Hepatoma Res 2018;4:21.10.20517/2394-5079.2018.44© The Author(s) 2018.
Open AccessReview

Pathway analysis provides insight into the genetic susceptibility to hepatocellular carcinoma and insight into immuno-therapy treatment response

1Computational Sciences and Informatics, Complex Adaptive Systems, Arizona State University, Tempe, AZ 85287, USA.

2Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85287, USA.

3School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA.

Correspondence Address: Dr. Kenneth Howard Buetow, Computational Sciences and Informatics, Complex Adaptive Systems, Arizona State University, Tempe, AZ 85287, USA. E-mail:

    Science Editor: Guang-Wen Cao | Copy Editor: Jun-Yao Li | Production Editor: Huan-Liang Wu

    © The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (, which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


    Clear evidence exists for genetic susceptibility to hepatocellular carcinoma (HCC). Genome-wide association studies have identified multiple candidate susceptibility loci. These loci suggest that genetic variation in the immune system may underpin HCC susceptibility. Genes for the antigen processing and presentation pathway have been observed to be significantly enriched across studies and the pathway is identified directly through genome-wide studies of variation using pathway methods. Detailed analysis of the pathway indicates both variation in the antigen presenting loci and in the antigen processing are different in cases in controls. Pathway analysis at the transcriptional level also shows difference between normal liver and liver in individuals with HCC. Assessing differences in the pathway may prove important in improving immune therapy for HCC and in identifying responders for immune checkpoint therapy.

    Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is ranked 5th in global incidence and 2nd in mortality[1]. With the exception of East Asia, the incidence of HCC is increasing in almost all regions of the world and has doubled in the USA since the early 1980s[2]. This increase is attributable to increases in obesity and type II diabetes[3,4]. Liver cancer’s 5-year survival is the second worst among all cancers (18.1%)[5].

    In this manuscript, the role of genetic susceptibility to HCC is examined. Novel tools that evaluate genetic data using collections of genes and their interactions within biologic networks are used to identify key biologic processes driving susceptibility. The relationship of germline and somatic variation is explored. The importance of these findings is assessed in the context of current therapeutic interventions for HCC.

    Somatic genetic etiology of HCC

    Like other solid tumors, at a somatic level, HCC appears to arise via alterations in numerous genes that modify multiple biologic processes. An early whole-genome sequencing effort identified an average of 9718 nucleotide alternations, 271 insertion/deletions, and 41 structural variations per tumor, with substantial variability from tumor to tumor[6]. Within coding sequences, it has been reported that there are an average of 21 synonymous and 64 non-synonymous mutations per tumor[7]. Tumors of larger size are observed to have greater numbers of point mutations, which are speculated to contribute to heterogeneity within the tumors. The Cancer Genome Atlas (TCGA) Research network’s evaluation of HCC[8] finds alterations over-represented in the RAS pathway, WNT pathway, cell cycle regulation pathways and chromatin modification pathways with high mutation rates in TP53 (31%), CTNNB1 (27%), AXIN1 (8%), ARID1A (7%), ARID2 (5%), RB1 (4%), PIK3CA (4%), CDKN2A (2%), KRAS (1%), NRAS (1%), high deletion frequencies of RB1 (19%), CDKN2A (13%), PTEN (7%) and amplification of CCND1 (6%). The most commonly mutated locus was TERT with promoter mutations found in 44% of tumors[8]. The TCGA data unexpectedly also showed high mutation rates in ALB (13%) and APOB (10%).

    Genetic susceptibility to HCC

    In contrast to other common tumors, genetic susceptibility to HCC remains poorly characterized. Studies have identified evidence for familiality of HCC, over and above familial exposures such as HBV infection[9-14]. For example, after accounting for HBV infection, individuals with a family history of HCC have a rate ratio of 2.4[10]. To date, these studies have examined only hepatitis virus associated HCC and have yet to explore the role of obesity and diabetes related susceptibility.

    A limited number of studies have been conducted to identify the loci underpinning this familiality. Original studies focused on candidate genes whose observed single nucleotide polymorphisms (SNPs) could plausibly modify known environmental risk factors for HCC including aflatoxin, alcohol, or tobacco. A meta-analysis of these studies found associations with 5 genes HFE, IL-1B, MnSOD, MDM, and 2UGT1A7[15].

    HCC has had a small number of genome wide association studies (GWAS) conducted with modest success in identifying risk loci. The NHGRI-EBI Catalog lists a total of 11 studies that have identified 22 loci[16]. These studies examine East Asian populations and have included HCC associated with hepatitis B virus (HBV), hepatitis C virus (HCV), and non-alcoholic steatohepatitis (NASH) etiologies. The studies have identified SNPs in the genomic proximity (intronic, upstream and/or downstream) of twenty protein coding loci.

    Clues to the biologic basis of HCC susceptibility across GWAS studies can be identified by looking for non-random enrichment. Using the resources of the Gene Ontology consortium (GO) (, the twenty protein coding loci were examined for biologic process enrichment in Homo sapiens. This enrichment analysis uses the tools of Panther ( Four high level GO processes were observed to be significantly enriched “T cell receptor signaling pathway” (P = 0.0366), “interferon-gamma-mediated signaling pathway” (P = 0.0026), “T cell costimulation” (P = 0.0020), and “antigen processing and presentation of exogenous peptide antigen via MHC class II” (P = 0.0001).

    We have previously looked for inherited susceptibility using genome-wide genotyping and a novel analytic approach that uses biologic networks - Pathways of Distinction Analysis (PoDA)[17]. In PoDA, the network is the unit of analysis and accounts for interactions among features within the network. In this analysis “antigen processing and presentation” was identified as having significant differences in variability in a population of Korean HBV associate HCC cases and controls. Consistent with the results of the enrichment analysis, re-analysis of this dataset with an extended set of 1200 pathways again identified “antigen processing and presentation”, but also “interferon gamma signaling”, “TCR signaling”, and “T cell receptor signaling pathway” [Table 1] suggesting that immune response may be a key driver of HCC susceptibility.

    Table 1

    Updated significant networks identified through pathway of distinction analysis

    PoDA pathway nameSourceDSORNo. of genesNo. of SNPs
    Axon guidance KEGG1.888 3.1699 24513,044
    GPCR downstream signaling REACTOME1.706 2.4122 69516,949
    Focal adhesion KEGG0.802 2.3329 1977999
    Pathways in cancer KEGG0.570 2.2487 28410,406
    MAPK signaling pathway KEGG0.620 2.1152 2457368
    PI3K-Akt signaling pathway KEGG-0.339 2.0837 31410,409
    Calcium signaling pathway KEGG-1.030 1.8479 1638684
    Regulation of actin cytoskeleton KEGG-1.004 1.8207 1955681
    Glycerolipid metabolism KEGG2.003 1.7607 551590
    Mechanism of gene regulation by peroxisome proliferators via ppara BIOCARTA2.371 1.7272 491076
    Interleukin-3, 5 and GM-CSF signalingREACTOME2.969 1.7235 411188
    Glycerophospholipid biosynthesisREACTOME2.201 1.7208 701714
    T cell receptor signaling pathwayBIOCARTA2.493 1.6792 551500
    Dopaminergic synapseKEGG-1.348 1.6651 1165396
    Stabilization and expansion of the E-cadherin adherens junctionNCI/NATURE2.142 1.6630 401449
    Eicosanoid metabolismBIOCARTA3.026 1.6620 16800
    Netrin-mediated signaling eventsNCI/NATURE1.965 1.6620 282400
    Pre-NOTCH expression and processing REACTOME3.240 1.6343 451451
    Purine metabolismKEGG-1.1901.62841504726
    Angiopoietin receptor Tie2-mediated signalingNCI/NATURE2.1631.5806471331
    Circadian entrainmentKEGG-1.4981.5738885919
    Systemic lupus erythematosusKEGG3.8731.5688821185
    Bioactive peptide induced signaling pathwayBIOCARTA2.2761.5677421260
    Role of mef2d in t-cell apoptosisBIOCARTA2.138 1.5522 30946
    Herpes simplex infection KEGG2.816 1.5388 1701994
    Glycosphingolipid biosynthesis - lacto and neolacto series KEGG2.756 1.5285 23535
    Multi-step regulation of transcription by pitx2 BIOCARTA2.935 1.5253 22526
    Retrograde endocannabinoid signaling KEGG-1.990 1.5208 944960
    TCR signaling REACTOME3.001 1.4913 511226
    TPO signaling pathway BIOCARTA2.556 1.4896 23635
    Growth hormone signaling pathway BIOCARTA2.144 1.4813 28768
    Rheumatoid arthritis KEGG2.895 1.4801 84978
    Huntington’s disease KEGG2.156 1.4658 1521647
    Inactivation of gsk3 by akt causes accumulation of b-catenin in alveolar macrophages BIOCARTA2.467 1.4628 32709
    Chaperones modulate interferon signaling pathway BIOCARTA2.486 1.4615 18313
    Phospholipase c signaling pathway BIOCARTA2.886 1.4577 10849
    GnRH signaling pathway KEGG-1.259 1.4488 843622
    Oocyte meiosis KEGG-1.285 1.4371 1022727
    Biosynthesis of unsaturated fatty acids KEGG1.927 1.4342 19495
    GMCSF-mediated signaling events NCI/NATURE1.843 1.4339 30841
    p75 NTR receptor-mediated signalling REACTOME-1.195 1.4335 762466
    E-cadherin signaling in keratinocytes NCI/NATURE2.123 1.4326 21477
    Signaling events mediated by HDAC Class III NCI/NATURE1.927 1.4323 26565
    Keratan sulfate/keratin metabolism REACTOME1.962 1.4251 28447
    Morphine addiction KEGG-3.158 1.4243 864524
    IL3-mediated signaling events NCI/NATURE2.199 1.4233 22399
    Intestinal immune network for IgA production KEGG3.740 1.4224 45506
    lectin induced complement pathway BIOCARTA2.541 1.4167 11359
    Leishmaniasis KEGG2.734 1.4130 68927
    Alternative complement pathway BIOCARTA2.196 1.4075 11236
    Autoimmune thyroid disease KEGG2.835 1.4056 39513
    Graft-versus-host disease KEGG3.079 1.4025 33240
    Activation of pkc through g-protein coupled receptors BIOCARTA1.733 1.3968 11892
    Allograft rejection KEGG3.327 1.3952 30253
    Costimulation by the CD28 family REACTOME2.648 1.3931 621270
    Eicosanoid ligand-binding receptors REACTOME2.798 1.3899 11174
    Staphylococcus aureus infection KEGG3.410 1.3766 52504
    Serotonergic synapse KEGG-1.854 1.3733 733128
    N-glycan antennae elongation in the medial/trans-Golgi REACTOME2.122 1.3729 14396
    Integrins in angiogenesis NCI/NATURE-1.929 1.3622 742110
    Tandem pore domain potassium channels REACTOME2.299 1.3589 4206
    Fatty acid elongation in mitochondria REACTOME2.226 1.3579 12170
    IL5-mediated signaling events NCI/NATURE2.080 1.3568 12304
    Antigen processing and presentation KEGG3.506 1.3397 65400
    Asthma KEGG3.713 1.3246 31200
    Neurotransmitter release cycle REACTOME1.846 1.3233 9326
    Classical complement pathway BIOCARTA2.682 1.3164 12239
    Antigen processing and presentation BIOCARTA2.938 1.2857 952
    Interferon gamma signaling REACTOME3.080 1.0558 612598
    Antigen processing-cross presentation REACTOME2.187 1.0371 591962

    The role of antigen processing and presentation in HCC

    To assess what might be the key factors within “antigen processing and presentation”, we performed analysis utilizing a modified version of PoDA using the Korean HCC dataset. In this analysis, all 400 of the SNPs genotyped in the data set for the 65 genes in the pathway were contrasted in the cases and controls. After assessing significance of the odds ratio for the entire set of SNPs, each individual SNP was removed one at a time from the dataset and the significance was re-assessed. The SNP which least affected the significance of the odds ratio was then removed and the process was repeated. SNPs were progressively removed in this “stepdown” procedure until the significance of the odds ratio was no longer improved. Interestingly, it was observed that initial removal of SNPs substantially improved significance of the difference between cases and controls. When stepdown was completed, a total of 49 SNPs in 26 genes were observed [Table 2].

    Table 2

    Significant genes and SNPs within the KEGG antigen processing and presentation pathway

    Gene symbolGene nameSNP (rs id)
    CD4CD4 moleculers1075835
    CD74CD74 molecule, major histocompatibility complex, class II invariant chainrs2748249
    CIITAClass II, major histocompatibility complex, transactivatorrs6498122
    CIITAClass II, major histocompatibility complex, transactivatorrs7203275
    CIITAClass II, major histocompatibility complex, transactivatorrs11074934
    CIITAClass II, major histocompatibility complex, transactivatorrs6498119
    CTSSCathepsin Srs11204722
    HLA-AMajor histocompatibility complex, class I, A rs12202296
    HLA-DMAMajor histocompatibility complex, class II, DM alphars11539216
    HLA-DMAMajor histocompatibility complex, class II, DM alphars17617515
    HLA-DMBMajor histocompatibility complex, class II, DM beta rs3132132
    HLA-DMBMajor histocompatibility complex, class II, DM beta rs714289
    HLA-DOAMajor histocompatibility complex, class II, DO alpha rs3129304
    HLA-DOAMajor histocompatibility complex, class II, DO alpha rs3129303
    HLA-DOAMajor histocompatibility complex, class II, DO alpha rs3130602
    HLA-DOAMajor histocompatibility complex, class II, DO alpha rs3129302
    HLA-DPB1Major histocompatibility complex, class II, DP beta 1rs9277378
    HLA-DQA2Major histocompatibility complex, class II, DQ alpha 2rs9275356
    HLA-DQA2Major histocompatibility complex, class II, DQ alpha 2rs9276427
    HLA-DQA2Major histocompatibility complex, class II, DQ alpha 2rs9469266
    HLA-DRAMajor histocompatibility complex, class II, DR alpha rs7194
    HLA-GMajor histocompatibility complex, class I, G rs2517898
    HSP90AB1Heat shock protein 90kDa alpha (cytosolic), class B member 1rs504697
    HSPA2Heat shock 70kDa protein 2rs4313734
    HSPA4Heat shock 70kDa protein 4rs7702889
    HSPA5Heat shock 70kDa protein 5rs12009
    HSPA8Heat shock 70kDa protein 8rs4936770
    KIR2DL3Killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 3rs9797797
    KIR2DL3Killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 3rs13344915
    KIR2DL4Killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 4rs10500318
    KIR2DL4Killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 4rs3865509
    KIR2DS4Killer cell immunoglobulin-like receptor, two domains, short cytoplasmic tail, 4rs11673276
    KLRD1Killer cell lectin-like receptor subfamily D, member 1 rs17206564
    LOC100509457HLA class II histocompatibility antigen, DQ alpha 1 chain-likers2647015
    LOC100509457HLA class II histocompatibility antigen, DQ alpha 1 chain-likers2859090
    LOC100509457HLA class II histocompatibility antigen, DQ alpha 1 chain-likers9272219
    RFXAPRegulatory factor X-associated proteinrs6563500
    TAP1Transporter 1, ATP-binding cassette, sub-family B (MDR/TAP)rs4148882
    TAP2Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP)rs3819720
    TAP2Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP)rs2228396
    TAP2Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP)rs241428
    TAP2Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP)rs9784758
    TAP2Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP)rs241431

    While the genes identified included key genes seen in the GWAS catalog, specifically members of HLA class II, other genes associated with antigen processing were also observed [Figure 1]. The design of Genome-wide association studies does not permit the specific etiologic effects of the variation. By design, the variation used in the studies is not chosen for function, but instead the ability to test differences between populations. The high linkage disequilibrium observed between variations in humans further complicates the capacity to interpret the molecular mechanisms of action.

    Figure 1. Gene-based SNPs associated with HCC in the antigen processing and presentation pathway. The genes and their relationships obtained from KEGG’s antigen processing and presentation pathway. Purple boxes with white letters indicate genes SNP variations associated with HCC from the PoDA stepdown analysis. Removal of these loci reduced the overall threshold of significance below that observed for the entire pathway. Genes in open boxes (with orange letters) indicate gens which could be removed without altering significance of the pathway’s association. HCC: hepatocellular carcinoma

    Nevertheless, this study identifies variation of genes of potential significance in etiology. Of particular interest are the proteasome (HSPA2, HSPA4, HSPA5 HSP90AB1), endoplasmic reticulum TAP1, TAP2, CANX), and exosome (LGMN) genes associated with the processing of antigens so that they may be presented by HLA loci. The pathway also identifies genes on the surface of immune cells - NK cells (KIR2DL3, KIR2DL4, and KIR2DL5) and CD4 T cells (CD4) that may compromise immune surveillance and regulation.

    It is possible to examine the intra-pathway associations of the variants. Using the analytic tool PLINK[18], one can estimate the association (r2) between loci in cases and controls [Table 3]. As expected by the PoDA analysis, variants within the pathways are associated with one another. Both variants within loci and between loci are observed to be associated. Interestingly, the magnitude of associations differs between cases and controls. This confirms that the pathway utilizes information (interactions between loci) that would not be observed in simple single locus GWAS assessments.

    Table 3

    Association of case and control SNP variation with r2 greater than 0.1 within the KEGG antigen processing and presentation pathway

    SNP_ASNP_BCase r2Control r2
    SNP_A-4289896 - KIR2DL3SNP_A-8561730 - KIR2DL30.880.95
    SNP_A-8566010 - HLA-DQA1LSNP_A-2200530 - TAP20.380.20
    SNP_A-8515749 - HLA-GSNP_A-8649593 - HLA-A0.160.37
    SNP_A-2214036 - HLA-DQA1LSNP_A-4206711 - HLADQA10.160.14
    SNP_A-8524421 - KIR2DL4SNP_A-8613821 - KIR2DS40.14< 0.1
    SNP_A-1985650 - HLA-DOASNP_A-8430032 - KIR2DL30.12< 0.1
    SNP_A-2214036 - HLA-DQA1LSNP_A-2200530 - TAP20.11< 0.1
    SNP_A-8451478 - TAP2SNP_A-8415280 - TAP20.10< 0.1
    SNP_A-2305613 - CSTBSNP_A-1944939 - CSTB< 0.11.00
    SNP_A-8566010 - HLA-DQA1LSNP_A-1985650 - HLA-DOA< 0.10.28
    SNP_A-4223083 - HLA-DQA1LSNP_A-8415280 - CIITA< 0.10.18
    SNP_A-4206711 - HLA-DQA1SNP_A-8451478 - TAP2< 0.10.16
    SNP_A-4277940 - HLA-DQA1LSNP_A-1985650 - HLA-DOA< 0.10.14

    “Antigen processing and presentation” transcriptional activity

    It is possible to assess whether the germline variation in “antigen processing and presentation” translates into functionally significant difference in normal liver when contrasted to tumor adjacent liver and HCC. This can be done by looking at the transcriptome of these tissues using publicly accessible data from the Gene Tissue Expression project (GTEx)[19-21] and the TCGA[8]. Data from both sources were processed with a common analytic pipeline that included realignment of sequencing reads to Hg38[22,23], uniform count scoring[24] and adjustment for over-dispersion[25,26].

    The scored transcript data was then evaluated using the novel pathway analysis tool PathOlogist[27-29]. PathOlogist utilizes the logical information contained within networks to compute network scores. By utilizing the structure of a network, in this approach the conditional state of genes determines expectations for the state of other members of the network. Two different scores are provided. The first assesses whether the activity state of the network differs. In the second, an assessment of the logical state of the network is measured as consistency. Consistency determines whether the transcription patterns follow the expected logic of the network.

    Examination of the transcriptional state of “antigen processing and presentation” provides additional insight into the susceptibility findings. First, “antigen processing and presentation” activity is observed to be significantly higher in normal liver (GTEx) compared to TCGA tumor-adjacent (adjusted P < 0.0001) and tumor (adjusted P < 0.0001) while no difference is observed between tumor adjacent and tumor (adjusted P = 0.87). This suggests that individuals with HCC have a different “antigen processing and presentation” profile in both their non-tumor and tumor than normal liver.

    No significant difference is observed between the consistency scores of normal liver (GTEx) and TCGA tumor-adjacent (adjusted P = 0.64) and tumor adjacent and tumor (adjusted P = 0.89b) for “antigen processing and presentation”. However, significant difference is observed between normal liver and tumor (adjusted P < 0.0001). This suggests that “antigen processing and presentation” may be a target of mutagenesis in HCC.

    Immune checkpoint therapy and “antigen processing and presentation”

    “Antigen processing and presentation” may be an important mediator of treatment response for HCC. Immune checkpoint therapy is dramatically altering the cancer therapeutic landscape[30]. Checkpoint therapy targets inhibitory signals to the immune system such as CTLA-4 and PD-1/PD-L1. These treatments show promising, durable response results in previously treatment resistant cancers such as melanoma[31] and non-small cell lung cancer[32]. The US FDA has approved checkpoint therapy for second line treatment of HCC. Numerous studies are in progress to assess the efficacy as 1st line treatment (

    Unfortunately only a minority of individuals respond to the treatments[33]. It is unknown what mediates response. Indicators of response include DNA mismatch repair capabilities[34] and tumor mutational burden[35]. But these have poor predictive capabilities.

    For checkpoint therapy to work, an intact immune response is required. As implied from the indicators of response, the immune system must have the capacity to recognize tumor antigens as foreign. This recognition is mediated through antigen processing and presentation. Inherited variability may indicate individuals in which this capacity is compromised. Moreover, variation in these processes may indicate individual response to immune directed therapeutic interventions.

    In conclusion, the results of the germline variation studies suggest that immune mediating processes are polymorphic in the population and systematically different in HCC. Individuals with HCC have significantly lower activity for these processes and HCC shows alterations in the “logic” of the processing and presentation pathways. As such, it may be possible to predict response to checkpoint therapy through the evaluation of the inherited genetic state of “antigen processing and presentation”. Understanding these differences may provide opportunities designing new immune checkpoint modulators and provide a rational basis for combinatorial therapy.



    The Korean HCC case-control study was collected by Dr. Myung Lyu (NCI/NIH/DHSS) and Dr. Young-Hwa Chung (Asan Medical Center, Seoul, South Korea).

    Authors’ contributions

    Data analysis: Lu YK, Brill JM, Aghili A

    Design of the work, data analysis, manuscript drafting and revising, and final approval of the version to be published: Buetow KH

    Availability of data and materials

    Not applicable.

    Financial support and sponsorship


    Conflicts of interest

    Buetow KH is an advisor for the Bristol Myers Squibb IO-ICON project.

    Ethical approval and consent to participate

    Not applicable.

    Consent for publication

    Not applicable.


    © The Author(s) 2018.


    • 1. Torre LA, Siegel RL, Ward EM, Jemal A. Global cancer incidence and mortality rates and trends - an update. Cancer Epidemiol Prev Biomark 2016;25:16-27.

    • 2. Petrick JL, Braunlin M, Laversanne M, Valery PC, Bray F, McGlynn KA. International trends in liver cancer incidence, overall and by histologic subtype, 1978-2007. Int J Cancer 2016;139:1534-45.

    • 3. McGlynn KA, Petrick JL, London WT. Global epidemiology of hepatocellular carcinoma: an emphasis on demographic and regional variability. Clin Liver Dis 2015;19:223-38.

    • 4. Makarova-Rusher OV, Altekruse SF, McNeel TS, Ulahannan S, Duffy AG, Graubard BI, Greten TF, McGlynn KA. Population attributable fractions of risk factors for hepatocellular carcinoma in the United States. Cancer 2016;122:1757-65.

    • 5. Jemal A, Ward EM, Johnson CJ, Cronin KA, Ma J, Ryerson B, Mariotto A, Lake AJ, Wilson R, Sherman RL, Anderson RN, Henley SJ, Kohler BA, Penberthy L, Feuer EJ, Weir HK. Annual report to the nation on the status of cancer, 1975-2014, featuring survival. J Natl Cancer Inst 2017;109:djx030.

    • 6. Fujimoto A, Furuta M, Totoki Y, Tsunoda T, Kato M, Shiraishi Y, Tanaka H, Taniguchi H, Kawakami Y, Ueno M, Gotoh K, Ariizumi S, Wardell CP, Hayami S, Nakamura T, Aikata H, Arihiro K, Boroevich KA, Abe T, Nakano K, Maejima K, Sasaki-Oku A, Ohsawa A, Shibuya T, Nakamura H, Hama N, Hosoda F, Arai Y, Ohashi S, Urushidate T, Nagae G, Yamamoto S, Ueda H, Tatsuno K, Ojima H, Hiraoka N, Okusaka T, Kubo M, Marubashi S, Yamada T, Hirano S, Yamamoto M, Ohdan H, Shimada K, Ishikawa O, Yamaue H, Chayama K, Miyano S, Aburatani H, Shibata T, Nakagawa H. Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer. Nat Genet 2016;48:500-9.

    • 7. Schulze K, Imbeaud S, Letouzé E, Alexandrov LB, Calderaro J, Rebouissou S, Couchy G, Meiller C, Shinde J, Soysouvanh F, Calatayud AL, Pinyol R, Pelletier L, Balabaud C, Laurent A, Blanc JF, Mazzaferro V, Calvo F, Villanueva A, Nault JC, Bioulac-Sage P, Stratton MR, Llovet JM, Zucman-Rossi J. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet 2015;47:505-11.

    • 8. Cancer Genome Atlas Research Network. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 2017;169:1327-41.e23.

    • 9. Shen FM, Lee MK, Gong HM, Cai XQ, King MC. Complex segregation analysis of primary hepatocellular carcinoma in Chinese families: interaction of inherited susceptibility and hepatitis B viral infection. Am J Hum Genet 1991;49:88-93.

    • 10. Yu MW, Chang HC, Liaw YF, Lin SM, Lee SD, Liu CJ, Chen PJ, Hsiao TJ, Lee PH, Chen CJ. Familial risk of hepatocellular carcinoma among chronic hepatitis B carriers and their relatives. J Natl Cancer Inst 2000;92:1159-64.

    • 11. Hassan MM, Spitz MR, Thomas MB, Curley SA, Patt YZ, Vauthey JN, Glover KY, Kaseb A, Lozano RD, El-Deeb AS, Nguyen NT, Wei SH, Chan W, Abbruzzese JL, Li D. The association of family history of liver cancer with hepatocellular carcinoma: a case-control study in the United States. J Hepatol 2009;50:334-41.

    • 12. Dragani TA. Risk of HCC: genetic heterogeneity and complex genetics. J Hepatol 2010;52:252-7.

    • 13. Turati F, Edefonti V, Talamini R, Ferraroni M, Malvezzi M, Bravi F, Franceschi S, Montella M, Polesel J, Zucchetto A, La Vecchia C, Negri E, Decarli A. Family history of liver cancer and hepatocellular carcinoma. Hepatology 2012;55:1416-25.

    • 14. Caruso S, Calderaro J, Letouzé E, Nault JC, Couchy G, Boulai A, Luciani A, Zafrani ES, Bioulac-Sage P, Seror O, Imbeaud S, Zucman-Rossi J. Germline and somatic DICER1 mutations in familial and sporadic liver tumors. J Hepatol 2017;66:734-42.

    • 15. Jin F, Xiong WJ, Jing JC, Feng Z, Qu LS, Shen XZ. Evaluation of the association studies of single nucleotide polymorphisms and hepatocellular carcinoma: a systematic review. J Cancer Res Clin Oncol 2011;137:1095-104.

    • 16. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, Junkins H, McMahon A, Milano A, Morales J, Pendlington ZM, Welter D, Burdett T, Hindorff L, Flicek P, Cunningham F, Parkinson H. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res 2017;45:D896-901.

    • 17. Braun R, Buetow K. Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data. PLoS Genet 2011;7:e1002101.

    • 18. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75.

    • 19. GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 2015;348:648-60.

    • 20. GTEx Consortium; Laboratory, Data Analysis &Coordinating Center (LDACC)-Analysis Working Group; Statistical Methods groups-Analysis Working Group; Enhancing GTEx (eGTEx) groups; NIH Common Fund; NIH/NCI; NIH/NHGRI; NIH/NIMH; NIH/NIDA; Biospecimen Collection Source Site-NDRI; Biospecimen Collection Source Site-RPCI; Biospecimen Core Resource-VARI; Brain Bank Repository-University of Miami Brain Endowment Bank; Leidos Biomedical-Project Management; ELSI Study; Genome Browser Data Integration & Visualization-EBI; Genome Browser Data Integration & Visualization-UCSC Genomics Institute, University of California Santa Cruz; Lead analysts; Laboratory, Data Analysis & Coordinating Center (LDACC); NIH program management; Biospecimen collection; Pathology; eQTL manuscript working group; Battle A, Brown CD, Engelhardt BE, Montgomery SB. Genetic effects on gene expression across human tissues. Nature 2017;550;204-13.

    • 21. Saha A, Kim Y, Gewirtz ADH, Jo B, Gao C, McDowell IC, GTEx Consortium GTE, Engelhardt BE, Battle A. Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Res 2017;27:1843-58.

    • 22. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods 2015;12:357-60.

    • 23. Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc 2016;11:1650-67.

    • 24. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014;30:923-30.

    • 25. Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 2014;15:R29.

    • 26. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47.

    • 27. Efroni S, Schaefer CF, Buetow KH. Identification of key processes underlying cancer phenotypes using biologic pathway analysis. PLoS One 2007;2:e425.

    • 28. Efroni S, Carmel L, Schaefer CG, Buetow KH. Superposition of transcriptional behaviors determines gene state. PLoS One 2008;3:e2901.

    • 29. Greenblum SI, Efroni S, Schaefer CF, Buetow KH. The PathOlogist: an automated tool for pathway-centric analysis. BMC Bioinformatics 2011;12:133.

    • 30. Sharma P, Allison JP. The future of immune checkpoint therapy. Science 2015;348:56-61.

    • 31. Garbe C, Peris K, Hauschild A, Saiag P, Middleton M, Bastholt L, Grob JJ, Malvehy J, Newton-Bishop J, Stratigos AJ, Pehamberger H, Eggermont AM, European Dermatology Forum (EDF) EDF, European Association of Dermato-Oncology (EADO)., European Organisation for Research and Treatment of Cancer (EORTC) EORT. Diagnosis and treatment of melanoma. European consensus-based interdisciplinary guideline - update 2016;63:201-17.

    • 32. Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, Patnaik A, Aggarwal C, Gubens M, Horn L, Carcereny E, Ahn MJ, Felip E, Lee JS, Hellmann MD, Hamid O, Goldman JW, Soria JC, Dolled-Filhart M, Rutledge RZ, Zhang J, Lunceford JK, Rangwala R, Lubiniecki GM, Roach C, Emancipator K, Gandhi L, KEYNOTE-001 Investigators. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med 2015;372:2018-28.

    • 33. Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 2017;168:707-23.

    • 34. Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, Lu S, Kemberling H, Wilt C, Luber BS, Wong F, Azad NS, Rucki AA, Laheru D, Donehower R, Zaheer A, Fisher GA, Crocenzi TS, Lee JJ, Greten TF, Duffy AG, Ciombor KK, Eyring AD, Lam BH, Joe A, Kang SP, Holdhoff M, Danilova L, Cope L, Meyer C, Zhou S, Goldberg RM, Armstrong DK, Bever KM, Fader AN, Taube J, Housseau F, Spetzler D, Xiao N, Pardoll DM, Papadopoulos N, Kinzler KW, Eshleman JR, Vogelstein B, Anders RA, Diaz LA Jr. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 2017;357:409-13.

    • 35. Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM, Miller V, Stephens PJ, Daniels GA, Kurzrock R. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol Cancer Ther 2017;16:2598-608.


    Cite This Article

    Lu YK, Brill JM, Aghili A, Buetow KH. Pathway analysis provides insight into the genetic susceptibility to hepatocellular carcinoma and insight into immuno-therapy treatment response. Hepatoma Res 2018;4:21.


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