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Supplementary Table S3. Related to Figure 3. Gene ontology for genes with higher expression in patient tumors vs. PDOXs. Supplementary Table S4. Related to Figure 3. List of the gene clusters defined across PDOXs #1-5 obtained from serial orthotopic passaging. Supplementary Table S5. Related to Figure 3. List of enriched Gene Ontology terms by cluster defined in PDOXs #1-5.

Supplementary Table S8. Related to Figure 5. List of top varying peptides based on SD across all samples derived from Patient/PDOX #5. Supplementary Table S9. Related to Figure 5. List of top varying phospho-peptides based on SD across all samples derived from Patient/PDOX #5. Supplementary Table S10. Related to Figure 5. List of enriched Gene Ontology terms by cluster derived from the protein clusters defined in Suppl. Table S8. Supplementary Table S11. Related to Figure 5. List of enriched Gene Ontology terms by cluster derived from the phospho-protein clusters defined in Suppl. Table S9. Supplementary Table S12. Related to Figure 5. List of differentially expressed proteins from group 1-3 of PDOX #5. Supplementary Table S13. Related to Figure 5. List of differentially expressed phosphorylated proteins from group 1-3 of PDOX #5. Supplementary Table S14. Related to Figure 5. List of peptides measured in the targeted PRM assay.

Supplementary Figure S2. Related to Figure 4. Intratumor heterogeneity is less prominent than intertumor heterogeneity.

Supplementary Table S6. Related to Figure 4. Top 1000 varying genes based on SD across all samples derived from Patient/PDOX #5. Supplementary Table S7. Related to Figure 4. List of enriched Gene Ontology terms by cluster derived from the gene clusters defined in Suppl. Table S6.

Supplementary Table S1. Related to Figure 1. List of all somatic mutations detected in patient tumors, and in PDOXs from low and high in vivo generations. Supplementary Table S2. Related to Figure 2. List of all chromosomal copy number changes in patient tumors, and in PDOXs from low and high in vivo generations.

Patient-derived xenografts (PDX) and the Avatar, a single PDX mirroring an individual patient, are emerging tools in preclinical cancer research. However, the consequences of intratumor heterogeneity for PDX modeling of biomarkers, target identification, and treatment decisions remain underexplored. In this study, we undertook serial passaging and comprehensive molecular analysis of neuroblastoma orthotopic PDXs, which revealed strong intrinsic genetic, transcriptional, and phenotypic stability for more than 2 years. The PDXs showed preserved neuroblastoma-associated gene signatures that correlated with poor clinical outcome in a large cohort of patients with neuroblastoma. Furthermore, we captured spatial intratumor heterogeneity using ten PDXs from a single high-risk patient tumor. We observed diverse growth rates, transcriptional, proteomic, and phosphoproteomic profiles. PDX-derived transcriptional profiles were associated with diverse clinical characteristics in patients with high-risk neuroblastoma. These data suggest that high-risk neuroblastoma contains elements of both temporal stability and spatial intratumor heterogeneity, the latter of which complicates clinical translation of personalized PDX–Avatar studies into preclinical cancer research.Significance: These findings underpin the complexity of PDX modeling as a means to advance translational applications against neuroblastoma. Cancer Res; 78(20); 5958–69. ©2018 AACR.

Supplementary methods and legends to supplementary figures and tables

Bull fertility is an important trait in breeding as the semen of one bull can, potentially, be used to perform thousands of inseminations. The high number of inseminations needed to obtain reliable measures from Non-Return Rates to oestrus creates difficulties in assessing fertility accurately. Improving molecular knowledge of seminal properties may provide ways to facilitate selection of bulls with good semen quality. In this study, liquid chromatography mass spectrometry (LC-MS/MS) was used to analyze the protein content from the seminal plasma of 20 bulls with Non-Return Rates between 35 and 60%, sampled across three seasons. Overall, 1343 proteins were identified and proteins with consistent correlation to fertility across multiple seasons found. From these, nine protein groups had a significant Pearson correlation (p < 0.1) with fertility in all three seasons and 34 protein groups had a similar correlation in at least two seasons. Among notable proteins showing a high and consistent correlation across seasons were Osteopontin, a lipase (LIPA) and N-acetylglucosamine-1phosphotransferase subunit gamma. Three proteins were combined in a multiple linear regression to predict fertility (r = 0.81). These sets of proteins represent potential markers, which could be used by the breeding industry to phenotype bull fertility. SIGNIFICANCE: The ability of bull spermatozoa to fertilize oocytes is crucial for breeding efficiency. However, the reliability of this trait from field measures is relatively low and the prediction of fertility given by conventional methods to evaluate sperm quality is currently not very accurate. In this work, we identify sets of proteins in bull seminal plasma from repeated samples collected at different times of the year that correlate to fertility in a consistent way. We combined these individual proteins to build a molecular signature predictive of fertility. This study provides an overview of proteins linked to fertility in seminal plasma, thereby increasing knowledge of the bull seminal plasma proteome. Protein signatures from the latter, potentially related to fertility, may be of use to predict fertility for individual bulls.

Technical biases are introduced in omics data sets during data generation and interfere with the ability to study biological mechanisms. Several normalization approaches have been proposed to minimize the effects of such biases, but fluctuations in the electrospray current during liquid chromatography-mass spectrometry gradients cause local and sample-specific bias not considered by most approaches. Here we introduce a software named NormalyzerDE that includes a generic retention time (RT)-segmented approach compatible with a wide range of global normalization approaches to reduce the effects of time-resolved bias. The software offers straightforward access to multiple normalization methods, allows for data set evaluation and normalization quality assessment as well as subsequent or independent differential expression analysis using the empirical Bayes Limma approach. When evaluated on two spike-in data sets the RT-segmented approaches outperformed conventional approaches by detecting more peptides (8-36%) without loss of precision. Furthermore, differential expression analysis using the Limma approach consistently increased recall (2-35%) compared to analysis of variance. The combination of RT-normalization and Limma was in one case able to distinguish 108% (2597 vs 1249) more spike-in peptides compared to traditional approaches. NormalyzerDE provides widely usable tools for performing normalization and evaluating the outcome and makes calculation of subsequent differential expression statistics straightforward. The program is available as a web server at http://quantitativeproteomics.org/normalyzerde .

Potato cyst nematodes (PCN) are important pests in crop production, especially since they persist in the soil and may affect further potato production for many years. Little is known about putative resistance and susceptibility targets as well as the general signaling in potato after interactions with PCN. Here we characterize a new potato breeding clone, SW-1015, found to harbor resistance to Globodera rostochiensis pathotype Ro1/4, the main PCN pathotype present in Sweden. SW-1015 contains the H1 resistance gene. We then describe susceptible and resistant reactions of potato infested by G. rostochiensis Ro1/4 in a global potato RNA-seq analysis. Only the resistant clone reacted to PCN infection quickly (8 hpi), and the reaction included up-regulation of a TSRF1 transcription factor. 48 h after PCN infection, massive RNA reprograming was evident in both resistant and susceptible clones. In the resistant interaction, several genes were up-regulated including germins and a cysteine protease, as well as a laccase. In contrast, the susceptible interaction involved up-regulation of genes for auxin transport and homeobox binding. Enriched GO terms for kinase activity, calmodulin, and Ca2+ ion binding in susceptible potato might reflect the initiation of nematode feeding structures. A TIR receptor like protein member was induced in the susceptible interaction only, making this a putative susceptibility factor. The RNA data is deposited at ArrayExpress with the number E-MTAB-5215.

Fusarium species are cereal pathogens that cause the Fusarium Head Blight (FHB) disease. FHB can reduce yield, cause mycotoxin accumulation in the grain and reduce germination efficiency of the harvested seeds. Understanding the biochemical interactions between the host plants and the pathogen is crucial for controlling the disease and for the development of cultivars with improved tolerance to FHB. Here, we studied morphological and proteomic differences between the susceptible oat variety Belinda and the more resistant variety Argamak using variety-specific transcriptome assemblies as references. Measurements of deoxynivalenol toxin levels confirmed the partial resistance in Argamak and the susceptibility in Belinda. To jointly investigate the proteomics- and sequence data, we developed an RShiny-based interface for interactive exploration of the dataset using univariate and multivariate statistics. When applying this interface to the dataset, quantitative protein differences between Belinda and Argamak were detected, and eighteen peptides were found uniquely in Argamak during infection, among them several lipoxygenases. Such proteins can be developed as markers for Fusarium resistance breeding. In conclusion, this study provides the first proteogenomic insight on molecular Fusarium-oat interactions at both morphological and molecular levels and the data are openly available through an interactive interface for further inspection. SIGNIFICANCE: Fusarium head blight causes widespread damage to crops, and chronic and acute toxicity to human and livestock due to the accumulation of toxins during infection. In the present study, two oat varieties with differing resistance were challenged with Fusarium to understand the disease better, and studied both at morphological and molecular levels, identifying proteins which could play a role in the defense mechanism. Furthermore, a proteogenomics approach allows joint profiling of expression and sequence level differences to identify potentially functionally differing mutations. Here such analysis is made openly available through an interactive interface which allows other scientists to draw further findings from the data. This study may both serve as a basis for understanding oat disease response and developing breeding markers for Fusarium resistant oat and future proteogenomic studies using the interactive approach described.

Patient-derived xenografts (PDX) and the Avatar, a single PDX mirroring an individual patient, are emerging tools in preclinical cancer research. However, the consequences of intratumor heterogeneity for PDX modeling of biomarkers, target identification, and treatment decisions remain under-explored. In this study, we undertook serial passaging and comprehensive molecular analysis of neuroblastoma orthotopic PDXs, which revealed strong intrinsic genetic, transcriptional, and phenotypic stability for more than 2 years. The PDXs showed preserved neuroblastoma-associated gene signatures that correlated with poor clinical outcome in a large cohort of patients with neuroblastoma. Furthermore, we captured spatial intratumor heterogeneity using ten PDXs from a single high-risk patient tumor. We observed diverse growth rates, transcriptional, proteomic, and phosphoproteomic profiles. PDX-derived transcriptional profiles were associated with diverse clinical characteristics in patients with high-risk neuroblastoma. These data suggest that high-risk neuroblastoma contains elements of both temporal stability and spatial intratumor heterogeneity, the latter of which complicates clinical translation of personalized PDX-Avatar studies into preclinical cancer research. Significance: These findings underpin the complexity of PDX modeling as a means to advance translational applications against neuroblastoma. (C) 2018 AACR.