Abstract

Background:Despite an increasing number of approved therapies, multiple myeloma (MM) remains an incurable disease with frequent drug resistsance. Resistance-mediating alterations are preexisting in early disease, and it is the selective pressure of antitumor therapy that promotes their clonal expansion from baseline tumor heterogeneity. Cross-talk between monoclonal plasma cells, platelets and endothelial cells enhances hypercoagulability. The prospective, longitudinal observational study ROADMAP-MM CAT (PROspective Risk Assessment anD bioMArkers of hyPercoagulability for the identification of patients with Multiple Myeloma at risk for Cancer Associated Thrombosis) was designed to explore alternative strategies for the development of risk stratification tools in patients with MM. Aim: We explored cellular and plasma hypercoagulability in newly diagnosed chemotherapy naïve MM (NDMM) patients to identify relevant biomarkers that can be used to classify patients at high risk of resistance to anti-myeloma treatment. Methods: Symptomatic NDMM were enrolled. Patients on anticoagulant treatment were excluded from the study. Study end-point was response to treatment at 3 months (T1) according the the International Myeloma Working Group response criteria. "Resistance to antimyeloma treatment" was defined as progressive disease (PD), stable disease (SD) or minor response (MR) at 3 months post treatment initiation. Blood samples were collected at inclusion. Procoagulant phospholipid-dependent clotting time (Procoag-PPL®), tissue factor activity (TFa), thrombomodulin activity (TMa), factor VIIa, factor V (FV), antithrombin (AT), fibrin monomers (FM) and D-Dimers were measured with respective assays from Diagnostica Stago (Asnieres, France) on a STA-R® analyzer (Diagnostica Stago). Plasma levels of P-Selectin and heparanase were measured with ELISA Kits from Cusabio Biotech (from CliniSciencies, France) and R&D Systems (Lille France), respectively. Samples of platelet-poor plasma (PPP) were assessed for thrombin generation (TG) with the TF 5 pM PPP-Reagent® on Calibrated Automated Thrombogram (Stago, France). Univariate logistic regression analysis examined the associations between biomarkers and treatment resistance. The cut-off values for studied biomarkers were selected based on ROC analysis. Results A total of 144 patients were enrolled (age 66.0±12.0 yrs; 53% male). Distribution of disease stage was as follows: 32% ISS I, 23% ISS II, 45% ISS III. Bone disease was present in 71% of patients and 19% of patients had high risk cytogenetic lesions. Proteasome inhibitor (PI) based therapy was given to 64% of patients, immunomodulatory drug (IMiD) based therapy in 32% and 4% received other regimens. At 3 months, 64% of patients showed treatment "resistance". At the univariate logistic regression "resistance to treatment" was associated with longer Procoag-PPL (32.4% in patients with Procoag-PPL ≥41.7 sec vs. 12.3% in patients with Procoag-PPL <41.7 sec; OR=3.41, 95%CI: 1.45-8.03, p=0.005), higher levels of D-dimers (36.0% in patients with D-dimers ≥1.44 μg/ml vs. 14.9% in patients with D-dimers <1.44 μg/ml; OR=3.21, 95%CI: 1.43-7.22, p=0.005) and higher Peak in the thrombogram (28.1% in patients with Peak ≥181.66 nM vs. 12.5% in patients with Peak <181.66 nM; OR=2.73, 95%CI: 1.03-7.23, p=0.043). On the other hand, PD/SD/MR rate was inversely associated with P-selectin levels (14.5% in patients with ≥23477 pg/ml vs. 42.3% in patients with P-selectin <23477 pg/ml; OR=0.23, 95%CI: 0.08-0.65, p=0.005). Conclusion. The prospective ROADMAP-CAT multiple myeloma study demonstrates for the first time that biomarkers of hypercoagulablity may also be useful to identify patients likely to display resistance to treatment. Among a large number of hypercoagulability biomarkers PPL-ct, that reflects the amount of procoagulant microparticles present in plasma, thrombin generation and the levels P-Selectine and D-Dimers were found to be significant predictors of poor treatment response. A prospective trial is required to evaluate the potential role of these biomarkers in anti-myeloma treatment optimization.

Disclosures

Terpos:Novartis: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant, Patents & Royalties; Genesis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant, Patents & Royalties; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant, Research Funding. Dimopoulos:Celgene: Honoraria; Takeda: Honoraria; Bristol-Myers Squibb: Honoraria; Janssen: Honoraria; Amgen: Honoraria.

Author notes

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Asterisk with author names denotes non-ASH members.