Development of a prognostic composite cytokine signature based on the correlation with nivolumab clearance: translational PK/PD analysis in patients with renal cell carcinoma

Antibody Drug Clearance: An Underexplored Marker of Outcomes with Checkpoint Inhibitors

Immune-checkpoint inhibitor (ICI) therapy has dramatically changed the clinical landscape for several cancers, and ICI use continues to expand across many cancer types. Low baseline clearance (CL) and/or a large reduction of CL during treatment correlates with better clinical response and longer survival. Similar phenomena have also been reported with other monoclonal antibodies (mAb) in cancer and other diseases, highlighting a characteristic of mAb clinical pharmacology that is potentially shared among various mAbs and diseases. Though tempting to attribute poor outcomes to low drug exposure and arguably low target engagement due to high CL, such speculation is not supported by the relatively flat exposure-response relationship of most ICIs, where a higher dose or exposure is not likely to provide additional benefit. Instead, an elevated and/or increasing CL could be a surrogate marker of the inherent resistant phenotype that cannot be reversed by maximizing drug exposure. The mechanisms connecting ICI clearance, therapeutic efficacy, and resistance are unclear and likely to be multifactorial. Therefore, to explore the potential of ICI CL as an early marker for efficacy, this review highlights the similarities and differences of CL characteristics and CL-response relationships for all FDA-approved ICIs, and we compare and contrast these to selected non-ICI mAbs. We also discuss underlying mechanisms that potentially link mAb CL with efficacy and highlight existing knowledge gaps and future directions where more clinical and preclinical investigations are warranted to clearly understand the value of baseline and/or time-varying CL in predicting response to ICI-based therapeutics.

The 5th Kidney Cancer Research Summit: Research Accelerating Cures for Renal Cell Carcinoma in 2023

The 5th Kidney Cancer Research Summit was a hybrid event hosted in Boston, MA in July 2023. As in previous editions, the conference attracted a wide representation of global thought leaders in kidney cancer spanning all stages of clinical and laboratory research. Sessions covered tumor metabolism, novel immune pathways, advances in clinical trials and immunotherapy, and progress toward biomarkers. The abstract presentations were published as a supplement in The Oncologist (https://academic.oup.com/oncolo/issue/28/Supplement_1). Aiming to be more concise than comprehensive, this commentary summarizes the most important emerging areas of kidney cancer research discussed and debated among the stakeholders at the conference, with relevant updates that have occurred since.

Therapeutic drug monitoring of immune checkpoint inhibitors: based on their pharmacokinetic properties and biomarkers

As a new means of oncology treatment, immune checkpoint inhibitors (ICIs) can improve survival rates in patients with resistant or refractory tumors. However, there are obvious inter-individual differences in the unsatisfactory response rate, drug resistance rate and the occurrence of immune-related adverse events (irAE). These questions have sparked interest in researchers looking for a way to screen sensitive populations and predict efficacy and safety. Therapeutic drug monitoring (TDM) is a way to ensure the safety and effectiveness of medication by measuring the concentration of drugs in body fluids and adjusting the medication regimen. It has the potential to be an adjunctive means of predicting the safety and efficacy of ICIs treatment. In this review, the author outlined the pharmacokinetic (PK) characteristics of ICIs in patients. The feasibility and limitations of TDM of ICIs were discussed by summarizing the relationships between the pharmacokinetic parameters and the efficacy, toxicity and biomarkers.

Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures

BACKGROUND

Immune checkpoint inhibitor (ICI) therapy has substantially improved the overall survival (OS) in patients with non-small-cell lung cancer (NSCLC); however, its response rate is still modest. In this study, we developed a machine learning-based platform, namely the Cytokine-based ICI Response Index (CIRI), to predict the ICI response of patients with NSCLC based on the peripheral blood cytokine profiles.

METHODS

We enrolled 123 and 99 patients with NSCLC who received anti-PD-1/PD-L1 monotherapy or combined chemotherapy in the training and validation cohorts, respectively. The plasma concentrations of 93 cytokines were examined in the peripheral blood obtained from patients at baseline (pre) and 6 weeks after treatment (early during treatment: edt). Ensemble learning random survival forest classifiers were developed to select feature cytokines and predict the OS of patients undergoing ICI therapy.

RESULTS

Fourteen and 19 cytokines at baseline and on treatment, respectively, were selected to generate CIRI models (namely preCIRI14 and edtCIRI19), both of which successfully identified patients with worse OS in two completely independent cohorts. At the population level, the prediction accuracies of preCIRI14 and edtCIRI19, as indicated by the concordance indices (C-indices), were 0.700 and 0.751 in the validation cohort, respectively. At the individual level, patients with higher CIRI scores demonstrated worse OS [hazard ratio (HR): 0.274 and 0.163, and p<0.0001 and p=0.0044 in preCIRI14 and edtCIRI19, respectively]. By including other circulating and clinical features, improved prediction efficacy was observed in advanced models (preCIRI21 and edtCIRI27). The C-indices in the validation cohort were 0.764 and 0.757, respectively, whereas the HRs of preCIRI21 and edtCIRI27 were 0.141 (p<0.0001) and 0.158 (p=0.038), respectively.

CONCLUSIONS

The CIRI model is highly accurate and reproducible in determining the patients with NSCLC who would benefit from anti-PD-1/PD-L1 therapy with prolonged OS and may aid in clinical decision-making before and/or at the early stage of treatment.

The components of tumor microenvironment as biomarker for immunotherapy in metastatic renal cell carcinoma

Substantial improvement in prognosis among metastatic renal cell carcinoma (mRCC) patients has been achieved, owing to the rapid development and utilization of immunotherapy. In particular, immune checkpoint inhibitors (ICIs) have been considered the backbone of systemic therapy for patients with mRCC alongside multi-targeted tyrosine kinase inhibitors (TKIs) in the latest clinical practice guidelines. However, controversies and challenges in optimal individualized treatment regarding immunotherapy remains still About 2/3 of the patients presented non-response or acquired resistance to ICIs. Besides, immune-related toxicities, namely immune-related adverse events, are still elusive and life-threatening. Thus, reliable biomarkers to predict immunotherapeutic outcomes for mRCC patients are needed urgently. Tumor microenvironment (TME), consisting of immune cells, vasculature, signaling molecules, and extracellular matrix and regulates tumor immune surveillance and immunological evasion through complex interplay, plays a critical role in tumor immune escape and consequently manipulates the efficacy of immunotherapy. Various studied have identified the different TME components are significantly associated with the outcome of mRCC patients receiving immunotherapy, making them potential valuable biomarkers in therapeutic guidance. The present review aims to summarize the latest evidence on the associations between the components of TME including immune cells, cytokines and extracellular matrix, and the therapeutic responses among mRCC patients with ICI-based treatment. We further discuss the feasibility and limitation of these components as biomarkers.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

IL-4, IL-10, CCL2 and TGF-β as potential biomarkers for severity in Plasmodium vivax malaria

Cytokines and chemokines are immune response molecules that display diverse functions, such as inflammation and immune regulation. In Plasmodium vivax infections, the uncontrolled production of these molecules is thought to contribute to pathogenesis and has been proposed as a possible predictor for disease complications. The objective of this study was to evaluate the cytokine profile of P. vivax malaria patients with different clinical outcomes to identify possible immune biomarkers for severe P. vivax malaria. The study included patients with non-severe (n = 56), or severe (n = 50) P. vivax malaria and healthy controls (n = 50). Patient plasma concentrations of IL-4, IL-2, CXCL10, IL-1β, TNF-α, CCL2, IL-17A, IL-6, IL-10, IFN-γ, IL-12p70, CXCL8 and active TGF-β1 were determined through flow cytometry. The levels of several cytokines and chemokines, CXCL10, IL-10, IL-6, IL-4, CCL2 and IFN-γ were found to be significantly higher in severe, compared to non-severe P. vivax malaria patients. Severe thrombocytopenia was positively correlated with IL-4, CXCL10, IL-6, IL-10 and IFN-γ levels, renal dysfunction was related to an increase in IL-2, IL-1β, IL-17A and IL-8, and hepatic impairment with CXCL10, MCP-1, IL-6 and IFN-γ. A Lasso regression model suggests that IL-4, IL-10, CCL2 and TGF-β might be developed as biomarkers for severity in P. vivax malaria. Severe P. vivax malaria patients present specific cytokine and chemokine profiles that are different from non-severe patients and that could potentially be developed as biomarkers for disease severity.

Plasmodium vivax is one of the main species responsible for malaria in the world. The pathogenic mechanisms leading to the development of severe P. vivax malaria are not yet fully understood. Immune system molecules such as cytokines and chemokines actively participate in the control of the infection, however, their uncontrolled production can influence alterations in organs such as the liver, kidneys, among others. In this study we show that there is a differential concentration of some cytokines and chemokines between patients with non-severe malaria and severe P. vivax malaria; and that there are associations between these molecules with manifestations that occur in severe malaria. Four molecules with potential to become biomarkers of severity were identified.

Hepcidin as a prognostic biomarker in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common malignancy of urologic neoplasms. Hepcidin is a pivotal modulator of iron metabolism involved in human cancers; however, the biological significance of hepcidin in ccRCC remains to be fully understood. Therefore, in this study, we evaluated the expression profiles of hepcidin in ccRCC from several public databases and found that hepcidin expression was upregulated in ccRCC, which was further validated in ccRCC cell lines, clinical samples, and tissue microarray (TMA) quantitative real-time PCR and immunohistochemistry. In addition, we found that the expression level of hepcidin was correlated with the age, T stage and pathologic stage of patients. Furthermore, hepcidin promoter methylation was significantly associated with the worse poor clinical parameters of ccRCC patients, and hepcidin was an independent prognostic factor. Mechanistically, enrichment analysis revealed that hepcidin participated in the immune-related and metabolism-related pathways. Hepcidin was positively correlated with not only immune infiltration and immune checkpoints but also tumor mutation burden and cytotoxic T lymphocyte. Finally, we validated the positive correlation of hepcidin with the marker of macrophage (CD68) in the TMA. Our findings provide insights into understanding the function and its underlying mechanism of hepcidin in ccRCC and suggest that hepcidin might serve as a potential predictive biomarker of response to immunotherapy and the prognosis of patients with ccRCC.

Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment

Machine learning (ML) opens new perspectives in identifying predictive factors of efficacy among a large number of patients’ characteristics in oncology studies. The objective of this work was to combine ML with population pharmacokinetic/pharmacodynamic (PK/PD) modeling of tumor growth inhibition to understand the sources of variability between patients and therefore improve model predictions to support drug development decisions. Data from 127 patients with hepatocellular carcinoma enrolled in a phase I/II study evaluating once‐daily oral doses of the fibroblast growth factor receptor FGFR4 kinase inhibitor, Roblitinib (FGF401), were used. Roblitinib  PKs was best described by a two‐compartment model with a delayed zero‐order absorption and linear elimination. Clinical efficacy using the longitudinal sum of the longest lesion diameter data was described with a population PK/PD model of tumor growth inhibition including resistance to treatment. ML, applying elastic net modeling of time to progression data, was associated with cross‐validation, and allowed to derive a composite predictive risk score from a set of 75 patients’ baseline characteristics. The two approaches were combined by testing the inclusion of the continuous risk score as a covariate on PD model parameters. The score was found as a significant covariate on the resistance parameter and resulted in 19% reduction of its variability, and 32% variability reduction on the average dose for stasis. The final PK/PD model was used to simulate effect of patients’ characteristics on tumor growth inhibition profiles. The proposed methodology can be used to support drug development decisions, especially when large interpatient variability is observed.

Exposure-Response Analysis to Support Nivolumab Once Every 4 Weeks Dosing in Combination with Cabozantinib in Renal Cell Carcinoma

PURPOSE

A benefit:risk assessment for a less-frequent nivolumab 480 mg Q4W + cabozantinib 40 mg QD dosing regimen was predicted using modeling and simulation of clinical trial data from nivolumab monotherapy studies and from the nivolumab 240 mg Q2W + cabozantinib 40mg QD dosing regimen, which demonstrated clinical benefit versus sunitinib in previously untreated advanced renal cell carcinoma (aRCC) in the phase III CheckMate 9ER trial (NCT03141177).

EXPERIMENTAL DESIGN

Multivariable Cox proportional-hazards analyses were conducted using nivolumab monotherapy data in previously treated aRCC and data from CheckMate 9ER to evaluate progression-free survival (PFS), overall survival (OS), and grade {greater than or equal to}2 immune-mediated adverse events (IMAEs).

RESULTS

Nivolumab 240 mg Q2W + cabozantinib versus nivolumab showed improvement in PFS (HR, 0.38; 95% CI, 0.31-0.47), OS (HR, 0.63 95% CI, 0.46-0.85), and increased risk of grade {greater than or equal to}2 IMAEs (HR, 2.19; 95% CI, 1.79-2.67). Nivolumab exposure was not a predictor of PFS/OS or grade {greater than or equal to}2 IMAEs. Lower nivolumab clearance, male sex, higher baseline bodyweight, and Karnofsky performance (100) were each associated with PFS/OS improvements. Region and IMDC poor score were negative OS predictors. Age, baseline albumin, and programmed death ligand-1 status were not significant PFS/OS predictors. Cabozantinib was a significant grade {greater than or equal to}2 IMAE predictor, driven by diarrhea and hepatic events. Model-predicted PFS/OS and grade {greater than or equal to}2 IMAE rates were similar (<2.5% difference) for nivolumab 240 mg Q2W + cabozantinib and 480 mg Q4W + cabozantinib.

CONCLUSIONS

Comparable benefit:risk was predicted for nivolumab 480 mg Q4W + cabozantinib and nivolumab 240 mg Q2W + cabozantinib.

Optimized Dosing: The Next Step in Precision Medicine in Non-Small-Cell Lung Cancer

In oncology, and especially in the treatment of non-small-cell lung cancer (NSCLC), dose optimization is often a neglected part of precision medicine. Many drugs are still being administered in "one dose fits all" regimens or based on parameters that are often only minor determinants for systemic exposure. These dosing approaches often introduce additional pharmacokinetic variability and do not add to treatment outcomes. Fortunately, pharmacological knowledge is increasing, providing valuable information regarding the potential of, for example, therapeutic drug monitoring. This article focuses on the evidence for the most promising and easily implemented optimized dosing approaches for the small-molecule inhibitors, chemotherapeutic agents, and monoclonal antibodies as treatment options currently approved for NSCLC. Despite limitations such as investigations having been conducted in oncological diseases other than NSCLC or the retrospective origin of many analyses, an alternative dosing regimen could be beneficial for treatment outcomes, prescriber convenience, or financial burden on healthcare systems. This review of the literature provides recommendations on the implementation of dose optimization and advice regarding promising strategies that deserve further research in NSCLC.

Novel emerging biomarkers to immunotherapy in kidney cancer

The treatment of metastatic renal cell carcinoma has significantly evolved in recent years, particularly with the advent of novel immune checkpoint inhibitors (ICI). Despite the striking benefits observed on a population level, outcomes vary and some patients do not respond to ICI-based regimens, ultimately require salvage therapies. An ever deeper understanding of the disease biology mediated by the development of multiple high-throughput molecular omics has led to significant progress in biomarkers discovery. But despite growing insights into the molecular underpinnings of the tumor microenvironment, biomarkers have not been integrated successfully into clinical practice. In this review, we discuss some of the novel emerging predictive biomarkers to ICIs in metastatic renal cell carcinoma.

Augmented clearance of nivolumab is associated with renal functions in chronic renal disease model rats

The clinically approved dose of nivolumab is 240 mg Q2W. However, previous studies have shown that baseline nivolumab clearance (CL) is associated with treatment outcomes in patients with solid cancers, thus motivating researchers to identify prognostic factors and indices influencing nivolumab CL. This study used chronic kidney disease model rats to investigate whether chronic renal impairment affected nivolumab CL and explored the surrogate markers associated with nivolumab CL. We observed that the total CL for nivolumab (CL_tot) was approximately 1.42-times higher in chronic kidney disease model rats than that in sham rats with an increased urinary excretion. Additionally, CL_tot showed positive correlation with renal CL for nivolumab (CL_R), but not with extrarenal CL. Furthermore, the baseline levels of creatinine, blood urea nitrogen, creatinine CL, and urinary albumin/creatine ratio based on laboratory data were also significantly correlated with CL_R Our findings suggest that nivolumab CL increases as renal function deteriorates due to an increased excretion of nivolumab in the urine; additionally, laboratory data reflecting renal function may be a feasible index to qualitatively estimate nivolumab CL prior to nivolumab treatment under conditions of renal impairment. Significance Statement We demonstrated that nivolumab was rapidly eliminated from the circulation in chronic kidney disease model rats compared to sham rats with an increased urinary nivolumab excretion. Moreover, nivolumab clearance was significantly correlated with the baseline levels of certain laboratory parameters reflecting renal functions. These results indicate the potential applicability of baseline renal function as a prognostic index to qualitatively estimate nivolumab clearance prior to nivolumab treatment under conditions with renal impairment.

Circulating cytokines associated with clinical response to systemic therapy in metastatic renal cell carcinoma

Background

Circulating cytokines and angiogenic factors have been associated with clinical outcomes in patients with metastatic renal cell carcinoma (RCC) receiving systemic therapy. However, none have yet examined cytokine concentrations in parallel cohorts receiving either immunotherapy or targeted therapy.

Methods

In this prospective correlative study, we enrolled 56 patients who were planned for treatment with either a vascular endothelial growth factor-tyrosine kinase inhibitor (VEGF-TKI) or immune checkpoint inhibitor (ICI). Eligibility requirements permitted any RCC histologic subtype, International Metastatic Renal Cell Carcinoma risk classification, and line of therapy. Immunologic profile was assessed at baseline and after 1 month on treatment using a Human Cytokine 30-plex protein assay (Invitrogen). Clinical benefit was defined as complete response, partial response, or stable disease ≥6 months per RECIST (Response Evaluation Criteria in Solid Tumors) V.1.1 criteria.

Results

Clinical benefit was similar between VEGF-TKI and ICI arms (65% vs 54%). Patients with clinical benefit from VEGF-TKIs had lower pretreatment levels of interleukin-6 (IL-6) (p=0.02), IL-1RA (p=0.03), and granulocyte colony-stimulating factor (CSF) (p=0.02). At 1 month, patients with clinical benefit from ICIs had higher levels of interferon-γ (IFN-γ) (p=0.04) and IL-12 (p=0.03). Among patients on VEGF-TKIs, those with clinical benefit had lower 1 month IL-13 (p=0.02) and granulocyte macrophage CSF (p=0.01) as well as higher 1 month VEGF (p=0.04) compared with patients with no clinical benefit.

Conclusion

For patients receiving VEGF-TKI or ICI therapy, distinct plasma cytokines were associated with clinical benefit. Our findings support additional investigation into plasma cytokines as biomarkers in metastatic RCC.

Confounding factors in exposure-response analyses and mitigation strategies for monoclonal antibodies in oncology

Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on 3 different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (i) Cox-proportional hazards modelling and case-matching; (ii) tumour growth inhibition-overall survival modelling; and (iii) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programmes. Therefore, the strengths and weaknesses of the other 2 approaches are considered, and the favourable utility of tumour growth inhibition-overall survival modelling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials and proposes that development programmes be designed to study more dose levels in earlier trials.

Cancer Immunotherapy Dosing: A Pharmacokinetic/Pharmacodynamic Perspective

Immune check-point inhibitors are drugs that are markedly different from other anticancer drugs because of their indirect mechanisms of antitumoral action and their apparently random effect in terms of efficacy and toxicity. This marked pharmacodynamics variability in patients calls for reconsidering to what extent approved dosing used in clinical practice are optimal or whether they should require efforts for customization in outlier patients. To better understand whether or not dosing could be an actionable item in oncology, in this review, preclinical and clinical development of immune checkpoint inhibitors are described, particularly from the angle of dose finding studies. Other issues in connection with dosing issues are developed, such as the flat dosing alternative, the putative role therapeutic drug monitoring could play, the rise of combinatorial strategies, and pharmaco-economic aspects.