Supplementary Figure S1 A B 7 Non Responders 4 Responders Figure S1: Extraction of paraffin-embedded PD-L1+ RCC tissues for RNA isolation. Brown staining indicates PD-L1 protein expression (IHC) in tumor foci. In (A), blue circles outline macroscopic tumor areas that were excised by manual scraping with a scalpel. In (B), focal areas of PD-L1+ tissue outlined with blue lines were excised by laser capture microdissection (LCM). Scale bars are equal to 500 um. Supplementary Figure S2 4 Relative Score p=0.36 p=0.81 p=0.32 p=0.67 p=0.46 3 2 1
0 R NR PD-1 R NR PD-L2 R NR LAG-3 R NR TIM-3 R NR Immune cell infiltrates Figure S2 legend: Molecules previously found to be up-regulated in PD-L1+ vs. PD-L1(-) melanomas are not differentially expressed in PD-L1+ RCCs from patients with divergent clinical outcomes after anti-PD-1 therapy. Expression of molecules previously found to be
associated with PD-L1 expression in melanoma (Taube et al., Clin Cancer Res 2015), and other candidate markers, were assessed by IHC in 13 PDL1+ RCC specimens, derived from 4 patients who responded to anti-PD-1 and 9 who did not. Specimens were scored for protein expression on the following scale: 0, absent expression; 1, focal expression, <5% of cells positive; 2, moderate expression, 5-50% of cells positive; 3, severe expression, >50% of cells positive. Horizontal bars indicate mean values. No significant differences were observed between responders (R) and non-responders (NR), using a two-sided Wilcoxon rank sum test. In data not shown, there were also no significant differences in FoxP3 expression or in CD4:CD8 ratios between the two groups. Gene target Supplementary Figure S3 GUSB AKR1C3 BACH2 BMP1 CACNB1 CCL3 CD24 E2F8 ENPP5 F2RL1 IL11RA KCNJ16 LTBP1 MAL MYLK2 NFATC1 PITX2 PLEC SLC23A1 SLC37A4
TNFRSF19 UCP3 UGT1A1 UGT1A3 UGT1A6 WHSC1 0 15 20 25 30 Cycle threshold 35 40 Figure S3 legend: Gene expression in RCC cell lines. The expression of 25 genes found to be differentially expressed in RCCs from anti-PD-1 responders vs. non-responders was assessed by qRT-PCR in 8 established RCC cell lines (786-0, A498, ACHN, Caki-1, RXF-393, TK-10, SN12C, and U0-31). The expression of each gene target in an individual cell line is shown as the average cycle threshold (Ct) value from triplicate reactions. Lower Ct values indicate higher gene expression. In cases of undetectable gene amplification, a Ct value of 40 was assigned (the maximum number of PCR cycles used). Vertical black bars indicate mean Ct values. The GUSB transcript was used as the internal reference. Results were visualized using GraphPad software (La Jolla, CA). RCC all stages (n=444)
FDR=0.78 0.6 High Low 0.2 B 0.0 RCC stage IV (n=71) 1.0 FDR=0.28 0.8 16 FDR =0.78 14 12 10 8 6 4 0.6 2 0.4 (n =7
1 Proportion surviving 0.4 ) 0.8 Supplementary Figure S4 expression 1.0 Relative No rm St al ( n= ag 72 e ) I (n St ag =2 1 St e II 4) ag (n =4 e 3)
I St II ( ag n= 11 e 6) IV A 0.2 0.0 Days after diagnosis Figure S4 legend: In silico analyses of TCGA RCC data do not demonstrate significant associations between UGT1A6 gene expression and overall survival or clinical stage. (A) Association of UGT1A6 expression with overall survival was assayed in silico in The Cancer Genome Atlas RCC database. A Cox regression model was used with continuous expression values of UGT1A6 in the whole patient dataset (N=444, upper panel), or only in patients with stage IV disease (n=71, lower panel). All p-values were adjusted by the Benjamini-Hochberg procedure (FDR, false discovery rate). KaplanMeier curves were generated using the median expression level to segregate samples into two groups, high and low UGT1A6 expressers.(B) The potential association of UGT1A6 mRNA expression levels in primary kidney specimens with clinical tumor stage was evaluated by fitting in a linear model using continuous expression levels of UGT1A6, and tumor stage (normal, or tumor stage I-IV) as a numeric value. The p-value adjusted by the Benjamini-Hochberg procedure (FDR, false discovery rate) is shown.
1) When Sir Philip Morton inherited his grandfather's land in Cumberland, he created two problems for the people of this area. Explain. 2) Peter, the main character, makes a very serious mistake at the end of chapter 1.
image or video clip. personal stories or anecdotes . Ask questions - involves, establishes rapport, helps support your arguments, gain feedback. Watch and learn from other presenters - replicate what they do well
IEEE and a well known semi-conductor company are developing a business partnership focused on developing an eLearning module to be part of the continuing education and training in wafer fabrication. The goal is to develop common education and training in...
A Strategy for Meeting the Challenges We Face Hammett Relationships pKa of benzoic acids Effect of electron withdrawing and donating groups based on rG = - RT ln Keq pKa Substituted Benzoic Acids log Ka - log KaH = K...
They introduce subject content progressively and constantly demand more of pupils. Teachers identify and support any pupil who is falling behind, and enable almost all to catch up. Classroom management (no preferred style). Teachers plan lessons very effectively, making maximum...
Susan Rea Welch, Ph.D., B.S.N., Informatics Research Fellow, Intermountain Healthcare ... Not Solved by CEM to Algorithm Data Mapping. Specific EHR structure/content used . Selection criteria of cases for input. ... Clinical "compound" model.
Convert the AC test data to corresponding phase values for a wye-connected motor. Determine R1 Determine R2 Determination of X1 and X2 From Table 5.10, for a design B machine, X1 = 0.4XBR,60 = 0.4(1.0182) = 0.4073Ω/phase X2 = 0.6XBR,60...
Ready to download the document? Go ahead and hit continue!