аЯрЁБс > ўџ Ь Ю ўџџџ Ш Щ Ъ Ы џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџьЅС я №П Z4 bjbjПCПC 4B н) н) Z, џџ џџ џџ З ј ј \ \ \ \ \ џџџџ p p p p $ p э d Ј p l n n n n n n $ Q Ж " \ \ J \ \ Ї J J J ж \ \ т J l J J J џџџџ АЭuєЌг џџџџ ю " J Ю Н 0 э J c" : c" J J \ J J э џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ џџџџ c" ј * " :
Materials and Methods
Toxicity analysis
C57BL/6 mice were placed in the different scheduling arms, as indicated. 9 weeks later, blood was collected via retro-orbital route.
Hematocrit Measurements
Blood was collected into heparin containing microhematocrit capillary tubes (Fisher Scientific) and centrifuged in a hematocrit rotor. Hematocrit values were determined by the ratio of the length of the red blood cell column to the total length of blood. Duplicate samples were prepared and averaged for each individual mouse.
Isolation of Peripheral Blood Leukocytes
One hundred microliters of blood was collected via retro-orbital route into heparin containing tubes, diluted with an equal volume of RPMI1640 (Corning Cellgro), and incubated for 2 minutes with 9 ml ACK lysing buffer (Fisher Scientific). Cells were collected after centrifugation and washed twice in PBS before use.
Flow cytometry
Isolated mouse blood leukocytes were stained in FACS buffer (PBS with 0.1% sodium azide and 1% BSA) with FITC-conjugated anti-Ly-6G/Ly-6C (Gr1) (BioLegend; RB6-8C5) and APC-conjugated anti-CD11b (BioLegend; M1/70). Flow cytometry analysis was conducted on LSRII flow cytometer (BD Biosciences). Data were analyzed using FlowJo software (Tree Star Inc).
In vivo experiment
In the short pulsatile scheduling study, mice bearing 1205Lu reporter xenografts were fed with combination chow (n=8) for four days and switched to single agent 429 mg/kg palbociclib (n=8) for three days. Control #4 cells or ComboR1 cells were injected at 5x106 per site. Mice were observed for tumor formation from day of injection.
RNA-Seq and Gene Set Enrichment
Technical replicates of cell lines generated from Control #4, ComboR1 and ComboR2 tumors were analyzed. RNA capture was performed with TruSeq RNA Library Prep Kit v2 (Illumina) and sequenced on a HiSeq4000. RNA counts were quantified from single-end reads using STAR aligner ADDIN EN.CITE Dobin2013158(1)15815817Dobin, A.Davis, C. A.Schlesinger, F.Drenkow, J.Zaleski, C.Jha, S.Batut, P.Chaisson, M.Gingeras, T. R.Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. dobin@cshl.eduSTAR: ultrafast universal RNA-seq alignerBioinformaticsBioinformatics15-21291AlgorithmsCluster AnalysisGene Expression ProfilingGenome, HumanHumansRNA SplicingSequence Alignment/*methodsSequence Analysis, RNA/methods*Software2013Jan 011367-4811 (Electronic)
1367-4803 (Linking)23104886https://www.ncbi.nlm.nih.gov/pubmed/23104886PMC353090510.1093/bioinformatics/bts635(1). Differential expression was performed using voom from R package limma ADDIN EN.CITE Law2014161(2)16116117Law, C. W.Chen, Y.Shi, W.Smyth, G. K.voom: Precision weights unlock linear model analysis tools for RNA-seq read countsGenome BiolGenome BiolR29152*AlgorithmsBase SequenceBayes TheoremComputer SimulationGene Expression ProfilingHigh-Throughput Nucleotide Sequencing/*methods*Linear ModelsRNA/*geneticsSequence Analysis, RNA2014Feb 031474-760X (Electronic)
1474-7596 (Linking)24485249https://www.ncbi.nlm.nih.gov/pubmed/24485249PMC405372110.1186/gb-2014-15-2-r29(2). Pathway analysis was performed using GSVA ADDIN EN.CITE Hanzelmann2013162(3)16216217Hanzelmann, S.Castelo, R.Guinney, J.Research Program on Biomedical Informatics, Hospital del Mar Medical Research Institute, Barcelona, Catalonia, Spain.GSVA: gene set variation analysis for microarray and RNA-seq dataBMC BioinformaticsBMC Bioinformatics714Analysis of VarianceFemaleGene Expression Profiling/*methodsGenetic VariationHumansLeukemia, Biphenotypic, Acute/genetics/metabolismOligonucleotide Array Sequence Analysis/*methodsOvarian Neoplasms/genetics/metabolism/mortalityPrecursor Cell Lymphoblastic Leukemia-Lymphoma/genetics/metabolismSequence Analysis, RNA/*methods*SoftwareStatistics, NonparametricSurvival Analysis2013Jan 161471-2105 (Electronic)
1471-2105 (Linking)23323831https://www.ncbi.nlm.nih.gov/pubmed/23323831PMC361832110.1186/1471-2105-14-7(3). Z-scores were calculated for each cell line from average normalized count values. The RNA-seq results have been deposited in GEO and are accessible through accession number GSE111005.
Statistical analysis for in vivo experiments
The log-transformed tumor volumes in experiments utilizing AZD2014 were analyzed using the longitudinal linear mixed effects model with the random effects of animal in slopes and intercepts of the animal-specific tumor growth curves. The fixed effects included the treatment group, the day and their interaction. The difference in tumor growth rates between treatment group was evaluated as an interaction between day and treatment group.
Scheduling experiments: The time trends in tumor volumes and tdTomato measures were not consistent with any low order polynomial or commonly used nonlinear models, and the variability of observations was heavily dependent on the observation day. Therefore, nonparametric rank-based longitudinal two-factor models with interaction ADDIN EN.CITE Brunner E2002155(4)15515517Brunner E, Langer F and Domhof SNonparametric Analysis of Longitudinal Data in Factorial ExperimentsJohn Wiley & Sons, New YorkJohn Wiley & Sons, New York2002(4) were fitted using the R package nparLD ADDIN EN.CITE Noguchi K2012156(5)15615617Noguchi K, Gel YR, Brunner E, and Konietschke FnparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial ExperimentsJournal of Statistical SoftwareJournal of Statistical SoftwareJ of Stats Software50(12)1-232012(5). In this model, the treatment group classification is considered a whole-plot factor (separates animals into 3 independent treatment groups) and the day is considered a sub-plot factor (corresponds to repeated at different day measurements within the same animal). This nonparametric approach effectively changes the response variables from the actual tumor volumes or tdTomato observations into their ranks within the entire data set. The pairwise difference between treatment groups were tested in terms of the differences in interaction effects (interaction between treatment group and day), which is equivalent to testing the difference in day slopes between treatment group in a liner mixed effects model.
References
ADDIN EN.REFLIST 1. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013;29(1):15-21.
2. Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 2014;15(2):R29.
3. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013;14:7.
4. Brunner E LFaDS. Nonparametric Analysis of Longitudinal Data in Factorial Experiments. John Wiley & Sons, New York 2002.
5. Noguchi K GY, Brunner E, and Konietschke F. nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments. J of Stats Software 2012;50(12)(1-23).
* D e А Б Ъ і
=
{ э я і
/
R
Ю
Я
ѓчѓжХЖЊЖХЖЖХЖХЖХveVJVЊJ hхв B*OJ QJ ph hF h,Zс B*OJ QJ ph hF h,Zс 5B*OJ QJ ph #hF h,Zс 56B*OJ QJ ph hF hRt B*OJ QJ \ph hF hRt 5B*OJ QJ phџ hж|Ъ B*OJ QJ ph hF hRt B*OJ QJ ph hF hRt 5B*OJ QJ ph hF hR q 5B*OJ QJ ph hF hU:с 5OJ QJ hF hRt 5OJ QJ * А Б Ъ
=
{ | ю я
Q R r Э$ Ю$ ќ$ Г&