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Abstract

  1. Top of page
  2. Abstract
  3. Renal Cell Cancer and the Hypoxia-inducible, Factor-mediated Pathway
  4. Biomarkers to Predict the Response to Sunitinib Therapy
  5. Biomarkers of Other Targeted Agents
  6. Conclusion
  7. Acknowledgments
  8. Disclosure Statement
  9. References

Sunitinib is an orally-administered, multitargeted tyrosine kinase inhibitor. The main targets are vascular endothelial growth factor receptor (VEGFR)-1, VEGFR-2, VEGFR-3, platelet-derived growth factor receptor (PDGFR)-α, and PDGFR-β. Among therapeutic targeting agents, it is the best available in the USA for patients with metastatic renal cell cancer (RCC). Well-constructed clinical trials have led to the worldwide approval of various agents for RCC. However, in clinical practice, it remains difficult to determine the best treatment strategy with these agents. Therefore, the identification of biomarkers to predict response and side-effects and to select optimal dosages is urgently needed. Potential mechanisms of action and resistance need to be understood in order to make accurate predictions. This article briefly reviews candidate biomarkers of sunitinib therapy in terms of clinical variables, genetic factors, and circulating proteins and endothelial cells. Although further validation and implementation is necessary, if validated, biomarkers will help measure the therapeutic response in individual patients and establish treatment strategies for metastatic RCC. (Cancer Sci, 102: 1949–1957)

Renal cell cancer (RCC) is the most lethal urologic malignancy, and its incidence is currently rising.(1) Radical nephrectomy remains the standard and only curative therapy for patients with localized RCC. However, at initial diagnosis, one-third of RCC patients exhibit visceral metastasis, and up to half of remaining patients eventually develop distant metastases.(2,3) For a long time, the only effective therapeutic and preventive agents for distant metastases and local recurrence have been interferon (IFN) and interleukin (IL)-2, although these agents only achieve a response rate of 15%.(2,3) Major recent breakthroughs have broadened our knowledge of the genetics and transduction pathways involved in various malignancies, including RCC.(4) This greater understanding of the molecular biology of RCC has led to the identification of the vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF), and mammalian target of rapamycin (mTOR) signaling pathways as rational targets for anticancer therapy for metastatic RCC.(4) Currently, two major subgroups of molecular-targeted agents are available in clinical practice: angiogenesis inhibitors, which include sorafenib (Nexavar; Bayer, West Haven, CT, USA), sunitinib (Sutent; Pfizer, New York, NY, USA), bevacizumab (Avastin; Genentech/Roche, Basle, Switzerland), pazopanib (Votrient; GlaxoSmithKline, Brentford, UK), and axitinib (AG-013736; Pfizer, Philadelphia, PA, USA);(5–9) and two specific inhibitors of the mTOR kinase, temsirolimus (Torisel; Pfizer) and everolimus (Afinitor; Novartis, Basel, Switzerland).(10,11) The RCC growth signals and the rationale behind these molecular-targeted agents are shown in Figure 1.

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Figure 1.  Genetic alterations and growth signals in renal cell cancer and various molecular-targeted agents. CA9, carbonic anhydrase 9; CXCR-4, C-X-C chemokine receptor type 4; EGF, epidermal growth factor; FH, fumarate hydratase; GLUT1, glucose transporter 1; HGFR, hepatocyte growth factor receptor; HIF, hypoxia inducible factor; HRE, hypoxia response element; IGF-1, insulin-like growth factor 1; MET, mesenchymal–epithelial transition factor; mTOR, mammalian target of rapamycin; PDGF, platelet-derived growth factor; PI3K, phosphatidyl-inositol; TFE3, transcription factor E3; TGF-α, transforming growth factor alpha; VEGF, vascular endothelial growth factor; VHL, von Hippel-Lindau tumor suppressor gene product, 3-kinases.

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Among these targeted agents, sunitinib is an orally-administered, multitargeted tyrosine kinase inhibitor (TKI). The main targets of sunitinib are VEGF receptor (VEGFR)-1, VEGFR-2, VEGFR-3, PDGF receptor (PDGFR)-α, and PDGFR-β.(12) Sunitinib is the most readily-available therapeutic targeting agent in the USA (Research from the Synovate Healthcare US Tandem Oncology Monitor 2007–2010) for patients with RCC. A randomized, multicenter, phase-III trial enrolled 750 patients with previously-untreated metastatic RCC to receive either repeated 6-week cycles of sunitinib (at a dose of 50 mg, given orally once daily for 4 weeks, followed by 2 weeks without treatment) or IFN-α (at a dose of 9 million units, given subcutaneously three times/week). This trial demonstrated the superiority of sunitinib over IFN in the objective response rate (ORR; 31%vs 6%), progression-free survival (PFS; 11 vs 5 months), and overall survival (OS; 26.4 vs 21.8 months) and its acceptable safety profile.(6,13) These results and those of the clinical trials for other targeting agents indicate an improved prognosis for patients with RCC in the era of targeted therapy.

Currently, various agents, including molecular-targeted agents and immunotherapeutic agents, are treatments of choice in clinical practice. Risk algorithms, molecular diagnostics, pharmacogenomics, and pharmacodynamics are important tools to improve treatment outcomes. One of the treatment goals is personalized medicine, which offers the right treatment for the right patient at the right time.(14) Therefore, the identification of biomarkers to predict response and side-effects, and to select optimal dosages, is urgently needed. This article provides a brief overview of the genetic changes of RCC and introduces sunitinib biomarkers in terms of clinical variables, genetic factors, and circulating proteins and endothelial cells.

Renal Cell Cancer and the Hypoxia-inducible, Factor-mediated Pathway

  1. Top of page
  2. Abstract
  3. Renal Cell Cancer and the Hypoxia-inducible, Factor-mediated Pathway
  4. Biomarkers to Predict the Response to Sunitinib Therapy
  5. Biomarkers of Other Targeted Agents
  6. Conclusion
  7. Acknowledgments
  8. Disclosure Statement
  9. References

Renal cell cancer is the most frequently-occurring solid lesion within the kidney.(1) Among RCC, clear-cell RCC (75%), alternatively called “conventional RCC”, is the most common, followed by papillary RCC (10%), chromophobe RCC (5%), and renal oncocytoma (5%). In addition, the World Health Organization system includes rare, newly-recognized cancers defined by genetic factors, such as Xp11.2 translocation-associated renal cancer.(15–17)

The most important molecular disorder in RCC involves the von Hippel-Lindau (VHL) tumor suppressor gene, which is responsible for clear-cell RCC. The protein product of the VHL gene, which is located on chromosome 3p25, prevents angiogenesis and suppresses tumors.(15) Inactivating the phosphorylated VHL protein activates hypoxia-inducible factor (HIF) and the induction of VEGF in clear-cell RCC. In addition, mesenchymal–epithelial transition factor (MET) and fumarate hydratase (FH) are the genes responsible for types 1 and 2 papillary RCC, respectively.(18,19) Mesenchymal–epithelial transition factor, which is a proto-oncogene, encodes a tyrosine kinase membrane receptor, and the activation of MET can indirectly promote angiogenesis and tumor growth through the overexpression of VEGF.(20,21) Fumarate hydratase is an enzyme in the mitochondrial tricarboxylic acid (TCA) cycle. The loss of FH leads to pseudohypoxia through the overexpression of HIF, resulting in an increase in downstream targets, including VEGF.(20,22) Therefore, the activation of MET and the loss of FH lead to angiogenesis.(4,15) Moreover, transcription factor E3 (TFE3) and transcription factor EB (TFEB), members of the microphthalmia transcription factor/TFE, are highly expressed in the nucleus as a result of chromosomal translocations, and are responsible for the development of juvenile renal cancer.(17) They also induce the HIF-mediated angiogenesis signaling cascade. The gene products responsible for RCC are indicated in Figure 1.

Biomarkers to Predict the Response to Sunitinib Therapy

  1. Top of page
  2. Abstract
  3. Renal Cell Cancer and the Hypoxia-inducible, Factor-mediated Pathway
  4. Biomarkers to Predict the Response to Sunitinib Therapy
  5. Biomarkers of Other Targeted Agents
  6. Conclusion
  7. Acknowledgments
  8. Disclosure Statement
  9. References

Clinical factors.  In the cytokine era, prognostic factors that could predict outcome in patients with metastatic RCC treated with IFN-α as initial systemic therapy were defined by Motzer at the Memorial Sloan Kettering Cancer Center (MSKCC).(23) The MSKCC group extracted five variable risk factors for short survival: low Karnofsky performance status (PS), high lactate dehydrogenase (LDH), low hemoglobin (Hb), high corrected serum calcium (Ca), and time from the initial RCC diagnosis to the start of IFN-α therapy of <1 year (Table 1).(23) Each patient was assigned to one of three risk groups: those with no risk factor (favorable risk), those with one or two risk factors (intermediate risk), and those with three or more risk factors (poor risk).(23) The median time to death was 30, 14, and 5 months in the favorable, intermediate, and poor-risk groups, respectively.(23) These five risk criteria are now widely used and are known as the Motzer score or the MSKCC score.

Table 1.   Clinical variables correlated with better/worse survival in patients with metastatic renal cell cancer
Investigators/referencesnAgentSettingEnd-pointHb <LLNCorrected Ca >ULNPoor PSFrom Dx to Tx <1 yearLDH >ULNOther prognostic factors
  1. Both, both first line and second line therapies; Ca, calcium; Dx, diagnosis; Hb, hemoglobin; LLN, lower limit of normal range; NS, not significant; OS, overall survival; PFS, progression-free survival; PS, performance status; TKI, tyrosine kinase inhibitor; Tx, therapy; ULN, upper limit of normal range.

Motzer et al.(23)670InterferonFirst lineOSSignificantSignificantSignificantSignificantSignificant 
Motzer et al.(24)251Various agentsSalvageOSSignificantSignificantSignificantNSNS 
Heng et al.(25)645TKIFirst lineOSSignificantSignificantSignificantSignificantNSNeutrophil count >ULN, Platelet count >ULN
Choueiri et al.(26)120TKIBothPFSNSSignificantSignificantSignificantNSNeutrophil count >4500/uL, Platelet count >300 000/uL
Patil et al.(27)375SunitinibFirst lineOSSignificantSignificantSignificantSignificantSignificantBone metastasis
Patil et al.(27)375InterferonFirst lineOSSignificantSignificantNSSignificantSignificantNeutrophil counts, bone metastasis, lymph node metastasis
Bamias et al.(28)109SunitinibBothOSNSNSSignificantSignificantNSMultiple metastatic sites
Yuasa et al.(29)63SunitinibBothOSSignificantNSNSNSSignificantNo history of nephrectomy, brain metastasis

Later, the same group analyzed the prognostic factors of previously-treated RCC patients who had received new agents as salvage therapy in clinical trials at the MSKCC. The median survival time for the 251 patients was 10.2 months, and the pretreatment features associated with a poor prognosis extracted by multivariate analysis were low Karnofsky PS; low Hb level; and high corrected Ca level.(24)

In the molecular-targeted therapy era, several studies have investigated clinical prognostic factors, and the findings are summarized in Table 1. Heng et al.(25) first reported the results from a large, multicenter study of 645 patients with anti-VEGF, therapy-naïve metastatic RCC. The study included three groups of patients: 396, 200, and 49 patients, respectively, treated with sunitinib, sorafenib, and bevacizumab, respectively; 560 patients (94%) had clear-cell RCC, while the remaining 35 patients (6%) were diagnosed with non-clear-cell RCC.(25) Four of the five adverse prognostic factors according to the MSKCC score (low Hb, high corrected Ca level, low Karnofsky PS, and time from diagnosis to treatment of <1 year) emerged as independent predictors of poor OS.(25) In addition, neutrophils greater than the upper limit of normal (ULN) range, and platelets greater than the ULN, emerged as independent adverse prognostic factors.(25)

Choueiri et al.(26) retrospectively identified the clinical factors associated with outcome in patients with clear-cell RCC receiving anti-VEGF agents, the majority of whom (84%) received either sorafenib or sunitinib. In total, 120 patients with metastatic clear-cell RCC were studied, and a prognostic model was constructed using PFS as an end-point.(26) In this study, all patients had undergone prior nephrectomy, and 45 patients (37%) received anti-VEGF treatment as first-line therapy, while 75 patients (63%) had previously received non anti-VEGF therapies.(26) The interval between diagnosis and anti-VEGF therapy, high corrected Ca level, poor PS, and increased platelet and neutrophil counts were identified as independent prognostic factors for poor PFS (Table 1).

Patil et al. reported the prognostic factors for PFS and OS, with sunitinib or IFN-α as first-line systemic therapy for patients with clear-cell metastatic RCC in a randomized, multicenter, phase-III trial, as described earlier.(6,12,27) For sunitinib, a PFS multivariate analysis identified five independent predictors, including high-serum LDH level, the presence of multiple metastatic sites, no prior nephrectomy, Eastern Cooperative Oncology Group (ECOG) PS, and baseline platelet count. The OS correlated with high LDH level, corrected Ca level, the time from diagnosis to treatment, Hb level, ECOG PS, and the presence of bone metastasis (Table 1). The authors concluded that the MSKCC model was applicable to targeted therapy.(27)

A multi-institutional, retrospective analysis of patients with metastatic RCC treated with sunitinib in six Greek oncology units was reported.(28) In this study, 109 patients were included, of whom 100 (91%) had clear-cell RCC and 17 (15%) had been treated with IFN-α, while 86 (79%) had undergone nephrectomy.(28) The time from diagnosis to the start of sunitinib of <1 year, multiple metastatic sites (P = 0.003), and poor PS were independently correlated with OS (Table 1).(28)

Finally, in our retrospective study of 63 native Japanese patients, all five MSKCC risk factors (ECOG PS >1, low Hb levels, high corrected Ca levels, high LDH levels, and the time from diagnosis to initial systemic therapy of <1 year) were associated with poorer OS by univariate analysis.(29) A multivariate analysis using the Cox proportional hazard model demonstrated that low Hb and elevated LDH were independently associated with poorer OS among MSKCC scores. Brain metastasis and no history of nephrectomy were also associated with poorer OS (Table 1).(29)

It is important to consider that the number of patients was different between these clinical studies, and that some studies, including our own, might be influenced by low statistical power due to relatively small sample sizes. Nevertheless, the MSKCC prognostic factors are still valid for predicting survival in metastatic RCC in the era of targeted therapy. These results indicate that the MSKCC scores are associated with the behavior of the disease, rather than with specific forms of therapy. In addition to the factors included in the MSKCC score, the number of neutrophils, the platelet count, and the number and/or location of metastatic lesions might be independent prognostic factors in patients treated with molecular-targeted agents (Table 1).

Genetic factors affecting pharmacokinetics and pharmacodynamics.  The clinical efficacy of sunitinib depends on the systemic exposure of the targeted organ to the active compounds. A recent meta-analysis of pharmacokinetic data in 443 patients treated with sunitinib showed that higher plasma levels of sunitinib and of its active metabolite SU12662 were associated with prolonged PFS and OS.(30) Orally-administered sunitinib is absorbed by the intestinal mucosa and metabolized in the liver. The primary metabolite, N-de-ethylated metabolite SU12662, reaches plasma concentrations that are similar to the parent compound sunitinib and has biochemically-equivalent activity to sunitinib, but its half-life is prolonged.(13,30) The efflux transporters and the cytochrome P450 3A (CYP3A) family play a role in the absorption and metabolism of the drug.(13,30) The active metabolite and the parent compound are multitargeted TKI that inhibit PDGFR-α and PDGFR-β; VEGFR-1, VEGFR-2, and VEGFR-3; stem cell factor receptor c-KIT; Fms-like tyrosine kinase 3 receptor (Flt-3); and the glial cell line-derived neutrophic factor receptor.(13) The efflux transporters also regulate the cytoplasmic concentration of these agents.(13) Therefore, the efficacy of sunitinib can be influenced by multiple genes encoding efflux transporters, metabolizing enzymes, and targeted tyrosine kinases. Figure 2 describes the processes and enzymes/proteins involved in sunitinib activity, and the genes relevant to sunitinib response and/or toxicity are summarized in Table 2. To achieve personalized medicine, the complete understanding of sunitinib pharmacogenomics and the molecular profile of each individual patient are necessary.(14)

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Figure 2.  Genetic prognostic factors in metastatic renal cell cancer patients treated with sunitinib. Orally-administered sunitinib is absorbed from the intestinal mucosa and metabolized in the liver to an active metabolite. Efflux transporters and the cytochrome P450 (CYP)3A family contribute to sunitinib absorption and metabolism. Metabolized active form and the parent compound inhibit platelet-derived growth factor receptor (PDGFR)-α and PDGFR-β, and vascular endothelial growth factor receptor (VEGFR)-1, VEGFR-2 and VEGFR-3. Therefore, pharmacokinetic and pharmacodynamic variables influence the efficacy of sunitinib. ABCB1, ATP-binding cassette transporter protein member B1; ABCG2, ATP-binding cassette transporter protein member G2; CYP3A4, cytochrome P450 3A4; CYP3A5, cytochrome P450 3A5; Flt-3, Fms-like tyrosine kinase receptor-3; NR1I2, nuclear receptor subfamily 1, group I, member 2; NR1I3, nuclear receptor subfamily 1, group I, member 3; PDGFR, platelet-derived growth factor receptor; VEGFR, vascular endothelial growth factor receptor.

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Table 2.   Genes relevant to sunitinib response and/or toxicity
FactorsGenotypers no.DescriptionReferences
  1. Description of haplotypes: *ABCB1 3435C/T, 1236C/T, and 2677G/T; †ABCG2 15622C/T and 1143C/T; ‡NR1I3 5719C/T, 7738A/C, and 7837T/G; §PDGFR-α1580T/C –1171C/G -735G/A and -573G/T. ABC, ATP-binding cassette; CYP3A5, cytochrome P450 3A5; Flt-3, Fms-like tyrosine kinase receptor-3; PDGFR, platelet-derived growth factor receptor; VEGFR, vascular endothelial growth factor receptor.

ABCB1TTT haplotype*rs1045642, rs1128503, rs2032582Increased risk of hand–foot syndrome32
ABCB1TCG haplotype*rs1045642, rs1128503, rs2032582Improved progression-free survival33
ABCG2421C>Ars2231142High concentration, increased risk of adverse events, such as hypertension and facial acne39
ABCG2TT haplotype†rs2622604Any toxicity >grade 232
CYP3A56986 A>Grs776746Improved progression-free survival33
Flt-3738 C>Trs1933437Increased risk of leukopenia and thrombocytopenia42
NR1I3Absence of a CAT haplotype‡rs2307424, rs2307418, rs4073054Improved progression-free survival33
NR1I3Absence of a CAG haplotype‡rs2307424, rs2307418, rs4073054Increased risk of leukopenia32
PDGFR-αHomozygous of GCGT§rs35597368, rs1800810, rs1800813, rs1800812Decreased overall survival33
VEGFR-21718 A>Trs1870377Improved overall survival33
VEGFR-21191 T>Crs2305948Any toxicity >grade 232

Absorption, excretion, and efflux of sunitinib.  Upon oral administration, sunitinib is absorbed from the gastrointestinal tract in a process regulated by efflux transporters.(30) The ATP-binding cassette (ABC) transporter proteins, particularly multidrug resistance-1//ABC member B1 (ABCB1), formerly known as P-glycoprotein, multidrug resistance-associated protein-1/ABCC1, and breast cancer resistance protein/ABCG2, formerly known as mitoxantrone-resistant protein, mediate absorption and/or excretion through the intestinal wall and regulate the efflux of a wide variety of anticancer drugs from target cells (Fig. 2). These transporter proteins might be involved in the efficacy of sunitinib and in the resistance to sunitinib.

The T genotype in ABCB1 3435C/T, which is associated with higher exposure to drugs transported by ABCB1 via decreased mRNA stability, and the consequent decreased expression of ABCB1 transporter,(31) might be a key factor in ABCB1-mediated sunitinib transport. A multicenter pharmacogenetic association study revealed that the ABCB1 TTT haplotype in 3435C/T, 1236C/T, and 2677G/T was related to hand–foot syndrome (HFS).(32) In addition, van der Veldt reported that the presence of a TCG copy in the same ABCB1 haplotype was a significant predictor of improved PFS.(33) Interestingly, in a report describing accelerated CYP3A4-mediated drug metabolism in Abcb1 knockout mice, Schuetz et al.(34)suggested that decreased ABCB1 expression activates enzymes involved in drug absorption or disposition.

Several studies reported higher sunitinib affinity for ABCG2 than ABCB1.(35,36) Shukla et al.(35) demonstrated that sunitinib stimulates ATP hydrolysis by both transporters in a concentration-dependent manner, and that the affinity for ABCG2 (IC50: 1.33 μM) is higher than that for ABCB1 (IC50: 14.2 μM). Kawahara et al. analyzed the kinetics of sunitinib inhibition on ABCG2- and ABCB1-mediated transport. The authors showed that sunitinib acts as a competitive inhibitor of the transporter function of ABCG2 and ABCB1, and that sunitinib has higher affinity for ABCG2 than ABCB1.(36) A previous genetic analysis revealed that, among single nucleotide polymorphism (SNP) in the ABCG2 gene, ABCG2 421C/A is the most common mutant allele in the Japanese population and in other Asian populations (>30%), and that it is associated with low ABCG2 protein expression. The authors also showed that this variable is rare in African Americans (<5%) and Caucasians (<10%).(37,38) This finding might explain the higher incidence of hematological adverse events in Asian patients. Indeed, a report suggested that the homozygous variant of ABCG2 421C/A might be involved in the elevated exposure to sunitinib and severe toxicity observed in a patient with RCC.(39) In addition, recent pharmacogenetic analyses revealed that two ABCG2 gene polymorphisms (–15622C /T and 1143C/T) were strongly associated with sunitinib-induced toxicity in patients.(32) Thus, ABCG2 genetic variants might lead to increased systemic exposure to sunitinib, resulting in dose-limiting toxicities.

Metabolism.  Sunitinib is metabolized in the liver, primarily by the CYP3A4 enzyme. No functional polymorphisms of CYP3A4 have been identified. The CYP3A5 enzyme metabolizes several TKI, including erlotinib, gefitinib, and imatinib, and might be a key determinant in the interindividual differences observed in CYP3A-mediated drug metabolism.(40) In addition, the expression of CYP3A4 and CYP3A5 is regulated by the ligand-activated nuclear receptors NR1I2 (nuclear receptor subfamily 1, group I, member 2 or pregnane X receptor [PXR]) and NR1I3 (nuclear receptor subfamily 1, group I, member 3 or constitutive androstane receptor [CAR]).(41)

The CYP3A5 gene, which is 83% homologous to CYP3A4, has a functional polymorphism, 6986A/G, in intron3. The variant G allele creates a cryptic acceptor splice site and transcribes variant mRNA with an excess 131-bp fragment between exons 3 and 4.(31) The CYP3A5 protein translated from the variant mRNA is truncated at a premature stop codon, resulting in a reduced amount of complete CYP3A5 protein. van der Veldt et al.(33) reported that the A allele of 6986A/G in the CYP3A5 gene, which creates the CYP3A5 expressor phenotype, is a predictive factor for prolonged PFS. The prolonged PFS observed in patients with the expressor phenotype might be caused by greater metabolism of sunitinib, and thereby increased levels of the active metabolite, which has a longer half-life than the parent compound.(33)

In addition, a relationship has been reported between the absence of the CAG haplotype in the NR1I3 gene, which encodes the CAR, and an increased risk for leukopenia.(32) Nuclear receptor NR1I3 plays an important role in the regulation of multiple drug detoxification genes, such as CYP3A4.(41) Another study also extracted the polymorphic variants of NR1I2 and NR1I3, identifying them as predictive factors for PFS and OS in sunitinib-treated metastatic RCC patients.(33)

Pharmacodynamics and targeted inhibition.  Besides pharmacokinetic factors, pharmacodynamic factors might affect the efficacy and toxicity of sunitinib. In RCC, the major therapeutic effect of sunitinib is thought to be the inhibition of the VEGFR on tumor-associated endothelium, leading to reduced tumor angiogenesis.(13,30) In addition, the inhibition of the PDGFR might increase the anti-angiogenic effects of sunitinib by targeting pericytes, which protect endothelial cells from apoptosis.(13,30)

The presence of the A allele of the 1718T/A VEGFR-2 polymorphism and the presence of two GCGT copies of the 1580T/C, –1171C/G, –735G/A, and –573G/T polymorphisms in PDGFR-α are associated with decreased OS and prolonged OS, respectively.(33) However, these polymorphisms are not significantly associated with prolonged PFS. These findings suggest that polymorphisms in VEGFR-2 and PDGFR-α might be associated with the nature of the disease, and might therefore be prognostic instead of predictive. The presence of the T allele of the VEGFR-2 1191C/T polymorphism is related to the development of any toxicity higher than grade 2, including fatigue, thrombocytopenia, and hypertension.(32) Polymorphisms are also predictive for the development of coronary heart disease due to the lower binding efficiency of VEGF to the polymorphic VEGFR-2.(33) The polymorphic receptor might therefore be involved in sunitinib-induced cardiac toxicity and the development of hypertension.

In addition, the association between the Flt-3 738T/C polymorphism and a reduced risk of leukopenia was reported.(32) The protective effect of the Flt-3 738C allele against sunitinib-induced thrombocytopenia was confirmed by van Erp et al.(42) Therefore, the Flt-3 738C/T polymorphism plays a role in the variability of sunitinib-induced bone marrow toxicity.

Circulating biomarkers.  Considering the pharmacological effect and biological activity of sunitinib, one should consider measuring the plasma levels of the soluble ligands (including the VEGF family and placental growth factor; PlGF), the specific ligands of VEGFR-1, and the soluble form of the receptors, which include soluble VEGFR (sVEGFR)-2 and sVEGFR-3. If the systemic exposure to sunitinib is a key factor in the predictive value, the less competitive ligands, which include VEGF and PDGF, might be associated with greater efficacy of sunitinib. Indeed, the basal levels and the fold changes of these specific ligands and their receptors have been reported as potential biomarkers of sunitinib.

The mechanisms underlying sunitinib-induced alterations in the levels of these growth factors and of their soluble receptors have not yet been elucidated. Sunitinib-induced angiogenesis inhibition might increase VEGF and PlGF as a positive feedback (Fig. 3). However, combining sunitinib with the soluble forms of the receptors might cause precipitation or degradation and decrease their levels. Rini et al. reported that the plasma levels of VEGF-A and PlGF in patients treated with sunitinib increased significantly after 28 days by 2.8-fold (range: 0.4- to 13.6-fold) and 3.9-fold (range: 0.8- to 20.4-fold) over baseline, respectively, whereas the mean sVEGFR-3 levels decreased by 37.6%.(43) DePrimo et al.(44) also reported that at the end of cycle 1 (day 28), VEGF and PlGF levels increased greater than threefold (relative to baseline) in 24 of 54 (44%) and 22 of 55 (40%) cases, respectively (P < 0.001). In contrast, sVEGFR-2 levels decreased by at least 20% in all patients, resulting in a 30% decrease in 50 of 55 (91%) patients (P < 0.001) during cycle 1, while sVEGFR-3 levels decreased ≥30% in 48 of 55 cases (87%) and ≥20% in all but two cases.(44) These levels tended to return to near baseline after 2 weeks of treatment, indicating that these effects were dependent on drug exposure. In addition to the soluble proteins, bone marrow-derived circulating endothelial progenitors (CEP) and circulating endothelial cells (CEC) increase when angiogenesis is required. Gruenwald et al. reported that CEC values in metastatic RCC patients are significantly higher than in healthy individuals (mean value: 49 ± 44 CEC/mL vs 8 ± 8 CEC/mL, P = 0.0001).(45) In this study, during the first course of sunitinib, the CEC of the patients increased from 49 ± 44 CEC/mL at baseline to 84 ± 59 CEC/mL after 14 days (P = 0.0331) and 89 ± 63 CEC/mL after 28 days (P = 0.0159) of treatment.(45) The CEC levels declined during the subsequent treatment-off period to baseline levels and below (range: 19–58 CEC/mL).(45)Figure 3 describes circulating soluble proteins and CEC/CEP as candidate biomarkers for sunitinib efficacy.

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Figure 3.  Circulating soluble proteins and endothelial cells as prognostic factors in metastatic renal cell cancer patients treated with sunitinib. Mechanisms underlying sunitinib-induced alterations in the levels of these growth factors and of their soluble receptors have not yet been elucidated. Sunitinib-induced angiogenesis inhibition might increase vascular endothelial growth factor (VEGF) and circulating endothelial cells and progenitor cells as a positive feedback. In contrast, combining sunitinib with the soluble forms of the receptors might cause precipitation or degradation, and decrease their levels. VEGF, vascular endothelial growth factor.

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Rini et al. first reported that baseline sVEGFR-3 and VEGF-C levels might be prognostic factors for PFS, as well as predictive factors for objective response. Patients with lower levels than median baseline values (sVEGFR-3, 47 000 pg/mL; VEGF-C, 722.1 pg/mL) had longer PFS than patients with greater levels than the median.(43) The lack of correlation between VEGF-A or PlGF levels and PFS could be due to the fact that the study consisted of bevacizumab-refractory patients. The predictive value of baseline serum VEGF-A was reported by other groups. Porta et al.(46) investigated the association between sunitinib-treatment outcome and baseline serum VEGF-A levels in 85 patients treated with sunitinib, among whom 60 had a pure clear-cell RCC, whereas 12 had a predominantly clear-cell mixed histology, and 13 had a pure non-clear-cell histology. In this study, patients with increased baseline VEGF-A had a significantly decreased PFS period (odds ratio: 2.14, 95% confidence interval [CI]: 1.324–3.459).(46) This study reported that patients with increased VEGF-A have a median PFS of 4.7 months (95% CI: 2.8–8.3), whereas patients with non-elevated VEGF-A have a median PFS of 11.2 months (95% CI: 6.5–15).(46)

The fold changes in these angiogenesis-associated proteins could also be a potential biomarker of sunitinib. DePrimo et al.(44) reported that significantly larger changes in VEGF, sVEGFR-2, and sVEGFR-3 levels were observed in patients exhibiting objective tumor response, compared to those exhibiting stable disease or disease progression (P < 0.05). In addition, total drug trough, sunitinib levels, and SU12662 levels correlated modestly with the change in mean sVEGFR-2 and sVEGFR-3 plasma levels relative to baseline by linear regression analysis.(44)

Endothelial progenitors and CEC also play an integral part in tumor angiogenesis, and might be suitable predictive and prognostic biomarkers for treatment with angiogenesis inhibitors. Gruenwald et al.(45) reported that in patients with PFS above the median value, CEC values increased significantly from baseline (mean value: 40 ± 41 CEC/mL to 111 ± 61, P = 0.0109) at day 28, whereas in patients with PFS below the median value, the increase remained insignificant (mean value: 53 ± 45 CEC/mL to 69 ± 61, P = 0.1848). Farace et al.(47) reported that although baseline CEC values were not associated with PFS or OS, baseline circulating progenitor cell values were associated with PFS (P = 0.01) and OS (= 0.006) in patients treated with TKI. In addition, changes in circulating progenitor cell values between days 1 and 14 were also associated with PFS (P = 0.03).(47)

Both circulating soluble proteins and endothelial cells have recently been measured in various clinical trials as potential biomarkers of the response to anti-angiogenesis agents, and research to further assess their utility is ongoing.

Others.  The known adverse effects of sunitinib include HFS, diarrhea, stomatitis, hypertension, fatigue, and hypothyroidism. If adverse effects depend on the degree of systemic exposure to sunitinib, on which clinical efficacy also depends, adverse effects might be potential predictors of sunitinib efficacy in metastatic RCC patients. Correlations have been reported between clinical response and hypertension, and hypothyroidism and HFS.(48–50)Figure 4 shows the correlation between the worst adverse effects and the best clinical response in RCC patients. Rixe et al. retrospectively analyzed the putative correlation between sunitinib activity and adverse effects in patients (n = 32) with metastatic RCC. The pattern of toxicity was compared between responders and non-responders.(48) The appearance or worsening of hypertension (grade 2 or above) was found to be the single independent predictor of improved clinical response (odds ratio: 2.33, 95% CI: 1.69–3.22, P = 0.009) by multivariate analysis using logistic regression. By univariate analysis, a higher response rate was observed in patients with stomatitis (P = 0.015), fatigue (P = 0.019), hypertension (P = 0.02), and testicular erythema (P = 0.04), as well as hair depigmentation (P = 0.042).(48)

image

Figure 4.  Adverse effects as candidate biomarkers of favorable efficacy for sunitinib. Correlations between sunitinib efficacy and adverse effects, including hypertension, hypothyroidism, and hand–foot syndrome, were reported.

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The possibility that the occurrence of hypothyroidism might affect the outcome of patients with metastatic RCC was prospectively investigated in consecutive patients who were to receive treatment with sunitinib or sorafenib.(49) Assessment included serum levels of thyroid-stimulating hormone (TSH), tri-iodothyronine (T3), and thyroxine (T4). Among these patients, subclinical hypothyroidism, defined as an increase in TSH above the ULN (>3.77 μM/mL), with normal T3 and T4 levels, was evident in five patients at baseline and occurred in 30 patients (36.1%) within the first 2 months after treatment initiation.(49) There was a statistically-significant correlation between the occurrence of subclinical hypothyroidism during treatment and the ORR (hypothyroid patients vs euthyroid patients: 28.3%vs 3.3%, respectively, P < 0.001).(49) Moreover, a multivariate analysis identified the development of subclinical hypothyroidism as an independent predictor of survival (hazard ratio: 0.31, P = 0.014).(49) Therefore, Schmidinger et al.(49) concluded that hypothyroidism might serve as a marker of favorable treatment outcome in metastatic RCC patients.

Very recently, at a meeting of the American Society of Clinical Oncology–Genitourinary, a report of a retrospective investigation of the correlation between HFS and sunitinib antitumor efficacy was presented.(50) In this study of 770 patients included in the analysis (after cycle 1, day 1), 179 (23%) developed HFS (all grades included), and 591 (77%) did not develop HFS. The median PFS (14.3 vs 8.3 months, < 0.0001), OS (38.2 vs 18.9 months, < 0.0001), and ORR (66.5%vs 31.8%, < 0.0001) were significantly higher in the group with HFS than in the group without HFS.(49) Moreover, a multivariate analysis also demonstrated that treatment-emergent HFS remained a significant independent predictor of survival benefit (= 0.001 and < 0.001 for PFS and OS, respectively) after adjusting for other significant independent prognostic markers, including MSKCC factors.(50)

We have also found initial tumor size to be a good predictor of tumor reduction. We retrospectively analyzed 139 metastatic lesions, 16 primary lesions, 86 sunitinib-treated lesions, and 69 sorafenib-treated lesions in 54 patients with metastatic RCC.(51) A linear, moderate-to-strong association between initial tumor size and tumor size reduction rate was demonstrated (correlation coefficient: −0.441, < 0.001). When these tumors were divided into two groups, according to threshold value (23.95 mm), which was determined by receiver-operating characteristic curve analysis, the smaller tumors demonstrated a significantly greater size reduction than the larger tumors by Mann–Whitney U-test (< 0.001).(51) We believe that this simple observation constitutes useful information for physicians who treat metastatic RCC.

Biomarkers of Other Targeted Agents

  1. Top of page
  2. Abstract
  3. Renal Cell Cancer and the Hypoxia-inducible, Factor-mediated Pathway
  4. Biomarkers to Predict the Response to Sunitinib Therapy
  5. Biomarkers of Other Targeted Agents
  6. Conclusion
  7. Acknowledgments
  8. Disclosure Statement
  9. References

In the present study, we provide a brief overview of biomarkers for other targeted agents used in the treatment of metastatic RCC. Several studies, which were introduced in this review, included patients treated with other angiogenesis inhibitors, such as sorafenib, bevacizumab, pazopanib, and axitinib,(25,26,47,49,51) and suggested that most factors might be possible universal biomarkers for these agents. Other studies investigated the association between efficacy and genetic characteristics, soluble plasma biomarkers, and clinical symptoms.

In a phase-III clinical trial of pazopanib in RCC, predictive genetic markers were explored.(52) Xu et al. reported that three polymorphisms in IL8 and HIF1A, and five polymorphisms in HIF1A, NR1I2, and VEGFA, showed a nominally significant association with PFS and response rate (RR), respectively.(52) From these results, they concluded that pharmacodynamic factors might predict treatment responses to pazopanib monotherapy in patients with RCC.(52)

Plasma proteins (VEGF, soluble VEGFR-2, carbonic anhydrase IX, tissue inhibitor of metalloproteinase-1 [TIMP-1], and Ras p21) were analyzed to identify prognostic biomarkers or indicators of response to sorafenib in patients enrolled in the phase-III clinical trial Treatment Approaches in Renal Cancer Global Evaluation Trial.(53) In this study, the reciprocal changes that were also observed in VEGF and sVEGFR-2 levels following sorafenib treatment were similar to those observed with sunitinib.(53) In addition, a multivariate analysis, which included ECOG PS, the MSKCC score, and the potential biomarkers, demonstrated that the elevated plasma level of TIMP-1, which inhibits most of the matrix metalloproteinases, was an independent, poor prognostic factor.(53) Although further investigation is necessary, TIMP-1 should be an important candidate as a potential biomarker for anti-angiogenesis therapy.(53)

Finally, some studies have suggested that hypertension is a predictive biomarker of efficacy in patients receiving targeted agents. Rini et al.(54) reported the final results of a phase-III trial of bevacizumab plus IFN-α versus IFN-α monotherapy in patients with metastatic RCC. In this study, patients who developed hypertension on bevacizumab plus IFN-α had a significantly improved PFS and OS versus patients without hypertension.(54) Similarly, axitinib efficacy was also reported to correlate with diastolic blood pressure. From five phase-II multicenter trials of axitinib in multiple solid tumors, including metastatic RCC, Rini et al. reported that the median OS (25.8 vs 14.9 months) and median PFS (10.2 vs 7.1 months) were greater in patients who developed hypertension.(55)

Conclusion

  1. Top of page
  2. Abstract
  3. Renal Cell Cancer and the Hypoxia-inducible, Factor-mediated Pathway
  4. Biomarkers to Predict the Response to Sunitinib Therapy
  5. Biomarkers of Other Targeted Agents
  6. Conclusion
  7. Acknowledgments
  8. Disclosure Statement
  9. References

In this review, we introduced the current candidate biomarkers of sunitinib therapy. Regarding the clinical factors, the MSKCC prognostic factors seem to be valid predictors of survival in metastatic RCC, as summarized in Table 1. Host genetic factors associated with efflux transporters, metabolizing enzymes, and targeted tyrosine kinases modify the efficacy and the toxicity of sunitinib (Fig. 2). Both circulating soluble proteins and cells, which include VEGFR and their ligands, and the CEC/CEP, have been considered as potential candidate biomarkers of the response to anti-angiogenesis agents, and research to further assess their utility is ongoing (Fig. 3). Finally, we introduced severe, adverse effects as candidate biomarkers of favorable efficacy (Fig. 4). Among the targeted agents, sunitinib is an attractive clinical tool, and biomarkers of sunitinib efficacy are desirable. An important caveat is that, to date, almost all of these studies have been retrospective. Although further implementation in prospective studies is necessary, if validated, these biomarkers can be utilized to measure therapeutic response and design treatment strategies for metastatic RCC.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Renal Cell Cancer and the Hypoxia-inducible, Factor-mediated Pathway
  4. Biomarkers to Predict the Response to Sunitinib Therapy
  5. Biomarkers of Other Targeted Agents
  6. Conclusion
  7. Acknowledgments
  8. Disclosure Statement
  9. References

This work was partly supported by the Takeda Science Foundation and Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology, Japan.

Disclosure Statement

  1. Top of page
  2. Abstract
  3. Renal Cell Cancer and the Hypoxia-inducible, Factor-mediated Pathway
  4. Biomarkers to Predict the Response to Sunitinib Therapy
  5. Biomarkers of Other Targeted Agents
  6. Conclusion
  7. Acknowledgments
  8. Disclosure Statement
  9. References

Kiyohiko Hatake obtained research funding from Takeda Pharmaceutical Co. Ltd., Chugai Pharmaceutical Co. Ltd., and Kyowa Hakko Kirin Co. Ltd.

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  2. Abstract
  3. Renal Cell Cancer and the Hypoxia-inducible, Factor-mediated Pathway
  4. Biomarkers to Predict the Response to Sunitinib Therapy
  5. Biomarkers of Other Targeted Agents
  6. Conclusion
  7. Acknowledgments
  8. Disclosure Statement
  9. References
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