Screening for AL Amyloidosis in Smoldering Multiple Myeloma
Authors: Toskic Denis , Mansukhani Mahesh , Zhou Ping , Scalia Stephanie , Ma Xun , Fogaren Teresa , Lee Lisa X. , Chung Alfred , Tuchman Sascha Alexander , Bianchi Giada , Godara Amandeep , Landau Heather J. , Bal Susan , Kaur Gurbakhash , Sanchorawala Vaishali , Morgan Gareth , Mann Hashim , Vescio Robert Allen , Chaulagain Chakra , Varga Cindy , Lentzsch Suzanne , Doros Gheorghe , Comenzo Raymond 
Published: December 2024

Background and Significance: Over half of new AL patients present with heart involvement. The failure to diagnose them early underlies their 20% mortality within 6 months. Eighty percent of AL patients harbor clonal free light chain (FLC) abnormalities for 10 years prior to presenting with symptoms of heart and kidney failure due to toxic FLC and amyloid deposits.(J Clin Oncol 2014;2699-704) Only efforts to diagnose AL in the precursor phase of the disease will reduce the early mortality rate. Smoldering multiple myeloma (SMM) is a disease in which patients can make FLC but be relatively asymptomatic; yet a fraction of them develop AL.(N Engl J Med 2007;2582-90; Blood 2018 S1;1903)
Study and Methods: In this study (NCT06365060; 1R01CA279808) we are screening patients with SMM for undiagnosed AL and risk of AL. Patients &gt; 40 years of age with SMM and FLC differential &gt; 23mg/L and no history of amyloidosis are eligible. Patients with prior biopsies positive for amyloid are ineligible. In order to develop a likelihood algorithm for AL in SMM patients we have created a multicenter network spanning the USA that will enroll 340 SMM patients and collect marrow and blood specimens and data for a training set of likelihood statistics. The likelihood algorithm employs 5 parameters: (1) the presence of SMM; (2) a difference between involved (pathologic) and uninvolved FLC &gt; 23mg/L; (3) clonal plasma cell studies showing t(11;14), cyclin D1 over-expression or gain 1q (covering 75% of AL cases), (4) AL-related κ or λ IGVL genes by NGS (the clonal κ and λ IGVL genes in AL cases are restricted), and (5) NT-proBNP &gt; 332pg/mL (the threshold for cardiac involvement with AL). All subjects will be evaluated for the presence of AL after enrollment and have their clonal IGVL genes identified by NGS enabling the creation and validation of a laboratory developed test in a precision medicine laboratory. Patients will be followed for progression to AL and to MM requiring therapy at their centers. Investigators will participate in regular conferences to track the patient status.
We have powered our study based on the hypothesis that presence of t(11;14) or gain 1q and of AL-related IGVL clonal genes discriminates between patients who have or are at risk for AL. Based on prior studies we estimate that at least 10% of SMM patients with dFLC &gt; 23mg/L, t(11;14) or gain 1q and AL-related IGVL genes may have or be at risk for AL. At a two-sided alpha of 0.05 and power of 0.8, assuming a 10% AL rate in SMM patients with t(11;14) or gain 1q and AL-related IGVL genes and a 2% rate of AL in SMM patients without t(11;14) or gain 1q, we need at least 150 SMM patients with t(11;14) or gain 1q and 150 SMM patients without t(11;14) or gain 1q to detect a significant effect of screening. Therefore, allowing for attrition, we plan a study population of 340 subjects in order to enable accrual of enough evaluable subjects in each arm.
Biostatistical analysis will begin with selected independent variables and moderator variables included as covariates selected through Least Absolute Shrinkage and Selection Operator (LASSO) with tuning parameter L1=0.1.(92) The five a priori identified factors: (1) presence of SMM; (2) dFLC &gt; 23mg/L; (3) AL-related plasma cell findings of t(11;14), cyclin D1 over-expression or gain 1q; (4) identification of the IGVL gene for a patient's clone as AL-related; and (5) NT-proBNP level above or below the threshold will be forced into the model, a linear combination of covariates () with weights derived from the model fit using LASSO. A cutoff-point, for the resulting be identified as the cutoff point that maximizes Youden's index. (BMC Bioinformatics 2015;S6:doi:10.1186/1471-2105-16-S6-S1) For the selected cutoff point, Sensitivity, Specificity, the negative likelihood ratio () for ruling out AL and the positive likelihood ratio () for ruling in AL will be estimated along with 95% confidence intervals. Models will be internally validated using bootstrap method as proposed by Efron.(J Am Stat Assoc 1986(81):461-70) Data analysis will be conducted using SAS 9.4 and R (Version 4.0.0, The R Foundation for Statistical Computing, package clogitLasso).
Conclusion: With this study we will identify SMM patients with AL or at risk for AL and create the training set for an algorithm to estimate the likelihood of having or progressing to AL from SMM. We will then design the validation study.