Supplementary Materials

Supplementary Material for:

Aristolochic acids and their derivatives are widely implicated in liver cancers in Taiwan and throughout Asia

Alvin W. T. Ng, Song Ling Poon, Mi Ni Huang, Jing Quan Lim, Arnoud Boot, Willie Yu, Yuka Suzuki, Saranya Thangaraju, Cedric C. Y. Ng, Patrick Tan, See-Tong Pang, Hao-Yi Huang, Ming-Chin Yu, Po-Huang Lee, Sen-Yung Hsieh,* Alex Y. Chang,* Bin T. Teh,* Steven G. Rozen*

*Corresponding author. Email: siming.shia{at}msa.hinet.net (S.-Y.H.); alexchang{at}imc.jhmi.edu (A.Y.C.); teh.bin.tean{at}singhealth.com.sg (B.T.T.); steve.rozen{at}duke-nus.edu.sg (S.G.R.)

Published 18 October 2017, Sci. Transl. Med. 9, eaan6446 (2017)
DOI: 10.1126/scitranslmed.aan6446

This PDF file includes:

  • Materials and Methods
  • Fig. S1. GISTIC analysis of significant amplifications and deletions in Taiwan HCCs.
  • Fig. S2. Mutational spectra of all 98 individual Taiwan HCCs.
  • Fig. S3. Comparison of COSMIC signature 22 with AA mutational signature extracted from all Taiwan HCCs.
  • Fig. S4. Associations between the number of AA signature mutations and clinical and epidemiological variables.
  • Fig. S5. Mutational spectra of all individual HCCs with the AA signature from publicly available data.
  • Fig. S6. Examples of clonal and subclonal AA SBS mutations in Taiwan HCCs.
  • Fig. S7. Two recall notices from Singapore for xi xin products containing aristolochic acid I.
  • Fig. S8. Length distributions of small indels in 98 Taiwan HCC exomes.
  • Fig. S9. Workflow for generating synthetic mutation data for testing.
  • Fig. S10. Receiver operating characteristics of LA-NMF for AA signature detection.
  • Fig. S11. Receiver operating characteristics for AA detection by mSigAct and LA-NMF.
  • Fig. S12. Correlations of AA exposure assigned by mSigAct and LA-NMF.
  • Table S10. Comparison of LA-NMF–extracted AA signatures with COSMIC 22.
  • Table S14. Subclonality analysis of AA mutations in published HCC multiregion sequencing studies.
  • Table S15. Likely AA-containing plants for sale on the internet.
  • Table S16. Selecting the negative binomial dispersion parameter for mSigAct.
  • Table S17. True- and false-positive rates for detection of the AA signature by mSigAct and LA-NMF.
  • Table S18. Comparison of detection of the AA signature by mSigAct and LA-NMF on 1400 publicly available HCC spectra.
  • References (82–90)

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Other Supplementary Material for this manuscript includes the following:

  • Table S1. (Microsoft Excel format). Clinicopathological parameters and statistics on sequencing for 98 HCCs and matched normal tissues from Taiwan.
  • Table S2 (Microsoft Excel format). Percent targeted bases at ≥30× coverage.
  • Table S3 (Microsoft Excel format). FDR estimated from IGV screenshots.
  • Table S4 (Microsoft Excel format). Somatic SBS mutations in Taiwan HCCs.
  • Table S5 (Microsoft Excel format). Somatic indel mutations in Taiwan HCCs.
  • Table S6 (Microsoft Excel format). Drivers identified by MutSigCV and 20/20+ in 98 Taiwan HCCs.
  • Table S7 (Microsoft Excel format). MutSigCV output for 98 Taiwan HCCs.
  • Table S8 (Microsoft Excel format). 20/20+ output for 98 Taiwan HCCs.
  • Table S9 (Microsoft Excel format). AA signature mutations and effects on driver genes in 98 Taiwan HCCs.
  • Table S11 (Microsoft Excel format). List of AA signature–positive HCCs from publicly available data.
  • Table S12 (Microsoft Excel format). Known oncogene and tumor suppressor drivers from COSMIC Cancer Gene Census.
  • Table S13 (Microsoft Excel format). Nonsilent mutations in known cancer driver genes plus genes identified by MutSigCV or 20/20+.
  • Code S1 (.zip format). Code for analyses presented in this paper, including mSigAct.v0.8.R and mSigTools.v0.7.R.
  • Code S2 (.zip format). Analysis and tests of HCCs with mSigAct and the NMF procedure from (3, 4).

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