Background: PHD2 is the central enzyme that controls hypoxia-inducible factor-␣ (HIF-␣) protein levels. Results: PHD2 binds a Pro-Xaa-Leu-Glu motif in two HSP90 co-chaperones, and knockdown of one of these, p23, augments hypoxia-induced HIF-1␣ protein levels. Conclusion: PHD2 is linked to the HSP90 pathway, facilitating its hydroxylation of HIF-1␣. Significance: This uncovers a new model by which PHD2 controls HIF-1␣.

Background: Tibetans have a genetic signature in the coding region of their PHD2 gene. Results: Tibetan PHD2 variant displays markedly impaired binding to the HSP90 cochaperone p23. Conclusion: Because p23 couples PHD2 to HIF-␣ hydroxylation, Tibetans possess a loss of function PHD2 allele. Significance: This study uncovers a mechanism for Tibetan adaptation to high altitude.

Prolyl hydroxylation is a post-translational modification (PTM) that plays an important role in the formation of collagen fibrils and in the oxygen-dependent regulation of Hypoxia Inducible Factor-α (HIF-α). While this modification has been well characterized in the context of these proteins, it remains unclear to what extent it occurs in the remaining mammalian proteome. We explored this question using mass spectrometry to analyze cellular extracts subjected to various fractionation strategies. In one strategy, we employed the von Hippel Lindau tumor suppressor protein (VHL), which recognizes prolyl hydroxylated HIF-α, as a scaffold for generating hydroxyproline capture reagents. We report novel sites of prolyl hydroxylation within five proteins: FK506-binding protein 10 (FKBP10), Myosin heavy chain 10 (MYH10), Hexokinase 2 (HK2), Pyruvate Kinase (PKM), and C-1 Tetrahydrofolate synthase (MTHFD1). Furthermore, we show that identification of prolyl hydroxylation presents a significant technical challenge owing to widespread isobaric methionine oxidation, and that manual inspection of spectra of modified peptides in this context is critical for validation.

A subset of patients with neuroblastoma are at extremely high risk for treatment failure, though they are not identifiable at diagnosis and therefore have the highest mortality with conventional treatment approaches. Despite tremendous understanding of clinical and biological features that correlate with prognosis, neuroblastoma at ultra-high risk for treatment failure remains a diagnostic challenge. As a first step towards improving prognostic risk stratification within the high-risk group of patients, we determined the feasibility of using computerized image analysis and proteomic profiling on single slides from diagnostic tissue specimens. After expert pathologist review of tumor sections to ensure quality and representative material input, we evaluated multiple regions of single slides as well as multiple sections from different patients' tumors using computational histologic analysis and semiquantitative proteomic profiling. We found that both approaches determined that intertumor heterogeneity was greater than intratumor heterogeneity. Unbiased clustering of samples was greatest within a tumor, suggesting a single section can be representative of the tumor as a whole. There is expected heterogeneity between tumor samples from different individuals with a high degree of similarity among specimens derived from the same patient. Both techniques are novel to supplement pathologist review of neuroblastoma for refined risk stratification, particularly since we demonstrate these results using only a single slide derived from what is usually a scarce tissue resource. Due to limitations of traditional approaches for upfront stratification, integration of new modalities with data derived from one section of tumor hold promise as tools to improve outcomes.

Introduction: Tumor heterogeneity at a histological and molecular level has been previously described in neuroblastoma (NB) and therefore concern exists about how representative the molecular profile of a single tissue section can be of an entire tumor. We hypothesized that a single tissue section would provide a representative proteomic signature of the whole tumor. Methods: As part of our effort to assess differential protein expression patterns from two maximally divergent groups with high-risk NB (i.e.: patients with early death from disease versus long-term survivors), we sampled multiple non-adjacent sections from blocks of 5 tumors within our larger cohort of 58 single section tumors. De-identified samples were obtained from the Children's Oncology Group biorepository. Using 5-10μ thick formalin fixed paraffin embedded tumor sections on a glass slide, we extracted 1 microgram of protein, performed tryptic digestion and desalting, and loaded peptides onto a reverse-phase column for separation by nanoflow chromatography. We performed peptide tandem mass spectrometry and protein expression vectors were identified using MaxQuant. The 1,495 most abundant proteins were evaluated across samples. Data were normalized and imputed. Hierarchical clustering analysis was performed using the pvclust R package between subjects with multiple sections and between all samples. We used average clustering criteria, correlation methods for distance, and pairwise covariance measures. Confidence of clusters was measured using approximately unbiased and bootstrap probability (1000 permutations) methods. Results: Intra-tumor distance was significantly shorter than the inter-tumor distance. When testing only subjects with multiple tumor samples, 4 out of 5 samples from the same subject clustered with >95% confidence. The samples from the remaining subject have a confidence >80% but seem to have differing rates of missing data, suggesting experimental errors. When using all samples available, tumor samples from the same subject still clustered together and had a greater correlation than tumor samples from different subjects. A validation cohort of 7 distinct sections from separate blocks of tumors of 3 high-risk patients confirmed that tumors cluster by protein expression. Conclusion and Future Directions: Intra-tumor heterogeneity exists but is significantly less than inter-tumor heterogeneity as assessed by proteomic profiling. We provide rationale to use single tissue section proteomics in ongoing research to define biologic drivers of primary refractory NB, a clinically important subgroup of patients who have highly lethal disease. Citation Format: Raquel Castellanos, Katherine Heaton-Johnson, Jonathan Chung, Edward Nieves, Michael Fremed, Stephen R. Master, Daniel Weiser. Limited intra-tumor versus inter-tumor heterogeneity as assessed by proteomic profiling of high-risk neuroblastoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3290. doi:10.1158/1538-7445.AM2015-3290