Tumor growth and overall survival modeling to support decision making in phase Ib/II trials: A comparison of the joint and two-stage approaches.

Model-based tumor growth inhibition (TGI) metrics are increasingly used to predict overall survival (OS) data in Phase III immunotherapy clinical trials. However, there is still a lack of understanding regarding the differences between two-stage or joint modeling methods to leverage Phase I/II trial data and help early decision-making. A recent study showed that TGI metrics such as the tumor growth rate constant KG may have good operating characteristics as early endpoints. This previous study used a two-stage approach that is easy to implement and intuitive but prone to bias as it does not account for the relationship between the longitudinal and time-to-event processes. A relevant alternative is to use a joint modeling approach. In the present article, we evaluated the operating characteristics of TGI metrics using joint modeling, assuming an OS model previously developed using historical data. To that end, we used TGI and OS data from IMpower150-a study investigating atezolizumab in over 750 patients suffering from non-small cell lung cancer-to mimic randomized Phase Ib/II trials varying in terms of number of patients included (40 to 15 patients per arm) and follow-up duration (24 to 6 weeks after the last patient included). In this context, joint modeling did not outperform the two-stage approach and provided similar operating characteristics in all the investigated scenarios. Our results suggest that KG geometric mean ratio could be used to support early decision-making provided that 30 or more patients per arm are included and followed for at least 12 weeks.

Comparison of two-stage and joint TGI-OS modeling using data from six atezolizumab clinical studies in patients with metastatic non-small cell lung cancer.

Two-stage and joint modeling approaches are the two main approaches to investigate the link between longitudinal tumor size data and overall survival (OS) and anticipate clinical trial outcome. We here used a large database composed of one phase II and five phase III clinical trials evaluating atezolizumab (an immunotherapy) in monotherapy or in combination with chemotherapies in 3699 patients with non-small cell lung cancer to evaluate the differences between both approaches in terms of parameter estimates, magnitude of covariate effects, and ability to predict OS. Although the two-stage approach may underestimate the magnitude of the impact of tumor growth rate (KG ) on OS compared to joint modeling approach (hazard ratios [HRs] of 0.42-2.52 vs. 0.25-2.85, respectively, for individual KG varying from the 5th and 95th percentiles), this difference did not lead into poorer performance of the two-stage approach to describe the OS distribution in the six clinical studies. Overall, two-stage and joint modeling approaches accurately predicted OS HR with a median (range) difference with the observed OS HR of 0.02 (0.01-0.18) and 0.03 (0.00-0.19), in all cases considered, respectively (e.g., for IMpower150: 0.80 [0.66-0.95] vs. 0.82 [0.70-0.95], respectively, whereas the observed OS HR was 0.80). In our setting, the two-stage approach accurately predicted the benefit of atezolizumab on OS. Further work is needed to verify if similar results are achieved using phase Ib or phase II clinical trials where the number of patients and measurements is limited as well as in other cancer indications.

Nonlinear multilevel joint model for individual lesion kinetics and survival to characterize intra-individual heterogeneity in patients with advanced cancer.

In advanced cancer patients, tumor burden is calculated using the sum of the longest diameters (SLD) of the target lesions, a measure that lumps all lesions together and ignores intra-patient heterogeneity. Here, we used a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify heterogeneity in lesion dynamics and measure their impact on survival. Using a nonlinear model of tumor growth inhibition, we estimated that dynamics differed greatly among lesions, and inter-lesion variability accounted for 21% and 28% of the total variance in tumor shrinkage and treatment effect duration, respectively. Next, we investigated the impact of individual lesion dynamics on survival. Lesions located in the liver and in the bladder had twice as much impact on the instantaneous risk of death compared to those located in the lung or the lymph nodes. Finally, we evaluated the utility of individual lesion follow-up for dynamic predictions. Consistent with results at the population level, the individual lesion model outperformed a model relying only on SLD, especially at early landmark times and in patients with liver or bladder target lesions. Our results show that an individual lesion model can characterize the heterogeneity in tumor dynamics and its impact on survival in advanced cancer patients.

Assessing the Increased Variability in Individual Lesion Kinetics During Immunotherapy: Does It Exist, and Does It Matter?

PURPOSE: Several studies have raised the hypothesis that immunotherapy may exacerbate the variability in individual lesions, increasing the risk of observing divergent kinetic profiles within the same patient. This questions the use of the sum of the longest diameter to follow the response to immunotherapy. Here, we aimed to study this hypothesis by developing a model that estimates the different sources of variability in lesion kinetics, and we used this model to evaluate the impact of this variability on survival. METHODS: We relied on a semimechanistic model to follow the nonlinear kinetics of lesions and their impact on the risk of death, adjusted on organ location. The model incorporated two levels of random effects to characterize both between- and within-patient variability in response to treatment. The model was estimated on 900 patients from a phase III randomized trial evaluating programmed death-ligand 1 checkpoint inhibitor atezolizumab versus chemotherapy in patients with second-line metastatic urothelial carcinoma (IMvigor211). RESULTS: The within-patient variability in the four parameters that characterize individual lesion kinetics represented between 12% and 78% of the total variability during chemotherapy. Similar results were obtained during atezolizumab, except for the durability of the treatment effects, for which the within-patient variability was markedly larger than during chemotherapy (40% v 12%, respectively). Accordingly, the occurrence of divergent profile consistently increased over time in patients treated with atezolizumab and was equal to about 20% after 1 year of treatment. Finally, we show that accounting for the within-patient variability provided a better prediction of most at-risk patients than a model relying solely on the sum of the longest diameter. CONCLUSION: Within-patient variability provides valuable information for the assessment of treatment efficacy and the detection of at-risk patients.

Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models.

Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.

Effect of remdesivir on viral dynamics in COVID-19 hospitalized patients: a modelling analysis of the randomized, controlled, open-label DisCoVeRy trial.

BACKGROUND: The antiviral efficacy of remdesivir in COVID-19 hospitalized patients remains controversial. OBJECTIVES: To estimate the effect of remdesivir in blocking viral replication. METHODS: We analysed nasopharyngeal normalized viral loads from 665 hospitalized patients included in the DisCoVeRy trial (NCT04315948; EudraCT 2020-000936-23), randomized to either standard of care (SoC) or SoC + remdesivir. We used a mathematical model to reconstruct viral kinetic profiles and estimate the antiviral efficacy of remdesivir in blocking viral replication. Additional analyses were conducted stratified on time of treatment initiation (≤7 or >7 days since symptom onset) or viral load at randomization (< or ≥3.5 log10 copies/104 cells). RESULTS: In our model, remdesivir reduced viral production by infected cells by 2-fold on average (95% CI: 1.5-3.2-fold). Model-based simulations predict that remdesivir reduced time to viral clearance by 0.7 days compared with SoC, with large inter-individual variabilities (IQR: 0.0-1.3 days). Remdesivir had a larger impact in patients with high viral load at randomization, reducing viral production by 5-fold on average (95% CI: 2.8-25-fold) and the median time to viral clearance by 2.4 days (IQR: 0.9-4.5 days). CONCLUSIONS: Remdesivir halved viral production, leading to a median reduction of 0.7 days in the time to viral clearance compared with SoC. The efficacy was larger in patients with high viral load at randomization.

Remdesivir plus standard of care versus standard of care alone for the treatment of patients admitted to hospital with COVID-19 (DisCoVeRy): a phase 3, randomised, controlled, open-label trial.

BACKGROUND: The antiviral efficacy of remdesivir against SARS-CoV-2 is still controversial. We aimed to evaluate the clinical efficacy of remdesivir plus standard of care compared with standard of care alone in patients admitted to hospital with COVID-19, with indication of oxygen or ventilator support. METHODS: DisCoVeRy was a phase 3, open-label, adaptive, multicentre, randomised, controlled trial conducted in 48 sites in Europe (France, Belgium, Austria, Portugal, Luxembourg). Adult patients (aged ≥18 years) admitted to hospital with laboratory-confirmed SARS-CoV-2 infection and illness of any duration were eligible if they had clinical evidence of hypoxaemic pneumonia, or required oxygen supplementation. Exclusion criteria included elevated liver enzymes, severe chronic kidney disease, any contraindication to one of the studied treatments or their use in the 29 days before random assignment, or use of ribavirin, as well as pregnancy or breastfeeding. Participants were randomly assigned (1:1:1:1:1) to receive standard of care alone or in combination with remdesivir, lopinavir-ritonavir, lopinavir-ritonavir and interferon beta-1a, or hydroxychloroquine. Randomisation used computer-generated blocks of various sizes; it was stratified on severity of disease at inclusion and on European administrative region. Remdesivir was administered as 200 mg intravenous infusion on day 1, followed by once daily, 1-h infusions of 100 mg up to 9 days, for a total duration of 10 days. It could be stopped after 5 days if the participant was discharged. The primary outcome was the clinical status at day 15 measured by the WHO seven-point ordinal scale, assessed in the intention-to-treat population. Safety was assessed in the modified intention-to-treat population and was one of the secondary outcomes. This trial is registered with the European Clinical Trials Database, EudraCT2020-000936-23, and ClinicalTrials.gov, NCT04315948. FINDINGS: Between March 22, 2020, and Jan 21, 2021, 857 participants were enrolled and randomly assigned to remdesivir plus standard of care (n=429) or standard of care only (n=428). 15 participants were excluded from analysis in the remdesivir group, and ten in the control group. At day 15, the distribution of the WHO ordinal scale was: (1) not hospitalised, no limitations on activities (61 [15%] of 414 in the remdesivir group vs 73 [17%] of 418 in the control group); (2) not hospitalised, limitation on activities (129 [31%] vs 132 [32%]); (3) hospitalised, not requiring supplemental oxygen (50 [12%] vs 29 [7%]); (4) hospitalised, requiring supplemental oxygen (76 [18%] vs 67 [16%]); (5) hospitalised, on non-invasive ventilation or high flow oxygen devices (15 [4%] vs 14 [3%]); (6) hospitalised, on invasive mechanical ventilation or extracorporeal membrane oxygenation (62 [15%] vs 79 [19%]); (7) death (21 [5%] vs 24 [6%]). The difference between treatment groups was not significant (odds ratio 0·98 [95% CI 0·77-1·25]; p=0·85). There was no significant difference in the occurrence of serious adverse events between treatment groups (remdesivir, 135 [33%] of 406 vs control, 130 [31%] of 418; p=0·48). Three deaths (acute respiratory distress syndrome, bacterial infection, and hepatorenal syndrome) were considered related to remdesivir by the investigators, but only one by the sponsor's safety team (hepatorenal syndrome). INTERPRETATION: No clinical benefit was observed from the use of remdesivir in patients who were admitted to hospital for COVID-19, were symptomatic for more than 7 days, and required oxygen support. FUNDING: European Union Commission, French Ministry of Health, Domaine d'intérêt majeur One Health Île-de-France, REACTing, Fonds Erasme-COVID-Université Libre de Bruxelles, Belgian Health Care Knowledge Centre, Austrian Group Medical Tumor, European Regional Development Fund, Portugal Ministry of Health, Portugal Agency for Clinical Research and Biomedical Innovation. TRANSLATION: For the French translation of the abstract see Supplementary Materials section.

Bayesian inference using Hamiltonian Monte-Carlo algorithm for nonlinear joint modeling in the context of cancer immunotherapy.

Treatment evaluation in advanced cancer mainly relies on overall survival and tumor size dynamics. Both markers and their association can be simultaneously analyzed by using joint models, and these approaches are supported by many softwares or packages. However, these approaches are essentially limited to linear models for the longitudinal part, which limit their biological interpretation. More biological models of tumor dynamics can be obtained by using nonlinear models, but they are limited by the fact that parameter identifiability require rich dataset. In that context Bayesian approaches are particularly suited to incorporate the biological knowledge and increase the information available, but they are limited by the high computing cost of Monte-Carlo by Markov Chains algorithms. Here, we aimed to assess the performances of the Hamiltonian Monte-Carlo (HMC) algorithm implemented in Stan for inference in a nonlinear joint model. The method was validated on simulated data where HMC provided proper posterior distributions and credibility intervals in a reasonable computational time. Then the association between tumor size dynamics and survival was assessed in patients with advanced or metastatic bladder cancer treated with atezolizumab, an immunotherapy agent. HMC confirmed limited sensitivity to prior distributions. A cross-validation approach was developed and identified the current slope of tumor size dynamics as the most relevant driver of survival. In summary, HMC is an efficient approach to perform nonlinear joint models in a Bayesian framework, and opens the way for the use of nonlinear models to characterize both the rapid dynamics and the intersubject variability observed during cancer immunotherapy treatment.

Longitudinal analysis of organ-specific tumor lesion sizes in metastatic colorectal cancer patients receiving first line standard chemotherapy in combination with anti-angiogenic treatment.

The purpose of this work is to assess the heterogeneity across organs of response to treatment in metastatic colorectal patient based on longitudinal individual target lesion diameters (ILD) in comparison to sum of tumor lesion diameters (SLD). Data were from the McCAVE trial, in which 189 previously untreated patients with metastatic colorectal carcinoma (mCRC) received either bevacizumab (control, C) or vanucizumab (experimental, E), on top of standard chemotherapy. Bayesian hierarchical longitudinal non-linear mixed effect models were fitted to the data using Hamilton Monte Carlo algorithm to characterize the time dynamics of the tumor burden, and to obtain estimates of the tumor shrinkage and regrowth rates. The ILD model brought more nuanced results than to the SLD model. Besides substantial differences in tumor size at baseline (with lesions located in liver more than twice as large as the ones in lungs), it revealed a more durable response in lesions located in lymph nodes and 'other organs' compared to liver and lungs. Specifically, in lymph nodes and 'other organs', the projected time to nadir was doubled in group E (2.12 and 2.44 years respectively) compared to group C (1.07 and 1.20 years respectively). This long period of tumor shrinkage associated with a slightly larger change from baseline at nadir (- 51.4% in lymph nodes and - 62.6% in 'other organs' in the group E, compared to - 46.2% and - 46.9% in group C) resulted in a clinically meaningful difference in the tumor dynamics of patients in group E compared to the group C. The proportion of variance explained by the inter-lesion variability for each model parameter was large (ranging between 10 and 56%), reflecting the heterogeneity in tumor dynamics across organs. These findings suggest that there is value in understanding both within- and between-patient variability in tumor size's time dynamics using an appropriate modeling framework, as this information may help in pairing the right treatment with individual patient profile.

Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models.

There is a need for new approaches and endpoints in oncology drug development, particularly with the advent of immunotherapies and the multiple drug combinations under investigation. Tumor dynamics modeling, a key component to oncology "model-informed drug development," has shown a growing number of applications and a broader adoption by drug developers and regulatory agencies in the past years to support drug development and approval in a variety of ways. Tumor dynamics modeling is also being investigated in personalized cancer therapy approaches. These models and applications are reviewed and discussed, as well as the limitations and issues open for further investigations. A close collaboration between stakeholders like clinical investigators, statisticians, and pharmacometricians is warranted to advance clinical cancer therapeutics.

Association Between Tumor Size Kinetics and Survival in Patients With Urothelial Carcinoma Treated With Atezolizumab: Implication for Patient Follow-Up.

We characterized the association between tumor size kinetics and survival in patients with advanced urothelial carcinoma treated with atezolizumab (anti-programmed death-ligand 1, Tecentriq) using a joint model. The model, developed on data from 309 patients of a phase II clinical trial, identified the time-to-tumor growth and the instantaneous changes in tumor size as the best on-treatment predictors of survival. On the validation dataset containing data from 457 patients from a phase III study, the model predicted individual survival probability using 3-month or 6-month tumor size follow-up data with an area under the receptor-occupancy curve between 0.75 and 0.84, as compared with values comprised between 0.62 and 0.75 when the model included only information available at treatment initiation. Including tumor size kinetics in a relevant statistical framework improves the prediction of survival probability during immunotherapy treatment and may be useful to identify most-at-risk patients in "real-time."

Nonlinear joint models for individual dynamic prediction of risk of death using Hamiltonian Monte Carlo: application to metastatic prostate cancer.

BACKGROUND: Joint models of longitudinal and time-to-event data are increasingly used to perform individual dynamic prediction of a risk of event. However the difficulty to perform inference in nonlinear models and to calculate the distribution of individual parameters has long limited this approach to linear mixed-effect models for the longitudinal part. Here we use a Bayesian algorithm and a nonlinear joint model to calculate individual dynamic predictions. We apply this approach to predict the risk of death in metastatic castration-resistant prostate cancer (mCRPC) patients with frequent Prostate-Specific Antigen (PSA) measurements. METHODS: A joint model is built using a large population of 400 mCRPC patients where PSA kinetics is described by a biexponential function and the hazard function is a PSA-dependent function. Using Hamiltonian Monte Carlo algorithm implemented in Stan software and the estimated population parameters in this population as priors, the a posteriori distribution of the hazard function is computed for a new patient knowing his PSA measurements until a given landmark time. Time-dependent area under the ROC curve (AUC) and Brier score are derived to assess discrimination and calibration of the model predictions, first on 200 simulated patients and then on 196 real patients that are not included to build the model. RESULTS: Satisfying coverage probabilities of Monte Carlo prediction intervals are obtained for longitudinal and hazard functions. Individual dynamic predictions provide good predictive performances for landmark times larger than 12 months and horizon time of up to 18 months for both simulated and real data. CONCLUSIONS: As nonlinear joint models can characterize the kinetics of biomarkers and their link with a time-to-event, this approach could be useful to improve patient's follow-up and the early detection of most at risk patients.

Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients.

Joint modeling is increasingly popular for investigating the relationship between longitudinal and time-to-event data. However, numerical complexity often restricts this approach to linear models for the longitudinal part. Here, we use a novel development of the Stochastic-Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed-effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prostate Specific Antigen (PSA) kinetics can be captured by using a mechanistic model defined by nonlinear ordinary differential equations. The use of a mechanistic model predicts that biological quantities that cannot be observed, such as treatment-sensitive and treatment-resistant cells, may have a larger impact than PSA value on survival. This suggests that mechanistic joint models could constitute a relevant approach to evaluate the efficacy of treatment and to improve the prediction of survival in patients.

Ebola Virus Infection: Review of the Pharmacokinetic and Pharmacodynamic Properties of Drugs Considered for Testing in Human Efficacy Trials.

The 2014-2015 outbreak of Ebola virus disease is the largest epidemic to date in terms of the number of cases, deaths, and affected areas. In October 2015, no antiviral agents had proven antiviral efficacy in patients. However, in September 2014, the World Health Organization inventoried and has since regularly updated a list of potential drug candidates with demonstrated antiviral efficacy in in vitro or animal models. This includes agents belonging to various therapeutic classes, namely direct antiviral agents (favipiravir and BCX4430), a combination of antibodies (ZMapp), type I interferons, RNA interference-based drugs (TKM-Ebola and AVI-7537), and anticoagulant drugs (rNAPc2). Here, we review the pharmacokinetic and pharmacodynamic information presently available for these drugs, using data obtained in healthy volunteers for pharmacokinetics and data obtained in human clinical trials or animal models for pharmacodynamics. Future studies evaluating these drugs in clinical trials are critical to confirm their efficacy in humans, propose appropriate doses, and evaluate the possibility of treatment combinations.

Nonlinear Mixed-effect Models for Prostate-specific Antigen Kinetics and Link with Survival in the Context of Metastatic Prostate Cancer: A Comparison by Simulation of Two-stage and Joint Approaches.

In metastatic castration-resistant prostate cancer (mCRPC) clinical trials, the assessment of treatment efficacy essentially relies on the time to death and the kinetics of prostate-specific antigen (PSA). Joint modeling has been increasingly used to characterize the relationship between a time to event and a biomarker kinetics, but numerical difficulties often limit this approach to linear models. Here, we evaluated by simulation the capability of a new feature of the Stochastic Approximation Expectation-Maximization algorithm in Monolix to estimate the parameters of a joint model where PSA kinetics was defined by a mechanistic nonlinear mixed-effect model. The design of the study and the parameter values were inspired from one arm of a clinical trial. Increasingly high levels of association between PSA and survival were considered, and results were compared with those found using two simplified alternatives to joint model, a two-stage and a joint sequential model. We found that joint model allowed for a precise estimation of all longitudinal and survival parameters. In particular, the effect of PSA kinetics on survival could be precisely estimated, regardless of the strength of the association. In contrast, both simplified approaches led to bias on longitudinal parameters, and two-stage model systematically underestimated the effect of PSA kinetics on survival. In summary, we showed that joint model can be used to characterize the relationship between a nonlinear kinetics and survival. This opens the way for the use of more complex and physiological models to improve treatment evaluation and prediction in oncology.

Patients with chronic hepatitis C without advanced fibrosis and hepatocellular carcinoma: a retrospective clinical-pathological study.

BACKGROUND: There are very few studies on the incidence and risk factors of hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) in the absence of advanced fibrosis. Our objective was to identify the clinical-pathological features of these patients. METHODS: We retrospectively reviewed 162 patients admitted to our hospital for HCV-related HCC between 2000 and 2010. Patients with hepatitis of other aetiologies, human immunodeficiency virus co-infection, or treated with interferon were excluded. We compared demographic, laboratory, clinical and outcome parameters of patients with and without advanced fibrosis. RESULTS: 137 patients had advanced fibrosis (85%). Median age was higher in the advanced fibrosis vs. the non-advanced fibrosis group (62 vs. 65 years, respectively; p = 0.025). Steatosis was significantly more frequent in patients with advanced fibrosis compared to those without advanced fibrosis (43% vs. 20%, respectively; p = 0.032). Independent predictors associated to the occurrence of HCC in patients without advanced fibrosis were hepatitis B core antigen (odds ratio: 3.86; p = 0.044) and duration of hepatitis C infection (odds ratio: 1.21; p = 0.003). CONCLUSIONS: Risk factors such as steatosis or diabetes were not frequent in patients without advanced fibrosis. Further studies are needed to evaluate the role of occult hepatitis B and the duration of hepatitis infection in patients with HCC and chronic hepatitis C without advanced fibrosis.

Effect of ribavirin on viral kinetics and liver gene expression in chronic hepatitis C.

OBJECTIVE: Ribavirin improves treatment response to pegylated-interferon (PEG-IFN) in chronic hepatitis C but the mechanism remains controversial. We studied correlates of response and mechanism of action of ribavirin in treatment of hepatitis C. DESIGN: 70 treatment-naive patients were randomised to 4 weeks of ribavirin (1000-1200 mg/d) or none, followed by PEG-IFNα-2a and ribavirin at standard doses and durations. Patients were also randomised to a liver biopsy 24 h before or 6 h after starting PEG-IFN. Hepatic gene expression was assessed by microarray and interferon-stimulated gene (ISG) expression quantified by nCounter platform. Temporal changes in ISG expression were assessed by qPCR in peripheral-blood mononuclear cells (PBMC) and by serum levels of IP-10. RESULTS: After 4 weeks of ribavirin monotherapy, hepatitis C virus (HCV) levels decreased by 0.5±0.5 log10 (p=0.009 vs controls) and ALT by 33% (p<0.001). Ribavirin pretreatment, while modestly augmenting ISG induction by PEG-IFN, did not modify the virological response to subsequent PEG-IFN and ribavirin treatment. However, biochemical, but not virological, response to ribavirin monotherapy predicted response to subsequent combination treatment (rapid virological response, 71% in biochemical responders vs 22% non-responders, p=0.01; early virological response, 100% vs 68%, p=0.03; sustained virological response 83% vs 41%, p=0.053). Ribavirin monotherapy lowered serum IP-10 levels but had no effect on ISG expression in PBMC. CONCLUSIONS: Ribavirin is a weak antiviral but its clinical effect seems to be mediated by a separate, indirect mechanism, which may act to reset IFN-responsiveness in HCV-infected liver.

Innate immune tolerance and the role of kupffer cells in differential responses to interferon therapy among patients with HCV genotype 1 infection.

BACKGROUND & AIMS: In patients with hepatitis C virus (HCV) infection, interferon alfa (IFN-α) alters expression of IFN-stimulated genes (ISGs), but little is understood about factors that determine outcomes of therapy. We used a systems biology approach to evaluate the acute response of patients with chronic hepatitis C to IFN-α therapy. METHODS: We collected liver biopsy samples from 8 treatment-naïve patients with chronic HCV genotype 1 infection at baseline and 24 hours after treatment with IFN-α-2a (10 MU subcutaneously). Blood samples were collected before and up to 48 hours after administration of IFN-α-2a to measure HCV RNA levels and for gene expression analysis. Patients then received pegylated IFN-α-2a and ribavirin on day 5 of the study; therapy continued for up to 48 weeks. RESULTS: Based on the kinetics of HCV RNA during the first 12 weeks of therapy, 2 patients were rapid virologic responders, 4 were early virologic responders, and 2 did not respond to therapy (nonresponders). Nonresponders had high pretreatment levels of ISG expression in the liver but not in peripheral blood mononuclear cells. In responders, after administration of IFN-α, intrahepatic ISG expression increased significantly from baseline and was associated with a rapid phase 1 decrease in HCV. We identified distinct hepatic expression and tissue distribution patterns of ISGs that segregated with treatment outcome. Importantly, Kupffer cells were a local source of IFN that promoted basal expression of ISG in hepatocytes of nonresponders. This finding was validated in cultured THP1 human macrophages that expressed IFN-ß after exposure to viable HCV 2a. When Huh7 K2040 and Huh7 L2198S hepatoma cells were incubated with IFN-α-2a, expression of ISGs peaked by 4 hours and decreased by 72 hours, associated with an increase in level of HCV RNA. This indicates that constitutive exposure to IFN causes hepatoma cells to become tolerant of ISG function. CONCLUSIONS: In patients with chronic HCV infection, IFN production by Kupffer cells might promote innate immune tolerance, characterized by a lack of response to IFN therapy. Strategies to disrupt the virus-host interactions that induce innate immune tolerance should improve therapy.