Nivolumab serum concentration in metastatic melanoma patients could be related to outcome and enhanced immune activity: a gene profiling retrospective analysis

Identification of biomarkers and target drugs for melanoma: a topological and deep learning approach

Introduction

Melanoma, a highly aggressive malignancy characterized by rapid metastasis and elevated mortality rates, predominantly originates in cutaneous tissues. While surgical interventions, immunotherapy, and targeted therapies have advanced, the prognosis for advanced-stage melanoma remains dismal. Globally, melanoma incidence continues to rise, with the United States alone reporting over 100,000 new cases and 7,000 deaths annually. Despite the exponential growth of tumor data facilitated by next-generation sequencing (NGS), current analytical approaches predominantly emphasize single-gene analyses, neglecting critical insights into complex gene interaction networks. This study aims to address this gap by systematically exploring immune gene regulatory dynamics in melanoma progression.

Methods

We developed a bidirectional, weighted, signed, and directed topological immune gene regulatory network to compare transcriptional landscapes between benign melanocytic nevi and cutaneous melanoma. Advanced network analysis tools were employed to identify structural disparities and functional module shifts. Key driver genes were validated through topological centrality metrics. Additionally, deep learning models were implemented to predict drug-target interactions, leveraging molecular features derived from network analyses.

Results

Significant topological divergences emerged between nevi and melanoma networks, with dominant functional modules transitioning from cell cycle regulation in benign lesions to DNA repair and cell migration pathways in malignant tumors. A group of genes, including AURKA, CCNE1, APEX2, and EXOC8, were identified as potential orchestrators of immune microenvironment remodeling during malignant transformation. The deep learning framework successfully predicted 23 clinically actionable drug candidates targeting these molecular drivers.

Discussion

The observed module shift from cell cycle to invasion-related pathways provides mechanistic insights into melanoma progression, suggesting early therapeutic targeting of DNA repair machinery might mitigate metastatic potential. The identified hub genes, particularly AURKA and DDX19B, represent novel candidates for immunomodulatory interventions. Our computational drug prediction strategy bridges molecular network analysis with clinical translation, offering a paradigm for precision oncology in melanoma. Future studies should validate these targets in preclinical models and explore network-based biomarkers for early detection.

Nivolumab plasma concentration and clearance associated with overall survival in patients with renal cell carcinoma

BACKGROUND

Nivolumab is an immune checkpoint inhibitor (ICI) that selectively inhibits programmed cell death protein 1 activation, restoring antitumor immunity. ICIs are indicated for various types of advanced solid tumors; however, not all patients benefit from them, and tools that could be used in the clinic to predict response to treatment represent an unmet need. Here we describe the development of a new population pharmacokinetic (PPK) model in patients treated with nivolumab in clinical trials. Applying the model to a patient population with renal cell carcinoma identified nivolumab clearance and plasma concentration as predictors of overall survival (OS).

METHODS

A custom liquid chromatography with tandem mass spectrometry method for quantifying nivolumab plasma concentration was developed and validated following the European Medicines Agency guidelines for bioanalytical method validation. The PPK model was developed using data from patients treated in the NIVIPIT (n=38) and NIVOREN (n=137) trials of nivolumab in metastatic melanoma and renal cell carcinoma, respectively. The PPK model was used to determine pharmacokinetic (PK) parameters such as baseline clearance and simulate individual clearance changes over time. The relationship between PK characteristics (including clearance at Cycle 1 (CLC1), plasma concentration at Cycle 3 and clinical outcomes was assessed in 137 patients treated in NIVOREN. Kaplan-Meier methodology was used in time-to-event analyses.

RESULTS

In 137 patients, the median nivolumab CLC1 was 6 mL/hour and the median plasma concentration at Cycle 3 was 48 µg/mL. Median follow-up was 21.0 months (95% CI 20.2 to 22.5 months) with a survival rate at 6 months of 91.2% and 77.9% at 12 months. In univariate analysis, OS was significantly higher in patients with CLC1<6 mL/hour versus ≥6 mL/hour (HR 2.2 (95% CI 1.2 to 4.1), p=0.0146). Shorter OS was observed in patients with plasma concentration at Cycle 3 below the median (48 µg/mL) versus those above the median (HR 0.4 (95% CI 0.2 to 0.8), p=0.0069). Multivariate analysis showed a trend towards lower clearance, but this did not reach statistical significance (p=0.0694).

CONCLUSIONS

Results of the study may potentially be used to predict outcomes of nivolumab therapy in patients with renal cell carcinoma. Additional applications may include guiding dose adjustments of nivolumab in those who are less likely to respond to the initial dose.

ICOSLG Is Associated with Anti-PD-1 and Concomitant Antihistamine Treatment Response in Advanced Melanoma

We previously demonstrated that patients with metastatic unresectable stage IIIb-IV melanoma receiving cetirizine (a second-generation H1 antagonist antihistamine) premedication with immunotherapy had better outcomes than those not receiving cetirizine. In this retrospective study, we searched for a gene signature potentially predictive of the response to the addition of cetirizine to checkpoint inhibition (nivolumab or pembrolizumab with or without previous ipilimumab). Transcriptomic analysis showed that inducible T cell costimulator ligand (ICOSLG) expression directly correlated with the disease control rate (DCR) when detected with a loading value > 0.3. A multivariable logistic regression model showed a positive association between the DCR and ICOSLG expression for progression-free survival and overall survival. ICOSLG expression was associated with CD64, a specific marker of M1 macrophages, at baseline in the patient samples who received cetirizine concomitantly with checkpoint inhibitors, but this association was not present in subjects who had not received cetirizine. In conclusion, our results show that the clinical advantage of concomitant treatment with cetirizine during checkpoint inhibition in patients with malignant melanoma is associated with high ICOSLG expression, which could predict the response to immune checkpoint inhibitor blockade.

Pharmacokinetics and clinical outcomes of low-dose nivolumab relative to conventional dose in patients with advanced cancer

PURPOSE

Nivolumab is approved at various doses, including 3 mg/kg, 240 mg and 480 mg flat doses at various dosing intervals. The concept of low-dose immunotherapy is gaining traction in recent years. However, there is a need to better understand the pharmacokinetics and clinical outcomes at lower doses.

METHODS

Patients were either administered 40 mg flat dose or 3 mg/kg Q2W/Q3W, depending on affordability as per prevailing hospital practice. All patients were hospitalized on day 1 and pharmacokinetic samples were collected at 0, 0.5, 1.0, 6.0, 24.0, 72.0 h and day 14 following administration of the first dose of nivolumab. Plasma nivolumab levels were measured by ELISA. Patients were followed up for response and toxicity.

RESULTS

Twenty five patients were included in the study. Fourteen received nivolumab at conventional dose (3 mg/kg), while 11 patients received low-dose (40 mg flat). The geometric means of dose normalized Cmax and AUC0-t were comparable between those who received conventional dose and low-dose of nivolumab (0.28 versus 0.23 µg/mL/mg and 0.0014 versus 0.0011 d/mL respectively). Nineteen patients were evaluable for response. ORR among patients who received conventional dose was 5/11 (45.5%) whereas it was 4/9 (44.4%) in the low-dose cohort. All 14 (100%) patients in conventional dosing group and 7/11 patients (63.64%) in low-dose group had treatment emergent adverse events. Grade ≥ 3 toxicities were observed in 4/14 patients in conventional dose group and none in low-dose group.

CONCLUSION

Low-dose nivolumab leads to lower exposure in patients as compared with conventional dose, but low-dose was better tolerated, while response rates were comparable to conventional dose.

A Combined Proteomic and Transcriptomic Signature Is Predictive of Response to Anti-PD-1 Treatment: A Retrospective Study in Metastatic Melanoma Patients

Resistance biomarkers are needed to identify patients with advanced melanoma obtaining a response to ICI treatment and developing resistance later. We searched a combination of molecular signatures of response to ICIs in patients with metastatic melanoma. In a retrospective study on patients with metastatic melanoma treated with an anti-PD-1 agent carried out at Istituto Nazionale Tumori-IRCCS-Fondazione "G. Pascale", Naples, Italy. We integrated a whole proteome profiling of metastatic tissue with targeted transcriptomics. To assess the prognosis of patients according to groups of low and high risk, we used PFS and OS as outcomes. To identify the proteins and mRNAs gene signatures associated with the patient's response groups, the discriminant analysis for sparse data performed via partial least squares procedure was performed. Tissue samples from 22 patients were analyzed. A combined protein and gene signature associated with poorer response to ICI immunotherapy in terms of PFS and OS was identified. The PFS and OS Kaplan-Meier curves were significantly better for patients with high expression of the protein signature compared to patients with low expression of the protein signature and who were high-risk (Protein: HR = 0.023, 95% CI: 0.003-0.213; p < 0.0001. Gene: HR = 0.053, 95% CI: 0.011-0.260; p < 0.0001). The Kaplan-Meier curves showed that patients with low-risk gene signatures had better PFS (HR = 0 0.221, 95% CI: 0.071-0.68; p = 0.007) and OS (HR = 0.186, 95% CI: 0.05-0.695; p = 0.005). The proteomic and transcriptomic combined analysis was significantly associated with the outcomes of the anti-PD-1 treatment with a better predictive value compared to a single signature. All the patients with low expression of protein and gene signatures had progression within 6 months of treatment (median PFS = 3 months, 95% CI: 2-3), with a significant difference vs. the low-risk group (median PFS = not reached; p < 0.0001), and significantly poorer survival (OS = 9 months, 95% CI: 5-9) compared to patients with high expression of protein and gene signatures (median OS = not reached; p < 0.0001). We propose a combined proteomic and transcriptomic signature, including genes involved in pro-tumorigenic pathways, thereby identifying patients with reduced probability of response to immunotherapy with ICIs for metastatic melanoma.

Immunosuppressants against acute kidney injury: what to prefer or to avoid?

BACKGROUND

Acute kidney injury (AKI) is a critical global health issue associated with high mortality rates, particularly in patients undergoing renal transplants and major surgeries. These individuals often receive immunosuppressants to dampen immune responses, but the impact of these drugs on AKI remains unclear.

OBJECTIVE

This review aims to provide a detailed understanding of the effects of different classes of immunosuppressants against AKI, elucidating their role in either exacerbating or mitigating the occurrence or progression of AKI.

METHODS

Several preclinical and clinical reports were analyzed to evaluate the impact of various immunosuppressants on AKI. Relevant preclinical and clinical studies were reviewed through different databases such as Scopus, PubMed, Google Scholar, and ScienceDirect, and official websites like https://clinicaltrials.gov to understand the mechanisms underlying the effects of immunosuppressants on kidney function.

RESULTS AND DISCUSSION

Specific immunosuppressants have been linked to the progression of AKI, while others demonstrate renoprotective effects. However, there is no consensus on the preferred or avoided immunosuppressants for AKI patients. This review outlines the classes of immunosuppressants commonly used and their impact on AKI, providing guidance for physicians in selecting appropriate drugs to prevent or ameliorate AKI.

CONCLUSION

Understanding the effects of immunosuppressants on AKI is crucial for optimizing patient care. This review highlights the need for further research to determine the most suitable immunosuppressants for AKI patients, considering both their efficacy and potential side effects.

Review - The impact of pharmacogenetics on the outcome of immune checkpoint inhibitors

The development of immune checkpoint inhibitors (ICIs) has a tremendous effect on the treatment options for multiple types of cancer. Nonetheless, there is a large interpatient variability in response, survival, and the development of immune-related adverse events (irAEs). Pharmacogenetics is the general term for germline genetic variations, which may cause the observed interindividual differences in response or toxicity to treatment. These genetic variations can either be single-nucleotide polymorphisms (SNPs) or structural variants, such as gene deletions, amplifications or rearrangements. For ICIs, pharmacogenetic variation in the human leukocyte antigen molecules has also been studied with regard to treatment outcome. This review presents a summary of the literature regarding the pharmacogenetics of ICI treatment, discusses the most important known genetic variations and offers recommendations on the application of pharmacogenetics for ICI treatment.