Biomarkers for Hepatobiliary Cancers : Hepatology (original) (raw)

Supported by the US Department of Defense (CA150272P3, to A.V.) and by INSERM with the “Cancer et Environnement” (Plan Cancer), MUTHEC, and TELOTHEP projects (to J.‐C.N.).

Potential conflict of interest: Dr. Villanueva consults for NGM, Gilead, Exact Sciences and Fuji. He advises Nucleix.

In 2030, more than 1 million people will die due to liver cancer worldwide.(1) In the United States, the liver cancer death rate increased 43% between 2000 and 2016.(1) With a 5‐year survival of 18%, liver cancer is the second most lethal malignancy after pancreatic cancer.(2) Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) account for 85% and 10% of all primary hepatic malignancies, respectively. The majority of patients are diagnosed at stages where curative therapies are not recommended. Most HCC cases occur in patients with underlying liver disease, mainly due to infections with hepatitis B or C virus, alcohol use disorder, or nonalcoholic fatty liver disease (NAFLD).(3) In HCC, early detection programs suffer from a low implementation rate and a suboptimal performance of recommended surveillance tools.(4) Also, tumor molecular profiling does not guide treatment allocation for systemic therapies in HCC, whereas in iCCA the treatment paradigm is changing, with several studies suggesting a role for biomarker‐guided therapies. Only high serum levels of alpha‐fetoprotein (AFP) can predict the response to the monoclonal antibody ramucirumab(5) in second line in patients with HCC at advanced stages. Biomarkers are key components of the clinical management of patients with cancer as they can contribute to major survival improvements and to optimize medical interventions (Figure 1). In HCC, the clinical niches where biomarkers are urgently needed are (1) risk stratification and early HCC detection, (2) prognosis prediction, and (3) prediction of response to systemic therapies. In cholangiocarcinoma (CCA), unmet needs exist in several areas: (1) diagnosis of CCA, especially in patients with primary sclerosing cholangitis; (2) prognosis prediction after curative treatment in order to guide adjuvant treatment; and (3) identification of biomarkers of response and of resistance to systemic therapies in first and second line. This review will summarize the current status of genomic biomarkers in HCC and iCCA, their main clinical applications, and future prospects for clinical implementation.

Fig. 1:

Biomarkers in hepatobiliary cancers. Shown are the different types of biomarkers as well as their potential uses in clinical practice in hepatobiliary cancers.

Biomarkers of HCC

The main driver somatic mutations in HCC affect telomere integrity (telomerase reverse transcriptase [_TERT_] promoter, 55%), cell cycle regulation (tumor protein 53 [_TP53_], 30%), and WNT signaling (catenin beta 1 [CTNNB1_], 30%).(6) Other genes less commonly mutated (5%‐10%) include AXIN1, AT‐rich interaction domain 1A (ARID1A), AT‐rich interaction domain 2 (ARID2), and kelch‐like ECH‐associated protein 1 (KEAP1) (Figure 2). With an average of two to three mutations per megabase,(7) HCC does not rank among the highest mutated solid tumors. In addition to mutation, other alterations include broad chromosome gains and losses with high‐level DNA amplifications of chromosome 6p21 and 11q13, loci of vascular endothelial growth factor A (VEGFA) and cyclin D1 (CCND1)/_fibroblast growth factor 19 (FGF19), respectively. These alterations are extensively reviewed elsewhere in this special issue of Hepatology.

Fig. 2:

Main genetic alterations in hepatobiliary cancers. We summarized the main somatic alterations in driver genes in HCC and CCA and the link with risk factors of tumor development. *, #, %, $underline an enrichment of a genetic alteration with a risk factor. AAV2, adeno‐associated virus type 2; CDKN2A, cyclin‐dependent kinase inhibitor 2A; ELF3, E74‐like ETS transcription factor 3; MLL, myeloid/lymphoid leukemia; NFE2L2, nuclear factor, erythroid 2 like 2; PRKACA/B, protein kinase cAMP‐activated catalytic subunit alpha/beta; PSC, primary sclerosing cholangitis; RB1, RB transcriptional corepressor 1; RPS6KA3, ribosomal protein S6 kinase A3; TSC1/2, TSC complex subunit 1/2.

Risk Stratification and Early Detection

Roughly 1% of the human population has chronic liver disease, and 15%‐20% of those afflicted will develop liver cancer during their lifetime.(3) Risk of HCC development varies across geographic area, etiology, degree of underlying liver disease, and family history.(1) For patients with liver disease but no cirrhosis (e.g., NAFLD, hepatitis B virus infection), the risk of HCC is lower and difficult to quantify. Thus, biomarkers that identify patients at highest risk are key to render surveillance cost‐effective in heterogeneous populations (e.g., NAFLD). In addition to clinical variables, a number of constitutional genetic variants have been associated with the risk of HCC development; but few have been robustly validated.(8) Two of the more robust variants are patatin‐like phospholipase domain containing 3 (PNPLA3) and transmembrane 6 superfamily member 2 (TM6SF2); both genes are involved in lipid metabolism and have been associated with HCC occurrence mainly in patients with alcohol‐associated liver disease and NAFLD,(9) whereas a variant of hydroxysteroid 17‐beta dehydrogenase 13 (HSD17B13) was protective of cirrhosis and HCC development.(10)

In addition to germline variants, there are liver gene signatures associated with risk of HCC development. A 186‐gene signature predicted death and HCC development in a cohort of 216 hepatitis C virus–related patients with cirrhosis followed for a median of 10 years(11) (Table 1). The gene signature, initially derived from the adjacent nontumoral tissue from HCC resection specimens,(12) was associated with survival and thought to capture the “cancer field effect” responsible for favoring malignant transformation. A more recent detailed genomic characterization of the inflammatory microenvironment of HCC using genomic data from 608 patients with cirrhosis defined an immune‐mediated cancer field molecular subclass.(13)

Table 1 - Prognostic Molecular Signatures in HCC and CCA

Abbreviations: EpCAM, epithelial cell adhesion molecule; TGFB, transforming growth factor beta.

The annual risk of HCC among patients with cirrhosis ranges between 1% and 3%.(1) Early detection increases survival,(14) which provides the basis to recommend biannual surveillance in patients at high risk of HCC development.(15,16) The gold standard for surveillance (i.e., abdominal ultrasound with or without serum AFP) has low sensitivity for early‐stage tumors (63%).(4) Also, HCC surveillance has a poor implementation rate, with <20% of patients at‐risk enrolled in early detection programs. Improvement in surveillance is urgently needed, either by developing better read‐outs of tumor burden or by facilitating implementation of surveillance programs through minimally invasive tools (i.e., serum biomarkers such as protein, microRNA, or circulating tumor DNA used in addition to liver ultrasonography for screening). Other strategies to increase the efficacy of HCC screening include the improvement of patient and provider education, better insurance coverage, and risk stratification based on etiologies.

Liquid biopsy has emerged as a potential source of early detection biomarkers in HCC. “Liquid biopsy” refers to the analysis of tumor components, mostly nucleic acids and tumor cells, released by tumors to the bloodstream.(17) The presence of circulating free DNA in the plasma of patients with cancer has been known for decades,(18) but its application to address clinical problems has significantly increased over the last 5 years. Recently, a composite panel of blood markers including circulating tumor DNA (ctDNA) sequencing (i.e., cancerSEEK), has shown promising results for the diagnosis of various types of cancer.(19) This study also included HCC cases, but only a few patients were at early stages. Thus, there are limited data on the performance of ctDNA analysis for the detection of early‐stage HCC. Two studies have shown how methylation analysis of ctDNA of a set of genes outperforms AFP in early HCC detection, despite the fact that most patients included were not at early stages.(20,21) Deep sequencing of ctDNA allows detection of mutations in patients at an early HCC stage,(22) and has been proposed as an alternative surveillance tool.(23) Circulating microRNAs have also been reported as potential tools for early HCC detection.(24) Lack of prospective data in at‐risk patients enrolled in surveillance and limited information on the performance of liquid biopsy in patients at early stages of HCC are bottlenecks for the implementation of this technique in clinical practice.

Prognosis Prediction

Prognostic biomarkers are key to providing information on life expectancy in oncology patients. To group patients based on common prognostic features is the basis for clinical staging systemics. In HCC, the Barcelona Clinic Liver Cancer (BCLC) algorithm(16,25) encapsulates the key prognostic variables for classifying patients according to tumor burden, underlying liver disease, and performance status. In addition to helping staging, prognostic biomarkers are crucial in clinical trial design to ensure fair comparisons between treatment and control groups, as an imbalance in prognostic factors between groups could significantly confound a drug‐related survival benefit (Figure 1). Genomic studies have tried to provide prognostic biomarkers in patients at early stages treated with resection. Initial studies used array‐based genomic technologies to query the expression of thousands of genes. These studies provided a basis for the first prognostic gene signatures in HCC,(26) which were shortly followed by molecular classifications of HCC, combining gene expression(27) with DNA copy number alterations.(28) The aim of HCC molecular classification is to define groups of patients based on homogenous molecular features. This definition could help refine clinical classification and identify patients with homogeneous phenotypes, such as response to specific therapies. HCC molecular classification is a response to the increasing need of subclassifying BCLC stages to account for intraclass patient heterogeneity.(29) Broadly, HCC can be classified in two major groups, the proliferation and nonproliferation classes.(30) Patients in the proliferation class have higher rates of aberrant activation of signaling pathways associated with active cell proliferation (e.g., AKT/mammalian target of rapamycin [mTOR], insulin‐like growth factor 2, RAS/mitogen‐activated protein kinase [MAPK]) and with stem cell features in a subset of tumors; high‐level DNA amplifications of known HCC driver genes(28) (CCND1 and FGF19), and clinical features suggestive of aggressive tumor behavior (e.g., vascular invasion and high AFP levels). The nonproliferation class is characterized by activation of the WNT canonical pathway, mostly through mutations in CTNNB1, and gene expression profiles resembling hepatocyte lineage.(27,28,30) Currently, these classes do not inform treatment decisions.

In addition to molecular classification associated with outcomes, numerous prognostic signatures have been reported in HCC.(31,32) Also, gene signatures derived from the adjacent nontumoral tissue have shown prognostic predictive capacity in HCC.(12,33) An integrative analysis of all these signatures in the same cohort of 278 HCC resected patients(34) concluded that (1) despite having different marker genes, most poor prognostic gene signatures identify the same patients and (2) tumor and nontumoral tissue provide complementary prognostic information. The companion biomarker study of the STORM trial failed to validate any of the gene signatures tested as predictors of recurrence in these patients,(35) which suggests that prospective validation is key to establishing the predictive capacity of any biomarker.

Predictive Biomarkers of Treatment Response

After 10 years of negative phase 3 clinical trials following the Food and Drug Administration (FDA) approval of sorafenib,(36) up to five systemic agents, mostly tyrosine kinase inhibitors, showed survival benefits in first and second line(1): lenvatinib, regorafenib, ramucirumab, cabozantinib, and the combination of atezolizumab and bevacizumab.(37) Despite the promising activity of the programmed death‐1 (PD1) immune checkpoint inhibitors (CPIs) nivolumab(38) and pembrolizumab(39) in phase 2 with tumor responses nearing 20%, the corresponding phase 3 trials failed to meet their primary endpoint.(40,41) This spurred extensive research to identify predictive biomarkers of CPI response and underscored the urgent need to identify the biologic subgroups most likely to respond to a given therapy in this heterogeneous cancer (Table 2). Predictive biomarkers of treatment response have been very successful in oncology. Thus, it is imperative to develop tools to stratify patients with HCC based on their likelihood to respond to systemic therapies. Proposed biomarkers of response to CPI include PD1 ligand (PDL1) expression, tumor mutational burden,(42) aneuploidy,(43) and gene signatures.(44) Activation of the WNT pathway has also been suggested as a biomarker of primary resistance to CPI in HCC.(45) More recently, FGF19 overexpression has been associated with increased response of FGF receptor 4 (FGFR4) tyrosine kinase inhibitors.(46) Besides AFP levels and treatment with ramucirumab,(5) none of these markers have been validated in large prospective phase 3 studies. Post hoc analysis of registration trials for sorafenib,(47) regorafenib,(48) and cabozantinib(49) did not identify any robust biomarker of response to any of these therapies, whereas increased serum FGF21 was predictive of reduced survival with sorafenib compared to lenvatinib.(50)

Table 2 - Predictive Biomarkers of Treatment Response in HCC and CCA

Abbreviations: EGFR, epidermal growth factor receptor; EZH2, enhancer of zeste 2 polycomb repressive complex 2 subunit; JAK1, Janus kinase 1; MMR, mismatch repair; NA, not available; OR, odds ratio; PFS, progression‐free survival; RR, relative risk; SMARCA4, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily A, member 4.

Biomarkers of CCA

CCA is a heterogeneous primary liver cancer in terms of risk factors, histological and molecular features. Several risk factors have been identified, such as primary sclerosing cholangitis, liver fluke infection, diabetes type 2, obesity, hepatitis B, hepatitis C, and cirrhosis.(51) However, most CCAs were not associated with known risk factors. CCAs are mainly divided into iCCA arising after the second‐order bile ducts and extrahepatic CCA (eCCA) including hilar CCA and distal CCA divided anatomically by the cystic duct.(51) Even if the number of somatic mutations in coding sequences is similar according to the localization, genetic drivers and molecular subclasses vary according to the localization and exposure to risk factors(52‐54) (Figure 2). The genetic alterations shared between iCCA and eCCA are KRAS mutations, with 15%‐20% of mutations in iCCA and eCCA, TP53 mutations which occur in 15% of iCAAs and 25% of eCCAs, and ARID1A, a member of the chromatin remodeling complex, which is mutated in 12%‐18% of iCAAs and eCAAs.(52‐54) Recurrent FGFR2 fusions with FGFR2 tyrosine kinase fused with different partners have been identified in 10%‐25% of iCCAs, whereas these are almost never observed in eCCA (<1%).(52,54,55)FGFR2 fusions lead to the autophosphorylation of FGFR2 independently of the ligand, activate downstream signaling pathways such as MAPK, and lead to uncontrolled cell proliferation and tumor growth.(55)

Recurrent hot spot mutations of isocitrate dehydrogenase nicotinamide adenine dinucleotide phosphate–positive 1 (IDH1) and IDH2 have been identified in 10%‐20% of iCCAs but are rare in eCCA (<2%).(52,53) These mutations lead to a gain of function and to the aberrant production of 2‐hydroxyglutarate, an oncometabolite. They induce an abnormal methylation profile characterized by DNA methylation at the CpG islands (“CpG island methylator phenotype”).(52,56) Interestingly, induction of IDH1 or IDH2 mutation in liver progenitor cells blocks hepatocyte differentiation and induces iCCA underlying the cell plasticity during liver carcinogenesis.(57) Mutations of breast cancer 1 (BRCA1) associated protein 1 (BAP1), encoding a protein involved in the polycomb deubiquitinase complex, are also enriched in iCCA, with 10% of mutations compared to 1% in eCCA. Other rare somatic genetic alterations have been identified such as activating mutations of B‐Raf proto‐oncogene, serine/threonine kinase (BRAF; 5%, enriched in iCCA), Erb‐B2 receptor tyrosine kinase 2 (ERBB2) amplification and mutation (4%‐8%, enriched in eCCA), or BRCA1/2 inactivating mutations (3%‐5%, in all subtypes of CCA).(52‐54) Finally, genomic studies showed an enrichment of TP53 mutations and ERBB2 amplifications together with less frequent IDH1/2 and BAP1 mutations in fluke‐related CCA.(52,53)

Early Detection and Diagnosis

Biomarkers derived from blood, urine, or brushing/bile samples have been tested for the diagnosis of CCA in front of a liver mass or biliary stenosis. Among blood‐derived biomarkers, one of the oldest serum biomarkers, carbohydrate antigen 19‐9 (CA‐19‐9), has a limited value for diagnosis due to its suboptimal sensitivity and specificity. Other serum proteins have been tested such as cytokeratin‐19 fragments (CYFRA21), matrix metallopeptidase 7, osteopontin, Dickkopf WNT signaling pathway inhibitor 1, and interleukin‐6; but none of them are currently used in clinical practice for the diagnosis of CCA.(58) Serum metabolic profiles (combination of metabolites phosphatidylcholine[34:3] and histidine) and circulating microRNA (miR) such as miR‐21, miR‐26a, miR‐150, miR‐222, and miR‐483‐5p have also been proposed as diagnostic tools for CCA.(58‐60)

Extracellular vesicles are small membrane‐covered particles released by cells that carry various molecules such as proteins, nucleic acids, and lipids. Recent studies have suggested that extracellular vesicles with specific surface components or that contain specific protein biomarkers could be used to distinguish patients with or without CCA.(61,62) Moreover, ctDNA released by dying tumor cells could be detected in patients with CCA. Detection of IDH1 mutations or circulating cell‐free DNA methylation biomarkers may be possible in the plasma even if their role in the diagnostic setting requires clarification.(63) Finally, conventional cytology from biliary brushing has a low sensitivity (40%) for the diagnosis of eCCA in front of a biliary stenosis. Identification of chromosomal instability (fluorescence in situ hybridization polysomy) in cytology brush sampling or detection of somatic mutations in bile by next‐generation sequencing could improve the sensitivity for the diagnosis of eCCA.(64,65) Other studies have identified specific metabolites, extracellular vesicles, or microRNA profiles in the bile of patients with CCA.(60,66)

Prognostic Biomarkers

Different prognostic biomarkers, mainly from blood and tissue, have been proposed in CCA. A high serum level of CA‐19‐9 has been associated with poor prognosis, and serum CA‐19‐9 has been proposed to monitor patients under treatment.(58) Serum biomarkers such as proteins (CYFRA21, osteopontin, circulating cytokines), circulating microRNA (miR‐106a, miR‐26a, or miR‐192), circulating tumor cells, and serum metabolites have been linked with prognosis.(58) The detection in the blood of 2‐hydroxyglutarate, the oncometabolite produced by IDH1/2 mutated CCA, is feasible and linked with tumor burden.(67)

In terms of tissue‐based biomarkers, molecular classification has been identified as a link with prognosis (see Table 1).(68,69) Moreover, TP53 and KRAS mutations have been associated with a higher risk of recurrence and death after resection of CCA.(69) In contrast, the prognostic significance of FGFR2 fusions and IDH1 mutations seems more controversial.(70) Overall, despite that several of these biomarkers seem promising, none have met a sufficiently high level of evidence to recommend their use in clinical practice.

Predictive Biomarkers of Treatment Response

If the main common genetic alterations in CCA such as TP53, ARID1A, and KRAS are not easily actionable, >40% of CCAs bear a druggable genetic alteration.(51,71)FGFR2 fusions are one of the main druggable targets of iCCA, and pan‐FGFR inhibitors such as BGJ398, erdafitinib, derazantinib, and the nonselective tyrosine‐kinase inhibitor ponatinib or pazopanib have been tested in CCA.(72) A phase 2 study of BGJ398/infigratinib in 62 patients harboring FGFR2 genetic alterations reported a 14.8% response rate with a 75.4% disease control rate.(73) Moreover, specific FGFR inhibitors against FGFR1, FGFR2, and FGFR3 are also under development, such as derazantinib (NCT01752920), Debio‐1347 (NCT01948297), and INCB054828/pemigatinib (NCT02924376, NCT02393248). A phase 2 trial using derazantinib in 29 patients with FGFR2 fusions showed a 20.7% response rate and an 82.8% disease control rate.(74) A phase 2 trial testing pemigatinib observed 35.5% of tumor response in CCA with FGFR rearrangements versus 0% in CCA without rearrangements.(75) Based on promising phase 2 data, pemigatinib and infigratinb are currently tested in phase 3 in first line versus the standard of care gemcitabine and cisplatin. Interestingly, acquired resistance to adenosine triphosphate competitive inhibitors of FGFR have been described due to secondary mutations of the kinase domain of FGFR2, and such resistance could be bypassed using an irreversible FGFR inhibitor (TAS‐120, NCT0205277).(76) These studies also demonstrated the possibility to detect FGFR2 mutation in ctDNA in order to monitor longitudinally the mechanisms of tumor resistance.(76,77)

Around 5% of CCAs harbor a BRAF V600E mutation. A phase 2 trial testing the BRAF inhibitor vemurafenib reported only a limited tumor response(78); however, the combination of dabrafenib (BRAF inhibitor) and trametinib (MAPK kinase [MEK] inhibitor) in BRAF mutated CCA showed promising results in a phase 2 trial, with a 41% tumor response.(79)

IDH1/2 hot spot mutations observed in iCCA are therapeutic targets currently tested in clinical trials. In cellulo data showed that the SRC inhibitor dasatinib decreases proliferation of CCA cells with IDH1/2 mutations.(80) Moreover, several specific inhibitors of IDH1 or IDH2 have been developed (AG220/ivosidenib or IDH305 against IDH1 mutations, AG221 against IDH2 mutation, and AG881 against IDH1/2 mutations) and are currently being tested in solid tumors including CCA.(81) Recently, a randomized phase 3 controlled trial showed that ivosidenib increased progression‐free survival in patients with IDH1 mutant CCA.(82) Potential therapeutic targets include BRCA1/2 or BAP1 mutations using poly(adenosine diphosphate‐ribose) polymerase (PARP) or ATM serine/threonine kinase (ATM) inhibitors, phosphatidylinositol‐4,5‐bisphosphate 3‐kinase catalytic subunit alpha (PIK3CA) mutations using mTOR inhibitors, or ERBB2 amplifications/mutations using human epidermal growth factor receptor 2 (HER2) antibody or a tyrosine kinase inhibitor against HER2 such as neratinib. Tumor response under neratinib has been described in a phase 2 clinical trial targeting CCA with HER2 mutations.(83)

Finally, a subset of CCAs (5%) have mismatch repair deficiency and are good candidates for immunotherapy such as treatment with anti‐PD1/PDL1 antibodies; a 40.9% objective response was observed among 22 CCAs with microsatellite instability treated by pembrolizumab.(84) In contrast, the efficacy of immunotherapy in nonselected CCA seems to be limited (5.8% objective response rate among 104 patients with CCA treated with pembrolizumab).(85)

Conclusion

Major advances have been made in the management of HCC with the rise of tyrosine kinase inhibitors and more recently of immunotherapy and anti‐VEGF antibody. However, a major gap remains in our ability to translate our knowledge of genomics of HCC to clinical practice. To tackle these limitations, we need biopsies/samples to correlate tumor biology with the natural history of HCC and tumor response to targeted therapy and immunotherapy. Moreover, the beginning of the era of precision medicine in HCC and its applicability to clinical practice will require well‐designed prospective multicentric/multidisciplinary research programs. As a proof of concept in liver tumors, biomarker‐guided therapies in CCA are closer to clinical practice, underlying the strong role of understanding liver cancer pathogenesis and biomarker development to improve the prognosis of these patients.

Author Contributions

J.C.N. and A.V. equally contributed to conception, drafting, revision and final approval of the manuscript.

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Author names in bold designate shared co‐first authorship.

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