Abstract
Purpose:
CALGB 90206 was a phase III trial of 732 mRCC patients comparing bevacizumab plus interferon alpha (BEV+IFN) versus interferon alpha alone (IFN). No difference in overall survival (OS) was observed. Baseline samples were analyzed to identify predictive biomarkers for survival benefit.
Experimental Design:
A total of 32 biomarkers were assessed in 498 consenting patients randomly assigned into training (n=279) and testing (n=219) sets. The proportional hazards model was used to test for treatment arm and biomarker interactions of OS. The estimated coefficients from the training set were used to compute a risk score for each patient and to classify patients by risk in the testing set. The resulting model was assessed for predictive accuracy using the time-dependent area under the receiver operating characteristic curve (tAUROC).
Results:
A statistically significant 3-way interaction between IL-6, HGF, and bevacizumab treatment was observed in the training set and confirmed in the testing set (p<0.0001). The model based on IL-6, HGF and bevacizumab treatment was predictive of OS (p<0.001) with the high and low risk groups having a median OS of 10.2 (95% CI=8.0–13.8) and 34.3 (95% CI 28.5–40.5) months, respectively. The average tAUROC for the final model of OS based on 100 randomly split testing sets was 0.78 (1st, 3rd quartiles=0.77, 0.79).
Conclusions:
IL-6 and HGF are potential predictive biomarkers of OS benefit from BEV+IFN in mRCC patients. The model based on key biological and clinical factors demonstrated predictive efficacy for OS. These markers warrant further validation in future anti-VEGF and immunotherapy in mRCC trials.
Keywords: bevacizumab, biomarker, IL-6, plasma, multiplex ELISA, renal cancer
Introduction
Renal cell carcinoma (RCC) accounts for 4–5% of all new cancers diagnosed each year and is the most common type of kidney cancer in the United States (1). Prior to the 1990s, cytokines such as interferon alpha and IL-2 represented the main therapeutic choices for RCC (2). In 2005, the first vascular endothelial growth factor (VEGF) inhibitor sorafenib was approved by Food and Drug Administration (FDA) to treat RCC (3). The median overall survival (OS) for untreated metastatic RCC (mRCC) patients improved from under 1 year to more than 33 months with the introduction of VEGF targeted therapy (4,5). This initial approval was followed by the approval of six additional VEGF inhibitors for use in mRCC patients, including sunitinib, pazopanib, axitinib, bevacizumab, cabozantinib, and lenvantinib (6).
In mRCC, the most widely used prognostic factor model is from Memorial Sloan-Kettering Cancer Center (MSKCC), which includes five negative predictive factors for short survival: low serum hemoglobin, high corrected serum calcium level, Karnofsky performance status less than 80%, high lactate dehydrogenase level, and time from diagnosis to treatment of less than one year(7). Motzer et al. validated the MSKCC model and added platelet and neutrophil counts as additional markers(8,9). Mekhail et al. further expanded the model by accommodating two prognostic factors: prior radiotherapy, as well as presence of hepatic, lung, and retroperitoneal nodal metastases (10). Currently, the most adopted system for OS prediction is the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria, consisting of two clinical variables (Karnofsky performance status and months from diagnosis to treatment start) and four laboratory variables (serum levels of calcium, hemoglobin, platelets, and neutrophils)(11,12).
In contrast to these extensively optimized clinical factors, circulating biomarkers are less understood in mRCC. VEGF-inhibitors represent an important option for the treatment of first-line mRCC patients, but no predictive biomarkers exist for this class of drugs. Since the general response rate to VEGF-inhibitors is less than 50% (13), it remains pivotal to identify predictive biomarkers to enable patient selection and to improve clinical outcomes (14). To this end, we have developed and optimized a protein multiplex array termed the Angiome, a panel of circulating biomarkers crucial in tumor angiogenesis, inflammation, and immune modulation (15–17). The Angiome biomarker platform is currently approved for use as an integrated biomarker by the National Cancer Institute Biomarker Review Committee in several ongoing NCTN studies.
In CALGB 90206, 732 mRCC patients were randomized with equal probability to receive interferon alpha alone (IFN) or bevacizumab plus interferon alpha (BEV+IFN) (18). Bevacizumab (Avastin™, Genentech/Roche Inc.) is a monoclonal antibody against VEGF, and has been approved for use in many cancers, including metastatic colorectal, non-small cell lung, renal cell, and glioblastoma(19). In CALGB 90206, bevacizumab significantly prolonged the progression-free survival (PFS) time, but no OS benefit was observed (18). Recognizing the potential value of biomarkers that might predict for sensitivity and resistance to bevacizumab, EDTA plasma samples were collected prospectively at baseline and after each restaging during treatment on CALGB 90206. Here, we report the Angiome analysis focusing on the pre-treatment (baseline) plasma samples from patients on CALGB 90206. Patients were randomly split into training and testing sets, and prognostic and predictive biomarkers for bevacizumab were first identified in the training set, and then validated in the testing set.
Materials and Methods
Patients
The design and results of CALGB 90206 have been described previously (18) and the trial was conducted in accordance with the Declaration of Helsinki. Briefly, eligible patients had mRCC with a clear cell histologic component, Karnofsky performance status of ≥70%, adequate end organ function, blood pressure less than 160/90 mmHg, and lack of central nerve system metastases, significant comorbidity, or recent history of bleeding or clotting. Prior chemotherapy for metastatic disease was not permitted. Institutional review board (IRB) approved, written informed consent was obtained from all the patients who opted to participate in this correlative analysis of CALGB 90206. The analysis conforms to the guidelines established by the REMARK criteria.
Sample Collection and Analysis
Peripheral venous blood was collected into EDTA anticoagulant vacutainers. The tubes were centrifuged within 30 minutes of collection at 2,500 g for 15 minutes. Plasma was aliquoted into cryovials and snap frozen, and samples were shipped on dry ice for centralized storage at −80°C at the CALGB Pathology Coordinating Office in Ohio State University. Before analysis, all patients’ samples were shipped to Duke Phase I Biomarker Laboratory, thawed on ice, re-aliquoted based on specific assay requirements and stored at −80°C. All assays were performed in duplicate at Duke University Medical Center, limited to 2 freeze-thaw cycles only, and all analyses were conducted while blinded to clinical outcomes.
All biomarkers (Ang-2, BMP-9, CRP, Endoglin, Gro-α, HGF, ICAM-1, IGFBP-1, IGFBP-2, IGFBP-3, IL-6, IL-8, MCP-1, OPN, PAI-1 total, PAI-1 active, PDGF-AA, PDGF-BB, PEDF, PlGF, P-selectin, SDF-1, TGF-β1, TGF-β2, TSP-2, VCAM-1, VEGF, VEGF-C, VEGF-D, VEGF-R1, and VEGF-R2) were measured using the SearchLight multiplex platform (Aushon Biosystems, Inc., now Quanterix, Billerica, MA), except for TGFβ-R3 which was tested as previously described (16).
Statistical Analysis
The primary endpoint used for the analysis was overall survival (OS), which was also the primary endpoint in CALGB 90206 and was defined as the interval from the date of random assignment to the date of death from any cause. Progression-free survival (PFS) was a secondary endpoint and was measured from the date of random assignment to the date of progression or death, whichever occurred first. A total of 498 CALGB 90206 patients (68%) consented to this correlative study and had baseline plasma available for analysis. Data were averaged among the duplicate measures of the markers and then transformed using the natural logarithm function. Furthermore, the data were randomly split into a 0.55:0.45 allocation ratio with 279 patients and 219 patients assigned to the training and testing sets, respectively. Spearman correlations coefficients were computed among the biomarkers and are presented visually in a dendrogram. This exploratory retrospective analysis had a pre-specified analysis and conforms to the reporting guidelines established by the REMARK criteria (20).
Model Building:
The objectives of this analysis were to identify prognostic and predictive factors of OS and PFS. A two-step procedure was used to identify and test for important prognostic markers for OS and PFS. In the first step, the proportional hazards models were utilized to test for the prognostic importance of the 32 markers in predicting OS and PFS. Biomarkers that had false discovery rate [Benjamini-Hockberg (BH) method] <0.05 in the univariate analysis were selected for the multivariable models(21). In the second step, the adaptive least absolute shrinkage and selection operator (ALASSO)(22) penalty was used to select important markers for the multivariable models of OS adjusting for the stratification factors (nephrectomy and Motzer criteria). The regularization parameter was chosen to minimize the Schwarz information criterion. The 95% CI for the ALASSO was derived by adopting the perturbation method (23).
The proportional hazards model was used to identify predictive biomarkers of OS and PFS. In each model, the main effect for each biomarker, treatment arm, and biomarker-treatment arm interaction terms were evaluated. Biomarkers-treatment arm interaction terms that had FDR<0.05 were considered statistically significant.
The final model of OS was built with the selected biomarkers and clinical variables. A risk score was computed for each patient in the training set from the estimated predictive regression coefficients.
Validation:
The estimated regression coefficients from the training set were used to compute risk scores for each patient in the testing set. The performance of the final predictive model of OS was evaluated for its discriminative ability utilizing the integrated time dependent area under the receiver operating characteristics curve (AUROC) with a follow-up period of up to 92 months (24). The final model of OS was validated using the testing set with the risk score as a continuous parameter. In addition, in order to assess the robustness of the final model of OS, the combined data (n=498) was randomly split into 100 training/testing sets of 0.55:0.45 allocation ratio. The final model of OS was fitted 100 times using the training sets and the tAUROC was computed from each testing set and averaged over the 100 testing sets. We report the first and third quartile for the tAUROC across the 100 randomly split testing sets.
In addition, the risk score based on the second quartile (median) was used to group patients into low or high-risk groups. The Kaplan-Meier product-limit method was utilized to estimate the PFS and OS distributions by the different groups and the log-rank statistic was used to test if the two risk groups have different survival outcomes. All statistical analyses for model development and validation were performed using the R package (25). Data collection and statistical analyses were conducted by the Alliance Statistics and Data Center. Data quality was ensured by review of data by the Alliance Statistics and Data Center following Alliance policies. All analyses were based on the study database frozen on March 24, 2009.
Results
Patient characteristics
Of the 732 metastatic renal cell carcinoma (mRCC) patients accrued to CALGB90206, a total of 498 patients had baseline plasma available for analysis. The training set consisted of 279 patients, 134 patients in the IFN arm, and 145 patients in the BEV+IFN arm; while the testing test consisted of 219 patients; 105 patients in the IFN arm, and 114 patients in the BEV+IFN arm. The CONSORT diagram is shown in Figure 1. The baseline clinical characteristics of the training and testing sets were similar and representative of the total patient population on the parent study (Table 1). The cutoff date is March 24, 2009 and the median duration of follow-up among surviving patients is 46.2 months(18).
Figure 1.
Patients CONSORT diagram.
Table 1.
Patient characteristics at baseline
| Variable | No Baseline Plasma (n=234) | Baseline Plasma Total (n=498) | Training Set (n=279) | Testing Set (n=219) | Total (n=732) |
|---|---|---|---|---|---|
|
| |||||
| Gender | |||||
| % Male | 71 | 68 | 67 | 70 | 69 |
| % Female | 29 | 32 | 33 | 30 | 31 |
|
| |||||
| Median Age, years | 62 | 61 | 63 | 60 | 62 |
| (inter-quartile range) | (56–71) | (55–70) | (56–71) | (54–68) | (55–70) |
|
| |||||
| ECOG performance status (%) | |||||
| 0 | 31 | 38 | 35 | 41 | 35 |
| 1 | 60 | 55 | 57 | 52 | 57 |
| 2 | 8 | 6 | 7 | 6 | 7 |
| Unknown | 1 | 1 | 1 | 1 | 1 |
|
| |||||
| Previous nephrectomy | 82 | 86 | 87 | 85 | 85 |
|
| |||||
| Common Sites of Metastases (%) | |||||
| Lung | 70 | 69 | 71 | 67 | 69 |
| Lymph node | 34 | 36 | 34 | 38 | 35 |
| Bone | 33 | 27 | 26 | 29 | 29 |
| Liver | 24 | 18 | 15 | 23 | 20 |
|
| |||||
|
Adverse risk factors (%)
(Motzer criteria) |
|||||
| 0 (favorable) | 26 | 27 | 29 | 23 | 26 |
| 1–2 (intermediate) | 62 | 64 | 62 | 66 | 64 |
| ≥ 3 (poor) | 12 | 9 | 9 | 11 | 10 |
|
| |||||
| Treatment Assignment (%) | |||||
| BEV + IFNα | 48 | 52 | 52 | 52 | 50 |
| IFNα | 52 | 48 | 48 | 48 | 50 |
ECOG: the Eastern Cooperative Oncology Group. BEV: bevacizumab. IFNα: interferon alpha.
Baseline Angiome measurement and correlation
In order to investigate prognostic and predictive biomarkers in this disease and treatment setting, we assessed 32 circulating protein biomarkers at baseline. Multiplex analyses demonstrated good sensitivity and reproducibility across all of the 32 markers with coefficients of variation (CVs) ranging from 5–20%. The median baseline levels and ranges for all markers were shown in Supplementary Table S1. Spearman-based correlation analyses identified five distinct clusters among the measured markers (Supplementary Fig. S1).
Prognostic markers
Twelve biomarkers were identified as prognostic of overall survival (OS) with a FDR <0.05, including IL-6, Ang-2, OPN, IL-8, CRP, IGFBP-1, HGF, IGFBP-2, PIGF, TSP-2, VEGFR-1, and VEGF. All of these markers have hazard ratios (HR) >1, indicating higher levels of these markers were associated with shorter survival duration (i.e., negative prognostic markers). The HRs and 95% confidence intervals (CI) for all 32 markers are shown in Supplementary Table S2.
Similarly, nine biomarkers were identified as prognostic of PFS with statistically significance (FDR <0.05). These were OPN, PlGF, IL-6, IGFBP-1, CRP, Ang-2, IL-8, HGF, and VEGF. All biomarkers identified as prognostic for PFS were also prognostic for the OS endpoint. The complete listing of HRs for the PFS endpoint and 95% CIs for all markers are presented in Supplementary Table S3.
In multivariable analyses of OS, we identified three prognostic markers: HGF, IL-6 and Ang-2. Higher levels of these markers were associated with greater risk of death (Table 2). Adaptive LASSO identified four prognostic biomarkers of PFS: OPN, IL-6, PlGF and Ang-2 (Table 2). A high degree of consistence was observed between the univariate and the multivariable analysis for prognostic markers.
Table 2.
Multivariable prognostic markers for overall survival (OS) and progression-free survival (PFS)
| OS | ||
| Factor | Hazard Ratio | 95% CI |
| HGF | 1.19 | 1.00 – 1.33 |
| IL-6 | 1.27 | 1.11 – 1.42 |
| Ang-2 | 1.45 | 1.17 – 1.71 |
| Motzer risk score (>=3 vs <3) |
2.15 | 1.25 – 3.22 |
| PFS | ||
| Factor | Hazard Ratio | 95% CI |
| Nephrectomy (yes vs. no) |
1.15 | 0.93 – 1.74 |
| OPN | 1.12 | 1.00 – 1.29 |
| IL-6 | 1.13 | 1.00 – 1.22 |
| PlGF | 1.22 | 1.00 – 1.47 |
| Ang-2 | 1.11 | 1.00 – 1.22 |
| Motzer risk score (>=3 vs <3) |
2.19 | 1.34– 3.58 |
Predictive Markers
Our next goal was to identify potential predictive biomarkers of bevacizumab benefit. From the training set, we identified two predictive markers for OS. IL-6 was identified as a predictive marker (FDR=0.0497; Supplementary Table S4). The Kaplan-Meier plots demonstrated that patients with higher than median IL-6 levels (>15.9 pg/ml) benefitted from BEV+IFN compared to those receiving IFN alone (Figure 2A). The median OS was 14.4 months (95% CI=9.8–20.0) and 10.1 months (95% CI=6.7–13.0) in patients with high levels IL-6 treated with BEV+IFN and IFN alone, respectively. In contrast, patients with low levels of IL-6 levels (≤15.9 pg/ml) treated with BEV+IFN had similar median OS [31.4 months (95% CI=24.3–43.3)] as that in patients treated with IFN alone [median OS 31.6 months (95% CI 25.1–41.6)] (Figure 2A).
Figure 2.
Kaplan-Meier plots showing the univariate predictive value of IL-6 (A) and HGF (B) by treatment arm in the training set of patients.
HGF was identified as a potential predictive marker for OS, but it did not meet the statistically significant threshold after multiple testing adjustment (FDR=0.3267; Supplementary Table S4). In contrast to IL-6, patients with lower than median HGF levels (≤173.6 pg/ml) benefitted from BEV+IFN treatment (median OS=40.8 months; 95% CI=26.9–55.1) compared to those treated with IFN alone (median OS=26.1 months; 95% CI=18.1–33.5). No benefit was seen with BEV+IFN (median OS=11.5, 95% CI=9.2–16.4) compared to IFN alone (median OS=11.0, 95% CI=8.6–18.4) in patients with high HGF levels (>173.6 pg/ml) (Figure 2B). A complete list of predictive values for all markers is shown in Supplementary Table S4.
None of the 32 biomarkers were predictive of PFS, as shown in Supplemental Table S5. Interestingly, IL-6 was the top candidate as a predictive biomarker for PFS (p=0.0735). No further data analyses were conducted on IL-6 as it did not meet the statistical significance threshold.
A three-way interaction model in the training set
In multivariable analyses, a three-way interaction was detected between IL-6, HGF, and treatment in the training set. For simplicity, the 3-way interaction is presented in Table 3 with the IL-6 and HGF as binary variables dichotomized by the observed median levels. Evaluation of HGF and IL-6 stratified by arm revealed that patients with high IL-6 levels benefitted from the addition of bevacizumab in both the low (median OS=33.8 in BEV+IFN vs. 13.9 months in IFN) and high HGF groups (median OS=9.8 in BEV+IFN vs. 6.7 months in IFN, Table 3). However, in patients with low IL-6, only those patients with low HGF levels benefitted from the addition of bevacizumab (median OS=43.0 months in BEV+IFN vs. 33.5 months in IFN); patients with low IL-6 and high HGF did not benefit from the addition of bevacizumab (median OS =17.1 months in BEV+IFN vs. 27.6 months in IFN) and trended toward a worse survival (Table 3). This model identifies a subgroup of patients (IL-6 high/ HGF low) who derived the most benefit from the addition of bevacizumab, while the group of patients (IL-6 low/ HGF high) are predicted for lack of benefit from the addition of bevacizumab.
Table 3.
Median overall survival (months, and 95% confidence interval) for HGF and treatment arm, stratified by IL-6 levels using data from the patients assigned to the Training Set
| High IL-6 Levels >15.90 pg/ml (n=138) | ||
| Arm | Low HGF ≤173.6 pg/ml (n=48) | High HGF >173.6 pg/ml (n=90) |
| IFN | 13.9 (10.1, 26.4) | 6.7 (5.0, 12.3) |
| BEV+IFN | 33.8 (20.0, 73.4) | 9.8 (7.4, 15.7) |
| Low IL-6 Levels ≤15.90 pg/ml (n=139) | ||
| Treatment | Low HGF ≤173.6 pg/ml (n=91) | High HGF >173.6 pg/ml (n=48) |
| IFN | 33.5 (27.7, 54.6) | 27.6 (18.4, 42.3) |
| BEV+IFN | 43.0 (27.1, 58.4) | 17.1 (10.9, 42.9) |
An alternate view of the 3-way interaction model is presented with Kaplan-Meier plots with the markers as binary in Figure 3. Based on the median levels of IL-6 and HGF, patients are grouped into the same four subgroups as described above, including IL-6 low/ HGF low; IL-6 low/ HGF high; IL-6 high/ HGF low; IL-6 high/ HGF high. In both the IFN and BEV+IFN arms, patients with IL-6 low/ HGF low exhibited superior OS while patients with IL-6 high/ HGF high exhibited worse outcomes. Interestingly, in the IFN arm, patients with IL-6 low/ HGF high had longer survival than patients with IL-6 high/ HGF low. This situation is reversed in the BEV+IFN arm, where patients with IL-6 high/ HGF low demonstrated longer survival compared to patients with IL-6 low/ HGF high (Figure 3).
Figure 3.
Kaplan-Meier plots showing the multivariable predictive value for overall survival benefit by IL-6 and HGF in the IFN (A) and BEV+ IFN (B) arms in the training set of patients.
Testing set validation
To validate these findings from the training set, we tested the predictive value of IL-6 and HGF using biomarker levels and OS durations in the testing set. No significant differences were noted in patient demographics or biomarker baseline levels in both the training and testing sets (Table 1, Supplementary Table S1). For each patient in the testing set, a risk score (RS) was computed using the estimated regression coefficients from the three-way interaction (IL-6, HGF, treatment arm), along with MSKCC risk score and prior nephrectomy. RS was highly predictive of OS with tAUROC of 0.80 (95% CI=0.76–0.88) based on one-split testing set. The average tAUROC based on 100 randomly splits testing sets was 0.78 (1st and 3rd quartile 0.77–0.78). Patients were classified into either high risk or low risk groups based on the median RS (Figure 4). The HR for death in the high-risk patients was 2.8 (95% CI = 2.1–3.8, p<0.0001) compared to low-risk patients. The high and low risk groups had median OS of 10.2 (95% CI=8.0–13.8) months and 34.3 (95% CI=28.5–40.5) months, respectively. This model based on the 3-way interaction of HGF and IL-6 and treatment identified patients who benefitted most from addition of bevacizumab to interferon treatment.
Figure 4.
The risk score model predicting overall survival in the testing set.
Discussion
In this retrospective biomarker study, IL-6 and HGF were identified as potential predictors of OS benefit from bevacizumab in untreated mRCC patients enrolled in CALGB 90206 in univariate analysis. These findings represent a comprehensive circulating biomarker analysis performed in first line mRCC patients and has the potential to guide current therapeutic approaches in mRCC treatment.
In the past five years, the RCC field has undergone a paradigm shift with immune checkpoint blockage becoming the first line treatment of choice, marked by the FDA approval of nivolumab in 2015 and ipilimumab in 2018 (26,27). It has been shown that co-administration of anti-angiogenic drugs and immune checkpoint inhibitors synergistically increase T cell infiltration into the tumor (28) and combinations of these two categories of drugs have been rigorously tested in RCC patients. As a result of this work, the FDA has approved the use of both pembrolizumab and axitinib as well as avelumab and axitinib in the treatment of RCC, based on the superior clinical outcomes revealed in two phase III trials, KEYNOTE 426 (29) and JAVELIN Renal 101 (30). In 2021, the FDA approved the combination of cabozantinib and nivolumab(31), lenvantinib and pembrolizumab(32) in advanced RCC. Even in this era of immuno-oncology, anti-angiogenic drugs remain a valid option with the potential to enhance immunotherapy and to modulate immune responses. Our biomarker findings have the potential to improve the application of not only anti-angiogenic agents, also immune checkpoint inhibitors in mRCC.
The phase III trial of CALGB 90206 was conducted before immune therapy was introduced to mRCC field, testing the efficacy of bevacizumab, a monoclonal antibody against VEGF. We identified 9 prognostic markers negatively associated with OS and PFS. Five of these markers clustered as two closely related biomarker groups in Dendrogram. One consisted of IL-6, IL-8, PlGF; the other consisted of HGF and VEGF (Supplementary Fig. S1). The clustering suggested that baseline expression of these markers were very similar. Prognostic value of these markers is consistent with the findings of Tran et al where they reported that serum concentrations of IL-6, IL-8, HGF were negatively associated with prognosis in a phase III trial with 344 RCC patients randomized to pazopanib or placebo(33). Escudier et al. reported baseline VEGF level associated with both PFS and OS in a phase III trial with 903 RCC patients randomized to sorafenib or placebo(34). Several groups have identified IL-6 as a prognostic marker in mRCC patients(35–37). These findings all align and confirm the prognostic value of circulating biomarkers as a complement to clinical parameters(38).
In addition to being significantly prognostic (Supplementary Table S2 and S3), IL-6 is the only significant predictive marker after multiple testing adjustment (FDR=0.0497). We observed that high baseline level of IL-6 was associated with improved OS with bevacizumab treatment over interferon alone (Supplement Table S4). Using a similar approach, Tran et al. identified high IL-6 levels as a predictive marker of improved PFS from a phase II trial where 215 RCC patients received pazopanib(39), then validated the predictive role of IL-6 in 344 RCC patients randomized to placebo or pazopanib in a phase III trial(40). Tran et al. showed that high expression level of IL-6 was predictive of PFS benefit from pazopanib compared with placebo with a Pinteraction of 0.009 (33). Together, these data revealed the predictive value of IL-6 for bevacizumab, a monoclonal antibody, and pazopanib, a multikinase inhibitor of VEGFR, raising the possibility that the finding may be applicable to general VEGF inhibitors in the disease setting of mRCC. Furthermore, we have recently reported a similar predictive role of IL-6 in ovarian cancer, where women with high IL-6 derived the most benefit from bevacizumab (41). Together these findings suggest that IL-6 may assist in directing front-line bevacizumab therapy to maximize benefit and minimize toxicity. Further testing to validate the role of IL-6 as a predictive biomarker is warranted.
Our analysis identified a predictive role of HGF that was IL-6 dependent (i.e., three-way interaction). By itself, HGF showed modest predictive value (p=0.0203), but did not reach statistical significance after multiple testing adjustment (Supplement Table S4). Upon evaluation of HGF-IL6 and treatment arm for patients, we found the subgroup of patients with high HGF and low IL-6 levels derived no benefit from bevacizumab. Therefore, high HGF and low IL-6 may be used as a predictor of lack of benefit from bevacizumab. In addition, the predictive importance of HGF has potential therapeutic implications. HGF is the ligand for c-MET, a receptor playing key roles in tumor angiogenesis, metastasis, and plasticity (42). Recently FDA approved cabozantinib, a novel anti-tumor drug that inhibits VEGFR2, MET, and other kinases (43). Cabozantinib has demonstrated clinical activity in RCC patients previously treated with VEGF inhibitors, as well in the first line setting as revealed in the METEOR and CABOSUN trials (44–46). At this time, no predictive biomarker for cabozantinib has been identified. Given the biological relevance, the predictive value of HGF in the context of VEGFR and MET dual inhibition should be investigated.
Lastly, the model combined key clinical risk factors (MSKCC risk score and history of nephrectomy) and biomarkers for bevacizumab, demonstrating a robust and reproducible association with OS that was validated in the testing set of CALGB 90206 patients (Figure 4). This model demonstrated that a combination of clinical and plasma biomarkers represent a stronger predictive marker than either one alone. Our findings of predictive markers and the resulted model have the potential to select patients who have the best chance to benefit from anti-angiogenic therapy, as well as those more likely to respond to other therapies. The application of our findings need to be validated prospectively in future trials.
Finally, we acknowledge several limitations to our study. Although bevacizumab has been tested in combination with immunotherapeutic drugs (47), it is not a commonly used regimen for RCC. Axitinib is more commonly used in combination with immune therapy, likely due to its high specificity as a VEGF inhibitor (48). Rather than targeting the soluble VEGF ligand, small-molecule, VEGFR-targeted therapies (sunitinib and pazopanib) remain the standard first-line treatment in combination with ICI for RCC (49). The biomarker findings reported here for bevacizumab may not be applicable to anti-VEGFR tyrosine kinase inhibitor drugs. Another limitation of our study is that our statistical analysis focused on OS, and the risk factor model was based on OS only. This may impede the application of our findings to other trials which use PFS as the primary endpoint.
In summary, we identified IL-6 and HGF, two factors in tumorigenesis and progression, as negative prognostic and predictive biomarkers in mRCC. Combined IL-6 and HGF together, a subgroup of patients (IL-6 high/ HGF low) benefited the most from BEV+IFN, while the other group (IL-6 low/ HGF high) benefited more from IFN alone. Application of these findings awaits further validation in prospective trials.
Supplementary Material
Translational relevance.
CALGB 90206 was a phase III trial comparing bevacizumab plus interferon alpha vs. interferon alpha alone as first line treatment for patients with advanced or metastatic renal cell carcinoma (mRCC). No significant improvement in overall survival (OS) was noted between the two arms. We conducted an exploratory analysis evaluating circulating angiogenic and inflammatory proteins and identified interleukin 6 (IL-6) and hepatocyte growth factor (HGF) as potential predictive biomarkers of bevacizumab benefit. Improved OS benefit was observed in a subpopulation of patients with higher than median IL-6 and lower than median HGF levels. With the recent advent of immunotherapy in RCC, the clinical need to personalize treatment has become more compelling. Our findings suggest that combining IL-6 and HGF may be effective in selecting RCC patients who are most likely to benefit from anti-angiogenic drugs. Given both IL-6 and HGF can be assessed non-invasively and cost-effectively, with the potential to become practice-changing in the future, prospective validation is warranted.
Acknowledgements
We gratefully acknowledge the invaluable contributions of the patients, their families, and the staff who participated in this study. The research for CALGB 90206 (Alliance) was supported, in part, by grants from the National Institutes of Health and by grants from National Cancer Institute to the Alliance for Clinical Trials in Oncology (Monica M. Bertagnolli, M.D., Chair) and to the Alliance Statistics and Data Center (Sumithra J. Mandrekar, Ph.D.,). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Cancer Institute.
More than 400 institutions participated in this study. Detailed information can be found at NCT00072046.
Support:
Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Numbers U10CA180821, U10CA180882, and U24CA196171 (to the Alliance for Clinical Trials in Oncology), UG1CA233180 and UG1CA233253. Detailed information is listed in https://acknowledgments.alliancefound.org. This work is also supported in part by funds from Genentech. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Disclosure of Potential Conflicts of Interest: A.B. Nixon has received research funding from Genentech, HTG Molecular Diagnostics, MedImmune/AstraZeneca, Medpacto, Promega Corporation, Seattle Genetics; and has received consultant/advisory compensation from AdjuVolt Therapeutics, Eli Lilly, GSK, Leap Therapeutics, Promega Corporation. H.I. Hurwitz declares ownership of Roche stock. H. Beltran has served as consultant/advisory board member for Janssen, Astellas, Astra Zeneca, Merck, Pfizer, Foundation Medicine, Blue Earth Diagnostics, Amgen, and has received research funding (inst) from Janssen, AbbVie/ Stemcentrx, Eli Lilly. E.J. Small has received consultant/advisory compensation from Janssen, Fortis, Teon Therapeutics, Ulragenyx, Beigene, Tolero; and declares ownership of Fortis Therapeutics and Harpoon Therapeutics stocks. D.L. George has received consultant/advisory compensation from Astellas, Astrazeneca, Axess Oncology, Bayer H/C Pharmaceuticals, BMS, Capio Biosciences, Constellation Pharmaceuticals, EMD Serono, Exelixis Inc., Flatiron, Ipsen, Janssen Pharmaceuticals, Merck Sharp & Dohme, Michael J Hennessey Associates, Modra Pharmaceuticals B. V., Myovant Sciences, Nektar Therapeutics, Physician Education Resource LLC, Pfizer, Propella TX, Rev Health LLC, Sanofi, UroGPO; and has received research funding from Astrazeneca, BMS, Calithera, Exelixis Inc., Janssen Pharmaceuticals, Novartis, Pfizer, Sanofi. The other authors declare no potential conflicts of interest.
References
- 1.Jemal A, Ward EM, Johnson CJ, Cronin KA, Ma J, Ryerson B, et al. Annual Report to the Nation on the Status of Cancer, 1975–2014, Featuring Survival. J Natl Cancer Inst 2017;109(9) doi 10.1093/jnci/djx030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Rini BI, Campbell SC, Escudier B. Renal cell carcinoma. Lancet 2009;373(9669):1119–32 doi 10.1016/S0140-6736(09)60229-4. [DOI] [PubMed] [Google Scholar]
- 3.Escudier B, Eisen T, Stadler WM, Szczylik C, Oudard S, Siebels M, et al. Sorafenib in advanced clear-cell renal-cell carcinoma. N Engl J Med 2007;356(2):125–34 doi 10.1056/NEJMoa060655. [DOI] [PubMed] [Google Scholar]
- 4.Motzer RJ, Tannir NM, McDermott DF, Aren Frontera O, Melichar B, Choueiri TK, et al. Nivolumab plus Ipilimumab versus Sunitinib in Advanced Renal-Cell Carcinoma. N Engl J Med 2018;378(14):1277–90 doi 10.1056/NEJMoa1712126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Motzer RJ, Hutson TE, Cella D, Reeves J, Hawkins R, Guo J, et al. Pazopanib versus sunitinib in metastatic renal-cell carcinoma. N Engl J Med 2013;369(8):722–31 doi 10.1056/NEJMoa1303989. [DOI] [PubMed] [Google Scholar]
- 6.Posadas EM, Limvorasak S, Figlin RA. Targeted therapies for renal cell carcinoma. Nat Rev Nephrol 2017;13(8):496–511 doi 10.1038/nrneph.2017.82. [DOI] [PubMed] [Google Scholar]
- 7.Fiala O, Finek J, Poprach A, Melichar B, Kopecky J, Zemanova M, et al. Outcomes According to MSKCC Risk Score with Focus on the Intermediate-Risk Group in Metastatic Renal Cell Carcinoma Patients Treated with First-Line Sunitinib: A Retrospective Analysis of 2390 Patients. Cancers (Basel) 2020;12(4) doi 10.3390/cancers12040808. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Patil S, Figlin RA, Hutson TE, Michaelson MD, Negrier S, Kim ST, et al. Prognostic factors for progression-free and overall survival with sunitinib targeted therapy and with cytokine as first-line therapy in patients with metastatic renal cell carcinoma. Ann Oncol 2011;22(2):295–300 doi 10.1093/annonc/mdq342. [DOI] [PubMed] [Google Scholar]
- 9.Motzer RJ, Escudier B, Bukowski R, Rini BI, Hutson TE, Barrios CH, et al. Prognostic factors for survival in 1059 patients treated with sunitinib for metastatic renal cell carcinoma. Br J Cancer 2013;108(12):2470–7 doi 10.1038/bjc.2013.236. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Mekhail TM, Abou-Jawde RM, Boumerhi G, Malhi S, Wood L, Elson P, et al. Validation and extension of the Memorial Sloan-Kettering prognostic factors model for survival in patients with previously untreated metastatic renal cell carcinoma. J Clin Oncol 2005;23(4):832–41 doi 10.1200/JCO.2005.05.179. [DOI] [PubMed] [Google Scholar]
- 11.Heng DY, Xie W, Regan MM, Warren MA, Golshayan AR, Sahi C, et al. Prognostic factors for overall survival in patients with metastatic renal cell carcinoma treated with vascular endothelial growth factor-targeted agents: results from a large, multicenter study. J Clin Oncol 2009;27(34):5794–9 doi 10.1200/JCO.2008.21.4809. [DOI] [PubMed] [Google Scholar]
- 12.Heng DY, Xie W, Regan MM, Harshman LC, Bjarnason GA, Vaishampayan UN, et al. External validation and comparison with other models of the International Metastatic Renal-Cell Carcinoma Database Consortium prognostic model: a population-based study. Lancet Oncol 2013;14(2):141–8 doi 10.1016/S1470-2045(12)70559-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Jayson GC, Kerbel R, Ellis LM, Harris AL. Antiangiogenic therapy in oncology: current status and future directions. Lancet 2016;388(10043):518–29 doi 10.1016/S0140-6736(15)01088-0. [DOI] [PubMed] [Google Scholar]
- 14.Di Paolo V, Colletti M, Ferruzzi V, Russo I, Galardi A, Alessi I, et al. Circulating Biomarkers for Tumor Angiogenesis: Where Are We? Curr Med Chem 2018. doi 10.2174/0929867325666180821151409. [DOI] [PubMed] [Google Scholar]
- 15.Liu Y, Starr MD, Brady JC, Rushing C, Pang H, Adams B, et al. Modulation of Circulating Protein Biomarkers in Cancer Patients Receiving Bevacizumab and the Anti-Endoglin Antibody, TRC105. Mol Cancer Ther 2018;17(10):2248–56 doi 10.1158/1535-7163.MCT-17-0916. [DOI] [PubMed] [Google Scholar]
- 16.Liu Y, Starr MD, Bulusu A, Pang H, Wong NS, Honeycutt W, et al. Correlation of angiogenic biomarker signatures with clinical outcomes in metastatic colorectal cancer patients receiving capecitabine, oxaliplatin, and bevacizumab. Cancer Med 2013;2(2):234–42 doi 10.1002/cam4.71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Nixon AB, Pang H, Starr MD, Friedman PN, Bertagnolli MM, Kindler HL, et al. Prognostic and predictive blood-based biomarkers in patients with advanced pancreatic cancer: results from CALGB80303 (Alliance). Clin Cancer Res 2013;19(24):6957–66 doi 10.1158/1078-0432.CCR-13-0926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Rini BI, Halabi S, Rosenberg JE, Stadler WM, Vaena DA, Archer L, et al. Phase III trial of bevacizumab plus interferon alfa versus interferon alfa monotherapy in patients with metastatic renal cell carcinoma: final results of CALGB 90206. J Clin Oncol 2010;28(13):2137–43 doi 10.1200/JCO.2009.26.5561. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Garcia J, Hurwitz HI, Sandler AB, Miles D, Coleman RL, Deurloo R, et al. Bevacizumab (Avastin(R)) in cancer treatment: A review of 15 years of clinical experience and future outlook. Cancer Treat Rev 2020;86:102017 doi 10.1016/j.ctrv.2020.102017. [DOI] [PubMed] [Google Scholar]
- 20.Altman DG, McShane LM, Sauerbrei W, Taube SE. Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): explanation and elaboration. PLoS Med 2012;9(5):e1001216 doi 10.1371/journal.pmed.1001216. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Sociaty: Series B (Methodological) 1995;57(1):289–300 doi 10.1111/j.2517-6161.1995.tb02031.x. [DOI] [Google Scholar]
- 22.Zhang HH, Lu W. Adaptive Lasso for Cox’s proportional hazards model. Biometrika 2007;94(3):691–703. [Google Scholar]
- 23.Lin CY, Halabi S. A Simple Method for Deriving the Confidence Regions for the Penalized Cox’s Model via the Minimand Perturbation. Commun Stat Theory Methods 2017;46(10):4791–808 doi 10.1080/03610926.2015.1085568. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Uno H, Cai T, Tian L, Wei LJ. Evaluating prediction rules for t-year survivors with censored regression models. Journal of the American Statistical Association 2012;102(478):527–37. [Google Scholar]
- 25.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria: 2020;URL https://www.R-project.org/. [Google Scholar]
- 26.Chow WH, Devesa SS. Contemporary epidemiology of renal cell cancer. Cancer J 2008;14(5):288–301 doi 10.1097/PPO.0b013e3181867628. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Deniz B, Ambavane A, Yang S, Altincatal A, Doan J, Rao S, et al. Treatment sequences for advanced renal cell carcinoma: A health economic assessment. PLoS One 2019;14(8):e0215761 doi 10.1371/journal.pone.0215761. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Yasuda S, Sho M, Yamato I, Yoshiji H, Wakatsuki K, Nishiwada S, et al. Simultaneous blockade of programmed death 1 and vascular endothelial growth factor receptor 2 (VEGFR2) induces synergistic anti-tumour effect in vivo. Clin Exp Immunol 2013;172(3):500–6 doi 10.1111/cei.12069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Rini BI, Plimack ER, Stus V, Gafanov R, Hawkins R, Nosov D, et al. Pembrolizumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med 2019;380(12):1116–27 doi 10.1056/NEJMoa1816714. [DOI] [PubMed] [Google Scholar]
- 30.Motzer RJ, Penkov K, Haanen J, Rini B, Albiges L, Campbell MT, et al. Avelumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med 2019;380(12):1103–15 doi 10.1056/NEJMoa1816047. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.FDA approves nivolumab plus cabozantinib for advanced renal cell carcinoma. https://wwwfdagov/drugs/resources-information-approved-drugs/fda-approves-nivolumab-plus-cabozantinib-advanced-renal-cell-carcinoma 2021. [DOI] [PubMed]
- 32.FDA approves lenvatinib plus pembrolizumab for advanced renal cell carcinoma. https://wwwfdagov/drugs/resources-information-approved-drugs/fda-approves-lenvatinib-plus-pembrolizumab-advanced-renal-cell-carcinoma 2021. [DOI] [PubMed]
- 33.Tran HT, Liu Y, Zurita AJ, Lin Y, Baker-Neblett KL, Martin AM, et al. Prognostic or predictive plasma cytokines and angiogenic factors for patients treated with pazopanib for metastatic renal-cell cancer: a retrospective analysis of phase 2 and phase 3 trials. Lancet Oncol 2012;13(8):827–37 doi 10.1016/S1470-2045(12)70241-3. [DOI] [PubMed] [Google Scholar]
- 34.Escudier B, Eisen T, Stadler WM, Szczylik C, Oudard S, Staehler M, et al. Sorafenib for treatment of renal cell carcinoma: Final efficacy and safety results of the phase III treatment approaches in renal cancer global evaluation trial. J Clin Oncol 2009;27(20):3312–8 doi 10.1200/JCO.2008.19.5511. [DOI] [PubMed] [Google Scholar]
- 35.Montero AJ, Diaz-Montero CM, Millikan RE, Liu J, Do KA, Hodges S, et al. Cytokines and angiogenic factors in patients with metastatic renal cell carcinoma treated with interferon-alpha: association of pretreatment serum levels with survival. Ann Oncol 2009;20(10):1682–7 doi 10.1093/annonc/mdp054. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Guida M, Casamassima A, Monticelli G, Quaranta M, Colucci G. Basal cytokines profile in metastatic renal cell carcinoma patients treated with subcutaneous IL-2-based therapy compared with that of healthy donors. J Transl Med 2007;5:51 doi 10.1186/1479-5876-5-51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Negrier S, Perol D, Menetrier-Caux C, Escudier B, Pallardy M, Ravaud A, et al. Interleukin-6, interleukin-10, and vascular endothelial growth factor in metastatic renal cell carcinoma: prognostic value of interleukin-6--from the Groupe Francais d’Immunotherapie. J Clin Oncol 2004;22(12):2371–8 doi 10.1200/JCO.2004.06.121. [DOI] [PubMed] [Google Scholar]
- 38.D’Aniello C, Berretta M, Cavaliere C, Rossetti S, Facchini BA, Iovane G, et al. Biomarkers of Prognosis and Efficacy of Anti-angiogenic Therapy in Metastatic Clear Cell Renal Cancer. Front Oncol 2019;9:1400 doi 10.3389/fonc.2019.01400. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Hutson TE, Davis ID, Machiels JP, De Souza PL, Rottey S, Hong BF, et al. Efficacy and safety of pazopanib in patients with metastatic renal cell carcinoma. J Clin Oncol 2010;28(3):475–80 doi 10.1200/JCO.2008.21.6994. [DOI] [PubMed] [Google Scholar]
- 40.Sternberg CN, Davis ID, Mardiak J, Szczylik C, Lee E, Wagstaff J, et al. Pazopanib in locally advanced or metastatic renal cell carcinoma: results of a randomized phase III trial. J Clin Oncol 2010;28(6):1061–8 doi 10.1200/JCO.2009.23.9764. [DOI] [PubMed] [Google Scholar]
- 41.Alvarez Secord A, Bell Burdett K, Owzar K, Tritchler D, Sibley AB, Liu Y, et al. Predictive Blood-Based Biomarkers in Patients with Epithelial Ovarian Cancer Treated with Carboplatin and Paclitaxel with or without Bevacizumab: Results from GOG-0218. Clin Cancer Res 2020;26(6):1288–96 doi 10.1158/1078-0432.CCR-19-0226. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Harshman LC, Choueiri TK. Targeting the hepatocyte growth factor/c-Met signaling pathway in renal cell carcinoma. Cancer J 2013;19(4):316–23 doi 10.1097/PPO.0b013e31829e3c9a. [DOI] [PubMed] [Google Scholar]
- 43.Yakes FM, Chen J, Tan J, Yamaguchi K, Shi Y, Yu P, et al. Cabozantinib (XL184), a novel MET and VEGFR2 inhibitor, simultaneously suppresses metastasis, angiogenesis, and tumor growth. Mol Cancer Ther 2011;10(12):2298–308 doi 10.1158/1535-7163.MCT-11-0264. [DOI] [PubMed] [Google Scholar]
- 44.Choueiri TK, Halabi S, Sanford BL, Hahn O, Michaelson MD, Walsh MK, et al. Cabozantinib Versus Sunitinib As Initial Targeted Therapy for Patients With Metastatic Renal Cell Carcinoma of Poor or Intermediate Risk: The Alliance A031203 CABOSUN Trial. J Clin Oncol 2017;35(6):591–7 doi 10.1200/JCO.2016.70.7398. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Choueiri TK, Escudier B, Powles T, Tannir NM, Mainwaring PN, Rini BI, et al. Cabozantinib versus everolimus in advanced renal cell carcinoma (METEOR): final results from a randomised, open-label, phase 3 trial. Lancet Oncol 2016;17(7):917–27 doi 10.1016/S1470-2045(16)30107-3. [DOI] [PubMed] [Google Scholar]
- 46.Choueiri TK, Escudier B, Powles T, Mainwaring PN, Rini BI, Donskov F, et al. Cabozantinib versus Everolimus in Advanced Renal-Cell Carcinoma. N Engl J Med 2015;373(19):1814–23 doi 10.1056/NEJMoa1510016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Rini BI, Powles T, Atkins MB, Escudier B, McDermott DF, Suarez C, et al. Atezolizumab plus bevacizumab versus sunitinib in patients with previously untreated metastatic renal cell carcinoma (IMmotion151): a multicentre, open-label, phase 3, randomised controlled trial. Lancet 2019;393(10189):2404–15 doi 10.1016/S0140-6736(19)30723-8. [DOI] [PubMed] [Google Scholar]
- 48.Atkins MB, Plimack ER, Puzanov I, Fishman MN, McDermott DF, Cho DC, et al. Axitinib in combination with pembrolizumab in patients with advanced renal cell cancer: a non-randomised, open-label, dose-finding, and dose-expansion phase 1b trial. Lancet Oncol 2018;19(3):405–15 doi 10.1016/S1470-2045(18)30081-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Choueiri TK, Motzer RJ. Systemic Therapy for Metastatic Renal-Cell Carcinoma. N Engl J Med 2017;376(4):354–66 doi 10.1056/NEJMra1601333. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.