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Poly-ligand profiling differentiates trastuzumabtreated breast cancer patients according to their outcomes

Authors:

Valeriy Domenyuk, Zoran Gatalica, Radhika Santhanam, Xixi Wei, Adam Stark, Patrick Kennedy, Brandon Toussaint, Symon Levenberg, Jie Wang, Nianqing Xiao, Richard Greil, Gabriel Rinnerthaler, Simon P. Gampenrieder, Amy B. Heimberger, Donald A. Berry, Anna Barker, John Quackenbush, John L. Marshall, George Poste, Jeffrey L. Vacirca, Gregory A. Vidal, Lee S. Schwartzberg, David D. Halbert, Andreas Voss, Daniel Magee, Mark R. Miglarese, Michael Famulok, Günter Mayer & David Spetzler

Assessing the phenotypic diversity underlying tumour progression requires the identification of variations in the respective molecular interaction networks. Here we report proof-of-concept for a platform called poly-ligand profiling (PLP) that surveys these system states and distinguishes breast cancer patients who did or did not derive benefit from trastuzumab. We perform tissue-SELEX on breast cancer specimens to enrich single-stranded DNA (ssDNA) libraries that preferentially interact with molecular components associated with the two clinical phenotypes. Testing of independent sample sets verifies the ability of PLP to classify trastuzumab-treated patients according to their clinical outcomes with ROC-AUC of 0.78. Standard HER2 testing of the same patients gives a ROC-AUC of 0.47. Kaplan–Meier analysis reveals a median increase in benefit from trastuzumab-containing treatments of 300 days for PLP-positive compared to PLP-negative patients. If prospectively validated, PLP may increase success rates in precision oncology and clinical trials, thus improving both patient care and drug development.

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