1. Introduction

Intra-tumor heterogeneity (ITH) is now thought to be a key factor that results in the therapeutic failures and drug resistance, which have arose increasing attention in cancer research. Here, we present an R package, MesKit, for characterizing cancer genomic ITH and inferring the history of tumor evolutionary. MesKit provides a wide range of analyses including ITH evaluation, enrichment, signature, clone evolution analysis via implementation of well-established computational and statistical methods. The source code and documents are freely available through Github (https://github.com/Niinleslie/MesKit). We also developed a shiny application to provide easier analysis and visualization.

1.1 Citation

In R console, enter citation("MesKit").

MesKit: a tool kit for dissecting cancer evolution from multi-region derived tumor biopsies via somatic mutations (Submitted)

2. Prepare input Data

To analyze with MesKit, you need to provide:

  • A MAF file of multi-region samples from patients (*.maf / *.maf.gz). Required
  • Cancer cell fraction (CCF) data of somatic mutations. Optional but recommended
  • A segmentation file. Optional
  • The GISTIC outputs. Optional

Note: Patient_ID and Tumor_Sample_Barcode should be consistant in all input files, respectively.

2.1 MAF file

Mutation Annotation Format (MAF) files are tab-delimited text files with aggregated mutations information from VCF Files. The input MAF file (or “*.maf.gz“) of MesKit should have additional columns named Patient_ID and Tumor_ID on the basis of standard MAF files. Besides, as for the Variant_Classificationcolumn, allowed values can be found at Mutation Annotation Format Page.
The following fields are required to be contained in the MAF files with MesKit.

Mandatory fields:

Hugo_Symbol, Chromosome, Start_Position, End_Position, Variant_Classification, Variant_Type, Reference_Allele, Tumor_Seq_Allele2, Ref_allele_depth, Alt_allele_dept, VAF, Tumor_Sample_Barcode, Patient_ID, Tumor_ID

Note: Multi-region samples from the a single tumor are indicated with the same Tumor_ID, such as “primary”, “metastasis” and “lymph”. In addition, values in the Hugo_Symbol field are not necessarily from the HUGO database. Example MAF file

##   Hugo_Symbol Chromosome Start_Position End_Position Variant_Classification
## 1      CFAP74          1        1880545      1880545                 Intron
## 2      TFAP2A          6       10159520     10159520                    IGR
## 3      IGSF21          1       18605309     18605309                 Intron
##   Variant_Type Reference_Allele Tumor_Seq_Allele2 Ref_allele_depth
## 1          SNP                C                 A               16
## 2          SNP                T                 A               29
## 3          SNP                A                 C              144
##   Alt_allele_depth    VAF Tumor_Sample_Barcode Patient_ID Tumor_ID
## 1                3 0.1578                   T1    HCC5647  Primary
## 2               17 0.3695                   T1    HCC6952  Primary
## 3               19 0.1165                   T4    HCC8031  Primary

2.2 CCF files

By default, there are six mandatory fields in input CCF file: Patient_ID, Tumor_Sample_Barcode, Chromosome, Start_Position, CCF and CCF_std/CCF_CI_High (required when identifying clonal/subclonal mutations). Chromosome field of mafFile and ccfFile should be in format (both in number or both start with “chr”). Notably, if CCF files contain other variants apart from SNVs, Reference_Allele and Tumor_Seq_Allele2 should also be included in the input CCF files.

Example CCF file

##   Patient_ID Tumor_Sample_Barcode Chromosome Start_Position       CCF
## 1    HCC5647                   T4         22       43190575 0.6112993
## 2    HCC5647                   T5         22       43190575 0.6239556
## 3    HCC5647                   T3         22       43190575 0.5121414
## 4    HCC5647                   T1         22       43190575 0.6891924
## 5    HCC5647                   T4          5      178224614 0.7668806
##      CCF_Std
## 1 0.19713556
## 2 0.19523997
## 3 0.17751275
## 4 0.21622254
## 5 0.09085722

2.3 Segmentation files

The segmentation file is a tab-delimited file with the following 6 or 7 columns:

  • Patient_ID - ID of a patient
  • Tumor_Sample_Barcode - Tumor sample barcode of samples
  • Chromosome - chromosome name or ID
  • Start_Position - genomic start position of segments (1-indexed)
  • End_Position - genomic end position of segments (1-indexed)
  • Segment_Mean/CopyNumber - segment mean value or absolute integer copy number

Note: Positions are in base pair units.

Example Segmentation file

##   Patient_ID Tumor_Sample_Barcode Chromosome Start_Position End_Position
## 1    HCC5647                   T1          1         138488      6479452
## 2    HCC5647                   T1          1        6504488    120906360
## 3    HCC5647                   T1          1      144921930    157805992
## 4    HCC5647                   T1          1      157809143    160394321
## 5    HCC5647                   T1          1      160604266    165877230
##   CopyNumber
## 1          2
## 2          2
## 3          6
## 4          2
## 5          8

3. Installation

Via GitHub

Install the latest version of this package by typing the commands below in R console:

4. Start with the Maf object

readMaf function creates Maf/MafList objects by reading MAF files and cancer cell fraction (CCF) data (optional but recommended). Parameter refBuild is used to set reference genome version for Homo sapiens reference ("hg18", "hg19" or "hg38").

5. Mutational landscape

5.1 Mutational profile

In order to explore the genomic alterations during cancer progression with multi-region sequencing approach, we provided classifyMut function to categorize mutations. The classification is based on shared pattern or clonal status (CCF data is required) of mutations, which can be specified by class option. Additionally, classByTumor can be used to reveal the mutational profile within tumors.

plotMutProfile function can visualize the mutational profile of tumor samples.