Supplementary Appendix s1

Supplementary Appendix

Supplementary Methods

Quality control of microRNA qPCR cDNA were generated for each RNA sample in duplicate reverse transcription (RT) reactions and tested for the expression of 5 miRNA (miR-103, miR-191, miR-23a, miR-30c, and miR-451) and a synthetic spike-in

RNA (UniSp6). An average Ct was calculated for the duplicate RTs, and evaluation of expression levels was performed based on raw Ct values. All five microRNA were detected in most samples, with a few samples showing less expression of individual microRNA. No RT-qPCR inhibition occurred as judged by the expression of the UniSp6 spike-in.

Analysis of the concordance between risk scores derived from RT-qPCR and microarray data

Spearman correlation coefficient was used to assess the concordance between the risk scores derived from measurement of the same set of patient samples using different methodologies (RT-qPCR and microarray).

Sensitivity analyses to test the robustness of the miRNA signature.

First, we modeled stage as a factor variable in the univariate associations and the multiple Cox regressions in the discovery cohort when we built the signature. The model coefficients of the 4-miRNA signature with stage as a factor variable (Table S1) were similar to those obtained with stage as a continuous variable

(Table 2). In the multivariate Cox model with stage as a factor, the adjusted log hazard ratio of stage 3 versus stage 1 (β_stage 3 = 2.20) was about twice of that of stage 2 versus stage 1 (β_ stage 2 = 1.16), which supported the linear assumption of the log-hazard ratio in the original multivariate Cox model with stage as a continuous variable. In sum, these results demonstrate the robustness of the identified 4-miRNA signature by various restrictions or statistical methods.

Second, we repeated the discovery and validation processes using logistic regression analysis. MiRNA were first ranked by univariate association of expression level for each miRNA with brain recurrence via logistic regression analysis with adjustment for tumor stage as a continuous variable. All miRNA were standardized into mean 0 and unit variance variables for scale consistency across cohorts. Top 50 ranking miRNA were used as candidates to be included in the multivariate logistic regression model. The resulting miRNA signature identified was the same four miRNAs to those obtained from Cox model plus one additional miRNA, hsa-miR-342-3p (Table S2).

Third, we estimated the C-index and AUCs of the signature within each stage as proof-of-principle that these miRNAs have prognostic potential independent of staging. Regression coefficients of the Cox regression model of the signature were applied to patients; and the AUCs and C-index were estimated for each stage separately. With stage alone as a predictor, the AUCs are 0.5 to distinguish Bmet from non-

Bmet patients within each staged patients. In contrast, using the expression of the four miRNAs, the performance of the classifier exceeded 60% for all three stages in the validation cohort; and for stage 2 patients in the independent cohort (Table S3).

Supplementary Figure Legends

Supplementary Figure S1. Waterfall plots of patient risk scores derived from the 4-miRNA

signature.

Individual patient risk scores calculated from the described 4-miRNA signature prognostic of brain metastasis development are plotted for (A) training (n = 92), (B) validation (n = 119), and (C) independent

(n = 45) patient cohorts. For plotting, patient risk scores were centered around the cut-off value of the cohort (linear risk score - cut-off value), which was determined by the Youden Index of the ROC curve.

Dark pink bars represent patients that have developed a documented brain metastasis, light pink bars represent patients with recurrent disease without documented brain metastasis, and light blue bars represent non-recurrent patients. Supplementary Figure S2. Brain metastasis-free and overall survival of patients separated by stage

and stratified using the 4-miRNA signature.

Within each cohort, risk scores for individual patients were defined as the linear combination of model predictors weighted by regression coefficients. For each stage, patients from all three cohorts were pooled and an optimal risk score cutoff using the Youden Index of the ROC curve was chosen to classify patients into high- and low-risk groups. Group data were plotted in Kaplan-Meier survival curves and statistical analysis performed by Wilcoxon tests for brain metastasis-free and overall survival of stage I (n = 74, A,

D), stage II (n = 111, B, E), and stage III (n = 71, C, F) patient subsets.

Supplementary Figure S3. Representative images of TILs and CD45 staining in brain metastatic vs.

non brain-metastatic primary melanomas.

Representative images of hematoxylin & eosin (H&E) (A,B) or CD45 (C,D) stained sections of primary melanomas that metastasized to brain, shown with non-brisk TILs (A,C) or that have not yet metastasized to brain, shown with brisk TILs (B,D). Arrows indicate representative lymphocytic infiltration in H&E and

CD45 images. Arrowheads indicate representative pigmentation, which is evident in the brain metastatic/non-brisk-TIL image as is distinguished from CD45+ lymphocytes by its diffuse, non-nuclear localization. Supplementary Tables

Table S1. Hazard ratios and p values of the 4-miRNA signature predicting brain metastasis derived from Cox PH regression, using stage as a categorical variable

HR (95% CI)* P-value hsa-miR-150-5p 0.31 (0.14, 0.68) 0.004 hsa-miR-15b-5p 4.52 (1.49, 13.74) 0.008 hsa-miR-16-5p 0.11 (0.03, 0.45) 0.002 hsa-miR-374b-3p 0.57 (0.39, 0.83) 0.004 stage 2 3.19 (0.71, 14.33) 0.129 stage 3 9.06 (1.79, 46.03) 0.008 * Hazard ratios (HR) and 95% confidence intervals (CI) of the standardized miRNA expressions obtained from discovery cohort.

Table S2. Odds ratios and p values of the 5-miRNA signature predicting brain metastasis derived from logistic regression modeling OR (95% CI)* P-value hsa-miR-150-5p 0.32 (0.12, 0.84) 0.02 hsa-miR-15b-5p 4.50 (1.07, 18.91) 0.04 hsa-miR-16-5p 0.13 (0.02, 0.74) 0.022 hsa-miR-342-3p 0.50 (0.23, 1.09) 0.083 hsa-miR-374b-3p 0.43 (0.19, 0.96) 0.039 stage 3.71 (1.39, 9.95) 0.009 * Odds ratios (OR) and 95% confidence intervals (CI) of the standardized miRNA expressions obtained from discovery cohort

Table S3. Sensitivities, specificities, positive predictive values (PPV), and negative predictive values (NPV) of the miRNA-based brain metastasis classifier in the indicated cohorts for patients restricted by ≥ 3 years follow-up or development of brain metastasis Training Validation Independent Sensitivity 80.8 % 64.5 % 76.9 %

Specificity 59.3 % 54.1 % 67.7 %

PPV 46.7 % 41.7 % 50.0 % NPV 87.5 % 75.0 % 87.5 % Table S4. Performance of the 4-miRNA signature by clinicopathologic stage

Training Validation Independent

n C-index 3 yrs. AUC n C-index 3 yrs. AUC n C-index 3 yrs. AUC FU (n) FU (n) FU (n)

19 85.0% 18 75.0% 21 65.0% 18 69.6% 34 54.1% 34 68.8%

52 79.4% 50 74.6% 50 62.2% 36 65.8% 89.5% 8 93.8%

Stage III 21 62.2% 17 78.8% 48 61.4% 38 59.7% NA 2 NA

AUC = area under the receiver operating characteristic curve; FU = follow-up