Research ArticleCancer

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

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Science Translational Medicine  18 Oct 2017:
Vol. 9, Issue 412, eaan6446
DOI: 10.1126/scitranslmed.aan6446
  • Fig. 1. Evidence of AA exposure in Taiwan HCCs.

    (A and B) Sample exome spectra of individual AA-exposed UTUCs (A) and BCs (B) from Taiwan. (C and D) Sample exome spectra of individual Taiwan HCCs with high (C) and moderate (D) levels of the AA signature. (E) Sample Taiwan HCC without AA signature. In the major plots in (A) to (E), each bar indicates the proportion of mutations in a particular trinucleotide context. In the AA signature (A to D), the overwhelming majority of mutations are T:A>A:T. By convention, mutations are shown as T>A (for example) rather than A>T, although AA mutations are physical consequences of adducts on adenines that cause A>T mutations (9, 20, 2224). In tumors strongly mutagenized by AA, the most prominent peak is at CTG>CAG (CAG>CTG on the complementary strand), indicated in (A), often with additional prominent peaks at CTA>CAA and ATG>AAG. Small plots at right in (A) to (E) show transcription strand bias. Mut count, mutation count. (F) Mutation spectra–based principal components analysis of HCCs from Taiwan, China (52), and Japan (53), plus AA-exposed UTUCs (29) and BCs (31) and an AA-exposed cell line (29). The most distinguishable features are the T>A mutations induced by AA, which are reflected in PC1. PC1 explains 35% of the variance, and PC2 explains 5.5%.

  • Fig. 2. Mutational signature exposures in Taiwan HCCs and summary of AA signature mutations.

    (A) Estimated numbers of mutations due to each mutational signature in each HCC. AA is COSMIC signature 22. W6 is from (53). COSMIC signatures 4 and 24 reflect known exogenous risk factors for HCC: tobacco smoking and aflatoxin exposure, respectively. MMR, mismatch repair. (B) Proportions of tumors with the AA signature in various groups of HCCs. “Southeast Asia” indicates Southeast Asia excluding Vietnam; “Mayo Clinic” denotes a group of HCCs from patients treated at that clinic for whom there was no country information and who we speculate may have traveled from Asia for treatment; “No information” denotes TCGA HCCs from biobanks for which there is no information on geographic origin. (C) Densities and counts of AA signature mutations among tumors with the AA signature. Each mutation is associated with a weighted assignment of the probability that it was caused by the AA signature (see Materials and Methods). The weighted count of AA signature mutations is the sum of these probabilities across all mutations in the tumors. The geographical regions indicated at the right of (B) also apply to (C).

  • Fig. 3. Sample spectra of HCCs with the AA signature.

    Display conventions are the same as in Fig. 1.

  • Fig. 4. Global distribution of mutagenesis associated with aristolochic acid and derivatives in HCCs.

    The pie chart labeled “Southeast Asia” includes both Vietnam and the other Southeast Asian HCCs. Pie chart areas are proportional to the number of HCCs in the given group.

  • Fig. 5. High burdens of AA signature mutations and predicted immunogenicity in Taiwan HCCs.

    (A) AA signature mutations constitute the majority of mutations in most Taiwan HCCs affected by the AA mutational signature. (B) Many more in silico predicted candidate neoantigens in AA HCCs than non-AA HCCs; P value by Wilcoxon rank sum test.

  • Table 1. Summary of AA signature mutations in HCCs.
    Geographic
    origin
    Number
    of HCCs
    Median
    (all SBS
    mutations/
    Mb)
    HCCs with
    AA signature
    Weighted counts* of AA signature
    SBS mutations among HCCs with the signature
    Number of
    HCCs with
    nonsilent AA
    signature
    SBS
    mutations in
    known
    drivers
    P value
    for difference
    from Taiwan
    in proportion
    of HCCs with
    the AA signature
    (Fisher’s exact
    test)
    SBS
    mutations/Mb
    Number of
    nonsilent SBS
    mutations
    Number of
    nonsilent SBS
    mutations in
    known driver
    Number
    of HCCs
    Proportion
    of HCCs
    MedianMeanMedianMeanMedianMean
    New data, this paper
      Taiwan983.33760.782.264.94101.6223.32.193.9457
    Previously published data
      China891.94420.470.291.075.832.60.060.5092.0 × 10−5
      Southeast Asia
    (excluding
    Vietnam)
    95.7450.562.923.0762.343.10.080.9720.22
      Vietnam263.1050.193.423.71125.4126.81.612.3657.3 × 10−8
      Korea2311.78290.131.001.2941.456.21.061.18192.1 × 10−30
      Japan4774.82130.0270.600.948.213.50.040.2332.2 × 10−60
      North
    America
    2092.36100.0480.992.6032.984.40.531.5152.6 × 10−40
      Europe2301.6740.0170.359.7215.216.00.340.3507.3 × 10−49
      Mayo Clinic892.94190.211.302.2739.277.00.891.46116.0 × 10−15
      No
    information
    302.7950.170.460.5713.917.10.300.7622.6 × 10−9
      Total
    non-Taiwan
    1400

    *We ascribe the AA signature to a proportion of each SBS as described in Materials and Methods. Because the AA signature is dominated by A:T>T:A SBS, when the activities of other signatures with these SBS mutations are low or absent, this approximates the number of A:T>T:A SBS mutations. The weighted counts are the sums of the proportions of SBS mutations ascribed to the AA signature.

    P = 7.1 × 10−7, for all Southeast Asia, including Vietnam.

    ‡Includes 10 additional TCGA HCCs: 1 Russia, 5 Brazil, 4 South Korea; 1 Brazil HCC has the AA signature.

    Supplementary Materials

    • www.sciencetranslationalmedicine.org/cgi/content/full/9/412/eaan6446/DC1

      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 S1. Clinicopathological parameters and statistics on sequencing for 98 HCCs and matched normal tissues from Taiwan.

      Table S2. Percent targeted bases at ≥30× coverage.

      Table S3. FDR estimated from IGV screenshots.

      Table S4. Somatic SBS mutations in Taiwan HCCs.

      Table S5. Somatic indel mutations in Taiwan HCCs.

      Table S6. Drivers identified by MutSigCV and 20/20+ in 98 Taiwan HCCs.

      Table S7. MutSigCV output for 98 Taiwan HCCs.

      Table S8. 20/20+ output for 98 Taiwan HCCs.

      Table S9. AA signature mutations and effects on driver genes in 98 Taiwan HCCs.

      Table S10. Comparison of LA-NMF–extracted AA signatures with COSMIC 22.

      Table S11. List of AA signature–positive HCCs from publicly available data.

      Table S12. Known oncogene and tumor suppressor drivers from COSMIC Cancer Gene Census.

      Table S13. Nonsilent mutations in known cancer driver genes plus genes identified by MutSigCV or 20/20+.

      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.

      Code S1. Code for analyses presented in this paper, including mSigAct.v0.8.R and mSigTools.v0.7.R.

      Code S2. Analysis and tests of HCCs with mSigAct and the NMF procedure from (3, 4).

      References (8290)

    • 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)

      [Download PDF]

      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).

      [Download Tables]

      [Download Codes S1 and S2]

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