Role of Biomarkers in Prediction of Response to Therapeutics in Metastatic Renal-Cell Carcinoma

Review

Role of Biomarkers in Prediction of Response to Therapeutics in Metastatic Renal-Cell Carcinoma Jacob J. Adashek,1 Meghan M. Salgia,2 Edwin M. Posadas,3 Robert A. Figlin,3 Jun Gong3 Abstract Renal-cell carcinoma remains one of the elusive cancers that lacks a biomarker. It is the eighth most commonly diagnosed malignancy in the United States, and the incidence has slowly trended upward. In addition to the increase in newly diagnosed cases, the prevalence and overall survival of individuals with kidney cancer also has increased substantially. This formal review synopsizes the literature regarding the current treatment landscape, the utility of biomarkers in renal-cell carcinoma, and future directions regarding next-generation sequencing of circulating tumor DNA. Clinical Genitourinary Cancer, Vol. 17, No. 3, e454-60 ª 2019 Elsevier Inc. All rights reserved. Keyword: ctDNA, IOs, mTOR, VEGF, VHL

Introduction Every year over 63,000 individuals are newly diagnosed with renal-cell carcinoma (RCC), and over 14,000 individuals will die from RCC in the United States.1,2 The median age at diagnosis is 64 years, with the majority, 53.5%, of all RCC patients ranging in age from 55 to 74 years.1 The Cancer Genome Atlas data collected on the subtypes of RCC notes that clear-cell disease makes up the majority of cases, 75% to 85%, with papillary subtype comprising roughly 10% to 15%, and other subtypes such as chromophobe (5-10%), oncolytic (3-7%), and collecting duct (<1%) making up the rest. With the introduction of targeted therapies and now immunotherapies (IOs) joining the treatment landscape, 5-year survival rates have markedly increased in the last 50 years, from 34% in 1954 to 76.0% in 2009.1,3 It is likely that the changes in the survival rates are primarily the result of earlier detection and better surgical techniques.4 Nearly one-third of cases undergoing surgical resection for localized disease experience recurrence, and > 10% of patients with organ-confined RCC (pT1-T2) experience disease 1 Western University of Health Sciences, College of Osteopathic Medicine of the Pacific, Pomona, CA 2 Department of Medical Oncology & Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA 3 Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA

Submitted: Oct 2, 2018; Revised: Dec 10, 2018; Accepted: Jan 8, 2019; Epub: Jan 15, 2019 Address for correspondence: Jun Gong, MD, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, AC 1042B, Los Angeles, CA 90048 Fax: (310) 423-4759; e-mail contact: [email protected]

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progression within 3 to 5 years.5 Roughly 17% of newly diagnosed RCC cases are already metastatic.4 The median overall survival (OS) for metastatic RCC (mRCC) in the current decade is approximately 30 months, which has improved from 13 months in the era of cytokine therapy nearly 3 decades ago; however, despite increases in OS, mRCC is still considered a terminal prognosis.6,7 Alterations in the VHL gene are found in 94% of cases of RCC, which is known to play a major role in cellular oxygen sensing and angiogenesis.8 Under normoxic conditions, the VHL protein’s role is to facilitate degradation of hypoxia-inducible factor 1 (HIF) a subunits, which prevents downstream expression of vascular endothelial growth factor (VEGF) and various other growth factors. In hypoxic conditions, HIF expression is up-regulated, allowing for increased angiogenesis by VEGF allowing normoxic levels be obtained through a feedback loop. This process allows for the regulation of oxygen within the cellular environment.9 In RCC, the dysregulation of VHL prevents the feedback loop from functioning properly and allows for uncontrolled expression of VEGF and subsequent excessive vessel proliferation and tumorigenesis. Understanding the pathogenesis and the molecular mechanisms of the development of RCC has been the basis for various treatment strategies, specifically the mainstay in treatment using antieVEGFdirected therapies. To date, there are no viable biomarkers in the prediction of response to treatment or progression of disease in mRCC. The VEGF pathway is well defined in the setting of mRCC, but it does not lend itself to the prediction to treatment. Here we provide an overview of current treatment options and the biomarker landscape in mRCC.

1558-7673/$ - see frontmatter ª 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.clgc.2019.01.004

Current Treatment Landscape in mRCC Currently, there are 12 US Food and Drug Administration (FDA)-approved medications for the treatment of mRCC. These include anti-VEGF tyrosine kinase inhibitors (TKIs; sunitinib, sorafenib, pazopanib, axitinib, cabozantinib, lenvatinib), anti-VEGF monoclonal antibody (bevacizumab), mammalian target of rapamycin (mTOR) inhibitors (temsirolimus, everolimus), biological response modifiers (interleukin-2, interferon-a), and programmed cell death 1 (PD-1) inhibitor (nivolumab).10 Despite the large market of drugs approved, many of the drugs occupying this space have not been compared to each other.11-15 The lack of comparative trials makes it difficult to guide best treatment-making decisions for providers and provide clear-cut evidence as to which treatments to provide to which patients. Additionally, the lack of available biomarkers makes the treatment of mRCC somewhat paradoxical. The basis of treatment is somewhat theoretical from the previously described pathogenesis, where loss of VHL gene leads to increased expression of VEGF and subsequent uncontrolled angiogenesis and tumorigenesis. However, evidence is lacking that identifying specific mutations in the VHL gene or even measuring VEGF levels before therapy can predict therapeutic benefit from these agents. Although the targets are known, there are no biomarkers. It is not clear which patients have disease that will respond to sunitinib and which may be better suited for temsirolimus, for example. Recently published data from the CABOSUN trial showed that cabozantinib compared to sunitinib led to significantly increased median progression-free survival (PFS) of 8.2 versus 5.6 months and was associated with a 34% reduction in rate of progression (hazard ratio [HR] ¼ 0.66; 95% confidence interval [CI], 0.46-0.95; P ¼ .012) and an objective response rate (ORR) of 46% for cabozantinib compared to 18% for sunitinib (95% CI, 34-57, 10-28, respectively). This trial demonstrated the superiority of cabozantinib over the current standard of care in the first-line setting for patients with intermediate- or poor-risk mRCC, leading to FDA approval of cabozantinib by the end of 2017.16 New on the forefront is the topic of IO for the treatment of several solid tumors, including mRCC. Currently nivolumab is approved for the second-line treatment of mRCC, but new evidence from the phase 3 CheckMate 214 trial may warrant a new schema for its combined use in the first-line setting. This ongoing clinical trial compares the efficacy of nivolumab plus ipilimumab (nivo-ipi) in the first-line setting to sunitinib for treatment-naive mRCC patients with intermediate- and poor-risk disease. The presented data from this ongoing trial showed that patients in the nivo-ipi arm had a significant increase in ORR of 42% compared to sunitinib at 27% and a significant increase in complete responses of 9% versus 1%, respectively. Additionally, the median PFS was also increased (11.6 vs. 8.4 months), but the results lacked statistical significance (HR ¼ 0.82; 95% CI, 0.64-1.05). An interesting note is that patients with favorable disease did not respond as well to nivo-ipi compared to sunitinib, with an ORR of 29% versus 52% and a median PFS of 15.3 months versus 25.1 months (HR ¼ 2.17; 95% CI, 1.46-3.22) for nivo-ipi and sunitinib, respectively.17 The median OS for the nivo-ipi cohort was not reached compared to 26.0 months

(HR ¼ 0.63; P < .001) for the sunitinib arm, thus leading to the FDA approval of nivo-ipi in the frontline setting in early 2018.18 The most recent innovation is combining IOs with VEGF TKIs. The first phase 3 trial to be presented was IMmotion151, which compared bevacizumab with atezolizumab (bev-atezo) to sunitinib. The ORR in programmed death ligand 1 (PD-L1)-positive patients was 43% in the bev-atezo arm compared to 35% in the sunitinib arm, with the duration of response not reached for the bev-atezo arm and 12.9 months for the sunitinib arm.19 In PD-L1epositive patients, the bev-atezo arm had a median PFS of 11.2 compared to 7.7 in the sunitinib group (HR ¼ 0.74; 95% CI, 0.57-0.96; P ¼ .0217). Additionally, all patients receiving bev-atezo also had a significant improvement of median PFS of 11.2 compared to 8.4 in the sunitinib group (HR ¼ 0.83; 95% CI, 0.70-0.97; P ¼ .0219).20 Data for OS are not yet available from this trial, but the ORR, duration of response, and median PFS results are all encouraging. The FDA has also granted breakthrough-therapy designation to the combination of axitinib with avelumab based on the phase 1b Javelin Renal 100 trial.21 Preliminary results from this phase 1b trial showed a confirmed ORR of 54.5% (95% CI, 40.6-68.0) in 55 enrolled patients treated with first-line axitinib with avelumab with a manageable toxicity profile. The most common adverse events (30% any grade) were fatigue and diarrhea (30.9% each), with 18.2% of patients having a maximum grade 3 adverse event and 1 patient (1.8%) experiencing a maximum grade 4 avelumab-related adverse event. Recently, top-line results for Javelin Renal 101 were released.22 This was the first positive phase 3 trial combining an IO with a VEGF-TKI, and it showed a statistically significant improvement in PFS for avelumab with axitinib over sunitinib in both treatment-naive patients with PD-L1 expression > 1% (primary end point) and the entire study population regardless of PD-L1 expression (secondary end point). No new safety signals were observed across both arms, and Javelin Renal 101 is currently proceeding to the final analysis for the other primary end point of (OS). Although these are two of the most promising new trials, there are key differences in treatment ideology, and ultimately this is where the difficulty in choosing which treatment to provide to which patient lies. The first trial compares an antieVEGF-directed therapy to the standard anti-VEGF therapy, whereas the second compares an IO to standard anti-VEGF. Although the comparator agents of both trials showed improved median PFS and ORR to the standard first-line medication for intermediate- and poor-risk mRCC, there is no specific biomarker. Multiple clinical trials in progress are comparing the efficacy of various agents in treating mRCC and are trying to predict which agents may be the most efficacious for various patients (Table 1). There is a large disconnect between the increasing advances in targeted therapies and having IOs readily available, and the lack of any predictable biomarkers, or one that could be monitored to assess response or progression.

Current Evidence on Biomarkers for mRCC VEGF-TKI and mTOR Inhibitors The current first-line approved medications—sunitinib, pazopanib, bevacizumab cabozantinib, and temsirolimus—are VEGFdirected therapies and the latter an mTOR antagonist. The use of

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Line of Therapy

Phase

N

Integral Versus Integrated Versus Exploratory Biomarkersa

SWOG 1500 (NCT02761057)

1-2

2

180

Integrated

SAVOIR trial (NCT03091192)

1-2

3

180

Integral

2

3

50

Exploratory

2-4

1

119

Exploratory

A Biomarker Study of Tivozanib in Subjects with Advanced Renal Cell Carcinoma (NCT01297244)

1-2

2

105

Integrated

Biomarker Trial of Everolimus in Patients with Advanced Renal Cell Carcinoma (NCT00827359) Pilot Study of Neo-Adjuvant Everolimus to Treat Advanced Renal Cell Carcinoma e Analysis of Biomarkers (NCT01107509) Neoadjuvant Pazopanib in Renal Cell Carcinoma (NCT01361113) Neo-adjuvant Temsirolimus in Patients With Advanced Renal Cell Carcinoma (NCT01404104) Brivanib Metastatic Renal Cell Carcinoma (NCT01253668)

After first line

2

40

Integrated

Neoadjuvant

2A

20

Integrated

Neoadjuvant

2

21

Integrated

Neoadjuvant

2A

11

Integrated

After first line

2

10

Integrated

After first line

4

64

Integrated

Study

An Exploratory Correlative Study of Biomarkers in Patients with Metastatic Renal Cell Carcinoma Who Have Progressed After Sunitinib Therapy (NCT00538772) Phase I Biomarker Study (BMS-936558) (NCT01358721)

Open Label, Single Arm Trial to Characterize Patients with Metastatic RCC Treated with Everolimus After Failures of the First VEGF-targeted Therapy (MARC-2) (NCT01266837)

Brief Summary

Drugs

Compare sunitinib to 3 MET-directed therapies in patients with mPRCC Savolitinib in MET-positive unresectable and locally advanced or metastatic PRCC vs. sunitinib Evaluate levels of serum VEGF and soluble VEGFR, circulating tumor cells, and endothelial cells to explore baseline patient factors, measurable disease response, and clinical progression Evaluate pharmacodynamic and biological properties of BMS-936558 in subjects with mRCC Evaluate biomarkers in blood and archived tissue samples, and their correlation with clinical activity and/or toxicity in subjects with advanced RCC Determine if certain features of tumor specimens sampled before therapy can predict likelihood of response to everolimus Gathers data on potential biomarkers in treatment of advanced RCC as well as test drug everolimus in a neoadjuvant setting

Sunitinib vs. crizotinib, cabozantinib, savolantinib Sunitinib vs. savolitinib

8 weeks of daily, oral neoadjuvant pazopanib before nephrectomy in evaluable patients with histologically confirmed localized RCC Study experimental use of temsirolimus 12 weeks before surgical removal of all or portion of kidney with tumor involvement Evaluate safety and effectiveness of investigational agent, brivanib, in patients with refractory mRCC, and determine whether imaging and molecular features of tumors can be used to predict response For patients with mRCC whose disease has progressed while or after receiving VEGF-targeted therapy

Sunitinib

BMS-936558 (antiePD-1) Tivozanib

Everolimus Everolimus

Pazopanib Temsirolimus (before surgery) Brivanib alaninate

Everolimus

Abbreviations: MET ¼ mesenchymaleepithelial transition; mPRCC ¼ metastatic papillary renal-cell carcinoma; mRCC ¼ metastatic renal-cell carcinoma; PRCC ¼ papillary renal-cell carcinoma; RCC ¼ renal-cell carcinoma; VEGF ¼ vascular endothelial growth factor; VEGFR ¼ vascular endothelial growth factor receptor. a Integral biomarkers assist in defining eligibility, stratification, and monitoring of disease or study end points. Integrated biomarkers have been incorporated into clinical trial design to test a hypothesis based on preexisting data.

Biomarkers in mRCC

Table 1 Biomarker-Guided Clinical Trials in Progress Comparing Efficacy of Various Agents in Treating mRCC

Jacob J. Adashek et al these medications is based on prospective studies.23,24 The RECORD-3 trial compared first-line sunitinib followed by everolimus versus first-line everolimus followed by sunitinib. As assessed retrospectively from archival specimens from the RECORD-3 trial, a mutation in PBRM1 was associated with a comparable PFS between everolimus and sunitinib (11.5 vs. 11.0 months, respectively), while patients with wild-type PBRM1 had more benefit from sunitinib than everolimus (8.3 vs. 5.3 months, respectively). Mutations in KDM5C also predicted activity of VEGF inhibition, where patients harboring KDM5C mutation had a median PFS of 20.6 months with sunitinib versus 9.8 months with everolimus in the first-line setting.25 A different study looking at 31 formalin-fixed, paraffinembedded tissue specimens investigated the association of genomic alterations and responses to VEGF-directed therapies. Patients with disease that had the best response to anti-VEGF agents more commonly had KDM5C, PBRM1, and VHL mutations in this study. These genes were identified in 27 patients who were deemed to have “exceptional responses,” meaning their duration of treatment lasted > 21 months.26 Another study retrospectively analyzed tumor DNA from 79 patients who experienced distinct clinical benefit with mTOR inhibitors. Using next-generation sequencing or analysis of formalin-fixed, paraffin-embedded DNA, 560 genes were analyzed for mutations. This study suggests that mTOR, TSC1, and TSC2 mutations may predict exceptional responses to everolimus, as these mutations were more common in subjects whose disease responded to therapy, 12 (28%) of 43, than in those with disease that did not respond to therapy, 4 (11%) of 36 (P ¼ .06). Additionally, 5 (42%) of 12 patients who experienced partial responses had mTOR, TSC1, and TSC2 mutations, whereas only 4 (11%) of 36 patients without response had a mutation in one of these 3 genes (P ¼ .03). However, it is important to note that a large proportion of those who experienced response did not harbor any mTOR mutations (56%).27 In the only prospective study for mRCC, specifically of the papillary subtype, researchers tested the use of savolitinib, a highly selective mesenchymaleepithelial transition (MET) TKI, in patients with MET-driven and MET-independent papillary mRCC. Median PFS for patients with MET-driven and MET-independent papillary mRCC was 6.2 and 1.4 months, respectively (HR ¼ 0.33; 95% CI, 0.20-0.52; P < .001).28 This study marks the beginning of biomarker-guided trials. There currently are several ongoing biomarker-guided trials for mRCC (Table 1).

Immunotherapies Although VEGF-directed therapies and mTOR inhibitors are the mainstay of treating mRCC, the introduction of IOs to the treatment arsenal has resulted in significant improvements in patient outcomes. As previously mentioned, nivolumab is approved for mRCC based on the data from the phase 3 CheckMate 025 trial comparing nivolumab with everolimus in patients with previously treated mRCC. This study showed that median OS for patients was 25.0 versus 19.6 months (HR ¼ 0.73; 98.5% CI 0.57-0.93; P ¼ .002) treated with nivolumab and everolimus, respectively. Additionally, the ORR was also higher in the nivolumab group compared to the everolimus group (25% vs. 5%; 95% CI, 3.68-9.72; P < .001). Importantly, this study also explored PD-L1

expression as a potential biomarker. In patients with  1% PD-L1 levels, the median OS was 21.9 versus 18.8 months (HR ¼ 0.79; 95% CI, 0.53-1.17) for the nivolumab and everolimus groups, respectively. In patients with  1% PD-L1 levels, the median OS was 27.4 versus 21.2 months (HR ¼ 0.77; 95% CI, 0.60-0.97) for the nivolumab and everolimus groups, respectively.13 Patients who received nivolumab clearly fared better regardless of their PD-L1 expression levels. However, in the previously mentioned CheckMate 214 trial, patients with PD-L1 levels  1% before treatment had an ORR of 58% versus 25% after receiving nivo-ipi versus sunitinib, respectively. These patients also had a median PFS of 22.8 versus 5.9 months (HR ¼ 0.48; 95% CI, 0.28-0.82; P ¼ .0003) with nivo-ipi versus sunitinib, respectively. After assessing previously published data, these researchers found that lower levels of PD-L1 expression were correlated with a more favorable risk.17 In the phase 2 IMmotion150 trial, treatment-naive mRCC patients were randomized to receive either atezolizumab with bevacizumab, atezolizumab alone, or sunitinib alone. This trial analyzed patients with PD-L1 levels  1% treated with first-line atezolizumab and bevacizumab compared to sunitinib and atezolizumab alone versus sunitinib, reporting PFS HRs of 0.64 and 1.03, respectively.29,30 With conflicting results regarding the potential utility of PD-L1 expression as a predictive biomarker for IO in mRCC, its use in the clinical setting is currently not ready. There continues to be ongoing research into somatic mutational burden and responses to IO. IOs have greater efficacy in diseases with higher mutation rates as a result of the increased abundance of neoantigens and greater mutant-binding specificity. In other tumor types, especially melanoma, somatic mutation burden in associated with better outcomes.31,32 Nonsynonymous mutations occur when the protein loses its function, as opposed to nonsynonymous mutations, where the point mutation does not lead to altered functional protein. The number of nonsynonymous mutations was directly correlated with positive outcomes in melanoma.33 Although this is true for melanoma and nonesmall-cell lung cancer, studies for kidney cancer have found conflicting results. Patients who bear mismatch repair mutations also benefit from IO.34 Colon cancer patients with microsatellite instability, for example, had disease that responded better to IO than patients without microsatellite instability.35-37 In another study, rather than measuring nonsynonymous mutation burden, the authors analyzed the number of frameshift mutations. Frameshift mutations are more immunogenic because they expose many more neoantigens to the cell surface for immune system targeting. Of all cancers, mRCC has the highest proportion and number of frameshift mutations.38 This is a double-edged sword: in part it allows for more specific neoantigen to target, which means immune cells could home in on these cells with higher specificity and perhaps better clinical response. However, it also means that there are an abundance of potential targets that all could potentially represent the same disease process. To date, there is no prospective study correlating frameshift mutations and response to checkpoint inhibitors in mRCC.39 Emerging data from gut microbiome studies have been shown to be able to predict outcomes in patients and specific resident bacteria species. In one study, researchers found that the efficacy of CTLA-4

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Biomarkers in mRCC Figure 1 Potential Predictive Biomarkers in mRCC. PBRM1 Encodes for Integral Protein of Complexes Needed for Ligand-dependent Transcriptional Activation by Nuclear Hormone Receptors. KDM5C Encodes for Lysine Demethylase Involved in Regulation of Transcription and Chromatin Remodeling. VHL Encodes for Protein that Facilitates Degradation of HIF a Subunits, Preventing Downstream Expression of VEGF and Various Other Growth Factors. MET Encodes for Tyrosine Kinase Receptor, Whose Activation Plays Roles in Cellular Survival, Embryogenesis, and Cellular Migration and Invasion. TP53 Encodes for Tumor Suppressor Protein With Transcriptional Activation, DNA Binding, and Oligomerization Domains. NF1 Encodes Product that Functions as Negative Regulator of Ras Signaling Pathway. PIK3CA, TSC1, TSC2, and mTOR Encode for Mediators of mTOR Signaling Pathway that Play Roles in Cellular Energetics, Growth, Proliferation, and Survival. Specifically, Roseburia and Faecalibacterium Were Significantly Elevated in Stool Microbiome of mRCC Patients With Disease that Responded to Nivolumab. *There Have Been Mixed Results Thus far in Large Prospective Trials on Predictive Value of PD-L1 Expression for Checkpoint Inhibitors in mRCC

Predictive Biomarkers for VEGF-TKIs

Predictive Biomarkers for mTOR Inhibitors

PBRM1 mutations

mTOR mutations

(Wild-type for sunitinib)

KDM5C mutations VHL mutations

TSC1 mutations TSC2 mutations

MET mutations TP53 mutations (Therapeutic resistance)

NF1 mutations (Therapeutic resistance)

PIK3CA mutations (Therapeutic resistance)

Predictive Biomarkers for IOs PD-L1 expression* Gut microbiome Loss of PBRM1

Abbreviations: HIF ¼ hypoxia-inducible factor 1; MET ¼ mesenchymaleepithelial transition; mRCC ¼ metastatic renal-cell carcinoma; mTOR ¼ mammalian target of rapamycin; PD-L1 ¼ programmed death ligand 1; VEGF ¼ vascular endothelial growth factor.

blockade was strongly influenced by the gut microbiome composition, with Bacteroidales-predominant flora leading to synergistic effect with CTLA-4 blockade.40 In another study of patients with metastatic melanoma, results showed that patients whose disease responded to CTLA-4 blockade therapy had higher levels of Clostridiales species compared to patients who did not respond, who had predominately Bacteroidales species. In this study, the abundance of specific bacteria in patients with disease that responded to therapy to antiePD-1 therapy was associated with improved PFS (HR ¼ 3.88; P ¼ .007).41 In a different study including 26 RCC patients, Akkermansia muciniphilaepredominant gut flora was associated with a PFS of > 3 months (P ¼ .004) and with better clinical responses.42 An ongoing study correlating the gut microbiome compositions in mRCC patient responses to nivolumab found a significant association with specific bacteria species. The preliminary results from 11 patients reported that Roseburia and Faecalibacterium were significantly higher in patients with disease that responded to nivolumab (P < .05).43 The important findings in the field of gut microbiome analysis may lead to further studies exploring the potential to manipulate gut microbiomes in order to increase the likelihood of responses from IO agents. The use of antiePD-1 therapy inherently changes the tumor microenvironment. These changes have been studied and have demonstrated that tumor-associated lymphocytes increase in the

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expression of antitumor activity, as well as there being an increase in the overall number of active lymphocytes. Interestingly, PD-L1 expression on tumor cells was assessed at both baseline and during treatment; however, no consistent changes were found on these cells.44 This is another example of how measuring specific markers within the tumor environment could help predict outcomes. A small but important caveat to the increasing use of IO, specifically antiePD-1/PD-L1, is the hyperprogressive disease states that can arise. Hyperprogressive disease was defined as a Response Evaluation Criteria in Solid Tumors progression from the initial evaluation of at least 2-fold increase in tumor growth rate.45 More work has been done to home in on specific mutations or markers that may predict which patients will potentially manifest hyperprogressive disease. In multivariate analysis, MDM2/MDM4 and EGFR mutations were associated with significantly increased tumor growth rates less than 2 months after initiation of PD-1/PD-L1 inhibitors.46 These identified mutations and ongoing research could lead to potential negative predictive biomarkers before therapy is initiated, thereby preventing negative outcomes in selected patients. In a recently published study, researchers performed wholeexome sequencing on 35 mRCC patients’ tumors. In patients whose tumors had a loss-of-function PBRM1 gene mutation (P ¼ .012), the authors reported clinical benefit. This finding was

Jacob J. Adashek et al confirmed with an independent validation cohort of 63 mRCC patients treated with either PD-1/PD-L1 blockade alone or in combination with CTLA-4 blockade (P ¼ .0071). The conclusion of this study was that loss of PBRM1 gene may alter the tumors and affect their responsiveness to IOs.47 Along with the listed gene mutations and microbiome composition data, there continue to be many reported variables that influence patient outcomes on IOs. This warrants further research into possible ways to predict and affect response while receiving IO.

Circulating Tumor DNA Clinicians routinely perform next-generation sequencing (NGS) to identify novel targetable genomic alterations for the purpose of directing therapy selection.48 One of these techniques is Guardant 360, which uses circulating tumor DNA (ctDNA) derived from serum or plasma to characterize 72 somatic cancereassociated genes. ctDNA NGS accounts for both temporal and spatial heterogeneity. These liquid biopsies are a safer and easier option for repetitive testing, decreasing the risk of complications for patients while still providing continually updated details of intratumoral genomic alterations.49 In addition to the advantages of ctDNA NGS, ctDNA assessment may also play an increasing role in metastatic disease as surrogate of tumor burden. In a 34-patient study, a significant correlation between ctDNA value and tumor burden was determined. It was shown that patients with detectable ctDNA had a higher sum of the long diameter of all measurable lesions, equating to a higher tumor burden compared to patients with no detectable ctDNA (8.81 vs. 4.49 cm; P ¼ .04).50 Other studies have found that early variations in ctDNA levels may provide the earliest measure of treatment response and predate radiographic changes by many months.51-54 If proven in prospective trials, ctDNA may be used to evaluate response and eliminate the need for radiographic imagining. Recent studies have also implicated ctDNA testing as important in determining therapeutic resistance in mRCC. Pal et al55 collected and analyzed ctDNA from 220 patients with mRCC, making it the largest assessment of ctDNA in mRCC to date. In this study, it was found that the frequency of certain genomic alterations, such as TP53 and NF1, increased considerably in patients receiving postefirst-line therapies. This variation between first-line and postefirst-line therapy suggests the development of therapeutic resistance. The noninvasive attribute of ctDNA profiling makes it an attractive method of acquiring real-time genomic data that could track potential therapeutic resistance in mRCC, allowing for resistant-directed therapies. Presently ctDNA has the ability to identify unique and changing targets in patients with mRCC, which may be able to contribute in the selection of subsequent therapy. The vast landscape of genomic alterations that can be found with ctDNA profiling could allow for the consideration of nonconventional therapies for mRCC for patients in the postefirst-line setting. Additionally, it could be used to facilitate entry into both certain clinical trials as well as basket studies, which target a specific genetic mutation found in the tumor.55 However, until this strategy proves to improve outcomes, it remains investigational.

Conclusion As the landscape of treating mRCC continues to rapidly change, the evolution and emergence of predictive biomarkers will continue to be a conundrum that we can only hope will be solved (Figure 1). It is safe to say that PD-L1 status is an ambiguous biomarker in the setting of treatment response; however, we know that patients with high levels of expression tend to fare worse overall. Efforts of retrospective gene analyses shed some light onto the biomarker prediction picture, but they do not yet have clinical utility. The most promising biomarkers may be more informative from efforts such as ctDNA and NGS, where mutation-guided therapeutic strategies seem to be a promising approach.56-58 Regardless of the method, potential biomarkers in mRCC require validation, ideally in large studies of prospective design, to establish their predictive value.

Disclosure The authors have stated that they have no conflict of interest.

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