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hepatocellular_carcinoma [2019/09/10 18:57]
admin [Current clinical risk assessment]
hepatocellular_carcinoma [2019/10/18 19:50] (current)
admin [Data]
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 ====== Hepatocellular carcinoma ====== ====== Hepatocellular carcinoma ======
 ===== Objectives ===== ===== Objectives =====
-\\ Hepatocellular carcinoma (HCC) is a the most common type of liver cancer and the third leading cause of cancer deaths in the world. The goal of project one is to identify the genes that are associated with regressing tumors (regardless of type of treatment) vs. those that are growing from the C3HeB/FeJ mouse model. Normal liver is used as a control. The results will be useful in identifying new therapeutic targets and potential drug combinations which could lead to more efficient treatments. Project two is related to knockdown of a specific protein that results in HCC in a different mouse model. The goal in the second project is to identify the genes and pathways that correlate with this protein.\\ \\ + 
 +\\ 
 +Hepatocellular carcinoma (HCC) is a the most common type of liver cancer and the third leading cause of cancer deaths in the world. [[https://​www.merckmanuals.com/​home/​liver-and-gallbladder-disorders/​fibrosis-and-cirrhosis-of-the-liver/​cirrhosis-of-the-liver#​v28485447|Cirrhosis]] of the liver is a major risk and contributing factor for HCC. The goal of project one is to identify the genes that are associated with regressing tumors (regardless of type of treatment) vs. those that are growing from the C3HeB/FeJ mouse model. Normal liver is used as a control. The results will be useful in identifying new therapeutic targets and potential drug combinations which could lead to more efficient treatments. Project two is related to knockdown of a specific protein that results in HCC in a different mouse model. The goal in the second project is to identify the genes and pathways that correlate with this protein. 
 + 
 ===== Data ===== ===== Data =====
  
-  - RNAseq from liver of 9 treated and 4 control samples ([[christis_data|Christi'​s data]]).+  - RNAseq from liver of 9 treated and 4 control samples ([[:christis_data|Christi'​s data]]).
   - The closest reference genome to our mouse strain is [[http://​www.csbio.unc.edu/​CCstatus/​index.py?​run=Pseudo|C3H/​HeJ]]. We can use fasta and MOD files from build 37 (mm9), which is more [[https://​www.biostars.org/​p/​81602/​|annotated]] than build 38 (mm10).   - The closest reference genome to our mouse strain is [[http://​www.csbio.unc.edu/​CCstatus/​index.py?​run=Pseudo|C3H/​HeJ]]. We can use fasta and MOD files from build 37 (mm9), which is more [[https://​www.biostars.org/​p/​81602/​|annotated]] than build 38 (mm10).
-  - Alternatively,​ we can map to the mouse reference transcriptome ([[|NCBI37]]/​mm9,​ rna.fa), and simplify the analysis in expense of losing upto 7% of reads. +  - Alternatively,​ we can map to the mouse reference transcriptome ([[:​hepatocellular_carcinoma|NCBI37]]/​mm9,​ rna.fa), and simplify the analysis in expense of losing upto 7% of reads. 
-  - Ron Walter'​s lab ran their pipeline to filter the fastq data. These files are stored in folder called Filtered_fastq_files. From Will Boswell: "PE stands for paired end reads. For example, you have a 500bp fragment and your target sequence size is 125bp. The fragment will be sequenced 125 bases from one end and 125 bases from the other end, and Illumina refers to this as paired end reads. SE stands for single end reads, which in our case is generated during our filtering process. If you look at the pre-filtered reads, you’ll see only PE1 and PE2 for each sample. During filtration, if one of the PE’s have low quality, it is tossed out leaving the other PE, and since it no longer has a mate pair, it’s kept as a single end sequence. Also, there are several files in the post-filtered directories that are considered intermediate files in the filtering process that we don’t need; these are process files used by the filtering script. The only files you should be concerned with are the _pe1.r.fastq,​ _pe2.r.fastq,​ _se.r.fastq,​ and _PE.filter.stats (gives you the number of reads mapped to the genome for each PE and SE)." A summary of the analysis can be found {{ :​mouse_hcc_liver_sequencing_summary.docx|here}}.+  - Ron Walter'​s lab ran their pipeline to filter the fastq data. These files are stored in folder called Filtered_fastq_files. From Will Boswell: "PE stands for paired end reads. For example, you have a 500bp fragment and your target sequence size is 125bp. The fragment will be sequenced 125 bases from one end and 125 bases from the other end, and Illumina refers to this as paired end reads. SE stands for single end reads, which in our case is generated during our filtering process. If you look at the pre-filtered reads, you’ll see only PE1 and PE2 for each sample. During filtration, if one of the PE’s have low quality, it is tossed out leaving the other PE, and since it no longer has a mate pair, it’s kept as a single end sequence. Also, there are several files in the post-filtered directories that are considered intermediate files in the filtering process that we don’t need; these are process files used by the filtering script. The only files you should be concerned with are the _pe1.r.fastq,​ _pe2.r.fastq,​ _se.r.fastq,​ and _PE.filter.stats (gives you the number of reads mapped to the genome for each PE and SE)." A summary of the analysis can be found {{:​mouse_hcc_liver_sequencing_summary.docx|here}}.
   - Sequencing was completed by Beckman Coulter using [[http://​www.illumina.com/​products/​truseq_rna_library_prep_kit_v2.html|TruSeq RNA Library Preparation Kit v2]] which is an unstranded protocol.   - Sequencing was completed by Beckman Coulter using [[http://​www.illumina.com/​products/​truseq_rna_library_prep_kit_v2.html|TruSeq RNA Library Preparation Kit v2]] which is an unstranded protocol.
-  - Jielei provided TruSeq {{ :​illumina_stranded_rnaseq_mapping.pdf|Stranded}} RNA-Seq data from 8 mice in August 2017 (See ~/​proj/​hcc/​data/​TPT1/​readme.txt),​ which was analyzed using TruSeq Stranded RNA-Seq.\\ ​\\ +  - Jielei provided TruSeq {{:​illumina_stranded_rnaseq_mapping.pdf|Stranded}} ​ RNA-Seq data from 8 mice in August 2017 (See ~/​proj/​hcc/​data/​TPT1/​readme.txt),​ which was analyzed using TruSeq Stranded RNA-Seq
 +  - Gao, Qiang, et al. "​Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma."​ //​[[https://​www.sciencedirect.com/​science/​article/​pii/​S0092867419310037|Cell//​]]//​ 179.2 (2019): 561-577. \\ "The data of WES, transcriptome sequencing, proteome, and phosphoproteome are available in [[https://​www.biosino.org/​node|NODE]] (accession # [[https://​www.biosino.org/​node/​experiment/​detail/​OEX001697|OEP000321]]). Survival data (~5 years of followup) were included in Table S1. 
 + 
 ====== **Sources of Human HCC RNA-seq Data** ====== ====== **Sources of Human HCC RNA-seq Data** ======
  
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 ==== Publication:​ ==== ==== Publication:​ ====
-Jessica Zavadil, Maryanne Herzig, Kim Hildreth, Amir Foroushani, William Boswell, Ronald Walter, Robert Reddick, Hugh White, Habil Zare. C3HeB/FeJ Mice Mimic Gene Expression ​and Pathobiological Features ​of Human Hepatocellular Carcinoma,  ​//Molecular Carcinogenesis//, In press+ 
-{{:​wiki:​public:​jessica_compare.png?​direct&​400|}}+Jessica Zavadil, Maryanne Herzig, Kim Hildreth, Amir Foroushani, William Boswell, Ronald Walter, Robert Reddick, Hugh White, Habil Zare. "C3HeB/FeJ Mice mimic many aspects of gene expression ​and pathobiological features ​of human hepatocellular carcinoma."​ [[https://onlinelibrary.wiley.com/doi/abs/10.1002/​mc.22929|Molecular carcinogenesis ]]58.3 (2019): 309-320. 
 + 
 +{{:​wiki:​public:​jessica_compare.png?​direct&​400}} 
 + 
 ==== Project 2: ==== ==== Project 2: ====
  
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   - Hart, Steven N., et al. "​Calculating sample size estimates for RNA sequencing data." //Journal of Computational [[http://​www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC3842884/​|Biology]]//​20.12 (2013): 970-978.Wu, Hao, Chi Wang, and Zhijin Wu. "​PROPER:​ comprehensive power evaluation for differential expression using RNA-seq."​ //​[[http://​bioinformatics.oxfordjournals.org/​content/​31/​2/​233.short|Bioinformatics]]//​31.2 (2015): 233-241.From [[http://​www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC3842884/​figure/​f2/​|Fig2]] and [[http://​www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC3842884/​figure/​f2/​|Fig3]] of Huang et al. paper, and [[http://​bioinformatics.oxfordjournals.org.libproxy.txstate.edu/​content/​31/​2/​233/​F5.expansion.html|Fig5]] of Hart et al., it seems that at least 5-7 samples are needed for each condition.   - Hart, Steven N., et al. "​Calculating sample size estimates for RNA sequencing data." //Journal of Computational [[http://​www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC3842884/​|Biology]]//​20.12 (2013): 970-978.Wu, Hao, Chi Wang, and Zhijin Wu. "​PROPER:​ comprehensive power evaluation for differential expression using RNA-seq."​ //​[[http://​bioinformatics.oxfordjournals.org/​content/​31/​2/​233.short|Bioinformatics]]//​31.2 (2015): 233-241.From [[http://​www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC3842884/​figure/​f2/​|Fig2]] and [[http://​www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC3842884/​figure/​f2/​|Fig3]] of Huang et al. paper, and [[http://​bioinformatics.oxfordjournals.org.libproxy.txstate.edu/​content/​31/​2/​233/​F5.expansion.html|Fig5]] of Hart et al., it seems that at least 5-7 samples are needed for each condition.
   - Ching, Travers, Sijia Huang, and Lana X. Garmire. "Power analysis and sample size estimation for RNA-Seq differential expression."​ //​[[http://​rnajournal.cshlp.org/​content/​early/​2014/​09/​22/​rna.046011.114|rna]]//​20.11 (2014): 1684-1696.   - Ching, Travers, Sijia Huang, and Lana X. Garmire. "Power analysis and sample size estimation for RNA-Seq differential expression."​ //​[[http://​rnajournal.cshlp.org/​content/​early/​2014/​09/​22/​rna.046011.114|rna]]//​20.11 (2014): 1684-1696.
-  - Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma, [[http://​www.cell.com/​cell/​abstract/​S0092-8674(17)30639-6?​innerTabgraphical_S0092867417306396|Cell]],​ 2017 [{{:​ally-copmprehensive_and_integrative_genomic_char_of_hcc-cell-2017.pdf|pdf}} ]. TCGA's HCC data and subtyping using DNA copy number, DNA methylation,​ mRNA expression, miRNA expression and RPPA (protein expression). Links to the MDACC dataset with 100 HCC samples. +  - Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma, [[http://​www.cell.com/​cell/​abstract/​S0092-8674(17)30639-6?​innerTabgraphical_S0092867417306396|Cell]],​ 2017 [{{:​ally-copmprehensive_and_integrative_genomic_char_of_hcc-cell-2017.pdf|pdf}} ​ ]. TCGA's HCC data and subtyping using DNA copy number, DNA methylation,​ mRNA expression, miRNA expression and RPPA (protein expression). Links to the MDACC dataset with 100 HCC samples. 
- +  Subramaniam,​ Somasundaram,​ Robin K. Kelley, ​and Alan P. Venook. "A review ​of hepatocellular carcinoma ​(HCCstaging systems." [[http://​cco.amegroups.com/​article/​view/​2528/​3943|Chinese clinical oncology]] 2.4 (2013).
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-===== Current clinical risk assessment ===== +
- +
-Percent rise in AFP and the total amount ​of AFP (alpha feto protein).  If it rises quickly it suggests aggressive tumor If AFP is over 500 it suggests vascular involvement or aggressiveness If regionally ablated and recurs within three months, this is also suggestive of an aggressive tumorAlso, disease burden, CP cirrhosis, portal vein thrombosis, AFP.  Also use CLIP score and BCLC score in clinic.+
  
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