Patient characteristics of training and validation set. Patient selection and inclusion overview can be found in Supp Data 9. Training set (103)

Transcription

1 Roepman P, et al. An immune response enriched 72-gene prognostic profile for early stage Non-Small- Supplementary Data 1. Patient characteristics of training and validation set. Patient selection and inclusion overview can be found in Supp Data 9. Training set (103) Validation set (69) Gender (%) (%) male female Age at diagnosis median range Hospital NKI Heidelberg Bialystok Gdansk Vumc Smoking current smoker former smoker non-smoker unknown Histology large cell carcinoma squamous cell carcinoma adenocarcinoma other Stage I II Follow-up period (months) median range Status alive / censored death lung cancer death other Relapse-free survival time (months) median range Overall survival time (months) median range Treatment before surgery yes no unknown

2 Supplementary Data 2 Hierarchical clustering (Euclidian distance, complete clustering) of NSCLC samples based on expression of all genes Hospital Histology Stage Survival Hospital Histology Stage Survival NKI-AvL Heidelberg Bialystok Gdansk VUmc squamous cell carcinoma adenocarcinoma other non-small cell stage I stage II alive or censored death by lung cancer

3 Supplementary Data 3 Schematic overview of the multiple samples procedure that was used for development of a robust nearest mean classifier. A 10-fold cross validation loop was used to identify genes which expression ratios correlate with overall and recurrence free survival time. The initial 103 training samples were randomly split into a training set (n=93) and in a test set (n=10). The training set was used to identify which gene correlate best with overall (OS) and recurrence-free (RFS) survival (based on three statistics to reduce selection of genes based on noise as much as possible: Welsh t-test, logrank test and a Cox proportional hazard ratio). Subsequently the top 100 genes were used for building a nearest mean classifier and the performance was tested on the test set. Repeating this procedure for different training and test splits (multiple sampling) resulted in a multiple sets of most prognostic genes. The multiple gene rankings for OS and RFS were combined and the set of most prognostic genes (most often selected genes during the multiple sampling procedure) was selected via a top-down approach. The performance of the optimal set of genes (72 genes) was evaluated using a leave-one-out approach on all training samples to define the classifier performance using the optimal threshold. The NSCLC 72 gene classifier was finally validated on the independent set of validation samples (n=69). 10-fold cross validation loop ±42,000 gene probes 103 samples (>2y follow-up) >500x Randomly split samples 93 train 10 test Gene scoring Test on test samples Welsh t-test Logrank test Cox proportional hazard ratio Score sample outcome Combine multiple gene rankings Score gene ranking Combine ranking Select top 100 genes Nearest mean classifier Combine Multiple predictions Select set of best prognostic genes (72) Sample prediction robustness & accuracy LOO cross-validation on 103 training samples classifier perfomance using optimal threshold Determine low-risk and high-risk profile Independent validation 69 samples

4 Supplementary Data 4. Kaplan-Meier plot survival estimates of overall survival (OS) (A) and recurrence-free survival (RFS) (B) based on the multiple sampling outcomes of the test samples using the 10-fold cross validation procedure described in Supp. Data 3. These results indicated that the multiple samples approach is suitable for development of a nearest mean classifier that is unbiased towards the training samples. A B overall survival% P= low-risk profile high-risk profile recurrence-free survival% P= low-risk profile high-risk profile months months

5 Supplementary Data 5 NSCLC survival associated genes. The set of 72 prognostic classifier genes is ranked according to the gene expression association with recurrence-free survival.

6 Supplementary Data 6 Kaplan-Meier plot survival estimates of overall survival based on the 72-gene classifier within tumour stage (A) and based on the 72-gene classifier within tumour histology (B). A overall survival% stage I & low-risk stage II & low-risk stage I & high-risk stage II & high-risk P< months B overall survival% scc & low-risk scc & high-risk adeno & low-risk adeno & high-risk P< months

7 Supplementary Data 7 Functional category analysis of the classifier signature. Functional categories within the set of 72 prognostic genes were identified using gene ontology (GO) analysis. Significantly (P<0.01) overrepresented GO categories are colored red. Blue shaded area indicted categories associated with immune response, green with antigen binding, yellow with protein modification and degradation. antimicrobial humoral response humoral defense mechanism lymphocyte activation humoral immune response hemopoiesis immune cell activation protein modification response to pest, pathogenor parasite response to stress immune response response to other organism lymphocyte differentiation defense response response to biotic stimulus hemopoietic or lymphoid organ development development protein catabolism protein catabolism ubiquitin cycle protein metabolism proteolysis during cellular protein catabolism cellular protein catabolism protein catabolism macromolecule metabolism cellular macromolecule catabolism dent macromolecule catabolism lytic vacuole response to stimulus physiological process mrna binding nucleic acid binding lysosome vacuole cytoplasm organelle integral to plasma membrane membrane biological process gene ontology cellular component binding molecular function catalytic activity antigen binding translation factor activity, nucleic acid binding translation regulator activity carboxylic ester hydrolase activity intrinsic to plasma membrane hydrolase activity thiolester hydrolase activity

8 Supplementary Data 8 Comparison of different early stage NSCLC prognostic gene classifiers

9 Supplementary Data 9 Patient selection and inclusion overview.