Bone Marrow Mastocytosis Is Independently Associated with Inferior Survival in Chronic Myelomonocytic Leukemia

Introduction: Systemic mastocytosis with associated hematologic neoplasm (SM-AHN) is defined by the presence of a concomitant hematologic malignancy with chronic myelomonocytic leukemia (CMML) being a particularly common partner. The overall survival of patients with SM-AHN is inferior to those with SM alone, even when matched for relevant prognostic covariates. However, the prognostic impact of mastocytosis in patients with CMML is unknown. Methods: CMML patients with concomitant mastocytosis were identified from the Moffitt Cancer Center CMML database. We assessed baseline demographic, clinical, and molecular findings and used Kaplan-Meier method to estimate overall survival (OS). The log-rank test was used to compare Kaplan-Meier curves, We then compared the SM-CMML cohort to a well-established institutional database of CMML patients. Baseline demographic and clinical variables were analyzed using GraphPad Prism and SPSS was used for Cox Regression Analysis. Results: Between 5/2004 and 5/2019 22 of 645 CMML patients (3.4%) were identified to have concomitant mastocytosis. The median follow-up for the 22 patients with SM-CMML was 51 months. Nine (41%) patients were diagnosed with de novo CMML prior to SM. In these cases, secondary SM-CMML occurred at a median time of 7 months after CMML diagnosis. Ten patients (45%) were diagnosed with CMML and SM concurrently and 3 (14%) were diagnosed with SM prior to CMML. Among 17 patients tested for KIT mutations, 12 were found to harbor a mutation. The remaining five patients did not undergo high-sensitivity KIT testing on a bone marrow aspirate. Eleven patients had extended gene sequencing performed with the most common additional mutations involving TET2 (45%), SRSF2 (55%), ASXL1 (27%), RAS (27%), DNMT3A (27%), and RUNX1 (27%). The median overall survival (OS) was estimated to be 38.6 months. Next, we compared this cohort of SM-CMML patients to a large, established database of CMML patients (excluding those with concomitant SM). Age at diagnosis, baseline white blood cell count, hemoglobin, and platelet count were well matched between the two groups. Applying the Mayo CMML Prognostic Model to the cohort of SM-CMML patients demonstrated that 32%, 41%, and 27% were low, intermediate and high risk, respectively. In the CMML cohort, 13%, 35%, and 51% were low, intermediate and high, risk respectively, suggesting the SM-CMML was more common in the lower-risk group (p=0.025). The median OS was similar between the two cohorts (median OS 31.3 vs 38.6 months, p = 0.43). However, multivariate analysis including Mayo Prognostic Scoring System, age > 65, and SM component revealed all three variables to be independently associated with survival (HR 1.8, p < 0.001; HR 1.7, p = 0.047; and HR 1.5, p 0.003, respectively). Assessing the impact of mastocytosis in low, intermediate, and high-risk groups separately, the inferior prognostic impact of mastocytosis was most prominent in high-risk patients (OS 19.6 mo vs. 5.4 mo; p = 0.049). Survival outcomes between SM-CMML and CMML were not statistically different in intermediate and low-risk groups (p = 0.47 and p = 0.19, respectively). Among 16 deaths in the SM-CMML cohort, cause of death was able to be assessed in 13 patients. Four (31%) patients died after transformation to acute myeloid leukemia (AML). These patients were either intermediate- or high-risk by Mayo Prognostic Model. Nine patients (69%) died due to multisystem organ failure due to progressive systemic mastocytosis without development of acute leukemia. Among these, 4 (44%) were low-risk, 3 (33%) were intermediate-risk, and 2 (22%) were high-risk. Conclusions: SM-CMML typically presented with lower-risk disease when graded by the Mayo CMML Prognostic Model. Compared head-to-head, OS was similar between SM-CMML and CMML; however multivariate analysis revealed the SM component to be a significant adverse prognostic factor. The presence of bone marrow mastocytosis is associated with inferior survival in high-risk CMML cases. Cause of death among SM-CMML patients was attributable to both progressive mastocytosis and transformation to AML. AML transformation was limited to intermediate- and high-risk group while progressive mastocytosis was seen across the risk spectrum. Future studies are warranted to determine if SM therapy can mitigate this outcome. Figure 1 Disclosures Kuykendall: Abbvie: Honoraria; Celgene: Honoraria; Incyte: Honoraria, Speakers Bureau; Janssen: Consultancy. Talati:Celgene: Honoraria; Agios: Honoraria; Jazz Pharmaceuticals: Honoraria, Speakers Bureau; Daiichi-Sankyo: Honoraria; Astellas: Honoraria, Speakers Bureau; Pfizer: Honoraria. Komrokji:DSI: Consultancy; Incyte: Consultancy; Agios: Consultancy; JAZZ: Consultancy; JAZZ: Speakers Bureau; Novartis: Speakers Bureau; pfizer: Consultancy; celgene: Consultancy.

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A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

BioData Mining ◽

10.1186/s13040-021-00261-y ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Qian Yan ◽

Wenjiang Zheng ◽

Boqing Wang ◽

Baoqian Ye ◽

Huiyan Luo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Risk Score ◽

Immune Cells ◽

Prognostic Model ◽

Risk Groups ◽

Low Risk ◽

Prognostic Performance ◽

Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

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An Enhanced International Prognostic Index (IPI) for Patients with Diffuse Large B-Cell Lymphoma (DLBCL) in the Rituximab Era Using the National Comprehensive Cancer Network (NCCN) Database.

Blood ◽

10.1182/blood.v120.21.2656.2656 ◽

2012 ◽

Vol 120 (21) ◽

pp. 2656-2656

Author(s):

Zheng Zhou ◽

Alfred W. Rademaker ◽

Leo I. Gordon ◽

Ann S. LaCasce ◽

Ann Vanderplas ◽

...

Keyword(s):

Bone Marrow ◽

Overall Survival ◽

High Risk ◽

International Prognostic Index ◽

Model Development ◽

Prognostic Index ◽

Risk Groups ◽

Ratio Test ◽

Validation Sample ◽

Gi Tract

Abstract Abstract 2656 Introduction: The International Prognostic Index (IPI) was first developed in 1993 to risk stratify patients with aggressive Non-Hodgkin's lymphoma (NHL) for outcome prediction (Shipp, NEJM, 1993). Since the addition of rituximab to conventional CHOP chemotherapy for the treatment of DLBCL, there have been many efforts to validate the IPI as well as to improve upon the model's capacity to distinguish subgroups with discrete clinical outcomes, especially high-risk patients. Previous studies have focused on adding clinical or biologic prognostic factor(s) to the original model or regrouping the original IPI score (R-IPI; Sehn, Blood, 2007). We, therefore, built anew a modern IPI based solely on clinical factors available in the real world NCCN clinical database. Methods: Using the nationwide population-based NHL lymphoma database from NCCN, patients with newly diagnosed DLBCL enrolled between June 2000 and Dec. 2010 at 7 NCCN cancer centers were included with at least 1 year and up to 5 years of follow-up. Clinical characteristics including age, Ann Arbor stage, ECOG performance status, disease in extranodal sites (including positivity in bone marrow, CNS, liver/GI tract, lung, other sites and spleen), LDH, presence of bulky disease (>10 cm) as well as B symptoms were studied as potential predictors for model development using COX proportional hazards regression. IPI scores were assigned proportionally based on parameter estimates of the statistically significant predictors in the final COX model. Model selection and its comparison to the original IPI model were made based on Akaike Criteria (AIC) and the likelihood ratio test. Categorization of age and LDH was decided by testing the linearity assumption and Martingale residuals. Kaplan-Meier curves were plotted for stratified risk groups per the new and original IPI. Finally, both IPI models were compared using the initial randomly selected 15% validation sample. Results: There were 1,650 DLBCL patients with complete clinical information included for model development. The new IPI model consisted of similar component predictors but used a maximum of 8 scoring points by further categorizing age group into >40–60 (score of 1), >60–75 (score of 2) and >75 yrs (score of 3), and normalized LDH between >1–3 times (score of 1) and 33 times (score of 2) upper limit of normal. These categorizations minimized the Martingale residuals. Age effect was linear and 20-year increments fit the model best, whereas the effect of normalized LDH was not linear and reached plateau at a ratio of 3. Lymphomatous involvement either of bone marrow, CNS, Liver/GI tract or lung appeared as a stronger predictor (p<0.001) than number of extranodal sites (p=0.91). Four risk groups (Low, Low-intermediate, High-intermediate and High) were identified using the current IPI (Table 1) with enhanced discrimination power when compared with the original IPI and better global model fitting statistics, i.e. smaller AIC and significant likelihood ratio test (p<0.001). It was possible to identify a high risk group (score 3 6) with 5-year overall survival of 33% (95% CI: 22%–45%). Better model prediction was also shown in the validation sample. Conclusions: We were able to develop an enhanced IPI model for clinical prediction among previously untreated DLBCL cases by using patient level data from the NCCN NHL database. The NCCN-IPI demonstrates better risk stratification and identifies a poor risk subgroup with <50% 5-year overall survival in the current real-world clinical setting as compared to the original IPI model developed for aggressive lymphoma prior to the rituximab era. Disclosures: No relevant conflicts of interest to declare.

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Development and validation of a prognostic model for overall survival in chemotherapy-naive men with metastatic castration-resistant prostate cancer (mCRPC) from the phase 3 prevail clinical trial.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.5022 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. 5022-5022

Author(s):

Andrew J. Armstrong ◽

Ping Lin ◽

Celestia S. Higano ◽

Cora N. Sternberg ◽

Guru Sonpavde ◽

...

Keyword(s):

Clinical Trial ◽

Overall Survival ◽

Prognostic Factors ◽

High Risk ◽

Prognostic Model ◽

Risk Groups ◽

Multivariable Model ◽

Training Set ◽

Phase 3 ◽

Testing Set

5022 Background: Prognostic models require updating to reflect contemporary medical practice. In a post hoc analysis of the phase 3 PREVAIL trial (enzalutamide vs placebo), we identified prognostic factors for overall survival (OS) in chemotherapy-naive men with mCRPC. Methods: Patients were randomly divided 2:1 into training (n = 1159) and testing (n = 550) sets. Using the training set, 23 predefined candidate prognostic factors (including treatment) were analyzed in a multivariable Cox model with stepwise procedures and in a penalized Cox proportional hazards model using the adaptive least absolute shrinkage and selection operator (LASSO) penalty (data cutoff June 1, 2014). A multivariable model predicting OS was developed using the training set; the predictive accuracy was assessed in the testing set using time-dependent area under the curve (tAUC). The testing set was stratified based on risk score tertiles (low, intermediate, high), and OS was analyzed using Kaplan-Meier methodology. Results: Demographics, disease characteristics, and OS were balanced between the training and testing sets; median OS was 32.7 months for both datasets. There were no enzalutamide treatment-prognostic factor interactions (predictors). The final multivariable model included 11 prognostic factors: prostate-specific antigen, treatment, hemoglobin, neutrophil-lymphocyte ratio, liver metastases, time from diagnosis to randomization, lactate dehydrogenase, ≥ 10 bone metastases, pain, albumin, and alkaline phosphatase. The tAUC was 0.74 in the testing set. Median (95% confidence interval [CI]) OS for the low-, intermediate-, and high-risk groups (testing set) were not yet reached (NYR) (NYR–NYR), 34.2 months (31.5–NYR), and 21.1 months (17.5–25.0). The hazard ratios (95% CI) for OS in the low- and intermediate-risk groups vs the high-risk group were 0.20 (0.14–0.29) and 0.40 (0.30–0.53), respectively. Conclusions: Our validated prognostic model incorporates factors routinely collected in chemotherapy-naive men with mCRPC treated with enzalutamide and identifies subsets of men with widely differing survival times. Clinical trial information: NCT01212991.

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Development and validation of a prognostic model for overall survival in chemotherapy-naïve men with metastatic castration-resistant prostate cancer (mCRPC) from the phase III PREVAIL clinical trial.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.6_suppl.138 ◽

2017 ◽

Vol 35 (6_suppl) ◽

pp. 138-138

Author(s):

Andrew J. Armstrong ◽

Ping Lin ◽

Celestia S. Higano ◽

Cora N. Sternberg ◽

Guru Sonpavde ◽

...

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

High Risk ◽

Prognostic Model ◽

Specific Antigen ◽

Risk Groups ◽

Phase Iii ◽

Multivariable Model ◽

Training Set ◽

Testing Set

138 Background: Prognostic models require updating to reflect contemporary medical practice. In a post hoc analysis of the phase 3 PREVAIL trial (enzalutamide vs placebo), we identified prognostic factors for overall survival (OS) in chemotherapy-naïve men with mCRPC. Methods: Patients were randomly divided 2:1 into training (n = 1159) and testing (n = 550) sets. Using the training set, 23 predefined candidate prognostic factors (including treatment) were analyzed in a multivariable Cox model with stepwise procedures and in a penalized Cox proportional hazards model using the adaptive least absolute shrinkage and selection operator (LASSO) penalty (data cutoff June 1, 2014). A multivariable model predicting OS was developed using the training set; the predictive accuracy was assessed in the testing set using time-dependent area under the curve (tAUC). The testing set was stratified based on risk score tertiles (low, intermediate, high), and OS was analyzed using Kaplan-Meier methodology. Results: Demographics, disease characteristics, and OS were balanced between the training and testing sets; median OS was 32.7 months for both datasets. There were no enzalutamide treatment-prognostic factor interactions (predictors). The final multivariable model included 11 prognostic factors: prostate-specific antigen, treatment, hemoglobin, neutrophil-lymphocyte ratio, liver metastases, time from diagnosis to randomization, lactate dehydrogenase, ≥ 10 bone metastases, pain, albumin, and alkaline phosphatase. The tAUC was 0.74 in the testing set. Median (95% confidence interval [CI]) OS for the low-, intermediate-, and high-risk groups (testing set) were not yet reached (NYR) (NYR–NYR), 34.2 months (31.5–NYR), and 21.1 months (17.5–25.0). The hazard ratios (95% CI) for OS in the low- and intermediate-risk groups vs the high-risk group were 0.20 (0.14–0.29) and 0.40 (0.30–0.53), respectively. Conclusions: Our validated prognostic model incorporates factors routinely collected in chemotherapy-naïve men with mCRPC treated with enzalutamide and identifies subsets of men with widely differing survival times.

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Prognositc Factors and Survival in Patients with Hypocellular Myelodysplastic Syndrome: Development of a Disease Specific Prognostic Score.

Blood ◽

10.1182/blood.v114.22.3819.3819 ◽

2009 ◽

Vol 114 (22) ◽

pp. 3819-3819 ◽

Cited By ~ 1

Author(s):

Wei-gang Tong ◽

Tapan Kadia ◽

Gautam Borthakur ◽

Elias Jabbour ◽

Sherry Pierce ◽

...

Keyword(s):

Bone Marrow ◽

Overall Survival ◽

High Risk ◽

Myelodysplastic Syndrome ◽

Median Survival ◽

Prognostic Model ◽

Prognostic Score ◽

Test Group ◽

Increased Risk ◽

High Risk Disease

Abstract Abstract 3819 Poster Board III-755 Background Myelodysplastic syndrome (MDS) is a heterogeneous group of bone marrow disorders characterized by dysplastic changes in the myeloid lineages, ineffective hematopoiesis, and an increased risk of transformation to acute myeloid leukemia (AML). In most cases, bone marrow is hyperceulluar but in 10 to 20% of cases, bone marrow can be hypocellular (defined as < 30% cellularity in patients < 70 years, or < 20% cellularity in patients 70 years or older), a condition that overlaps and is difficult to differentiate from aplastic anemia (AA). Currently, there are no good prognostic model for patients with hypocellular MDS. Methods In order to improve the prognostic assessment and to better understand the natural history of hypoplastic MDS, we analyzed the associations between disease characteristics and survival in 253 cases of hypocellular MDS presented to MDACC between 1993 and 2007. This is the largest study so far on patients with hypocellular MDS. We also compared the presenting characteristic and survival between these patients and a group of patients with hyper/normocelluar MDS (n=1725) during the same time period. Results Patients with hypocellular MDS usually presented with more significant thrombocytopenia (p< 0.019), neutropenia (p< 0.001), low β-2 microglobulin (p< 0.001), more transfusion dependency (p< 0.001), and more intermediate-2/high risk disease (57% vs. 42%, p= 0.02) compared to their hyper/normocelluar counterparts. There was no difference in overall survival between the hypocellular and the hyper/normocellular groups (p= 0.312). We divided the patients randomly into study and test group, and a multivariate analysis of prognostic factor identified the following adverse, independent factors (p < 0.001): poor performance status (ECOG 2-4), poor bone marrow cytogenetics (chromosome 7 or complex), anemia (< 10 g/dl), increased bone marrow blasts (≥ 5%) and high serum LDH (> 600 IU/l). In this model, each characteristic has a score of 1. A new prognostic model based on these factors could classify this group of patients into three risk categories independent of IPSS score. Patients with low risk (n= 66; scores 0-1) had a median survival of 30 months, and 2-year/3-year survival of 62%/44%. Patients with intermediate risk (n=44; score 2) had a median survival of 19.4 months, and 2-year/3-year survival of 43%/20%. Patients with high risk disease (n= 59; scores 3-5) had a median survival of 7.3 months, and 2-year/3-year survival of 12%/6%. When this new prognostic model was applied to test group (n=84), the median survival was 55.7, 13.5 and 8.6 months (p< 0.0001) for patients in low, intermediate and high risk groups, respectively. Patients that received immunotherapy (ATG/cyclosporine) had a better median survival and overall survival than patients treated with supportive care, hypomethylating agents, or induction chemotherapy (p< 0.0001). Conclusions Here, we proposed a new simple prognostic model that allows to predict prognosis in patients with hypocellular MDS. Analysis of prognostic factors in patients with hypocellular MDS may help us understand the biology of the disease, and develop risk-adapted therapies for this group of patients. Disclosures: No relevant conflicts of interest to declare.

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A New Prognostic Model for Extranodal Natural Killer/T Cell Lymphoma, Nasal Type

Blood ◽

10.1182/blood.v122.21.1769.1769 ◽

2013 ◽

Vol 122 (21) ◽

pp. 1769-1769

Author(s):

Qingqing Cai ◽

Xiaolin Luo ◽

Ken H. Young ◽

Huiqiang Huang ◽

Guanrong Zhang ◽

...

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

High Risk ◽

Prognostic Model ◽

Cell Lymphoma ◽

Prognostic Index ◽

Roc Curves ◽

Risk Groups ◽

Survival Outcomes ◽

Nasal Type

Abstract Background Extranodal natural killer (NK)/T–cell lymphoma, nasal type (ENKTL) is an aggressive disease with a poor prognosis. A better risk stratification is beneficial for clinical management in affected patients. Our recent study has shown that fasting blood glucose (FBG) was a novel, prognostic factor, (Cai et al, British Journal of Cancer, 108: 380–386,2013). This finding has not been integrated in the previous prognostic models for ENKTL Therefore, we aimed to design a new prognostic model, including FBG, for ENKTL which supports to identify high–risk patients eligible for advanced or more aggressive therapy. Patients and methods 158 newly diagnosed patients with ENKTL were analyzed between January 2003 and January 2011 at Sun Yat–sen University Cancer Center, China. Overall survival (OS) and progression free survival (PFS) were estimated using the Kaplan–Meier method. The significance of differences between survival was tested using the Log–rank test. Significant variables in the univariate analysis were selected as variables for the multivariate analysis of survival. The latter was performed by the Cox regression mode. We constructed receiver operating characteristic (ROC) curves and compared the areas under the ROC curves of total protein (TP), FBG, Korean Prognostic Index (KPI) and their combinations in comparison to the survival outcome. Results Of 158 patients, 156 patients had complete clinical information for the parameters of the International Prognostic Index (IPI) model and KPI model. The estimated 5–year overall survival rate in 158 patients was 59.2%. Independent prognostic factors included TP < 60 g/L, FBG > 100 mg/dL, KPI score ≥ 2. A new prognostic model was constructed by combining these prognostic factors: Group 1 (64 cases, 41.0%), no adverse factors; Group 2 (58 cases, 37.2%), one adverse factor; and Group 3 (34 cases, 21.8%), two or three adverse factors. The 5–year overall survival of these groups were 88.9%, 35.6% and 12.7%, respectively (p < 0.001). The survival curves according to the new prognostic model are shown in Fig. 1. The new model categorized three groups with significantly different survival outcomes. The new prognostic model was also efficient in discriminating the patients with low to low–intermediate risk IPI group and high–intermediate to high risk IPI group into three subgroups with different survival outcomes (p < 0.001). The KPI model balanced the distribution of patients into different risk groups better than IPI prognostic model (score 0: 12 cases, 7.7%; score 1: 38 cases, 24.4%; score 2: 42 cases, 26.9%; score 3–4: 64 cases, 41.0%), and it was able to differentiate patients with different survival outcomes (p < 0.001). In addition, the new prognostic model had a better prognostic value than did KPI model alone (p < 0.001), suggesting that TP and FBG reinforced the prognostic ability of KPI model (Table 1). Conclusions The new prognostic model we proposed for ENKTL, including the new prognostic indicator total protein and FBG, demonstrated balanced distribution of patients into different risk groups with better prognostic discrimination as compared to KPI model alone. Disclosures: No relevant conflicts of interest to declare.

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The Combination of SKY92 and ISS Provides a Powerful Tool to Identify Both High Risk and Low Risk Multiple Myeloma Cases, Validation in Two Independent Cohorts

Blood ◽

10.1182/blood.v126.23.2970.2970 ◽

2015 ◽

Vol 126 (23) ◽

pp. 2970-2970 ◽

Cited By ~ 3

Author(s):

Martin van Vliet ◽

Joske Ubels ◽

Leonie de Best ◽

Erik van Beers ◽

Pieter Sonneveld

Keyword(s):

Overall Survival ◽

High Risk ◽

Research Funding ◽

Prognostic Markers ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Kaplan Meier ◽

Standard Risk

Abstract Introduction Multiple Myeloma (MM) is a heterogeneous disease with a strong need for robust markers for prognosis. Frequently occurring chromosomal abnormalities, such as t(4;14), gain(1q), and del(17p) etc. have some prognostic power, but lack robustness across different cohorts. Alternatively, gene expression profiling (GEP) studies have developed specific high risk signatures such as the SKY92 (EMC92, Kuiper et al. Leukemia 2012), which has shown to be a robust prognostic factor across five different clinical datasets. Moreover, studies comparing prognostic markers have indicated that the SKY92 signature outperforms all other markers for identifying high risk patients, both in single and multivariate analyses. Similarly, when assessing the prognostic value of combinations of various prognostic markers, the SKY92 combined with ISS was the top performer, and also enables detection of a low risk group (Kuiper et al. ASH 2014). Here, we present a further validation of the low and high risk groups identified by the SKY92 signature in combination with ISS on two additional cohorts of patients with diverse treatment backgrounds, containing newly diagnosed, previously treated, and relapsed/refractory MM patients. Materials and Methods The SKY92 signature was applied to two independent datasets. Firstly, the dataset from the Total Therapy 6 (TT6) trial, which is a phase 2 trial for symptomatic MM patients who have received 1 or more prior lines of treatment. The TT6 treatment regime consists of VTD-PACE induction, double transplant with Melphalan + VRD-PACE, followed by alternating VRD/VMD maintenance. Affymetrix HG-U133 Plus 2.0 chips were performed at baseline for n=55 patients, and OS was made available previously (Gene Expression Omnibus identifier: GSE57317). However, ISS was not available for this dataset. Secondly, a dataset of patients enrolled at two hospitals in the Czech Republic, and one in Slovakia (Kryukov et al. Leuk&Lymph 2013). Patients of all ages, and from first line up to seventh line of treatment were included (treatments incl Bort, Len, Dex). For n=73 patients Affymetrix Human Gene ST 1.0 array, OS (n=66), and ISS (n=58) was made available previously (ArrayExpress accession number: E-MTAB-1038). Both datasets were processed from .CEL files by MAS5 (TT6), and RMA (Czech), followed by mean variance normalization per probeset across the patients. The SKY92 was applied as previously described (Kuiper et al. Leukemia 2012), and identifies a High Risk and Standard Risk group. In conjunction with ISS, the SKY92 Standard Risk group is then further stratified into low and intermediate risk groups (Kuiper et al. ASH 2014). Kaplan-Meier plots were created, and the Cox proportional hazards model was used to calculate Hazard Ratios (HR), and associated 1-sided p-values that assess whether the SKY92 High Risk group has worse survival than SKY92 Standard Risk group (i.e. HR>1). Results Figure 1 shows the Kaplan Meier plots of the SKY92 High Risk and Standard Risk groups on the TT6 and Czech cohorts. On the TT6 dataset, the SKY92 signature identifies 11 out of 55 patients (20%) as High Risk. In both datasets, the SKY92 High Risk group has significantly worse overall survival, HR=10.3, p=7.4 * 10-6 (TT6), and HR=2.6, p=2.2 * 10-2 (Czech). In addition, the combination of SKY92 with ISS on the Czech dataset identifies a low risk group of 14 out of 61 patients (23%), with a five year overall survival estimate of 100% versus 28.7% in the SKY92 High Risk group (HR=inf). Robustness of the SKY92 signature is further demonstrated by the fact that it validates on both datasets, despite different microarray platforms being used. Conclusions The SKY92 high risk signature has been successfully validated on two independent datasets generated using different microarray platforms. In addition, on the Czech data, the low risk group (SKY92 Standard Risk combined with ISS 1) has been successfully validated. Together, this signifies the robust nature of the SKY92 signature for high and low risk prediction, across treatments, and with applicability in newly diagnosed, treated, and relapsed/refractory MM patients. Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Disclosures van Vliet: SkylineDx: Employment. Ubels:SkylineDx: Employment. de Best:SkylineDx: Employment. van Beers:SkylineDx: Employment. Sonneveld:Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Karyopharm: Research Funding; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Research Funding.

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Comorbidities Are Major Drivers of Overall Survival of Chronic Myelomonocytic Leukemia

Blood ◽

10.1182/blood-2018-99-119339 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 5521-5521

Author(s):

Tyson Broadbent ◽

Srinivas K. Tantravahi ◽

Sowmya Ravulapati ◽

Morgan Ward ◽

Alexandra Asay ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Medical Records ◽

Risk Groups ◽

Advisory Committees ◽

Kaplan Meier ◽

Comorbidity Index ◽

Equity Ownership ◽

Myelomonocytic Leukemia ◽

Risk Categories

Abstract Introduction:Chronic myelomonocytic leukemia (CMML) is a genetically heterogeneous myeloid neoplasm characterized by the presence of both dysplastic and proliferative features and highly variable clinical outcome. A CMML specific prognostic system (CPSS) has been developed that stratifies patients in to low, intermediate and high risk groups based on WHO subtype, FAB subtype, transfusion dependent anemia, and karyotype. Somatic mutations and DNA methylation patterns can increase prognostic precision, but fail to explain a large part of the clinical variation, suggesting that additional variables, including comorbidities, may be major determinants of overall survival (OS) in CMML. Methods : We retrospectively identified CMML patients diagnosed between 1996 and 2017 at the Huntsman Cancer Hospital, University of Utah, using ICD codes, tumor registry data and electronic medical records. For all patients a diagnosis of CMML was confirmed based on 2008 WHO diagnostic criteria. Data on comorbidities at the time of diagnosis were obtained by search of electronic medical records using a customized rule based algorithm utilizing linguimatics text mining software (Natural language processing). The comorbidities were scored and categorized as per previously published reports: low, intermediate and high risk groups for MDS Comorbidity Index (MDS-CI) and low, mild, moderate/high (moderate and high included in the same group due to small number of patients) for the Charlson Comorbidity Index (CCI). Continuous variables were transformed into categorical variables, based on cutoffs used in previously published studies. Univariate analysis was performed using the Cox proportional hazards model for categories: MDS-CI (low, intermediate and high) and CCI (low, mild, moderate/high). Other variables analyzed included age (<70 or >70 years), sex (male or female), hemoglobin (<10 gm/dL or >10 gm/dL), platelet count (<100k/uL or >100k/uL), WHO subtype (CMML-0, CMML-1 and CMML-2), FAB subtype (CMML-MD or CMML-MP), karyotype (low, intermediate and high risk) and treatment with hypomethylating agents (yes or no). Kaplan-Meier methods were used for plotting OS. All analysis was performed using R statistical programming software version 3.2.1 (The R Foundation for Statistical Computing, Vienna, Austria). Results shown are censored at the time of allogeneic stem cell transplant. For OS the "Low" category is reference and the p-values are for comparison to this category using the Cox model. Results : We identified 94 patients with confirmed diagnosis of CMML. The median age was 76 (range 33-91 years) and 61 patients were men (65%). Fifty-five (58.5%), 34 (36.2%) and 5 (5.3%) patients were categorized as MDS-CI low, intermediate and high risk respectively. Sixty-two (66%), 26 (27.6%) and 6 (6.4%) were categorized as low, mild and moderate/high CCI risk. Hazard ratios (HR) for MDS-CI risk categories were: intermediate=1.26 (95% CI 0.71 to 2.23; p=0.425) and high risk=2.22 (95% CI 0.86-5.75); p=0.101). HR for CCI risk categories were: mild=1.01 (95% CI 0.56-1.82; p=0.964) moderate/high=4.18 (95% CI 1.57 to 11.10; p=0.004). HR for other variables are shown in Table 1. Kaplan-Meier curve representing the OS of the entire cohort categorized according to CCI and MDS-CI risk categoriesis shown in Figure 1. Estimated median survival for MDS-CI low, intermediate and high is 36, 36, and 23 months respectively. Median survival for CCI-CI low, mild, moderate/high risk categories was 36, 33, and 10 months respectively (Figure 1). Conclusions: High risk CCI and MDS-CI category patients are at markedly higher risk of death, suggesting that co-morbidities are major host-related determinant of OS in CMML. Given the association of clonal hematopoiesis of indeterminate potential (CHIP) with coronary heart disease (Jaiswal et al. N Engl J Med 2017; 377:111-121) and the fact that CHIP genes such as TET2are frequently mutated in CMML, it is conceivable that CMML causally contributes to comorbidities. Somatic mutation data are being collected for inclusion in a multivariate model that will be presented at the conference. Disclosures Shami: JSK Therapeutics: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Lone Star Biotherapies: Equity Ownership; Pfizer: Consultancy; Baston Biologics Company: Membership on an entity's Board of Directors or advisory committees. Kovacsovics:Abbvie: Research Funding; Amgen: Honoraria, Research Funding. Deininger:Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Blueprint: Consultancy.

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Correlation Of Outcomes Of Allogeneic Stem Cell Transplants For Chronic Myelomonocytic Leukemia With The Mayo Prognostic Model

Blood ◽

10.1182/blood.v122.21.5226.5226 ◽

2013 ◽

Vol 122 (21) ◽

pp. 5226-5226

Author(s):

Michelle T Patzelt ◽

Mark R. Litzow ◽

William Hogan ◽

Shahrukh K Hashmi ◽

Michelle Elliott ◽

...

Keyword(s):

Bone Marrow ◽

Stem Cell ◽

High Risk ◽

Univariate Analysis ◽

Disease Status ◽

Chronic Myelomonocytic Leukemia ◽

Prognostic Impact ◽

Cell Transplant ◽

Comorbidity Index ◽

Myelomonocytic Leukemia

Abstract Abstract 5226 Background Chronic myelomonocytic leukemia (CMML) is a clonal stem cell disorder with overlapping features between myelodysplastic syndromes and myeloproliferative neoplasms. Allogeneic stem-cell transplant (SCT) is considered to be a potentially curative option (Eur J Haematol. 2013; 90:355); with karyotype and comorbidity index (CI) being independent prognostic factors. The role of newer CMML prognostic models remains to be elucidated. We carried out this study to analyze the predictive value of the Mayo model (Leukemia 2013; 27:1504) in assessing transplant outcomes for patients with CMML. Methods After due IRB approval, 25 patients with WHO-defined CMML that underwent allo-SCT at Mayo Clinic from 1992 through 2013 were identified. All patients underwent bone marrow examination and cytogenetic evaluation at diagnosis. Clinical features including status at transplant, graft-versus-host disease (GVHD) prophylaxis, donor-recipient HLA-matching, donor-recipient blood types and CMV status were documented. Patients were evaluated for development of GVHD, disease relapse, remission status, and death from all causes. We evaluated the prognostic relevance of clinical and laboratory parameters including those previously identified by the MDAPS (Blood 2002;99:840), Spanish cytogenetic stratification (Haematologica 2011;96:375), and the recently described Mayo model Results Among 25 study patients, 14 (56%) were males and 15 (60%) had WHO defined CMML-1 (6-symptomatic/transfusion dependent, 3-monosomy 7). The median age at transplant was 51 years (range, 18-66 years). Graft sources included: 19 (76%) peripheral blood, 5 (20%) bone marrow, and 1 (4%) double umbilical cord blood. Ten (40%) patients received a reduced intensity conditioning. At last follow up, 15 (60%) deaths were documented while the remainders are alive and disease free. There were six (25%) post-transplant relapses all resulting in mortality. The 5-year OS was 42% and the 5-year non relapse mortality was 35%. Based on the Mayo model, 15 (60%) patients received a high-risk prognostication, 6 (24%) were intermediate and 3 (12%) were low. In an univariate analysis that included: demographics, blood counts at diagnosis and transplant, WHO classification (p=0.19), Spanish karyotypic stratification (p=0.67), prognostication according to the MDAPS (p=0.35) and Mayo model, disease status at transplant, SCT comorbidity index (p=0.06), graft sources, transplant conditioning regimens (p=0.08), development of acute (p=0.64) and chronic GVHD (p=0.06); thrombocytopenia at diagnosis (p=0.04), disease status at transplant (p=0.02), and a high risk prognostication using the Mayo model (p=0.01) were statistically significant. On a multivariable analysis only high risk prognostication by the Mayo model (p=0.02) retained its negative prognostic impact. In an univariate analysis for relapse-free survival, risk stratification by the Spanish cytogenetic system (p=0.04) and the Mayo model were prognostic (p=0.01), with the high risk prognostication by the Mayo model retaining its negative prognostic impact (p=0.01). Conclusions Allogeneic SCT remains a viable treatment option for patients with CMML. The Mayo model serves as a valuable tool, helping with the identification of high risk CMML patients. These patients seem to benefit from allo-SCT in comparison to non-transplant therapeutic options. Given the smaller sample size, validation in a larger patient cohort is needed. Disclosures: No relevant conflicts of interest to declare.

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Validation of the Low Risk Prognostic Scoring System (LR-PSS) in Patients with VERY Low, Low and Intermediate Risk IPSS-R Myelodysplastic Syndrome. Results from a Single Center

Blood ◽

10.1182/blood.v126.23.2902.2902 ◽

2015 ◽

Vol 126 (23) ◽

pp. 2902-2902 ◽

Cited By ~ 1

Author(s):

Helena Pomares ◽

Isabel Sánchez-Ortega ◽

Esther Alonso ◽

Javier Grau ◽

Rafael F. Duarte ◽

...

Keyword(s):

Bone Marrow ◽

Overall Survival ◽

Myeloid Leukemia ◽

Low Risk ◽

Prognostic Impact ◽

Score System ◽

Free Survival ◽

Kaplan Meier ◽

Acute Myeloid

Abstract Background: Myelodysplastic syndrome (MDS) therapeutic decisions have been traditionally based on the IPSS; however, this score system does not allow the identification of patients with low risk disease (low or intermediate-1 IPSS) but a poor prognosis, who could benefit from an early intervention. Garcia-Manero et al (Leukemia 2008) described a specific prognostic scoring system for this subgroup of patients (LR-PSS) based on age ≥60 years, hemoglobin <10g/dl, platelet count <50k/uL or 50-200k/uL, bone marrow blasts ≥4% and unfavorable cytogenetics (non-del(5q), non-diploid). This LR-PSS score system enables the stratification of low risk MDS patients into 3 different risk categories; interestingly, the third category identifies a subgroup of patients with a median overall survival (OS) similar to that of patients classified as intermediate-2 and high risk IPSS. Besides, the IPSS-R described by Greenberg et al (Blood 2012) has demonstrated a strong prognostic value for OS and LFS as compared to the IPSS when applied to different independent series of MDS patients. The prognostic impact of the LR-PSS has not been analyzed in MDS patients with very low-, low- and intermediate IPSS-R scores. Aim: To analyze the prognostic impact according to OS and leukemia free survival of the LR-PSS when applied to a population MDS patients with very low, low and intermediate IPSS-R. Methods: A total of 789 consecutive patients diagnosed with MDS (01/1992-12/2014) at the Catalan Institute of Oncology of Barcelona were included in the study. 413 (52%) had available cytogenetics and therefore, IPSS-R was calculated. Overall, 371 (89%) patients were classified as very low, low and intermediate IPSS-R and included in the study. Results: 123 (30%) patients were classified as very low, 182 (44%) low and 66 (16%) intermediated IPSS-R risk MDS; median age 72 years (range 32-101) and 258 (69%) male. 1.4 % CRDU, 7.6 % RA, 41.6 % RCMD, 16.2 % RAEB‐1, 4.1 % RAEB‐2, 25.9 % CMML and 3.2 % MDS‐U with isolated 5q deletion according to the 2008 WHO classification. At diagnosis, median hemoglobin, platelet and bone marrow blast were 11.8 g/dL (5.5-17.1), 152 x109/L (1-1492) and 3 % (0-17), respectively and fifty-three (14.3 %) patients had unfavorable LR-PSS cytogenetics. For the whole population, median follow up was 6.6 years (range 6-7.7). At the time of last follow up, 48.2 % (179) had died and only 49 (13%) had progressed to acute myeloid leukemia. When the LR-PSS was applied to the very low, low and intermediate IPSS-R subgroups three well-differentiated prognostic categories could be identified: 58 patients (15.6%) category 1, scores 0-2; 277 (74.6%) patients category 2, scores 3-4 and 36 (9.8%) patients category 3, scores 5-7 with significantly different overall survival and leukemia free survival. Median OS for categories 1 (9.4 years; 95% CI 6.7-12), 2 (6 years; 95% CI 5-7.1) and 3 (2.6 years; 95% CI 2.1-3) were significantly different (p<0.001; Figure 1). Moreover, the rate of progression to acute myeloid leukemia was 5% (3/58), 13% (37/277) and 25% (9/36) for categories 1, 2 and 3, respectively. Summary/Conclusion: When applied to a low risk (very low, low and intermediate) IPSS-R cohort of MDS population, the LR-PSS identifies a subgroup of patients with a significantly worse prognosis who could benefit from an early intervention. Further studies are warranted. Fig 1. Kaplan-Meier survival for patients with very low-, low- and intermediate IPSS-R risk assigned to categories 1 to 3 by LR-PSS. Fig 1. Kaplan-Meier survival for patients with very low-, low- and intermediate IPSS-R risk assigned to categories 1 to 3 by LR-PSS. Disclosures Sureda: Takeda: Consultancy, Speakers Bureau.

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