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Introduction

This page describes the file formats that cancer study data should assume in order to be successfully imported into the database. Unless otherwise noted, all data files are in tabular-TSV (tab separated value) format and have an associated metadata file which is in a multiline record format. The metadata and data files should follow the same naming convention and end with a ".txt" extension. For example, if the data file is called data_CNA.txt, there should be a respective meta_CNA.txt file within the same directory. We have provided an example dataset on our Downloads page for use as reference when creating data files.

Note, as of Dec. 9, 2014, the software makes a distinction between patients and their samples. That is, the software can support multiple samples/patient. In general, data files should contain sample ids and patient lists should contain patient ids. The [clinical data][12] file is used to capture both clinical attributes and the mapping between patient and sample ids.

Formats

Cancer Study

For each cancer study to be importer by the Importer Tool, the following two files need to exist in the cancer study directory:

  • cancer_type.txt: This file contains a single record describing the cancer study's cancer type. This file is necessary if the cancer type does not already exist in the type_of_cancer database table. The file is comprised of the following columns in the order specified:

    1. type_of_cancer: The cancer type abbreviation, e.g., "brca".
    2. name: The name of the cancer type, e.g., "Breast Invasive Carcinoma".
    3. clinical_trial_keywords: A comma separated list of keywords used to help associated clinical trial data with this cancer study, e.g., "breast,breast invasive".
    4. dedicated_color: The color associated with this cancer study, e.g., "HotPink". We follow the awareness ribbons color schema. This color is associated with the cancer study on various web pages within the cBioPortal.
    5. short_name: A short name used for display used on various web pages within the cBioPortal, e.g., "Breast".

    An example record would be:

brca<TAB>Breast Invasive Carcinoma<TAB>breast,breast invasive<TAB>HotPink<TAB>Breast

  • meta_study.txt: This file contains metadata about the cancer study. The file contains the following fields:

    1. type_of_cancer: The cancer type abbreviation, e.g., "brca". This should be the same cancer type specified in the cancer_type.txt file in step 1.
    2. cancer_study_identifier: A string used to uniquely identify this cancer study within the database, e.g., "brca_joneslab_2013". This identifier will be used throughout the remaining cancer study data files.
    3. name: The name of the cancer study, e.g., "Breast Cancer (Jones Lab 2013)".
    4. description: A description of the cancer study, e.g., "Comprehensive profiling of 103 breast cancer samples. Generated by the Jones Lab 2013". This description may contain one or more URLs to relevant information.
    5. citation (optional): A relevant citation, e.g., "TCGA, Nature 2012".
    6. pmid (optional): A relevant pubmed id.
    7. short_name: A short name used for display used on various web pages within the cBioPortal, e.g., "BRCA (Jones)".
    8. groups (optional): When using an authenticating cBioPortal, lists the user-groups that are allowed access to this study. Multiple groups are separated with a semicolon ";". The study will be invisible to users not in at least one of the listed groups, as if it wasn't loaded at all. e.g., "PUBLIC;GDAC;SU2C-PI3K". see User-Authorization for more information on groups

    An example meta_study.txt file would be:

type_of_cancer: brca
cancer_study_identifier: brca_joneslab_2013
name: Breast Cancer (Jones Lab 2013)
description: Comprehensive profiling of 103 breast cancer samples. Generated by the Jones Lab 2013.

Discrete Copy Number Data

The discrete copy number data file contain values that would be derived from copy-number analysis algorithms like GISTIC or RAE. For each gene (row) in the data file, the following columns are required in the order specified:

  1. Hugo_Symbol: A HUGO gene symbol.
  2. Entrez_Gene_Id: A Entrez Gene identifier.
  3. An additional column for each sample in the dataset using the sample id as the column header.

For each gene-sample pair, a copy number level is specified. "-2" is a deep loss, possibly a homozygous deletion, "-1" is a single-copy loss (heterozygous deletion), "0" is diploid, "1" indicates a low-level gain, and "2" is a high-level amplification.

An example data file which includes the required column header would look like:

Hugo_Symbol<TAB>Entrez_Gene_Id<TAB>SAMPLE_ID_1<TAB>SAMPLE_ID_2<TAB>...
ACAP3<TAB>116983<TAB>0<TAB>-1<TAB>...
AGRN<TAB>375790<TAB>2<TAB>0<TAB>...
...
...

The discrete copy number metadata file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. genetic_alteration_type: COPY_NUMBER_ALTERATION.
  3. datatype: DISCRETE.
  4. stable_id: typically the cancer_study_identifier with an "_gistic" suffix.
  5. show_profile_in_analysis_tab: true.
  6. profile_name: A name for the discrete copy number data, e.g., "Putative copy-number alterations from GISTIC".
  7. profile_description: A description of the copy number data, e.g., "Putative copy-number from GISTIC 2.0. Values: -2 = homozygous deletion; -1 = hemizygous deletion; 0 = neutral / no change; 1 = gain; 2 = high level amplification.".

An example metadata file would be:

cancer_study_identifier: brca_tcga_pub
genetic_alteration_type: COPY_NUMBER_ALTERATION
datatype: DISCRETE
stable_id: brca_tcga_pub_gistic
show_profile_in_analysis_tab: true
profile_description: Putative copy-number from GISTIC 2.0. Values: -2 = homozygous deletion; -1 = hemizygous deletion; 0 = neutral / no change; 1 = gain; 2 = high level amplification.
profile_name: Putative copy-number alterations from GISTIC

Log2 Copy Number Data

The log2 copy number data file follows the same format as expression data files. See Expression Data for a description of the expression data file format.

The log2 copy number metadata file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. genetic_alteration_type: COPY_NUMBER_ALTERATION.
  3. datatype: LOG-VALUE.
  4. stable_id: typically the cancer_study_identifier with an "_log2CNA" suffix.
  5. show_profile_in_analysis_tab: false.
  6. profile_name: A name for the log2 copy number data, e.g., "Log2 copy-number values".
  7. profile_description: A description of the copy number data, e.g., "Log2 copy-number values for each gene (from Affymetrix SNP6).".

An example metadata file would be:

cancer_study_identifier: brca_tcga_pub
genetic_alteration_type: COPY_NUMBER_ALTERATION
datatype: LOG-VALUE
stable_id: brca_tcga_pub_log2CNA
show_profile_in_analysis_tab: false
profile_description: Log2 copy-number values for each gene (from Affymetrix SNP6).
profile_name: Log2 copy-number values

Segmented Data

A SEG file (segmented data; .seg or .cbs) is a tab-delimited text file that lists loci and associated numeric values. The first row contains column headings and each subsequent row contains a locus and an associated numeric value. The segmented data file format is the output of the Circular Binary Segmentation algorithm (Olshen et al., 2004). Segment data for import into the cBioPortal should be based on build 37 (hg19).

An example data file which includes the required column header would look like:

'ID<TAB>chrom<TAB>loc.start<TAB>loc.end<TAB>num.mark<TAB>seg.mean
SAMPLE_ID_1<TAB>1<TAB>3208470<TAB>245880329<TAB>128923<TAB>0.0025
SAMPLE_ID_2<TAB>2<TAB>474222<TAB>5505492<TAB>2639<TAB>-0.0112
SAMPLE_ID_2<TAB>2<TAB>5506070<TAB>5506204<TAB>2<TAB>-1.5012
SAMPLE_ID_2<TAB>2<TAB>5512374<TAB>159004775<TAB>80678<TAB>-0.0013
...
...

The segmented metadata file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. genetic_alteration_type: SEGMENT.
  3. datatype: SEGMENT.
  4. stable_id: typically the cancer_study_identifier with an "_segment" suffix.
  5. show_profile_in_analysis_tab: false.
  6. profile_name: A name for the segmented data, e.g., "Segmented data values".
  7. profile_description: A description of the segmented data, e.g., "Segment data for the XYZ cancer study.".

An example metadata file would be:

cancer_study_identifier: brca_tcga_pub
genetic_alteration_type: SEGMENT
datatype: SEGMENT
stable_id: brca_tcga_pub_segment
show_profile_in_analysis_tab: false
profile_description: Segment data for the BRCA study.
profile_name: Segment data values

Expression Data

An expression data file is a two dimensional matrix with a gene per row and a sample per column. For each gene-sample pair, a real number represents the gene expression in that sample. For each gene (row) in the data file, the following tab-delimited values are required in the order specified:

  1. Hugo_Symbol: A HUGO gene symbol.
  2. Entrez_Gene_Id: A Entrez Gene identifier.
  3. A real number for each sample id (column) in the dataset.

For mRNA and microRNA expression data, we typically compute the relative expression of an individual gene and tumor to the gene's expression distribution in a reference population. That reference population is either all tumors that are diploid for the gene in question, or, when available, normal adjacent tissue. The returned value indicates the number of standard deviations away from the mean of expression in the reference population (Z-score). This measure is useful to determine whether a gene is up- or down-regulated relative to the normal samples or all other tumor samples. Note, the importer tool can create normalized (z-score) expression data on your behalf. Please visit the Importer Tool wiki page for more information.

An example data file which includes the required column header would look like:

Hugo_Symbol<TAB>Entrez_Gene_Id<TAB>SAMPLE_ID_1<TAB>SAMPLE_ID_2<TAB>...
ACAP3<TAB>116983<TAB>-0.005<TAB>-0.550<TAB>...
AGRN<TAB>375790<TAB>0.142<TAB>0.091<TAB>...
...
...

The expression metadata file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. genetic_alteration_type: MRNA_EXPRESSION.
  3. datatype: CONTINUOUS (or Z-SCORE).
  4. stable_id: typically the cancer_study_identifier with an "mrna" or "mrna_median_Zscores" suffix.
  5. show_profile_in_analysis_tab: false (true if Z-SCORE).
  6. profile_name: A name for the expression data, e.g., "mRNA expression (microarray)".
  7. profile_description: A description of the expression data, e.g., "Expression levels (Agilent microarray).".

An example metadata file would be:

cancer_study_identifier: brca_tcga_pub
genetic_alteration_type: MRNA_EXPRESSION
datatype: Z-SCORE
stable_id: brca_tcga_pub_mrna
profile_description: Expression levels (Agilent microarray).
show_profile_in_analysis_tab: false
profile_name: mRNA expression (microarray)

Mutation Data

The mutation data file follows the Mutation Annotation Format (MAF) created as part of the Cancer Genome Atlas project. If your mutation data is already in VCF format (which by default most variant callers produce) you can use this vcf2maf converter.

A minimal mutation file can contain just four columns:

  • Tumor_Sample_Barcode: This is the sample ID. Either a TCGA barcode (patient identifier will be extracted), or for non-TCGA data, a literal SAMPLE_ID as listed in the clinical data file.
  • Hugo_Symbol: A HUGO gene symbol.
  • Variant_Classification: Translational effect of variant allele, e.g. Missense_Mutation, Silent, etc.
  • Amino_Acid_Change: Amino Acid Change, e.g. p.V600E.

A more detailed example MAF can be found on our Downloads page. It has 32 columns + 4 columns with information on reference and variant allele counts in tumor and normal samples. Description of each column is provided below:

  1. Hugo_Symbol (Required): A HUGO gene symbol.
  2. Entrez_Gene_Id (Optional, but desired): A Entrez Gene identifier.
  3. Center (Optional): The sequencing center.
  4. NCBI_Build (Optional): Must be "37".
  5. Chromosome (Optional): A chromosome number, e.g., "7".
  6. Start_Position (Optional): Start position of event.
  7. End_Position (Optional): End position of event.
  8. Strand (Optional): We assume that the mutation is reported for the + strand.
  9. Variant_Classification (Optional): Translational effect of variant allele, e.g. Missense_Mutation, Silent, etc.
  10. Variant_Type (Optional): Variant Type, e.g. SNP, DNP, etc.
  11. Reference_Allele (Optional): The plus strand reference allele at this position.
  12. Tumor_Seq_Allele1 (Optional): Primary data genotype.
  13. Tumor_Seq_Allele2 (Optional): Primary data genotype.
  14. dbSNP_RS (Optional): Latest dbSNP rs ID.
  15. dbSNP_Val_Status (Optional): dbSNP validation status.
  16. Tumor_Sample_Barcode (Required): This is the sample ID. Either a TCGA barcode (patient identifier will be extracted), or for non-TCGA data, a literal SAMPLE_ID as listed in the clinical data file.
  17. Matched_Norm_Sample_Barcode (Optional): The sample ID for the matched normal sample.
  18. Match_Norm_Seq_Allele1 (Optional): Primary data.
  19. Match_Norm_Seq_Allele2 (Optional): Primary data.
  20. Tumor_Validation_Allele1 (Optional): Secondary data from orthogonal technology.
  21. Tumor_Validation_Allele2 (Optional): Secondary data from orthogonal technology.
  22. Match_Norm_Validation_Allele1 (Optional): Secondary data from orthogonal technology.
  23. Match_Norm_Validation_Allele2 (Optional): Secondary data from orthogonal technology.
  24. Verification_Status (Optional): Second pass results from independent attempt using same methods as primary data source.
  25. Validation_Status (Optional): -- "Valid" or "Unknown".
  26. Mutation_Status (Optional): Ideally "Somatic".
  27. Sequencing_Phase (Optional): Indicates current sequencing phase.
  28. Sequence_Source (Optional): Molecular assay type used to produce the analytes used for sequencing.
  29. Validation_Method (Optional): The assay platforms used for the validation call.
  30. Score (Optional): Not in use.
  31. BAM_File (Optional): Not used.
  32. Sequencer (Optional): Instrument used to produce primary data.
  33. t_alt_count (Optional): Variant allele count (tumor).
  34. t_ref_count (Optional): Reference allele count (tumor).
  35. n_alt_count (Optional): Variant allele count (normal).
  36. n_ref_count (Optional): Reference allele count (normal).

Mutation data that you find on cBioPortal.org comes from MAF files that have been enriched with information from mutationassessor.org

The mutation metadata file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. genetic_alteration_type: MUTATION_EXTENDED.
  3. datatype: MAF.
  4. stable_id: typically the cancer_study_identifier with an "_mutations" suffix.
  5. show_profile_in_analysis_tab: true.
  6. profile_name: A name for the mutation data, e.g., "Mutations".
  7. profile_description: A description of the mutation data, e.g., "Mutation data from whole exome sequencing.".

An example metadata file would be:

cancer_study_identifier: brca_tcga_pub
genetic_alteration_type: MUTATION_EXTENDED
datatype: MAF
stable_id: brca_tcga_pub_mutations
show_profile_in_analysis_tab: true
profile_description: Mutation data from whole exome sequencing.
profile_name: Mutations

Fusion Data

A fusion data file is a two dimensional matrix with one gene per row. For each gene (row) in the data file, the following tab-delimited values are required in the order specified:

  1. Hugo_Symbol: A HUGO gene symbol.
  2. Entrez_Gene_Id: A Entrez Gene identifier.
  3. Center: The sequencing center.
  4. Tumor_Sample_Barcode: This is the sample ID.
  5. Fusion: A description of the fusion, e.g., "TMPRSS2-ERG fusion".
  6. DNA support: Fusion detected from DNA sequence data, "yes" or "no".
  7. RNA support: Fusion detected from RNA sequence data, "yes" or "no".
  8. Method: Fusion detected algorithm/tool.
  9. Frame: "in-frame" or "frameshift".

An example data file which includes the required column header would look like:

Hugo_Symbol<TAB>Entrez_Gene_Id<TAB>Center<TAB>Tumor_Sample_Barcode<TAB>Fusion<TAB>DNA support<TAB>RNA support<TAB>Method<TAB>Frame>
ALK<TAB>238<TAB>center.edu<TAB>SAMPLE_ID_1<TAB>Fusion<TAB>unknown<TAB>yes<TAB>unknown<TAB>in-frame
ALK<TAB>238<TAB>center.edu<TAB>SAMPLE_ID_2<TAB>Fusion<TAB>unknown<TAB>yes<TAB>unknown<TAB>in-frame
RET<TAB>5979<TAB>center.edu<TAB>SAMPLE_ID_3<TAB>Fusion<TAB>unknown<TAB>yes<TAB>unknown<TAB>in-frame
...
...

The fusion metadata file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. genetic_alteration_type: FUSION.
  3. datatype: FUSION.
  4. stable_id: typically the cancer_study_identifier with a "fusion" suffix.
  5. show_profile_in_analysis_tab: true.
  6. profile_name: A name for the fusion data, e.g., "Fusions.".
  7. profile_description: A description of the fusion data.

An example metadata file would be:

cancer_study_identifier: brca_tcga_pub
genetic_alteration_type: FUSION
datatype: FUSION
stable_id: brca_tcga_pub_mutations
profile_description: Fusions.
show_profile_in_analysis_tab: true
profile_name: Fusions

Methylation Data

The methylation data file follows the same format as expression data files. See [Expression Data][6] for a description of the expression data file format. The Portal expects a single value for each gene in each sample, usually a beta-value from the Infinium methylation array platform.

The methylation metadata file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. genetic_alteration_type: METHYLATION.
  3. datatype: CONTINUOUS.
  4. stable_id: typically the cancer_study_identifier with an "_methylation_hm27" or "_methylation_hm450" suffix (depending on platform).
  5. show_profile_in_analysis_tab: false.
  6. profile_name: A name for the methylation data, e.g., "Methlytation (HM27)".
  7. profile_description: A description of the methlytation data, e.g., "Methylation beta-values (HM27 platform). For genes with multiple methylation probes, the probe least correlated with expression is selected.".

An example metadata file would be:

cancer_study_identifier: brca_tcga_pub
genetic_alteration_type: METHYLATION
datatype: CONTINUOUS
stable_id: brca_tcga_pub_methylation_hm27
profile_description: Methylation beta-values (HM27 platform). For genes with multiple methylation probes, the probe least correlated with expression is selected.
show_profile_in_analysis_tab: false
profile_name: Methylation (HM27)

RPPA Data

An RPPA data file is a two dimensional matrix with an antibody per row and a sample per column. For each antibody-sample pair, a real number represents the RPPA level for that sample. The antibody information should contain a HUGO gene symbol and an antibody ID pair separated by the "|" symbol.

An example data file which includes the required column header would look like:

Composite.Element.REF<TAB>SAMPLE_ID_1<TAB>SAMPLE_ID_2<TAB>...
BRAF|B-Raf-M-NA<TAB>1.09506676325<TAB>0.5843256495...
EGFR|EGFR-R-C<TAB>1.70444582025<TAB>1.0982864685...
...

The RPPA metadata file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. genetic_alteration_type: RPPA.
  3. datatype: CONTINUOUS.
  4. stable_id: typically the cancer_study_identifier with an "rppa" suffix.
  5. show_profile_in_analysis_tab: false.
  6. profile_name: A name for the RPPA data, e.g., "RPPA data".
  7. profile_description: A description of the RPPA data, e.g., "RPPA levels.".

An example metadata file would be:

cancer_study_identifier: brca_tcga_pub
genetic_alteration_type: RPPA
datatype: CONTINUOUS
stable_id: brca_tcga_pub_rppa
profile_description: RPPA levels.
show_profile_in_analysis_tab: false
profile_name: RPPA data

Clinical Data

A clinical data file is a two dimensional matrix with multiple clinical attributes which can be attached to either a patient or a sample.

The first five rows of a clinical data file contain tab-delimited metadata about the clinical attributes and should start with a '#' symbol:

  1. Display Name: The display name for each clinical attribute.
  2. Description: Long(er) description of each clinical attribute.
  3. Datatype: The datatype of each clinical attribute (must be one of: STRING, NUMBER, BOOLEAN).
  4. Attribute Type: The type of each clinical attribute, e.g. should this attribute be attached to a PATIENT or SAMPLE.
  5. Priority: A number which indicates the importance of each attribute. In the future, higher priority attributes will appear in more prominent places than lower priority ones on relevant pages. A lower number indicates a higher priority.

Following the metadata rows comes a tab delimited list of clinical attributes (column headers).

The following tab-delimited values are required in the order specified:

  1. PATIENT_ID: A patient ID.
  2. SAMPLE_ID: A sample ID
  3. [Clinical Attribute Headers]

Note that each row must have a PATIENT_ID and a SAMPLE_ID, and this mapping is the only mechanism we have for associating a sample with a patient. It also enables one to associate multiple samples to one patient -- for example, a single patient may have had multiple biopsies, each of which has been genomically profiled. Note that reusing the deprecated "CASE_ID" will lead to erroneous display after loading.

[Clinical Attribute Headers] are freeform, you can add any parameter you have tracked, and cBio will interpret them (be sure to provide the correct 'Datatype' and 'attribute type' as described above, for optimal search results and filtering).

An example data file would look like:

#Patient Identifier<TAB>Sample Identifier<TAB>Subtype<TAB>Overall Survival Status<TAB>Overall Survival (Months)<TAB>Disease Free Status<TAB>Disease Free (Months)<TAB>...
#Patient identifier<TAB>Sample Identifier<TAB>Subtype description<TAB>Overall survival status<TAB>Overall survival in months since diagnosis<TAB>Disease free status<TAB>Disease free in months since treatment<TAB>...
#STRING<TAB>STRING<TAB>STRING<TAB>STRING<TAB>NUMBER<TAB>STRING<TAB>NUMBER<TAB>...
#PATIENT<TAB>SAMPLE<TAB>SAMPLE<TAB>PATIENT<TAB>PATIENT<TAB>PATIENT<TAB><PATIENT>...
#1<TAB>1<TAB>1<TAB>1<TAB>1<TAB>1<TAB>1<TAB>
PATIENT_ID<TAB>SAMPLE_ID<TAB>SUBTYPE<TAB>OS_STATUS<TAB>OS_MONTHS<TAB>DFS_STATUS<TAB>DFS_MONTHS<TAB>...
PATIENT_ID_1<TAB>SAMPLE_ID_1<TAB>basal-like<TAB>DECEASED<TAB>17.97<TAB>Recurred/Progressed<TAB>30.98<TAB>...
PATIENT_ID_2<TAB>SAMPLE_ID_2<TAB>Her2 enriched<TAB>LIVING<TAB>63.01<TAB>DiseaseFree<TAB>63.01<TAB>...
...
...

The clinical metadata file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. genetic_alteration_type: CLINICAL.
  3. datatype: FREE-FORM.
  4. stable_id: typically the cancer_study_identifier with an "_clinical".
  5. show_profile_in_analysis_tab: false.
  6. profile_name: A name for the clinical data, e.g., "Clinical".
  7. profile_description: A description of the clinical data, e.g., "Clinical data for 55 patients."

An example metadata file would be:

cancer_study_identifier: brca_tcga_pub
genetic_alteration_type: CLINICAL
datatype: FREE-FORM
stable_id: brca_tcga_pub_clinical
profile_description: Clinical data for 55 patients.
show_profile_in_analysis_tab: false
profile_name: Clinical

Case Lists

There can be zero or more case lists associated with each cancer study, although it is recommended you have at least one case list which contains all sample ids. Each case list file should be placed in a sub-directory called "case_lists" which exists along side all the other cancer study data. The case list filename must end with a ".txt" extension.

The case list file should contain the following fields:

  1. cancer_study_identifier: same value specified in meta_study.txt.
  2. stable_id: typically the cancer_study_identifier with an relevant suffix, e.g., "_all".
  3. case_list_name: A name for the patient list, e.g., "All Tumors".
  4. case_list_description: A description of the patient list, e.g., "All tumor samples (825 samples).".
  5. case_list_ids: A tab-delimited list of sample ids from the dataset.

An example case list file would be:

cancer_study_identifier: brca_tcga_pub
stable_id: brca_tcga_pub_all
case_list_name: All Tumors
case_list_description: All tumor samples (825 samples)
case_list_ids: SAMPLE_ID_1<TAB>SAMPLE_ID_2<TAB>SAMPLE_ID_3<TAB>...

In order for sample counts to propagate to the data sets widget on the home page and the table on the Data Sets page, the following case list suffixes need to be used in the stable_id property:

  • All Samples: "_all" e.g. "brca_tcga_pub_all".
  • Sequenced Samples: "_sequenced" e.g. "brca_tcga_pub_sequenced".
  • CNA Patients: "_cna" e.g. "brca_tcga_pub_cna".
  • mRNA (RNA-SeqV2): "_rna_seq_v2_mrna" e.g. "brca_tcga_pub_rna_seq_v2_mrna".
  • mRNA (microarray): "_mrna" e.g. "brca_tcga_pub_mrna".
  • miRNA: _microrna" e.g. "brca_tcga_pub_microrna".
  • Methylation (HM27): "_methylation_hm27" e.g. "brca_tcga_pub_methylation_hm27".
  • RPPA: "_rppa" e.g. "brca_tcga_pub_rppa".
  • Complete: "_3way_complete" e.g. "brca_tcga_pub_3way_complete", (mRNA, CNA, & sequencing).

Timeline Data

The timeline data is a representation of the various events that occur during the course of treatment for a patient from initial diagnosis. For timeline data to be imported, the following files need to be created and maintained:

  1. data_timeline_<event_type>.txt: This file contains all the events that each patient undergoes. These can be multiple files depending upon the events occurring for the patient set and comprises of the following:

i) PATIENT_ID: The patient ID from the dataset.

ii) START_DATE(required): This is start point of any event, calculated in days from the date of diagnosis (which will act as point zero on the timeline scale).

iii) STOP_DATE (depending upon event type): Only required if the EVENT_TYPE is TREATMENT. The end date is calculated in days from the date of diagnosis (which will act as point zero on the timeline scale).

iv) EVENT_TYPE: In this category, the event type is defined. This can be TREATMENT, STATUS, SPECIMEN, IMAGING or SURGERY

Based on the type of event, the following attributes can be used:

a) EVENT_TYPE: TREATMENT

  • TREATMENT_TYPE: This can be either Medical Therapy or Radiation Therapy.
  • SUBTYPE: Depending upon the TREATMENT_TYPE, this can either be Chemotherapy, Hormone Therapy, Targeted Therapy etc. (for Medical Therapies) or WPRT, IVRT etc. (for Radiation Therapies).
  • AGENT: for medical therapies, the agent is defined with number of cycles if applicable and for radiation therapy, the agent is defined as standard dose given to the patient during the course.
  • Based on different cancer types you can add additional data here.

b) EVENT_TYPE: STATUS

  • STATUS: If the EVENT_TYPE is status, data is entered under STATUS to define either the best response from the treatment or if there is a diagnosis of any stage progression etc.
  • SOURCE: Where the status was monitored.
  • Based on different cancer types you can add additional data here.

c) EVENT_TYPE: SPECIMEN

  • SPECIMEN_REFERENCE_NUMBER: This corresponds to the SAMPLE_ID.
  • SPECIMEN_SITE: This is the site from where the specimen was collected.
  • SPECIMEN_TYPE: This can either be tissue or blood.
  • SOURCE: Where was the specimen collection done.
  • Based on different cancer types you can add additional data here.

d) EVENT_TYPE: IMAGING

  • DIAGNOSTIC_TYPE: This attribute will cover the different diagnostics tools used (for example: MRI, CT scan etc.)
  • DIAGNOSTIC_TYPE_DETAILED: Detailed description of the event type.
  • RESULT: Results of the diagnostic tests
  • SOURCE: Where was the Imaging done.
  • Based on different cancer types you can add additional data here.

v) NOTE: Any additional information regarding the event

  1. meta_timeline_<event_type>.txt: This file contains metadata about the timeline data. The file contains the following fields:

i) cancer_study_identifier: A string used to uniquely identify this cancer study within the database, e.g., "brca_joneslab_2013". This identifier will be used throughout the remaining cancer study data files.

ii) genetic_alteration_type: TIMELINE.

cBioPortal, 2015

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