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Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies

Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies abstract original reports Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies Purpose Recent advances have uncovered therapeutic interventions that might reduce the risk of progression of premalignant diagnoses, such as monoclonal gammopathy of undetermined signif- icance (MGUS) to multiple myeloma (MM). It remains unclear how to best screen populations at risk and how to evaluate the ability of these interventions to reduce disease prevalence and mor- tality at the population level. To address these questions, we developed a computational modeling framework. Materials and Methods We used individual-based computational modeling of MGUS incidence and progression across a population of diverse individuals to determine best screening strategies in terms of screening start, intervals, and risk-group specificity. Inputs were life tables, MGUS inci- dence, and baseline MM survival. We measured MM-specific mortality and MM prevalence after MGUS detection from simulations and mathematic modeling predictions. Results Our framework is applicable to a wide spectrum of screening and intervention scenarios, including variation of the baseline MGUS to MM progression rate and evolving MGUS, in which progression increases over time. Given the currently available point estimate of progression risk reduction to 61% risk, starting screening at age 55 years and performing follow-up screening every 6 years reduced total MM prevalence by 19%. The same reduction could be achieved with starting screening at age 65 years and performing follow-up screening every 2 years. A 40% progression risk reduction per patient with MGUS per year would reduce MM-specific mortality by 40%. Specifically, screening onset age and screening frequency can change disease prevalence, and progression risk reduction changes both prevalence and disease-specific mortality. Screen- ing would generally be favorable in high-risk individuals. Conclusion Screening efforts should focus on specifically identified groups with high lifetime risk of MGUS, for which screening benefits can be significant. Screening low-risk individuals with Philipp M. Altrock MGUS would require improved preventions. Jeremy Ferlic Clin Cancer Inform. © 2018 by American Society of Clinical Oncology Licensed under the Creative Commons Attribution 4.0 License Tobias Galla Michael H. Tomasson INTRODUCTION Men show higher age-adjusted incidence rates than women. There are also racial disparities; Franziska Michor Multiple myeloma (MM) is the second most MGUS prevalence in African Americans age 40 common hematologic malignancy in the United years is roughly equivalent to MGUS prevalence Author affiliations and States, representing 1.8% of new cancer cases support information (if in non-African Americans age 50 years. applicable) appear at the and 2.1% of deaths resulting from annually. Recent advances suggest that the rate of pro- end of this article. MM is an incurable plasma-cell malignancy. Licensed under the gression to MM can be altered by therapeu- Patients show abnormal levels of the paraprotein Creative Commons 8,9 tic interventions. Obesity—a modifiable risk Attribution 4.0 License M protein, indicating a monoclonal cell popula- factor for MM—is associated with increased tion and end-organ damage such as lytic bone 10-12 risk. Furthermore, metformin is associated Corresponding author: lesions. Almost all patients with MM experience with a reduced progression of MGUS to MM, Philipp M. Altrock, PhD, progression from a precursor condition called potentially delaying MM by 4 years in patients Moffitt Cancer Center, monoclonal gammopathy of undetermined sig- 9 12902 USF Magnolia with type 2 diabetes with MGUS. Reduced risk Drive, SRB 24007, nificance (MGUS), displaying only M protein is also associated with regular use of aspirin. Tampa, FL 33612; spikes. The MGUS condition exists in approx- Although causal relationships and molecular e-mail: philipp.altrock@ imately 2% of the population age ≥ 50 years. mechanisms of these associations are uncertain, moffitt.org. © 2018 by American Society of Clinical Oncology ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 1 these findings suggest that pharmacologic and epidemiologic changes, implemented after detec- Fig 1. Population other interventions have the potential to reduce tion. Our model is based on life tables and epide- dynamics of unscreened the risk of MGUS progression. It is therefore of miologic data of MGUS and MM, which depend and screened individuals 15,16 with monoclonal gam- particular interest to investigate the effects of on genetic background, sex, and age and mopathy of undetermined 17 screening for MGUS, especially in specific sub- correlate with ethnicity. Using simulations significance (MGUS) populations, and screening distribution across and analytic results, we assessed whether a as well as those with risk groups. The goals of screening are to detect given reduction in progression risk after a pos- multiple myeloma (MM). (A) Possible individual MGUS early and reduce MM prevalence and itive MGUS screen could reduce MM preva- transitions from healthy mortality as a result of mild interventions leading lence and lead to changes in MM-specific to MGUS to MM can be to an MGUS to MM progression risk reduction. mortality (or survival). Our work can be used to modeled as a Markov identify optimal screening strategies and can chain. The transitions Independent of intervention-based progression describe incidence and assess the utility of interventions targeting MM risk reduction, precursor state knowledge can screening of MGUS and precursor states. progression to MM. The also affect mortality and comorbidity in patient four possible states are 13 cohorts. Sigurdardottir et al found that patients healthy (blue), undetected with MM with prior knowledge of MGUS had MATERIALS AND METHODS MGUS (pink), detected improved overall survival (median, 2.8 years) MGUS (pink with dashed We developed a Markov chain model (Fig 1A) in outline), and MM (red). compared with patients with MM without prior which healthy individuals transition to an unde- (B) Example time evolu- knowledge (median, 2.1 years), overshadowed tion of a cohort at risk for tected MGUS stage, from which they can transi- by a larger extent of relative comorbidities in MGUS and subsequent tion to detected MGUS if screened. An individual patients with prior knowledge. The authors MM without screening. with MGUS progresses to overt MM at a certain Undetected MGUS cases concluded that earlier treatment of MM, as a rate per year; however, a positive MGUS screen- accumulate and can lead result of prior knowledge, leads to better survival to a baseline number ing result reduces the rate of progression to (potentially conflicted by lead bias). Clinical fol- of MM cases. (C) Time MM (Figs 1B and 1C). Individuals may die at low-up in cases of accidental MGUS detection evolution of a cohort with any point, but mortality is greater for those with screening and interven- may be important regardless of (anticipated) risk MM. We performed stochastic simulations and tion that reduces MGUS 13 type, and follow-up preceding the diagnosis to MM progression. derived an analytic framework to assess MM of MGUS-associated malignancy may lead to MGUS cases accumu- mortality and prevalence reduction after screen- improved survival. Screening for MGUS might late; individuals are ing (Data Supplement). screened and receive have additional merit because < 10% of MM preventive treatment if diagnoses currently are knowingly associated positive for MGUS, lead- 13,14 with preexisting MGUS. ing to a lower number of Model Inputs and Outputs MM cases (red indicates We designed a computational model that describes a few screened individu- We were interested in screening outcomes in incidence of MGUS and progression to MM, spe- als who may develop MM mixture populations composed of individuals with nonetheless). cific MGUS screening scenarios, and potential different MGUS lifetime risks. We distinguished non-African American and African Americans as low-risk (baseline) and high-risk individuals, respectively. From baseline, high-risk individu- als carry an average two-fold increase in lifetime Incidence Progression Healthy Undetected MGUS MM 16,18 Calculations of the respective risk of MGUS. Other Screening Death (age|MM) cause of death (age) Reduced progression MGUS incidence rates are displayed in the Data Detected MGUS; Supplement. Furthermore, we used a crude birth Dead Dead intervention initiated rate for the total population and life tables to cal- culate death events of healthy individuals and those with MGUS (high- and low-risk men and women), MM-specific death rates, and a fixed MGUS to MM progression rate for unscreened individuals. A screening scenario was specified by three parameters: age of the individual when Screened without MGUS (to be screened again) receiving the first screen (a ), spacing between follow-up screens (Δa), and risk reduction r after a positive screen (Table 1). As model out- Individual at risk Undetected MGUS Detected MGUS Individual with MM puts, we were interested in the effects of varying 2 ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 24 MGUS-positive but unscreened individuals. Table 1. Important Parameters Used for Computational and Mathematic Modeling Screening meant that starting at age a , indi- Parameter Description Range or Value Reference viduals were screened each year with probability a Age 0-100 years 1/Δa, such that their average time between 1,19 d(a) Probability of dying as 0-1, age dependent screens was Δa. Positively screened individu- a result of any cause at als were assumed to experience progression at age a a reduced rate of r × p. Recent studies have 21,22 d Probability of dying as 0.1295 per patient with MM estimated r = 0.61 for regular aspirin users. a result of MM (Data MM per year From simulations, individual ages, MGUS status, Supplement) MGUS screening, and MM status were recorded m(a) Incidence rate of MGUS 0-1 per person per year, , this work age dependent, risk- (Data Supplement). This approach allowed us to group dependent calculate MGUS and MM prevalence, distribu- 23-25 p Probability of progression 0-0.15 per person per , this tion of age at diagnosis of MM, and MM-specific from MGUS to MM year, depending on work mortality. We also devised a model to calculate progression model, MGUS and MM prevalence and mortality analyt- disease evolution ically (Data Supplement). Using this framework, a Age at first MGUS screen 20-50 years This work we calculated the fractions of individuals with Δa Interval between screens 1-15 years This work MGUS M at a specific age for any risk group, 8,9 r Reduction in 0-1; for example, if r = the fraction of individuals with MM proportional progression, conditional 0.5, then p = 0.5 × 0.01 to M, and the MM-specific mortality for a given on MGUS detection = 0.005 per patient with MGUS per year number of years after MGUS detection. Abbreviations: MGUS, monoclonal gammopathy of undetermined significance; MM, multiple myeloma. RESULTS Prevalence of MM When Screening for MGUS screening scenarios on MM-specific mortality after MGUS detection and on the fraction of We performed stochastic simulations of our individuals with MM of all ages. We initiated all agent-based model to investigate the effects of simulated populations according to the age dis- different conditions on MGUS and MM preva- tribution of the population in the United States lence and mortality. As expected, the propor- 19,20 according to the 2013 census, with a fixed tions of individuals with MGUS and MM varied fraction of healthy high-risk individuals of 20%. with the fraction of high-risk persons in the pop- Although the fraction of African Americans in the ulation (Data Supplement). An increasing risk United States is approximately 13%, we esti- reduction after a positive MGUS screen dras- mated that the genetic diversity in the United tically diminished the fraction of patients with States would further contribute to high risk. MM while increasing the fraction of those with MGUS (Fig 2A). To validate our results, we com- pared our findings with those of Birmann et al, Stochastic Model where in a cohort of 163,810 men and women, 82 individuals were associated with the baseline We simulated the Markov chain model (Fig 1A; progression risk and 44 were associated with the Data Supplement) by using a fixed crude birth lowest progression risk measured, with a value age-dependent death rates for healthy rate, of r = 0.61 in long-term aspirin users (95% CI, individuals and those with MGUS individuals, 0.41 to 0.95). Birmann et al reported a reduc- and a fixed death rate for patients with MM. tion linked to aspirin use of 40% in patients with From the baseline low-risk MGUS incidence MM. On the basis of this study, we estimated a we calculated adapted from Therneau et al, reduced risk in progression from MGUS to MM elevated incidence rates per life-year for spe- of r = 0.61 (point estimate). For this value, our cific risk groups. In our simulations, high-risk predictions of approximately 60% lie in the CI of African Americans experience MGUS incidence Birmann et al for r. that exponentially increases with age such that lifetime risk is approximately two-fold higher Changes in onset age of screening a and spac- 16,28 Progression than that at baseline (low risk). ing Δa affected MM risk reduction similarly (Fig to MM was mostly constant across risk groups 2B; Data Supplement). For example, for a fixed = 45 years and Δa = 8 years reduced and occurred at a rate of p = .01 per year in r = 0.61, a ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 3 A B 80 8 20 MM MGUS 0.0 0.2 0.4 0.6 0.8 1.0 45 50 55 60 65 Risk Reduction Factor r (a = 50 years) Age at First Screen a (years) 0 0 C D 100 100 20 30 40 50 60 70 20 30 40 50 60 70 Age at First Screen a (r = 0.61; years) Age at First Screen a (r = 0.10; years) 0 0 Fig 2. Number of patients with multiple myeloma (MM), age at MM diagnosis, and variability of screening strategy. (A) When monoclonal gam- mopathy of undetermined significance (MGUS) screening was applied, we measured the number of patients diagnosed with MGUS (dashed line, open circles) and MM (solid line, filled circles) relative to the r = 1 values, with respect to changing the risk reduction factor r (circles, simulations; lines, analytic model; Data Supplement), with a = 50 years and Δa = 1 year. At r = 0.61, the MM fraction dropped to < 70% of its value at r = 1 (where screening had no effect on progression). (B) Variability in MM fraction at r = 0.61, with respect to changes in a and Δa (analytic approach, point estimates; Table S4, Data Supplement). (C, D) Distributions of age at MM diagnosis (Δa = 1 year), with varying a and fixed r of (C) 0.61 or (D) 0.1. Width in these violin plots is equal to probability of MM diagnosis at that age. All point estimates were calculated from a simulation of approxi- mately 10 individuals. MM prevalence to 77.2%, whereas a = 65 years changes in progression risk, screening interval, and Δa = 8 years reduced MM prevalence to and screening start age. 78.6% relative to r = 1, respectively. Even for nearly complete risk reduction (r close to 0) and Lead-Time Bias and Cumulative MM-Specific rare screening (Δa = 8 years), a = 45 years Mortality reduced cases of MM by 60% and a = 65 years Screening can cause lead-time bias; the sur- by approximately 38%. Figures 2C to 2F show the impact of Δa and a on the age distribution of vival time after a positive MGUS screening out- come is typically longer than the survival time MM diagnoses, varying r. These normalized vio- lin plots give the probability of finding an individ- after direct clinical presentation of MM, with or without screening; the difference between these ual of a specific age with MM in our simulations. 29,30 The bottleneck near a is more pronounced for two times is the lead-time bias. Because lower r values. Hence, both the number of cases lead-time bias overshadows actual survival ben- of MM and age at MM diagnosis are sensitive to efits of screening in clinical settings where this 4 ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics Age at Diagnosis of MM (years) Patients Diagnosed (% of r = 1) Age at Diagnosis of MM (years) Years Between Screens Δa Patients Diagnosed With MM at r = 0.61 (% of r = 1) t time difference may not be directly observed, by the β × (1 − β) (Fig 3; Data Supplement). We disease-specific mortality is a more appropri- inferred that individuals with nonevolving MGUS ate measure. We determined the expected experience progression at β = 0.007, which well lead-time bias by a comparison of survival in approximates our constant progression rate of unscreened (control) and screened population p = 0.01. Individuals with evolving MGUS expe- simulations (Fig 3A). Median survival after MM rience progression with a 10-fold higher value diagnosis in the control group was 4 to 5 years. (β = 0.07). MM-specific mortality increases con- Median survival after MGUS detection (a = 50 siderably with evolving MGUS rate (Fig 3F) and years; Δa = 1 year) was 15 years for r = 1.0 (and decreases with r (Fig 3G). In addition to pop- similar for r = 0.61) and 17 years for r = 0.1. ulation-based diversity, global migration could Thus, the lead-time bias here would be 10 years. 33 affect the value of screening, as discussed in the Data Supplement using data from Ghana. We calculated the cumulative MM-specific mor - Realistic levels of immigration of high-risk indi- tality after MGUS detection, defined as the prob- viduals are unlikely to affect US MGUS or MM ability that an individual would die as a result of statistics (Data Supplement). MM within a predefined number of years after detection of MGUS at a fixed age. We distin- guished death events resulting from MM and Equal Reduction of MM Prevalence Can Serve deaths resulting from other causes. In Figure 3B, As a Criterion for Optimal Screening Frequency we display the MM-specific mortality as well as Among High- and Low-Risk Populations competing risk for MGUS detection at ages 50, 60, and 70 years. In younger groups, the chance We sought to identify best screening distributions of dying as a result of MM was comparable to among different risk groups to minimize MM the chance of dying as a result of other causes; prevalence (Data Supplement). A fraction y of the latter increased with age. MM-specific mor - available screenings could be applied to high- tality varied strongly with the risk reduction factor risk individuals and the remainder, 1 − y, to low- r (Fig 3C). As shown, using the analytic model risk individuals. There can exist a value of y for in the Data Supplement, MM-specific mortality which MM prevalences are equal. If r = 1, no should not be affected by the screening param- intercept exists, and all screening efforts would eters a and Δa, which only determine age- go to high-risk individuals (Fig 4A). The point specific prevalences. estimate r = 0.61 also gave y = 1. Lower values of r could permit values of y < 1 (Fig 4B), rang- ing from y = 71% (r = 0.0) to y = 96% (r = 0.3), MGUS to MM Progression Variability and given a = 50 years (Fig 4C; Data Supplement); Evolving MGUS y was between 81% and 93% for Δa = 1 and Our framework allows assessment of the impact between 79% and 95% for Δa = 4 (fixed r = 0.1; of variation in MGUS progression rates, as well Fig 4D; Data Supplement). as the impact of evolving MGUS, in which the progression rate changes over time. Variability in MGUS progression rate p (per individual per Groups With Higher Than Two-Fold Lifetime Risk year) can lead to large variability in mortality Could Bnefit Strongly From Regular Screening 10 years after MGUS detection if screening has Multiple factors determine increased lifetime no effect (r = 1.0), but this effect is reduced as risk of MGUS, notably family history of MM. We risk reduction takes effect (r < 1; Fig 3C). analyzed the sensitivity of MM prevalence and MM-specific mortality to screening frequency Patients with MGUS belong either to a large and risk reduction. Both risk reduction and spac- group of individuals who experience progres- ing of screens have more pronounced effects in sion at a constant rate or to a small group who higher-risk groups, but in those groups, steeper experience progression at an accelerating rate. increase in mortality was observed with decreas- Of 359 cases of MGUS reported by Rosiñol ing screening frequency (Fig 4E). Importantly, et al, 330 (92%) were nonevolving and 29 (8%) the increase in MM-specific deaths saturated were evolving (Fig 3E). We approached this effect by assuming that for each individual, the with increasing progression rate, indicating that in high-risk groups, mortality reduction can be rate to progress after exactly t years was given ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 5 A B Unscreened MM Competing cause, MGUS at 50 years 100 0.5 Annual MGUS screening, r = 1.0 Competing cause, MGUS at 60 years Annual MGUS screening, r = 0.1 Competing cause, MGUS at 70 years 0.4 MM specific, MGUS at 50 years MM specific, MGUS at 60 years MM specific, MGUS at 70 years 60 0.3 Lead bias 40 0.2 20 0.1 0 20 40 60 0 123 4 5 678 910 Time Since Disease Detection (years) Time Since MGUS Diagnosis (years) MGUS detection ages: 60 years 80 years C D Distribution of MGUS progression rates 0.10 0.10 0.08 0.5 0.06 r = 1.0 (control) 0.04 0.08 0.4 0.02 r = 0.5 0 0.02 0.04 0.06 0.08 0.10 0.06 0.3 r = 0.1 MGUS Progression Rate per Year 0.2 0.04 0.1 0.02 0.0 0 2 46 810 r = 1.00 r = 0.61 r = 0.10 Time Since MGUS Diagnosis (years) E F G Evolving MGUS progression(t) = β(1-β) Age at MGUS detection, 60 years Evolving, E = 0.071 0.4 0.4 r = 1.00 Age at MGUS detection, 80 years Nonevolving, E = 0.007 1.0 r = 0.61 r = 0.10 0.8 0.3 0.3 0.6 0.2 0.2 0.4 0.1 0.1 0.2 0.0 0 5 10 15 20 25 00.02 0.04 0.06 0.08 0.10 0 0.02 0.04 0.06 0.08 0.10 Time Since MGUS Detection (years) Evolving MGUS−Parameter E Evolving MGUS−Parameter E Fig 3. Lead-time bias, cumulative multiple myeloma (MM) –specific mortality, and monoclonal gammopathy of undetermined significance (MGUS) to MM progression variability. All simulations were performed with populations of 10 healthy individuals (20% high risk). (A) Potential lead-time bias, comparing median survival after MM diagnosis without screening (blue: median survival, 4 years) and with screening (gold: median survival, 15 years; gray: median survival, 17 years after MGUS screen, respectively). Without screening, disease detection was the event of MM diagnosis. With screening, disease detection was diagnosis of asymptomatic MGUS. (B) Cumulative MM-specific mortality in years after MGUS detection was measured for the groups of 50, 60, and 70 years of age at MGUS detection (a = 50 years, Δa = 1, and r = 1). In older patients, death resulting from other cause becomes more dominant. (C) MM-specific mortality changed dramatically with r (a = 50 years, Δa = 1), here shown for individuals diagnosed with MGUS at age 60 years, sampled from simulations. (D) MM-specific mortality is influenced by variability in MGUS to MM progression rate (inset, truncated normal distribution\; mean, 0.01; standard deviation, 0.03), for different r, using the analytic model (Δa = 1; t 25 Data Supplement). (E) Simple evolving MGUS progression rates [β × (1 − β) ], fitted to data from Rosiñol et al (filled circles; nonevolving: 10% at 10 years, 13% at 20 years follow-up; evolving: 55% at 10 years, 80% at 20 years follow-up), for which we show 95% CIs. Nonevolving MGUS con- firms the low value of β (here 0.007; R = 0.996), corresponding to constant progression risk p (Table 1). Evolving MGUS led to a progression rate of p = .071 (R = 0.975). (F, G) Impacts of age at MGUS detection and progression risk reduction r on MM-specific mortality as a function of evolving progression rate calculated as described in Data Supplement: (F) r = 0.61 and (G) age at MGUS detection 60 years. 6 ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics Cumulative MGUS Progression Cumulative MM-Specific Mortality (probability) (probability) Survival (%) 10-Year MM-Specific Mortality (probability) 10year MM−Specific Mortality (probability) Cumulative MM-Specific Mortality (probability) Probability 10-Year MM-Specific Mortality (probability) A B High-risk population High-risk population Low-risk population Low-risk population Optimum 10 10 0 20 40 60 80 100 0 20 40 60 80 100 High-Risk Screens (%) High-Risk Screens (%) C D 0.0 50 0.1 55 0.2 60 0.3 65 1234 1234 Years Between Screens Δa Years Between Screens Δa Four-fold lifetime risk (relative to low risk) E F Three-fold lifetime risk 1.5 Two-fold lifetime risk 1.0 r = 1.00 r = 0.61 r = 0.10 0.5 00.2 0.40.6 0.81.0 Risk Reduction r 60 2 1234 12 3 4 1 234 00.020.040.060.080.10 Screening Interval Δa (a = 50 years; years) MGUS Progression (r = 0.61; per year) Fig 4. Equal disease fractions as a criterion for optimal screening distribution. (A, B) Comparing multiple myeloma (MM) fractions in the high-risk and low-risk populations (men and women, respectively), with a = 50 years and Δa = 1 year, for different r. (A) For r = 0.61, equality could not be observed for any percentage of high-risk screens. (B) For r = 0.1, equality was observed at approximately 81% high-risk screens. Thus, an optimal fraction of screens was defined as the point where the fractions of patients with MM in both subpopulations were the same. (C) Location of the optimal fraction (scale) under variation of r and Δa (Table S5, Data Supplement), with a = 50 years. Changing r from 0 to 0.3 would lead to up to 20% change in the optimal high-risk fraction of screens. Changing Δa from 1 to 4 would lead to 1% to 3% change in the optimal high-risk fraction of screens. (D) For fixed r = 0.1, changes in a had more drastic effects than changes in Δa (Table S6, Data Supplement). (E) For risk groups with a lifetime risk higher than two-fold, we examined the effect of risk reduction and screening interval (a = 50 years) on the number of patients with MM (Data Supplement). (F) MM-specific deaths per 100,00 were calculated as the product of screened individuals with monoclonal gammopathy of undetermined significance (MGUS) at age 60 years and the 10-year follow-up MM-specific mortality (a = 50 years and Δa = 1; age at MGUS detection, 60 years). Both risk reduction and spacing of screens have more pronounced effects in higher-risk groups. ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 7 No. of Patients With MM Risk Reduction Factor r per 100,000 No. of Patients With MM per 100,000 Optimal Percentage of High-Risk Screens MM-Specific Deaths per 100,000 Age at First Screen a No. of Patients With MM per 100,000 10 Years After MGUS Detection achieved in subgroups of intermediate progres- Length-time bias, in contrast, is a form of selec- sion rates (Fig 4F). tion bias that occurs because of heterogeneity in the progression speed of a malignancy. This bias was absent in our study because we modeled DISCUSSION uniform progression of the disease (ie, a high- MM remains incurable for a majority of patients, risk person with early incidence of MGUS expe- and decreasing mortality is of as much interest rienced progression to MM equally as fast as a as decreasing its prevalence. All patients seem low-risk person with late MGUS incidence; the to experience progression to symptomatic MM time spent in the MGUS state in the no-screening from a premalignant, asymptomatic stage called scenario was independent of age). Therefore, MGUS. The fact that there are outstanding these common sources of bias in epidemiologic diagnostic tests for MGUS implies the possibil- prevention studies did not confound our results. ity of delaying progression of MGUS to MM by 36 Using a stochastic simulation framework and an screening and early identification. Because analytic model, we measured MGUS and MM precise estimates of MGUS prevalence have 5-7,37 prevalence and MM-specific mortality in differ - changed over the past decade, we consid- ent risk groups for different screening strategies ered relative changes in prevalence (using as a and varying progression risk reduction after baseline no effect of screening on progression MGUS detection. For effective MM prevalence risk reduction). We evaluated a range of possible reduction, better screening results are expected screening strategies based on the consideration for screening as early as possible and frequent that diagnosis of MGUS permits progression follow-up. Improved chemoprevention, effec- reduction as a result of several possible interven- tively reducing progression risk, may also reduce tions or modifiable risk factors, including aspirin, MM-specific mortality. We found that this effect metformin, or mediation such as exercise or diet is more pronounced in individuals with evolving 8,9,11,12,36 alterations. MGUS, especially in individuals with higher than The promise of early intervention in MGUS should two-fold lifetime MGUS risk. be viewed with caution. Our current understand- We did not explicitly address screening toxicity, ing comes from retrospective observational stud- nor did we model smoldering MM—an inter- ies. Our results, however, suggest that research mediate stage between MGUS and MM with a to identify effective chemoprevention agents in much higher rate of progression to full MM of high-risk MGUS can be justified. It will take time approximately 30% per year—in part because to develop a more comprehensive understand- it remains unclear whether smoldering MM is a ing of the intricate relationship between early requisite intermediate between MGUS and MM. intervention utilities and potential adverse effects However, our framework can be adjusted and on a wider scale, related to health care costs expanded. and psychological burden. Patients with MGUS may experience psychological distress similar Assessments of screening and prevention in solid to that experienced by those with MM, and the tumors (eg, prostate cancer) have been contro- identification of cancer precursor states must versial and lacking in evidence for screening in be accompanied by a discussion of the utility large prospective trials. We share the skepti- 38-42 of follow-up in individual patients. Promising cism of potential medicalization of asymptomatic efforts that evaluate MGUS screening and con- conditions. However, the biology of MGUS and tinuous follow-up before clinical manifestation of the robust laboratory tests demand careful eval- MM are under way in a long-term, prospective, uation of the role of screening and prevention. three-armed randomized trial (iStopMM). Such With notable similarities in the epidemiology of long-term efforts highlight the utility of predictive prostate cancer and MGUS (ie, most low-grade tools such as the one developed here. lesions will not proceed to lethal disease), major Our approach allowed us to quantify the amount differences in technology of screening tests for of risk reduction needed to result in certain these diseases are critical. Prostate-specific anti- reductions in MM-specific mortality and MM gen tests for prostate cancer are burdened by prevalence (measured as MM fraction). To avoid substantial false-positive (21% to 32% sensitiv- lead-time bias, we evaluated screening scenar- ity) and false-negative rates (85% to 91% spec- In contrast, serum testing for MGUS is ios in terms of mortality and MM prevalence. ificity). 8 ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics straightforward. The sensitivity of serum protein by lowering the incidence of MM, provided effec- electrophoresis and free light chain testing for tive and nontoxic interventions can be identified. MGUS is close to 100%, and the specificity is Without further study of chemoprevention strat- 99%. These differences underline the evalua- egies, regular screening of MGUS candidates tion of the role of screening and prevention in should start as early as possible, with biannual MGUS and MM. We have shown that the reduc- follow-up, and focus on high-risk individuals, tion of cases of MM and MM-specific mortality especially those with a family history of MM, in high- and low-risk subpopulations can be or on groups with strong indication for evolving achieved, but only for drastic reduction in pro- MGUS progression. gression risk. Until highly effective agents are developed, identification and follow-up of high- DOI: https://doi.org/10.1200/CCI.17.00131 risk individuals are important. Screening for Published online on ascopubs.org/journal/cci on MGUS may have significant population benefits March 22, 2018. AUTHOR CONTRIBUTIONS Philipp M. Altrock No relationship to disclose Conception and design: All authors Financial support: Franziska Michor Jeremy Ferlic Administrative support: Franziska Michor No relationship to disclose Collection and assembly of data: Philipp M. Altrock, Jeremy Ferlic, Michael H. Tomasson Tobias Galla Data analysis and interpretation: All authors Honoraria: Eli Lilly (I), UCB Pharma (I) Manuscript writing: All authors Consulting or Advisory Role: Eli Lilly (I) Final approval of manuscript: All authors Travel, Accommodations, Expenses: Amgen (I) Accountable for all aspects of the work: All authors Michael H. Tomasson No relationship to disclose AUTHORS' DISCLOSURES OF Franziska Michor POTENTIAL CONFLICTS OF INTEREST No relationship to disclose The following represents disclosure information provided by authors of this manuscript. All relationships are ACKNOWLEDGMENT considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My We thank Graham Colditz (St Louis) and Nicola Camp (Salt Institution. Relationships may not relate to the subject Lake City) as well as members of the Michor laboratory at matter of this manuscript. For more information about the Dana-Farber Cancer Institute (Boston) and members of ASCO's conflict of interest policy, please refer to www. Integrated Mathematical Oncology at Moffitt Cancer Center asco.org/rwc or ascopubs.org/jco/site/ifc. and Research Institute (Tampa) for their feedback. Affiliations Philipp M. Altrock, Moffitt Cancer Center and Research Institute; Morsani College of Medicine, University of South Florida, Tampa, FL; Jeremy Ferlic and Franziska Michor, Dana-Farber Cancer Institute and Harvard University; Harvard T.H. Chan School of Public Health, Boston; Franziska Michor, Center for Cancer Evolution, Dana-Farber Cancer Institute, and The Ludwig Center at Harvard, Boston; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA; Tobias Galla, University of Manchester, Manchester, United Kingdom; and Michael H. Tomasson, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA. Support Supported by Grant No. LPDS 2012-12 from Deutsche Akademie der Naturforscher Leopoldina (P.M.A. [for work at Harvard University]), by National Cancer Institute Grant No. U54CA193461 to the Dana-Farber Cancer Institute Physical Sciences Oncology Center (F.M.), by Grant No. EP/K037145/1 from the Engineering and Physical Sciences Research Council (T.G), and by the Moffitt Cancer Center and Research Institute (P.M.A.). REFERENCES 1. 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Carson KR, Bates ML, Tomasson MH: The skinny on obesity and plasma cell myeloma: A review of the literature. Bone Marrow Transplant 49:1009-1015, 2014 12. Birmann BM, Giovannucci E, Rosner B, et al: Body mass index, physical activity, and risk of multiple myeloma. Cancer Epidemiol Biomarkers Prev 16:1474-1478, 2007 13. Sigurdardottir EE, Turesson I, Lund SH, et al: The role of diagnosis and clinical follow-up of monoclonal gammopathy of undetermined significance on survival in multiple myeloma. JAMA Oncol 1:168-174, 2015 14. Go RS, Gundrum JD, Neuner JM: Determining the clinical significance of monoclonal gammopathy of undetermined significance: A SEER-Medicare population analysis. Clin Lymphoma Myeloma Leuk 15:177.e4-186.e4, 2015 15. Therneau TM, Kyle RA, Melton LJ III, et al: Incidence of monoclonal gammopathy of undetermined significance and estimation of duration before first clinical recognition. Mayo Clin Proc 87:1071- 1079, 2012 16. Landgren O, Gridley G, Turesson I, et al: Risk of monoclonal gammopathy of undetermined significance (MGUS) and subsequent multiple myeloma among African American and white veterans in the United States. Blood 107:904-906, 2006 17. Waxman AJ, Mink PJ, Devesa SS, et al: Racial disparities in incidence and outcome in multiple myeloma: A population-based study. Blood 116:5501-5506, 2010 18. Landgren O, Katzmann JA, Hsing AW, et al: Prevalence of monoclonal gammopathy of undetermined significance among men in Ghana. Mayo Clin Proc 82:1468-1473, 2007 19. US Census Bureau: American FactFinder 2016. http://factfinder2.census.gov 20. National Cancer Institute: Surveillance, Epidemiology, and End Results (SEER) program populations (1969-2014). http://www.seer.cancer.gov/popdata 21. Palumbo A, Anderson K: Multiple myeloma. N Engl J Med 364:1046-1060, 2011 22. Kumar SK, Rajkumar SV, Dispenzieri A, et al: Improved survival in multiple myeloma and the impact of novel therapies. 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International Myeloma Foundation: iStopMM: Black Swann Initiative. https://www.myeloma.org/ istopmm 44. Tabayoyong W, Abouassaly R: Prostate cancer screening and the associated controversy. Surg Clin North Am 95:1023-1039, 2015 45. Hayes JH, Barry MJ: Screening for prostate cancer with the prostate-specific antigen test: A review of current evidence. JAMA 311:1143-1149, 2014 46. Katzmann JA: Screening panels for monoclonal gammopathies: Time to change. Clin Biochem Rev 30:105-111, 2009 12 ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JCO Clinical Cancer Informatics Wolters Kluwer Health

Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies

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Wolters Kluwer Health
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2473-4276
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10.1200/CCI.17.00131
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abstract original reports Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies Purpose Recent advances have uncovered therapeutic interventions that might reduce the risk of progression of premalignant diagnoses, such as monoclonal gammopathy of undetermined signif- icance (MGUS) to multiple myeloma (MM). It remains unclear how to best screen populations at risk and how to evaluate the ability of these interventions to reduce disease prevalence and mor- tality at the population level. To address these questions, we developed a computational modeling framework. Materials and Methods We used individual-based computational modeling of MGUS incidence and progression across a population of diverse individuals to determine best screening strategies in terms of screening start, intervals, and risk-group specificity. Inputs were life tables, MGUS inci- dence, and baseline MM survival. We measured MM-specific mortality and MM prevalence after MGUS detection from simulations and mathematic modeling predictions. Results Our framework is applicable to a wide spectrum of screening and intervention scenarios, including variation of the baseline MGUS to MM progression rate and evolving MGUS, in which progression increases over time. Given the currently available point estimate of progression risk reduction to 61% risk, starting screening at age 55 years and performing follow-up screening every 6 years reduced total MM prevalence by 19%. The same reduction could be achieved with starting screening at age 65 years and performing follow-up screening every 2 years. A 40% progression risk reduction per patient with MGUS per year would reduce MM-specific mortality by 40%. Specifically, screening onset age and screening frequency can change disease prevalence, and progression risk reduction changes both prevalence and disease-specific mortality. Screen- ing would generally be favorable in high-risk individuals. Conclusion Screening efforts should focus on specifically identified groups with high lifetime risk of MGUS, for which screening benefits can be significant. Screening low-risk individuals with Philipp M. Altrock MGUS would require improved preventions. Jeremy Ferlic Clin Cancer Inform. © 2018 by American Society of Clinical Oncology Licensed under the Creative Commons Attribution 4.0 License Tobias Galla Michael H. Tomasson INTRODUCTION Men show higher age-adjusted incidence rates than women. There are also racial disparities; Franziska Michor Multiple myeloma (MM) is the second most MGUS prevalence in African Americans age 40 common hematologic malignancy in the United years is roughly equivalent to MGUS prevalence Author affiliations and States, representing 1.8% of new cancer cases support information (if in non-African Americans age 50 years. applicable) appear at the and 2.1% of deaths resulting from annually. Recent advances suggest that the rate of pro- end of this article. MM is an incurable plasma-cell malignancy. Licensed under the gression to MM can be altered by therapeu- Patients show abnormal levels of the paraprotein Creative Commons 8,9 tic interventions. Obesity—a modifiable risk Attribution 4.0 License M protein, indicating a monoclonal cell popula- factor for MM—is associated with increased tion and end-organ damage such as lytic bone 10-12 risk. Furthermore, metformin is associated Corresponding author: lesions. Almost all patients with MM experience with a reduced progression of MGUS to MM, Philipp M. Altrock, PhD, progression from a precursor condition called potentially delaying MM by 4 years in patients Moffitt Cancer Center, monoclonal gammopathy of undetermined sig- 9 12902 USF Magnolia with type 2 diabetes with MGUS. Reduced risk Drive, SRB 24007, nificance (MGUS), displaying only M protein is also associated with regular use of aspirin. Tampa, FL 33612; spikes. The MGUS condition exists in approx- Although causal relationships and molecular e-mail: philipp.altrock@ imately 2% of the population age ≥ 50 years. mechanisms of these associations are uncertain, moffitt.org. © 2018 by American Society of Clinical Oncology ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 1 these findings suggest that pharmacologic and epidemiologic changes, implemented after detec- Fig 1. Population other interventions have the potential to reduce tion. Our model is based on life tables and epide- dynamics of unscreened the risk of MGUS progression. It is therefore of miologic data of MGUS and MM, which depend and screened individuals 15,16 with monoclonal gam- particular interest to investigate the effects of on genetic background, sex, and age and mopathy of undetermined 17 screening for MGUS, especially in specific sub- correlate with ethnicity. Using simulations significance (MGUS) populations, and screening distribution across and analytic results, we assessed whether a as well as those with risk groups. The goals of screening are to detect given reduction in progression risk after a pos- multiple myeloma (MM). (A) Possible individual MGUS early and reduce MM prevalence and itive MGUS screen could reduce MM preva- transitions from healthy mortality as a result of mild interventions leading lence and lead to changes in MM-specific to MGUS to MM can be to an MGUS to MM progression risk reduction. mortality (or survival). Our work can be used to modeled as a Markov identify optimal screening strategies and can chain. The transitions Independent of intervention-based progression describe incidence and assess the utility of interventions targeting MM risk reduction, precursor state knowledge can screening of MGUS and precursor states. progression to MM. The also affect mortality and comorbidity in patient four possible states are 13 cohorts. Sigurdardottir et al found that patients healthy (blue), undetected with MM with prior knowledge of MGUS had MATERIALS AND METHODS MGUS (pink), detected improved overall survival (median, 2.8 years) MGUS (pink with dashed We developed a Markov chain model (Fig 1A) in outline), and MM (red). compared with patients with MM without prior which healthy individuals transition to an unde- (B) Example time evolu- knowledge (median, 2.1 years), overshadowed tion of a cohort at risk for tected MGUS stage, from which they can transi- by a larger extent of relative comorbidities in MGUS and subsequent tion to detected MGUS if screened. An individual patients with prior knowledge. The authors MM without screening. with MGUS progresses to overt MM at a certain Undetected MGUS cases concluded that earlier treatment of MM, as a rate per year; however, a positive MGUS screen- accumulate and can lead result of prior knowledge, leads to better survival to a baseline number ing result reduces the rate of progression to (potentially conflicted by lead bias). Clinical fol- of MM cases. (C) Time MM (Figs 1B and 1C). Individuals may die at low-up in cases of accidental MGUS detection evolution of a cohort with any point, but mortality is greater for those with screening and interven- may be important regardless of (anticipated) risk MM. We performed stochastic simulations and tion that reduces MGUS 13 type, and follow-up preceding the diagnosis to MM progression. derived an analytic framework to assess MM of MGUS-associated malignancy may lead to MGUS cases accumu- mortality and prevalence reduction after screen- improved survival. Screening for MGUS might late; individuals are ing (Data Supplement). screened and receive have additional merit because < 10% of MM preventive treatment if diagnoses currently are knowingly associated positive for MGUS, lead- 13,14 with preexisting MGUS. ing to a lower number of Model Inputs and Outputs MM cases (red indicates We designed a computational model that describes a few screened individu- We were interested in screening outcomes in incidence of MGUS and progression to MM, spe- als who may develop MM mixture populations composed of individuals with nonetheless). cific MGUS screening scenarios, and potential different MGUS lifetime risks. We distinguished non-African American and African Americans as low-risk (baseline) and high-risk individuals, respectively. From baseline, high-risk individu- als carry an average two-fold increase in lifetime Incidence Progression Healthy Undetected MGUS MM 16,18 Calculations of the respective risk of MGUS. Other Screening Death (age|MM) cause of death (age) Reduced progression MGUS incidence rates are displayed in the Data Detected MGUS; Supplement. Furthermore, we used a crude birth Dead Dead intervention initiated rate for the total population and life tables to cal- culate death events of healthy individuals and those with MGUS (high- and low-risk men and women), MM-specific death rates, and a fixed MGUS to MM progression rate for unscreened individuals. A screening scenario was specified by three parameters: age of the individual when Screened without MGUS (to be screened again) receiving the first screen (a ), spacing between follow-up screens (Δa), and risk reduction r after a positive screen (Table 1). As model out- Individual at risk Undetected MGUS Detected MGUS Individual with MM puts, we were interested in the effects of varying 2 ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 24 MGUS-positive but unscreened individuals. Table 1. Important Parameters Used for Computational and Mathematic Modeling Screening meant that starting at age a , indi- Parameter Description Range or Value Reference viduals were screened each year with probability a Age 0-100 years 1/Δa, such that their average time between 1,19 d(a) Probability of dying as 0-1, age dependent screens was Δa. Positively screened individu- a result of any cause at als were assumed to experience progression at age a a reduced rate of r × p. Recent studies have 21,22 d Probability of dying as 0.1295 per patient with MM estimated r = 0.61 for regular aspirin users. a result of MM (Data MM per year From simulations, individual ages, MGUS status, Supplement) MGUS screening, and MM status were recorded m(a) Incidence rate of MGUS 0-1 per person per year, , this work age dependent, risk- (Data Supplement). This approach allowed us to group dependent calculate MGUS and MM prevalence, distribu- 23-25 p Probability of progression 0-0.15 per person per , this tion of age at diagnosis of MM, and MM-specific from MGUS to MM year, depending on work mortality. We also devised a model to calculate progression model, MGUS and MM prevalence and mortality analyt- disease evolution ically (Data Supplement). Using this framework, a Age at first MGUS screen 20-50 years This work we calculated the fractions of individuals with Δa Interval between screens 1-15 years This work MGUS M at a specific age for any risk group, 8,9 r Reduction in 0-1; for example, if r = the fraction of individuals with MM proportional progression, conditional 0.5, then p = 0.5 × 0.01 to M, and the MM-specific mortality for a given on MGUS detection = 0.005 per patient with MGUS per year number of years after MGUS detection. Abbreviations: MGUS, monoclonal gammopathy of undetermined significance; MM, multiple myeloma. RESULTS Prevalence of MM When Screening for MGUS screening scenarios on MM-specific mortality after MGUS detection and on the fraction of We performed stochastic simulations of our individuals with MM of all ages. We initiated all agent-based model to investigate the effects of simulated populations according to the age dis- different conditions on MGUS and MM preva- tribution of the population in the United States lence and mortality. As expected, the propor- 19,20 according to the 2013 census, with a fixed tions of individuals with MGUS and MM varied fraction of healthy high-risk individuals of 20%. with the fraction of high-risk persons in the pop- Although the fraction of African Americans in the ulation (Data Supplement). An increasing risk United States is approximately 13%, we esti- reduction after a positive MGUS screen dras- mated that the genetic diversity in the United tically diminished the fraction of patients with States would further contribute to high risk. MM while increasing the fraction of those with MGUS (Fig 2A). To validate our results, we com- pared our findings with those of Birmann et al, Stochastic Model where in a cohort of 163,810 men and women, 82 individuals were associated with the baseline We simulated the Markov chain model (Fig 1A; progression risk and 44 were associated with the Data Supplement) by using a fixed crude birth lowest progression risk measured, with a value age-dependent death rates for healthy rate, of r = 0.61 in long-term aspirin users (95% CI, individuals and those with MGUS individuals, 0.41 to 0.95). Birmann et al reported a reduc- and a fixed death rate for patients with MM. tion linked to aspirin use of 40% in patients with From the baseline low-risk MGUS incidence MM. On the basis of this study, we estimated a we calculated adapted from Therneau et al, reduced risk in progression from MGUS to MM elevated incidence rates per life-year for spe- of r = 0.61 (point estimate). For this value, our cific risk groups. In our simulations, high-risk predictions of approximately 60% lie in the CI of African Americans experience MGUS incidence Birmann et al for r. that exponentially increases with age such that lifetime risk is approximately two-fold higher Changes in onset age of screening a and spac- 16,28 Progression than that at baseline (low risk). ing Δa affected MM risk reduction similarly (Fig to MM was mostly constant across risk groups 2B; Data Supplement). For example, for a fixed = 45 years and Δa = 8 years reduced and occurred at a rate of p = .01 per year in r = 0.61, a ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 3 A B 80 8 20 MM MGUS 0.0 0.2 0.4 0.6 0.8 1.0 45 50 55 60 65 Risk Reduction Factor r (a = 50 years) Age at First Screen a (years) 0 0 C D 100 100 20 30 40 50 60 70 20 30 40 50 60 70 Age at First Screen a (r = 0.61; years) Age at First Screen a (r = 0.10; years) 0 0 Fig 2. Number of patients with multiple myeloma (MM), age at MM diagnosis, and variability of screening strategy. (A) When monoclonal gam- mopathy of undetermined significance (MGUS) screening was applied, we measured the number of patients diagnosed with MGUS (dashed line, open circles) and MM (solid line, filled circles) relative to the r = 1 values, with respect to changing the risk reduction factor r (circles, simulations; lines, analytic model; Data Supplement), with a = 50 years and Δa = 1 year. At r = 0.61, the MM fraction dropped to < 70% of its value at r = 1 (where screening had no effect on progression). (B) Variability in MM fraction at r = 0.61, with respect to changes in a and Δa (analytic approach, point estimates; Table S4, Data Supplement). (C, D) Distributions of age at MM diagnosis (Δa = 1 year), with varying a and fixed r of (C) 0.61 or (D) 0.1. Width in these violin plots is equal to probability of MM diagnosis at that age. All point estimates were calculated from a simulation of approxi- mately 10 individuals. MM prevalence to 77.2%, whereas a = 65 years changes in progression risk, screening interval, and Δa = 8 years reduced MM prevalence to and screening start age. 78.6% relative to r = 1, respectively. Even for nearly complete risk reduction (r close to 0) and Lead-Time Bias and Cumulative MM-Specific rare screening (Δa = 8 years), a = 45 years Mortality reduced cases of MM by 60% and a = 65 years Screening can cause lead-time bias; the sur- by approximately 38%. Figures 2C to 2F show the impact of Δa and a on the age distribution of vival time after a positive MGUS screening out- come is typically longer than the survival time MM diagnoses, varying r. These normalized vio- lin plots give the probability of finding an individ- after direct clinical presentation of MM, with or without screening; the difference between these ual of a specific age with MM in our simulations. 29,30 The bottleneck near a is more pronounced for two times is the lead-time bias. Because lower r values. Hence, both the number of cases lead-time bias overshadows actual survival ben- of MM and age at MM diagnosis are sensitive to efits of screening in clinical settings where this 4 ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics Age at Diagnosis of MM (years) Patients Diagnosed (% of r = 1) Age at Diagnosis of MM (years) Years Between Screens Δa Patients Diagnosed With MM at r = 0.61 (% of r = 1) t time difference may not be directly observed, by the β × (1 − β) (Fig 3; Data Supplement). We disease-specific mortality is a more appropri- inferred that individuals with nonevolving MGUS ate measure. We determined the expected experience progression at β = 0.007, which well lead-time bias by a comparison of survival in approximates our constant progression rate of unscreened (control) and screened population p = 0.01. Individuals with evolving MGUS expe- simulations (Fig 3A). Median survival after MM rience progression with a 10-fold higher value diagnosis in the control group was 4 to 5 years. (β = 0.07). MM-specific mortality increases con- Median survival after MGUS detection (a = 50 siderably with evolving MGUS rate (Fig 3F) and years; Δa = 1 year) was 15 years for r = 1.0 (and decreases with r (Fig 3G). In addition to pop- similar for r = 0.61) and 17 years for r = 0.1. ulation-based diversity, global migration could Thus, the lead-time bias here would be 10 years. 33 affect the value of screening, as discussed in the Data Supplement using data from Ghana. We calculated the cumulative MM-specific mor - Realistic levels of immigration of high-risk indi- tality after MGUS detection, defined as the prob- viduals are unlikely to affect US MGUS or MM ability that an individual would die as a result of statistics (Data Supplement). MM within a predefined number of years after detection of MGUS at a fixed age. We distin- guished death events resulting from MM and Equal Reduction of MM Prevalence Can Serve deaths resulting from other causes. In Figure 3B, As a Criterion for Optimal Screening Frequency we display the MM-specific mortality as well as Among High- and Low-Risk Populations competing risk for MGUS detection at ages 50, 60, and 70 years. In younger groups, the chance We sought to identify best screening distributions of dying as a result of MM was comparable to among different risk groups to minimize MM the chance of dying as a result of other causes; prevalence (Data Supplement). A fraction y of the latter increased with age. MM-specific mor - available screenings could be applied to high- tality varied strongly with the risk reduction factor risk individuals and the remainder, 1 − y, to low- r (Fig 3C). As shown, using the analytic model risk individuals. There can exist a value of y for in the Data Supplement, MM-specific mortality which MM prevalences are equal. If r = 1, no should not be affected by the screening param- intercept exists, and all screening efforts would eters a and Δa, which only determine age- go to high-risk individuals (Fig 4A). The point specific prevalences. estimate r = 0.61 also gave y = 1. Lower values of r could permit values of y < 1 (Fig 4B), rang- ing from y = 71% (r = 0.0) to y = 96% (r = 0.3), MGUS to MM Progression Variability and given a = 50 years (Fig 4C; Data Supplement); Evolving MGUS y was between 81% and 93% for Δa = 1 and Our framework allows assessment of the impact between 79% and 95% for Δa = 4 (fixed r = 0.1; of variation in MGUS progression rates, as well Fig 4D; Data Supplement). as the impact of evolving MGUS, in which the progression rate changes over time. Variability in MGUS progression rate p (per individual per Groups With Higher Than Two-Fold Lifetime Risk year) can lead to large variability in mortality Could Bnefit Strongly From Regular Screening 10 years after MGUS detection if screening has Multiple factors determine increased lifetime no effect (r = 1.0), but this effect is reduced as risk of MGUS, notably family history of MM. We risk reduction takes effect (r < 1; Fig 3C). analyzed the sensitivity of MM prevalence and MM-specific mortality to screening frequency Patients with MGUS belong either to a large and risk reduction. Both risk reduction and spac- group of individuals who experience progres- ing of screens have more pronounced effects in sion at a constant rate or to a small group who higher-risk groups, but in those groups, steeper experience progression at an accelerating rate. increase in mortality was observed with decreas- Of 359 cases of MGUS reported by Rosiñol ing screening frequency (Fig 4E). Importantly, et al, 330 (92%) were nonevolving and 29 (8%) the increase in MM-specific deaths saturated were evolving (Fig 3E). We approached this effect by assuming that for each individual, the with increasing progression rate, indicating that in high-risk groups, mortality reduction can be rate to progress after exactly t years was given ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 5 A B Unscreened MM Competing cause, MGUS at 50 years 100 0.5 Annual MGUS screening, r = 1.0 Competing cause, MGUS at 60 years Annual MGUS screening, r = 0.1 Competing cause, MGUS at 70 years 0.4 MM specific, MGUS at 50 years MM specific, MGUS at 60 years MM specific, MGUS at 70 years 60 0.3 Lead bias 40 0.2 20 0.1 0 20 40 60 0 123 4 5 678 910 Time Since Disease Detection (years) Time Since MGUS Diagnosis (years) MGUS detection ages: 60 years 80 years C D Distribution of MGUS progression rates 0.10 0.10 0.08 0.5 0.06 r = 1.0 (control) 0.04 0.08 0.4 0.02 r = 0.5 0 0.02 0.04 0.06 0.08 0.10 0.06 0.3 r = 0.1 MGUS Progression Rate per Year 0.2 0.04 0.1 0.02 0.0 0 2 46 810 r = 1.00 r = 0.61 r = 0.10 Time Since MGUS Diagnosis (years) E F G Evolving MGUS progression(t) = β(1-β) Age at MGUS detection, 60 years Evolving, E = 0.071 0.4 0.4 r = 1.00 Age at MGUS detection, 80 years Nonevolving, E = 0.007 1.0 r = 0.61 r = 0.10 0.8 0.3 0.3 0.6 0.2 0.2 0.4 0.1 0.1 0.2 0.0 0 5 10 15 20 25 00.02 0.04 0.06 0.08 0.10 0 0.02 0.04 0.06 0.08 0.10 Time Since MGUS Detection (years) Evolving MGUS−Parameter E Evolving MGUS−Parameter E Fig 3. Lead-time bias, cumulative multiple myeloma (MM) –specific mortality, and monoclonal gammopathy of undetermined significance (MGUS) to MM progression variability. All simulations were performed with populations of 10 healthy individuals (20% high risk). (A) Potential lead-time bias, comparing median survival after MM diagnosis without screening (blue: median survival, 4 years) and with screening (gold: median survival, 15 years; gray: median survival, 17 years after MGUS screen, respectively). Without screening, disease detection was the event of MM diagnosis. With screening, disease detection was diagnosis of asymptomatic MGUS. (B) Cumulative MM-specific mortality in years after MGUS detection was measured for the groups of 50, 60, and 70 years of age at MGUS detection (a = 50 years, Δa = 1, and r = 1). In older patients, death resulting from other cause becomes more dominant. (C) MM-specific mortality changed dramatically with r (a = 50 years, Δa = 1), here shown for individuals diagnosed with MGUS at age 60 years, sampled from simulations. (D) MM-specific mortality is influenced by variability in MGUS to MM progression rate (inset, truncated normal distribution\; mean, 0.01; standard deviation, 0.03), for different r, using the analytic model (Δa = 1; t 25 Data Supplement). (E) Simple evolving MGUS progression rates [β × (1 − β) ], fitted to data from Rosiñol et al (filled circles; nonevolving: 10% at 10 years, 13% at 20 years follow-up; evolving: 55% at 10 years, 80% at 20 years follow-up), for which we show 95% CIs. Nonevolving MGUS con- firms the low value of β (here 0.007; R = 0.996), corresponding to constant progression risk p (Table 1). Evolving MGUS led to a progression rate of p = .071 (R = 0.975). (F, G) Impacts of age at MGUS detection and progression risk reduction r on MM-specific mortality as a function of evolving progression rate calculated as described in Data Supplement: (F) r = 0.61 and (G) age at MGUS detection 60 years. 6 ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics Cumulative MGUS Progression Cumulative MM-Specific Mortality (probability) (probability) Survival (%) 10-Year MM-Specific Mortality (probability) 10year MM−Specific Mortality (probability) Cumulative MM-Specific Mortality (probability) Probability 10-Year MM-Specific Mortality (probability) A B High-risk population High-risk population Low-risk population Low-risk population Optimum 10 10 0 20 40 60 80 100 0 20 40 60 80 100 High-Risk Screens (%) High-Risk Screens (%) C D 0.0 50 0.1 55 0.2 60 0.3 65 1234 1234 Years Between Screens Δa Years Between Screens Δa Four-fold lifetime risk (relative to low risk) E F Three-fold lifetime risk 1.5 Two-fold lifetime risk 1.0 r = 1.00 r = 0.61 r = 0.10 0.5 00.2 0.40.6 0.81.0 Risk Reduction r 60 2 1234 12 3 4 1 234 00.020.040.060.080.10 Screening Interval Δa (a = 50 years; years) MGUS Progression (r = 0.61; per year) Fig 4. Equal disease fractions as a criterion for optimal screening distribution. (A, B) Comparing multiple myeloma (MM) fractions in the high-risk and low-risk populations (men and women, respectively), with a = 50 years and Δa = 1 year, for different r. (A) For r = 0.61, equality could not be observed for any percentage of high-risk screens. (B) For r = 0.1, equality was observed at approximately 81% high-risk screens. Thus, an optimal fraction of screens was defined as the point where the fractions of patients with MM in both subpopulations were the same. (C) Location of the optimal fraction (scale) under variation of r and Δa (Table S5, Data Supplement), with a = 50 years. Changing r from 0 to 0.3 would lead to up to 20% change in the optimal high-risk fraction of screens. Changing Δa from 1 to 4 would lead to 1% to 3% change in the optimal high-risk fraction of screens. (D) For fixed r = 0.1, changes in a had more drastic effects than changes in Δa (Table S6, Data Supplement). (E) For risk groups with a lifetime risk higher than two-fold, we examined the effect of risk reduction and screening interval (a = 50 years) on the number of patients with MM (Data Supplement). (F) MM-specific deaths per 100,00 were calculated as the product of screened individuals with monoclonal gammopathy of undetermined significance (MGUS) at age 60 years and the 10-year follow-up MM-specific mortality (a = 50 years and Δa = 1; age at MGUS detection, 60 years). Both risk reduction and spacing of screens have more pronounced effects in higher-risk groups. ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics 7 No. of Patients With MM Risk Reduction Factor r per 100,000 No. of Patients With MM per 100,000 Optimal Percentage of High-Risk Screens MM-Specific Deaths per 100,000 Age at First Screen a No. of Patients With MM per 100,000 10 Years After MGUS Detection achieved in subgroups of intermediate progres- Length-time bias, in contrast, is a form of selec- sion rates (Fig 4F). tion bias that occurs because of heterogeneity in the progression speed of a malignancy. This bias was absent in our study because we modeled DISCUSSION uniform progression of the disease (ie, a high- MM remains incurable for a majority of patients, risk person with early incidence of MGUS expe- and decreasing mortality is of as much interest rienced progression to MM equally as fast as a as decreasing its prevalence. All patients seem low-risk person with late MGUS incidence; the to experience progression to symptomatic MM time spent in the MGUS state in the no-screening from a premalignant, asymptomatic stage called scenario was independent of age). Therefore, MGUS. The fact that there are outstanding these common sources of bias in epidemiologic diagnostic tests for MGUS implies the possibil- prevention studies did not confound our results. ity of delaying progression of MGUS to MM by 36 Using a stochastic simulation framework and an screening and early identification. Because analytic model, we measured MGUS and MM precise estimates of MGUS prevalence have 5-7,37 prevalence and MM-specific mortality in differ - changed over the past decade, we consid- ent risk groups for different screening strategies ered relative changes in prevalence (using as a and varying progression risk reduction after baseline no effect of screening on progression MGUS detection. For effective MM prevalence risk reduction). We evaluated a range of possible reduction, better screening results are expected screening strategies based on the consideration for screening as early as possible and frequent that diagnosis of MGUS permits progression follow-up. Improved chemoprevention, effec- reduction as a result of several possible interven- tively reducing progression risk, may also reduce tions or modifiable risk factors, including aspirin, MM-specific mortality. We found that this effect metformin, or mediation such as exercise or diet is more pronounced in individuals with evolving 8,9,11,12,36 alterations. MGUS, especially in individuals with higher than The promise of early intervention in MGUS should two-fold lifetime MGUS risk. be viewed with caution. Our current understand- We did not explicitly address screening toxicity, ing comes from retrospective observational stud- nor did we model smoldering MM—an inter- ies. Our results, however, suggest that research mediate stage between MGUS and MM with a to identify effective chemoprevention agents in much higher rate of progression to full MM of high-risk MGUS can be justified. It will take time approximately 30% per year—in part because to develop a more comprehensive understand- it remains unclear whether smoldering MM is a ing of the intricate relationship between early requisite intermediate between MGUS and MM. intervention utilities and potential adverse effects However, our framework can be adjusted and on a wider scale, related to health care costs expanded. and psychological burden. Patients with MGUS may experience psychological distress similar Assessments of screening and prevention in solid to that experienced by those with MM, and the tumors (eg, prostate cancer) have been contro- identification of cancer precursor states must versial and lacking in evidence for screening in be accompanied by a discussion of the utility large prospective trials. We share the skepti- 38-42 of follow-up in individual patients. Promising cism of potential medicalization of asymptomatic efforts that evaluate MGUS screening and con- conditions. However, the biology of MGUS and tinuous follow-up before clinical manifestation of the robust laboratory tests demand careful eval- MM are under way in a long-term, prospective, uation of the role of screening and prevention. three-armed randomized trial (iStopMM). Such With notable similarities in the epidemiology of long-term efforts highlight the utility of predictive prostate cancer and MGUS (ie, most low-grade tools such as the one developed here. lesions will not proceed to lethal disease), major Our approach allowed us to quantify the amount differences in technology of screening tests for of risk reduction needed to result in certain these diseases are critical. Prostate-specific anti- reductions in MM-specific mortality and MM gen tests for prostate cancer are burdened by prevalence (measured as MM fraction). To avoid substantial false-positive (21% to 32% sensitiv- lead-time bias, we evaluated screening scenar- ity) and false-negative rates (85% to 91% spec- In contrast, serum testing for MGUS is ios in terms of mortality and MM prevalence. ificity). 8 ascopubs.org/journal/cci JCO™ Clinical Cancer Informatics straightforward. The sensitivity of serum protein by lowering the incidence of MM, provided effec- electrophoresis and free light chain testing for tive and nontoxic interventions can be identified. MGUS is close to 100%, and the specificity is Without further study of chemoprevention strat- 99%. These differences underline the evalua- egies, regular screening of MGUS candidates tion of the role of screening and prevention in should start as early as possible, with biannual MGUS and MM. We have shown that the reduc- follow-up, and focus on high-risk individuals, tion of cases of MM and MM-specific mortality especially those with a family history of MM, in high- and low-risk subpopulations can be or on groups with strong indication for evolving achieved, but only for drastic reduction in pro- MGUS progression. gression risk. Until highly effective agents are developed, identification and follow-up of high- DOI: https://doi.org/10.1200/CCI.17.00131 risk individuals are important. Screening for Published online on ascopubs.org/journal/cci on MGUS may have significant population benefits March 22, 2018. AUTHOR CONTRIBUTIONS Philipp M. Altrock No relationship to disclose Conception and design: All authors Financial support: Franziska Michor Jeremy Ferlic Administrative support: Franziska Michor No relationship to disclose Collection and assembly of data: Philipp M. Altrock, Jeremy Ferlic, Michael H. Tomasson Tobias Galla Data analysis and interpretation: All authors Honoraria: Eli Lilly (I), UCB Pharma (I) Manuscript writing: All authors Consulting or Advisory Role: Eli Lilly (I) Final approval of manuscript: All authors Travel, Accommodations, Expenses: Amgen (I) Accountable for all aspects of the work: All authors Michael H. Tomasson No relationship to disclose AUTHORS' DISCLOSURES OF Franziska Michor POTENTIAL CONFLICTS OF INTEREST No relationship to disclose The following represents disclosure information provided by authors of this manuscript. All relationships are ACKNOWLEDGMENT considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My We thank Graham Colditz (St Louis) and Nicola Camp (Salt Institution. Relationships may not relate to the subject Lake City) as well as members of the Michor laboratory at matter of this manuscript. For more information about the Dana-Farber Cancer Institute (Boston) and members of ASCO's conflict of interest policy, please refer to www. Integrated Mathematical Oncology at Moffitt Cancer Center asco.org/rwc or ascopubs.org/jco/site/ifc. and Research Institute (Tampa) for their feedback. Affiliations Philipp M. Altrock, Moffitt Cancer Center and Research Institute; Morsani College of Medicine, University of South Florida, Tampa, FL; Jeremy Ferlic and Franziska Michor, Dana-Farber Cancer Institute and Harvard University; Harvard T.H. Chan School of Public Health, Boston; Franziska Michor, Center for Cancer Evolution, Dana-Farber Cancer Institute, and The Ludwig Center at Harvard, Boston; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA; Tobias Galla, University of Manchester, Manchester, United Kingdom; and Michael H. Tomasson, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA. Support Supported by Grant No. LPDS 2012-12 from Deutsche Akademie der Naturforscher Leopoldina (P.M.A. [for work at Harvard University]), by National Cancer Institute Grant No. U54CA193461 to the Dana-Farber Cancer Institute Physical Sciences Oncology Center (F.M.), by Grant No. EP/K037145/1 from the Engineering and Physical Sciences Research Council (T.G), and by the Moffitt Cancer Center and Research Institute (P.M.A.). REFERENCES 1. 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JCO Clinical Cancer InformaticsWolters Kluwer Health

Published: Mar 22, 2018

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