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Supplemental Table 4-6. Table S4: Detail for each sample of the PBRM1 and VHL-mutation status. NA stands for Not Available. Table S5: For each data type, clinical, transcriptome, methylome or SNP array, sensitity and specificity of the two predictors: PD vs (PR+SD) and PR vs (PD+SD) in the training and validation sets. The 'N()' columns indicate the numbers of each responder type. Table S6: List of the 27 genes firstly used by the classifier to predict the subtypes ccrcc2, ccrcc3 and the joint subtype ccrcc14 and list of the 8 genes used by the second classifier to assign the ccrcc14 samples to the two subtypes ccrcc1 and ccrcc4.

Supplemental Figure 7-10. Figure S7: Summary of the results obtained on the TCGA data; Figure S8: Survival analysis of the patients included in the TCGA according to our classification ccrcc 1 to 4; Figure S9: Illustration of the gradient assumption defined by subtype order

Purpose: Selecting patients with metastatic clear-cell renal cell carcinoma (m-ccRCC) who might benefit from treatment with targeted tyrosine kinase inhibitors (TKI) is a challenge. Our aim was to identify molecular markers associated with outcome in patients with m-ccRCC treated with sunitinib.Experimental Design: We performed global transcriptome analyses on 53 primary resected ccRCC tumors from patients who developed metastatic disease and were treated with first-line sunitinib. We also determined chromosome copy-number aberrations, methylation status, and gene mutations in von Hippel–Lindau and PBRM1. Molecular data were analyzed in relation with response rate (RR), progression-free survival (PFS), and overall survival (OS). Validation was performed in 47 additional ccRCC samples treated in first-line metastatic setting with sunitinib.Results: Unsupervised transcriptome analysis identified 4 robust ccRCC subtypes (ccrcc1 to 4) related to previous molecular classifications that were associated with different responses to sunitinib treatment. ccrcc1/ccrcc4 tumors had a lower RR (P = 0.005) and a shorter PFS and OS than ccrcc2/ccrcc3 tumors (P = 0.001 and 0.0003, respectively). These subtypes were the only significant covariate in the multivariate Cox model for PFS and OS (P = 0.017 and 0.006, respectively). ccrcc1/ccrcc4 tumors were characterized by a stem-cell polycomb signature and CpG hypermethylation, whereas ccrcc3 tumors, sensitive to sunitinib, did not exhibit cellular response to hypoxia. Moreover, ccrcc4 tumors exhibited sarcomatoid differentiation with a strong inflammatory, Th1-oriented but suppressive immune microenvironment, with high expression of PDCD1 (PD-1) and its ligands.Conclusions: ccRCC molecular subtypes are predictive of sunitinib response in metastatic patients, and could be used for personalized mRCC treatment with TKIs, demethylating or immunomodulatory drugs. Clin Cancer Res; 21(6); 1329–39. ©2015 AACR.

Supplemental Figure 1-3. Figure S1: Expression of immune modulators among the 4 molecular subgroups and normal Samples; Figure S2: Expression of cell-type specific metagenes among the different subgroups and normal samples; Figure S3: CD8+ cell infiltration according to the molecular ccRCC subtype classification.

Supplementary Figure and Table Legends. Legends of all supplemental Figures and Tables

Supplemental Figure 4-6. Figure S4: Characterisation of the ccRCC molecular subtypes: methylation status and Benporath polycomb signature; Figure S5: Cytogenetic characterization of the ccRCC molecular subtypes; Figure S6: Characterization of the ccRCC molecular subtypes: Myc methylation and (target) amplification

Supplemental Table 7-10. Table S7: Area under the ROC curve or c-index for the predictive and prognostic markers presented in Table 1 and Figures 3A, 3B and 3C. Table S8: P-values of the pathway enrichment analysis for the genes differentially expressed, the genes differentially methylated and the genes differentially methylated and anti-correlated to expression data in the four unsupervised subgroups. Table S9: List of the recurrent chromosomal aberrations characterizing ccrcc4-samples with a sensitivity and a specificity greater than 0.65. Table S10: Correlation of the centroids of our classification in four subtypes with the Brannon classification in three subgroups ccA, ccB and cluster_3 and the subtypes predicted in the TCGA cohort

This research identifies key variables that influence fuel consumption that might be improved through eco-driving training programs under three circumstances that have been scarcely studied before: (a) heavy- and medium-duty truck fleets, (b) long-distance freight transport, and (c) the Latin American region. Based on statistical analyses that include multivariate regression of operational variables on fuel consumption, the impacts of an eco-driving training campaign were measured by comparing ex ante and ex post data. Operational variables are grouped into driving errors, trip conditions, driver behavior, driver profile, and vehicle attributes. The methodology is applied in a freight fleet with nationwide transport operations located in Colombia, where the steepness of its roads plays an important role in fuel consumption. The fleet, composed of 18 trucks, is equipped with state-of-the-art real-time data logger systems. During four months, 517 trips traveling a total distance of 292,512 km and carrying a total of 10,034 tons were analyzed. The results show a baseline average fuel consumption (FC) of 1.716 liters per ton-100 km. A different logistics performance indicator, which measures FC in liters per ton transported each 100 km, shows an average of 3.115. After the eco-driving campaign, reductions of 6.8% and 5.5% were obtained. Drivers’ experience, driving errors, average speed, and weight-capacity ratio, among others, were found to be highly relevant to FC. In particular, driving errors such as acceleration, braking and speed excesses are the most sensitive to eco-driving training, showing reductions of up to 96% on the average number of events per trip.

Tertiary lymphoid structures (TLS) form at sites of chronic inflammation, and locally prime the immune system independently of secondary lymphoid organs. The intratumoral presence of TLS has been associated with a positive prognostic outcome for patients with solid tumors, including colorectal carcinoma. Thus, the therapeutic induction of TLS would be hypothesized to confer clinical benefit to patients. Indeed, the therapeutic introduction of the IL-1 family cytokine IL-36 gamma into the tumor microenvironment (TME) of murine MC38 colon carcinoma leads to delayed tumor progression and the development of TLS at 5 days post-treatment. The TLS present with characteristics of secondary lymphoid organs, including PNAd+ high endothelial venules (HEV) surrounded by CD3+ T cells and CD11c+ dendritic cells, but are considered as “non-classical” due to the absence of a defined B cell zone. The formation of TLS is dependent upon the adaptive immune response, as IL-36 gamma-treated tumors grown in Rag2−/− animals fail to present with PNAd+ vasculature within the tumor. Thus, crosstalk between the immune system and cells of the vasculature is key to the efficacy of the IL-36 gamma-induced anti-tumor immune response. In human colorectal carcinoma, IL-36 gamma was also observed to promote the local anti-tumor immune response. Expression of IL-36 gamma by vascular endothelial cells of HEV was associated with an increased density of CD20+ B cells in TLS. Specific IL-36 gamma production by CD68+ macrophages was associated with an increased infiltration of CD4+ central memory T cells into the TME. Therefore, IL-36 gamma is a pro-inflammatory mediator involved in the anti-tumor immune response in both murine and human colorectal cancer.

We describe the use of a gas bubbler apparatus in which the gas phase is bubbled into a fixed amount of absorbent under standard conditions as a uniform procedure for determining the absorption capacity of solvents. The method was systematically applied to determine the CO2absorbing capacity of MEA (Ac) at several aqueous MEA (β) and gas-phase CO2concentrations.Acapproached the nominal CO2absorbing capacity of MEA (720 g CO2/kg MEA) at very lowβlevels, increasing from447.9±18.1to581.3±32.3 g CO2/kg MEA asβwas reduced from 30 to 2.5% (w/w).Acdid not depend on the CO2concentration in the inlet gas stream as long as the gas stream did not include other amine sensitive components. During the bubbling tests the outlet CO2concentration profiles exhibited a sigmoidal shape that could be described by an exponential equation characterized by an efficiency factor (a) and a form factor (n). Statistical analysis based on correlation analysis indicated that in all cases the experimental data fit the equation well when a was6.1±0.35andnwas2.5±0.12. The results of these experiments may be used to optimize scrubber designs for CO2sequestration from fossil fuel derived flue gases.

Background Lymphocytes achieve diversity in antigen recognition in part by rearranging genomic DNA at loci encoding antibodies and cell surface receptors. The process, termed V(D)J recombination, juxtaposes modular coding sequences for antigen binding. Erroneous recombination events causing chromosomal translocations are recognized causes of lymphoid malignancies. Here we show a hybridization based method for sequence enrichment can be used to efficiently and selectively capture genomic DNA adjacent to V(D)J recombination breakpoints for massively parallel sequencing. The approach obviates the need for PCR amplification of recombined sequences. Results Using tailored informatics analyses to resolve alignment and assembly issues in these repetitive regions, we were able to detect numerous recombination events across a panel of cancer cell lines and primary lymphoid tumors, and an EBV transformed lymphoblast line. With reassembly, breakpoints could be defined to single base pair resolution. The observed events consist of canonical V(D)J or V-J rearrangements, non-canonical rearrangements, and putatively oncogenic reciprocal chromosome translocations. We validated non-canonical and chromosome translocation junctions by PCR and Sanger sequencing. The translocations involved the MYC and BCL-2 loci, and activation of these was consistent with histopathologic features of the respective B-cell tumors. We also show an impressive prevalence of novel erroneous V-V recombination events at sites not incorporated with other downstream coding segments. Conclusions Our results demonstrate the ability of next generation sequencing to describe human V(D)J recombinase activity and provide a scalable means to chronicle off-target, unexpressed, and non-amplifiable recombinations occurring in the development of lymphoid cancers.

Background: Trypanosoma cruzi persistence has been associated with cardiac and gastrointestinal tissue damage in nearly 30% of the infected individuals; however, the pathogenic mechanisms are yet unknown. This study’s goal was to compare the activation status and proliferative capacity of T lymphocytes among chronic chagasic patients and uninfected controls. Methodology: Twenty-seven chronic chagasic patients, 20 healthy individuals and 28 non-chagasic cardiomyopathy donors were analyzed. Peripheral blood cells were stained with CD3, CD4, CD8, CD28, HLA-DR and CD38. PBMCs were labeled with CFSE and co-cultivated with PHA or T. cruzi lysate; at the fifth day post-stimulation cells were stained for CD3, CD4 and CD8. Results: Chagasic patients displayed higher frequencies of CD4+ (P=0.0001) and CD8+ (P=0.0002) T cells co-expressing HLA-DR and CD38 in comparison with healthy and non-chagasic cardiomyopathy donors. Also, the former group exhibited lower percentages of CD8+/CD28+ T lymphocytes (P=0.0005). After 5 days of stimulation, the proliferation index was lower in both CD4+ (P=0.01) and CD8+ (P=0.04) PHA-stimulated T cells from chagasic donors when compared with both control groups. Conclusions: Despite their increased activation status, the T lymphocytes from chagasic donors displayed reduced proliferative capacity after mitogenic stimulation, corroborating a dysfunctional cellular immune response in the chronic stages of the disease.