Peptide spectra in Wilms tumor that associate with adverse outcomes

Abstract

Background

The 2013 Children's Oncology Group (COG) blueprint for renal tumor research challenges investigators to develop new, risk-specific biological therapies for unfavorable histology and higher-risk Wilms tumor (WT) in an effort to close a persistent survival gap and to reduce treatment toxicities. As an initial response to this call from the COG, we used imaging mass spectrometry to determine peptide profiles of WT associated with adverse outcomes.

Materials and methods

We created a WT tissue microarray containing 2-mm punches of formalin-fixed, paraffin-embedded specimens archived from 48 sequentially treated WT patients at our institutions. Imaging mass spectrometry was performed to compare peptide spectra between three patient groups as follows: unfavorable versus favorable histology, treatment success versus failure, and COG higher- versus lower-risk disease. Statistically significant peptide peaks differentiating groups were identified and incorporated into a predictive model using a genetic algorithm.

Results

One hundred thirty-one peptide peaks were differentially expressed in unfavorable versus favorable histology WT (P < 0.05). Two hundred three peaks differentiated treatment failure from success (P < 0.05). Seventy-one peaks differentiated COG higher-risk disease from the very-low, low, and standard-risk groups (P < 0.05). These peaks were used to develop predictive models that could differentiate among patient groups 98.49%, 94.46%, and 98.55% of the time, respectively. Spectral patterns were internally cross-validated using a leave-20% out model.

Conclusions

Peptide spectra can discriminate adverse behavior of WT. After future external validation and refinement, these models could be used to predict WT behavior and to stratify intensity of chemotherapy regimens. Furthermore, peptides discovered in the model could be sequenced to identify potential risk-specific drug targets.

Introduction

Wilms tumor (WT) is the most common childhood kidney cancer worldwide, with overall 5-y survival currently exceeding 90% in developed nations. Despite the remarkable successes in treatment achieved through large-scale cooperative trials conducted over the last 40 y, there remain patient populations who experience suboptimal outcomes. The most significant survival gap remains for patients whose tumors exhibit anaplasia (unfavorable histology), which is defined by nuclear gigantism, hyperchromasia, and bizarre multipolar mitoses [1]. Although anaplasia is found in only 6% of WT cases, it is associated with significant treatment resistance and accounts for 50% of WT deaths [2]. Therefore, the Children's Oncology Group (COG) 2013 blueprint for renal tumors calls on the scientific community to identify biomarkers that associate with treatment resistance and also to seek novel risk-specific therapies [3]. The current COG risk-stratification algorithm, which has been extensively validated and refined by decades of cooperative trials, now stratifies patients according to high-risk molecular features including loss of heterozygosity (LOH) at the chromosomal loci 1p and 16q. These genetic alterations are accurate surrogates for aggressive biologic behavior and are useful to assign more or less intensive therapies but currently are not themselves targetable moieties [2], [4].

In an effort to eventually identify cell-specific, potentially targetable molecular signatures of high-risk disease, we established a WT tissue microarray (TMA) containing specimens from 48 sequentially treated WT patients at our respective institutions. We hypothesized that three patient groups with disparate outcomes would have unique underlying peptide signatures as follows: unfavorable versus favorable histology, treatment failure (death or disease relapse) versus success, and COG higher-risk tumors versus very-low, low, and standard-risk tumors. Tissues were analyzed using matrix absorption laser desorption ionization time of flight imaging mass spectrometry (IMS) to derive a peptide signature that differentiates samples grouped according to adverse or standard-risk disease features.