Welcome to CAMOIP!

CAMOIP is a tool for analyzing the expression data and mutation data from the TCGA and the ICI-treated projects, using a standard processing pipeline. CAMOIP provides multiple functions such as patient survival analysis, expression analysis, mutational landscape analysis, immune infiltration analysis, immunogenicity analysis and pathway enrichment analysis.

News and Updates

Updates: 2022-04-11

The DataTable error of expression analysis has been fixed.
The paper of CAMOIP is online! PubMed Link(PMID: 35395670)/Preprint Link.
CAMOIP citation: CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer(DOI: 10.1093/bib/bbac129)

Updates: 2022-02-26

CAMOIP release 1.1
Cox-regression model added, including clinical characteristics (e.g. age, gender, TNM stage).
Speed up and UI updated.
Pathway Enrichment updated, including GSEA and ssGSEA.
The comparison between high-expression and low-expression supported (High versus. Low).
Multiple immune infiltration algorithms supported, including CIBERSORT, EPIC, IPS, MCPcounter, and quanTIseq.

Updates: 2021-10-20

CAMOIP release 1.0

Gene Mutation
Gene Expression

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol:

Color 1:

Color 2:

Mismatch between Step1 and Step2

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol:

Color 1:

Color 2:

Mismatch between Step1 and Step2

Gene Mutation
Gene Expression

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol:

Mismatch between Step1 and Step2

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol:

Mismatch between Step1 and Step2

Gene Mutation
Gene Expression

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Select, search a gene symbol (X axis):

Select, search 1~10 gene symbol(s) (Y axis):

Color 1:

Color 2:

Mismatch between Step1 and Step2

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Select, search a gene symbol (X axis):

Select, search 1~10 gene symbol(s) (Y axis):

Color 1:

Color 2:

Mismatch between Step1 and Step2

Gene Mutation
Gene Expression

Table

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol:

Table

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol:

Gene Mutation
Gene Expression

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Select, search a gene symbol (X axis):

Select, search 1~10 gene symbol(s) (Y axis):

Color 1:

Color 2:

Mismatch between Step1 and Step2

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Select, search a gene symbol (X axis):

Select, search 1~10 gene symbol(s) (Y axis):

Color 1:

Color 2:

Mismatch between Step1 and Step2

Gene Mutation
Gene Expression

Plot

Parameters

Search, select a dataset:

Select, search a gene symbol (X axis):

Select, search 1~10 immune scores (Y axis):

Color 1:

Color 2:

Mismatch between Step1 and Step2

Plot

Parameters

Search, select a dataset:

Select, search a gene symbol (X axis):

Select, search 1~10 immune scores (Y axis):

Color 1:

Color 2:

Mismatch between Step1 and Step2

Gene Mutation
Gene Expression

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol (X axis):

Color 1:

Color 2:

Mismatch between Step1 and Step2

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol:

Color 1:

Color 2:

Mismatch between Step1 and Step2

Gene Mutation
Gene Expression

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol:

Color 1:

Color 2:

Mismatch between Step1 and Step2

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Search, select a gene symbol:

Color 1:

Color 2:

Mismatch between Step1 and Step2

Gene Mutation
Gene Expression

Plot

Parameters

Search, select a dataset:

Search, select a gene symbol:

Color 1:

Color 2:

Mismatch between Step1 and Step2

Plot

Parameters

Search, select a dataset:

Search, select a gene symbol:

Color 1:

Color 2:

Mismatch between Step1 and Step2

Gene Mutation
Gene Expression

Table

Step1:Table-Parameters

Which type are you interested:

TCGA-Cohort

ICI-Cohort

Search, select a dataset:

Search, select a gene symbol:

Mismatch between Step1 and Step2

Table

Step1:Table-Parameters

Which type are you interested:

TCGA-Cohort

ICI-Cohort

Search, select a dataset:

Search, select a gene symbol:

Mismatch between Step1 and Step2

Gene Mutation
Gene Expression

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Select, search a gene symbol (X axis):

Select, search 1~10 pathway(s) (Y axis):

Color 1:

Color 2:

Mismatch between Step1 and Step2

Plot

Parameters

Which type are you interested:

ICI-Cohort

TCGA-Cohort

Search, select a dataset:

Select, search a gene symbol (X axis):

Select, search 1~10 pathway(s) (Y axis):

Color 1:

Color 2:

Mismatch between Step1 and Step2

CAMOIP

CAMOIP is a tool for analyzing the expression data and mutation data from the TCGA and the ICI-treated projects, using a standard processing pipeline.

CAMOIP provides multiple functions such as patient survival analysis, expression analysis, mutational landscape analysis, immune infiltration analysis, immunogenicity analysis and pathway enrichment analysis.

This tool is developed by Anqi Lin, Jian Zhang and Peng Luo et.al., Southern Medical University.

By using CAMOIP , experimental biologists can easily explore the large TCGA and ICI-treated datasets, ask specific questions, and test their hypotheses in a higher resolution.

How to cite CAMOIP

Lin A, Qi C, Wei T, Li M, Cheng Q, Liu Z, Luo P, Zhang J. CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer. Brief Bioinform. 2022 Apr 9:bbac129. doi: 10.1093/bib/bbac129. Epub ahead of print. PMID: 35395670.

Contact us

If you have any questions about the CAMOIP, please feel free to contact us: luopeng@smu.edu.cn (Peng Luo); zhangjian@i.smu.edu.cn (Jian Zhang)

Acknowledgement

We thank the Biotrainee-JianMing Zeng, elegant-r-Shiya Wang, yikeshuzj-Jing Zhang, Zhiwei Bao and Jiacheng Lou for providing technical support.

1.How to get the raw data from CAMOIP?

Reply: Unfortunately, we cannot provide the raw data download since we don't own these data. Users could download the related data with the 'DATA' menu.

2.Why do some datasets have so few candidate genes based on mutation status?

Reply: The inclusion criteria for the candidate genes based on mutation status is that the mutation frequency of the gene is between 5% and 95%.

3.Why Rose et.al. cohort did not contain immune infiltration analysis and pathway enrichment analysis?

Reply: The expression data of the Rose et.al. cohort had already been processed, which was not suitable for the immune infiltration analysis and pathway enrichment analysis.

4.Why Mariathasan et.al and Motzer et.al cohort did not contain mutational landscape analysis?

Reply: The Variant Classification data of Mariathasan et.al and Motzer et.al was not Frame_Shift_Del, Frame_Shift_Ins, In_Frame_Del, In_Frame_Ins, Missense Mutation, Nonsense_Mutation, Nonstop_Mutation, Splice_Site, and Translation_Start_Site.

5.The full name of the alterations in the mutational landscape analysis.

Reply: Splice_Site: Splice_Site; Missense: Missense Mutation; FrameShift: Frame_Shift_Del and Frame_Shift_Ins; Inframe ins/del: In_Frame_Del and In_Frame_Ins; Nonsense: Nonsense_Mutation.

6.Why the sample number of the MANTIS scores analysis was less than other analyses?

Reply: The MANTIS scores of some samples were not applicable (NA).

7.Abbreviation

Reply: LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; NSCLC: non-small cell lung cancer; GBM: Glioblastoma multiforme; BLCA: Bladder Urothelial Carcinoma; KIRC: Kidney renal clear cell carcinoma; SKCM: Skin Cutaneous Melanoma; READ: Rectum adenocarcinoma; HNSC: Head and Neck squamous cell carcinoma; ESCA: Esophageal carcinoma; COAD: Colon adenocarcinoma; HCC: Hepatocellular carcinoma; GSEA: gene sets enrichment analysis; ssGSEA: single sample gene sets enrichment analysis; ICI: Immune checkpoint inhibitor; TCGA: The Cancer Genome Atlas; ACC: Adrenocortical carcinoma; BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: Cholangiocarcinoma; DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; KICH: Kidney Chromophobe; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate adenocarcinoma; SARC: Sarcoma; STES: Stomach and Esophageal carcinoma; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; UCEC: Uterine Corpus Endometrial Carcinoma; UCS: Uterine Carcinosarcoma; UVM: Uveal Melanoma; CRC: Colorectal cancer; OS: Overall Survival; PFS: Progress Free Survival. MT: mutant-type; WT: wild-type; High: high-expression; Low: low-expression; MHC: Major Histocompatibility Complex.

8.Why some datasets did not include cox-regression analysis?

Reply: The dataset used in the cox-regression analysis must contain the Age, Gender, and/or Stage variables.

8.What is the definition of the MANTIS Score?

Reply: The MANTIS score, which predicts the MSI status of tumors, was published by Bonneville et al. (PMID: 29850653).