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Editorial
. 2021 Jul;9(7):e003299.
doi: 10.1136/jitc-2021-003299.

Author response to Cunha et al

Rivka R Colen  1   2 , Christian Rolfo  3 , Murat Ak  4   2 , Mira Ayoub  4   2 , Sara Ahmed  5 , Nabil Elshafeey  6 , Priyadarshini Mamindla  2 , Pascal O Zinn  7 , Chaan Ng  8 , Raghu Vikram  8 , Spyridon Bakas  9 , Christine B Peterson  10 , Jordi Rodon Ahnert  11 , Vivek Subbiah  11 , Daniel D Karp  11 , Bettzy Stephen  11 , Joud Hajjar  12   13 , Aung Naing  14
Affiliations
Editorial

Author response to Cunha et al

Rivka R Colen et al. J Immunother Cancer. 2021 Jul.

Abstract

The need to identify biomarkers to predict immunotherapy response for rare cancers has been long overdue. We aimed to study this in our paper, 'Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers'. In this response to the Letter to the Editor by Cunha et al, we explain and discuss the reasons behind choosing LASSO (Least Absolute Shrinkage and Selection Operator) and XGBoost (eXtreme Gradient Boosting) with LOOCV (Leave-One-Out Cross-Validation) as the feature selection and classifier method, respectively for our radiomics models. Also, we highlight what care was taken to avoid any overfitting on the models. Further, we checked for the multicollinearity of the features. Additionally, we performed 10-fold cross-validation instead of LOOCV to see the predictive performance of our radiomics models.

Keywords: immunotherapy.

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Conflict of interest statement

Competing interests: CN reports grant support and personal fees from General Electric Healthcare, outside the submitted work; SB reports grant support from National Institutes of Health, outside the submitted work; JRA reports personal fees from Novartis, Eli Lilly, Orion Pharmaceuticals, Servier Pharma, Peptomyc, and Merck Sharpe, on the advisory board for Novartis, Eli Lilly, Orion Pharmaceuticals, Servier Pharma, Peptomyc, Merck Sharpe and Dome, Kelun Pharma/Klus Pharma, Pfizer, Roche Pharma, and Elipses Pharma, research funding from Bayer, Novartis, Spectrum Pharmaceuticals, Tocagen, Symphogen, BioAtla, Pfizer, GenMab, CytomX, KELUN-BIOTECH, Takeda-Millenium, GLAXOSMITHKLINE, and Ipsen, from null, outside the submitted work; VS reports clinical trial research funding from Novartis, Bayer, GlaxoSmithKline, Nanocarrier, Vegenics, Celgene, Northwest Biotherapeutics, Berghealth, Incyte, Fujifilm, Pharmamar, D3, Pfizer, Multivir, Amgen, Abbvie, Alfa-sigma, Agensys, Boston Biomedical, Idera Pharma, Inhibrx, Exelixis, Blueprint medicines, Loxo oncology, Takeda and Roche/ Genentech, National Comprehensive Cancer Network, NCI-CTEP, and UT MD Anderson Cancer Center, outside the submitted work; JH reports grants from Immune Deficiency Foundation, Jeffrey Modell Foundation and Chao Physician-Scientist, and Baxalta, and has served as an advisory board member for Takeda, CSL Behring, and Horizon Pharma outside the submitted work; AN reports research support and non-financial support from Merck Sharp and Dohme, grants from NCI/NIH, research support from the University of Texas MD Anderson Cancer Center, during the conduct of the study; grants from NCI, research support from EMD Serono, MedImmune, Healios Onc. Nutrition, Atterocor, Amplimmune, ARMO BioSciences, Karyopharm Therapeutics, Incyte, Novartis, Regeneron, Merck, Bristol Myers Squibb, Pfizer, CytomX Therapeutics, Neon Therapeutics, Calithera BioSciences, TopAlliance BioSciences, Eli Lilly, Kymab, PsiOxus, Arcus Biosciences, NeoImmuneTech, ImmuneOncia, and Surface Oncology, non-financial support for travel and accommodation from ARMO BioSciences, and has served as an advisory board member for Novartis, CytomX Therapeutics, Genome and Company, STCube Pharmaceuticals, OncoSec KEYNOTE-695, and Kymab, outside the submitted work. RRC, CR, MAk, MAyoub, SA, NE, PM, POZ, RV, CP, BS, and DDK declare no competing interests.

Figures

Figure 1
Figure 1
(A) Receiver operating characteristic (ROC) curve representing the performance of the predictive model using top 10 radiomic features to predict RECIST response with 10-fold cross-validation over 10 iterations. (B) ROC curve representing the performance of the predictive model using top 10 radiomic features to predict irRECIST response with 10-fold cross validation over 10 iterations.
See this image and copyright information in PMC

Comment on

References

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