Therneau, Terry M.

Abstract Smoldering Waldenström’s Macroglobulinemia (SWM) is defined as a serum IgM monoclonal protein ≥3 g/dL and/or ≥10% bone marrow lymphoplasmacytic infiltration but no evidence of constitutional symptoms, symptomatic anemia, or hyperviscosity. Forty-eight patients with SWM were identified at Mayo Clinic from 1974 to 1995. Patients with a diagnosis of chronic lymphocytic leukemia or lymphoma or history of any antineoplastic therapy before diagnosis of SWM were excluded. The median age at diagnosis was 63 years (range 39–87 years). Only one patient (2%) was < 40 years of age, but 14 percent were < 50 years old. Thirty-two (67%) were men, and 16 (33%) were women. At diagnosis, hepatomegaly was noted in 10%, splenomegaly in 4%, and lymphadenopathy in 8%. The initial hemoglobin level ranged from 8.7 to 15.3 g/dL (median 11.8). The anemia in all 4 patients with an initial hemoglobin < 10 g/dL was due to other causes such as myelodysplastic syndrome, bronchopleural fistula, Barrett’s esophagus, and Coumadin administration. The initial leukocyte level ranged from 3.2 to 13.2 × 109/L (median 5.7), and platelets ranged from 101 to 578 × 109/L (median 285.5). Only one patient had a serum creatinine > 2.0 mg/dL and it was unrelated to SWM. The serum monoclonal protein level at the time of diagnosis ranged from 1.5 to 5.2 g/dL (median 3.3); 12 (25%) were < 3 g/dL, while 10 (21%) were ≥4 g/dL. IgM kappa accounted for 75%, IgM lambda 21%, and 2 (4%) were biclonal. Immunofixation of the urine was positive in 97% (kappa 80%, lambda 17%, indeterminate 3%) of those tested. The size of the urine M protein ranged from unmeasurable to 1.4 g/24h (median 0.04 g/24h). Serum albumin ranged from 2.5 to 4.3 g/dL (median 3.6). Five patients (10%) had an albumin value < 3 g/dL. Ten (26%) had a reduction of 1 uninvolved immunoglobulin, while 8 (21%) had a reduction of both IgG and IgA immunoglobulins. Lymphoplasmacytic infiltration of the bone marrow ranged from 3% to 80%, (median 30%). Three (6%) had less than 10% infiltration, while 13 (27%) had 50% or greater infiltration. The 48 patients were followed for a total of 292 (range 0.5 to 22.6 years; median 3.7) person-years, during which time 33 progressed to symptomatic WM and one to AL amyloidosis. The expected number of WM based on the SEER data was 0.004 cases. Thus, relative risk of progression to WM was increased 7,586-fold (95% CI, 5,083 to 10,373). The median time to progression was 4.6 years. The absolute risk of progression to WM was 6% at 1 year, 39% at 3 years, and 55% at 5 years. The median survival after progression to symptomatic WM was 5.1 years. Seventy-three percent of patients died, indicating a robust follow-up. We evaluated sex, hemoglobin level, size of serum M protein, reduction of uninvolved immunoglobulins, presence of urinary monoclonal light chains, serum albumin level, and the proportion of lymphoplasmacytic infiltration of the bone marrow as risk factors for progression. Significant risk factors for progression with univariate analysis included size of the serum M protein, hemoglobin level, reduction in uninvolved immunoglobulins, and degree of bone marrow lymphoplasmacytic cell infiltration. Multivariate modeling revealed that the size of the M protein and the degree of bone marrow infiltration were the most important risk factors. We conclude that smoldering WM is a distinct clinical entity that needs to be differentiated from IgM MGUS and symptomatic Waldenstrom’s Macroglobulinemia.

Abstract Background: Monoclonal gammopathy of undetermined significance (MGUS) is defined by a monoclonal protein concentration in serum of 3 g/dL or less and a proportion of plasma cells in the bone marrow of 10% or less, absence of lytic bone lesions, anemia, hypercalcemia, and renal insufficiency related to the proliferation of the monoclonal plasma cells. Two major questions remain unanswered: What proportion of patients with MGUS in the population is unrecognized clinically? What is the likely duration of MGUS at the time of its discovery? Methods: We examined the serum of 21,463 (77%) of the 28,038 enumerated residents of Olmsted County, Minnesota, in a population-based screening study and found that MGUS was present in 1.3% of persons 50 years of age and 3.8% of those 70 years of age. The risk of progression of MGUS to multiple myeloma (MM), primary amyloidosis (AL), Waldenstrom’s macroglobulinemia (WM), or related plasma cell disorders was 1% per year. Results: By age 70, only 21% of Olmsted County residents with MGUS, as determined by the prevalence study, had been recognized by routine clinical practice (Fig. 1). This is probably higher in Olmsted County than elsewhere because serum protein electrophoresis is frequently ordered on Mayo Clinic patients. The incidence of MGUS, estimated from the prevalence data, indicates onset of the condition long before clinical recognition. The duration of MGUS prior to detection at age 70 is shown in Fig. 2. Thus, 30% of patients recognized to have MGUS at age 70 probably had MGUS onset before age 50 and 55% had a prior (unrecognized) MGUS already by age 60. The median duration of MGUS prior to its recognition on the basis of clinical practice is 11 years (median). Linear extrapolation of the incidence curve implies a first appearance of MGUS around 30 years of age. Figure 1 Figure 1 Figure 2 Figure 2

Background and Aims Statins have been proven to be cytotoxic to human cholangiocarcinoma cells by inhibiting cell division and inducing apoptosis. We aimed to determine the effect of statin use on the risk of cancer development and survival in patients with extrahepatic cholangiocarcinoma (ECC), including perihilar cholangiocarcinoma (pCCA) and distal cholangiocarcinoma (dCCA). Approach and Results A total of 394 patients with ECC and hyperlipidemia who received care at Mayo Clinic Rochester between 2005 and 2015 were matched by age, sex, race, ethnicity, and residency to 788 controls with hyperlipidemia. Clinical and outcome data were abstracted. The odds ratios (ORs) for risk and hazard ratios for outcomes were calculated. The mean age and standard deviation (SD) for cases and controls was 65.6 years (13.8). The number of statin users in cases and controls was 73 (19%) and 403 (51%), respectively. Hepatitis C virus infection (OR, 15.84; 95% confidence interval [CI], 4.06‐61.87; P < 0.001) was the most significant risk factor for pCCA followed by inflammatory bowel disease and cirrhosis, whereas other liver disease, including biliary stone disease (OR, 4.06; CI, 2.24‐7.36; P < 0.001), was the only significant risk factor for dCCA. Statin use was associated with significantly reduced risk for all ECC (OR, 0.22; CI, 0.16‐0.29) as well as for the subtypes pCCA (OR, 0.3; CI, 0.21‐0.41) and dCCA (OR, 0.06; CI, 0.03‐0.14), all P < 0.0001. Moderate‐intensity dosage was found to decrease the risk of ECC (OR, 0.48; CI, 0.34‐0.67; P < 0.001). Comparing statin ever users to nonusers, patients with dCCA who used statins had significantly overall better survival (hazard ratio = 0.53; CI, 0.29‐0.97; P = 0.04). Conclusions This case‐control study suggests that statins decrease the risk of ECC and may improve survival in patients with dCCA. Additional validation studies are warranted.

Background/aims: The difference in mean survival time, which quantifies the treatment effect in terms most meaningful to patients and retains its interpretability regardless of the shape of the survival distribution or the proportionality of the treatment effect, is an alternative endpoint that could be used more often as the primary endpoint to design clinical trials. The underuse of this endpoint is due to investigators’ lack of familiarity with the test comparing the mean survival times and the lack of tools to facilitate trial design with this endpoint. The aim of this article is to provide investigators with insights and software to design trials with restricted mean survival time as the primary endpoint. Methods: A closed-form formula for the asymptotic power of the test of restricted mean survival time difference is presented. The effects of design parameters on power were evaluated for the mean survival time test and log-rank test. An R package which calculates the power or the sample size for user-specified parameter values and provides power plots for each design parameter is provided. The R package also calculates the probability that the restricted mean survival time is estimable for user-defined trial designs. Results: Under proportional hazards and late differences in survival, the power of the mean survival time test can approach that of the log-rank test if the restriction time is late. Under early differences, the power of the restricted mean survival time test is higher than that of the log-rank test. Duration of accrual and follow-up have little influence on the power of the restricted mean survival time test. The choice of restriction time, on the other hand, has a large impact on power. Because the power depends on the interplay among the design factors, plotting the relationship between each design parameter and power allows the users to select the designs most appropriate for their trial. When modification is necessary to ensure the difference in restricted mean survival time is estimable, the three available modifications all perform adequately in the scenarios studied. Conclusion: The restricted mean survival time is a survival endpoint that is meaningful to investigators and to patients and at the same time requires less restrictive assumptions. The biggest challenge with this endpoint is selection of the restriction time. We recommend selecting a restriction time that is clinically relevant to the disease and the clinical setting of the trial of interest. The practical considerations and the R package provided in this work are readily available tools that researchers can use to design trials with restricted mean survival time as the primary endpoint.

Purpose: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the US. Epidemiological studies demonstrate a 2-3 fold higher incidence of the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) and MM with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). With equal access to care, AAs have better overall survival compared to EAs (Waxman, Blood, 2010). This disparity may be explained by ancestral-associated genetic predisposition of AAs to development of monoclonal gammopathies and to specific acquired, cytogenetically-defined subtypes. Using calculated genetic ancestry data, we have previously identified a higher prevalence of IgH translocations t(11;14), t(14;16) and t(14;20) in individuals with >80% African ancestry (Baughn, BCJ, 2018). Since SNP rs9344 encoding the CCND1 870G>A polymorphism has been reported in association with increased risk of t(11;14) (Weinhold, Nature Genetics, 2013), we investigated whether rs9344 correlates with African ancestry and with t(11;14) in our cohort of patients with plasma cell dyscrasias. Methods: We studied 898 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014). Plasma cell proliferative disorder FISH of immunoglobulin (cIg)-stained positive plasma cells was performed as described (Baughn, BCJ, 2018). Individuals were divided into three ancestral groups: 1. EAs (<0.1% African ancestry and <30% Asian ancestry); 2. AAs (> 80% African ancestry); and 3. Other. Chi-squared test was used to determine the overall comparison between the 3 ancestral groups and also between ancestral groups 1 vs 2, and 2 vs 3 using pairwise comparison. All tests were two-sided with alpha level set at 0.05 for overall statistical significance. Pairwise comparison was considered statistically significant when p<0.025 based on Bonferroni method for multiple comparisons. Results: We identify increased risk of development of either a t(11;14), t(14;16) or t(14;20) in AAs (48.8%) compared to EAs (33.6%) (p-value=0.0051). To explore the genetic basis of increased t(11;14) specifically (37.4% AAs vs. 27.3% p-value=0.049), we evaluated the frequency of the G risk allele of rs9344 in relation to African ancestry. The frequency of the G risk allele was higher in AAs (0.81) compared to EAs (0.59) (p-value <0.0001) and also higher in t(11;14) cases (0.73) compared to non-t(11;14) controls (0.58) (p-value <0.0001). A multivariate model identified only rs9344 as significantly associated with t(11;14) after adjusting for age, gender and race group suggesting that it plays a role in the development of t(11;14) (p-value <0.001 for GG, p-value=0.005 for AG). To test if these results are replicable, we studied the MMRF CoMMpass cohort. This cohort includes individuals with newly diagnosed MM along with self-report race information and translocation data from long insert whole genome sequencing. Although t(11;14) was not enriched in self-reported Black individuals from this cohort (17.5% Black vs. 20.4% White, p-value=0.47), there was association between the rs9344 and self-reported Black race (p-value <0.0001) and with the presence of t(11;14) when the t(11;14) was analyzed by long insert whole genome sequencing (p-value=0.0001). Conclusions: To our knowledge, this study includes the largest group of African individuals with an abnormal plasma cell clone along with uniformly collected FISH, genotyping and ancestry data. We have identified in this diverse population the association of the CCND1 870G>A polymorphism (rs9344) with African ancestry and with t(11;14) suggesting that it plays a role in the development of t(11;14) plasma cell dyscrasias. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Research Funding.

Disease monitoring in nonalcoholic steatohepatitis (NASH) is limited by absence of noninvasive biomarkers of disease regression or progression. We aimed to examine the role of multiparametric three‐dimensional magnetic resonance elastography (3D‐MRE) and magnetic resonance imaging proton density fat fraction (MRI‐PDFF) in the detection of NASH regression after interventions. This is a single‐center prospective clinical trial of 40 patients who underwent bariatric surgery. Imaging and liver biopsies were obtained at baseline and 1 year after surgery. The imaging protocol consisted of multifrequency 3D‐MRE to determine the shear stiffness at 60 Hz and damping ratio at 40 Hz, and MRI‐PDFF to measure the fat fraction. A logistic regression model including these three parameters was previously found to correlate with NASH. We assessed the model performance in the detection of NASH resolution after surgery by comparing the image‐predicted change in NAFLD activity score (delta NAS) to the histologic changes. A total of 38 patients (median age 43, 87% female, 30 of 38 with NAS ≥ 1, and 13 of 38 with NASH) had complete data at 1 year. The NAS decreased in all subjects with NAS ≥ 1 at index biopsy, and NASH resolved in all 13. There was a strong correlation between the predicted delta NAS by imaging and the delta NAS by histology (r = 0.73, P < 0.001). The strength of correlation between histology and the predicted delta NAS using single conventional parameters, such as the fat fraction by MRI‐PDFF or shear stiffness at 60 Hz by MRE, was r = 0.69 (P < 0.001) and r = 0.43 (P = 0.009), respectively. Conclusion: Multiparametric 3D‐MRE and MRI‐PDFF can detect histologic changes of NASH resolution after bariatric surgery. Studies in a nonbariatric setting are needed to confirm the performance as a composite noninvasive biomarker for longitudinal NASH monitoring.

Abstract Introduction: Immunoglobulin light chain (AL) amyloidosis is associated with significant morbidity and mortality. Several models were developed to allow survival prediction, but head-to-head model comparison is lacking. Patients and Methods: Five prognostic models were compared: Mayo 2004; Mayo 2012; 2013 European modification of Mayo 2004 (where Mayo 2004 stage 3 is subdivided into 3 subgroups based on NT-proBNP at 8500 ng/L and systolic blood pressure at 100 mmHg); 2015 European modification of Mayo 2004 (where Mayo 2004 stage 3 is subdivided into 2 subgroups based on NT-proBNP at 8500 ng/L); and organ model (a model based on number of involved organs). All patients with available baseline data for the 5 models were included in this study (n=1005). Improvements in predictive accuracy of the various models were determined by calculating a net improvement in survival prediction for each pair of prognostic models. Net improvement was considered as the proportion where model A outperforms model B minus the proportion where model B outperforms model A. Results: For all models, there was increasing risk for death with increasing stage. The organ model was the least powerful system with a hazard ratio of 2.8 for ≥4 organs compared to 1 involved organ. The organ model was consistently outperformed by the other 4 systems. In the cohort as a whole, the biomarker models (Mayo 2004; Mayo 2012; European 2013; European 2015) were comparable in ability to predict survival (Figure). However, a slightly superior performance of the European 2013 modification of the Mayo 2004 system was seen over the Mayo 2004 system in the group as a whole (net improvement 4.1%, P=0.023) and when restricted to non-ASCT population (net improvement 6.3% P=0.014). The European 2015 modification performed slightly better than the Mayo 2004 system in the ASCT population (net improvement 3.4%, P=0.025). 1-year mortality was better predicted by the European models (net improvement 11-14%, P<0.05), while the Mayo 2012 had better survival prediction to all other biomarker models in 3-year landmark comparison (net improvement 7-9%, P<0.05). Conclusion: Using biomarker based staging is a clear improvement over the organ staging system. The different biomarker staging systems' abilities to predict for survival were relatively comparable, solidifying the importance of soluble cardiac biomarkers and free light chain measurement as prognosticators in AL amyloidosis. Mayo 2004-based models better predict for early death while Mayo 2012 model has a better prediction for long-term survival. Figure. Figure. Disclosures Gertz: Ionis: Honoraria; celgene: Consultancy; spectrum: Consultancy, Honoraria; janssen: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; Medscape: Consultancy; annexon: Consultancy; Teva: Consultancy; Research to Practice: Consultancy; Apellis: Consultancy; Alnylam: Honoraria; Prothena: Honoraria; Physicians Education Resource: Consultancy. Lacy:Celgene: Research Funding. Dingli:Millennium Takeda: Research Funding; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.. Kapoor:Celgene: Research Funding; Takeda: Research Funding. Russell:Vyriad: Equity Ownership. Kumar:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding.

Abstract Monoclonal gammopathies, including multiple myeloma (MM), represent a group of plasma cell (PC) disorders that comprise of mostly incurable hematopoietic malignancies with an increasing incidence in the US. Previous epidemiological studies demonstrated a 2-3 fold higher incidence of monoclonal gammopathy of undetermined significance (MGUS) and a similarly higher incidence of MM along with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). This suggests a possible ancestral-associated genetic predisposition of AAs to the development of monoclonal gammopathies (Landgren, Blood, 2006). When access to care is equal, AAs have a better overall survival compared to EAs suggesting that AAs may have a genetic predisposition that renders them better responders to treatment or have more favorable cytogenetic subtypes of MM (Waxman, Blood, 2010). Previous efforts to understand this disparity have relied on self-reported race rather than genetic ancestry. We hypothesize that quantifying genetic ancestry is necessary to fully understand the genetic mechanisms of racial disparities of monoclonal gammopathies. We describe our study of 881 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from the bone marrow samples was genotyped on the Precision Medicine Research Array. Biogeographical ancestry was assessed using the Geographic Population Structure Origins tool, which provides the ancestral breakdown of 36 admixture components for each individual representing different geographic regions and outputs admixture proportions corresponding to those ancestries (Elhaik, Nat Commun, 2014). We evaluated whether an increase in the percentage of African Ancestry altered the odds of any primary cytogenetic abnormality. We demonstrated that a 10% increase in the percentage of African ancestry was associated with a 6% increase in the odds of detecting either an t(11;14), t(14;16) or t(14;20) (odds ratio = 1.06, 95% CI: 1.02 - 1.11; p-value = 0.05). Although we observed an increase in the odds of each of the individual translocations with 10% increase in African ancestry, l t(11;14) (OR=1.03) t(14;16) (OR=1.11) and t(14;20) (OR=1.10), these three abnormalities were combined to achieve greater statistical power. The differences in cytogenetic abnormalities were most striking among individuals with ≥80.0% African (n=120) compared to individuals with <0.1% African ancestry (n=235). Using these cutoff values comparing the ≥80.0% and <0.1% African ancestry cohorts, there was a higher prevalence of t(11;14), t(14;16) and t(14;20) (51% vs. 33%, respectively, p-value=0.008) and a lower prevalence of trisomies (with or without IgH translocations) (48.3% vs. 61.3%, respectively, p-value=0.066). In addition, the ≥80% African ancestry cohort also displayed a slightly lower prevalence of monosomy 13/13q deletion compared to the cohort with <0.1% African ancestry (34.2% vs. 38.7%, respectively, p-value=0.021). Further, we observed a significantly higher prevalence in the proportion of females with monoclonal gammopathies among the ≥80% African ancestry cohort compared to the cohort with <0.1% African ancestry (56.7% vs. 42.1%, respectively, p-value=0.028). We complement our past studies that relied on self-reported race and characterized patients' demographic and uniformly collected cytogenetic data in relation to genetically defined African ancestry. We demonstrate the prevalence of having one of three specific subtypes (i.e., t(11;14), t(14;16), or t(14;20)) was significantly higher in those with highest (≥80%) compared to lowest (<0.1%) African ancestry demonstrating African ancestry is associated with a subtype distribution of MM that differs from non-African individuals. These differences were only revealed after analysis of individuals with the highest and lowest percentage of African ancestry. Since 38.3% of the individuals with ≥80% African ancestry have the favorable t(11;14) translocation (in comparison to 26.8% of <0.1% African individuals), this finding may explain improved survival with equal access to care. Understanding the genetic mechanisms of this disparity is a fundamental step to understanding differences in incidence and outcomes of patients with monoclonal gammopathies. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding; Celgene: Research Funding. Stewart:Amgen Inc., BMS, Celgene, Takeda, Roche, Seattle Genetics, Janssen, Ono: Consultancy; Amgen Inc., Celgene, Roche, Seattle Genetics: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Kumar:Novartis: Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding.