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Reply to: ‘IMPRES does not reproducibly predict response to immune checkpoint blockade therapy in metastatic melanoma’

The Original Article was published on 05 December 2019

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Fig. 1: IMPRES training set is unbiased and reproducibly predicts response to immune checkpoint blockade therapy.

Data availability

All data analyzed in this manuscript are publicly available, and the accessions or web links to each dataset are provided either in the original publications or in the relevant parts of this manuscript.

Code availability

The feature selection from the original training set and all melanoma ICB response prediction codes are available from the original publication: The additional code to sample similar training sets is provided at


  1. Carter, J. A. et al. IMPRES does not reproducibly predict response to immune checkpoint blockade therapy in metastatic melanoma. Nat. Med. (2019).

  2. Auslander, N. et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma.Nat. Med. 24, 1545–1549 (2018).

    Article  CAS  Google Scholar 

  3. Rudlof, S. & Köppen, M. Stochastic hill climbing with learning by vectors of normal distributions. in Proceedings of the First Online Workshop on Soft Computing (WSC1) 60–70 (1996).

  4. Patrick, K. Comparison of simulated annealing and hill climbing in the course timetabling problem. Afr. J. Math. Comput. Sci. Res. 5, 176–178 (2012).

    Article  Google Scholar 

  5. Jacobson, S. H. & Yücesan, E. Analyzing the performance of generalized hill climbing algorithms. J. Heuristics 10, 387–405 (2004).

    Article  Google Scholar 

  6. Vinh, N. X., Chetty, M., Coppel, R. & Wangikar, P. P. Globalmit: learning globally optimal dynamic Bayesian network with the mutual information test criterion. Bioinformatics 27, 2765–2766 (2011).

    Article  CAS  Google Scholar 

  7. Zhang, Y., Lei, T., Barzilay, R. & Jaakkola, T. Greed is good if randomized: new inference for dependency parsing. in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing 1013–1024 (Association for Computational Linguistics, 2015).

  8. Burke, E. K. & Bykov, Y. The late acceptance hill-climbing heuristic. Eur. J. Oper. Res. 258, 70–78 (2017).

    Article  Google Scholar 

  9. Efron, B. Introduction to the Bootstrap (Statistical Science, 2003).

  10. Fu, W. J., Carroll, R. J. & Wang, S. Estimating misclassification error with small samples via bootstrap cross-validation. Bioinformatics 21, 1979–1986 (2005).

    Article  CAS  Google Scholar 

  11. Popovici, V. et al. Effect of training-sample size and classification difficulty on the accuracy of genomic predictors. Breast Cancer Res. 12, R5 (2010).

    Article  Google Scholar 

  12. Pang, S., Havukkala, I., Hu, Y. & Kasabov, N. Classification consistency analysis for bootstrapping gene selection. Neural Comput. Appl. 16, 527–539 (2007).

    Article  Google Scholar 

  13. Zhang, Y. Q. & Rajapakse, J. C. (eds) Machine Learning in Bioinformatics (Wiley, 2008).

  14. Huang, A. C. et al. A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma. Nat. Med. 25, 454–461 (2019).

    Article  CAS  Google Scholar 

  15. Wagner, G. P., Kin, K. & Lynch, V. J. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 131, 281–285 (2012).

  16. Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

  17. Wu, D. C., Yao, J., Ho, K. S., Lambowitz, A. M. & Wilke, C. O. Limitations of alignment-free tools in total RNA-seq quantification. BMC Genomics 19, 510 (2018).

  18. Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949 (2017).

    Article  Google Scholar 

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Author information

Authors and Affiliations



N.A. performed the additional analysis and provided the additional code; N.A. and E.R. designed experiments; N.A., E.R. and J.S.L wrote the manuscript.

Corresponding author

Correspondence to Eytan Ruppin.

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Competing interests

The authors declare no competing interests.

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Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The performance of scores selected with different sampled training sets, and survival prediction for an independent NB dataset.

(a) Boxplots showing the area under the ROC curve (AUC) for responder/non-responder classification obtained with 200 feature sets selected over the NB data, for each of the melanoma datasets (b) The fraction of features recurring in these 200 feature sets (Original IMPRES features are colored in red). (c) IMPRES score predicts survival for an independent NB dataset from TARGET, where the top 15 features reported by Carter et al. fail to do that (a strong indication of overfitting). The NB data is RPKM values downloaded from cBioPortal spanning 39 NB patients of age 1.5 years or less, as described originally. We predicted overall survival using the original IMPRES features (with the same Kaplan-Meier codes used in Figure 2C in our original publication). The P-values are for two-sided log-rank test.

Extended Data Fig. 2 IMPRES performance on TPM and FPKM data.

(a) ROC curves quantifying the accuracy of IMPRES response prediction in two different TPM generated datasets generated by the Van Allen group ( (b) Boxplots showing IMPRES performance when calculated on FPKM values of the Riaz et al. dataset, for progressive diseases (PD), stable disease (SD), partial response (PR) and complete response (CR). The P-values are for one-sided rank-sum test, comparing IMPRES scores of each non-PD group (SD/PR/CR) to that of PD.

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Auslander, N., Lee, J.S. & Ruppin, E. Reply to: ‘IMPRES does not reproducibly predict response to immune checkpoint blockade therapy in metastatic melanoma’. Nat Med 25, 1836–1838 (2019).

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