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

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

doi:10.1038/s41467-018-03631-z

Abstract

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.

Highlights

Assessing the phenotypic diversity underlying tumour progression requires the identification of variations in the respective molecular interaction networks
To reflect bona fide clinical practice, we designed our study retrospectively by using patient samples routinely submitted to certified central laboratories
time to next therapy (TTNT) is defined as the time that elapsed between the initiation of trastuzumab-based therapy and the initiation of a subsequent line of therapy[26]

Summary

Introduction

Assessing the phenotypic diversity underlying tumour progression requires the identification of variations in the respective molecular interaction networks. Precision oncology emanates from the premise that clinical outcomes will be improved by the selection of treatment regimens that act on specific molecular targets identified by multi-Omics profiling This premise has been validated in several clinical studies[1,2,3] and provides a rationale for ongoing trials, such as TAPUR (NCT02693535), there are still troubling examples of discordance in which patients positive for the deemed target(s) for drug action fail to respond and patients presumably lacking the target(s) respond[4,5]. Profiling this level of network diversity requires an unbiased, hypothesis-free approach that must employ an equal or greater number of potential detector molecules[22] Towards this goal, here we use tissue-SELEX on formalin-fixed paraffin-embedded (FFPE) tumour tissue samples from breast cancer patients to enrich libraries of single-stranded DNA (ssDNA) that utilize Watson–Crick base pairing, sequencespecific protein binding or aptameric binding similar to antibodies for interacting with molecular targets and protein complexes in the sample. PLP may increase success rates in precision oncology and of pivotal drug trials in the future and facilitate both drug development and patient care

Methods

Results

Conclusion