Genomic and experimental evidence that ALKATI does not predict single agent sensitivity to ALK inhibitors
Haider Inam,
Ivan Sokirniy,
Yiyun Rao,
Anushka Shah,
Farnaz Naeemikia,
Edward O'Brien,
Cheng Dong,
David M. McCandlish,
Justin R. Pritchard
Affiliations
Haider Inam
Department of Biomedical Engineering, 211 Wartik Lab, The Pennsylvania State University, University Park, PA 16802, USA
Ivan Sokirniy
The Huck Institute for the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
Yiyun Rao
The Huck Institute for the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
Anushka Shah
Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
Farnaz Naeemikia
Department of Biomedical Engineering, 211 Wartik Lab, The Pennsylvania State University, University Park, PA 16802, USA
Edward O'Brien
Department of Chemistry, The Pennsylvania State University, University Park, PA 16802, USA
Cheng Dong
Department of Biomedical Engineering, 211 Wartik Lab, The Pennsylvania State University, University Park, PA 16802, USA
David M. McCandlish
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
Justin R. Pritchard
Department of Biomedical Engineering, 211 Wartik Lab, The Pennsylvania State University, University Park, PA 16802, USA; The Huck Institute for the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA; Corresponding author
Summary: Genomic data can facilitate personalized treatment decisions by enabling therapeutic hypotheses in individual patients. Mutual exclusivity has been an empirically useful signal for identifying activating mutations that respond to single agent targeted therapies. However, a low mutation frequency can underpower this signal for rare variants. We develop a resampling based method for the direct pairwise comparison of conditional selection between sets of gene pairs. We apply this method to a transcript variant of anaplastic lymphoma kinase (ALK) in melanoma, termed ALKATI that was suggested to predict sensitivity to ALK inhibitors and we find that it is not mutually exclusive with key melanoma oncogenes. Furthermore, we find that ALKATI is not likely to be sufficient for cellular transformation or growth, and it does not predict single agent therapeutic dependency. Our work strongly disfavors the role of ALKATI as a targetable oncogenic driver that might be sensitive to single agent ALK treatment.
