Pediatric/Congenital/DevelopmentalPeptide spectra in Wilms tumor that associate with adverse outcomes
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.
Section snippets
Selection of WT patient groups for comparison
We hypothesized that three patient-group comparisons could have underlying biologic differences associated with outcome discrepancies. The first comparison was conducted among six patients having unfavorable histology (diffuse anaplasia) and 39 patients having favorable histology tumors (Table). Two tumors containing focal anaplasia were excluded from this analysis. The second comparison was conducted between 12 patients who experienced treatment failure, defined as death or disease relapse,
Unfavorable versus favorable histology
To determine a molecular signature of unfavorable histology and therefore treatment resistance, we compared the peptide spectra obtained from the blastemal compartment of six tumors with unfavorable histology (defined as diffuse anaplasia) and 39 with favorable histology. Analysis of peptide spectra revealed 131 differentiating peptide peaks with P values <0.05. Principal component analysis was used to plot two peptide peaks (6612 and 1263 Da) that differentiated most between the favorable and
Discussion
This study is the first to use IMS to identify peptide spectra associated with adverse outcomes in WT. We sought spectra that reliably differentiated between unfavorable and favorable histology tumors, treatment failure (death or disease relapse) and success, and higher-risk and very-low, low, and standard-risk tumors. For each of these three comparisons, an internally validated model was created to assign spectra from a given tumor to one group or the other. For example, one model created in
Conclusions
This preliminary study identifies peptide spectra that differentiate WT based on COG risk-stratification criteria and adverse outcomes.
Acknowledgment
This work was supported by funding generously provided through the National Cancer Institute (5R00CA135695-05 [H.N.L. and J.P.] and T32CA106183 [A.J.M.]). The authors recognize the expertise within the Vanderbilt Translational Pathology Shared Resource for assistance with TMA development and histologic processing of all clinical specimens that comprise their laboratory tissue repository. This Shared Resource is supported by the Vanderbilt-Ingram Cancer Center (grant P30 CA68485). Mass
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2017, Biochimica et Biophysica Acta - Proteins and ProteomicsCitation Excerpt :However, a small percentage of patients exhibit significant treatment resistance and associated poor survival [77]. Murphy and coworkers analyzed tissue microarrays with samples from 48 Wilms tumors by MALDI IMS in order to identify molecular signatures associated with high-risk disease [78]. Using a genetic algorithm based classification model incorporating 15 m/z values 72.7% of the 12 patients with treatment failure and 91.5% of the 22 patients with treatment success could be identified correctly.
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