One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial, and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effects of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of 100 independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), α‐lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation, but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design.

Breast cancer (BC) is the second most common type of cancer and a major cause of death for women. Commonly, BC patients are assigned to risk groups based on the combination of prognostic and prediction factors (eg, patient age, tumor size, tumor grade, hormone receptor status, etc). Although this approach is able to identify risk groups with different prognosis, patients are highly heterogeneous in their response to treatments. To improve the prediction of BC patients, we extended clinical models (including prognostic and prediction factors with whole-omic data) to integrate omics profiles for gene expression and copy number variants (CNVs). We describe a modeling framework that is able to incorporate clinical risk factors, high-dimensional omics profiles, and interactions between omics and non-omic factors (eg, treatment). We used the proposed modeling framework and data from METABRIC (Molecular Taxonomy of Breast Cancer Consortium) to assess the impact on the accuracy of BC patient survival predictions when omics and omic-by-treatment interactions are being considered. Our analysis shows that omics and omic-by-treatment interactions explain a sizable fraction of the variance on survival time that is not explained by commonly used clinical covariates. The sizable interaction effects observed, together with the increase in prediction accuracy, suggest that whole-omic profiles could be used to improve prognosis prediction among BC patients.

Glioblastoma multiforme (GBM) has been recognized as the most lethal type of malignant brain tumor. Despite efforts of the medical and research community, patients’ survival remains extremely low. Multi-omic profiles (including DNA sequence, methylation and gene expression) provide rich information about the tumor. These profiles are likely to reveal processes that may be predictive of patient survival. However, the integration of multi-omic profiles, which are high dimensional and heterogeneous in nature, poses great challenges. The goal of this work was to develop models for prediction of survival of GBM patients that can integrate clinical information and multi-omic profiles, using multi-layered Bayesian regressions. We apply the methodology to data from GBM patients from The Cancer Genome Atlas (TCGA, n = 501) to evaluate whether integrating multi-omic profiles (SNP-genotypes, methylation, copy number variants and gene expression) with clinical information (demographics as well as treatments) leads to an improved ability to predict patient survival. The proposed Bayesian models were used to estimate the proportion of variance explained by clinical covariates and omics and to evaluate prediction accuracy in cross validation (using the area under the Receiver Operating Characteristic curve, AUC). Among clinical and demographic covariates, age (AUC = 0.664) and the use of temozolomide (AUC = 0.606) were the most predictive of survival. Among omics, methylation (AUC = 0.623) and gene expression (AUC = 0.593) were more predictive than either SNP (AUC = 0.539) or CNV (AUC = 0.547). While there was a clear association between age and methylation, the integration of age, the use of temozolomide, and either gene expression or methylation led to a substantial increase in AUC in cross-validaton (AUC = 0.718). Finally, among the genes whose methylation was higher in aging brains, we observed a higher enrichment of these genes being also differentially methylated in cancer.

Despite recent advances in treatment, cancer continues to be one of the most lethal human maladies. One of the challenges of cancer treatment is the diversity among similar tumors that exhibit different clinical outcomes. Most of this variability comes from wide-spread molecular alterations that can be summarized by omic integration. Here, we have identified eight novel tumor groups (C1-8) via omic integration, characterized by unique cancer signatures and clinical characteristics. C3 had the best clinical outcomes, while C2 and C5 had poorest. C1, C7, and C8 were upregulated for cellular and mitochondrial translation, and relatively low proliferation. C6 and C4 were also downregulated for cellular and mitochondrial translation, and had high proliferation rates. C4 was represented by copy losses on chromosome 6, and had the highest number of metastatic samples. C8 was characterized by copy losses on chromosome 11, having also the lowest lymphocytic infiltration rate. C6 had the lowest natural killer infiltration rate and was represented by copy gains of genes in chromosome 11. C7 was represented by copy gains on chromosome 6, and had the highest upregulation in mitochondrial translation. We believe that, since molecularly alike tumors could respond similarly to treatment, our results could inform therapeutic action.

7Despite recent advances in treatment, cancer continues to be one of the most lethal human 8 maladies. One of the challenges of cancer treatment is the extreme diversity among seemingly 9 identical tumors: while some tumors may have good prognosis and are treatable, others are quite 10 aggressive, and may lack of effective therapies. Most of this variability comes from wide-spread 11 mutations and epigenetic alterations. Using a novel omic-integration method, we have exploited 12 this molecular information to re-classify tumors beyond the constraints of cell type. Eight novel 13 tumor groups (C1-8) emerged, characterized by unique cancer signatures. C3 had better prognosis, 14 genome stability, and immune infiltration. C2 and C5 had higher genome instability and poorer 15 clinical outcomes. Remaining clusters were characterized by worse outcomes, along with higher 16 genome instability. C1, C7, and C8 were upregulated for cellular and mitochondrial translation, 17 and relatively low proliferation. C6 and C4 were also downregulated for cellular and mitochondrial 18 translation, and had high proliferation rates. C4 was represented by copy losses on chromosome 19 6, and had the highest number of metastatic samples. C8 was characterized by copy losses on 20 chromosome 11, having also the lowest lymphocytic infiltration rate. C6 had the lowest natural 21 killer infiltration rate and was represented by copy gains of genes in chromosome 11. C7 was 22 represented by copy gains on chromosome 6, and had the highest upregulation in mitochondrial 23 translation. We believe that, since molecularly alike tumors could respond similarly to treatment, 24 our results could inform therapeutic action. 25 Significance 26 Cancer has been traditionally studied as a family of different diseases from different anatomical 27 sites. Nevertheless, regardless of the tissue of origin, cancer can be characterized by molecular 28 alterations on mechanisms controlling cell fate and progression. In this study, we integrate 33 29 cancer types and show the existence of eight clusters with unique genomic signatures and clinical 30 characteristics, beyond the site of origin of the tumor. The study and treatment of cancer, based on 31 predominant molecular features, rather than site of origin, can potentially aid in the discovery of 32 novel therapeutic alternatives.33