Article Text

Original article
A genomic and clinical prognostic index for hepatitis C-related early-stage cirrhosis that predicts clinical deterioration
  1. Lindsay Y King1,
  2. Claudia Canasto-Chibuque2,
  3. Kara B Johnson1,
  4. Shun Yip2,
  5. Xintong Chen2,
  6. Kensuke Kojima2,
  7. Manjeet Deshmukh2,
  8. Anu Venkatesh2,
  9. Poh Seng Tan2,3,
  10. Xiaochen Sun2,
  11. Augusto Villanueva4,
  12. Angelo Sangiovanni5,
  13. Venugopalan Nair6,
  14. Milind Mahajan7,
  15. Masahiro Kobayashi8,
  16. Hiromitsu Kumada8,
  17. Massimo Iavarone5,
  18. Massimo Colombo5,
  19. Maria Isabel Fiel9,
  20. Scott L Friedman2,
  21. Josep M Llovet2,10,11,
  22. Raymond T Chung1,
  23. Yujin Hoshida2
  1. 1Liver Center and Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
  2. 2Liver Cancer Program, Division of Liver Diseases, Department of Medicine, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA
  3. 3Division of Gastroenterology and Hepatology, University Medicine Cluster, National University Health System, Singapore
  4. 4Institute of Liver Sciences, King's College London, London, UK
  5. 5M. & A. Migliavacca Center for Liver Disease and 1st Division of Gastroenterology, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
  6. 6Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, USA
  7. 7Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
  8. 8Department of Hepatology, Toranomon Hospital, Tokyo, Japan
  9. 9Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, USA
  10. 10HCC Translational Research Laboratory, Barcelona Clinic Liver Cancer Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer Centro de Investigaciones en Red de Enfermedades Hepáticas y Digestivas, Hosptial Clínic Barcelona, Barcelona, Catalonia, Spain
  11. 11Institució Catalana de Recerca i Estudis Avancats (ICREA), Barcelona, Catalonia, Spain
  1. Correspondence to Dr Yujin Hoshida, Liver Cancer Program, Tisch Cancer Institute, Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, 1470 Madison Ave, Box 1123, New York, NY 10029, USA; yujin.hoshida{at}mssm.edu Dr Raymond T. Chung, Liver Center and Gastrointestinal Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Warren 1007, Boston, MA 02114, USA; rtchung{at}partners.org

Abstract

Objective The number of patients with HCV-related cirrhosis is increasing, leading to a rising risk of complications and death. Prognostic stratification in patients with early-stage cirrhosis is still challenging. We aimed to develop and validate a clinically useful prognostic index based on genomic and clinical variables to identify patients at high risk of disease progression.

Design We developed a prognostic index, comprised of a 186-gene signature validated in our previous genome-wide profiling study, bilirubin (>1 mg/dL) and platelet count (<100 000/mm3), in an Italian HCV cirrhosis cohort (training cohort, n=216, median follow-up 10 years). The gene signature test was implemented using a digital transcript counting (nCounter) assay specifically developed for clinical use and the prognostic index was evaluated using archived specimens from an independent cohort of HCV-related cirrhosis in the USA (validation cohort, n=145, median follow-up 8 years).

Results In the training cohort, the prognostic index was associated with hepatic decompensation (HR=2.71, p=0.003), overall death (HR=6.00, p<0.001), hepatocellular carcinoma (HR=3.31, p=0.001) and progression of Child–Turcotte–Pugh class (HR=6.70, p<0.001). The patients in the validation cohort were stratified into high-risk (16%), intermediate-risk (42%) or low-risk (42%) groups by the prognostic index. The high-risk group had a significantly increased risk of hepatic decompensation (HR=7.36, p<0.001), overall death (HR=3.57, p=0.002), liver-related death (HR=6.49, p<0.001) and all liver-related adverse events (HR=4.98, p<0.001).

Conclusions A genomic and clinical prognostic index readily available for clinical use was successfully validated, warranting further clinical evaluation for prognostic prediction and clinical trial stratification and enrichment for preventive interventions.

  • HEPATITIS C
  • CIRRHOSIS
  • GENE EXPRESSION
View Full Text

Statistics from Altmetric.com

Video abstract

Significance of this study

What is already known about this subject?

  • HCV-related early-stage cirrhosis is increasing and overtaxing the medical resources for regular follow-up and hepatocellular carcinoma (HCC) surveillance as evidenced by the low application rate (only 12% of new patients with HCC were diagnosed through the surveillance in the USA).

  • Eradication of HCV reduces, but does not eliminate the risk of disease progression including HCC development especially when cirrhosis is present. Therefore, prognostic prediction is critical in clinical management of this sizeable patient population.

  • Clinical prognostic variables have demonstrated limited capability with HRs <2 in identifying a subset of patients who most need close follow-up for long-term clinical deterioration and mortality.

What are the new findings?

  • A prognostic index comprised of a gene-expression signature, serum bilirubin and platelet count showed significant association with disease progression and death with substantially higher HRs up to 6.70 in a cohort of patients with HCV cirrhosis prospectively followed for a median of 10 years.

  • The gene signature test was implemented in an assay platform specifically designed for clinical use.

  • The prognostic index based on the clinically applicable gene signature assay was successfully validated in an independent cohort of patients with early-stage HCV cirrhosis for disease progression and death with HRs as high as 7.36.

How might it impact on clinical practice in the foreseeable future?

  • The prognostic index will help prioritise a subset of patients with early-stage HCV cirrhosis for regular follow-up and HCC surveillance.

  • The prognostic index can be used for clinical trial stratification and enrichment for preventive intervention with anticirrhosis and/or anticarcinogenic therapies.

Introduction

Liver cirrhosis affects 1%–2% of the world's population and leads to more than a million deaths every year worldwide.1 ,2 Chronic HCV infection is one of the major aetiologies of cirrhosis in developed countries. More than a million individuals in the USA, representing the ‘baby boomer’ population, are projected to develop HCV-related cirrhosis, hepatic decompensation or hepatocellular carcinoma (HCC) by 2020.3 ,4 HCV superseded HIV as a cause of death by 2007, and costs for patient management are estimated to reach $8.6 billion by 2015 (excluding drug costs) in the USA and increase by 60% by 2032 in Canada.4–6 Despite the emergence of direct-acting antiviral agents, eradication of HCV reduces but does not eliminate the risk of the lethal complications of cirrhosis, especially when more advanced fibrosis is present.7 In addition, cost of the direct-acting antivirals will limit their wider use to prevent disease progression.8 ,9

Cirrhosis is the major driver in the development of HCC, the second leading cause of cancer mortality worldwide and the fastest and only rising cancer death in the USA.10 ,11 The extremely high HCC incidence in HCV-related cirrhosis (up to 7% per year) justifies regular HCC surveillance in the practice guidelines.12 ,13 However, only 12% of new HCV-related HCC cases are diagnosed through surveillance in the USA, clearly indicating that current medical resources are challenged by this sizeable patient population and underscoring the urgent need for prognostic biomarkers to identify a subset of patients with cirrhosis who most require surveillance and close follow-up.14 Recent emergence of non-invasive fibrosis assessment tests such as elastography may actually increase the burden of close follow-up and cancer screening by bringing a larger number of patients with early-stage, asymptomatic cirrhosis to medical attention.15 Prognostic prediction in early-stage cirrhosis, the largest group among patients with cirrhosis, is particularly challenging because of the lack of clinical prognostic indicators. The availability of an accurate prognostic biomarker will also help identify patients with cirrhosis who will benefit from preventive intervention to reduce the incidence of cirrhosis complications.16

In previous studies, we identified and validated a prognostic 186-gene signature together with prognostic clinical variables in patients with HCV-related early-stage cirrhosis from Asia, Europe and the USA.17–20 A cost-effectiveness analysis suggested that the signature enables personalised HCC surveillance with reduced net medical care cost and extended patient life expectancy.19 For clinical application of the finding, we implemented the 186-gene signature in an FDA-approved clinical diagnostic assay platform, constructed a genomic and clinical prognostic index and externally validated the index in an independent cohort of patients with HCV-related, early-stage cirrhosis with long-term follow-up.

Methods

Patient cohorts

A cohort of 216 Italian patients with HCV-related, early-stage cirrhosis was used to develop a prognostic index based on a Cox regression model reported in our previous study (training cohort).19 The cohort was prospectively enrolled and followed for a median of 10 years to evaluate clinical utility of HCC surveillance.19 ,21 ,22 A subset of the training cohort (n=90) who had poor-prognosis or good-prognosis prediction and had left-over RNA samples were reanalysed by the digital transcript counting assay (see ‘Gene expression profiling’ for details) to compare the gene signature-based prediction with the genome-wide profiles from the previous study (NCBI Gene Expression Omnibus, accession number GSE15654).

External validation of the prognostic index was performed by using archived liver biopsy specimens from an independent cohort of 145 patients with HCV-related, compensated cirrhosis who had a liver biopsy between 1990 and 2007 and were followed at Massachusetts General Hospital (validation cohort). Enrolled patients were aged >18 years at the time of biopsy. HCV infection was confirmed by serum HCV antibody and/or RNA. A diagnosis of cirrhosis was defined histologically as having Ishak23 fibrosis stage 5/6 or Metavir24 fibrosis stage 4. No co-infection of HIV or HBV was present. Patients with prior history of ascites, variceal bleeding, hepatic encephalopathy, HCC and liver transplantation were excluded. One hundred and sixty-nine patients were identified as having formalin-fixed paraffin-embedded (FFPE) tissue blocks. Analysed clinical end points include overall death (primary end point) development of hepatic decompensation (ascites, variceal bleeding, hepatic encephalopathy, spontaneous bacterial peritonitis and hepatorenal syndrome), HCC, liver-related death (deaths related to complications of end-stage liver disease, HCC, hepatorenal syndrome, portopulmonary hypertension) and a composite of all liver-related adverse events (hepatic decompensation, HCC and liver-related death). No patient was lost from follow-up for overall death. Serial biopsy specimens were available for three patients for longitudinal analysis. Liver tissue from the right and left lobes of an explanted liver were probed in one patient to assess sampling variability. The study was approved and the requirement for written informed consent was waived by the institutional review board based on the condition that all samples were anonymous.

Tissue processing and RNA extraction

Total RNA was isolated from three to five 10-μm-thick FFPE tissue sections by using High Pure RNA Paraffin kit (Roche) according to the manufacturer's instructions. RNA fragmentation was evaluated by quantitative reverse transcriptase (qRT)-PCR of a housekeeping gene RPL13A as previously described18 and all samples were confirmed to have cross-over threshold (Ct) value <33. One hundred and fifty-five samples with RNA concentration >20 ng/μL were subjected to gene expression profiling with the nCounter assay.

Gene expression profiling

The 186-gene signature was implemented in the digital transcript counting (nCounter) assay (NanoString). Expression profiling was performed with 100–400 ng total RNA by using nCounter Digital Analyzer system (NanoString) according to the manufacturer’s instructions. For the analysis of the training cohort, the first generation of reagent plate (‘white’ Prep Plate) was used. For the validation cohort, a newer version (‘green’ New Prep Plate) with improved sensitivity for signal detection was used. Poor quality profiles were detected based on maximum signal intensity from positive control probes <8000 U for the older reagent plate and median signal intensity >100 U for the newer reagent plate according to the manufacturer's recommendations. Raw transcript count data were log transformed and scaled by geometric mean of control probe data by using NanoString normaliser module implemented in GenePattern genomic analysis toolkit (http://www.broadinstitute.org/genepattern). Genome-wide expression profiling for paired biopsies and explanted liver was performed by using whole-genome cDNA-mediated annealing, selection, extension and ligation (DASL) assay (Illumina) according to the manufacturer’s instructions. Scanned data were extracted by Genome Studio software V.3 (Illumina) and normalised by cubic spline algorithm implemented in GenePattern Illumina normaliser module as previously described.18

Bioinformatics and statistical analysis

The 186-gene signature-based clinical outcome prediction was performed based on a previously reported prediction model and algorithm without making any modification18 by using the nearest template prediction algorithm25 implemented in GenePattern. A prediction of poor or good outcome was determined based on prediction p<0.05, and the rest of the samples with intermediate expression level of the poor or good prognosis-correlated genes in the signature were classified as having intermediate prognosis as previously reported.19 Reduction of signature genes was performed based on a series of cut-offs of absolute Cox scores calculated in the original training data set (DASL)18 as well as recalculated Cox scores in the nCounter data of the training cohort. Gene expression data sets are available at NCBI Gene Expression Omnibus database (accession number GSE54102). All bioinformatics analyses were performed by using GenePattern and R statistical language (http://www.r-project.org).

For the validation cohort, the date of the liver biopsy documenting HCV-related cirrhosis was defined as the time of enrolment. Prognostic association of the gene signature-based prediction with clinical outcome was evaluated by Kaplan–Meier curves, log rank test and Cox regression modelling. In the analysis of liver-related death, competing risk was additionally adjusted for non-liver-related death by using proportional subdistribution hazards regression modelling,26 Clinical outcomes that occurred in more than 20% of the cohort were analysed in the Cox regression analysis to achieve a statistical power of 0.8 to detect HR of 3.0 (α=0.05). Annual incidence rate for each clinical outcome was calculated using declining exponential approximation of life expectancy27 based on cumulative 5-year incidence. A prognostic index was calculated using the following formula based on our previously reported multivariable Cox regression model19:0.848× gene-signature-based prediction of poor prognosis (0: no, 1: yes)+0.998×serum bilirubin (0:≤1.0 mg/dL, 1:>1.0 mg/dL)+0.905×platelet count (0:≥100 000/mm3, 1:<100 000/mm3). The tertiles of the scores in the training cohort (0.848, 1.846) were used as the cut-off values to classify the patients into high-risk, intermediate-risk and low-risk groups. A subgroup analysis within Child–Turcotte–Pugh class A patients was performed to evaluate robustness of outcome association for the prognostic index. Correction for multiple hypothesis testing was performed using Bonferroni's method when needed. A two-tailed p value <0.05 was regarded as statistically significant. R statistical language and SAS (SAS Institute, Cary, North Carolina, USA) V.9.3 were used for statistical analyses.

Results

Technical evaluation of 186-gene-signature assay

To assess the prognostic index in a clinically relevant setting, we implemented the 186-gene signature in an FDA-approved clinical diagnostic assay platform, the nCounter system, and we reassayed a subset of the training cohort previously analysed by genome-wide DNA microarray.19 Among the 106 samples with leftover RNA that were reassayed by the nCounter assay, 90 (85%) passed the predetermined quality threshold. Prognostic prediction was performed by applying the same prediction model and algorithm reported in the original studies18 without making any modification. Thirty-five (39%), 19 (21%) and 36 (40%) samples presented poor-prognosis, intermediate-prognosis and good-prognosis signatures, respectively (figure 1A). Inconsistent poor or good prediction between the nCounter assay and genome-wide microarray was observed in only three samples (3%) (figure 1B). Stability of the gene signature-based prediction in longitudinal and multisite liver sampling was assessed in paired core needle liver biopsy specimens obtained with the time interval ranging from 1 month to 7½ years from three individuals, and liver tissues from right and left lobes of explanted liver with chronic HCV infection. Histologically, necroinflammatory grade and fibrosis stage23 were comparable within each pair. There was no instance of inconsistent poor or good prediction between the paired samples (see online supplementary table S1). Changes of prediction were observed only between poor/good and intermediate prognosis, which may reflect subtle changes in molecular status of the liver unrecognisable by histological assessment.

Figure 1

Technical assessment of the 186-gene signature nCounter assay in a subset of the training cohort (n=90). (A) Expression pattern of the 186-gene signature. Red and blue colours indicate high and low gene expression, respectively. (B) Prediction concordance between the nCounter assay and genome-wide microarray used in our previous study.19

Prognostic index in the training cohort

The prognostic index, developed using the entire training cohort (n=216), classified patients in the training cohort into high-risk (n=78, 36%), intermediate-risk (n=80, 37%) and low-risk (n=58, 27%) groups. The high-risk group showed significantly more frequent development of hepatic decompensation (HR=2.71, p=0.003), overall death (HR=6.00, p<0.001), HCC development (HR=3.31, p=0.001) and progression of Child–Turcotte–Pugh class (HR=6.70, p<0.001) (see online supplementary table S2). The probabilities of overall death at 5 years and 10 years were 19% and 44% in the high-risk group, 7% and 21% in the intermediate-risk group, and 3% and 10% in the low-risk group, respectively. Clinical demographics of the training cohort were reported in our previous study19 and summarised in online supplementary table S3.

Validation of the prognostic index

We next assessed the prognostic index in an external independent cohort of patients with HCV-related compensated cirrhosis in the USA using the latest version of nCounter assay. One hundred and fifty-five samples with sufficient tissue to isolate more than 100 ng total RNA were subjected to the nCounter assay, among which 145 samples (94%) yielded high-quality profiles (figure 2). Table 1 summarises clinical demographics of the 145 patients. Compared with retrospective or prospective cohorts of HCV-related compensated cirrhosis in the literature and the training cohort in the current study, patients in the validation cohort were approximately 10 years younger and more predominantly men (see online supplementary table S3). The median follow-up time was 8 years (IQR: 6.3–11.1 years, range: 1.2–22.9 years). Forty-five patients (31%) developed at least one episode of hepatic decompensation. The first decompensation events were as follows: ascites, n=19; variceal haemorrhage, n=11; hepatic encephalopathy, n=8; ascites and hepatic encephalopathy, n=4; ascites and variceal haemorrhage, n=1; variceal haemorrhage and hepatic encephalopathy, n=1; and ascites, variceal haemorrhage and hepatic encephalopathy, n=1. The annual incidence rate of hepatic decompensation (3.9%) was comparable, whereas annual incidence rates of death (1.7%) and HCC development (1.3%) were lower than reported in the published cohorts and the training cohort of the current study, possibly due to the younger age. HCC was analysed together with all liver-related adverse events because only 21 patients (14%) had HCC during the follow-up presumably due to the younger age. The probabilities of hepatic decompensation at 5 years and 10 years were 18% and 37%, respectively (see online supplementary figure S1). The probabilities of overall death at 5 years and 10 years were 8% and 35%, respectively, and the probabilities of liver-related death at 5 years and 10 years were 7% and 27%, respectively. The probabilities of all liver-related adverse events at 5 years and 10 years were 24% and 51%, respectively.

Table 1

Clinical characteristics of the validation cohort at enrolment

Figure 2

The validation cohort used to assess the prognostic index. FFPE, formalin-fixed paraffin-embedded.

Gene signature-based prognostic prediction was performed using the nCounter assay, and 53 (37%), 32 (22%) and 60 (41%) patients had poor-prognosis, intermediate-prognosis and good-prognosis signatures, respectively (figure 3), which was significantly associated with overall survival (p=0.02) (see online supplementary figure S2). The prognostic index classified the patients in the validation cohort into high-risk (n=22, 16%), intermediate-risk (n=56, 42%) and low-risk (n=57, 42%) groups. The high-risk group experienced significantly more frequent development of hepatic decompensation (HR=7.36, p<0.001), overall death (HR=3.57, p=0.002), liver-related death (HR=6.49, p<0.001) and all liver-related adverse events (HR=4.98, p<0.001) (table 2, figure 4). When competing risk was adjusted for non-liver-related death, the association of the high-risk group with liver-related death remained statistically significant (HR=3.65, 95% CI 1.75 to 7.61, p<0.001). The probabilities of overall death at 5 years and 10 years were 18% and 57% in the high-risk group, 11% and 42% in the intermediate-risk group and 4% and 18% in the low-risk group, respectively. The prognostic association of the prognostic index remained statistically significant in a subgroup of Child–Turcotte–Pugh class A patients (figure 5).

Table 2

Association of the prognostic index with clinical outcome (Cox regression, validation cohort)

Figure 3

Expression pattern of the 186-gene signature nCounter assay in the validation cohort. Red and blue colours indicate high and low gene expression, respectively.

Figure 4

Probability of overall death according to the prognostic index in training cohort (A) and validation cohort (B). p Values were calculated by log rank test.

Figure 5

Prognostic association of the prognostic index with clinical outcomes in all patients (A) and Child–Turcotte–Pugh class A patients (B). HR and 95% CI in log scale (base 10) for the high-risk group in comparison with the low-risk group is shown.

The full 186-gene signature captures markers of molecular pathway deregulation, including upregulation of epidermal growth factor, nuclear factor κ-B, interleukin 6 and interferon pathways, hepatic stellate cell activation, and downregulation of metabolic and protein synthesis pathways and DNA damage repair machinery.18 ,19 Although some of the information may be lost, a reduction in the number of signature genes could decrease complexity of the signature and enable its adaptation to a lower throughput assay platform and thus enable more flexible use of the signature in clinical practice. By using two different approaches, we found that the number of signature genes could be reduced to 32 or 11 (see online supplementary figure S3, tables S4 and S5), which were still significantly associated with overall death (see online supplementary figure S4). The prognostic index based on the reduced signatures showed relatively inferior, but still statistically significant associations with all of the analysed clinical outcomes (see online supplementary tables S6 and S7), warranting further evaluation in future studies.

Discussion

We have demonstrated that a prognostic index combining genomic and clinical information successfully predicts prognosis of patients with HCV-related compensated cirrhosis, for which clinical prognostic information is limited. Several clinical prognostic indicators have been proposed to discriminate late-stage cirrhosis from early-stage cirrhosis28 or to discriminate progressive cirrhosis from less advanced or no fibrosis with HRs smaller than 2.0.29–31 However, prognostic prediction within established but early-stage cirrhosis is more critical because this specific disease stage comprises the majority of the target patient population indicated for regular follow-up and HCC surveillance as recommended in the clinical practice guidelines. Recently emerging non-invasive blood test-based or elastography-based methods of liver fibrosis detection are not sensitive enough to classify patients with early-stage cirrhosis into prognostic subgroups.15 ,32 In addition, the increasing use of these non-invasive methods promises to increase the number of newly identified patients with early-stage cirrhosis, a prospect that may tax current medical resources.14 Therefore, highly sensitive and accurate molecular prognostic biomarkers are sorely needed. Clinical deployment of a gene-expression-based molecular biomarker has been challenging because of less reproducible measurements.33 Recent development of assay platforms, which are capable of inexpensively analysing archived FFPE tissues with minimal experimental variation, is a breakthrough that will facilitate clinical implementation of gene signature tests.34–36

Risk prediction, early detection and prevention of lethal cirrhosis complications including hepatic decompensation and HCC have been recognised as the most effective strategies to substantially impact patient prognosis.16 ,37 Recent large-scale HCC chemoprevention trials have demonstrated the proof of concept for this strategy, although none of the therapies tested in the trials have been established as a standard of care due to the modest effect and/or unacceptable toxicities.38–40 These studies and others also highlight the challenge in conducting chemoprevention trials, that is, requirement of larger sample size and longer follow-up time compared with therapeutic trials enrolling patients with advanced diseases such as end-stage cancer.41 Enrichment of high-risk populations through use of prognostic indices like ours could overcome this handicap and facilitate the implementation of chemoprevention trials.42 In a preclinical animal study, the 186-gene signature was correlated with antifibrotic and anticarcinogenic effect of an FDA-approved kinase inhibitor, erlotinib.43 Interestingly, the study showed that the gene signature was strikingly induced before histologically recognisable accumulation of fibrosis, suggesting that the signature sensitively detected activated fibrogenic pathways in the liver. Based on the study, a clinical trial of the drug has been planned with assessment of the 186-gene signature assay as a companion diagnostic (cancerpreventionnetwork.org). The significant association of the signature with long-term clinical outcomes confirmed in multiple patient cohorts may support the use of the gene signature as a surrogate end point in other chemoprevention trials.

Recently emerging highly effective and less toxic direct-acting anti-HCV drugs will eventually halt the development of HCV-induced advanced fibrosis and cirrhosis and prevent lethal complications in newly infected patients.9 However, recent epidemiological studies have suggested that patients with advanced fibrosis are still at risk of progressive disease even after viral eradication.7 Also, given the already high worldwide prevalence of HCV infection affecting 170 million individuals and anticipated obstacles in disseminating the expensive direct-acting anti-HCV drugs, it is expected that risk prediction and chemopreventive interventions will remain relevant in the coming decade and beyond.

Recent emergence of selective molecular targeted agents has highlighted the need to obtain tissue specimens for more precise characterisation of the molecular targets and subsequent personalised therapeutic decisions.13 ,44 Circulating cells or biomolecules such as microRNAs may be alternative sources to obtain similar molecular information less invasively, although more studies are needed.45 The current validation cohort may have been limited for assessment of HCC development due to an insufficient number of clinical events. Further follow-up data from this cohort will require evaluation. We have also made the assumption that the variables in the prognostic index, the 186-gene signature, bilirubin and platelet count, capture molecular and clinical information for progressive cirrhosis in general and speculate that the index will also be prognostic in cirrhosis caused by other aetiologies, namely chronic hepatitis B, alcohol and non-alcoholic fatty liver diseases.46 This prospect should also be evaluated in future studies. The risk index validation was conducted in a retrospective cohort enrolled from a tertiary referral centre. Therefore, the possibility of presence of selection biases and information biases caused by variation in quality and reliability in the outcome definition and measurement exists.

In conclusion, our novel genomic and clinical prognostic index was successfully validated for prognostic capability in patients with HCV-related early-stage cirrhosis, in whom clinical prognostic indicators are limited. One of the components of the prognostic index, the gene signature, was implemented in an assay platform readily available for clinical implementation. The prognostic index has the potential to refine the intensity of follow-up and enable more cost-effective clinical trials of chemoprevention and/or anticirrhosis therapy by enriching for high-risk populations, which could contribute to substantial improvements in predicting a patient's prognosis and improving outcomes, a vital need in view of the projected growth of the population of patients with cirrhosis and its associated medical care costs.

Acknowledgments

The nCounter assay was performed at Mount Sinai qPCR Shared Resource Facility. Bioinformatics analysis was performed by using High Power Computing facility at Mount Sinai Genomics Core and Department of Scientific Computing.

References

View Abstract

Supplementary materials

  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    Files in this Data Supplement:

Footnotes

  • Contributors Study concept and design: LYK, RTC, YH. Acquisition of data: LYK, KBJ, AS, AV, MK, HK. Analysis and interpretation of data: LYK, SY, XC, KK, PST, YH. Drafting of the manuscript: LYK, RTC, YH. Critical revision of the manuscript for important intellectual content: LYK, MI, MC, SLF, JML, RTC, YH. Statistical analysis: LYK, SY, XC, KK, PST, XS, YH. Obtained funding: SLF, JML, RTC, YH. Technical or material support: MD, AV, VN, MM, MK, HK. Study supervision: RTC, YH.

  • Funding This research was supported by the National Institute of Health (DK099558 to YH; DA033541, DK098079, DK078772 to RTC; DK56621 and AA020709 to SLF; DK076986 to JML; DK007191 to LYK and KBJ); the European Commission Framework Programme 7 (Heptromic, proposal number 259744 to YH, JML); the Samuel Waxman Cancer Research Foundation, the Spanish National Health Institute (SAF-2010-16055) and the Asociación Española para el Estudio del Cáncer (AECC) to JML.

  • Competing interests YH, AV, and JML are named investigators on a pending patent application entitled “Compositions, kits, and methods for detecting, characterising, preventing, and treating hepatic disorders (USPTO application #: #20110263441)”. NanoString has secured the option to an exclusive worldwide license. NanoString has no role in conduction of the current study. There is no other relevant declaration relating to employment, consultancy, patents, products in development or modified products, etc.

  • Ethics approval IRB of Icahn School of Medicine at Mount Sinai, Mass General Hospital.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement Gene-expression data sets are available at NCBI Gene Expression Omnibus (GSE54102).

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.