Gene expression profiling (GEP) in multiple myeloma (MM): Distinguishing relapses with high-risk transformation from those with sustained low risk.

2010 ◽  
Vol 28 (15_suppl) ◽  
pp. 8122-8122
Author(s):  
J. Sawyer ◽  
J. D. Shaughnessy ◽  
J. Haessler ◽  
A. Hoering ◽  
B. Barlogie
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Low Risk ◽  
Risk Transformation

scholarly journals Superior results of Total Therapy 3 (2003-33) in gene expression profiling–defined low-risk multiple myeloma confirmed in subsequent trial 2006-66 with VRD maintenance

Blood ◽  
2010 ◽  
Vol 115 (21) ◽  
pp. 4168-4173 ◽  
Author(s):  
Bijay Nair ◽  
Frits van Rhee ◽  
John D. Shaughnessy ◽  
Elias Anaissie ◽  
Jackie Szymonifka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Risk Model ◽  
Complete Response ◽  
Low Risk ◽  
Cure Fraction ◽  
Total Therapy

The Total Therapy 3 trial 2003-33 enrolled 303 newly diagnosed multiple myeloma patients and was noted to provide superior clinical outcomes compared with predecessor trial Total Therapy 2, especially in gene expression profiling (GEP)–defined low-risk disease. We report here on the results of successor trial 2006-66 with 177 patients, using bortezomib, lenalidomide, and dexamethasone maintenance for 3 years versus bortezomib, thalidomide, and dexamethasone in year 1 and thalidomide/dexamethasone in years 2 and 3 in the 2003-33 protocol. Overall survival (OS) and event-free survival (EFS) plots were super-imposable for the 2 trials, as were onset of complete response and complete response duration (CRD), regardless of GEP risk. GEP-defined high-risk designation, pertinent to 17% of patients, imparted inferior OS, EFS, and CRD in both protocols and, on multivariate analysis, was the sole adverse feature affecting OS, EFS, and CRD. Mathematical modeling of CRD in low-risk myeloma predicted a 55% cure fraction (P < .001). Despite more rapid onset and higher rate of CR than in other molecular subgroups, CRD was inferior in CCND1 without CD20 myeloma, resembling outcomes in MAF/MAFB and proliferation entities. The robustness of the GEP risk model should be exploited in clinical trials aimed at improving the notoriously poor outcome in high-risk disease.

scholarly journals Erratum: Dose-dense and less dose-intense total therapy 5 for gene expression profiling-defined high-risk multiple myeloma

2016 ◽  
Vol 6 (9) ◽  
pp. e471-e471 ◽  
Author(s):  
Y Jethava ◽  
A Mitchell ◽  
M Zangari ◽  
S Waheed ◽  
C Schinke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Total Therapy ◽  
Dose Dense

Ninety Percent Sustained Complete Response (CR) Rate Projected 4 Years after Onset of CR in Gene Expression Profiling (GEP)-Defined Low-Risk Multiple Myeloma (MM) Treated with Total Therapy 3 (TT3): Basis for GEP-Risk-Adapted TT4 and TT5

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 162-162 ◽  
Author(s):  
Bart Barlogie ◽  
Elias J. Anaissie ◽  
John D. Shaughnessy ◽  
Frits van Rhee ◽  
Mauricio Pineda-Roman ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Complete Response ◽  
Low Risk ◽  
Event Free Survival ◽  
Free Survival ◽  
Response Onset ◽  
Dose Dense

Abstract We have previously reported on the remarkable activity of the TT3 program that incorporated both bortezomib (V) and thalidomide (T) into the up-front management of 303 patients. TT3 consisted of 2 cycles each of induction prior to and of dose-reduced consolidation therapy with VTD-PACE (cisplatin, doxorubicin, cyclophosphamide, etoposide) after melphalan 200mg/m2 (M200)-based tandem transplants, followed by maintenance therapy for 3 years with VTD and, in later stages, VRD (substituting T for lenalidomide, R). Characteristics included a median age of 59yr (range, 33–75yr), B2M &gt;=4mg/L in 37%, albumin &lt;3.5g/dL in 26%, ISS stages II and III in 33% and 21%, cytogenetic abnormalities (CA) in 33% and gene expression profiling (GEP)-defined high-risk MM in 15% of the 275 patients with such data. With a median follow-up of 39 months, 4-yr overall survival (OS) and event-free survival (EFS) estimates were 78% and 71%, respectively, including 84% and 77% among the 85% with GEP-defined low-risk MM contrasting with 43% and 33% in the remainder with high-risk MM (both p&lt;0.0001). Near-CR and CR, attained in 86% and 63%, were sustained at 4 years from response onset in 78% and 87%, which pertained to 83% and 90% with low-risk MM but to only 44% and 57% with high-risk MM (all p &lt;0.0001). These results were corroborated in a TT3 extension trial (TT3E) that enrolled 175 additional patients, comprising higher proportions of CA (42%) and GEP-defined high-risk MM (21%). Two-year estimates of OS and EFS are 85% and 85%, with 94% and 92% in low-risk patients versus 61% and 62% in high-risk MM (p=0.0001, p=0.0003); the 2-yr estimate of remaining in CR is 93% including 100% in low-risk and 77% in high-risk MM (p=0.01). Multivariate analysis of features linked to OS in TT3 included GEP-defined high-risk, CA, B2M and LDH elevation, collectively accounting for 41% of outcome variability by R2 statistics; the corresponding R2 values for EFS and n-CR duration were 38% and 39%. Compared to the predecessor trial, TT2, that evaluated the role of T in a randomized trial design in 668 patients, TT3 data were superior for OS (p=0.08), EFS (&lt;0.0001), n-CR duration (p&lt;0.0001) and CR duration (p=0.0002). In the low-risk subgroup, EFS (p=0.0001), n-CR duration (p&lt;0.0001) and CR duration (Figure 1a; p=0.0002) all were superior in TT3 versus TT2; whereas, in the high-risk MM group, outcomes remained poor also with TT3 despite superior EFS (Figure 1b; p=0.03). Based on these data, we have now started a GEP-risk-based algorithm of assigning separate therapies to good-risk (TT4) and poor-risk MM (TT5). As the TT3 results for low-risk are difficult to improve upon, TT4 randomizes patients between standard TT3 and TT3-LITE that employs only 1 cycle each of induction and consolidation (with anticipated further improvement in compliance) and 4-day-fractionated M50×4 to enable the addition of VTD and thus exploit synergistic drug interactions to occur. In order to sustain tolerable effective therapies for at least 3 years and prevent recurrence from previous drug-free or insufficiently effective phases in TT3, TT5 for high-risk MM employs less dose-intense and more dose-dense highly synergistic combination therapy, utilizing M10-VTD-PACE for induction, M80 (in 4 daily fractions of M20) plus VRD-PACE tandem transplants, separated by 2 cycles of M20 (in 4 daily fractions of M5) plus VTD-PACE, and followed by 2 years of monthly alternating R-VD and M-VD. Figure 1a: Superior CR duration with TT3 v TT2 in GEP-low-risk MM: Figure 1a:. Superior CR duration with TT3 v TT2 in GEP-low-risk MM: Figure 1b: Superior event-free survival with TT3 v TT2 in GEP-high-risk MM: Figure 1b:. Superior event-free survival with TT3 v TT2 in GEP-high-risk MM:

MyPRSR Molecular Subtypes of Multiple Myeloma Represent All High-Risk FISH Translocations Included in the mSMART 2.0 and R-ISS Guidelines

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3264-3264 ◽  
Author(s):  
Ryan K Van Laar ◽  
Ivan Borrelo ◽  
David Jabalayan ◽  
Ruben Niesvizky ◽  
Aga Zielinski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Research Funding ◽  
Plasma Cells ◽  
Molecular Subtypes ◽  
Risk Status ◽  
Significant Difference

Abstract Background: There is a global consensus that multiple myeloma patients with high-risk disease require additional monitoring and therapy compared to low/standard risk patients in order to maximize their chances of survival. Current diagnostic guidelines recommend FISH-based assessment of chromosomal aberrations to determine risk status (i.e. t(14;20), t(14;16), t(4;14) and/or Del17p), however, studies show FISH for MM may have a 20-30% QNS rate and is up to 15% discordant between laboratories, even when starting from isolated plasma cells. In this study we demonstrate that MyPRS gene expression profiling reproduces the key high risk translocations for MM risk stratification, in addition to having other significant advantages. Methods: Reproducibility studies show that MyPRS results are less than 1% discordant starting from isolated plasma cells and return successful results in up to 95% of cases. 270 MM patients from Johns Hopkins University (MD) and Weill Cornell Medicine (NY) had both FISH and MyPRS gene expression profiling performed between 2012 and 2016 using standard and previously published methodology, respectively. Results: Retrospective review of the matched FISH and MyPRS results showed: 25/28 (89%) patients wish FISH-identified t(4;14) were classified as MMSET (MS) subtype. 10/10 (100%) patients with t(14;16) or t(14;20) were classified as MAF-like (MF) subtype 62/67 (93%) patients with t(11;14) were assigned to the Cyclin D (1 or 2) subtype. Patients with FISH hyperdiploidy status were classified as the Hyperdiploid (HY) subtype or had multiple gains detected by the separate MyPRS Virtual Karyotype (VK) algorithm, included in MyPRS. TP53del was seen in patients with multiple molecular subtypes, predominantly Proliferation (PR) and MMSET (MS). Assessment of TP53 function by gene expression is a more clinically relevant prognostic marker than TP53del, as dysregulation of the tumor suppressor is affected by mutations as well as deletions. Analysis of the TP53 expression in the 39 patients with delTP53 showed a statistically significant difference, compared to patients without this deletion (P<0.0001). Conclusion: Gene expression profiling is a superior and more reliable method for determining an individual patients' prognostic risk status. The molecular subtypes of MM, as reported by Signal Genetics MyPRS assay, are driven by large-scale changes in gene expression caused by or closely associated with chromosomal changes, including translocations. Physicians who are managing myeloma patients and wishing to base their assessment of risk on R-ISS or mSMART Guidelines may obtain the required data points from either FISH or MyPRS, with the latter offering lower QNS rates, higher reproducibility, assessment of a larger number of cells and a substantially lower price point ($5,480 vs. $1,912; 2016 CMS data). A larger cohort study is now underway to further validate these observations. Figure GEP-based TP53 expression in patients with and without Del17p. P<0.0001 Figure. GEP-based TP53 expression in patients with and without Del17p. P<0.0001 Disclosures Van Laar: Signal Genetics, Inc.: Employment. Borrelo:Sidney Kimmel Cancer Institute: Employment. Jabalayan:Weill Cornell Medical Center: Employment. Niesvizky:Celgene: Consultancy, Research Funding, Speakers Bureau; Takeda: Consultancy, Research Funding, Speakers Bureau; Onyx: Consultancy, Research Funding, Speakers Bureau. Zielinski:Signal Genetics, Inc.: Employment. Leigh:Signal Genetics, Inc.: Employment. Brown:Signal Genetics, Inc.: Employment. Bender:Signal Genetics, Inc.: Employment.

Proteomic Profiling of Multiple Myeloma: Correlation of Protein and Gene Expression Data.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1705-1705
Author(s):  
Ricky D Edmondson ◽  
Sheeno P Thyparambil ◽  
Veronica MacLeod ◽  
Bart Barlogie ◽  
John D. Shaughnessy
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Expression Profiles ◽  
Single Agent ◽  
Clinical Samples ◽  
Proteomics Data

Abstract Although melphalan-based autologous stem cell transplantation has improved prognosis for patients diagnosed with Multiple Myeloma, survival varies from a few months to more than 15 years with an individual’s risk not accurately predicted with standard prognostic variables. Correlating genome-wide mRNA expression profiles in purified myeloma cells with outcome, we recently showed that that the differential expression of 70 genes could identify patients at high risk for early disease related death [1]. The utility of a high throughput proteomics platform in the analysis of clinical samples has great potential but as of yet none have been firmly established. Herein, we describe the use of such a platform and its utility in stratifying patients with Multiple Myeloma in terms of high and low risk disease. Preliminary analysis indicates that the proteomics data can separate the patients into risk groups, although the proteins responsible for the assignment are not identical to the 70 genes identified in the gene expression profiling experiments. In addition to the proteomic analysis of plasma cells enriched using anti-CD138 immunomagnetic beads from mononuclear cell fractions of bone marrow aspirates from newly diagnosed myeloma patients; we have performed (in triplicate) LCMS profiling on plasma cells from 30 patients isolated prior to and 48 hours after a single test-dose application of bortezomib at 1.0mg/m2. An aliquot of 100,000 plasma cells was enzymatically digested with trypsin and a fraction (~5,000 cells) analyzed using our proteomics platform (an Eksigent nanoHPLC coupled to a ThermoElectron LTQ-Orbitrap with data analyzed using the Elucidator software package from Rosetta Biosoftware). The correlation of the proteomic profiles to gene expression profiles and clinical parameters will be presented. The analysis of proteins that were observed to change (p&lt;0.01) in abundance after the single agent dose of the proteasome inhibitor bortezomib yielded an unanticipated finding; the abundance of 30 proteins associated with the proteasome were observed to increase in a subset of patients. The majority of the patients with the increased levels of proteasome related proteins are predicted by GEP to have high risk disease. The proteomic data will be discussed in terms of its utility in the identification of activated pathways as well as in the development of a prognostic indicator as was achieved using gene expression profiling.

Intra-Tumoral Heterogeneity In Multiple Myeloma Gleaned From Gene Expression Profiling Analysis Of Plasma Cells and Bone Marrow Biopsies: Comparison Of Random Bone Marrow Samples and CT-Guided Fine Needle Aspirations Of MRI-Defined Focal Lesions

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3123-3123
Author(s):  
Bart Barlogie ◽  
Emily Hansen ◽  
Sarah Waheed ◽  
Jameel Muzaffar ◽  
Monica Grazziutti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
P Value ◽  
Focal Lesions ◽  
Molecular Subgroup

Abstract Intra-tumoral heterogeneity (ITH) is increasingly viewed as the Achilles heel of treatment failure in malignant disease including multiple myeloma (MM). Most MM patients harbor focal lesions (FL) that are recognized on MRI long before bone destruction is detectable by conventional X-ray examination. Serial MRI examinations show that eventually 60% of patients will achieve resolution of FL (MRI-CR). However, this will lag behind the onset of a clinical CR by 18 to 24 months, thus attesting to the biological differences between FL and diffuse MM growth patterns. Consequently, we performed concurrent gene expression profiling (GEP) analyses of plasma cells (PC) from both random bone marrow (RBM) via iliac crest and FL. Our primary aims were to first compare the molecular profiles of FL vs. RBM, second to determine if ITH existed (as defined molecular subgroup and risk), and finally to investigate if the bone marrow micro-environment (ME) contained a biologically interesting signature. A total of 176 patients were available for this study with a breakdown of: TT3 (n=23), TT4 for low-risk (n=131) and TT5 for high-risk MM (n=22). Regarding the molecular analyses of PCs, GEP-based risk (GEP-70, GEP-5) and molecular subgroup correspondence were examined for commonalties and differences between RBM and FL. A “filtering” approach for ME genes was also developed and bone marrow biopsy (BMBx) GEP data derived from this method is under analysis. PC risk correspondence between FL and RBM was 86% for GEP70 and 88% for the GEP5 model. Additionally, 82% had a molecular subgroup concordance, however, they did differ among subgroups (p=0.020) by Fisher's Exact Test. A lower concordance was noted in the CD2, LB, and PR subgroups (67%, 69%, 73%, respectively). GEP70 and GEP5 risk concordance between RBM and FL samples by molecular subgroup was also examined. The overall correlation coefficients were 0.619 (GEP70) and 0.597 (GEP5). The best correspondence was noted for CD1, MF and PR subgroups especially for the GEP5 model. HY, LB and MS showed intermediate correlations, while CD2 fared worst with values of only 0.322 for GEP70 and 0.267 for GEP5 model. Figure 1 portrays these data in more detail for the GEP70 and GEP5 models. Good correlations were noted between RBM and FL based risk scores in case of molecular subgroup concordance (left panels) in both GEP5 and GEP70 risk models, whereas considerable scatter existed in case of subgroup discordance (right panels). The clinical implications in TT4 regarding RBM and FL derived risk and molecular subgroup information, viewed in the context of standard prognostic baseline variables are portrayed in Table 1. High B2M levels at both cut-points imparted inferior OS and PFS as did low hemoglobin. Although present in 42% of patients, cytogenetic abnormalities (CA) did not affect outcomes. FL-based GEP5-defined high-risk designation conferred poor OS and PFS. B2M>5.5mg/L and FL-derived GEP5 high-risk MM, pertaining to 29% and 11% of patients, survived the multivariate model for both OS and PFS. Next, in examining PC-GEP differences among RBM and FL sites, 199 gene probes were identified with a false discovery rate (FDR) of 1x10-6. Additionally, 55 of the 199 belong to four molecular networks of inter related genes associated with: lipid metabolism, cellular movement, growth and proliferation, and cell-to-cell interactions. Multivariate analysis identified the GEP5 high risk designation of focal lesion PCs to be significantly prognostic with a HR=3.73 (p=0.023).Table 1Cox regression analysis of variables linked to overall and progression-free survival in TT4.Overall SurvivalProgression-Free SurvivalVariablen/N (%)HR (95% CI)P-valueHR (95% CI)P-valueMultivariateB2M > 5.5 mg/L38/130 (29%)3.71 (1.49, 9.22)0.0053.84 (1.58, 9.31)0.003FL GEP5 High Risk14/130 (11%)3.68 (1.19, 11.41)0.0243.73 (1.20, 11.62)0.023HR- Hazard Ratio, 95% CI- 95% Confidence Interval, P-value from Wald Chi-Square Test in Cox RegressionNS2- Multivariate results not statistically significant at 0.05 level. All univariate p-values reported regardless of significance.Multivariate model uses stepwise selection with entry level 0.1 and variable remains if meets the 0.05 level.A multivariate p-value greater than 0.05 indicates variable forced into model with significant variables chosen using stepwise selection. Disclosures: No relevant conflicts of interest to declare.

scholarly journals The role of fluorescence in situ hybridization and gene expression profiling in myeloma risk stratification

2011 ◽  
Vol 139 (suppl. 2) ◽  
pp. 84-89 ◽  
Author(s):  
Dirk Hose ◽  
Anja Seckinger ◽  
Anna Jauch ◽  
Thierry Reme ◽  
Jerome Moreaux ◽  
...  
Keyword(s):  
Gene Expression ◽  
Risk Assessment ◽  
Multiple Myeloma ◽  
Prognostic Factors ◽  
In Situ Hybridization ◽  
High Risk ◽  
Risk Stratification ◽  
Gene Expression Profiling ◽  
Expression Profiling

Multiple myeloma patients? survival under treatment varies from a few months to more than 15 years. Clinical prognostic factors, especially beta2-microglobulin (B2M) and the international staging system (ISS), allow risk assessment to a certain extent, but do not identify patients at very high risk. As malignant plasma cells are characterized by a variety of chromosomal aberrations and changes in gene expression, a molecular characterization of CD138-purified myeloma cells by interphase fluorescence in situ hybridization (iFISH) and gene expression profiling (GEP) can be used for improved risk assessment. iFISH allows a risk stratification with presence of a translocation t(4;14) and/or deletion of 17p13 being the best documented adverse prognostic factors. A deletion of 13q14 is no longer considered to define adverse risk. Patients harbouring a t(4;14) seems to benefit from a bortezomib- or lenalidomide containing regimen, whereas patients with deletion 17p13 seem only to benefit from a high dose therapy approach using long term bortezomib (in induction and maintenance) and autologous tandem-transplantation as used in the GMMG-HD4 trial, or the total therapy 3 concept. Gene expression profiling allows the assessment of high risk scores (IFM, UAMS), remaining prognostic despite treatment with novel agents, and prognostic surrogates of biological factors (e.g. proliferation) and (prognostic) target gene expression (e.g. Aurora-kinase A). Thus, assessment of B2M and ISS-stage, iFISH, and GEP is considered extended routine diagnostics in therapy requiring multiple myeloma patients for risk assessment and, even now, to a certain extent selection of treatment.

scholarly journals Comprehensive Biologic Characterization of 99 Multiple Myeloma Patients Using Cytomorphology, FISH, Gene Expression Profiling and Mutation Screening Leads to Important Clinical and Therapeutic Insights

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4424-4424
Author(s):  
Simone Weber ◽  
Marietta Truger ◽  
Wolfgang Kern ◽  
Martin H van Vliet ◽  
Erik H van Beers ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Risk Groups ◽  
Published Data ◽  
Employment Equity ◽  
Equity Ownership ◽  
1Q Gain

Abstract Introduction: Multiple myeloma (MM) is a genetically heterogeneous disease regarding both chromosomal abnormalities (CA) and dysregulated gene expression. Lately, several gene mutations (mut) have been identified further contributing to the genetic complexity. However, data on parallel assessment of morphologic, cytogenetic and molecular genetic parameters is scarce. Aim: Integration of morphological and extensive genetic information in an MM cohort to improve understanding of the disease biology and classification, providing a basis for evaluation of the most suitable therapy for MM patients (pts) by this holistic approach for future studies. Methods: We investigated 99 newly diagnosed MM pts (46 female, 53 male; median age 69 years, range 43 - 88). Plasma cell (PC) in bone marrow by cytomorphology ranged from 10 to 96% (median 54%). PC were enriched by magnetic-activated cell sorting targeting CD138 (median purity 95%) before interphase FISH was performed to detect hyperdiploidy, del(13q), del(17p), t(4;14), t(11;14), t(14;16), t(14;20), t(6;14), 1q gain, del(12p) and MYC rearrangements. Purified samples were further analyzed by next generation sequencing (NGS) using a comprehensive 36-gene panel targeting genes previously described mutated in MM. Library was prepared by TSCA-LI Multiple Myeloma Panel (Illumina, San Diego, CA). Gene expression profiling (GEP) was performed using Affymetrix HG U133 Plus 2.0 arrays. The MMprofiler assay algorithms were used to calculate the SKY92 signature classification into standard/high risk groups (Kuiper et al., 2012). Results: The frequencies of CA detected by FISH were consistent with published data. According to R-ISS high risk (hr) CA was defined by del(17p), t(4;14) and/or t(14;16) (28/98 pts, 29%). All other cases (71%) were standard risk (sr) (Palumbo et al., 2015). First, GEP were analyzed in relation to the CA risk groups. Cluster analysis revealed the majority of hr CA pts clustering together with overexpression of genes including ROBO1, CCNB2, FGFR3, WHSC1, DSG2 and PBX1, consistent with prior publications on hr GEP signature (Shaugnessy et al., 2007; Zhan et al., 2006). However, 9 pts assigned hr by CA clustered together with sr CA cases. Thus, in 10% of our pts GEP clusters would not be concordant with the risk classification by CA. In addition, the expression data were also analyzed based on the SKY92 signature. In consistency with published data this analysis assigned 16 pts (17%) as hr (Kuiper et al., 2012). Interestingly, out of the 9 hr CA pts mentioned above which clustered with sr CA 8 pts were also assigned sr by SKY92 classifier. Further, regarding the hr CA group, 8/28 pts (29%) also revealed a hr SKY92 signature. These patients may need further attention. Further, regarding the sr CA group, 7/65 (11%) revealed a hr SKY92 signature. Focusing on NGS, we found 115 mut (with mut load ≥10%) in 67/93 pts (72%; range 0-5) affecting 17 genes. Most commonly mut genes were NRAS (26%), KRAS (21%), TLR4 (11%), BRAF (8%), FAM46C (8%) and TP53 (7%). No difference in mut frequency between hr and sr CA pts was observed. However, association of FAM46Cmut with hyperdiploidy as well as CCND1mut with t(11;14) could be corroborated (12% vs 0%, p=0.095; 9% vs 0%, p=0.056, respectively). Besides, FAM46Cmut was associated with del(17p) (23% vs 5%, p=0.058) and a strong association of KRASmut to 1q gain was found (32% vs 11%, p=0.029), while KRASmut and NRASmut were mutually exclusive of del(12p) and t(4;14). Of note, 3/6 TP53mut pts concomitantly harbored del(17p) detected by FISH. According to CA 2/3 of these pts without del(17p) would have been classified sr. Thus, molecular data might improve risk classification. Considering biological pathways (pw) connected to currently used therapeutics (e.g. vemurafenib, bortezomib), we found the following mut frequencies in genes associated with the respective pw: 46% in MAPK/ERK-pw, 16% in NFkB-pw, 2% in HOXA9-pw and 15% associated with RNA processing. Interestingly, we found mut in IKZF1 (6%), IKZF2 (6%), IKZF3 (1%) and IRF4 (4%), which represent critical targets of the immunomodulatory drug/CRBN mediated anti-tumor activity. Conclusion: 1) A comprehensive analysis of MM based on cytogenetic, gene expression and mutation data may lead to the identification of new biologic subgroups. 2) Molecular mutations should be further evaluated to allow precision medicine approaches including respective pathway components. Disclosures Weber: MLL Munich Leukemia Laboratory: Employment. Truger:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. van Vliet:SkylineDx: Employment. van Beers:SkylineDx: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Haupt:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

scholarly journals Clinical Validation of MCL35 in Mantle Cell Lymphoma Patients ≥65 Years Receiving Bendamustine-Rituximab

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3517-3517
Author(s):  
Allison C. Rosenthal ◽  
Colleen Ramsower ◽  
Raphael Mwangi ◽  
Matthew J. Maurer ◽  
Diego Villa ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Board Of Directors ◽  
Expression Profiling ◽  
Research Funding ◽  
Advisory Board ◽  
Low Risk ◽  
Advisory Committees ◽  
Scientific Advisory

Abstract BACKGROUND: Mantle cell lymphoma (MCL) is a B-cell non-Hodgkin lymphoma with variable clinical outcomes. Commonly used risk stratification tools (Ki67 IHC, MIPI) in newly diagnosed MCL are not frequently used when selecting therapy, resulting in treatment choice being dictated by age and co-morbidities rather than disease biology. The MCL35 risk score was developed as a more reliable measure of proliferation and has been shown to be prognostic and can risk stratify younger transplant eligible MCL patients into three groups with significantly different overall survival (OS; Scott et al. 2017; Holte et al. 2018) but has not been evaluated in older transplant ineligible patients. We report results evaluating the prognostic value of the MCL35 assay in older MCL patients (≥65) treated with frontline bendamustine/rituximab (BR). METHODS: Archived tissue samples from 119 patients age ≥65 years treated with BR from collaborating Lymphoma/Leukemia Molecular Profiling Project (LLMPP) sites and the LEO/MER cohort were collected and analyzed using the MCL35 assay and stratified into three distinct risk groups (low, standard, and high risk). Association between MCL35 proliferation scores and OS were estimated by the Kaplan-Meier method and hazard ratios were calculated. Associations between Ki67, s-MIPI, p53 IHC status, morphology and OS were also evaluated. RESULTS: The MCL35 assay was run on tissue samples from 119 patients. Median patient age was 74 (range 65-93) and 69.5% were male. Ki67 was &lt;30% in 29 patients (24%) and ≥30% in 90 patients (76%). Simplified MIPI (s-MIPI) score was 0-3 in 21 patients (24%), 4-5 in 42 patients (48%) and ≥6 in 25 patients (28%). Thirty-one did not have sufficient data to calculate a s-MIPI score. MCL35 was low risk in 51 patients (43%), standard risk in 39 patients (33%) and high risk in 29 patients (24%). Eleven patients had blastic morphology, 7 had pleomorphic morphology and the remainder were classic morphology (n=56). Of 57 samples with p53 IHC staining 7 (12.3%) were positive. At a median follow up of 33.4 months, 82 patients were alive and 35 had died. Patients with high risk MCL35 score had inferior OS compared to low risk (HR 2.27, 95% CI: 1.03-5.00; p=0.042) while standard risk was not statistically significant compared to low risk (HR 0.87, 95% CI: 0.37-2.0; p=0.740)(Figure 1). Ki67 IHC using a cutoff of ≥ 30% and 10%-29% was not significantly associated with OS compared to Ki67 &lt;10% ( Ki67 ≥ 30% vs. Ki67 &lt; 10%, HR 0.87, 95% CI: 0.12-6.41; p=0.892, Ki67 ≥ 10%-29% vs. Ki67 &lt; 10%, HR 0.32, 95% CI: 0.04-2.83; p=0.303), however high s-MIPI score (≥6) (s-MIPI ≥6 vs. s-MIPI 0-3, HR 3.86, 95% CI 1.20-12.5; p=0.024) and positive p53 IHC (HR: 9.51, 3.26-27.7; p &lt;0.001) were both associated with poor OS. Eighteen cases were blastic/pleomorphic by morphology, 12 of which were in the high-risk group by MCL35, and this subset also had worse survival than classic MCL (p=0.0052). CONCLUSIONS: These results suggest high risk MCL35 score is a prognostic biomarker of poor OS in patients &gt;65 with MCL treated with BR. Conversely, Ki67 was not significantly associated with OS in these patients. Additional clinical validation using a larger sample size from the E1411 study is planned. If similar results are found, the MCL35 assay in combination with s-MIPI and p53 status may have utility in stratifying patients into risk adapted treatment arms in future prospective clinical trial designs. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Villa: Janssen: Honoraria; Gilead: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Seattle Genetics: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; NanoString Technologies: Honoraria. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Cohen: Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo, BeiGene, Adaptive: Consultancy; Genentech, BMS/Celgene, LAM, BioINvent, LOXO, Astra Zeneca, Novartis, M2Gen, Takeda: Research Funding. Hill: Celgene (BMS): Consultancy, Honoraria, Research Funding; Epizyme: Consultancy, Honoraria; Gentenech: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Other: Travel Support, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Incyte/Morphysis: Consultancy, Honoraria, Research Funding. Raess: Scopio Labs: Research Funding. Scott: Celgene: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. ; Rich/Genentech: Research Funding; Janssen: Consultancy, Research Funding; Incyte: Consultancy; Abbvie: Consultancy; AstraZeneca: Consultancy. Rimsza: NanoString Technologies: Other: Fee-for-service contract.

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