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 of Multi-region Tumor Biopsies through Somatic Alterations (In production)

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
  • A clinical file contains the clinical data of tumor samples from each patient. Required
  • Cancer cell fraction (CCF) data of somatic mutations. Optional but recommended
  • A segmentation file. Optional
  • The GISTIC outputs. Optional

Note: Tumor_Sample_Barcode should be consistent in all input files, respectively.

2.1 MAF files

Mutation Annotation Format (MAF) files are tab-delimited text files with aggregated mutations information from VCF Files. The input MAF file could be gz compressed, and allowed values of Variant_Classificationcolumn 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_depth, VAF, Tumor_Sample_Barcode

Note:

  • The Tumor_Sample_Barcode of each sample should be unique.
  • The VAF (variant allele frequencie) can be on the scale 0-1 or 0-100.

Example MAF file

##   Hugo_Symbol Chromosome Start_Position End_Position Variant_Classification
## 1      KLHL17          1         899515       899515                 Silent
## 2       MFSD6          2      191301342    191301342      Missense_Mutation
## 3    KIAA0319          6       24596837     24596837      Missense_Mutation
##   Variant_Type Reference_Allele Tumor_Seq_Allele2 Ref_allele_depth
## 1          SNP                C                 T               85
## 2          SNP                G                 A               53
## 3          SNP                C                 T                6
##   Alt_allele_depth   VAF Tumor_Sample_Barcode
## 1                1 0.012             V402_P_1
## 2                0 0.000             V750_P_2
## 3                0 0.000            V750_BM_3

2.2 Clinical data files

Clinical data file contains clinical information about each patient and their tumor samples, and mandatory fields are Tumor_Sample_Barcode, Tumor_ID, Patient_ID, and Tumor_Sample_Label.

Example clinical data file

##   Tumor_Sample_Barcode Tumor_ID Patient_ID Tumor_Sample_Label
## 1             V402_P_1        P       V402                P_1
## 2             V402_P_2        P       V402                P_2
## 3             V402_P_3        P       V402                P_3
## 4             V402_P_4        P       V402                P_4
## 5            V402_BM_1       BM       V402               BM_1

2.3 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). The Chromosome field of your MAF file and CCF file should be in the same format (both in number or both start with “chr”). Notably, Reference_Allele and Tumor_Seq_Allele2 are also required if you want include contains INDELs in the CCF file.

Example CCF file

##   Patient_ID Tumor_Sample_Barcode Chromosome Start_Position   CCF CCF_Std
## 1       V402             V402_P_1          1         899515 0.031   0.126
## 2       V402             V402_P_1          1         982996 0.031   0.117
## 3       V402             V402_P_1          1        2452742 0.125   0.239
## 4       V402             V402_P_1          1        6203883 0.422   0.750
## 5       V402             V402_P_1          1       11106655 0.094   0.324

2.4 Segmentation files

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

  • Patient_ID - patient ID
  • 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)
  • SegmentMean/CopyNumber - segment mean value or absolute integer copy number
  • Minor_CN - copy number of minor allele Optional
  • Major_CN - copy number of major allele Optional
  • Tumor_Sample_Label - the specific label of each tumor sample. Optional

Note: Positions are in base pair units.

Example Segmentation file

##   Patient_ID Tumor_Sample_Barcode Chromosome Start_Position End_Position
## 1       V402             V402_P_1          1              1      1650882
## 2       V402             V402_P_1          1        1650883     33159352
## 3       V402             V402_P_1          1       33159353     33610373
## 4       V402             V402_P_1          1       33610374     88509894
## 5       V402             V402_P_1          1       88509895     89462108
##   CopyNumber Minor_CN Major_CN Tumor_Sample_Label
## 1          2        1        1                P_1
## 2          1        0        1                P_1
## 3          3        0        3                P_1
## 4          2        1        1                P_1
## 5          2        0        2                P_1

4. Start with the Maf object

readMaf function creates Maf/MafList objects by reading MAF files, clinical 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"). You should set use.indel.ccf = TRUE when your ccfFile contains INDELs apart from SNVs.

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.

5.2 CNA profile

The plotCNA function can characterize the CNA landscape across samples based on copy number data from segmentation algorithms. Besides, MesKit provides options to integrate GISTIC2 results, which can be obtained from http://gdac.broadinstitute.org. Please make sure the genome version based on these results is consistent with refBuild of the Maf/MafList object .

## --Processing COREAD_amp_genes.conf_99.txt
## --Processing COREAD_del_genes.conf_99.txt
## --Processing COREAD_all_lesions.conf_99.txt

6. Measurement of intra-tumor heterogeneity

6.1 Within tumors

6.1.1 MATH score

The mathScore function estimates ITH per sample using mutant-allele tumor heterogeneity (MATH) approach 1. Typically, the higher the MATH score is, more heterogeneous a tumor sample is 2 3. For MRS, this function can estimate the MATH score within tumor based on the merged VAF when withTumor = TRUE.

## $V402
##   Patient_ID Tumor_Sample_Barcode MATH_Score
## 1       V402            V402_BM_1    123.550
## 2       V402            V402_BM_2    105.900
## 3       V402            V402_BM_3    101.664
## 4       V402            V402_BM_4    108.724
## 5       V402             V402_P_1     80.191
## 6       V402             V402_P_2     99.897
## 7       V402             V402_P_3    100.515
## 8       V402             V402_P_4     92.337
6.1.2 AUC of CCF

The ccfAUC function calculates the area under the curve (AUC) of the cumulative density function based on the CCFs per tumor. Tumors with higher AUC values are considered to be more heterogeneous 4.

## [1] "AUC.value"        "CCF.density.plot"

6.1.3 Mutation clustering

For a single region/tumor, mutCluster function clusters mutations in one dimension by VAFs or CCFs based on a Gaussian finite mixture model (using mclust). This function only focuses on heterozygous mutations within copy-number neutral and loss of heterozygosity (LOH)-free regions when clustering VAFs, as copy number aberrations will alter the fraction of reads bearing mutations. Note that, “low VAF/CCF” populations might be a mixture of subclones mutations represent admixed polyphyletic lineages 14.

6.2 Between regions/tumors

To quantify the genetic divergence of ITH between regions or tumors, we introduced two classical metrics derived from population genetics, which were Wright’s fixation index (Fst) 5 6 7 and Nei’s genetic distance 8.

6.2.1 Fixation index
## $Fst.avg
## [1] 0.03600727
## 
## $Fst.pair
##            BM_1       BM_2       BM_3       BM_4
## BM_1 1.00000000 0.03825504 0.05133115 0.02173604
## BM_2 0.03825504 1.00000000 0.04722441 0.02393010
## BM_3 0.05133115 0.04722441 1.00000000 0.03356684
## BM_4 0.02173604 0.02393010 0.03356684 1.00000000
## 
## $Fst.plot

6.2.2 Nei’s genetic distance
## $Nei.dist.avg
## [1] 0.01008835
## 
## $Nei.dist
##             BM_1        BM_2       BM_3        BM_4
## BM_1 1.000000000 0.007271303 0.01751193 0.004240568
## BM_2 0.007271303 1.000000000 0.01573008 0.004302810
## BM_3 0.017511933 0.015730084 1.00000000 0.011473404
## BM_4 0.004240568 0.004302810 0.01147340 1.000000000
## 
## $Nei.plot

7. Metastatic routes inference

Metastasis remains poorly understood despite its critical clinical significance, and the understanding of metastasis process offers supplementary information for clinical treatments.

7.2 Jaccard similarity index

The Jaccard similarity index (JSI) can be used to calculate mutational similarity between regions, which is defined as the ratio of shared variants to all variants for sample pairs 11. Users can distinguish monoclonal versus polyclonal seeding in metastases (including lymph node metastases and distant metastases) via calJSI function, and higher JSI values indicate the higher possibility of polyclonal seeding 12.

## [1] "JSI.multi" "JSI.pair"
## [1] 0.3410853
##            P        BM
## P  1.0000000 0.3410853
## BM 0.3410853 1.0000000

7.3 Neutral evolution

The subclonal mutant allele frequencies of a tumor follow a simple power-law distribution predicted by neutral growth 13. Users can evaluate whether a tumor follows neutral evolution or not under strong selection via the testNeutral function. Tumors with R2 >= R2.threshold (Default: 0.98) are considered to follow neutral evolution. Besides, this function can also generate the model fitting plot of each sample if the argument plot is set toTRUE. Please note that this analysis has been superseded by mobster 14.

8. Phylogenetic tree analysis

8.1 Phylogenetic tree construction

With MesKit, phylogenetic tree construction for each individual is based on the binary present/absence matrix of mutations across all tumor regions.
Based on the Maf object, getPhyloTree function reconstructs phylogenetic tree in different methods, including “NJ” (Neibor-Joining) , “MP” (maximum parsimony), “ML” (maximum likelihood), “FASTME.ols” and “FASTME.bal”, which can be set by controlling the method parameter. The phylogenetic tree would be stored in a phyloTree/phyloTreeList object, and it can be further used for functional exploration, mutational signature analysis and tree visualization.

8.2 Compare Phylogenetic trees

Comparison between phylogenetic trees can reveal consensus patterns of tumor evolution. The compareeTree function computes distances between phylogenetic trees constructed through different methods, and it returns a vector containing four distances by treedist from phangorn R package. See treedist for details.

## 4 clades are common between two trees.
## $compare.dist
##     Symmetric.difference       KF-branch distance          Path difference 
##                 4.000000               115.559715                 5.291503 
## Weighted path difference 
##               715.926657 
## 
## $compare.plot

8.4 Mutational characteristics analysis

The sequence context of the base substitutions can be retrieved from the corresponding reference genome to construct a mutation matrix with counts for all 96 trinucleotide changes using “mut_matrix”. Subsequently, the 6 base substitution type spectrum can be plotted with “plot_spectrum” , which can be divided into several sample groups.

mutTrunkBranch function calculates the fraction of branch/trunk mutations occurring in each of the six types of base substitution types (C>A, C>G, C>T, T>A, T>C, T>G) and conducts two-sided Fisher’s exact tests. For C>T mutations, it can be further classified as C>T at CpG sites or other sites by setting CT = TRUE. This function provides option plot to show the distribution of branch/trunk mutations. Substitutions types with significant difference between trunk and branch mutations are marked with Asterisks.

## [1] "mutTrunkBranch.res"  "mutTrunkBranch.plot"

triMatrix function generates a mutation count matrix of 96 trinucleotides based on somatic SNVs per sample, which can be later fed into the fitSignatures function to estimates the optimal contributions of known signatures to reconstruct a mutational profile. Besides, the signature matrix can be specified ("cosmic_v2", "exome_cosmic_v3" and "nature2013") or provided by users via signaturesRef parameter. plotMutSigProfile function can be utilized to visualize both original mutational profile and reconstructed mutational profile.

9. Phylogenetic tree visualization

The plotPhyloTree functionan of MesKit implemented an auto-layout algorithm to visualize rooted phylogenetic trees with annotations. The branches can be either colored according to classification of mutations or putative known signatures by branchCol parameter. Argument show.bootstrap is provided to show the support values of internal nodes. Additionally, a phylogenetic tree along with a heatmap of mutation profile (via mutHeatmap functionan) enable a better depiction of mutational patterns in tumor phylogeny.

10. Shiny APP with video tutorial

The guidance video for MesKit Shiny APP can be found at http://meskit.renlab.org/video.html.

11. References

  1. Mroz, E. A., & Rocco, J. W. (2013). MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma. Oral oncology, 49(3), 211–215. https://doi.org/10.1016/j.oraloncology.2012.09.007

  2. Mroz, E. A., Tward, A. D., Pickering, C. R., Myers, J. N., Ferris, R. L., & Rocco, J. W. (2013). High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma. Cancer, 119(16), 3034–3042. https://doi.org/10.1002/cncr.28150

  3. Mroz, E. A., Tward, A. D., Hammon, R. J., Ren, Y., & Rocco, J. W. (2015). Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas. PLoS medicine, 12(2), e1001786. https://doi.org/10.1371/journal.pmed.1001786

  4. Charoentong, P., Finotello, F., Angelova, M., Mayer, C., Efremova, M., Rieder, D., Hackl, H., & Trajanoski, Z. (2017). Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell reports, 18(1), 248–262. https://doi.org/10.1016/j.celrep.2016.12.019

  5. Weir, B. S., & Cockerham, C. C. (1984). ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE. Evolution; international journal of organic evolution, 38(6), 1358–1370. https://doi.org/10.1111/j.1558-5646.1984.tb05657.x

  6. Bhatia, G., Patterson, N., Sankararaman, S., & Price, A. L. (2013). Estimating and interpreting FST: the impact of rare variants. Genome research, 23(9), 1514–1521. https://doi.org/10.1101/gr.154831.113

  7. Holsinger, K. E., & Weir, B. S. (2009). Genetics in geographically structured populations: defining, estimating and interpreting F(ST). Nature reviews. Genetics, 10(9), 639–650. https://doi.org/10.1038/nrg2611

  8. Puente, X. S., Pinyol, M., Quesada, V., Conde, L., Ordóñez, G. R., Villamor, N., Escaramis, G., Jares, P., Beà, S., González-Díaz, M., Bassaganyas, L., Baumann, T., Juan, M., López-Guerra, M., Colomer, D., Tubío, J. M., López, C., Navarro, A., Tornador, C., Aymerich, M., … Campo, E. (2011). Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia. Nature, 475(7354), 101–105. https://doi.org/10.1038/nature10113

  9. Hu, Z., Ding, J., Ma, Z., Sun, R., Seoane, J. A., Scott Shaffer, J., Suarez, C. J., Berghoff, A. S., Cremolini, C., Falcone, A., Loupakis, F., Birner, P., Preusser, M., Lenz, H. J., & Curtis, C. (2019). Quantitative evidence for early metastatic seeding in colorectal cancer. Nature genetics, 51(7), 1113–1122. https://doi.org/10.1038/s41588-019-0423-x

  10. Xue, R., Chen, L., Zhang, C., Fujita, M., Li, R., Yan, S. M., Ong, C. K., Liao, X., Gao, Q., Sasagawa, S., Li, Y., Wang, J., Guo, H., Huang, Q. T., Zhong, Q., Tan, J., Qi, L., Gong, W., Hong, Z., Li, M., … Zhang, N. (2019). Genomic and Transcriptomic Profiling of Combined Hepatocellular and Intrahepatic Cholangiocarcinoma Reveals Distinct Molecular Subtypes. Cancer cell, 35(6), 932–947.e8. https://doi.org/10.1016/j.ccell.2019.04.007

  11. Makohon-Moore, A. P., Zhang, M., Reiter, J. G., Bozic, I., Allen, B., Kundu, D., Chatterjee, K., Wong, F., Jiao, Y., Kohutek, Z. A., Hong, J., Attiyeh, M., Javier, B., Wood, L. D., Hruban, R. H., Nowak, M. A., Papadopoulos, N., Kinzler, K. W., Vogelstein, B., & Iacobuzio-Donahue, C. A. (2017). Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer. Nature genetics, 49(3), 358–366. https://doi.org/10.1038/ng.3764

  12. Hu, Z., Li, Z., Ma, Z., & Curtis, C. (2020). Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases. Nature genetics, 52(7), 701–708. https://doi.org/10.1038/s41588-020-0628-z

  13. Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A., & Sottoriva, A. (2016). Identification of neutral tumor evolution across cancer types. Nature genetics, 48(3), 238–244. https://doi.org/10.1038/ng.3489

  14. Caravagna, G., Heide, T., Williams, M. J., Zapata, L., Nichol, D., Chkhaidze, K., Cross, W., Cresswell, G. D., Werner, B., Acar, A., Chesler, L., Barnes, C. P., Sanguinetti, G., Graham, T. A., & Sottoriva, A. (2020). Subclonal reconstruction of tumors by using machine learning and population genetics. Nature genetics, 52(9), 898–907. https://doi.org/10.1038/s41588-020-0675-5

12. Session Info

## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
## 
## locale:
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##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] grid      stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] ComplexHeatmap_2.14.0             BSgenome.Hsapiens.UCSC.hg19_1.4.3
##  [3] BSgenome_1.66.0                   rtracklayer_1.58.0               
##  [5] Biostrings_2.66.0                 XVector_0.38.0                   
##  [7] GenomicRanges_1.50.0              GenomeInfoDb_1.34.0              
##  [9] clusterProfiler_4.6.0             org.Hs.eg.db_3.16.0              
## [11] AnnotationDbi_1.60.0              IRanges_2.32.0                   
## [13] S4Vectors_0.36.0                  Biobase_2.58.0                   
## [15] BiocGenerics_0.44.0               MesKit_1.8.0                     
## 
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