Somatic mutations as preoperative predictors of metastases in patients with localized clear cell renal cell carcinoma - An exploratory analysis

Revealing the characteristics of SETD2-mutated clear cell renal cell carcinoma through tumor heterogeneity analysis

Background

Renal cell carcinoma (RCC) is the most prevalent type of malignant kidney tumor in adults, with clear cell renal cell carcinoma (ccRCC) comprising about 75% of all cases. The SETD2 gene, which is involved in the modification of histone proteins, is often found to have alterations in ccRCC. Yet, our understanding of how these SETD2 mutations affect ccRCC characteristics and behavior within the tumor microenvironment is still not fully understood.

Methods

We conducted a detailed analysis of single-cell RNA sequencing (scRNA-seq) data from ccRCC. First, the data was preprocessed using the Python package, "scanpy." High variability genes were pinpointed through Pearson's correlation coefficient. Dimensionality reduction and clustering identification were performed using Principal Component Analysis (PCA) and the Leiden algorithm. Malignant cell identification was conducted with the "InferCNV" R package, while cell trajectories and intercellular communication were depicted using the Python packages "VIA" and "cellphoneDB." We then employed the R package "Deseq2" to determine differentially expressed genes (DEGs) between groups. Using high-dimensional weighted gene correlation network analysis (hdWGCNA), co-expression modules were identified. We intersected these modules with DEGs to establish prognostic models through univariate Cox and the least absolute shrinkage and selection operator (LASSO) method.

Results

We identified 69 and 53 distinctive cell clusters, respectively. These were classified further into 12 unique cell types. This analysis highlighted the presence of an abnormal tumor sub-cluster (MT + group), identified by high mitochondrial-encoded protein gene expression and an indication of unfavorable prognosis. Investigation of cellular interactions spotlighted significant interactions between the MT + group and endothelial cells, macrophaes. In addition, we developed a prognostic model based on six characteristic genes. Notably, risk scores derived from these genes correlated significantly with various clinical features. Finally, a nomogram model was established to facilitate more accurate outcome prediction, incorporating four independent risk factors.

Conclusion

Our findings provide insight into the crucial transcriptomic characteristics of ccRCC associated with SETD2 mutation. We discovered that this mutation-induced subcluster could stimulate M2 polarization in macrophages, suggesting a heightened propensity for metastasis. Moreover, our prognostic model demonstrated effectiveness in forecasting overall survival for ccRCC patients, thus presenting a valuable clinical tool.

A Phase II Trial of the WEE1 Inhibitor Adavosertib in SETD2-Altered Advanced Solid Tumor Malignancies (NCI 10170)

We sought to evaluate the efficacy of WEE1 inhibitor adavosertib in patients with solid tumor malignancies (cohort A) and clear cell renal cell carcinoma (ccRCC; cohort B). NCT03284385 was a parallel cohort, Simon two-stage, phase II study of adavosertib (300 mg QDAY by mouth on days 1-5 and 8-12 of each 21-day cycle) in patients with solid tumor malignancies harboring a pathogenic SETD2 mutation. The primary endpoint was the objective response rate. Correlative assays evaluated the loss of H3K36me3 by IHC, a downstream consequence of SETD2 loss, in archival tumor tissue. Eighteen patients were enrolled (9/cohort). The median age was 60 years (range 45-74). The median duration of treatment was 1.28 months (range 0-24+). No objective responses were observed in either cohort; accrual was halted following stage 1. Minor tumor regressions were observed in 4/18 (22%) evaluable patients. Stable disease (SD) was the best overall response in 10/18 (56%) patients, including three patients with SD > 4 months. One patient with ccRCC remains on treatment for >24 months. The most common adverse events of any grade were nausea (59%), anemia (41%), diarrhea (41%), and neutropenia (41%). Nine patients (50%) experienced a Grade ≥3 adverse event. Of eight evaluable archival tissue samples, six (75%) had a loss of H3K36me3 by IHC. Adavosertib failed to exhibit objective responses in SETD2-altered ccRCC and other solid tumor malignancies although prolonged SD was observed in a subset of patients. Combination approaches may yield greater depth of tumor response.

SIGNIFICANCE

WEE1 inhibition with adavosertib monotherapy demonstrated limited clinical activity in patients with SETD2-altered solid tumors despite compelling preclinical data indicating a synthetic lethal effect, which did not translate into robust tumor regression. Loss of the H3K36me3 trimethylation mark caused by SETD2-deficiency was confirmed in the majority of evaluable tumors. A subset of patients derived clinical benefit as manifested by minor tumor regressions and prolonged SD.

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.

Current Landscape of Genomic Biomarkers in Clear Cell Renal Cell Carcinoma

CONTEXT

Dramatic gains in our understanding of the molecular biology of clear cell renal cell carcinoma (ccRCC) have created a foundation for clinical translation to improve patient care.

OBJECTIVE

To review and contextualize clinically impactful data surrounding genomic biomarkers in ccRCC.

EVIDENCE ACQUISITION

A systematic literature search was conducted focusing on genomic-based biomarkers with an emphasis on studies assessing clinical outcomes.

EVIDENCE SYNTHESIS

The advancement of tumor sequencing techniques has led to a rapid increase in the knowledge of the molecular underpinnings of ccRCC and with that the discovery of multiple candidate genomic biomarkers. These include somatic gene mutations such as VHL, PBRM1, SETD2, and BAP1; copy number variations; transcriptomic multigene signatures; and specific immune cell populations. Many of these biomarkers have been assessed for their association with survival and a smaller number as potential predictors of a response to systemic therapy. In this scoping review, we discuss many of these biomarkers in detail. Further studies are needed to continue to refine and validate these molecular tools for risk stratification, with the ultimate goal of improving clinical decision-making and patient outcomes.

CONCLUSIONS

While no tissue or blood-based biomarkers for ccRCC have been incorporated into routine clinical practice to date, the field continues to expand rapidly. There remains a critical need to develop and validate these tools in order to improve the care for patients with kidney cancer.

PATIENT SUMMARY

Genomic biomarkers have the potential to better predict outcome and select the most appropriate treatment for patients with kidney cancer; however, further research is needed before any of these currently developed biomarkers are adopted into clinical practice.

Prognostic and predictive biomarkers for immunotherapy in advanced renal cell carcinoma

The therapeutic algorithm of renal cell carcinoma has been revolutionized by the approval of immunotherapy agents by regulatory agencies. However, objective and durable responses are still not observed in a large number of patients, and prognostic and predictive biomarkers for immunotherapy response are urgently needed. Prognostic models used in clinical practice are based on clinical and laboratory factors (such as hypercalcaemia, neutrophil count or Karnofsky Performance Status), but, with progress in molecular biology and genome sequencing techniques, new renal cell carcinoma molecular features that might improve disease course and outcomes prediction have been highlighted. An implementation of current models is needed to improve the accuracy of prognosis in the immuno-oncology era. Moreover, several potential biomarkers are currently under evaluation, but effective markers to select patients who might benefit from immunotherapy and to guide therapeutic strategies are still far from validation.