Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments

Posted: Wednesday, April 28, 2021

We are pleased to announce that WIN's article “Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival” has been published in npj Precision Oncology today.

Abstract

The expanding targeted therapy landscape requires combinatorial biomarkers for patient stratification and treatment selection. This requires simultaneous exploration of multiple genes of relevant networks to account for the complexity of mechanisms that govern drug sensitivity and predict clinical outcomes. We present the algorithm, Digital Display Precision Predictor (DDPP), aiming to identify transcriptomic predictors of treatment outcome. For example, 17 and 13 key genes were derived from the literature by their association with MTOR and angiogenesis pathways, respectively, and their expression in tumor versus normal tissues was associated with the progression-free survival (PFS) of patients treated with everolimus or axitinib (respectively) using DDPP. A specific eight-gene set best correlated with PFS in six patients treated with everolimus: AKT2, TSC1, FKB-12, TSC2, RPTOR, RHEB, PIK3CA, and PIK3CB (r = 0.99, p = 5.67E−05). A two-gene set best correlated with PFS in five patients treated with axitinib: KIT and KITLG (r = 0.99, p = 4.68E−04). Leave-one-out experiments demonstrated significant concordance between observed and DDPP-predicted PFS (r = 0.9, p = 0.015) for patients treated with everolimus. Notwithstanding the small cohort and pending further prospective validation, the prototype of DDPP offers the potential to transform patients’ treatment selection with a tumor- and treatment-agnostic predictor of outcomes (duration of PFS).

Introduction

The application of personalized medicine to oncology has resulted in prominent successes that have led to approved, molecularly specific, biomarker-defined indications for targeted therapies. As examples, the use of EGFR mutation/erlotinib1, KIT mutation/imatinib2, BRAF mutation/vemurafenib3, ALK translocation/crizotinib4, and high tumor mutation burden or microsatellite instability high/pembrolizumab5,6,7,8, have dramatically changed the treatment landscape in many cancers including, but not limited to melanoma, non-small cell lung carcinoma (NSCLC), colorectal carcinoma (CRC), and head and neck (HN) cancers.

However, despite the advent of personalized precision oncology, cancer remains one of the leading causes of deaths all over the world. Globally, 9.6 million deaths are attributed to cancer, representing 13% of all deaths9.

Extending the application of precision medicine requires a deeper understanding of tumor biology. Furthermore, improvement in the ability to select patients is needed, both with respect to identifying sensitive versus resistant tumors and in pinpointing patients at risk for severe toxicities.

With the number of validated drug targets increasing, testing each patient’s tumor for all markers related to all possible targeted therapies becomes infeasible due to the limited amount of tissue usually obtained by biopsies, pointing out the limitation of the classic companion diagnostic approach.

A comprehensive analysis of all relevant genes in a single assay would enable the exploration of all drug targets simultaneously to inform therapeutic options for patients. Furthermore, the complexity of cancer biology requires investigation of multiple genes in networks of pathways to understand the variability of clinical outcomes observed, that cannot be achieved by investigating single genes (as performed with most companion diagnostic tests).

We report the Digital Display Precision Predictor (DDPP), a biomarker strategy and tool, able to predict duration of progression-free survival (PFS) for multiple targeted treatments, based on the comprehensive investigation of the whole transcriptome. The DDPP prototype presented here was derived from analysis of transcriptomic and clinical outcomes in patients with advanced cancer enrolled in the WINTHER clinical trial and treated with: everolimus (mTOR inhibitor), axitinib (VEGFR receptors inhibitor), afatinib (pan-HER inhibitor), trametinib (MEK inhibitor), FGFR inhibitors, and anti-PD-1/PDL-1 antibodies (pembrolizumab, nivolumab and atezolizumab). The WINTHER trial (NCT 01856296) explored, for the first time in a prospective clinical trial, the use of differences in gene expression between tumor and analogous organ-matched normal tissues to guide treatment selection10. The trial demonstrated that transcriptomic analysis, based on tumor/normal tissue comparison, was feasible and increased, by about one third, the number of patients that could be matched to a targeted therapy as compared to genomic analysis alone.

https://www.nature.com/articles/s41698-021-00171-6

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