Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model

Prognostic model development using novel genetic signature associated with adenosine metabolism and immune status for patients with hepatocellular carcinoma

The high mortality rate of hepatocellular carcinoma (HCC) is partly due to advanced diagnosis, emphasizing the need for effective predictive tools in HCC treatment. The aim of this study is to propose a novel prognostic model for HCC based on adenosine metabolizing genes and explore the potential relationship between them. Regression analysis was performed to identify differentially expressed genes associated with adenosine metabolism in HCC patients using RNA sequencing data obtained from a public database. Adenosine metabolism-related risk score (AMrisk) was derived using the least absolute shrinkage and selection operator (LASSO) Cox regression and verified using another database. Changes in adenosine metabolism in HCC were analyzed using functional enrichment analysis and multiple immune scores. The gene expression levels in patient samples were validated using quantitative reverse transcription polymerase chain reaction. Thirty adenosine metabolism-related differentially expressed genes were identified in HCC, and six genes (ADA, P2RY4, P2RY6, RPIA, SLC6A3, and VEGFA) were used to calculate the AMrisk score; the higher the risk scores, the lower the overall survival. Moreover, immune infiltration activation and immune checkpoints were considerably higher in the high-risk group. Additional in vitro experiments validated the enhanced expression of these six genes in HCC. The established predictive model demonstrated that adenosine metabolism-related genes was significantly associated with prognosis in HCC patients.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Engineering dendritic cell biomimetic membrane as a delivery system for tumor targeted therapy

Targeted immunotherapies make substantial strides in clinical cancer care due to their ability to counteract the tumor's capacity to suppress immune responses. Advances in biomimetic technology with minimally immunogenic and highly targeted, are addressing issues of targeted drug delivery and disrupting the tumor's immunosuppressive environment to trigger immune activation. Specifically, the use of dendritic cell (DC) membranes to coat nanoparticles ensures targeted delivery due to DC's unique ability to activate naive T cells, spotlighting their role in immunotherapy aimed at disrupting the tumor microenvironment. The potential of DC's biomimetic membrane to mediate immune activation and target tumors is gaining momentum, enhancing the effectiveness of cancer treatments in conjunction with other immune responses. This review delves into the methodologies behind crafting DC membranes and the fusion of dendritic and tumor cell membranes for encapsulating therapeutic nanoparticles. It explores their applications and recent advancements in combating cancer, offering an all-encompassing perspective on DC biomimetic nanosystems, immunotherapy driven by antigen presentation, and the collaborative efforts of drug delivery in chemotherapy and photodynamic therapies. Current evidence shows promise in augmenting combined therapeutic approaches for cancer treatment and holds translational potential for various cancer treatments in a clinical setting.

The complex role of immune cells in antigen presentation and regulation of T-cell responses in hepatocellular carcinoma: progress, challenges, and future directions

Hepatocellular carcinoma (HCC) is a prevalent form of liver cancer that poses significant challenges regarding morbidity and mortality rates. In the context of HCC, immune cells play a vital role, especially concerning the presentation of antigens. This review explores the intricate interactions among immune cells within HCC, focusing on their functions in antigen presentation and the modulation of T-cell responses. We begin by summarizing the strategies that HCC uses to escape immune recognition, emphasizing the delicate equilibrium between immune surveillance and evasion. Next, we investigate the specific functions of various types of immune cells, including dendritic cells, natural killer (NK) cells, and CD8+ T cells, in the process of antigen presentation. We also examine the impact of immune checkpoints, such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and the pathways involving programmed cell death protein 1 (PD-1) and programmed death ligand 1 (PD-L1), on antigen presentation, while taking into account the clinical significance of checkpoint inhibitors. The review further emphasizes the importance of immune-based therapies, including cancer vaccines and CAR-T cell therapy, in improving antigen presentation. In conclusion, we encapsulate the latest advancements in research, propose future avenues for exploration, and stress the importance of innovative technologies and customized treatment strategies. By thoroughly analyzing the interactions of immune cells throughout the antigen presentation process in HCC, this review provides an up-to-date perspective on the field, setting the stage for new therapeutic approaches.

Comprehensive assessment of TECENTRIQ® and OPDIVO®: analyzing immunotherapy indications withdrawn in triple-negative breast cancer and hepatocellular carcinoma

Atezolizumab (TECENTRIQ®) and nivolumab (OPDIVO®) are both immunotherapeutic indications targeting programmed cell death 1 ligand 1 (PD-L1) and programmed cell death 1 (PD-1), respectively. These inhibitors hold promise as therapies for triple-negative breast cancer (TNBC) and hepatocellular carcinoma (HCC) and have demonstrated encouraging results in reducing the progression and spread of tumors. However, due to their adverse effects and low response rates, the US Food and Drug Administration (FDA) has withdrawn the approval of atezolizumab in TNBC and nivolumab in HCC treatment. The withdrawals of atezolizumab and nivolumab have raised concerns regarding their effectiveness and the ability to predict treatment responses. Therefore, the current study aims to investigate the immunotherapy withdrawal of PD-1/PD-L1 inhibitors, specifically atezolizumab for TNBC and nivolumab for HCC. This study will examine both the structural and clinical aspects. This review provides detailed insights into the structure of the PD-1 receptor and its ligands, the interactions between PD-1 and PD-L1, and their interactions with the withdrawn antibodies (atezolizumab and nivolumab) as well as PD-1 and PD-L1 modifications. In addition, this review further assesses these antibodies in the context of TNBC and HCC. It seeks to elucidate the factors that contribute to diverse responses to PD-1/PD-L1 therapy in different types of cancer and propose approaches for predicting responses, mitigating the potential risks linked to therapy withdrawals, and optimizing patient outcomes. By better understanding the mechanisms underlying responses to PD-1/PD-L1 therapy and developing strategies to predict these responses, it is possible to create more efficient treatments for TNBC and HCC.

Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery

Due to the lack of treatment options, there remains a need to advance new therapeutics in hepatocellular carcinoma (HCC). The traditional approach moves from initial molecular discovery through animal models to human trials to advance novel systemic therapies that improve treatment outcomes for patients with cancer. Computational methods that simulate tumors mathematically to describe cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico, potentially greatly accelerating delivery of new therapeutics to patients. To facilitate the design of dosing regimens and identification of potential biomarkers for immunotherapy, we developed a new computational model to track tumor progression at the organ scale while capturing the spatial heterogeneity of the tumor in HCC. This computational model of spatial quantitative systems pharmacology was designed to simulate the effects of combination immunotherapy. The model was initiated using literature-derived parameter values and fitted to the specifics of HCC. Model validation was done through comparison with spatial multiomics data from a neoadjuvant HCC clinical trial combining anti-PD1 immunotherapy and a multitargeted tyrosine kinase inhibitor cabozantinib. Validation using spatial proteomics data from imaging mass cytometry demonstrated that closer proximity between CD8 T cells and macrophages correlated with nonresponse. We also compared the model output with Visium spatial transcriptomics profiling of samples from posttreatment tumor resections in the clinical trial and from another independent study of anti-PD1 monotherapy. Spatial transcriptomics data confirmed simulation results, suggesting the importance of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. Our findings demonstrate that incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides a novel approach for patient outcome prediction and biomarker discovery. Significance: Incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides an effective approach for patient outcome prediction and biomarker discovery.

Cancer immunotherapy and its facilitation by nanomedicine

Cancer immunotherapy has sparked a wave of cancer research, driven by recent successful proof-of-concept clinical trials. However, barriers are emerging during its rapid development, including broad adverse effects, a lack of reliable biomarkers, tumor relapses, and drug resistance. Integration of nanomedicine may ameliorate current cancer immunotherapy. Ultra-large surface-to-volume ratio, extremely small size, and easy modification surface of nanoparticles enable them to selectively detect cells and kill cancer cells in vivo. Exciting synergistic applications of the two approaches have emerged in treating various cancers at the intersection of cancer immunotherapy and cancer nanomedicine, indicating the potential that the combination of these two therapeutic modalities can lead to new paradigms in the treatment of cancer. This review discusses the status of current immunotherapy and explores the possible opportunities that the nanomedicine platform can make cancer immunotherapy more powerful and precise by synergizing the two approaches.

Crosstalk between Epigenetics and Metabolic Reprogramming in Metabolic Dysfunction-Associated Steatotic Liver Disease-Induced Hepatocellular Carcinoma: A New Sight

Epigenetic and metabolic reprogramming alterations are two important features of tumors, and their reversible, spatial, and temporal regulation is a distinctive hallmark of carcinogenesis. Epigenetics, which focuses on gene regulatory mechanisms beyond the DNA sequence, is a new entry point for tumor therapy. Moreover, metabolic reprogramming drives hepatocellular carcinoma (HCC) initiation and progression, highlighting the significance of metabolism in this disease. Exploring the inter-regulatory relationship between tumor metabolic reprogramming and epigenetic modification has become one of the hot directions in current tumor metabolism research. As viral etiologies have given way to metabolic dysfunction-associated steatotic liver disease (MASLD)-induced HCC, it is urgent that complex molecular pathways linking them and hepatocarcinogenesis be explored. However, how aberrant crosstalk between epigenetic modifications and metabolic reprogramming affects MASLD-induced HCC lacks comprehensive understanding. A better understanding of their linkages is necessary and urgent to improve HCC treatment strategies. For this reason, this review examines the interwoven landscape of molecular carcinogenesis in the context of MASLD-induced HCC, focusing on mechanisms regulating aberrant epigenetic alterations and metabolic reprogramming in the development of MASLD-induced HCC and interactions between them while also updating the current advances in metabolism and epigenetic modification-based therapeutic drugs in HCC.

DMPK perspective on quantitative model analysis for chimeric antigen receptor cell therapy: Advances and challenges

Chimeric antigen receptor (CAR) cells are genetically engineered immune cells that specifically target tumor-associated antigens and have revolutionized cancer treatment, particularly in hematological malignancies, with ongoing investigations into their potential applications in solid tumors. This review provides a comprehensive overview of the current status and challenges in drug metabolism and pharmacokinetics (DMPK) for CAR cell therapy, specifically emphasizing on quantitative modeling and simulation (M&S). Furthermore, the recent advances in quantitative model analysis have been reviewed, ranging from clinical data characterization to mechanism-based modeling that connects in vitro and in vivo nonclinical and clinical study data. Additionally, the future perspectives and areas for improvement in CAR cell therapy translation have been reviewed. This includes using formulation quality considerations, characterization of appropriate animal models, refinement of in vitro models for bottom-up approaches, and enhancement of quantitative bioanalytical methodology. Addressing these challenges within a DMPK framework is pivotal in facilitating the translation of CAR cell therapy, ultimately enhancing the patients' lives through efficient CAR cell therapies.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

Antitumor immunostimulatory activity of the traditional Chinese medicine polysaccharide on hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy, often associated with compromised immune function in affected patients. This can be attributed to the secretion of specific factors by liver cancer cells, which hinder the immune response and lead to a state of immune suppression. Polysaccharides derived from traditional Chinese medicine (TCM) are valuable constituents known for their immunomodulatory properties. This review aims to look into the immunomodulatory effects of TCM polysaccharides on HCC. The immunomodulatory effects of TCM polysaccharides are primarily manifested through the activation of effector T lymphocytes, dendritic cells, NK cells, and macrophages against hepatocellular carcinoma (HCC) both in vivo and in vitro settings. Furthermore, TCM polysaccharides have demonstrated remarkable adjuvant antitumor immunomodulatory effects on HCC in clinical settings. Therefore, the utilization of TCM polysaccharides holds promising potential for the development of novel therapeutic agents or adjuvants with advantageous immunomodulatory properties for HCC.

Emerging biomolecules for practical theranostics of liver hepatocellular carcinoma

Most cases of hepatocellular carcinoma (HCC) are able to be diagnosed through regular surveillance in an identifiable patient population with chronic hepatitis B or cirrhosis. Nevertheless, 50% of global cases might present incidentally owing to symptomatic advanced-stage HCC after worsening of liver dysfunction. A systematic search based on PUBMED was performed to identify relevant outcomes, covering newer surveillance modalities including secretory proteins, DNA methylation, miRNAs, and genome sequencing analysis which proposed molecular expression signatures as ideal tools in the early-stage HCC detection. In the face of low accuracy without harmonization on the analytical approaches and data interpretation for liquid biopsy, a more accurate incidence of HCC will be unveiled by using deep machine learning system and multiplex immunohistochemistry analysis. A combination of molecular-secretory biomarkers, high-definition imaging and bedside clinical indexes in a surveillance setting offers a comprehensive range of HCC potential indicators. In addition, the sequential use of numerous lines of systemic anti-HCC therapies will simultaneously benefit more patients in survival. This review provides an overview on the most recent developments in HCC theranostic platform.

Translational Pharmacokinetic/Pharmacodynamic Modeling and Simulation of Oxaliplatin and Irinotecan in Colorectal Cancer

Despite the recent advances in this field, there are limited methods for translating organoid-based study results to clinical response. The goal of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model to facilitate the translation, using oxaliplatin and irinotecan treatments with colorectal cancer (CRC) as examples. The PK models were developed using qualified oxaliplatin and irinotecan PK data from the literature. The PD models were developed based on antitumor efficacy data of SN-38 and oxaliplatin evaluated in vitro using tumor organoids. To predict the clinical response, translational scaling of the models was established by incorporating predicted ultrafiltration platinum in plasma or SN-38 in tumors to PD models as the driver of efficacy. The final PK/PD model can predict PK profiles and responses following treatments with oxaliplatin or irinotecan. After generation of virtual patient cohorts, this model simulated their tumor shrinkages following treatments, which were used in analyzing the efficacies of the two treatments. Consistent with the published clinical trials, the model simulation suggested similar patient responses following the treatments of oxaliplatin and irinotecan with regards to the probabilities of progression-free survival (HR = 1.05, 95%CI [0.97;1.15]) and the objective response rate (OR = 1.15, 95%CI [1.00;1.32]). This proposed translational PK/PD modeling approach provides a significant tool for predicting clinical responses of different agents, which may help decision-making in drug development and guide clinical trial design.

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

Cancer immune escape: the role of antigen presentation machinery

The mechanisms of antigen processing and presentation play a crucial role in the recognition and targeting of cancer cells by the immune system. Cancer cells can evade the immune system by downregulating or losing the expression of the proteins recognized by the immune cells as antigens, creating an immunosuppressive microenvironment, and altering their ability to process and present antigens. This review focuses on the mechanisms of cancer immune evasion with a specific emphasis on the role of antigen presentation machinery. The study of the immunopeptidome, or peptidomics, has provided insights into the mechanisms of cancer immune evasion and has potential applications in cancer diagnosis and treatment. Additionally, manipulating the epigenetic landscape of cancer cells plays a critical role in suppressing the immune response against cancer. Targeting these mechanisms through the use of HDACis, DNMTis, and combination therapies has the potential to improve the efficacy of cancer immunotherapy. However, further research is needed to fully understand the mechanisms of action and optimal use of these therapies in the clinical setting.

Glycolysis in Chronic Liver Diseases: Mechanistic Insights and Therapeutic Opportunities

Chronic liver diseases (CLDs) cover a spectrum of liver diseases, ranging from nonalcoholic fatty liver disease to liver cancer, representing a growing epidemic worldwide with high unmet medical needs. Glycolysis is a conservative and rigorous process that converts glucose into pyruvate and sustains cells with the energy and intermediate products required for diverse biological activities. However, abnormalities in glycolytic flux during CLD development accelerate the disease progression. Aerobic glycolysis is a hallmark of liver cancer and is responsible for a broad range of oncogenic functions including proliferation, invasion, metastasis, angiogenesis, immune escape, and drug resistance. Recently, the non-neoplastic role of aerobic glycolysis in immune activation and inflammatory disorders, especially CLD, has attracted increasing attention. Several key mediators of aerobic glycolysis, including HIF-1α and pyruvate kinase M2 (PKM2), are upregulated during steatohepatitis and liver fibrosis. The pharmacological inhibition or ablation of PKM2 effectively attenuates hepatic inflammation and CLD progression. In this review, we particularly focused on the glycolytic and non-glycolytic roles of PKM2 in the progression of CLD, highlighting the translational potential of a glycolysis-centric therapeutic approach in combating CLD.

Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager

Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients' immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy.

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition

Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition

Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.

Case Report: Solitary metastasis to the appendix after curative treatment of HCC

BACKGROUND

Liver cancer is now the fourth most common cancer in China. The most important factor in decreasing the overall survival is recurrence. Nearly 40%-70% of patients would be detected with intrahepatic or extrahepatic recurrence in 5 years after R0 resection. The intestine is not a usual site for extrahepatic metastasis. Only one case of hepatocellular carcinoma (HCC) metastasis to the appendix has been reported so far. So, it poses a difficulty for us to develop treatment plan.

CASE PRESENTATION

Here, we report a very rare case of a recurrent HCC patient. R0 resection was first performed on this 52-year-old men who was diagnosed with Barcelona Clinic Liver Cancer stage A HCC. Different from other cases, a solitary metastasis to the appendix was detected 5 years after the R0 resection. After discussing with the multidisciplinary team, we decided to perform surgical resection again. The final postoperative pathology confirmed HCC. Complete responses were detected in this patient after the combined treatment of transarterial chemoembolization, angiogenesis inhibitors, and immune checkpoint inhibitors.

CONCLUSION

Because solitary metastasis to the appendix in HCC is very rare, this case might be the first reported in HCC patients after R0 resection. This case report highlights the efficacy of the combination of surgery, local regional therapy, angiogenesis inhibitors, and immune treatment in HCC patients with solitary metastasis to the appendix.

Mortality Evaluation and Life Expectancy Prediction of Patients with Hepatocellular Carcinoma with Data Mining

BACKGROUND

The complexity of systemic variables and comorbidities makes it difficult to determine the best treatment for patients with hepatocellular carcinoma (HCC). It is impossible to perform a multidimensional evaluation of every patient, but the development of guidelines based on analyses of said complexities would be the next best option. Whereas conventional statistics are often inadequate for developing multivariate predictive models, data mining has proven more capable. Patients, methods and findings: Clinical profiles and treatment responses of 537 patients diagnosed with Barcelona Clinic Liver Cancer stages B and C from 2009 to 2019 were retrospectively analyzed using 4 decision tree algorithms. A combination of 19 treatments, 7 biomarkers, and 4 states of hepatitis was tested to determine which combinations would result in survival times greater than a year in duration. Just 2 of the algorithms produced complete models through single trees, which made them only the ones suitable for clinical judgement. A combination of alpha fetoprotein ≤210.5 mcg/L, glutamic oxaloacetic transaminase ≤1.13 µkat/L, and total bilirubin ≤ 0.0283 mmol/L was shown to be a good predictor of survival >1 year, and the most effective treatments for such patients were radio-frequency ablation (RFA) and transarterial chemoembolization (TACE) with radiation therapy (RT). In patients without this combination, the best treatments were RFA, TACE with RT and targeted drug therapy, and TACE with targeted drug therapy and immunotherapy. The main limitation of this study was its small sample. With a small sample size, we may have developed a less reliable model system, failing to produce any clinically important results or outcomes.

CONCLUSION

Data mining can produce models to help clinicians predict survival time at the time of initial HCC diagnosis and then choose the most suitable treatment.

FDA-Approved Monoclonal Antibodies for Unresectable Hepatocellular Carcinoma: What Do We Know So Far?

Unresectable hepatocellular carcinoma (HCC) is an advanced primary liver malignancy with a poor prognosis. The Food and Drug Administration (FDA) has, to date, approved nivolumab, pembrolizumab, ramucirumab, nivolumab/ipilimumab, atezolizumab/bevacizumab, as well as tremelimumab/durvalumab, as first- or second-line monoclonal antibodies (mAbs) for unresectable HCC. The present review examines the current state of knowledge, and provides a useful update on the safety and efficacy of these therapeutic agents, thus attempting to define the suitability of each mAb for different patient subgroups.

Adjuvant ICIs Plus Targeted Therapies Reduce HCC Recurrence after Hepatectomy in Patients with High Risk of Recurrence

BACKGROUND

The high recurrence rate of hepatocellular carcinoma (HCC) after hepatectomy usually results in poor prognosis. To the best of our knowledge, no study has reported the efficacy of immune checkpoint inhibitors (ICIs) plus targeted therapies on preventing HCC recurrence after hepatectomy. Thus, the aim of this study was to investigate the benefits and safety of applying adjuvant ICIs plus targeted therapies after hepatectomy for patients at high risk of HCC recurrence.

METHODS

A total of 196 patients with any risk factors for recurrence who underwent hepatectomy for HCC were reviewed in this retrospective study.

RESULTS

Compared with the control group (n = 158), ICIs plus targeted therapies (n = 38) had a significantly higher recurrence-free survival (RFS) rate in univariate analysis (HR, 0.46; 95% confidence interval [CI], 0.24-0.90; p = 0.020), multivariate analysis (adjusted HR, 0.62; 95%CI, 0.49-0.79; p < 0.001) and propensity score-matched analysis (HR, 0.35; 95%CI, 0.16-0.75; p = 0.005). Subgroup analyses also showed that postoperative adjuvant ICIs plus targeted therapies might reduce HCC recurrence in patients with the most of risk factors.

CONCLUSION

Postoperative adjuvant ICI plus targeted therapies may reduces early HCC recurrence in patients with a high risk of recurrence, and the treatments are well tolerated.

Immunotherapy for Hepatocellular Carcinoma in the Setting of Liver Transplantation: A Review

The emerging field of immuno-oncology has brought exciting developments in the treatment of hepatocellular carcinoma (HCC). It has also raised urgent questions about the role of immunotherapy in the setting of liver transplantation, both before and after transplant. A growing body of evidence points to the safety and efficacy of immunotherapeutic agents as potential adjuncts for successful down-staging of advanced HCCs to allow successful transplant in carefully selected patients. For patients with recurrent HCC post-transplant, immunotherapy has a limited, yet growing role. In this review, we describe optimal regimens in the setting of liver transplantation.

New Challenges Facing Systemic Therapies of Advanced HCC in the Era of Different First-Line Immunotherapy-Based Combinations

The standard of care of first-line systemic therapy for advanced hepatocellular carcinoma (HCC) is currently changing with the results of the IMbrave150 trial which are demonstrating superiority of the atezolizumab-bevacizumab combination over sorafenib, modifying this line of treatment for the first time in over 10 years. Recently, other immunotherapy-based combinations (durvalumab-tremelimumab, lenvatinib-pembrolizumab, cabozantinib-atezolizumab, and camrelizumab-rivoceranib) reported results in phase III studies, and might challenge this new standard of care. This revolution will lead to a considerable change in practice, and highlight challenges for future drug development. In this review, we will, firstly, describe results of the different combinations, and discuss the difficulties in selecting the first-line treatment. We will then present the different recommendations about second-line treatment following the first-line immunotherapy-based combination, discussing the rationale for the differences in existing recommendations. We will finally discuss the challenges for future drug development in advanced HCC.