Combinatorial biomarker for predicting outcomes to anti-PD-1 therapy in patients with metastatic clear cell renal cell carcinoma

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.

Established and emerging biomarkers of immunotherapy in renal cell carcinoma

Immunotherapies, such as immune checkpoint inhibitors, have heralded impressive progress for patient care in renal cell carcinoma (RCC). Despite this success, some patients' disease fails to respond, and other patients experience significant side effects. Thus, development of biomarkers is needed to ensure that patients can be selected to maximize benefit from immunotherapies. Improving clinicians' ability to predict which patients will respond to immunotherapy and which are most at risk of adverse events - namely through clinical biomarkers - is indispensable for patient safety and therapeutic efficacy. Accordingly, an evolving suite of therapeutic biomarkers continues to be investigated. This review discusses biomarkers for immunotherapy in RCC, highlighting current practices and emerging innovations, aiming to contribute to improved outcomes for patients with RCC.

Exploring histological predictive biomarkers for immune checkpoint inhibitor therapy response in non-small cell lung cancer

Treatment challenges persist in advanced lung cancer despite the development of therapies beyond the traditional platinum-based chemotherapy. The early 2000s marked a shift to tyrosine kinase inhibitors targeting epidermal growth factor receptor, ushering in personalized genetic-based treatment. A further significant advance was the development of immune checkpoint inhibitors (ICIs), especially for non-small cell lung cancer. These target programmed death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4, which enhanced the immune response against tumor cells. However, not all patients respond, and immune-related toxicities arise. This review emphasizes identifying biomarkers for ICI response prediction. While PD-L1 is a widely used, validated biomarker, its predictive accuracy is imperfect. Investigating tumor-infiltrating lymphocytes, tertiary lymphoid structure, and emerging biomarkers such as high endothelial venule, Human leukocyte antigen class I, T-cell immunoreceptors with Ig and ITIM domains, and lymphocyte activation gene-3 counts is promising. Understanding and exploring additional predictive biomarkers for ICI response are crucial for enhancing patient stratification and overall care in lung cancer treatment.

Association between pathologic response and survival after neoadjuvant therapy in lung cancer

Neoadjuvant immunotherapy plus chemotherapy improves event-free survival (EFS) and pathologic complete response (0% residual viable tumor (RVT) in primary tumor (PT) and lymph nodes (LNs)), and is approved for treatment of resectable lung cancer. Pathologic response assessment after neoadjuvant therapy is the potential analog to radiographic response for advanced disease. However, %RVT thresholds beyond pathologic complete response and major pathologic response (≤10% RVT) have not been explored. Pathologic response was prospectively assessed in the randomized, phase 3 CheckMate 816 trial (NCT02998528), which evaluated neoadjuvant nivolumab (anti-programmed death protein 1) plus chemotherapy in patients with resectable lung cancer. RVT, regression and necrosis were quantified (0-100%) in PT and LNs using a pan-tumor scoring system and tested for association with EFS in a prespecified exploratory analysis. Regardless of LN involvement, EFS improved with 0% versus >0% RVT-PT (hazard ratio = 0.18). RVT-PT predicted EFS for nivolumab plus chemotherapy (area under the curve = 0.74); 2-year EFS rates were 90%, 60%, 57% and 39% for patients with 0-5%, >5-30%, >30-80% and >80% RVT, respectively. Each 1% RVT associated with a 0.017 hazard ratio increase for EFS. Combining pathologic response from PT and LNs helped differentiate outcomes. When compared with radiographic response and circulating tumor DNA clearance, %RVT best approximated EFS. These findings support pathologic response as an emerging survival surrogate. Further assessment of the full spectrum of %RVT in lung cancer and other tumor types is warranted. ClinicalTrials.gov registration: NCT02998528 .

CXCL14 as a potential marker for immunotherapy response prediction in renal cell carcinoma

Background

Epigenetic mechanisms play vital roles in the activation, differentiation, and effector function of immune cells. The breast and kidney-expressed chemokine (CXCL14) mainly contributes to the regulation of immune cells. However, its role in shaping the tumor immune microenvironment (TIME) is yet to be elucidated in renal cell carcinoma (RCC).

Objectives

This study aimed to elucidate the role of CXCL14 in predicting the efficacy of immunotherapy in patients with RCC.

Methods

CXCL14 expression and RNA-sequencing, single-cell RNA-sequencing (scRNA-seq), and survival datasets of RCC from public databases were analyzed, and survival was compared between different CXCL14 levels. The correlation between CXCL14 and immune infiltration and human leukocyte antigen (HLA) gene expression was analyzed with TIMER2.0 and gene expression profiling interactive analysis. Institutional scRNA-seq and immunohistochemical staining analyses were used to verify the relationship between CXCL14 expression level and the efficacy of immunotherapy.

Results

CXCL14 was expressed in fibroblast and malignant cells in RCC, and higher expression was associated with better survival. Enrichment analysis revealed that CXCL14 is involved in immune activation, primarily in antigen procession, antigen presentation, and major histocompatibility complex assemble. CXCL14 expression was positively correlated with T-cell infiltration as well as HLA-related gene expression. Among the RCC cohort receiving nivolumab in Checkmate 025, the patients with CXCL14 high expression had better overall survival than those with CXCL14 low expression after immunotherapy. scRNA-seq revealed a cluster of CXCL14+ fibroblast in immunotherapy responders. Immunohistochemistry analysis verified that the patients with high CXCL14 expression had an increased proportion of high CD8 expression simultaneously. The expression level of CXCL14 was associated with CXCR4 expression in RCC.

Conclusion

CXCL14 expression is associated with immunotherapy response in RCC. It is a promising biomarker for immunotherapy response prediction and may be an effective epigenetic modulator in combination with immunotherapy approaches.

Head-to-head comparisons of enhanced CT, 68Ga-PSMA-11 PET/CT and 18F-FDG PET/CT in identifying adverse pathology of clear-cell renal cell carcinoma: a prospective study

OBJECTIVES

Accurate preoperative prediction of adverse pathology is crucial for treatment planning of renal cell carcinoma (RCC). Previous studies have emphasized the potential of prostate-specific membrane antigen positron emission tomography / computed tomography (PSMA PET/CT) in differentiating between benign and malignant localized renal tumors. However, there is a scarcity of case reports elucidating the identification of aggressive pathological features using PET/CT. Our study was designed to prospectively compare the diagnostic value of enhanced CT, 68Ga-PSMA-11 and 18F-fluorodeoxyglucose (18F-FDG) PET/CT in clear-cell renal cell carcinoma (ccRCC) with necrosis or sarcomatoid or rhabdoid differentiation.

MATERIALS AND METHODS

A prospective case series of patients with a newly diagnosed renal mass who underwent enhanced CT, 68Ga-PSMA-11 and 18F-FDG PET/CT within 30 days prior to nephrectomy was included. Complete preoperative and postoperative clinicopathological data were recorded. Patients who received neoadjuvant targeted therapy, declined enhanced CT or PET/CT scanning, refused surgical treatment or had non-ccRCC pathological indications were excluded. Radiological parameters were compared within subgroups of pathological characteristics. Bonferroni corrections were used to adjust for multiple testing and statistical significance was set at a p-value less than 0.017.

RESULTS

Seventy-two patients were available for the final analysis. Enhanced CT demonstrated poor performance in identifying necrosis, sarcomatoid or rhabdoid differentiation and adverse pathology (all P > 0.05). The maximum standardized uptake value (SUVmax) of 68Ga-PSMA-11 PET/CT was more effective than 18F-FDG PET/CT in identifying tumor necrosis and adverse pathology, with an area under the curve (AUC) of 0.85 (cutoff value=25.26, p<0.001; Delong test z=2.709, p=0.007) for tumor necrosis and AUC of 0.90 (cutoff value=25.26, p<0.001; Delong test z=3.433, p<0.001) for adverse pathology. However, no significant statistical difference was found between 68Ga-PSMA-11 and 18F-FDG PET/CT in predicting sarcomatoid or rhabdoid feature (AUC of 0.91 vs.0.75, Delong test z=1.998, p=0.046). Subgroup analyses based on age, sex, tumor location, maximal diameter, stage and WHO/ISUP grade demonstrated that 68Ga-PSMA-11 PET/CT SUVmax had a significant predictive value for adverse pathology. Enhanced CT value and SUVmax demonstrated strong reliability [intraclass correlation coefficient (ICC) > 0.80], indicating a robust correlation.

CONCLUSIONS

68Ga-PSMA-11 PET/CT demonstrates distinct advantages in identifying aggressive pathological features of primary ccRCC when compared to enhanced CT and 18F-FDG PET/CT. Further research and assessment are warranted to fully establish the clinical utility of 68Ga-PSMA-11 PET/CT in ccRCC.

Genomic Profiling and Molecular Characterization of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) treatment has undergone three major paradigm shifts in recent years, first with the introduction of molecular targeted therapies, then with immune checkpoint inhibitors, and, more recently, with immune-based combinations. However, to date, molecular predictors of response to targeted agents have not been identified for ccRCC. The WHO 2022 classification of renal neoplasms introduced the molecularly defined RCC class, which is a first step in the direction of a better molecular profiling of RCC. We reviewed the literature data on known genomic alterations of clinical interest in ccRCC, discussing their prognostic and predictive role. In particular, we explored the role of VHL, mTOR, chromatin modulators, DNA repair genes, cyclin-dependent kinases, and tumor mutation burden. RCC is a tumor whose pivotal genomic alterations have pleiotropic effects, and the interplay of these effects determines the tumor phenotype and its clinical behavior. Therefore, it is difficult to find a single genomic predictive factor, but it is more likely to identify a signature of gene alterations that could impact prognosis and response to specific treatment. To accomplish this task, the interpolation of large amounts of clinical and genomic data is needed. Nevertheless, genomic profiling has the potential to change real-world clinical practice settings.

Single-Cell RNA-Seq Analysis Reveals Ferroptosis in the Tumor Microenvironment of Clear Cell Renal Cell Carcinoma

In this study, we investigated the role of ferroptosis in the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC), the leading cause of renal cancer-related death. We analyzed single-cell data from seven ccRCC cases to determine cell types most correlated with ferroptosis and performed pseudotime analysis on three myeloid subtypes. We identified 16 immune-related ferroptosis genes (IRFGs) by analyzing differentially expressed genes between cell subgroups and between high and low immune infiltration groups in the TCGA-KIRC dataset and the FerrDb V2 database. Using univariate and multivariate Cox regression, we identified two independent prognostic genes, AMN and PDK4, and constructed an IRFG score model immune-related ferroptosis genes risk score (IRFGRs) to evaluate its prognostic value in ccRCC. The IRFGRs demonstrated excellent and stable performance for predicting ccRCC patient survival in both the TCGA training set and the ArrayExpress validation set, with an AUC range of 0.690-0.754, outperforming other commonly used clinicopathological indicators. Our findings enhance the understanding of TME infiltration with ferroptosis and identify immune-mediated ferroptosis genes associated with prognosis in ccRCC.