Advances in renal cell carcinoma (RCC) treatment algorithms, fostered by a better understanding of molecular and genomic aberrations underlying kidney cancer pathogenesis and the addition of immune checkpoint inhibitors (ICIs) to frontline advanced RCC management, have led to significant improvements in patient response rates and survival outcomes.¹ Several immunotherapies, as ICI-ICI combinations or combined with tyrosine kinase inhibitors (TKIs) targeting VEGF, are now approved for the management of metastatic RCC (mRCC), based on data from phase 3 clinical studies.^1,2 However, immunotherapy-based combination regimens are associated with high rates of treatment-related toxicities and do not yield objective responses in a significant proportion of patients.^3,4

Wenxin (Vincent) Xu, MD

Medical Oncologist

Dana-Farber Cancer Institute

Instructor in Medicine

Harvard Medical School

Boston, MA

“From a clinician’s standpoint, [current] biomarkers are not very good for kidney cancer, and a lot of work has been done and is ongoing to look for other biomarkers. But as of today, if I see a patient in the clinic, I am still dependent on clinical biomarkers,” said Wenxin (Vincent) Xu, MD, an oncologist at the Lank Center for Genitourinary Oncology at Dana-Farber Cancer Institute and instructor in medicine at Harvard Medical School, both in Boston, Massachusetts, in an interview with Targeted Therapies in Oncology.

The Challenge of Identifying Biomarkers in RCC

The different pathogenic mechanisms and biological underpinnings for RCC and other cancers may add to the challenges of biomarker discovery in RCC. Many biomarkers, both prognostic and predictive, are currently being evaluated in RCC; however, Xu noted that although some biomarkers, such as PD-L1 expression and high tumor mutational burden (TMB), correspond with response to immunotherapy in certain cancers, such as non–small cell lung cancer (NSCLC),^5,6 the data for biomarkers predictive of response to immunotherapy in RCC are mixed and/ or not promising.⁴

For instance, in the CheckMate 025 study (NCT01668784) of nivolumab (Opdivo) in patients who had progressed on prior VEGF-targeted therapy, response was seen both in patients with PD-L1–positive and –negative tumors⁷; however, in the IMmotion150 study (NCT01984242), which compared atezolizumab (Tecentriq) with or without bevacizumab (Avastin) with sunitinib (Sutent) in mRCC in the frontline setting, PD-L1 expression was correlated with response to ICI-based treatment.⁸ Xu said that TMB correlates better with immunotherapy response in NSCLC or melanoma, which are characterized by higher inherent mutational rates than those in RCC.^4,9

In the IMmotion150 and -151 studies, which compared atezolizumab with or without bevacizumab vs sunitinib in patients with mRCC, PD-L1 expression correlated with response to ICI-based treatment.^8,10

“Some of the more interesting predictive biomarkers that are currently being studied include RNA gene expression signatures, which have been developed from several different trials, [including] IMmotion151 [NCT02420821] and JAVELIN Renal 101 [NCT02684006],” Xu said. “[These RNA signatures] seem to have some ability to predict which patients respond to immunotherapy vs angiogenic therapies. But it is not really known whether these [signatures] are sensitive and specific enough to be used in routine decision-making in the clinic,” he continued.

JAVELIN Renal 101 data showed that first-line avelumab (Bavencio) with axitinib (Inlyta) significantly prolonged PFS, compared with sunitinib, in patients with advanced RCC.¹¹ Subsequent molecular analyses of baseline tumor samples helped identify tumor immune features that differentiated outcomes with the ICI and antiVEGF combination regimen.¹²

In the COMPARZ study (NCT00720941), which compared pazopanib (Votrient) with sunitinib in untreated mRCC, increased expression of angiogenesis genes signifi cantly correlated with better outcomes compared with those with lower expression.^13,14 These data suggest that a higher angiogenesis gene expression signature may predict better outcomes with TKI-based treatment.¹⁴

Biomarkers in Non–Clear Cell RCC

Speaking to the state of biomarker discovery in non–clear cell RCC (nccRCC), Xu said, “Non–clear cell kidney cancer is not 1 type of cancer, but it is 20 or 30 different kinds of cancer from different cells of origin inside the kidney. That said, there are specific types of non–clear cell kidney cancer for which we have more data on biomarkers than [for] others and specifically capillary kidney cancer, which is the most common subtype.”

This cancer type comprises a genetically and histologically diverse set of cancers distinct from clear cell RCC that account for 25% of all RCC cases, with papillary RCC accounting for the majority (approximately 15% of all RCC cases).¹⁵ Comprehensive molecular analyses of papillary RCC showed that almost 80% of the type 1 tumors harbored a MET alteration (amplification, mutation, or duplication) or a gain of chromosome 7 (where MET is located).¹⁶ Savolitinib is considered to be of special interest in this context due to its high specificity for MET.⁴ However, the phase 3 SAVOIR study (NCT03091192) comparing savolitinib with sunitinib in patients with MET-driven papillary RCC did not meet the prespecifi ed primary end point of PFS.¹⁷

Although the crizotinib (Xalkori) and savolitinib arms of the phase 2 PAPMET study (NCT02761057) comparing different TKIs in patients with papillary RCC were removed based on futility analyses, data comparing the efficacy of cabozantinib (Cabometyx) to sunitinib in MET-altered and MET- expressing tumors are expected to be reported later in 2023.¹⁸ In the CALYPSO phase 2 trial (NCT02819596), which is evaluating savolitinib, tremelimumab, and MED14736, the primary end point of confi rmed response rate (cRR) was missed; however, cRR was higher in the patients with MET-driven disease at 53% (95% CI, 28-77) vs the overall cohort at 29% (95% CI, 16-46).¹⁹

Emerging Biomarkers and Evolving Data

The intensive search for validated prognostic and predictive biomarkers in RCC is highlighted by new data on biomarkers presented at the 2023 American Society of Clinical Oncology Annual Meeting (summarized in the TABLE). Overall, the data for these novel and emerging biomarkers, including the microbiome or gut microflora, immune signatures in the tumor immune microenvironment (TME), and circulating tumor DNA (ctDNA), are still in the early stages of development, according to Xu.

Although the data from studies such as HCRN GU16-260 (NCT03117309; TABLE) are “thought provoking, [these studies are] exploratory. [They have] not taken us to a clinical test yet to measure who is going to be responding to immunotherapy. The hope is that by studying the TME this way, we are going to find a surrogate [biomarker] for immunotherapy response. These correlative analyses, such as in this study, cannot be generalized to all patients, as they involve TME deep sequencing or single cell RNA analysis, and are not scalable.”

Data from a next-generation sequencing analysis of ctDNA showed that mutations in VHL, TP53, BAP1, PBRM1, and SETD2 were most frequent in ctDNA isolated from patients with RCC.²⁶ Speaking about the value of ctDNA as a biomarker, Xu said, “I think ctDNA, including sequencing ctDNA, is an emerging field in kidney cancer. [ctDNA] is challenging in kidney cancer, compared with prostate cancer or lung cancer, because of not only the lower TMB but also due to the lower tumor DNA shedding rate into blood associated with kidney cancer.”

Xu also alluded to emerging data for endogenous retroviruses and KIM-1 as biomarkers of interest in RCC. Data from studies have shown, for instance, that human endogenous retroviruses can not only be prognostic markers, but also be predictive for patients with advanced RCC who received anti–PD-1 therapy.^27,28 Xu’s previous work showed that postnephrectomy plasma KIM-1 is associated with survival outcomes in RCC and may be a biomarker for microscopic residual disease.²⁹ Xu said, “This is a prognostic biomarker, in the sense that it measures how much cancer is present in a person’s body. But it is possible that changes in the prognostic biomarkers may be predictive of therapeutic response or lack thereof. So the line between prognostic and predictive biomarkers can be somewhat blurry.”

Although studies of novel RCC biomarkers continue to progress, Xu said that none of these exploratory biomarkers are poised to transform immediate medical practice. He concluded, “The take-home message is that we need better biomarkers. As the RCC treatments have [expanded] and continue to expand, we have more treatments than we have ways to choose between them and that is defi nitely a problem, clinically.

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