EFFECT OF RADIATION THERAPY ON SURVIVAL IN PATIENTS WITH RESECTED MERKEL CELL CARCINOMA: A POPULATION-BASED ANALYSIS JULIAN A. KIM, M.D.

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1 EFFECT OF RADIATION THERAPY ON SURVIVAL IN PATIENTS WITH RESECTED MERKEL CELL CARCINOMA: A POPULATION-BASED ANALYSIS by JULIAN A. KIM, M.D. Submitted in partial fulfillment of the requirements For the degree of Master of Science Clinical Research Scholars Program Center for Clinical Investigation CASE WESTERN RESERVE UNIVERSITY August 2010

2 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of candidate for the degree *. (signed) (chair of the committee) (date) *We also certify that written approval has been obtained for any proprietary material contained therein.

3 This work is dedicated to my family, mentors and all of the patients who battle cancer every day 3

4 Table of Contents List of Tables.5 List of Figures 6 Abstract..7 Introduction 8 Methods.14 Results 20 Discussion..36 Appendix A 40 Appendix B 41 References.42 4

5 List of Tables Table Univariate analysis of Factors Associated with Adjuvant Radiation Therapy Table Interaction Analysis for Covariates Associated with the use of Radiation Therapy Table 3a 23 Multivariate Analysis of Factors Associated with Adjuvant Radiation Therapy Table 3b 24 Multivariate Analysis of Factors Associated with Adjuvant Radiation Therapy- Interaction Term Results Table 4a 26 Interaction analysis for covariates associated with MCC-specific survival Table 4b 27 Interaction Analysis for Covariates Associated with Overall Survival Table 5a 28 Cox Proportional Hazards Model for MCC-Specific and Overall Survival Table 5b 29 Cox Proportional Hazards Model for MCC-specific Survival Table 6a 30 Patient Characteristics before and after Propensity Score Matching Table 6b 32 Tumor and Treatment Characteristics before and after Propensity Score Matching 5

6 List of Figures Figure 1 15 Geographic regions covered by SEER (A) and socioeconomic characteristics of SEER population as compared to total U.S. (B). Figure 2 25 Kaplan-Meier survival analysis of MCC-specific (A) and overall survival (B) in the cohort of 747 study patients with resected MCC Figure 3 33 Absolute standardized differences of covariates before and after propensity score matching Figure 4 34 Kaplan-Meier survival analysis of MCC-specific (A) and overall survival (B) for the 269 pairs of patients matched by propensity score Figure 5.35 Sensitivity analysis of survival from propensity score matched pairs of patients who received either surgery alone or surgery plus radiation 6

7 Effect of Radiation Therapy on Survival in Patients with Resected Merkel Cell Carcinoma: A Population-Based Analysis Abstract by JULIAN A. KIM, M.D. Merkel cell carcinoma (MCC) is an uncommon cutaneous malignancy that despite surgical resection, can cause death. The primary purpose of this study was to determine whether patients in the Surveillance, Epidemiology and End Results (SEER) database who received radiation therapy after resection demonstrate improved survival. Univariate, multivariate, Cox proportional hazards models and propensity scoring with matched pair analyses were performed. Factors that were independently associated with the use of radiation therapy included marital status, disease stage and lymph node surgery. Factors associated with both MCC-specific and overall survival included age and disease stage. Propensity scoring and matched pair analysis demonstrated that patients who received radiation therapy had an improved overall survival but not MCCspecific survival. These data suggest that the improvement in overall survival in patients who receive radiation therapy following surgical resection of MCC may be a result of selection bias or unmeasured factors and not radiation therapy. 7

8 Introduction Merkel cell carcinoma (MCC) is an uncommon cutaneous malignancy that can be highly aggressive and ultimately lethal. Although the exact etiology of MCC is not known, there is evidence that suggests that skin damage related to prolonged sun exposure is contributory and may be cumulative, since there is a higher incidence of MCC in the elderly population [1, 2]. Using U.S. population data (Surveillance Epidemiology and End Results or SEER), the age-adjusted incidence rate is estimated to be 0.24 per 100,000 person-years. The incidence of MCC rises sharply beyond age 65, and age-adjusted incidence per 100,000 persons has been steadily increasing in both males and females since the late 1980 s. This increased incidence has been attributed to increased awareness of the diagnosis and improved histopathologic diagnostic techniques. The most common anatomic location is in the head and neck area, followed by upper and lower extremities and trunk. Unfortunately, the low incidence rate of MCC has made it difficult to design and accrue to prospective randomized clinical trials. A major concern associated with MCC is that there can be a significant proportion of patients who are not diagnosed until the disease has spread to regional lymph nodes. SEER data suggests that nearly 50% of all patients entered into the database between had evidence of regional spread of disease at diagnosis, whereas a singleinstitutional study from Memorial Sloan-Kettering Cancer Center suggests that the incidence may be slightly lower [1, 3]. However, both studies are in agreement that the overall survival of patients is significantly reduced once the MCC has spread to the regional lymph nodes, with 5-year MCC-specific survival estimated to range from 45% - 59% in node positive patients and 64% in patients at all stages of presentation. The poor 8

9 outcome of patients with this disease and the lack of prospective, randomized clinical trials to determine best clinical practice has lead to controversies and variability in evidence-based approaches to treatment. Diagnosis and surgical treatment in patients with MCC Although the first report of MCC as trabecular carcinoma of the skin has been attributed to Toker in 1972, the modern literature describing the histopathologic features of MCC as a neuroendocrine tumor of the skin with specific immunohistochemical staining patterns emerged primarily in the 1980 s [4-9]. The typical presentation of MCC as a painless, rubbery growth within the skin which is sometimes erythematous and can become ulcerated is relatively benign. Because the growth is painless and can mimic a number of benign cutaneous entities such as cysts or acneiform lesions, it is frequently observed by both patients and physicians until it starts to increase in size [10]. This delay in diagnosis can contribute to the high incidence of patients who present with metastatic disease to the regional lymph nodes, which significantly worsens survival. Surgical therapy is the primary therapy for cutaneous MCC. Local excision with clear microscopic margins is the goal, although the ability to achieve clear microscopic margins on lesions of the head and neck may be limited by anatomic structures such as the eyelids. Larger MCCs require a more radical resection to achieve clear margins, although resection of underlying muscle and bone is relatively uncommon. Since it was observed that the first site of recurrence following excision of the primary MCC was in the regional lymph node basin, several clinicians have offered prophylactic lymph node dissection in clinically node negative patients [11]. However, these patients did not demonstrate improved survival as compared to patients who did not undergo lymph node 9

10 dissection, and this practice has been abandoned in the absence of biopsy-proven lymph node metastases. The advent of sentinel lymph node biopsy to selectively sample a small number of regional lymph nodes at the time of excision of the MCC has demonstrated a high sensitivity for identifying patients who have node positive disease [12]. Unfortunately, with no randomized clinical trial data available, the therapeutic efficacy of sentinel node biopsy followed by lymph node dissection in patients with node positive disease is unknown. Adjuvant therapy of patients with resected cutaneous MCC Although it is generally well-accepted that adjuvant systemic chemotherapy is not indicated in patients with surgically resected MCC, the use of radiation therapy has remained any area of intense interest and debate. Early studies of radiation therapy to the MCC excision site demonstrated local recurrence rates that were thought to be low, although most studies were limited by lack of a matched comparison group, small sample size and limited patient follow-up [13, 14]. In the head and neck location, radiation therapy to both the MCC excision site and the regional lymph node site has been advocated because locoregional control of disease is particularly difficult. However, radiation of both the primary MCC site and the regional lymph node bed is now becoming widely adopted in other anatomic sites without compelling supportive data. A meta-analysis of 1254 patients reported by Lewis demonstrated an improved MCCspecific and overall survival in patients who underwent surgery plus radiation therapy as compared to those who underwent surgery alone, but these improvements were not statistically significant [15]. 10

11 In an attempt to add to the exisiting literature on the therapeutic benefit of radiation therapy in patients with surgically-resected MCC, Mojica et al used the SEER database ( ) to compare survival in patients who did and did not receive radiation therapy [16]. Cox regression analysis of a subgroup of 603 patients with complete data demonstrated a reduced hazard ratio of death from all causes associated with the use of radiation therapy of 0.85 (95% CI= ). Unadjusted survival analysis using Kaplan-Meier in a total of 1166 patients demonstrated an improved overall survival in patients who had radiation therapy as compared to patients who did not (p<0.0001, log-rank). Interestingly, the authors did not do an analysis of MCC-specific survival. In addition, Housman et al suggested that there was significant bias present in the Cox analysis because the number of patients who had an overall survival of less than 4 months were over-represented in the surgery alone group [17]. Elimination of patients with less than 4 months survival from the analysis resulted in no significant association of radiation therapy and overall survival. The authors of the study responded that the elimination of the patients with less than 4 months survival resulted in an underpowered analysis, and that firm conclusions could not be drawn. In any event, the question of whether radiation therapy improves MCC-specific survival remains unresolved. Statistical methods in observational studies The retrospective nature of observational studies in oncology can result in challenges when attempting to compare groups of patients who either received a treatment (such as surgery plus radiation therapy) or did not (surgery alone). Because the assignment of treatment outside of a clinical trial is rarely a random event, it is expected that certain patient and tumor factors will influence whether patients receive treatments or 11

12 not. These factors can include patient performance status, socioeconomic status, geographical region and many others which can bias outcome. Commonly used statistical techniques to reduce the effect of bias include multivariate analysis using logistic regression, which can identify covariates that are independently associated with the odds of receiving or not receiving a particular treatment. In addition, Cox proportional hazards models are employed to estimate adjusted hazard ratios of events such as disease-specific survival and overall survival. These methods are robust, frequently used and generally can be used in instances where there are significant covariate imbalances between the patients in the treatment and control groups. An alternative method of adjustment is to perform matching of similar patients between groups. Propensity scoring and matched pair analysis is a method which reduces covariate imbalance between patients in the treatment and control groups [18]. The method involves generation of a propensity score for each subject which is an estimate of the conditional probability of receiving a certain exposure or treatment. Using the logit of the propensity score, patients in the control group can be matched with patients in the treatment group with similar characteristics. The result of the matching process is the reduction of covariate imbalance between the treatment and control groups, allowing for analysis of treatment effect among matched pairs of subjects. Propensity score matching has been used in several areas of biostatistical research, and has been applied to oncology as well. One example of the use of propensity scoring with matched pair analysis in the oncology literature relates to the use of adjuvant chemotherapy for node positive colon cancer in the elderly population. In general, patients over the age of 65 have been under-represented in adjuvant chemotherapy trials 12

13 of patients with node positive colon cancer. A review of 6 randomized controlled trials which demonstrated a survival benefit associated with the use of adjuvant chemotherapy in patients with node positive disease also showed that only 15% of the patients enrolled were greater than 70 years old [19]. Despite the positive results from several randomized clinical trials, a SEER study demonstrated that a significant proportion of elderly patients in the U.S. were not receiving adjuvant chemotherapy [20]. The dilemma was that despite convincing evidence of the benefit of adjuvant chemotherapy, it was unclear that this effect could be reproduced in elderly patients who not well represented in the studies. In an attempt to answer this question, Iwashnya et al used propensity score matching in patients diagnosed over the age of 67 from the SEER database ( ) to demonstrate an improvement in overall survival in elderly patients who received chemotherapy as compared to those that did not [21]. The study illustrated the potential benefit of using SEER data to guide clinical practice for a subgroup of patients where a randomized controlled trial would not be feasible. The Iwashnya study and other similar studies using propensity score matching techniques to help determine treatment effect in populations of cancer patients that lacked evidenced-based treatment algorithms provided the rationale for the statistical analyses used in the present observational study. The primary aim of this study was to examine whether radiation therapy following surgical resection of cutaneous MCC was associated with MCC-specific and overall survival using Cox proportional hazards models and propensity score matching techniques. A secondary aim of this study is to determine factors which are associated with the use of adjuvant radiation therapy in patients with resected MCC in the U.S. 13

14 Methods The Surveillance, Epidemiology and End Results (SEER) Program- Overview SEER is a National Cancer Institute (NCI)-sponsored database of cancer patients in the United States which was initiated in 1971 by the Richard Nixon administration as part of the National Cancer Act [22]. Since that time, SEER has become a valuable resource for U.S. population-based studies of cancer epidemiology and cancer outcomes [22-30]. SEER is a public-use database which is supported within the Division of Cancer Control and Population Sciences, and captures patient information from approximately 14% of the U.S. population. SEER locations were determined in order to generally capture cancer incidence and outcomes data from a representative sample of the racial and ethic groups across the U.S. A. B. Geographic areas covered by the SEER Program. Selected characteristics of SEER areas versus the total United States. Hankey B F et al. Cancer Epidemiol Biomarkers Prev 1999;8: by American Association for Cancer Research Hankey B F et al. Cancer Epidemiol Biomarkers Prev 1999;8: by American Association for Cancer Research Figure 1. Geographic regions covered by SEER (A) and socioeconomic characteristics of SEER population as compared to total U.S. (B). It is interesting to note that there appears to be a large proportion of the U.S. population that is not included in the SEER coverage (Figure 1A). However, the socioeconomic characteristics of the SEER-covered population appear to closely match 14

15 the total U.S. population in terms of proportion of patients below poverty level and achievement of high school degree or higher educational level. Urban dwellers are overrepresented within SEER as compared to U.S. (89.2% versus 75.2% respectively) and also foreign born are over-represented as compared to U.S. born (15.5% versus 7.9% respectively). Approximately 14% of all races are represented in the SEER coverage areas with a total population of over 34 million in 1999 [1]. 13% of the White U.S. population is included within the SEER coverage areas as well as 12% of the Black population, 25% of Hispanic, 31% of American Indian and even higher proportions of the Asian and Hawaiian U.S. populations. The total population covered by the SEER program includes 16.4 million White, 3.7 million Black, 5.6 million Hispanic and less than 1 million each of Chinese, Korean, Japanese, Filipino and Vietnamese. Development of the MCC-SEER study population and database management Permission to use the SEER data was obtained by signing a user s agreement which has strict rules concerning the confidentiality of patient information. These include the use of safeguards to protect the SEER data as well as permission to publish the results of analyses while appropriately citing the source of the data from SEER. A compact disc was obtained from SEER containing data from as well as statistical software SEER*Stat In order to first determine the feasibility of studying the effect of radiation therapy on the survival of patients with MCC, frequency analyses were performed using SEER*Stat to look at all of the variables associated with patients who had an MCC diagnosis. In particular, it was important to determine the total number of patients with a diagnosis of MCC within the SEER database, the number of patients who had 15

16 histological confirmation of diagnosis, the number of patient who had undergone surgical resection of the MCC and the number of patients who had undergone radiation therapy. Based upon these initial frequency queries as well as previously published studies on MCC using earlier versions of the SEER database, it was determined that the number of subjects would be sufficient to carry out the proposed study. The MCC-SEER study population was derived from the SEER data using SEER*Stat and then saved into a Microsoft Excel spreadsheet for data management prior to analysis. The algorithm used to come up with a final subset of 747 study patients for analysis is outlined in Appendix A. To summarize, there were a total of 1888 patients who had a microscopically-confirmed diagnosis of MCC and also had no other primary malignancy. The exclusion of patients with other malignancies was necessary because it is not possible to determine whether the radiation therapy was administered specifically for the MCC, which would confound any analysis of radiation therapy specific to the treatment of MCC. Approximately 200 patients were eliminated from the study due to survival less than 4 months, lack of lymph node surgery variable prior to 1998, unknown marital status and other surgery codes. A total of 724 patients were eliminated because of unknown primary tumor size, which was felt to be a potentially critical variable for patient matching and adjusted survival analysis. The 747 patient study population was then analyzed for missing data, and there appeared to be significant missing data in the covariate of histological grade. In current medical practice, determination of grade of MCC is not uniform and for this reason, histological grade was not included as a variable in the analyses. 16

17 All variables were checked to confirm that they were character or numeric, and normality distribution check was performed. Certain variables such as geographic region, tumor location and lymph node surgery were placed into groups which had clinical relevance. Age was maintained as a continuous variable and all other variables with the exception of survival were treated as categorical variables. Univariate and multivariate statistical analyses Univariate and multivariate analyses were performed using either SAS v. 9.2 or R Commander v statistical software packages. Univariate analysis was performed using Student s t-test for testing of differences between means and Pearson s chi-square for differences between proportions. Significance level was set at p<0.05. Multivariate analysis of factors associated with the use of radiation therapy was performed using logistic regression. Since all of the covariates in the study were felt to be potentially clinically relevant, step-wise elimination of variables was not performed. Interactions between covariates were tested by including the interaction term into the logistic regression model. Interaction terms which had a significant p-value were included into the final logistic regression model if the interaction was determined to be clinically or biologically relevant. Adjusted odds ratios and 95% confidence intervals were calculated for each covariate. Propensity scoring and matching procedures Propensity scores to determine the conditional probability of receiving radiation therapy were generated using logistic regression as described by Rosenbaum and Rubin [31]. The logit of the propensity score (PS) was then used for patient matching with the 17

18 calipers set in the method described by Normand [32]. The formulas used were as follows: Logit PS= log odds of PS= log((1 PS)/PS) Standard error of the Logit PS= (Standard error of the Logit PS)= :1 patient matching with replacement was selected for this analysis. This means that subjects could be used more than once in order to find a match for the subjects in the treatment (radiation) group. The SAS code used to perform the 1:1 matching with replacement with a caliper of (highlighted in yellow) was from Coca-Perraillon [33]: data Matched(keep= IdSelectedControl MatchedToTreatID); length pscorec 8; length idc 8; if _N_= 1 then do; declare hash h(dataset: "Control", ordered: 'no'); declare hiter iter('h'); h.definekey('idc'); h.definedata('pscorec', 'idc'); h.definedone(); call missing(idc, pscorec); end; set Treatment; retain BestDistance 99; rc=iter.first(); if (rc=0) then BestDistance= 99; do while (rc= 0); if (pscoret 0.01) <= pscorec <= (pscoret ) then do; ScoreDistance= abs(pscoret pscorec); if ScoreDistance < BestDistance then do; BestDistance= ScoreDistance; IdSelectedControl= idc; MatchedToTreatID= idt; end; end; rc = iter.next(); if (rc ~= 0) and BestDistance~= 99 then do; output; end; end; run; A total of 269 matched pairs of patients who had either surgery alone or surgery plus radiation was generated from the propensity score and matching procedure. The standardized differences for each covariate between the patients who received surgery 18

19 alone and surgery plus radiation were then calculated and compared prior to and after the matching process to determined covariate balance between the two groups [34]. Survival analysis and Cox proportional hazards modeling Unadjusted survival analysis was performed using Kaplan-Meier estimates using log-rank tests with significance level set at p<0.05. For survival analysis of the propensity score matched pairs of patients, the censored-data version of the sign test of Klein and Moeschberger was used [35]: Z = (D control D treatment ) / sqrt (D control + D treatment ) D control refers to the number of patients within the pairs where the control person failed first (died) and was not censored D treatment refers to the number of patients within the pairs were the treatment person failed first (died) and was not censored Cox proportional hazards models were performed using the PROC PHREG statement in SAS. Proportionality check was performed using log-log proportional hazards curves. Covariates were tested for interactions by including the interaction term in the Cox model. If the interaction term was associated with a p<0.05 and also it made clinical sense it was left in the final model. Hazard ratios and 95% confidence intervals were reported for each covariate in the Cox model with significance level set at p<0.05. Results Appendix A shows the algorithm used to select the patients for analysis in the study. A total of 747 patients made up the final study population, of which 343 had surgery alone and 404 surgery plus radiation. 19

20 Univariate analysis of factors associated with use of radiation therapy In order to determine factors associated with the use of radiation therapy following surgical resection of MCC, both univariate and multivariate analyses were performed. Table 1 demonstrates the results of the univariate analysis of factors significantly associated with radiation therapy. Table 1: Univariate Analysis of Factors Associated with Adjuvant Radiation Therapy (N=747) Covariate Surgery Alone Surgery plus Radiation p value (N=343) (N=404) Age Sex* Female Male 43% 57% 43% 57% Race* White Non White Diagnosis Period* Marital Status* Married Unmarried Geographic Region* East Midwest South West Tumor Histology* Cut. neuroendocrine MCC Tumor Site* Head and Neck Upper extremity Trunk Lower extremity Tumor Size* < 2 cm > 2 cm Disease Stage* Localized Regional + t test * Chi square 95% 5% 21% 40% 39% 57% 43% 19% 9% 12% 60% 1% 99% 40% 27% 13% 20% 65% 35% 63% 37% 96% 4% 15% 38% 47% 65% 35% 15% 10% 12% 63% 1% 99% 41% 27% 14% 18% 56% 43% 44% 56% <

21 Table 1: Univariate Analysis of Factors Associated with Adjuvant Radiation Therapy (N=747) cont. Covariate Surgery Alone Surgery plus Radiation p value Surgery Type* Local excision Radical excision Lymph Node Surgery Type* No LN biopsy SLN biopsy Lymph node dissection * Chi Square 45% 55% 61% 19% 20% 55% 45% 47% 20% 33% < Unadjusted univariate analysis suggested that patients who underwent surgery alone were slightly younger age ( years vs for surgery and radiation) and a higher proportion were married (57% married for surgery alone vs. 65% married for surgery and radiation). Although a larger proportion of the study population was from the West geographic location, there was no significant association between geographic region and the use of radiation therapy. There was a significant association observed in the proportion of patients who received radiation therapy based upon diagnosis period, with a steady increase in the proportion of patients who received radiation therapy over time (p=0.0085). As might be expected, there was a significant association between both larger tumor size (> 2cm vs. < 2 cm, p=0.0242) as well as the presence of regional versus localized disease (p<0.0001) and the use of radiation therapy. Finally, although surgery type (local excision vs. radical excision) was not significantly associated with use of radiation therapy, the type of lymph node surgery was, with patients who had a lymph node dissection more likely not to have radiation therapy (p<0.0001). 21

22 Multivariate analysis of factors associated with use of radiation therapy In order to determine which covariates were independently associated with the use of radiation therapy while adjusting for other factors, multivariate logistic regression was performed. Prior to multivariate logistic regression, covariates were tested for interactions. Table 2 demonstrates the p-values associated with each pair of covariates when the interaction term was entered into the logistic regression model. There was a strong interaction of surgery type*lymph node surgery (p=0.007) as well as race*site (p=0.02). Table 2.: Interaction analysis for covariates associated with use of radiation therapy (p values) Age Dec Sex Race Marital Region Dx Per ICD Site Size Stage Surg LN Surg Age Dec Sex Race Marital Region Dx Per ICD Site Size Stage Surg LN Surg From a clinical perspective, there is a possibility that the interaction between the surgery of the primary MCC and the type of lymph node surgery could influence the decision to give radiation. For this reason, the interaction term was included in the multivariate analysis. The interaction between race*site was not placed into the regression as the non-white race was such a low proportion of the study population. 22

23 Table 3a: Multivariate Analysis of Factors Associated with Adjuvant Radiation Therapy (N=747)* Covariate p value Adjusted Odds Ratio 95% CI Age Sex Female Male Referent Marital Status Married Unmarried Referent Disease Stage Localized < Regional Referent *Likelihood Ratio Test of Global Null Hypothesis p< When adjusting for other factors, patient age was no longer significantly associated with the use of radiation therapy (Table 3a). Interestingly, married patients had an almost 1.5 odds of receiving radiation therapy when adjusting for other covariates (OR=1.46, 95%CI= ). Patients who had localized disease had an approximately 50% less odds of having radiation therapy following surgical resection as compared to patients who had regional metastasis of disease (OR= 0.50, 95% CI= ). The results of the multivariate analysis specific to surgery type, lymph node surgery and the interaction of surgery type*lymph node surgery are reported in Table 3b. Interestingly, the association of radiation therapy to lymph node surgery was influenced by the surgical treatment of the primary MCC. In patients who had local excision of the primary MCC, those who underwent sentinel node biopsy experienced over three times higher odds of receiving undergone radiation therapy after surgery as compared to patients who had local excision and no lymph node procedure (OR=3.122, 95% CI= ). By contrast, in patients who had radical excision of the primary MCC, those who went on to lymph node dissection had a nearly two-fold odds of undergoing 23

24 radiation therapy as compared to those who had no lymph node surgery (OR=1.948, 95% CI= ). These results illustrate the complexity of the decision-making behind whether patients receive radiation therapy, and also suggest that other unmeasured factors may play a role in that decision as well. Table 3b: Multivariate Analysis of Factors Associated with Adjuvant Radiation Therapyinteraction term results (N=747)* Covariate p value Adjusted Odds Ratio 95% CI Surgery type Local Excision Radical Excision Referent Lymph node surgery Sentinel node biopsy Lymph node dissection No Lymph node biopsy Referent Surgery type/lymph node Interaction term Local Excision/Sentinel node Local Excision/Lymph node dissection Local excision/no LN biopsy Referent Radical excision/sentinel node Radical excision/lymph node dissection Radical excision/no LN biopsy Referent Effect of radiation therapy on MCC-specific and overall survival: univariate and multivariate analyses In order to determine whether radiation therapy in addition to surgery is associated with differences in survival in patients with surgically-resected MCC, unadjusted univariate (Kaplan-Meier estimates) analysis as well as adjusted multivariate (Cox proportional hazards model) analyses were performed. Kaplan-Meier survival analysis demonstrated no significant association of the use of radiation therapy and MCC-specific survival in the cohort of 747 study patients (log-rank= , Figure 1a). 24

25 A. MCC Specific Survival Log rank= (months) B. Overall Survival Log rank= (months) Figure 2. Kaplan Meier survival analysis of MCC specific (A) and overall survival (B) in the cohort of 747 study patients with resected MCC. 25

26 There was a trend towards increased overall survival in the group of patients that received radiation therapy, but this was not statistically significant (log-rank= , Figure 1b). Examination of the survival curves demonstrates that the majority of MCCspecific deaths occured within the first 20 months of diagnosis, although there are still a number of censored events prior to 20 months. By contrast, the overall survival of both patient groups continues to decline over a period of months, suggesting that mortality due to factors other than MCC might significantly affect the long-term survival of the study population. Multivariate analysis using Cox proportional hazards was performed to determine which covariates were independently associated with MCC-specific and overall survival. Interactions which were significant included sex*stage (p=0.02), sex*marital (p=0.03), and site*stage (p=0.04, Table 4a). The site*stage interaction was felt to be possibly clinically relevant with respect to survival, and it was included in the Cox proportional hazards model. Table 4a: Interaction analysis for covariates associated with MCC specific survival (p values) Sex Rac Mar Region Dx ICD Site Size Stage Surg LN Rad e ital Per Surg Age Sex Race Marital Region Dx Per ICD Site Size Stage Surg LN surg

27 The interactions between covariates with respect to overall survival were geographic region*stage (p=0.03) and diagnosis period*radiation therapy (p=0.03). Cox models were developed both with and without the two interaction terms (Table 4b). Table 4b: Interaction analysis for covariates associated with overall survival (p values) Sex Rac Mar Region Dx ICD Site Size Stage Sur LN Rad e ital Per g Surg Age Sex Race Marital Region Dx Per ICD Site Size Stage Surg LNsurg 0.50 Results of the Cox proportional hazards model are demonstrated in Table 5a. Covariates which were significantly associated with MCC-specific survival included age (HR=1.03, 95%CI= ); non-white race (HR=0.29, 95%CI= ); and the interaction between disease stage and anatomic site of MCC. Several covariates were also significantly associated with overall or all cause survival. Age (HR=1.04, 95%CI= ) and female sex (HR=0.55, 95%CI= ) were both associated with hazard of death. Tumor size < 2cm (HR=0.77, 95%CI= ) and localized disease (HR=0.64, 95%CI= ) were both significantly associated with decreased hazard of death. Patients who underwent SLN biopsy had a lower hazard of death as compared to those who had no lymph node surgery (HR=0.59, 95%CI= ). Finally, in contrast to MCC-specific survival, the lack of radiation therapy was significantly associated with a higher hazard ratio of death from all causes (HR=1.28, 95%CI= ). 27

28 Table 5a: Cox Proportional Hazards Model for MCC Specific and Overall Survival (N=747) Covariate MCC Specific Survival Overall Survival Hazard 95% P value Hazard 95% P value Ratio Confidence Interval Ratio Confidence Interval Age < Sex 0.09 < Female ** ** Male Reference Reference Race Non White White Reference Reference Marital Status Married Unmarried Reference Reference Tumor Histology CNC MCC Reference Reference Tumor Size cm > 2 cm Reference Reference Disease Stage Localized ** ** Regional Reference Reference Surgery Type Local destruction Radical Reference Reference Excision Lymph Node Surgery Type SLNB NOS Regional nodes removed No nodes Reference Reference removed Radiation Therapy No Yes Reference Reference 28

29 When examining the interactions between disease stage with sex and with anatomic site, localized disease stage was associated with a lower risk of MCC-specific death among all anatomic sites in females with the exception of lower extremity (Table 5b). In male patients, localized disease stage was associated with reduced risk of death in head and neck location (HR=0.38, 95% CI= ). Radiation therapy was not significantly associated with MCC-specific hazard of death (no radiation therapy HR=0.94, 95%CI= ). Similarly, tumor histology, tumor size, surgery type and lymph node surgery were not significantly associated with MCC-specific hazard of death. Table 5b: Cox proportional Hazards model for MCC specific survival site*stage and sex*stage interaction terms (N=747) Site and Stage Female Male Hazard Ratio 95% CI Hazard Ratio 95% CI Head and Neck Localized vs. Regional Disease Trunk Localized vs. Regional Disease Upper Extremity Localized vs. Regional Disease Lower Extremity Localized vs. Regional Disease Other Localized vs. Regional Disease In summary, radiation therapy was not associated with MCC-specific survival by unadjusted, univariate Kaplan-Meier analysis as well as multivariate Cox proportional hazards analysis. Multivariate analysis did demonstrate a significant association of radiation therapy and reduced hazard of death from all causes (HR= 1.28, 95% CI= ). 29

30 Propensity scoring and matched pair analysis Propensity scoring was used to generate a subset of patient pairs who were matched based upon similar patient and tumor characteristics. Covariate balance between the surgery alone and surgery plus radiation therapy groups before and and after propensity score matching. Table 6a: Patient characteristics before and after propensity score matching Before matching After matching Covariate Surgery Surgery plus d* Surgery Surgery plus d* Alone Radiation (%) Alone Radiation (%) (N = 343) (N = 404) (N = 269) (N = 269) Age Mean ± SD 74.9 ± 12.4 yr 72.2 ± 12.6 yr ± 12.5 yr 73.5 ± 11.9 yr 0.2 Median 77 yr 75 yr 76 yr 75 yr Sex Female 149 (43.4%) 172 (42.6%) (43.5%) 115 (42.7%) 1.5 Male 194 (56.6%) 232 (57.4%) 152 (56.5%) 154 (57.3%) Race White 325 (94.7%) 386 (95.5%) (94.8%) 254 (94.4%) 1.7 Non White 18 (5.3%) 18 (4.5%) 14 (5.2%) 15 (5.6%) Diagnosis Period (21.0%) 59 (14.6%) (19.7%) 50 (18.6%) (39.4%) 153 (37.9%) (37.5%) 106 (39.4%) (39.6%) 192 (47.5%) (42.8%) 113 (42.0%) 1.5 Marital Status Married 197 (57.4%) 261 (64.6%) (61.3%) 160 (59.5%) 3.8 Unmarried 146 (42.6%) 143 (35.4%) 104 (38.7%) 109 (40.5%) Geographic Region East 66 (19.2%) 59 (14.6%) (17.1%) 41 (15.2%) 5.1 Midwest 30 (8.8%) 45 (10.4%) (10.0%) 30 (11.2%) 3.6 South 42 (12.2%) 48 (11.9%) (11.9%) 34 (12.6%) 2.3 West 205 (59.8%) 255 (63.1%) (61.0%) 164 (61.0%) 0 *d= Standardized difference Covariate differences between group with respect to patient characteristics are shown in Table 6a. Age, sex and race were fairly equally distributed between the surgery alone and surgery plus radiation therapy groups both prior to and after matching. 30

31 However, there were significant differences between the two groups with respect to proportions of patients within specific diagnosis periods, marital status and geographic regions prior to propensity scoring and matching, and the standardized differences prior to matching. For example, prior to matching there were varying proportions of patients in the surgery alone group diagnosed between either (21% vs. 14.6%) and (39.6% vs. 47.5%) as compared to the surgery plus radiation group, with standardized differences of and 15.9 respectively. By contrast, following propensity scoring and matching the standardized differences between groups were reduced to -2.8 ( ) and -1.5 ( ) respectively. Covariate differences between groups among tumor and treatment characteristics also became more balanced following propensity score matching (Table 6b). Prior to matching, the proportion of patients with tumors < 2cm was 65% in the surgery alone group and 56.7% in the surgery plus radiation group with a standardized difference of -17.1%. After propensity matching, the proportion of patients with tumors < 2cm was 62% in the surgery alone group and 61% in the surgery plus radiation group with a reduction in the standardized difference to -2.3%. With respect to the proportion of patients with localized extent of disease, the surgery alone group had 62.7% prior to matching and the surgery plus radiation group had 44.3% whereas after matching the proportions were 57% and 58% respectively. Finally, the distribution of lymph node surgery types was better balanced after matching, with standardized differences for no lymph node surgery reduced from -28.3% to 1.5% and for lymph node dissection reduced from 30.0% to -2.6%. It is not surprising that significant imbalances were present in covariates among patients who were assigned to surgery or surgery plus radiation, as the 31

32 decision to add radiation therapy is likely not based upon random chance but instead patient and tumor characteristics that may place patients at a higher or lower risk of recurrence and death from MCC and other co-morbidities. Table 6b: Tumor and Treatment Characteristics before and after propensity score matching Before matching After matching Covariate Surgery Alone Surgery plus Radiation d* (%) Surgery Alone Surgery plus Radiation d* (%) (N = 343) (N = 404) (N = 269) (N = 269) MCC 339 (98.8%) 400 (99.0%) (98.9%) 266 (98.9%) 0 CNC 4 (1.2%) 4 (1.0%) 3 (1.1%) 3 (1.1%) Tumor Site Head and 137 (39.9%) 164 (40.6%) (40.9%) 109 (40.5%) 2.8 Neck Trunk 44 (12.9%) 55 (13.6%) (10.8%) 34 (12.7%) 5.8 Upper 93 (27.1%) 110 (27.2%) (27.5%) 73 (27.1%) 0.8 Extremity Lower 69 (20.1%) 72 (17.8%) (20.8%) 53 (19.7%) 0.8 Extremity Other 0 (0%) 3 (0.8%) (0%) 0 (0%) 0 Tumor Size 2 cm 223 (65.0%) 229 (56.7%) (62.1%) 164 (61.0%) 2.3 > 2 cm 120 (35.0%) 175 (43.3% 102 (37.9%) 105 (39.0%) Disease Stage Localized 215 (62.7%) 179 (44.3%) (57.3%) 156 (58.0%) 1.5 Regional 128 (37.3%) 225 (55.7%) 115 (42.7%) 113 (42.0%) Surgery Type Local 155 (45.2%) 172 (42.6%) (43.1%) 125 (46.5%) 6.7 Excision Radical Excision 188 (54.8%) 232 (57.4%) 153 (56.9%) 144 (53.5%) Lymph Node Surgery Type No Nodes 210 (61.2%) 191 (47.3%) (56.1%) 153 (56.9%) 1.5 Removed SLNB NOS 65 (19.0%) 80 (19.8%) (19.7%) 54 (20.1%) 0.9 Lymph node dissection 68 (19.8%) 133 (32.9%) (24.2%) 62 (23.0%) 2.6 *d= Standardized difference 32

33 The standardized differences of all covariates both before and after propensity score matching were plotted and are illustrated in Figure 2. Prior to matching, 14 of the 23 covariates had standardized differences between the surgery alone and surgery plus radiation groups which were less than 10%. Alternatively, nine of 23 covariates had standardized differences of greater than 10%, and it is important to note that covariates such as localized disease, tumor size and lymph node surgery were found to be significantly associated with hazard of death from all causes by Cox proportional hazards model of the 747 unmatched study population (Table 5a). By contrast, after propensity scoring and matching the standardized difference between the groups was reduced to less than 10% for all 23 covariates, and even less than 5% in most cases, illustrating the ability of propensity scoring methods to achieve evenly matched groups of patients for comparison. Localized disease Regional nodes removed No nodes removed Tumor size 2 cm Figure 3. Absolute standardized differences of covariates before and after propensity score matching (N=269 patient pairs) 33

34 Survival analysis of propensity score matched patient pairs Kaplan-Meier analysis was used to plot the survival estimates for MCC-specific and overall survival of the 269 propensity score-matched patient pairs. MCC Specific Survival A. p=0.26 (Censored data version of the sign test) (months) B. Overall Survival p=0.028 (Censored data version of the sign test) (months) Figure 4. Kaplan Meier survival analysis of MCC specific (A) and overall survival (B) for the 269 pairs of patients matched by propensity score 34

35 MCC-specific survival between the propensity score matched patients who had surgery alone or surgery plus radiation was not significantly different (p=0.26). The finding that radiation therapy was not associated with MCC-specific survival is similar to that seen in both the unadjusted Kaplan Meier analysis of the patients prior to any matching (Figure 1a) and also the multivariate Cox proportional hazards model results (Table 5a). By contrast, the matching process and paired analysis did demonstrate a significant association between radiation therapy and improved overall survival, which consistent with the results of the Cox proportional hazard model (Table 5a). Sensitivity analysis was performed of the matched pairs with respect to overall survival using the method of Rosenbaum [31]. This yielded a gamma value of 1.03, which is low and suggests that the analysis may not be very sensitive to hidden bias. Data Total # of Pairs With A Clear Winner 99 T = # of Pairs Where Exposed Outlives Control 60 Sensitivity Analysis Gamma Values Insert Gamma Value Below 2 tail P value (lower bound) 2 tail P value (upper bound) P+ P E(T+) E(T ) SD(T+) tail P value (lower bound) 2 tail P value (upper bound) P+ P E(T+) E(T ) SD(T+) Figure 5. Sensitivity analysis of survival from propensity score matched pairs of patients who received either surgery alone or surgery plus radiation (N=269 pairs). 35

36 Discussion To date, there have been no prospective randomized clinical trials which have tested whether radiation therapy in addition to surgery improves disease-specific or overall survival in patients with MCC. The majority of the clinical reports regarding outcome in patients with MCC undergoing radiation therapy in addition to surgery lack a proper control or comparison group. As mentioned previously Mojica et al performed an analysis of patients within the SEER database from and found an improved overall survival in the group of patients who underwent adjuvant radiation therapy following surgical resection [16]. However, a major criticism of that study was the inclusion of patients who had survival less than 4 months, which were over-represented in the surgery alone group and may have biased the survival results [17]. In addition, the study did not assess MCC-specific survival, which may be a better indicator of the therapeutic efficacy of adjuvant radiation therapy. The results of the present study suggest that although SEER patients who received radiation therapy demonstrated an increased overall or all cause survival as compared to patients who received surgery alone, the hazard of death from MCC is not significantly associated with radiation therapy. Cox proportional hazards models estimated the hazard of death from all causes at nearly 30% higher (HR=1.29, 95%CI= ) in patients who did not receive radiation therapy as compared to those that did. However, the hazard of death from MCC was not significantly different in patients who received radiation therapy as compared to those that did not. Thus, these results are in agreement with those of Mojica et al with respect to improved overall survival associated with radiation therapy, but in addition the present study suggests that the improvement is not due to a therapeutic effect of radiation therapy. The lack of improvement in MCC-specific 36

37 survival in the present study suggests that the observed improvement in overall survival may be related to selection bias and/or differences in unmeasured factors between groups. The lack of inclusion of patients with survival less than 4 months in the present study negates that covariate as a contributor to the selection bias. The sensitivity analysis of the Cox model suggests that the results may be significantly altered by hidden bias, which suggests that incorporation of other unmeasured factors into a revised analysis may result in the lack of association of radiation therapy and overall survival. The use of SEER data for studying patients with rare tumors such as MCC allows analysis of outcomes of large numbers of patients. However, the inherent limitations of the use of SEER data the present study include several key factors. First, details concerning certain characteristics related to MCC biologic behavior such as histologic grade were missing, which may have served as an important matching or adjustment covariate. Second, details concerning the patient treatment were lacking and include margin status after surgical resection, radiation dose, timing of radiation therapy and whether radiation was administered to only the primary tumor site or included the regional lymph nodes. Finally, details of patient co-morbidities were lacking, which could significantly bias the overall survival analysis. Known limitations of all SEER studies include missing or incorrect data entry, inability to obtain critical details related to patient, tumor or treatment characteristics and skewed sample population. The results of the present study provide insight into patient and clinical factors which are associated with the use of radiation therapy following surgical resection of MCC in the U.S. First, the fact that there were approximately 40% of the patients within the study population who did not undergo radiation therapy suggests that adjuvant 37