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. 2021 May 18;16(5):e0250939. doi: 10.1371/journal.pone.0250939

Uveal melanoma: Long-term survival

Tomas Radivoyevitch 1, Emily C Zabor 1, Arun D Singh 2,*
Editor: Pukhraj Rishi3
PMCID: PMC8130945  PMID: 34003826

Abstract

Purpose

The long-term survival of uveal melanoma patients in the US is not known. We compared long-term survival estimates using relative survival, excess absolute risk (EAR), Kaplan-Meier (KM), and competing risk analyses.

Setting

Population based cohort study.

Study population

Pooled databases from Surveillance, Epidemiology, and End Results data (SEER, SEER-9+SEER-13+SEER-18).

Main outcome measure

Overall Survival (OS), Metastasis Free Survival (MFS) and relative survival, computed directly or estimated via a model fitted to excess mortality.

Results

There were 10678 cases of uveal melanoma spanning a period of 42 years (1975–2016). The median age at diagnosis was 63 years (range 3–99). Over half the patients were still alive at the end of 2016 (53%, 5625). The KM estimates of MFS were 0.729 (0.719, 0.74), 0.648 (0.633, 0.663), and 0.616 (0.596, 0.636) at 10, 20, and 30 years, respectively. The cumulative probabilities of melanoma metastatic death at 10, 20 and 30 years were 0.241 (0.236, 0.245), 0.289 (0.283, 0.294), and 0.301 (0.295, 0.307). In the first 5 years since diagnosis of uveal melanoma, the proportion of deaths attributable to uveal melanoma were 1.3 with rapid fall after 10 years. Death due to melanoma were rare beyond 20 years. Relative survival (RS) plateaued to ~60% across 20 to 30 years. EAR parametric modeling yielded a survival probability of 57%.

Conclusions

Relative survival methods can be used to estimate long term survival of uveal melanoma patients without knowing the exact cause of death. RS and EAR provide more realistic estimates as they compare the survival to that of a normal matched population. Death due to melanoma were rare beyond 20 years with normal life expectancy reached at 25 years after primary therapy.

Introduction

There is a paucity of uveal melanoma survival data beyond 15 years. A multicenter collaborative effort to develop American Joint Commission on Cancer (AJCC) staging for uveal melanoma reported that 19% of patients had died due to metastatic melanoma at 10 years [1]. The Collaborative Ocular Melanoma Study (COMS) reported 20% died of melanoma (10% vs 30% for smaller vs larger sizes in younger vs older patients) at 12 years [2], and two studies reported 40% died of melanoma at 10 years [3, 4]. Beyond 15 years following ocular treatment, cohort thinning presents challenges [57]. Large population-based datasets with long-term follow up are needed to assess long-term survival in uveal melanoma patients [8, 9]. In a Danish study of 302 patients diagnosed in 1943–1952, 50% died of uveal melanoma at 25 years [8]. In a Finnish study of 289 patients diagnosed in 1962–1981, 52% died of uveal melanoma at 35 years [9]. Here we add new US estimates based on Surveillance, Epidemiology, and End Results (SEER) cases diagnosed in 1975–2016.

Critical to estimating cancer-specific long-term death rates is how the presence of metastasis is ascertained. Hospital registries [5, 7] and national databases can be inaccurate [9]. As such, COMS designed its own system of documenting metastasis [10]. In relative survival (RS) [11] approaches, survival probabilities observed in those with uveal melanoma are divided by survival probabilities expected in age-year-sex-matched simulated “normal” individuals from the same population. RS avoids use of cause of death (COD) and uses background mortalities instead. In a hazard focused approach, person-years at risk of death (PY) in the same age-year-sex bin are tallied across patients and multiplied into background mortality rates to form, for each arbitrarily chosen time interval after diagnosis, expected numbers of deaths E [12]. Such E, with total PY and observed deaths (O) in the same intervals, yield time courses of relative risks (RR = O/E) and excess absolute risks (EAR = (O-E)/PY) of mortality. In this framework, an RR of 2 implies life-expectancy halving and an EAR(t) of 0.05 implies a death rate increase of 5% per year at time t.

SEER cancer registry data [13] meet the prerequisites of long-term analyses, such as large numbers of patients [14] and long follow-up [15]. It is the source of summary statistics that are published annually as the national cancer report in the United States [16]. We use SEER cancer registry data here to provide a US-based estimate of uveal melanoma lifetime mortality. We compare estimates obtained using EAR methods to those obtained using RS and COD-based methods.

Methods

Data selection

Since SEER data are deidentified and publicly available, IRB approval was not necessary to conduct this study. SEER database ASCII files that include treatment information, provided in SEER_1975_2016_CUSTOM_TEXTDATA.d04122019.zip, were downloaded on April 16th, 2019 and processed and analyzed using the R package SEERaBomb [17]. This software merges all three SEER databases (SEER-9+SEER-13+SEER-18) to provide more cases than can be retrieved conventionally using SEER*stat. It also accesses background US mortality rates in the Human Mortality Database (https://www.mortality.org/). Uveal melanoma cases were identified using International Classification of Disease for Oncology (ICD-O-3) morphology (melanoma: 8,720–8,790) and site [C69.3 (choroid), C69.4 (ciliary body and iris), and C69.2 (retina)] codes [14]. Diagnoses occurred between 1/1/1975 and 12/31/2016. R scripts that produced our results are provided (S1 Text).

Data analysis

The Kaplan-Meier (KM) method [18] in the R package survival was used to form estimates of overall survival (OS) and metastasis free survival (MFS) [19, 20]. Both were calculated from dates of diagnoses. For MFS, times to metastasis are approximated as times of deaths due to the uveal melanoma and deaths due to other causes were treated as right-censored. Cumulative incidence (CI) of mortality [21] was computed using the competing risks function cuminc in the R package cmprsk [22], in which deaths due to uveal melanoma were the events of interest and deaths due to other causes were treated as a competing event. RS [23] denominator survival probabilities were computed using the R function simSurv in the R package SEERaBomb [17]. This function uses Human Mortality Database US mortality rates to generate the probability of survival in a comparable (same age-sex-year) US cohort. It accomplishes this by simulating each individual forward in time from age at diagnosis, incrementing year and age one year at a time and using updated morality rates at each step (i.e. age and year both increase by one year at each step). If a death arises in a step, the time of it within the year is selected from a uniform distribution over that year. For each uveal melanoma case, three normal counterparts were simulated to form the control cohort. Relative survival was then calculated as observed uveal melanoma OS divided by the simulated expected OS. EAR estimates were computed using the SEERaBomb R function mortality since diagnosis (msd).

Results

Study demographics

There were 10678 cases of uveal melanoma spanning a period of 42 years (1975–2016).

The median age at diagnosis was 63 years (range 3–99). The vast majority of cases were race-classified as white (97%). Over half the patients were alive (5625) as of 12/31/2016. Of 5053 cases that died by this date, the cause of death was uveal melanoma in 2266 (44.8%) (Table 1).

Table 1. Uveal melanoma.

Demographic distributions (n = 10678).

ASPECT Group Number of cases (%)
Year of Diagnosis 1973–1982 864 (8.1)
1983–1992 1014 (9.5)
1993–2002 2302 (21.6)
2003–2016 6498 (60.9)
Gender Male 5580 (52.3)
Female 5098 (47.7)
Age at Diagnosis Range 3–99
Q1-Q3 52–72
Median 63
Laterality Right Eye 5266 (49.3)
Left Eye 5317 (49.8)
Bilateral 2 (0.1)
Unknown 93 (0.8)
Race White 10347 (96.9)
Black 80 (0.75)
Other / Unknown 251 (2.4)
Method of treatment Radiation only 6013 (56.3)
Surgery Only 4440 (41.6)
Combination 194 (1.8)
Other/ Not specified 31 (0.3)
Status/ Metastasis Alive 5625*
Dead with metastasis 2266*
Dead with other causes 2787*

*Percentages of these numbers are omitted intentionally as they could be misleading.

Greater proportions of deaths were attributed to causes other than uveal melanoma metastasis beyond 10 years (77%–94%) (Fig 1).

Fig 1. In the first 5 years since diagnosis of uveal melanoma, the proportion of deaths attributable to uveal melanoma were 1.3 with rapid fall after 10 years.

Fig 1

Death due to melanoma were rare beyond 20 years.

In 2000, SEER expanded from 13 to 18 registries. As a result, there were disproportionately more person-years (PY) at risk of death 0–16 years after diagnoses, wherein risks of death by melanoma are greater. Focusing on SEER-9 diagnoses in 1975–1986, 37% [399/(399+678)] were due to uveal melanoma. Assuming 163 still alive at the end of 2016 will die of other causes, the proportion falls to 32%. This raw proportion is our first estimate of the lifetime risk of death due to uveal melanoma.

Survival metrics

Kaplan Meier (KM) analysis

The KM estimates of survival probabilities at 10, 20, and 30 years were 0.529 (95% confidence interval (CI): 0.518, 0.541), 0.305 (95% CI: 0.291, 0.319), and 0.181 (95% CI: 0.166, 0.198) for OS and 0.729 (95% CI: 0.719, 0.74), 0.648 (95% CI: 0.633, 0.663), and 0.616 (95% CI: 0.596, 0.636) for MFS, respectively (Fig 2A).

Fig 2.

Fig 2

Kaplan-Meier plots (A). OS is difficult to interpret since everyone dies in the long run. MFS counts only deaths due to melanoma. Note that very few patients die due to melanoma 10 years after diagnosis. Cumulative incidence of death (B). These cumulative probabilities of death are integrals of death probability densities that account for competing risks. OS observed vs expected (C). Cumulative incidence of death. OS observed vs expected (C). Relative survival (D) is the ratio of the lower curve divided by the upper curve. The long-term mortality attributable to melanoma is estimated from the stable portion of this curve that lies between 20 and 30 years. (E) Trigam model fit to excess absolute risks of death due to uveal melanoma. Algebraic forms of the triangle wave and gamma function are provided above. Poisson regression was used to fit their sum to EAR “data” generated by the R function msd in SEERaBomb. This excess hazard adds to a subject’s background hazard when diagnosed with uveal melanoma. (F). Comparison of survival methods. The EAR and the RS curves ignore cause of death. The other two curves rely on cause of death information. The KM-based 1-MFS curve is incorrect relative to the cumulative incidence (CI) curve because it does not account for competing risks.

OS was worse than MFS at all time points, as expected, as the assumption under MFS is that a patient can only die of uveal melanoma. As MFS flattens after 30 years, MFS = 0.606 (95% CI: 0.584, 0.629) at 35 years, and by calculating 1-MFS we obtain our second estimate of lifetime risk of death due uveal melanoma of 39% (95% CI: 37.1%, 41.6%) (Table 2).

Table 2. Long term survival estimates: Comparison of metrics (95%, CI).
Kaplan–Meier Estimates: Overall and Metastasis Free survival
Year Overall Survival Metastasis Free Survival
5 0.707 (0.697, 0.716) 0.823 (0.815, 0.831)
10 0.529 (0.518, 0.541) 0.729 (0.719, 0.740)
15 0.405 (0.393, 0.418) 0.686 (0.674, 0.699)
20 0.305 (0.291, 0.319) 0.648 (0.633, 0.663)
25 0.236 (0.222, 0.251) 0.630 (0.613, 0.647)
30 0.181 (0.166, 0.198) 0.616 (0.596, 0.636)
35 0.140 (0.124, 0.158) 0.606 (0.584, 0.629)
Cumulative Probabilities of Death
Other Causes Metastatic Deaths
5 0.126 (0.119, 0.133) 0.163 (0.155, 0.170)
10 0.228 (0.219, 0.238) 0.241 (0.231, 0.250)
15 0.325 (0.314, 0.337) 0.269 (0.259, 0.279)
20 0.404 (0.390, 0.417) 0.289 (0.278, 0.300)
25 0.466 (0.450, 0.482) 0.297 (0.286, 0.309)
30 0.516 (0.499, 0.533) 0.301 (0.289, 0.313)
35 0.555 (0.537, 0.574) 0.305 (0.292, 0.317)
Relative Survival Estimates
.. Relative Survival
5 .. 0.807 (0.797, 0.818)
10 .. 0.700 (0.685, 0.715)
15 .. 0.644 (0.625, 0.664)
20 .. 0.601 (0.574, 0.629)
25 .. 0.599 (0.563, 0.638)
30 .. 0.612 (0.561, 0.668)
35 .. 0.660 (0.586, 0.743)

Competing risk analysis

To properly control for competing risks by treating both death by other causes and death by uveal melanoma on equal footings, a standard R package for competing risk analyses was used. This yielded cumulative probabilities of death at 10, 20 and 30 years of 0.228 (95% CI: 0.224, 0.233), 0.404 (95% CI: 0.397, 0.411), and 0.516 (95% CI: 0.507, 0.525) for other causes of death and 0.241 (95% CI: 0.236, 0.245), 0.289 (95% CI: 0.283, 0.294), and 0.301 (95% CI: 0.295, 0.307) for death by metastatic uveal melanoma (Table 2). Thus, 30% (95% CI: 29%, 31%) is our third estimate of the lifetime risk of death due to uveal melanoma. Plots of these risks at all time points (Fig 2B) reveal that the risk of death from metastatic uveal melanoma is higher than other causes in the first 10 years since the ocular diagnosis/ treatment and lower thereafter as melanoma death risks begin to plateau but risks of death due to other causes continue to rise.

Relative survival

We first compare OS to its corresponding expected survival curve obtained by simulating three US age-sex-year matched normal “healthy” individuals for each age-sex-year at diagnosis of uveal melanoma (Fig 2C).

OS was worse in uveal melanoma patients than in the simulated normal population, which can be thought of as survival expected if the uveal melanoma patients did not actually have uveal melanoma. The gap between the curves narrows after approximately 25 years (Fig 2C). Relative survival (RS) is the ratio of the lower curve divided by the upper curve. It plateaus to ~60% across 20 to 30 years (Fig 2D).

Using 1-RS at 25 years yields a fourth estimate of death due to uveal melanoma of 40% (95% CI: 36%, 44%).

Excess absolute risks

RS instability at large times due to cohort thinning and the unit of data being individuals is solved in the EAR approach by pooling PY at risk in time intervals across patients and increasing interval lengths as follow up times increase to boost numbers of deaths in late intervals. Modeling EAR as the sum of a smooth Gamma function wave and a triangle wave yields an EAR area under the curve (AUC) of 0.555 (Fig 2E) and thus a survival probability of e-0.555 = 0.57. Our fifth estimate of the probability of death due to uveal melanoma is thus 43%.

Comparison of metrics

The lifetime risk of death due to uveal melanoma, estimated 5 different ways, ranged from 30%-43%. A comparison of curves underlying 4 of the estimates is presented in Fig 2F.

Age dependence

Uveal melanoma mediated death risks are dominant for ~40 years in those younger than 50 years old at diagnosis (Fig 3A), but only for ~10 years in those ≥50 years old (Fig 3B). Lifetime risks of death by melanoma in these two age groups are ~30% (Fig 3C) and ~50% (Fig 3D), respectively, based on relative survival. Furthermore, in the younger age group (Fig 3E) relative to the older age group (Fig 3F), the peak EAR is lower and the triangle wave is not detectable. Negative EARs at large times in Fig 3F are possible, if additional surveillance results in better health care, but 95% confidence intervals of these estimates are wide and include 0, so we cannot claim this: the estimates of the last two EARs are -0.0188 (-0.0598, 0.0222) and -0.0241 (-0.113, 0.0648).

Fig 3. Dependence on age.

Fig 3

Those diagnosed before the age of 50 years are shown on the left (A, C, E) and those diagnosed at ≥50 years are shown on the right (B, D, F). There are fewer competing risks in younger vs older patients (A vs B), so the cross-over time is much later. Fewer competing risks in younger patients does not, however, imply a greater overall probability of death by melanoma, as final percentage are ~30% vs ~50% (C vs D). The reason is that the disease itself is less lethal in younger patients (E vs F).

Discussion

Determining long term mortalities attributable to uveal melanoma poses specific practical challenges that include a paucity of long-term data in institutional studies, the role of competing other causes of death that confound interpretations, and difficulty in ascertaining exact cause of death. Of the few published studies, most are based on nationwide populations wherein data collection was based on National Cancer Registries [8, 24, 25]. There is some variability in the reported outcomes due to variations in the study population (iris melanoma included [6, 8] or excluded [7, 9, 25]) size of the tumors, and follow up duration [69, 25]. The method of ascertaining the diagnosis of metastasis as the cause of death is perhaps the biggest contributor to the variability of the reported outcomes (Table 3) [9].

Table 3. Uveal melanoma: Long-term survival.

Published data and methods.

Author, Year Setting N Follow up years Median (range) Method Year since diagnosis / treatment Comment
15 20 25 30 35
1 Jensen 1981 National (Denmark) 302 25 (25–25) 1-MFS 50 58 60 X X Iris included
2 Kujala 2003 National Finland 289 28 Competing Risks 45 48 49 50 52 Iris excluded
3 Shields 2009 Institutional 8033 2.8 (0–36.3) 1-MFS^ 32 37 X X X Iris included
4 Lane 2015 Institutional 1490 12.3 (1–33.5) 1-MFS 25 26 26 X X Iris excluded
5 Bagger 2018 National Denmark 1637 5.1 (0.01–32.6) Competing Risks 34 44 44 44 X Iris excluded
6 Present Study 2020 National US 10678 5.3 (0.02–42.6) 1-MFS 31 35 37 38 39 Iris included
Competing Risks 27 29 30 30 31
1-RS 35 40 40 40 *
EAR 37 41 42 42 43

^data shown for medium sized tumors only.

*unstable.

With a median age at diagnosis of 62 years [14], it is not surprising that patients with uveal melanoma die from other causes over the long term [7]. As concluded in Danish [8] and Finnish studies [9], other causes of death are increasingly relevant over the long term [25]. In fact, in our large population-based cohort, other causes of death were more common than melanoma metastatic deaths (Table 1). Beyond 15 years since diagnosis, a patient with uveal melanoma was over 5 times (482 vs 85) more likely to die due to other causes than from uveal melanoma metastasis (Fig 1). The difference between KM-based OS and MFS increases progressively after 5 years since diagnosis, further indicating increasing burdens of other causes of death as patients age (Fig 2A). Accounting for competing risks, the cumulative probability of death due to other causes is greater than due to melanoma after 10 years since diagnosis (Fig 2B). This is also observed as a narrowing of the gap between OS and the corresponding expected survival curve after 25 years (Fig 2C). Moreover, uveal melanoma mediated death risks were dominant for longer duration in those younger than 50 years old at diagnosis (Fig 3A) than those ≥50 years old (Fig 3B) (~40 years and ~10 years, respectively).

Another important variable is the statistical method used to analyze the data. Mortality due to melanoma is often calculated as 1-MFS [26] which adjusts for variable follow up duration of each patient [26, 27] but considers deaths due to other causes as censored, which could introduce bias [28]. In contrast, cumulative incidence (CI) methods, as used in the COMS [2, 3] and Finnish [9] studies, treat deaths by melanoma and other causes as two competing events. Intuitively, this is more appealing. Uveal melanoma 1-MFS estimates are greater than CI estimates by as much as 25% (40% vs 50% survival) [9, 29].

Although CI estimates are more appropriate than KM estimates, they still rely on accurate cause of death recordings [9, 29]. To circumvent the need to identify the exact cause of death (metastasis or other) [5, 7, 10], relative survival has been used to report survival outcomes [11, 30]. We used SEERaBomb to merge all three SEER databases (SEER-9+SEER-13+SEER 18) to extract more cases (10000+) than can be analyzed conventionally using SEER*stat [17]. The demographics of the present study cohort are comparable to those in an earlier SEER-based uveal melanoma study [14].

Life expectancy approaches to uveal melanoma data analysis were pioneered by Damato, Eleteri, Taktak and Coupland in the setting of uveal melanoma prognostication [28, 31]. Our approach of estimating EAR is similar. In it, individuals are pooled such that, instead of contributing times to events, they contribute person years at risk and deaths to time intervals. This converts survival analysis problems into Poisson regressions of models of numbers of deaths in each time interval. Such analyses require large amounts of data, as is provided by SEER. Our findings of modeled EAR values that return to 0 at around 25 years is consistent with limited published long-term data. Lane et al reported that annual rates of death from melanoma decreased gradually after year 6, but did not drop below 1% until 14 years after treatment [7]. In a Finnish study 90% (95% CI, 84–95) of the uveal melanoma deaths occurred within 15 years [9]. A Swedish study showed that excess mortality reached baseline values by ~15 years [4]. Force of mortality peak values of 0.06/y were also similar to those of 0.08/y in the Swedish study [4].

The estimated lifetime risk of death due to melanoma, using a variety of statistical methods, ranged from 30–43%. The lower values of 30% (95% CI: 29%-31%) and 32% were estimated by competing risk analysis and by simple calculation of raw proportions, respectively. Both these methods rely on knowing the exact cause of death. Given that these values are on the lower end of the range of our estimates and also lower than those reported in Danish (50%) [8] and Finnish (52%) [9] studies, where exact cause of death was established with meticulous details due to low number of study cases (302 and 289, respectively), under reporting of metastatic deaths in the SEER data set is the most likely explanation [32].

The estimated lifetime risk of death due to melanoma by KM analysis of 39% (95% CI: 37.1%, 41.6%) was 9 percentage point higher than that estimated by competing risk analysis (30%, 95% CI: 29%-31%), a value that is strikingly similar to one reported by Kjujala et al. As causes death in any cancer patient cannot be ascertained with high accuracy outside of a well-defined study, with competing causes of death in an aging population, the lifetime risk of death by uveal melanoma is likely 40–43% in the US. For patient counseling purposes, conditional survival, which is a dynamic estimate of survival probability with additional years survived may be more relevant particularly for patients who have survived initial 5–10 years [33]. Exploration of EAR over the long term and the time at which the overall EAR reaches 0 may be used to estimate time to cure and cured fractions in uveal melanoma [34].

In conclusion, relative survival methods can be used to estimate long term survival of uveal melanoma patients without knowing the exact cause of death. RS and EAR provide more realistic estimates as they compare the survival to that of a normal matched population. Death due to melanoma are rare beyond 20 years. The majority of uveal melanoma patients will die from unrelated causes and those still alive will reach a normal life expectancy about 25 years after ocular therapy.

Supporting information

S1 Text. R scripts used in “uveal melanoma: Long-term survival”.

(PDF)

Data Availability

The full data set is publicly available via the NCI’s SEER program upon signing a Data Use Agreement with the NCI (https://seer.cancer.gov/data/access.html). Anyone accessing this data at the individual level must sign this agreement. Grouped data used in the EAR Poisson regressions of Fig 2E are available via GitHub (https://github.com/radivot/SEERaBomb).

Funding Statement

There is no funding associated with this work.

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Decision Letter 0

Pukhraj Rishi

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

13 Apr 2021

PONE-D-21-05440

Uveal Melanoma : Long-Term Survival

PLOS ONE

Dear Dr. Singh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The authors present ways of estimating the probability of long-term survival in uveal melanoma patients in US using statistical methods on data from SEER databases. This is an important question to answer. There are some concerns by the reviewers regarding methodology and discussion which can hopefully be addressed.

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Pukhraj Rishi

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PLOS ONE

Additional Editor Comments:

This is an interesting article on ways of estimating the probability of long-term survival in uveal melanoma patients in US using statistical methods on data from SEER databases. This is an important question to answer. There are some concerns by the reviewers regarding methodology and discussion which can hopefully be addressed.

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Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. The authors present ways of estimating the probability of long-term survival in uveal melanoma patients using statistical methods on data from SEER databases.

2.The data comprises all patients diagnosed as uveal melanoma between 1976 and 2016. The authors report that 5625 of the 10678 patients are alive as of 12/31/2016. Effectively the follow up data available is restricted to the end of year 2016 which limits the follow up period of the patients diagnosed with melanoma in the later part of this cohort. In fact, for the patients diagnosed in the year 2016, there is almost no follow up data.

This is also borne out from the table 3 where they indicate that the mean follow up in this study was 5.3 yrs (range of 0.02 to 42.6). The National Denmark study in contrast had a mean follow up of 25 yrs although the total number of cases in that study was far less.

Intuitively one would feel that including subjects with no follow up would affect the model being constructed to predict the long-term overall survival rates.

The authors may consider restricting the analysis to patients with a minimum of 5 yrs follow up to enable better inferences to be drawn.

3. Since very few patients die of melanoma after 10 yrs follow up. Can one sub analyse the age groups less than 50 Vs more than 50 whether this statement holds true for all age groups. In other words, is it possible that in the older population, the concurrent natural causes of death overtake the melanoma in causing death (after 10yrs) while in younger population, it is possible that melanoma may still be important even after 10 yrs in the causation of death.

Reviewer #2: This is a nice manuscript on an important and poorly investigated topic of the long term survival of uveal melanoma patients. As the authors point out this is a difficult subject to evaluate.

As this is mostly a statistical paper in terms of methodology, it is really very dense and difficult to read. I realize that the information needs to be displayed but it may be worthwhile to use % in areas whenever possible (instead of presenting the percentages as 0.xx) for readability and to include more summative statements and tables. right now I suspect that the vast majority of clinicians, who the paper is directed to, will stop reading this in its current version.

Even the abstract and discussion require some more clear, declarative summary statements than in this current version.

Some other considerations for statements that will be impactful for clinicians and patients:

risk of melanoma related death per year after diagnosis of melanoma. Risk of non-melanoma related death per year.

risk in first 5 years, 10 years

risk that decreases after 5 years in terms of melanoma specific mortality

basically, please try to add some clear, clinically relative take home statements in a clear and readable fashion

Figure 2a and 2b are invert of the same information, if space is needed one could be removed

I am not sure if Figure 3 adds significantly to Figure 2

**********

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PLoS One. 2021 May 18;16(5):e0250939. doi: 10.1371/journal.pone.0250939.r002

Author response to Decision Letter 0


14 Apr 2021

Editor Comments:

This is an interesting article on ways of estimating the probability of long-term survival in uveal melanoma patients in US using statistical methods on data from SEER databases. This is an important question to answer. There are some concerns by the reviewers regarding methodology and discussion which can hopefully be addressed.

Thank you! Reviewer concerns are addressed point-by-point below.

Journal Requirements:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at …

The manuscript is now formatted for PLOS ONE.

2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

The data is publicly available via the NCI’s SEER program upon signing a Data Use Agreement with the NCI. Anyone accessing this data at the individual level must sign this agreement. Thus, regarding your link above, we are not restricting access for any reasons stated therein.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Grouped data used in the EAR Poisson regressions of Figure 2E (formerly Fig S1) is now available via a GitHub link provided at the end of S1 Text.

3. Please include captions for all your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

We now follow the Supporting Information formatting guidelines described in the link above.

4. Thank you for stating the following financial disclosure:

[NO].

At this time, please address the following queries:

Please clarify the sources of funding (financial or material support) for your study. List the grants or organizations that supported your study, including funding received from your institution.

State what role the funders took in the study. If the funders had no role in your study, please state: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

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There is no funding associated with this work.

5. Thank you for stating the following in your Competing Interests section:

[NO].

Please complete your Competing Interests on the online submission form to state any Competing Interests. If you have no competing interests, please state "The authors have declared that no competing interests exist.", as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now

This information should be included in your cover letter; we will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

We have no competing interests.

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Thank you!

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

Thank you!

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

See response above regarding our inclusion of grouped data underlying Fig. 2E.

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Thank you!

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1:

The authors present ways of estimating the probability of long-term survival in uveal melanoma patients using statistical methods on data from SEER databases. The data comprises all patients diagnosed as uveal melanoma between 1976 and 2016. The authors report that 5625 of the 10678 patients are alive as of 12/31/2016. Effectively the follow up data available is restricted to the end of year 2016 which limits the follow up period of the patients diagnosed with melanoma in the later part of this cohort. In fact, for the patients diagnosed in the year 2016, there is almost no follow up data.

This is also borne out from the table 3 where they indicate that the mean follow up in this study was 5.3 yrs (range of 0.02 to 42.6). The National Denmark study in contrast had a mean follow up of 25 yrs although the total number of cases in that study was far less. Intuitively one would feel that including subjects with no follow up would affect the model being constructed to predict the long-term overall survival rates.

The authors may consider restricting the analysis to patients with a minimum of 5 yrs follow up to enable better inferences to be drawn.

We appreciate this point as it is important in crude estimates of percentage of deaths caused by uveal melanoma. Thus, the first paragraph in our Results section provides this estimate focusing on SEER-9 diagnoses in 1975-1986 (i.e. cases with at least 30 years of follow up). “ Focusing on SEER-9 diagnoses in 1975-1986, 37% [399/(399+678)] were due to uveal melanoma. Assuming 163 still alive at the end of 2016 will die of other causes, the proportion falls to 32%. This raw proportion is our first estimate of the lifetime risk of death due to uveal melanoma.” For hazard function-based results in Fig. 2, however, we do not expect this to be an issue.

Since very few patients die of melanoma after 10 yrs follow up. Can one sub analyse the age groups less than 50 Vs more than 50 whether this statement holds true for all age groups. In other words, is it possible that in the older population, the concurrent natural causes of death overtake the melanoma in causing death (after 10yrs) while in younger population, it is possible that melanoma may still be important even after 10 yrs in the causation of death.

Thank you, this is a very interesting idea.

Added new analysis in Results, new Figure 3 and also in discussion .

Age Dependence

Uveal melanoma mediated death risks are dominant for ~40 years in those younger than 50 years old at diagnosis (Figure 3A), but only for ~10 years in those ≥50 years old (Figure 3B). Lifetime risks of death by melanoma in these two age groups are ~30% (Figure 3C) and ~50% (Figure 3D), respectively, based on relative survival. Furthermore, in the younger age group (Figure 3E) relative to the older age group (Figure 3F), the peak EAR is lower and the triangle wave is not detectable. Negative EARs at large times in Figure 3F are possible, if additional surveillance results in better health care, but 95% confidence intervals of these estimates are wide and include 0, so we cannot claim this: the estimates of the last two EARs are -0.0188 (-0.0598, 0.0222) and -0.0241 (-0.113, 0.0648).

Reviewer #2:

This is a nice manuscript on an important and poorly investigated topic of the long term survival of uveal melanoma patients. As the authors point out this is a difficult subject to evaluate.

As this is mostly a statistical paper in terms of methodology, it is really very dense and difficult to read. I realize that the information needs to be displayed but it may be worthwhile to use % in areas whenever possible (instead of presenting the percentages as 0.xx) for readability and to include more summative statements and tables. right now I suspect that the vast majority of clinicians, who the paper is directed to, will stop reading this in its current version.

Even the abstract and discussion require some more clear, declarative summary statements than in this current version.

Thank you for this feedback. Fractions were changed to percentages to improve readability.

Some other considerations for statements that will be impactful for clinicians and patients:

risk of melanoma related death per year after diagnosis of melanoma. Risk of non-melanoma related death per year. risk in first 5 years, 10 yearsrisk that decreases after 5 years in terms of melanoma specific mortality

Thank you for these suggestions. We believe that required info is covered in Figure 1 displayed as bar chart and in Table 2. Made it explicit by modifying the legend and added in results and abstract.

“In the first 5 years since diagnosis of uveal melanoma, the proportion of deaths attributable to uveal melanoma were 1.3 with rapid fall after 10 years. Death due to melanoma are rare beyond 20 years (Figure 1).”

The reviewer is correct in pointing out about study density of the paper as the emphasis is on the methodology and variations in the estimates based upon the method used.

Survival stats at 5 and 10 years after diagnosis were calculated, conditioned on various numbers of years already survived is in other published work by us. Added in discussion

“For patient counseling purposes, conditional survival, which is a dynamic estimate of survival probability with additional years survived may be more relevant particularly for patients who have survived initial 5-10 years.[33] Exploration of EAR over the long term and the time at which the overall EAR reaches 0 may be used to estimate time to cure and cured fractions in uveal melanoma [34].”

Reference 33. Zabor, EC et al. Conditional Survival in Uveal Melanoma, Ophthalmol Retina. 2020 Sep 23;S2468-6530(20)30395-X. doi: 10.1016/j.oret.2020.09.015.

Reference 34. Singh et al . Cured fractions in uveal melanoma . JAMA Ophthal 2021

Figure 2a and 2b are invert of the same information, if space is needed one could be removed

Not really. In 2A death by other causes equates to loss of follow up and in 2B it is on an equal footing as death by uveal melanoma. Please note that there is cross-over in 2B not present in 2A.

I am not sure if Figure 3 adds significantly to Figure 2

Agreed. As it is a summary of Fig. 2, it is now Fig 2F (Figure S1 is now Fig. 2E).

Attachment

Submitted filename: Survival PLOS ONE Rebuttals.docx

Decision Letter 1

Pukhraj Rishi

19 Apr 2021

Uveal Melanoma : Long-Term Survival

PONE-D-21-05440R1

Dear Dr.  Singh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Pukhraj Rishi

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Pukhraj Rishi

28 Apr 2021

PONE-D-21-05440R1

Uveal Melanoma : Long-Term Survival

Dear Dr. Singh:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Pukhraj Rishi

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Text. R scripts used in “uveal melanoma: Long-term survival”.

    (PDF)

    Attachment

    Submitted filename: Survival PLOS ONE Rebuttals.docx

    Data Availability Statement

    The full data set is publicly available via the NCI’s SEER program upon signing a Data Use Agreement with the NCI (https://seer.cancer.gov/data/access.html). Anyone accessing this data at the individual level must sign this agreement. Grouped data used in the EAR Poisson regressions of Fig 2E are available via GitHub (https://github.com/radivot/SEERaBomb).


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