Impact of intra-individual molecular heterogeneity in personalized treatment of hepatocellular carcinoma


  • Augusto Villanueva,

    1. HCC Translational Research Laboratory Barcelona-Clínic Liver Cancer Group Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Liver Unit, Hospital Clínic Barcelona, Spain
    2. Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) Instituto Carlos III Madrid, Spain
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  • Josep M. Llovet

    1. HCC Translational Research Laboratory Barcelona-Clínic Liver Cancer Group Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Liver Unit, Hospital Clínic Barcelona, Spain
    2. Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) Instituto Carlos III Madrid, Spain
    3. Liver Cancer Program Mount Sinai School of Medicine New York, NY
    4. Institució Catalana de Recerca i Estudis Avançats Barcelona, Spain
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  • Potential conflict of interest: Nothing to report.

Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883-892. (Reprinted with permission.)


Intratumor heterogeneity may foster tumor evolution and adaptation and hinder personalized-medicine strategies that depend on results from single tumor-biopsy samples. To examine intratumor heterogeneity, we performed exome sequencing, chromosome aberration analysis, and ploidy profiling on multiple spatially separated samples obtained from primary renal carcinomas and associated metastatic sites. We characterized the consequences of intratumor heterogeneity using immunohistochemical analysis, mutation functional analysis, and profiling of messenger RNA expression. Phylogenetic reconstruction revealed branched evolutionary tumor growth, with 63 to 69% of all somatic mutations not detectable across every tumor region. Intratumor heterogeneity was observed for a mutation within an autoinhibitory domain of the mammalian target of rapamycin (mTOR) kinase, correlating with S6 and 4EBP phosphorylation in vivo and constitutive activation of mTOR kinase activity in vitro. Mutational intratumor heterogeneity was seen for multiple tumor-suppressor genes converging on loss of function; SETD2, PTEN, and KDM5C underwent multiple distinct and spatially separated inactivating mutations within a single tumor, suggesting convergent phenotypic evolution. Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor. Allelic composition and ploidy profiling analysis revealed extensive intratumor heterogeneity, with 26 of 30 tumor samples from four tumors harboring divergent allelic-imbalance profiles and with ploidy heterogeneity in two of four tumors. Intratumor heterogeneity can lead to underestimation of the tumor genomics landscape portrayed from single tumor-biopsy samples and may present major challenges to personalized-medicine and biomarker development. Intratumor heterogeneity, associated with heterogeneous protein function, may foster tumor adaptation and therapeutic failure through Darwinian selection. (Funded by the Medical Research Council and others.)


Clinical decision-making for mainstream cancer therapies (i.e., surgery, conventional chemotherapy, and radiation) is mostly based on tumor stage. In these instances, molecular prognostic or predictive variables are usually not included in cancer management algorithms. However, with the advent of molecular-targeted therapies, personalized approaches are increasingly being introduced in routine clinical cancer care. Under this new framework, selective therapies are administered based on the molecular alterations that dominate tumor progression on an individual basis. There are some successful examples of personalized oncology (Table 1),1-6 as is the recent case of vemurafenib in BRAF-mutated melanoma2 or crizotinib in lung cancer with ALK rearrangements.5 The efficacy of this model pivots on the identification and selective blockade of previously identified oncogene addiction loops, a concept that establishes a hierarchy among the constellation of molecular changes present in a given tumor.7 From a therapeutic standpoint, those drivers of tumor progression are the ideal targets for therapies, since they lead to outstanding antitumoral responses. Personalized oncology is not only restricted to tailored therapies but also to prognosis prediction; there are gene signatures defining disease progression and the need for adjuvant chemotherapy (e.g., MammaPrint, which has been approved by the US Food and Drug Administration for breast cancer).

Table 1. Oncogene Addiction Loops Exploited Therapeutically (Biomarkers Obtained from Single Biopsy)
BiomarkerDrugTumorClinical BenefitReference
  1. EGFR, epidermal growth factor receptor; GI, gastrointestinal; HR, hazard ratio; OS, overall survival; PFS, progression-free survival; RR, response rate; TTP, time to disease progression.

EGFR mutationErlotinib/GefitinibLungImproved PFS (10.8 months versus 5.4 months, HR 0.3) and RR (73% versus 30%)1
BRAF mutationVemurafenibMelanomaImproved OS (6 months, 84% versus 64%) and RR (48% versus 5%)2
HER2 amplificationTrastuzumabBreastImproved TTP (7.4 versus 4.6) and RR (50% versus 32%)3
CD117 overexpressionImatinibGI stromal tumorPartial response (53%), stable disease (28%)4
EML4-ALK fusionCrizotinibLungOverall response (57%), 6-month PFS (72%)5
KRAS wild-typeCetuximabColorectalImproved OS (9.5 months versus 4.8 months, HR 0.55) and RR (12.8 versus 1.2%)6

Unfortunately, only a limited number of cancer patients will benefit from personalized approaches. For instance, around 3% of non–small cell lung cancers have ALK rearrangements; consequently, proof-of-concept trials are needed that will screen 1,500 patients to ultimately treat 82 cases.5 In most tumors, as is the case with hepatocellular carcinoma (HCC), no oncogenic addiction loops have yet been identified. Although molecular therapies such as sorafenib are effective in advanced HCC,8 its wide range of targets makes it difficult to identify specific molecular drivers in these patients. This partially justifies the lack of predictive biomarkers of sorafenib response from a recent phase 3 registration trial.9 Many candidate oncogenic addiction loops have been evaluated in experimental models of HCC (e.g., CTNNB1, IGF1R, FGF19, CCND1, IGF2), but none has yet entered advanced clinical developmental phases using trial enrichment schemes.10

To fully deploy personalized approaches in oncology, it is necessary to identify driver molecular events in a given tumor by using markers from tissue biopsies, and less commonly from serum samples. Hence, it has been assumed that molecular information found in a tumor biopsy (e.g., mutations, DNA copy number changes, DNA rearrangements) recapitulates the molecular events of the whole neoplasm. This concept has been challenged by Gerlinger et al., who reported intratumor heterogeneity in primary renal cell carcinoma and associated metastasis by testing dense scrutiny of mutations using next-generation sequencing. Sampling included nine specimens from the primary tumor and additional specimens from two metastatic sites, all from the same individual. They identified 128 nonsynonymous mutations with different regional distribution. The results included 40 mutations ubiquitous to all specimens, 59 shared by several but not all regions and 29 unique to specific specimens (so-called private mutations). Thus, most somatic mutations (∼65%) were not detected across every tumor region explored. One such target was the mutation of mammalian target of rapamycin (mTOR) affecting the kinase domain (L2431P), which correlated with mTOR pathway activation in human samples and in experimental models of renal cancer. This finding suggests that genetic intratumor heterogeneity was also inducing functional heterogeneity. Interestingly, one of the samples from the primary tumor shared mutations with the metastatic sites. The gene expression data revealed that this same specimen also shared a gene signature with the metastasis, pointing toward a possible location of metastasis-enabling cells within the primary tumor. Based on these data, authors inferred ancestral relationships and were able to construct a phylogenetic tree with all tumor specimens from the same individual. These findings are in line with the hypothesis of clonal evolution,11 a model that applies Darwinian selection rules to justify constant evolutionary changes in cancers and provides a general mechanistic framework to explain tumor heterogeneity and drug resistance.12

Additional evidence in other malignancies suggests frequent intra-individual heterogeneity in advanced cancer stages. For instance, a study analyzing mutations in different lesions from a patient with metastatic pancreatic cancer found a mixture of cellular subclones in the primary tumor that correlated with molecular changes in metastasis, an additional clue for the presence of metastasis-enabling cells in the primary tumor.13 Data from a similar report focusing on chromosomal aberrations also showed considerable intratumor heterogeneity in pancreatic cancer, probably responsible for independent metastasis.14 Strikingly, sophisticated mathematical modeling of pancreatic metastasis kinetics indicates that all patients are expected to harbor subclones of metastasis-enabling cells in the primary tumor at the time of diagnosis, even when tumor size is fairly small.15 There is also some evidence of intratumor heterogeneity in HCC. A sequencing study performed on a recurrent HCC after surgical resection showed 10 different alterations and distinct cell populations across the primary tumor and the recurrence.16, 17 Authors were able to identify molecular aberrations that favored clonal outgrowth and conferred a more aggressive phenotype.

Overall, these studies raise several concerns about the validity of single tumor-biopsy to infer genomic information applicable in patient decision-making. In other words, is the whole model of personalized oncology jeopardized until tools are available to accurately assess intra-individual tumor heterogeneity? Certainly, the presence of molecular heterogeneity introduces a new variable in the personalized oncology approach. Intra-individual heterogeneity probably explains why, despite effective blockade of oncogenic addiction loops, we are still unable to attain a 100% complete response rate and cure the disease. It may also justify why targeted therapies in solid tumors are less effective compared with hematological malignancies. Nonetheless, there are still many unanswered questions, such as the accurate distribution of the different mutational variants present in a given tumor and their predominance in tumor progression.

Liver biopsy results are subject to sample variability and require a careful interpretation. In HCC, noninvasive criteria are accepted for the diagnosis of this neoplasm,18, 19 but recent guidelines recommend collecting tissue samples in a systematic manner in the context of clinical trials and research studies.19 Study investigators testing molecular heterogeneity using next-generation sequencing are encouraged to determine whether additional mutations identified in different tumor sites or in multiple tumors have any functional impact on progression, resistance to therapy, and dissemination of this cancer. Our studies exploring transcriptomic heterogeneity within single early HCC tumors showed quite homologous molecular subclasses in samples obtained from the same nodule, albeit no next-generation testing was conducted.20

In conclusion, solid evidence indicates that blocking oncogenic addiction loops improves survival in cancer patients (Table 1), even when drivers are evaluated in a single tumor biopsy. These examples reflect what Gerlinger et al. state at the end of their Discussion: “larger series will probably identify genes that can be targeted in the trunks of the phylogenetic tree for each tumor type.” Hence, despite its limitations, working with single biopsies for exploring common oncogenic drivers improves outcome in patients with cancer. This does not diminish the need for new readouts of tumor biology and heterogeneity (e.g., tumor circulating cells and functional imaging21). Hopefully, in the near future, translational studies applying high-throughput technologies will provide an accurate estimation of the true impact of intra-individual variability in personalized oncology for HCC.