecular response, mutation development and leukemic transformation on imatinib therapy. Therefore, it is of particular interest to ascertain whether epigenetic changes are distinct and potentially biologically relevant in these poor risk patients. Aim: To investigate the global DNA methylation profile in CP-CML patients with a particular focus on poor risk patients (very low OCT-1 activity), and to ascertain whether aberrant epigenetic programming may underlie their poor response. Method: Cells were isolated from the blood of 55 CP-CML patients at diagnosis and 5 normal individuals. CP-CML patients were classified according to their OA values, with 29 classified as poor (very low OA) and 26 standard risk (high OA). Whole genome DNA methylation analysis was performed using the Illumina Infinium® HumanMethylation450 BeadChip. Analysis of array data was performed with R v2.15.0, using the minfiBioconductor package. Results: The methylation profile of CP-CML was significantly different to that of normal individuals, as shown in Table 1. GeneGo enrichment analysis revealed a significant enrichment in CML for genes known to be involved in other leukemias (p=4.92e−26) particularly AML and CLL, suggesting similar pathways may be under epigenetic control in CML. A significant number of polycomb group (BMI1 and EZH2) target genes were also identified, suggesting the likely involvement of this pathway in CML. Table 1: Summary of significant CpGs and corresponding genes when comparisons of CP-CML to normal individuals, and poor to standard risk patients, are made using methylation profiles. A significant difference was also observed when the methylation profiles of poor and standard risk CP-CML patients were analysed (Table 1). GeneGo analysis again identified polycomb group (SUZ12 and EZH2) target enrichment and significant enrichment of NOTCH, Hedgehog and WNT signalling (p=7.93e−9, p=2.42e−5 and p=3.66e−5 respectively) in poor risk patients, indicating these pathways may play a significant role in the unfavourable responses observed in many of these patients. Of particular interest were the ten CpGs where a fold change >4 was observed between the methylation profiles of poor and standard risk patients. Using the Prediction Analysis of Microarrays supervised learning algorithm, a classifier for patient OA prediction based on this 10 CpG signature was evaluated. This classifier had an overall accuracy of 94% (sensitivity: 95%, specificity: 93%). Conclusion: We present a comprehensive global DNA methylation analysis of CP-CML that indicates significant and widespread changes to the CML epigenome, compared with that of normal individuals. Importantly, we have generated a classifier, which identifies the poor risk patient subgroup (very low OA) with 94% accuracy. Validation of this classifier is currently in progress. The epigenetic changes identified here may contribute to CML pathogenesis, and also to the response heterogeneity observed between CP-CML patients treated with TKI therapy. Disclosures: Hughes: Novartis Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. White:Novartis Oncology: Honoraria, Research Funding; BMS: Research Funding; CSL: Research Funding.

The Value of Risk Stratification Tools in Multiple Myeloma (MM) in the Real-World: Validation of the Revised- International Staging System (R-ISS) at Initiation of Treatment and Relevance of the ISS and the R-ISS for Risk Stratification in the Relapsed Setting Using Data from the Czech Registry of Monoclonal Gammopathies (RMG)

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2418-2418 ◽  
Author(s):  
Roman Hájek ◽  
Jiri Jarkovsky ◽  
Walter Bouwmeester ◽  
Maarten Treur ◽  
DeCosta Lucy ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Board Of Directors ◽  
Real World ◽  
Research Funding ◽  
Risk Group ◽  
Stage I ◽  
Advisory Committees ◽  
Standard Practice ◽  
First Relapse ◽  
Using Data

Abstract The ISS stratifies survival risk in patients with MM based on β2-microglobulin and albumin levels. The R-ISS is an improved stratification tool, which also uses chromosomal abnormalities (CA) and lactate dehydrogenase (LDH). It was developed based on clinical trial data in the first-line setting but, has not been validated outside clinical trials or for use in the relapsed setting. Using data from the RMG, we assessed the real-world validity of the R-ISS at diagnosis. Additionally, as it is standard practice to re-stage patients after first relapse, we explored the value of re-estimating ISS stage in the relapsed setting and exploring the carry on effect of R-ISS from diagnosis. Re-estimation of R-ISS at relapse is not possible as standard practice often does not include CA measurement at first relapse. Assessment of improvement in stratification was based on visual comparison of median OS, hazard ratios (HR) and confidence intervals. Eligible patients were diagnosed with symptomatic MM between May 2007 and April 2016. A Cox regression model and Kaplan-Meier analyses assessed the performance of the ISS and R-ISS for stratifying patients based on survival both at diagnosis and at first relapse. Overall, there were 3027 patients at diagnosis however only 493 were included in these analyses due to unavailable CA values (84% of patients). ISS and R-ISS stage distribution at diagnosis was ISS I 31.2%, II 29.1% and III 39.6%; and R-ISS I 12% II 57% and III 31% (Table 1). Median overall survival (OS) in months (95% confidence interval [CI]), from diagnosis was 73.5 (68.0-NE), 40.5 (31.0-50.0) and 29.0 (20.9-37.2) in patients with ISS stage I, II and III, respectively, and not reached (NR), 46.6 (39.2-54.1) and 26.0 (18.2-33.8) in patients with R-ISS stage I, II and III, respectively. Table 2 shows HR, which indicate OS assessed in alternative ways was significantly different among the three stages for both ISS and R-ISS. R-ISS provided refined stratification than ISS alone, since R-ISS stage III classified patients with higher risk than ISS III alone, (shorter median OS, narrower CI, and stronger HR vs. ISS I and II). From the original sample of 493 patients at diagnosis, only 250 went on to receive further treatment after first relapse. The median OS months (95% CI) after first relapse was 46.4 (32.0-60.8), 22.8 (13.4-32.1) and 14.9 months (9.0-20.8) in patients staged as ISS stage I, II and III at diagnosis, respectively. In patients staged as R-ISS stage I, II and III at diagnosis it was NR, 25.6 (20.8-30.3) and 10.4 (6.7-14.2), respectively. Data to enable re-estimation of ISS and R-ISS at first relapse were available for 187 patients (R-ISS re-stratification was using CA data at diagnosis only). Median OS months (95% CI) from first relapse was 32.2 (15.5-49.0), 25.6 (11.5-39.6) and 10.8 (8.6-13.0) in patients at ISS stage I, II and III, respectively, and 23.3 (NE-NE), 28.4 (20.8-36.0) and 9.7 (6.5-12.9) in patients at R-ISS stage I, II and III, respectively. The HRs comparing OS from first relapse stratified by ISS at diagnosis indicated that re-estimating ISS did not improve stratification (Table 2). For R-ISS, compared with staging patients at diagnosis, staging at first relapse resulted in refined stratification between stage II and III, however assessment of HRs comparing to stage I was difficult owing to small sample sizes. Re-estimation of R-ISS stage at first relapse resulted in 26% of patients having their stage reclassified; the main drivers of reclassification to a lower R-ISS risk group were β2-microglobulin and albumin levels, and to a higher risk group, LDH levels. Our real-world data show that, at diagnosis R-ISS provides refined risk stratification compared with ISS. Further refinement seemed to be added by restaging at first relapse using R-ISS, not ISS however, CA measurements are not currently routinely measured at first relapse, limiting the practical utility of the R-ISS at this stage. Therefore, re-estimating R-ISS stage after first relapse may enable physicians improve estimation of patient prognosis. Both ISS and R-ISS have been developed for use at diagnosis when there is less evidence to predict prognosis, therefore risk stratification after first relapse should also consider other historical patient, disease and treatment factors contributing to improved risk stratification and improved treatment selection and outcomes. Disclosures Hájek: Novartis: Consultancy, Research Funding; Celgene: Research Funding; Amgen: Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Bouwmeester:Amgen: Consultancy. Treur:Amgen: Consultancy. Lucy:Amgen: Employment, Other: Amgen Stock. Campioni:Amgen: Employment, Other: Holds Amgen Stock. Delforge:Celgene: Honoraria; Amgen: Honoraria; Janssen: Honoraria. Raab:BMS: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Research Funding; Novartis: Consultancy, Research Funding. Schoen:Amgen: Employment, Other: Holds Amgen Stock. Szabo:Amgen: Employment, Other: Holds Amgen Stock. Gonzalez-McQuire:Amgen: Employment, Other: Holds Amgen Stock.

The Genomic Landscape of Childhood and Adult Acute Erythroid Leukemia

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 39-39 ◽  
Author(s):  
Ilaria Iacobucci ◽  
Ji Wen ◽  
Manja Meggendorfer ◽  
Catherine Carmichael ◽  
John K Choi ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Tyrosine Kinase ◽  
Board Of Directors ◽  
Transcription Regulation ◽  
Research Funding ◽  
Complex Karyotype ◽  
Advisory Committees ◽  
Nih3t3 Cells ◽  
Genomic Landscape

Abstract Introduction: The genetic basis of several acute myeloid leukemia (AML) subtypes remains poorly characterized, such as that of acute erythroid leukemia (AEL, AML M6) which is currently subclassified by morphology alone, and is associated with poor outcome. Here we sought to perform a definitive genomic analysis of AEL and translate these findings into faithful experimental models and novel therapeutic approaches. Methods: We studied 151 AEL cases (19.2% pediatric, 4.6% young adult, 21.9% adult and 54.3% older adult). Diagnosis of AEL was centrally confirmed and subclassified according to WHO 2008 and revised 2016 criteria. Whole exome and/or genome sequencing, RNA-sequencing and SNP array analysis were performed on all cases and in 2 AEL cell lines (TF-1 and Hel). Genomic data were compared to those from non-M6 childhood and adult AML from TARGET (n=192) and TCGA (n=197) studies. The functional effects of fusion transcripts and mutated genes were examined in IL-3 dependent Ba/F3 cells, NIH3T3 cells for focus formation assays and/or mouse lineage negative hematopoietic stem cells (lin- HSC) for colony forming and transplantation assays. Avatars of human AEL were established in immunocompromised NSGS and MISTRG mice for preclinical studies. Results: a) Genomic landscape of AEL. We identified 2,250 non-synonymous clonal and subclonal somatic mutations in 1,723 genes with a mean of 16.4 per case (range 2-88) and with missense and frameshift mutations accounting for 47.1% and 22.5% of all mutations, respectively. 78 genes were recurrently mutated in at least 3 cases. In frame fusions were detected in 31% of childhood and 27.5% of adult cases, and were more frequent in cases with complex karyotype. 124 potential driver genes were identified by statistical analysis or known pathogenic role in cancer, 9 of which were recurrent novel targets of mutation, most commonly involving chromatin modification (60.3%), cell cycle/tumor suppression (TP53, 33.8%), DNA methylation (28.5%), transcription regulation (26.5%), splicing (15.9%), NPM1 (11.9%), Ras (11.3%), JAK-STAT signaling (9.9%), the cohesin complex (8.6%), ALK/NTRK1 (4.6%) and PI3K signaling (3.3%). Overall, 33% of cases harbored a mutation in signaling genes amenable to inhibition by tyrosine kinase/Ras inhibitors. Mutations in TP53 and DNA methylation genes were significantly more frequent in adults while mutations in transcription regulators and Ras pathway were more frequent in children. Splicing mutations correlated with MDS phenotype and PI3K alterations with therapy-related AEL. Based on the co-occurrence and exclusivity of mutations 7 main distinct AEL genetic subtypes were defined: 1) pediatric AEL with NUP98-rearrangements (3.3% of all cases); 2) adult complex karyotype AEL with TP53 mutations (33.8%); 3) AEL with MLL-rearrangements (12.6%); 4) NPM1-mutated AEL (11.9%); 5) DNA-methylation/splicing mutated AEL (17.8%); 6) splicing/Ras/transcription regulation mutated AEL (21.2%) and 7) Other (8.6%) (Fig.1A). Mutations of chromatin modifiers occurred independently of karyotype, age and subtype (Fig.1B). NUP98-fusions and mutations in PTPN11, UBTF and GATA1 were more frequent in pediatric AEL compared to non-M6 AML. Among adults, mutations in TP53 and MLL were more frequent in AEL while FLT3, NPM1, DNMT3A and IDH1 were more in frequent in non-M6 subtypes. A complex karyotype, therapy-related AEL, TP53 mutations and NUP98-rearrangements were associated with poor outcome. b) Functional AEL modeling and therapeutic translations. Expression of NUP98-JARID1A in lin- HSC resulted in sustained self-renewal and development of an aggressive transplantable leukemia. At least three classes of signaling pathway mutations are targetable in AEL. ALK mutations in the extracellular MAM domain transformed Ba/F3 cells which were sensitive to crizotinib in vitro. Mutations in the tyrosine kinase domain of NTRK1 transformed NIH3T3 cells and were sensitive to entrectinib in vitro. Targeting of JAK-STAT, mTOR and PI3K pathways were examined in xenografts and sensitivity to JAK2 inhibitor ruxolitinib was confirmed in vivo. Conclusions: We provided the first comprehensive landscape of genomic alterations in AEL and defined distinct genomic groups with unique patterns of mutation occurrence compared to non-M6 AML. Finally, we showed that several pathogenic pathways are amenable to inhibition by approved targeted compounds. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Wei:Novartis: Honoraria, Research Funding. Loh:Abbvie: Research Funding; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Mullighan:Amgen: Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees; Loxo Oncology: Research Funding.

scholarly journals Modeling of the Epigenome of the Cell-of-Origin Identifies Cancer-Specific DNA Methylation Patterns in CLL

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3885-3885
Author(s):  
Justyna Anna Wierzbinska ◽  
Reka Toth ◽  
Naveed Ishaque ◽  
Jan-Phillip Mallm ◽  
Karsten Rippe ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cell Differentiation ◽  
B Cells ◽  
B Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
B Cell Differentiation ◽  
Advisory Committees ◽  
Normal Differentiation ◽  
Methylation Patterns

Abstract Normal B cells undergo extensive epigenetic programming during normal differentiation and distinct B cell differentiation stages represent unique DNA methylation patterns. Chronic Lymphocytic Leukemia (CLL) originates from rapidly differentiating B cells and their DNA methylation signature is stably propagated in CLL. Consequently, CLL methylome data can be used to infer the putative cell-of-origin (COO) for each individual CLL case. We define the COO of CLL as the cell that has acquired a first oncogenic hit and which will initiate tumorigenic growth if one or more additional hits have been acquired. This means that two factors contribute to the epigenetic profile of CLL cells: first, the epigenetic profile of the founder B cell at the time of malignant transformation and second, CLL-specific epigenetic alterations that are acquired during leukemogenesis and progression of the disease. Previous studies using peripheral blood CD19+ B cells as a reference for aberrant methylation calls completely neglected the massive epigenetic programming that occurs during normal B cell differentiation. Thus, novel strategies aiming at identifying truly CLL-specific methylation changes considering the highly dynamic methylome during normal B cell differentiation were urgently needed. Here we outline a new analytical framework to delineate CLL-specific DNA methylation. We demonstrate how this approach can be applied to detect epigenetically deregulated transcripts in CLL. Firstly, we modeled the epigenome dynamics occurring during normal B cell differentiation using linear regression. The DNA methylomes of CLL cells were then precisely positioned onto the normal B cell differentiation trajectory to define the closest normal B cell methylome for every CLL patient, the COO. The epigenome of the COO then served as a reference for aberrant DNA methylation calls. We dissected two categories of CLL-specific methylation events: those occurring at sites undergoing epigenetic programming during B cell differentiation and those that normally do not change during B cell differentiation. The first group was further subdivided into class A and B, displaying exaggerated methylation loss or gain, respectively, and class C showing both hyper- and hypomethylation relative to the normal differentiation. The second group was classified into class D displaying hypo- and class E showing hypermethylation. Overall, only 1.6% of the CpG-sites (7,248 CpGs) represented on the Illumina 450k array were affected by disease-specific methylation programming, mostly hypomethylation (6,680 CpGs). Next, the molecular programs underlying the CLL-specific methylation patterns were investigated. We tested enrichment of chromatin states and of transcription factor binding sites (TFBS) as identified in an immortalized B cell line (GM12878). This indicated that disease-specific methylation events target transcriptionally relevant cis-regulatory elements in CLL (enhancers, weak and poised promoters and insulator regions). In line with this, CLL-specific differentially methylated regions affected TFBS associated with signaling pathways known to be important in normal B-cell differentiation (i.e. BATF, EBF1). We also observed altered methylation at CTCF binding sites suggesting their involvement in CLL pathogenesis. In the present work, we dissected CLL methylomes to distinguish between normal B cell differentiation-associated methylation patterns and CLL-specific methylation events. We showed that this approach is indispensable to identify key pathogenic events driving CLL pathogenesis. The relevance of our approach was demonstrated by contrasting the number of epigenetically deregulated miRNAs and protein-coding genes to those determined with a classic analysis using CD19+ B cells as controls. This highlights the extent of overcalling of CLL-specific methylation patterns in previous studies (~30-fold for protein-coding genes and ~10-fold for miRNAs) and stresses the importance to consider normal differentiation trajectories for the identification of aberrant DNA methylation events. Here we propose 11 protein-coding genes (e.g. DOK2, CLLU1) and 4 miRNAs (e.g. miR-486, miR-195) as being epigenetically deregulated in CLL. Our analytical approach provides a general framework for the identification of disease-specific epigenomic changes that should be applicable to other cancers in the future. Disclosures Küppers: the Takeda Advisory Board: Membership on an entity's Board of Directors or advisory committees. Stilgenbauer:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genzyme: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmcyclics: 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, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Hoffmann La-Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

scholarly journals Wide Variation in Use and Interpretation of Gene Mutation Profiling Panels Among Health Care Providers of Patients with Myelodysplastic Syndromes (MDS): Results of a Large Web-Based Survey

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1825-1825 ◽  
Author(s):  
Alexander B Pine ◽  
Nora B Chokr ◽  
Maximilian Stahl ◽  
David P. Steensma ◽  
Mikkael A. Sekeres ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Board Of Directors ◽  
Gene Mutation ◽  
Health Care Providers ◽  
Research Funding ◽  
Specific Gene ◽  
Care Providers ◽  
Advisory Committees ◽  
Web Based ◽  
Mutation Profiling

Abstract Background. Gene mutation profiling is increasingly employed for diagnosis, risk stratification, and clinical management in patients with MDS. However, current World Health Organization MDS classification is still based on histologic findings (with the exception of SF3B1 for MDS-RS), and guidelines generally suggest that clinical decisions be guided by clinico-pathologic risk stratification tools such as Revised International Prognostic Scoring System (IPSS-R). We sought to study beliefs and patterns of practice with respect to gene mutation profiling among health care providers who manage patients with MDS. Methods. A link to a 23-question web-based survey was emailed to members of the Eastern Cooperative Oncology Group (ECOG)-ACRIN Cancer Research Group, Alliance for Clinical Trials in Oncology (Alliance), and the Southwest Oncology Group (SWOG), and the Cancer Trials Support Unit (CTSU) on 5/1/2018 with 6 subsequent weekly reminders. The Qualtrics survey platform was used to record anonymous responses. We used descriptive statistics to analyze the data. No incentive was provided for responses. Results. Of 371 received responses, 262 were received from providers who did not manage MDS patients or lacked analyzable data and therefore were excluded. Of 109 eligible responses, 108 responders were from institutions representing 31 US states (one respondent was from South America). Median age of respondents was 48 years (range, 33-75); 43 (39%) were women. A third of responders (32%) worked at a university hospital, while 25%, 17%, and 5% worked at a community hospital, private practice, or other settings, respectively. While 37% of participants worked at institutions with guidelines for clinical care of MDS patients, 28% reported that their institutional guidelines recommended MDS-specific gene mutation profiling. Such testing was performed at institutions of 13% participants; institutions of 26% of responders tested a general AML panel that included MDS-specific genes. The total number of respondents whose institutions sent out either an MDS-specific gene panel or a general AML gene panel with MDS-specific genes was similar, 25% and 12%, respectively (Fig. 1). Of those who routinely perform molecular testing, 94% do so at diagnosis, 56% at relapse, 33% during preparation for stem cell transplant, 31% after the failure of hypomethylating agents (HMA), 24% during screening for a clinical trial, and 15% at initial treatment (Fig. 2). MDS gene mutation profiling was felt to be most helpful in diagnosis (rarely 11%; sometimes 49%; often 30%; always 9%), risk stratification (sometimes 31%; often 51%; always 15%), and prognosis (sometimes 31%; often 51%; always 14%); its role was more limited in response assessment (never 12%; rarely 25%; sometimes 44%; often 14%) and to predict responses to HMAs (never 5%; rarely 28%; sometimes 52%, often 14%) (Fig. 3). Various types of evidence were used to stratify MDS risk and prognosis: genetic mutations were used by nearly everyone (95%); 70% relied on morphologic findings, while gene expression/transcriptome profiling was used by 40%. Eighty-four percent of responders reported relying on conventional prognostic models like IPSS-R to identify high-risk patients for whom they would consider intensive treatment options. For this purpose, 62% would also rely on mutation profiling, and 32% would also consider higher frequency of gene mutations. While mutations in the p53 pathway were felt to be helpful in terms of risk stratification and treatment decisions by 70% of responders, 43%, 39%, 31%, 26% 23%, 20%, and 3% considered mutations in spliceosome, DNA methylation, transcription factors, histone modification, signaling, RAS pathway, and cohesin genes, respectively, to be useful as well. Approximately 31% of responders were not certain as to which mutations would affect risk stratification and management choices and said they needed to review literature. The respondents also cited multiple limitations to wider clinical use of MDS gene mutation profiling (Fig. 4). Conclusions. Our survey demonstrates widespread use of gene mutation profiling in the management of patients with MDS, but also reveals substantial variability in beliefs, practices, testing logistics, and interpretation of molecular profiling. Our findings emphasize the need for high-quality data to develop consensus evidence-based guidelines for gene profiling of MDS patients. Disclosures Sekeres: Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Bejar:AbbVie/Genentech: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Foundation Medicine: Consultancy; Astex/Otsuka: Consultancy, Honoraria; Modus Outcomes: Consultancy; Takeda: Research Funding; Genoptix: Consultancy. Gore:Celgene: Consultancy, Research Funding. Zeidan:Ariad: Consultancy, Speakers Bureau; Gilead: Consultancy; Incyte: Employment; Celgene: Consultancy; Abbvie: Consultancy; Agios: Consultancy; Novartis: Consultancy; Pfizer: Consultancy.

scholarly journals Evaluation of the International Prognostic Index for Chronic Lymphocytic Leukemia (CLL-IPI) and Validation of a Proposed Novel Risk Model (BALL Score) in Real-World Relapsed/Refractory (R/R) CLL Patients Receiving Idelalisib and Rituximab

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5485-5485
Author(s):  
Massimo Gentile ◽  
Gianluigi Reda ◽  
Francesca Romana Mauro ◽  
Paolo Sportoletti ◽  
Luca Laurenti ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Board Of Directors ◽  
Research Funding ◽  
Advisory Board ◽  
Low Risk ◽  
Intermediate Risk ◽  
Advisory Committees ◽  
Scientific Advisory Board ◽  
Scientific Advisory

The CLL-IPI score, which combines genetic, biochemical, and clinical parameters, represents a simple worldwide model able to refine risk stratification for CLL patients. This score, developed in the era of chemo-immunotherapy, has not been gauged extensively in R/R-CLL patients treated with novel targeted agents, such as BCR and BCL2 inhibitors. Soumerai et al (Lancet Hematol 2019) assembled a novel risk model for OS in the setting of R/R-CLL receiving targeted therapies in clinical trials. This model, consisting of four accessible markers (β2M, LDH, Hb, and time from initiation of last therapy; BALL score), is able to cluster 3 groups of CLL patients with significantly different OS. This multicenter, observational retrospective study aimed to validate the proposed Soumerai (BALL) and/or CLL-IPI scores for R/R-CLL real-world patients treated with idelalisib and rituximab (IDELA-R). The primary objectives were to determine whether: i) the CLL-IPI retains its prognostic power also in R/R patients treated with IDELA-R; ii) the BALL score is of prognostic value for IDELA-treated R/R-CLL patients, and iii) the BALL score is predictive of PFS. This study, sponsored by Gilead (ISR#IN-IT-312-5339), included CLL patients collected from 12 Italian centers, who received IDELA-R (idelalisib 150 mg b.i.d. and a total of 8 rituximab infusions intravenously) outside clinical trials as salvage therapy with available data for the calculation of the CLL-IPI and BALL scores at the time of treatment start. OS was estimated for all subgroups of both scores. Additionally, risk-specific PFS was assessed. Kaplan-Meier curve, log-rank test, and Cox regression analyses were performed. The prognostic accuracy of the predictive model was assessed by Harrell's C-index. Overall, 120 CLL patients were included in this analysis. The majority of patients were Binet stage B and C (94.2%). The median age was 75 years and 83 cases (69.2%) were male. The median number of previous therapies was 3 (range 1-9) Baseline patient features are listed in Table 1. After a median follow-up of 1.6 years (1 month to 5.8 years), 33 patients had died and 39 experienced an event (death or progression). CLL-IPI scoring (115/120 evaluable cases) indicated that 6 patients (5.2%) were classified as low-risk, 24 (20.9%) as intermediate-risk, 58 (50.4%) as high-risk, and 27 (23.5%) as very high-risk. Stratification of patients according to the CLL-IPI score did not allow prediction of significant differences in OS. Thus, low-risk patients had a 2-year OS probability of 75% (HR=1), with an intermediate-risk of 68% (HR=2.9, 95%CI 0.37-23.3, P=0.3), high-risk of 83% (HR=1.58, 95%CI 0.2-12.5, P=0.66), and very high-risk of 63% (HR=5.9, 95%CI 0.78-45.2, P=0.86). Next, we tested a modified CLL-IPI by assigning a more balanced score to the original CLL-IPI variables (Soumerai et al, Leukemia Lymphoma 2019), partially overlapping previous results. Specifically, modified CLL-IPI high-risk group showed a significantly different OS as compared with intermediate- and low-risk groups. However, differently from the original report no difference was observed between low- and intermediate-risk). According to the BALL score (120/120 evaluable cases), 33 patients (27.5%) were classified as low-risk, 68 (56.7%) as intermediate-risk, and 19 (15.8%) as high-risk. Stratification of patients according to the BALL score predicted significant differences in terms of OS. Thus, low-risk patients had a 2-year OS probability of 92% (HR=1), intermediate-risk of 76% (HR=5.47, 95%CI 1.3-23.7, P=0.023), and high-risk of 54% (HR=15.1, 95%CI 3.4-67, P<0.0001) (Figure 1). Harrell's C-statistic was 0.68 (P<0.001) for predicting OS. To note, BALL score failed to significantly stratify patients in terms of PFS. As for Soumerai et al (Leukemia Lymphoma 2019), the original CLL-IPI score did not retain discriminative power in term of OS in R/R-CLL patients receiving IDELA-R. The modified CLL-IPI failed to stratify low- and intermediate-risk groups, likely due to the number of cases analysed in the current cohort and the heterogeneous IDELA-containing regimens included in the Soumerai study (Soumerai et al, Leukemia Lymphoma 2019). The CLL-IPI was designed for CLL patients treated with first-line chemo-immunotherapy. Herein, we confirm the prognostic power of the BALL score in this real-world series for OS, while losing the predictive impact of patient outcomes in terms of PFS. Disclosures Mauro: Gilead: Consultancy, Research Funding; Jannsen: Consultancy, Research Funding; Shire: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding; Roche: Consultancy, Research Funding. Coscia:Abbvie: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Karyopharm Therapeutics: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Varettoni:ABBVIE: Other: travel expenses; Roche: Consultancy; Janssen: Consultancy; Gilead: Other: travel expenses. Rossi:Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Honoraria, Other: Scientific advisory board; Janseen: Honoraria, Other: Scientific advisory board; Roche: Honoraria, Other: Scientific advisory board; Astra Zeneca: Honoraria, Other: Scientific advisory board. Gaidano:AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sunesys: Consultancy, Honoraria; Astra-Zeneca: Consultancy, Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

scholarly journals Acute Leukemia of Ambiguous Lineage: A Comprehensive Survival Analysis Enables Designing New Treatment Strategies

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 584-584 ◽  
Author(s):  
Ondrej Hrusak ◽  
Valerie De Haas ◽  
Ales Luks ◽  
Iveta Janotova ◽  
Ester Mejstrikova ◽  
...  
Keyword(s):  
Acute Leukemia ◽  
Board Of Directors ◽  
Molecular Genetics ◽  
Financial Support ◽  
Research Funding ◽  
Residual Disease ◽  
Inadequate Response ◽  
Advisory Committees ◽  
Travel Costs ◽  
All Treatment

Abstract Acute leukemia (AL) of ambiguous lineage (AMBI-L) comprises up to 5% of AL cases in both children and adults. Although several definitions exist, a general treatment guideline has been missing. Single country studies usually report fewer than 50 cases of children or adults. Accordingly, the international iBFM AMBI2012 Study/Registry collected 275 AMBI-L cases in patients <18y from Australia, Austria, Brazil, Czechia, Germany, Greece, Israel, Italy, Netherlands, NOPHO (Denmark, Estonia, Finland, Norway, Sweden, Iceland, Latvia and Lithuania), PINDA (Chile), Poland, SAHOP (Argentina), Slovakia, St. Jude's Children Research Hospital (USA), Texas Children's Cancer Center (USA), Ukraine and United Kingdom. Each center/country reported all consecutive patients with AMBI-L from a 2 to 13 year period ending May 31, 2015. Apart from the study itself, the central database served also as a basis for consulting individual patients during the diagnostic workup. Preliminary results of this study were first introduced in ASH 2015 and now the complete detailed analysis of updated findings including significance of immunophenotype, molecular genetics, blast clearance and transplant are shown. In total, 275 patients were included in the study. Of these, 240 fulfilled the definitions of biphenotypic/mixed phenotype AL, partially overlapping with cases in whom two clones had been identified (n=68) and 15 cases presented with undifferentiated AL. Most patients started their treatment with an ALL-type protocol (n=161), 79 with AML therapy, 27 with a combined regimen, including the Interfant protocols, 2 patients were not treated, 2 received other treatment, and in 4 patients such information was missing. The 5yEFS of the entire cohort was 56±3.7% and 5y overall survival was 67±3.3%. Patients treated by ALL-type protocols had superior 5 year event free survival (5yEFS) (70±4.6%, n=158) compared to those who started AML-type treatment (5yEFS: 40±6.4%, n=78) or hybrid ALL/AML treatment (5yEFS: 50±11%, n=27). Although protocol selection was likely biased, we recommend ALL treatment, when diagnostic findings, including molecular genetics, fail to indicate AML therapy. Although myeloperoxidase (MPO) has been used as the ultimate marker of myeloid lineage, patients who started with ALL-type treatment demonstrated a better prognosis even among cases classified as MPOpos/part pos (Fig. 1). These differences by initial choice of treatment are most prominent when CD19pos/part pos cases are analyzed regardless of the overall lineage (Fig. 2). This shows that at least for CD19pos/part pos cases in the absence of RUNX1/RUNX1T1 fusion, treatment should not start with current AML-type protocols. Until week 12, patients with higher leukemia burden were slightly overrepresented compared to non-AMBI ALL patients (data not shown). In addition, patients with higher residual disease had a much poorer prognosis. Thus, Prednisone poor and good responders (based on day 8 blood blast counts) had a 5yEFS of 50±9.7%, n=38 and 81±5.8%, n=82, respectively (p=0.005). By day 15 bone marrow (BM), only cutoffs of 10-4 and 10-3 were analyzed and neither showed significant associations with EFS. At the end of induction, patients with BM residual disease ≥10-3 had a 5yEFS of 51±10%, n=49 compared to 90±4.3% for those with lower levels, n=75 (p=0.0002). Especially higher residual disease at week 12 was associated with an extremely poor EFS (Fig. 3). Early identification of patients with inadequate response and designing alternative treatment for them is our important challenge. No overall benefit of transplantation was seen in patients who started on ALL treatment or hybrid ALL/AML treatment. Again, this may be caused by a biased selection of more severe cases for transplant. In patients who started with AML treatment, transplant appeared to improve prognosis (Fig. 4). This study provides the basis for improved treatment of future patients with AMBI-L, with more accurate diagnostics. OH, AL, IJ, EM and JS were supported by Czech Health Research Council 15-28525A. Disclosures Bleckmann: JazzPharma: Other: financial support of travel costs. Moricke:JazzPharma: Honoraria, Other: financial support of travel costs. Inaba:Arog: Research Funding. Kattamis:Novartis: Honoraria, Research Funding; ApoPharma: Honoraria. Reinhardt:Boehringer Ingelheim: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Jazz Pharma: Other: Travel Accomodation; Celgene: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Epigenetic Phenocopying Expands Molecular Risk Assessment in Acute Myeloid Leukemia (Alliance)

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 803-803
Author(s):  
Brian Giacopelli ◽  
Salma Abdelbaky ◽  
Kyoko Yamaguchi ◽  
Jessica Kohlschmidt ◽  
Krzysztof Mrózek ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Board Of Directors ◽  
Genetic Markers ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Prognostic Markers ◽  
Risk Groups ◽  
Advisory Committees ◽  
Biological Features ◽  
Acute Myeloid

Abstract Genetic profiling of leukemic cells forms the basis for risk stratification in acute myeloid leukemia (AML). Genetic markers in AML are used to make recommendations for distinct treatment approaches, such as remission consolidation with chemotherapy rather than stem cell transplant for patients with favorable risk genetics as defined by the current guidelines from the European LeukemiaNet (ELN). Yet, several limitations remain, such as overall rarity of many mutations, hierarchical complexity in cases with multiple mutations, conflicting reports of associations with outcomes for some genetic markers, and the absence of markers with prognostic significance in some patients. We have recently described genome-wide DNA methylation signatures that underlie biological features of AML cells and their utility to classify patients [Giacopelli et al. Genome Res. 2021;31:747]. The additional value of epigenetic information for risk assessment in AML in the context of current genetic and other clinical prognostic markers remains largely unexplored. In this study, we have first developed a targeted approach for assessment of DNA methylation-based signatures and employed it to classify 1,262 patients with de novo AML enrolled onto the Cancer and Leukemia Group B/Alliance for Clinical Trials in Oncology studies. We successfully classified 87.5% of patients into one of 13 DNA methylation subgroups, termed 'epitypes' (Figure 1A,B). We found that epitypes are composed of a majority of patients with a specific genetic alteration (or a unique combination of alterations) in 9 of 13 epitypes. However, we also identified subgroups of patients that lack these highly recurrent alterations, and, instead, represent an epigenetic phenocopy of the dominant genetic feature (epiphenocopy). Epiphenocopies within epitypes were often enriched in specific lower frequency mutations, suggesting convergence of biological function(s) for these rare mutations. Epiphenocopying was also exhibited by patients displaying a DNA methylation signature involving hypomethylation of STAT DNA sequence motifs (termed the STAT hypomethylation signature, SHS) that mimicked FLT3-ITD mutations. Epitype and SHS DNA methylation signatures affected clinical outcomes separately to ELN risk groups (P&lt;0.0001; Figures 1C,D), and FLT3-ITD status (P&lt;0.0001; Figure 1E), respectively. To broadly examine the prognostic power of DNA methylation signatures, we combined methylation-based classifications into a knowledge bank containing a compendium of other prognostic markers. Using a recently developed machine-learning approach [Gerstung et al. Nat Genet. 2017;49(3):332], we found that DNA methylation retained a high degree of importance for clinical outcomes, including overall survival (Figure 1F). Specifically, SHS and 6 epitypes were the most significant features negatively associated with overall survival along with age (P&lt;0.0001; Figure 1G). SHS and epitype were among the most significantly associated features for all other endpoints, such as early death, remission and relapse (P&lt;0.0001) and improved concordance between all predicted to actual outcomes. Finally, we used DNA methylation to reconstruct all 4 genetic features that define the ELN Favorable risk group. We found that patients with epiphenocopies of t(8;21)/inv(16) (CBF-AML), and CEBPA-dm had favorable outcome indistinguishable from that of patients with the respective genetic markers. NPM1-mutated, FLT3-ITD-negative patients displaying SHS-positivity had adverse risk despite lacking FLT3-ITD. Re-classifying patients with CBF-AML and CEBPA-dm epiphenocopies from more unfavorable risk groups into favorable group and excluding KMT2A/MLL-like and SHS-positive patients substantially improves the definition of favorable risk AML (P&lt;0.0001; Figure 1H). Our study demonstrates that DNA methylation signatures advance our understanding of the biology of AML and improve risk stratification through the identification of patients with epiphenocopies that mimic genetic mutations and other biological features. Use of DNA methylation signatures may lead to more effective assignment of patients to existing and novel therapeutic approaches. Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org; ClinicalTrials.gov Identifiers: NCT00048958 (8461), NCT00899223 (9665), and NCT00900224 (20202) Figure 1 Figure 1. Disclosures Blachly: KITE: Consultancy, Honoraria; INNATE: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria. Blum: Abbvie: Honoraria; AmerisourceBergen: Honoraria; Celyad Oncology: Research Funding; Xencor: Research Funding; Nkarta: Research Funding; Forma Therapeutics: Research Funding; Leukemia and Lymphoma Society: Research Funding; Syndax: Honoraria. Stone: Agios Pharmaceuticals Inc, Novartis;: Research Funding; ACI Clinical, Syntrix Pharmaceuticals, Takeda Oncology: Other: Data Safety & Monitoring; AbbVie Inc, Actinium Pharmaceuticals Inc, Aprea Therapeutics, BerGenBio ASA, ElevateBio, Foghorn Therapeutics, GEMoaB, GlaxoSmithKline, Innate Pharma, Syndax Pharmaceuticals Inc, Syros Pharmaceuticals Inc, Takeda Oncology: Other: Advisory Committee. Eisfeld: Karyopharm (spouse): Current Employment. Byrd: Novartis, Trillium, Astellas, AstraZeneca, Pharmacyclics, Syndax: Consultancy, Honoraria; Vincerx Pharmaceuticals: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Newave: Membership on an entity's Board of Directors or advisory committees.

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