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. Author manuscript; available in PMC: 2023 Jan 1.
Published in final edited form as: Clin Cancer Res. 2022 Jul 1;28(13):2779–2788. doi: 10.1158/1078-0432.CCR-21-2389

Plasma protein biomarkers in advanced or metastatic colorectal cancer patients receiving chemotherapy with bevacizumab or cetuximab: Results from CALGB 80405 (Alliance)

Andrew B Nixon 1,2, Alexander B Sibley 2,3, Yingmiao Liu 1, Ace J Hatch 1, Chen Jiang 4, Flora Mulkey 4, Mark D Starr 1, John C Brady 1, Donna Niedzwiecki 2,3, Alan P Venook 5, Luis Baez-Diaz 6, Heinz-Josef Lenz 7, Bert H O’Neil 8, Federico Innocenti 9, Jeffrey A Meyerhardt 10, Eileen M O’Reilly 11, Kouros Owzar 2,3, Herbert I Hurwitz 1
PMCID: PMC9240111  NIHMSID: NIHMS1768529  PMID: 34965954

Abstract

Purpose:

CALGB 80405 compared the combination of first-line chemotherapy with cetuximab or bevacizumab in the treatment of advanced or metastatic colorectal cancer (mCRC). While similar clinical outcomes were observed in the cetuximab-chemotherapy group and the bevacizumab-chemotherapy group, biomarkers could identify patients deriving more benefit from either biologic agent.

Experimental design:

In this exploratory analysis, the Angiome, a panel of 24 soluble protein biomarkers were measured in baseline plasma samples in CALGB 80405. Prognostic biomarkers were determined using univariate Cox proportional hazards models. Predictive biomarkers were identified using multivariable Cox regression models including interaction between biomarker level and treatment.

Results:

In the total population, high plasma levels of Ang-2, CD73, HGF, ICAM-1, IL-6, OPN, TIMP-1, TSP-2, VCAM-1, and VEGF-R3 were identified as prognostic of worse progression-free survival (PFS) and overall survival (OS). PlGF was identified as predictive of lack of PFS benefit from bevacizumab (bevacizumab HR = 1.51, 95% CI 1.10–2.06; cetuximab HR = 0.94, 95% CI 0.71–1.25; Pinteraction = 0.0298) in the combined FOLFIRI/FOLFOX regimens. High levels of VEGF-D were predictive of lack of PFS benefit from bevacizumab in patients receiving FOLFOX regimen only (FOLFOX/bevacizumab HR = 1.70, 95% CI 1.19–2.42; FOLFOX/cetuximab HR = 0.92, 95% CI 0.68–1.24; Pinteraction = 0.0097).

Conclusions:

In this exploratory, hypothesis-generating analysis, the Angiome identified multiple prognostic biomarkers and two potential predictive biomarkers for mCRC patients enrolled in CALGB80405. PlGF and VEGF-D predicted lack of benefit from bevacizumab in a chemo-dependent manner.

Keywords: predictive biomarker, prognostic biomarker, bevacizumab, cetuximab, metastatic colorectal cancer

Introduction

Colorectal cancer (CRC) is a leading cause of cancer mortality in the United States. Significant progress has been made in the past 20 years in both detection and treatment of CRC(1). In 1990s fluorouracil became the only cytotoxic drug in CRC as continuous infusion of fluorouracil improved the overall survival (OS) from 12 to 15 months(2). Since then, combination therapies of fluorouracil with leucovorin and either irinotecan [FOLFIRI regimen(3)] or oxaliplatin [FOLFOX regimen(4)] became the first line chemotherapeutic treatment for CRC. In 2000s, targeted therapies were added to chemotherapy, including monoclonal antibodies targeting epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF) (5,6).

Two anti-EGFR antibodies, panitumumab and cetuximab, have been approved by Food and Drug Administration (FDA) to treat mCRC patients as monotherapies and combined with chemotherapy(7,8). EGFR binding to its ligands results in the activation of the mitogenic MAPK/ERK signaling cascade via RAS GTPases (9). Patients whose tumors harbor activating RAS mutations do not respond to EGFR-targeting agents (10–12). Current guidelines recommend the use of panitumumab and cetuximab only for CRC patients with wild-type RAS (13).

There are currently four FDA approved biological agents for targeting the VEGF-pathway in CRC patients: first line bevacizumab which binds VEGF-A(5); second line ramucirumab which binds VEGFR-2 (14), and ziv-aflibercept, which binds VEGFs and PlGF (15); and third line regorafenib which blocks activity of all VEGF receptors and other tyrosine kinases(16,17). Extensive effort has been made to identify biomarkers to guide anti-VEGF drugs, including markers at the molecular, cellular, and tissue levels(18).

Circulating protein biomarkers have gained increasing attention given the advantages of ease to obtain, cost-effectiveness, and amenability to repeated sampling. We have developed and optimized the Angiome, a panel of circulating protein biomarkers and analyzed their predictive potential in multiple trials involving thousands of patients in different disease settings(19–22). Ang-2, SDF-1, and VEGF-D plasma levels were identified as predictive biomarkers for bevacizumab in patients with pancreatic cancer treated with gemcitabine ± bevacizumab in a study conducted by Cancer and Leukemia Group B (CALGB), now part of the Alliance for Clinical Trials in Oncology cooperative group, CALGB 80303 (20). Benefit from bevacizumab was associated with high levels of Ang-2 and SDF-1 and low levels of VEGF-D. The VEGF-D effect was strongest in patients in the lowest quartile of plasma VEGF-D (20). Consistently, negative predictive effect of VEGF-D has been reported in metastatic CRC (mCRC) patients treated in the Australian Gastro-Intestinal Trials Group testing Mitomycin, Avastin, and Xeloda (AGITG MAX trial) (23). Despite a number of promising candidates including VEGF-D, currently there are no validated biomarkers to guide the applications of anti-angiogenic agents in any disease settings(24).

CALGB and the Southwest Oncology Group (SWOG), conducted the intergroup study, CALGB/SWOG 80405, to better guide clinical decision making for patients with advanced or metastatic CRC (25). CALGB 80405 was initially designed to compare bevacizumab, cetuximab, and the combination of both agents in addition to FOLFIRI or FOLFOX chemotherapy regimen chosen by the physician and patient. Early in the course of the study, the combination regimen was discontinued due to results from the CAIRO2 and PACCE studies showing increased toxicity and a lack of increased survival benefit in the arms with dual VEGF and EGFR inhibition (26,27). CALGB80405 was negative for overall survival (OS), the primary endpoint for this study. The clinical results of CALGB 80405 showed similar progression-free survival (PFS) and OS benefits from either bevacizumab-chemotherapy or cetuximab-chemotherapy groups (25).

To improve outcomes in future clinical trials of bevacizumab in mCRC, a homogenous selected patient population would be pivotal. Disease parameters and circulating biomarkers would greatly facilitate patient selection to enrich patient deriving the most benefit from anti-angiogenic drugs. As stated earlier, our Angiome analysis has successfully identified prognostic and predictive biomarkers for anti-angiogenic agents, bevacizumab included. While prognostic biomarkers reflect how well a patient perform, independent of treatments; predictive biomarkers have the potential to identify patients benefitting the most from a specific treatment.

In this study, we assessed the Angiome expression in plasma samples collected at baseline in the primary cohort of CALGB 80405. These patients habored KRAS wild-type tumors, received either bevacizumab or cetuximab in addition to FOLFIRI or FOLFOX. Angiome analysis provided a snapshot of tumor angiogenesis, inflammation, and immune status at baseline. Potential prognostic and predictive biomarkers were identified.

Materials and Methods

Study Design and Patients

The study design of CALGB 80405 and clinical results have been reported (25) and detailed information can be found at ClinicalTrials.gov (NCT00265850). Briefly, patients with locally advanced or metastatic CRC were randomized to receive either bevacizumab, cetuximab, or the combination of both in conjunction with chemotherapy (FOLFIRI or FOLFOX) at the discretion of the treating physician. The study was later amended to stop enrollment in the combination arm and to implement KRAS mutation testing. The samples used in this study were collected at baseline from the primary cohort of CALGB 80405. The primary cohort consists of 1137 KRAS wild type patients randomized to the bevacizumab + chemotherapy or cetuximab + chemotherapy treatment groups. This correlative study of CALGB 80405 was approved by the Institutional Review Board at all participating centers, adhered to Guidelines for Good Clinical Practice and guiding principles laid out in the Declaration of Helsinki. Written informed consent was obtained from patients who opted to participate in this analysis.

Sample Collection

Peripheral venous blood was collected at baseline from consenting patients into vacutainers containing EDTA anticoagulant. Samples were centrifuged at 2500g for 15 min within 30 min of collection. The resulting plasma was spun again at 2500g for 15 min. Double-spun, platelet-poor plasma was aliquoted, frozen, and shipped on dry ice to the Alliance Pathology Coordinating Office for centralized storage. Prior to analyses, samples were shipped to Duke Phase I Biomarker Laboratory, thawed once on ice, re-aliquoted, and stored at −80°C until assayed.

Protein Analysis

Levels of 24 soluble protein biomarkers were measured in duplicates with the personnel blinded to the clinical results. Plasma samples from patients in the primary cohort were analyzed using the multiplex enzyme-linked immunosorbent assay (ELISA) techniques on the Quanterix (Billerica, MA) and Meso Scale Discovery (Rockville, MD) platforms as previously described (20,28). The analysis conforms to the guidelines established by the REMARK criteria.

Statistical Considerations

All biomarker levels were log-transformed prior to analysis, to de-emphasize outliers and better approximate a normal distribution. All analyses were performed using baseline data from patients in the primary cohort with continuous values for the protein analytes. Biomarkers prognostic of clinical outcome [overall survival (OS) or progression free survival (PFS)] were determined using univariate Cox proportional hazards models independent of treatment arm, and the resulting hazard ratios (HR), 95% confidence intervals (CI), and asymptotic P-values based on the score test were reported. Multivariable Cox regression models were used to test for interactions between biomarker levels and treatment arms to identify biomarkers predictive of benefit from bevacizumab or cetuximab and asymptotic P-values based on the Wald test for the interaction terms were reported. All analyses were stratified by type of chemotherapy (FOLFIRI or FOLFOX) to control for any differences in baseline survival rate among these two groups. Biomarker levels were also tested for interaction with treatment arm separately within each chemotherapy subpopulation. The reported p-values were not adjusted for multiple testing.

Hierarchical agglomerative clustering, using Euclidean distance and complete linkage, was used to identify groupings among protein biomarkers based on their baseline levels. The results were illustrated using a dendrogram. Forest plots were created as illustrations of prognostic effect sizes (HRs and corresponding 95% CIs). For the purposes of illustrating interaction effects, biomarker levels were dichotomized as “high” or “low” relative to the median, and Kaplan–Meier plots of OS and PFS were created, with separate curves for each combination of treatment group and dichotomized biomarker level. It has previously been shown that low levels of VEGF-D (first quartile) were predictive of benefit from bevacizumab in advanced pancreatic cancer and colon cancer, respectively (20,23), so we also considered VEGF-D levels separated by quartiles. Unless otherwise stated, the results reported pertain to the analysis of biomarkers as continuous measures.

The Alliance Statistics and Data Center conducted data collection and statistical analyses on the basis of clinical data locked as of December 15, 2015. The R software environment for statistical computing and graphics and the survival extension package were used to conduct the statistical analyses and to generate the figures(29,30). The analyses were carried out with adherence to the principles of reproducible analysis using the knitr package(31) for generation of dynamic reports and GitLab for source code management.

Data Availability Statement

The code for replicating the statistical analysis has been made accessible through a public source code repository (https://gitlab.oit.duke.edu/dcibioinformatics/pubs/calgb-80405-plasma). Codes used for all analyses will be made available upon request.

Results

Patient Characteristics

In CALGB 80405 primary cohort, mCRC patients with KRAS wild type were randomized to receive bevacizumab (BEV) or cetuximab (Cetux), in addition to either FOLFIRI or FOLFOX as chemotherapeutic agents per physician choice. The last date of follow up was December 15, 2015 and the median follow up for surviving patients was 47.4 months(25). The demographic and survival outcomes for the total primary cohort population (n=1137) and the biomarker population (n=715) are shown in Figure 1 and Supplementary Table S1. No significant differences were noticed regarding age, race, gender, or clinical outcomes between the biomarker population and the total primary cohort. To better present the four patient cohorts receiving different treatments, a detailed demographic table was generated. As reported in the clinical paper, there is no statistically significant difference in overall survival (OS) and progression free survival (PFS) in the bevacizumab-chemotherapy group and the cetuximab-chemotherapy group(25). All four patient cohorts showed similar baseline characteristics (Table 1).

Figure 1. Patient CONSORT diagram.

Figure 1.

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Bev: bevacizumab; Cetux: cetuximab.

Table 1.

Distribution of the biomarker subpopulation by treatment.

Chemotherapy FOLFIRI/ FOLFOX/ FOLFIRI/ FOLFOX/ Primary cohort
Target therapy Bev Bev Cetux Cetux
N 98 259 94 264 1137
Age Median 61 58 60 59 59
(range) (34–82) (22–84) (36–83) (23–84) (20–89)
Race African American 9 (9.2%) 22 (8.5%) 10 (10.6%) 31 (11.7%) 129 (11.3%)
Asian 4 (4.1%) 4 (1.5%) 2 (2.1%) 9 (3.4%) 35 (3.1%)
Caucasian 84 (85.7%) 228 (88.0%) 81 (86.2%) 218 (82.6%) 934 (82.1%)
Multiple 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (0.4%) 5 (0.4%)
Other 1 (1.0%) 5 (1.9%) 1 (1.1%) 5 (1.9%) 34 (3.0%)
Sex Female 37 (37.8%) 95 (36.7%) 37 (39.4%) 103 (39.0%) 440 (38.7%)
Male 61 (62.2%) 164 (63.3%) 57 (60.6%) 161 (61.0%) 697 (61.3%)
Median Outcome (months) OS 35.1 30.3 31.9 28.9 29.4
(95% CI) (29.4–41.3) (27.1–33.2) (26.8–40.2) (25.3–34.6) (27.6–31.4)
PFS 12.4 10.8 10.9 10 10.6
(95% CI) (10.4–14.0) (9.5–11.7) (9.2–14.4) (9.2–11.3) (9.8–11.1)
ECOG N (%) 0 60 (61.2%) 153 (59.1%) 55 (58.5%) 162 (61.4%) 657 (57.8%)
1 38 (38.8%) 105 (40.5%) 39 (41.5%) 102 (38.6%) 478 (42.0%)
2 1 (0.4%) 2 (0.2%)
Tumor Biology Metachronous 36 (36.7%) 33 (12.7%) 34 (36.2%) 45 (17.1%) 236 (20.8%)
Synchronous 62 (63.3%) 225 (86.9%) 57 (60.6%) 217 (82.2%) 892 (78.5%)
unknown 1 (0.4%) 3 (3.2%) 2 (0.8%) 9 (0.8%)
CRC Tumor Location N (%) Left 63 (64.3%) 147 (56.8%) 63 (67.0%) 164 (62.1%) 689 (60.6%)
Right 24 (24.5%) 69 (26.6%) 16 (17.0%) 61 (23.1%) 280 (24.6%)
Transverse 3 (3.1%) 18 (7.0%) 7 (7.5%) 17 (6.4%) 62 (5.5%)
Multiple 1 (0.4%) 1 (0.1%)
unknown 8 (8.2%) 25 (9.7%) 8 (8.5%) 21 (8.0%) 105 (9.2%)
Primary tumor unresected at study entry No 76 (77.6%) 192 (74.1%) 75 (79.8%) 196 (74.2%) 845 (74.3%)
Yes 22 (22.4%) 67 (25.9%) 19 (20.2%) 68 (25.8%) 292 (25.7%)
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Abbreviations: Bev, bevacizumab; Cetux, cetuximab.

Notes: metachronous indicates metastasis subsequent to diagnosis of primary tumor; synchronous indicates metastasis present at diagnosis of primary tumor. Values from the primary cohort are as reported in Venook et al (25).

Biomarker assessment

A total of 24 circulating protein biomarkers were assessed using baseline plasma samples for 715 mCRC patients enrolled in CALGB80405. The median levels and ranges for all biomarkers are shown in Supplementary Table S2. A dendrogram illustrating the associations among these 24 biomarkers is shown in Supplementary Fig. S1. As observed in previous analyses, we noted several consistent relationships among the protein biomarkers. PlGF was tightly associated with general angiogenic biomarkers such as Ang-2 and TSP-2, while VEGF-A, -C, -D were more clustered with factors in TGF-β signaling pathway. CD73 and HER-3, two previously identified biomarkers predictive of benefit from cetuximab(28), were tightly clustered with each other.

Prognostic Analyses

The clinical report showed similar OS and PFS across patients in all treatment groups, therefore, prognostic analysis was conducted with all patients combined. We identified ten biomarkers (Ang-2, CD73, HGF, ICAM-1, IL-6, OPN, TIMP-1, TSP-2, VCAM-1, and VEGFR-3) to be prognostic for both OS and PFS in the overall population, as illustrated by the Forrest plots (Figure 2). All of the biomarkers identified were negative prognostic biomarkers, i.e., higher expression levels were associated with shorter times to event (OS or PFS). The strongest prognostic effects with respect to hazard ratio (HR) for OS and PFS were observed for VCAM-1 (for OS, HR=2.49, 95%CI=2.01–3.09, p<0.0001; for PFS, HR=1.55, 95%CI=1.28–1.88, p<0.0001). A complete list of the prognostic value for all 24 biomarkers for OS and PFS was shown in Supplementary Tables S3 and S4, respectively. All biomarkers tested were independently prognostic from one another. PlGF and VEGF-A were prognostic for OS (PlGF, HR=1.31, 95% CI=1.05–1.63, p=0.0166; VEGF-A, HR=1.10, 95% CI=1.02–1.19, p=0.0143; Supplementary Table S3), but not for PFS (PlGF, HR=1.16, 95% CI=0.95–1.41, p=0.1473; VEGF-A, HR=1.06, 95% CI=0.99–1.14, p=0.0811; Supplementary Table S4).

Figure 2. Prognostic biomarkers.

Figure 2.

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Forest plots of hazard ratios (HRs) illustrate the prognostic association of biomarkers for both overall survival (A) and progression-free survival (B) in the overall population. All 10 were negative prognostic indicating that higher biomarker levels are associated with higher HR or favors lower expression.

Predictive Analyses

We identified PlGF as the only predictive biomarker of PFS benefit in the overall population. A high baseline level of PlGF was associated with better PFS when treated by cetuximab (BEV arm: HR = 1.51, 95% CI 1.10–2.06; Cetux arm: HR = 0.94, 95% CI 0.71–1.25; Pintx = 0.0298) (Table 2). No other biomarkers were observed to be predictive of OS or PFS in the chemotherapy-pooled populations (Supplemental Tables S5 and S6). A Kaplan-Meier curve illustrating the predictive effect of PlGF is shown in Figure 3. Patients with lower than median PlGF exhibited longer PFS and benefited more from bevacizumab than cetuximab.

Table 2.

Predictive effects of PlGF and VEGF-D.

OS PFS
Chemo pooled Chemo pooled
Biomarker Bev Cetux Pintx Bev Cetux Pintx
PlGF 1.65 (1.16–2.36) 1.12 (0.83–1.51) 0.1157 PlGF 1.51 (1.10–2.06) 0.94 (0.71–1.25) 0.0298
VEGF-D 1.31 (0.98–1.74) 1.12 (0.84–1.50) 0.4346 VEGF-D 1.34 (1.03–1.73) 0.97 (0.75–1.27) 0.1069
FOLFIRI FOLFIRI
PlGF 1.47 (0.74–2.94) 0.84 (0.45–1.57) 0.2079 PlGF 1.34 (0.71–2.51) 0.80 (0.46–1.41) 0.2487
VEGF-D 0.84 (0.49–1.45) 1.10 (0.54–2.26) 0.5643 VEGF-D 0.95 (0.61–1.48) 1.37 (0.73–2.56) 0.4336
FOLFOX FOLFOX
PlGF 1.71 (1.13–2.59) 1.23 (0.88–1.71) 0.2540 PlGF 1.56 (1.08–2.24) 0.99 (0.72–1.37) 0.0656
VEGF-D 1.65 (1.17–2.33) 1.12 (0.81–1.54) 0.1151 VEGF-D 1.70 (1.19–2.42) 0.92 (0.68–1.24) 0.0097
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Note: median overall survival or progression-free survival time are presented in months, with 95% confidence intervals in parenthesis. Abbreviations: Bev, bevacizumab; Cetux, cetuximab; Pintx, interaction p value.

Figure 3. Kaplan-Meier curves of PlGF and treatment for overall survival (Top) and progression-free survival (Bottom).

Figure 3.

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For illustrative purposes, PlGF levels were dichotomized as “high” or “low” relative to the median. P-values from the test for interaction of the marker as a continuous measure are reported.

To control for the possibility of the interaction effects across chemotherapies used in CALGB 80405, the biomarkers were re-analyzed for predictive associations within chemotherapy subpopulations. In patients receiving FOLFOX, low VEGF-D was associated with better PFS and benefit from bevacizumab (BEV arm: HR = 1.70, 95% CI 1.19–2.42; Cetux arm: HR = 0.92, 95% CI 0.68–1.24; Pintx = 0.0097, Table 2). While a borderline trend was noted for PlGF, the PFS benefit did not reach statistical significance (Pintx =0.0656, Table 2). Kaplan-Meier curves showed the predictive effects of VEGF-D in chemotherapy combined, as well as in FOLFOX and FOLFIRI subpopulation respectively (Figure 4). No other biomarkers were observed to be predictive for OS or PFS when patients were stratified by chemotherapy (Supplementary Table S7 and S8).

Figure 4. Kaplan-Meier curves showing the effects of VEGF-D levels and chemotherapy on overall survival (Top panels) and progression-free survival (Bottom panels).

Figure 4.

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For illustrative purposes, VEGF-D levels were dichotomized as “high” or “low” relative to the first quartile. P-values from the test for interaction of the maker as a continuous measure are reported. Patients with VEGF-D levels in the lowest quartile receiving FOLFOX as the chemotherapy derived more benefit from bevacizumab (the interaction p-value for the marker as a continuous measure was 0.0097), as shown in the middle bottom figure.

To optimize the cut-point of VEGF-D level, Forest plots were generated to illustrate the effects of VEGF-D by quartile on OS and PFS in patients receiving FOLFOX (Figure 5). Patients with VEGF-D levels in the lowest quartile had better OS (HR =0.62, 95% CI=0.41–0.92) and PFS (HR =0.59, 95% CI=0.41–0.85).

Figure 5. Predictive effect of VEGF-D.

Figure 5.

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Forest plots of VEGF-D quantiles in the FOLFOX-treated subpopulation for overall survival (A) and progression-free survival (B). Patients with VEGF-D levels in the lowest quantile derived benefit from bevacizumab.

Discussion

CALGB 80405 is the largest phase III trial to directly compare bevacizumab and cetuximab, two of the most commonly used targeted agents, in addition to FOLFIRI or FOLFOX as first-line treatment in advanced or metastatic CRC. Blood samples collected during CALGB 80405 provide a unique and valuable source of materials for biomarker studies that may help guide the use of these therapeutics. Our retrospective biomarker analysis identified PlGF and VEGF-D, two chemo-dependent predictive biomarkers, as well as numerous prognostic biomarkers.

We identified ten biomarkers with highly consistent prognostic impact across both OS and PFS. These biomarkers play important roles in angiogenesis (Ang-2, HGF, TSP-2, VEGF-R3), inflammatory and immune modulating (CD73, IL-6, ICAM-1, VCAM-1), and extracellular matrix remodeling (OPN, TIMP-1). Intrinsic high expression of all these biomarkers was associated with worse outcomes, reflecting the coordinated interplay of angiogenesis, inflammation, and immune modulation. Interestingly, PlGF and VEGF-A were found to be prognostic for OS, but not for PFS. This is likely due to the manifest of stronger prognostic effect at later stage of the disease or over longer time, as the median OS was approximately 20 months longer than median PFS(25).

With respect to EGFR inhibition, we previously identified two predictive biomarkers for cetuximab benefit (HER-3 and CD73) across all patients (KRAS mutant and wild-type) in CALGB 80203(28), a randomized study evaluating the efficacy of cetuximab in combination with FOLFOX or FOLFIRI in mCRC. However, neither biomarker predicted benefit from cetuximab here in CALGB80405 (Supplementary Table S5–S8). HER-3 belongs to the same family of receptor tyrosine kinases as EGFR, with the potential to serve as a resistance mechanism to cetuximab(32). CD73, an immune modulatory AMP phosphatase, was associated with cetuximab benefit in previous studies (28,33). The lack of predictive value in this study may be due to the potential association between CD73 and RAS mutational status(34). In this correlative study, all patients included were KRAS wild type at codons 12 and 13. Exclusion of patients with RAS mutations may confound the analysis. Although RAS wild-type has been widely applied as a prerequisite for patients to receive anti-EGFR inhibitors, not all patients with wild-type RAS respond to EGFR inhibitors. Our Angiome analysis could provide novel insight regarding the patient population most likely to derive benefit from the EGFR-targeting drugs.

With respect to VEGF inhibition, VEGF-A has long been considered a candidate for predictive biomarker for anti-angiogenic therapies, and high levels of VEGF-A may indicate increased biological dependency on this angiogenic factor. Yet a meta-analysis of more than 1800 patients participating in phase III trials in CRC, non-small cell lung cancer, and renal cell carcinoma confirmed that pretreatment VEGF-A levels have only prognostic, but not predictive value (35). In the MERiDiAN trial, pre-selecting metastatic breast cancer patients based on high plasma levels of short isoforms of VEGF-A for bevacizumab treatment revealed no significant association of PFS by VEGF-A interaction, failing to support using baseline VEGF-A to identify patients benefitting most from bevacizumab(36). Similarly, PlGF has prognostic value, but has not been shown to be predictive for anti-angiogenic therapy(37). Our data provide novel insight into the potential predictive role of these VEGF family members.

In this study, we found PlGF was predictive of lack of PFS benefit from bevacizumab independent of the type of chemotherapy used (Table 2 and Figure 3). PlGF belongs to the VEGF family; it binds and activates signaling through VEGF-R1, but does not directly signal through VEGF-R2 (38). PlGF modulates VEGF-A signaling both at the ligand level (by forming heterodimers with VEGF-A) and the receptor level (by engaging VEGF-R1) (39). PlGF has been noted as a pharmacodynamic biomarker with circulating levels consistently increasing in response to anti-VEGF agents (22,40,41), but this is the first report of a potential predictive association between baseline PlGF levels and outcome in CRC. Patients with lower than median levels of PlGF derived more PFS benefit from bevacizumab (Pintx=0.0298, Figure 3), independent of chemotherapy received. It is important to note that the comparative arm used in this study is an active therapy (cetuximab) and not a true placebo. This may explain why we observed PlGF as a predictive biomarker only in this specific study population.

VEGF-D was identified as the strongest predictive biomarker for lack of PFS benefit from bevacizumab; however, this was only observed in the FOLFOX treated patients (Pintx=0.0097, Table 2). VEGF-D is another VEGF family member; it binds VEGF-R2 promoting angiogenesis, as well as VEGF-R3 where it promotes lymphangiogenesis (42). The predictive effect was largest in patients with VEGF-D levels in the lowest quartile, where patients derived the most benefit from bevacizumab (Figure 5). Importantly, far more patients in CALGB 80405 study received FOLFOX (n=523) than FOLFIRI (n=192). It is possible that additional clinical factors related to the use of FOLFOX over FOLFIRI may confound the VEGF-D effect observed here. The exact mechanism for this chemotherapy effect remains unclear. The VEGF-D effect is consistent with results seen previously in patients with pancreatic cancer treated with bevacizumab and gemcitabine in CALGB 80303 (20), and in patients with CRC treated in the Australian Gastro-Intestinal Trials Group testing Mitomycin, Avastin, and Xeloda (AGITG MAX trial) (23). In all cases, low VEGF-D predicts benefit from bevacizumab, while high VEGF-D predicts for lack of benefit from bevacizumab. Excessive VEGF-D can provide compensatory signaling in the VEGF axis through VEGF-R2, serving as a resistance mechanism upon VEGF-A blockage by bevacizumab. Therefore, patients are less likely to benefit from bevacizumab given the presence of a readily available compensatory mechanism. The predictive roles of PlGF and VEGF-D noted here in CALGB 80405 may suggest that alternate VEGF family members may mediate intrinsic resistance mechanisms to bevacizumab, which selectively binds VEGF-A.

While no other biomarkers reach statistical significance, a few interesting trends were noted. First, VEGF-C, an additional VEGF family member with potent angiogenic and lymphangiogenic properties, appears to predict for OS benefit from cetuximab in FOLFIRI subgroup (Pintx=0.0791, Supplementary Table S7). Additionally, in the subgroup of patients receiving FOLFIRI, we identified three TGF-β related biomarkers to be potentially predictive of PFS benefit, including TGF-β1 (Pintx=0.0524), PDGF-AA (Pintx=0.0511), and SDF-1 (Pintx=0.0688; Supplementary Table S8). Higher levels of TGF-β1 and PDGF-AA potentially predict for benefit from cetuximab. These biomarkers may reflect overlapping biologies contributing to cetuximab sensitivity. Interplay between EGFR and TGF-β signaling pathways have been extensively reviewed(43).

Despite extensive effort, no predictive biomarker has been identified that would enable a more personalized use of bevacizumab. Angiogenesis is a complex process involving diverse cell types, cytokines, and growth factors signaling through multiple pathways. The biological redundancies eventually lead to anti-angiogenic drug resistance. As the biology of angiogenesis and its role in tumor development varies across tumor types, suitable biomarkers may be tumor type specific. While angiogenic factors (PlGF and VEGF-D) were noted to be associated with PFS benefit in mCRC patients in CALGB80405, other studies noted the predictive potential of the inflammatory cytokine IL-6 in the context of anti-angiogenic therapy in renal cancer and ovarian cancer(21,44).

This analysis has several limitations. The patients receiving FOLFIRI were much fewer than those receiving FOLFOX, therefore underpowered to explore key subgroups. The unbalanced use of FOLFIRI and FOLFOX may introduce uncontrolled bias into these analyses. While all patients are considered KRAS WT, KRAS testing was only performed at codons 12 and 13. It is possible that some patients with KRAS mutant were included in our analysis. Genetic information such as extended RAS and RAF mutations (45), microsatellites instability and tumor mutational burdens(46), consensus molecular subtypes (CMSs) representing intrinsic heterogeneity of CRC at the genetic expression level (47) will be further investigated in subsequent analysis. Additionally, this retrospective biomarker analysis was intended to be exploratory and hypothesis-generating in nature, hence the p-values presented here were not adjusted for multiple testing. The current findings are promising and biologically plausible but should be considered preliminary and not ready to guide patient care at this time. In order to advance any of these putative biomarkers along the validation path, a predefined cut-point would need to be established and tested. Ideally, any biomarker cut-point would be tested prospectively as a stratification factor or for entry onto the trial. The establishment of a cut-point is a crucial step for clinical decision making when considering the prospective use of any prognostic or predictive biomarker. As such, validation of VEGF-D as a biomarker with a specific cut-point awaits further investigation.

Discovery and validation of predictive biomarkers is challenging and requires a detailed understanding of the target expression and biology in patients, technical optimization of biomarker assays, and clinical validation of any findings. This analysis in CALGB 80405 is the culmination for nearly two decades of work by this group. The findings of predictive biomarkers may be applied to other FDA-approved drugs for mCRC. Notably, ziv-aflibercept, a recombinant receptor containing the ligand-binding domains of VEGF-R1 and -R2, binds both VEGFs and PlGF(15,48); while ramucirumab blocks the binding of both VEGF-A, -C, and -D to VEGF-R2(49,50). It is not known whether these agents may be more active in patients with higher levels of plasma PlGF and VEGF-D. Nevertheless, our data support the rationale for biomarker-driven clinical studies to address this possibility.

In conclusion, our biomarker analysis of CALGB 80405 identified two candidate predictive biomarkers for lack of benefit from bevacizumab: PlGF and VEGF-D. Future studies to validate and refine these findings are warranted.

Supplementary Material

1
2

Translational relevance.

CALGB 80405 was a randomized phase III trial in advanced or metastatic colorectal cancer (mCRC) patients testing whether the addition of cetuximab or bevacizumab to the combination of either leucovorin, fluorouracil, and irinotecan (FOLFIRI) or the combination of leucovorin, fluorouracil, and oxaliplatin (FOLFOX) regimen was superior as first-line therapy in this setting. The results of the trial were negative, similar overall survival (OS) and progression-free survival (PFS) were observed for the cetuximab-chemotherapy group and the bevacizumab-chemotherapy group. In this report, we conducted an exploratory, hypothesis-generating, retrospective analysis evaluating a panel of angiogenic and inflammatory biomarkers using baseline plasma samples collected from patients enrolled in CALGB80405 and identified PlGF and VEGF-D as potential predictive biomarkers. High PlGF levels predict lack of PFS benefit from bevacizumab independent of the chemotherapeutic regimen, while low VEGF-D levels predict PFS benefit from bevacizumab in patients receiving FOLFOX. Notably, the predictive potential of VEGF-D for anti-angiogenic agents has been previously reported by several groups. Given the lack of a true control arm, the findings are exploratory and hypothesis-generating in nature.

Acknowledgements

We gratefully acknowledge the invaluable contributions of the patients, their families, and the staff who participated in this study. The authors would also like to thank the Alliance Biorepository at the Ohio State University and administrative staff. More than 680 institutions participated in this study. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of National Cancer Institute.

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, UG1CA233253, UG1CA233290, UG1CA233337, UG1CA233373, U10CA180820 (ECOG-ACRIN), U10CA180888 (SWOG). The following have provided support to Alliance for Clinical Trials in Oncology and Alliance Foundation Trials programs (https://acknowledgments.alliancefound.org). This study was also supported in part by funds from Bristol-Myers, Genentech, Pfizer, and Sanofi. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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.J. Lenz has served as advisory Board and consulting roles in Bayer, BMS, Roche, Genentech, Merck KG, Merck, Fulgent, Oncocyte, G1 Therapeutics, Jazz Therapeutics, Biacara. J.A. Meyerhardt has received institutional research funding from Boston Biomedical, has served as an advisor/ consultant to COTA Healthcare, and served on a grant review panel for the National Comprehensive Cancer Network funded by Taiho Pharmaceutical. E.M. O’Reilly has received funding from Genentech/Roche, Celgene/BMS, BioNTech, BioAtla, AstraZeneca, Arcus, Elicio, Parker Institute, AstraZeneca, Pertzye; and has served consulting role in Cytomx Therapeutics (DSMB), Rafael Therapeutics (DSMB), Sobi, Silenseed, Tyme, Seagen, Molecular Templates, Boehringer Ingelheim, BioNTech, Ipsen, Polaris, Merck, IDEAYA, Cend, AstraZeneca, Noxxon, BioSapien, Cend Therapeutics, Bayer (spouse), Genentech-Roche (spouse), Celgene-BMS (spouse), Eisai (spouse), Research To Practice. H.I. Hurwitz declares ownership of Roche stock. The other authors declare no potential conflicts of interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1768529-supplement-1.docx (4.2MB, docx)
2
NIHMS1768529-supplement-2.docx (48.4KB, docx)

Data Availability Statement

The code for replicating the statistical analysis has been made accessible through a public source code repository (https://gitlab.oit.duke.edu/dcibioinformatics/pubs/calgb-80405-plasma). Codes used for all analyses will be made available upon request.

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