Identifying the Molecular Determinants of Pazopanib Response in Advanced Soft Tissue Sarcoma


Pazopanib (Votrient) is a small molecule inhibitor of multiple tyrosine kinases that act as important molecular mediators of essential tumour angiogenic and oncogenic pathways (Table 1). Pazopanib treatment has been shown to improve progression-free survival in advanced soft tissue sarcomas in phase II and III trials.1,2 Whilst fewer than 10% of patients experience objective tumour shrinkage following pazopanib treatment, stabilisation of tumour growth is seen in the majority, with up to 20% of patients achieving disease control extending beyond 6 months.3 Despite significant biological diversity at histopathological, genomic and gene expression levels, disease control with pazopanib has been demonstrated across a range of STS subtypes. The shared mechanisms of treatment effect within this heterogeneous patient population are not well established and it is currently not possible to prospectively predict which patients will benefit from pazopanib. 

Table 1. IC50 of Pazopanib against Selected Kinase Targets
Kinase Kinase IC50
VEGFR1 (FLT1) 0.01
VEGFR2 (KDR) 0.03
VEGFR3 0.05
PDGFRα 0.07
cKIT 0.07
PDGFRβ 0.08
FGFR1 0.14
FGFR3 0.13
FGFR4 0.8
FAK .8
Abl1 2
JNK1 2.46
Tie-2 4.52
Met 6
IGF-1R 8
Akt, CDK2, EGFR,

Disappointingly, this evidence of clinical activity of pazopanib failed to translate to significant prolongation of overall survival compared to placebo in the mixed STS trial cohort.4 As has been the suspected case in numerous sarcoma drug trials, it is likely that the recruitment of patients with a diverse assortment of disease biology contributed significantly to masking the drug’s efficacy signal, reducing overall effect size and subsequently impacting upon the study’s statistical power.

Nevertheless, the failure to meet an overall survival endpoint adversely impacted cost-effectiveness appraisals of pazopanib, resulting in withdrawal of funding support by the Cancer Drug Fund in the UK.5

The ability to prospectively stratify patients within and across different STS subtypes for likelihood of pazopanib response would greatly improve the effectiveness of the drug, allowing for the targeted delivery of treatment to patients most likely to benefit whilst sparing those unlikely to benefit from futile treatment and possible toxicity. There is a clear and urgent need for research with the aim of developing clinically useful predictive biomarkers for this and other targeted treatments of STS.

The aim of our research is to identify molecular signatures and develop assays that can identify patients most likely to respond to pazopanib, with a view to future stratification of STS patient cohorts. Supported by a research grant from the Liddy Shriver Sarcoma Initiative, we have performed DNA sequencing, mRNA transcript quantification and immunohistochemistry on primary tumour material to investigate the mutational and gene expression profiles of the tumour of patients treated with pazopanib. Additionally, we have developed mass spectrometry approaches to analyse FFPE tissue and applied it to generate proteomic profiles of sarcoma archival material, adding an additional level of detail to our molecular profiling. We have also generated cell lines with acquired pazopanib resistance to investigate mechanisms potentially employed by tumours to escape the effects of treatment.


Assembling a pazopanib-treated tumour tissue cohort

Report Figure 1: Tissue microarray of pre-treatment tumour tissue

Figure 1: Tissue microarray of pre-treatment tumour tissue.

Clinically annotated, good quality tumour tissue from sarcoma patients is a valuable resource that can form the basis for important translational research. Historically, the treatment of sarcoma was often not centralised and processes for adequate archiving of tissue specimen were not in place. More recently, high quality tumour banks have been maintained by centralised sarcoma units and have provided material for many important studies. At our institution, we have the benefit of a diagnostic archive dating back more than 20 years, containing cohorts of both more common and rare STS subtypes that among the largest in the world. From this archive, we have identified pre-treatment tissue from close to 50 patients who received pazopanib, representing a cohort larger than any currently reported. Using this material, we have constructed a tissue microarray that will allow for high throughput analysis of protein expression in these tumours and correlation with clinical outcome. Our focus so far has been on expression of receptor tyrosine kinase targets of pazopanib and their downstream signalling intermediaries (Figure 1).

In addition to archival FFPE tissue, fresh tumour biopsies were collected from patients enrolled in the EMPRASS study (Elucidation of Molecular Signatures of Pazopanib Response in Soft Tissue Sarcomas including Solitary Fibrous Tumours), a prospective tissue-driven translational study of pazopanib treatment. In addition to pre-treatment biopsies, peripheral blood and dual energy CT staging scans were performed at pre-treatment baseline and at 8 weekly intervals until progression.
In this protocol, patients underwent pre-treatment dual energy CT imaging, blood testing and percutaneous biopsy of tumour prior to commencement of pazopanib. Bloods and imaging were repeated during treatment up until the point of progression. Pre-treatment fresh frozen tissue from 5 patients was available for analysis from this study, now closed following withdrawal of CDF support of the drug.


Sizeable tissue cohorts are a valuable resource for identifying molecular features that are shared between tumours that exhibited clinical response to pazopanib therapy.

A multi-omic approach to molecular profiling of STS

Report Figure 2: Processing and analysis of fresh frozen tumour biopsies

Figure 2: Processing and analysis of fresh frozen tumour biopsies

Prospectively collected fresh frozen tumour samples were collected by percutaneous biopsy of metastatic disease sites from 5 patients (2 leiomyosarcoma, 2 solitary fibrous tumour, 1 malignant PEComa) prior to study closure following withdrawal of CDF drug funding.  Multiple tumour cores from each patient were available for analysis by the Protein Networks team at the Institute of Cancer Research. H&E staining was performed to confirm >75% viable tumour content of each core prior to parallel extraction of DNA, RNA and protein. These materials were used for downstream applications that included immunohistochemistry, whole exome sequencing, gene expression microarray and mass spectrometry-based proteomics (Figure 2).

Whole exome sequencing was performed on DNA from all suitable cores from each patient, along with germline DNA derived from matched peripheral blood. Following sequence alignment, single nucleotide variants (SNV) and small insertions or deletions (indels) were identified and filtered by confirmed or predicted functional impact using the IMPACT bioinformatics pipeline.6 In our five patient cohort, the number of unique, functionally significant SNVs and indels per tumour ranged between 12- 52 and 582 -943 respectively (Table 2). Only a minority proportion of these mutations were shared between more than 2 cores from the same tumour – between 17-48% of SNVs and 8-14% of indels – suggesting that only a minority of these genomic events were likely to represent core disease-driving mutations.

Table 2. Number of SNVs and Indels Identified in Fresh Frozen Tissue Cohort
Tumour Total unique SNVs SNVs shared by >50% of cores Total unique Indels Indels shared by >50% cores
SFT 1 15 5 (33%) 582 78 (13%)
SFT 2 12 2 (17%) 761 69 (9%)
LMS 1 30 6 (20%) 661 51 (8%)
LMS 2 52 25 (48%) 943 132 (14%)
PEComa 22 6 (27%) 723 72 (9%)

Missense mutations of predicted functional impact and with existing annotation within COSMIC database are shown in Table 3.  Despite the genomic complexity seen in even this small case series, likely significant mutations in key cancer genes were identified and shared between several tumours. The most significant of these was p53.

Genome-wide transcriptional analysis using Illumina HT12 BeadChip microarray was performed on RNA from all cores from each tumour. Gene set enrichment analysis of normalised transcript abundance data was used to produce a gene expression profile for each tumour core. Unsupervised clustering was then used to assess expression signatures that could be compared between cores from the same tumour and between different tumours.

Table 3. Key Mutations Identified in Fresh Tissue Cohort, Predicted to Have Functional Impact
Gene Mutation Sample
KMT2C p.Y987H LMS2
TP53 p.Q136E
HRH2 p.V39I SFT1
KIR3DL1 p.R270C LMS2 + SFT1

Whilst genomic complexity is seen in common STS subtypes, the number of clear oncogenic driver mutations that can be identified is often low. The integration of genomic, transcriptomic and proteomic data derived from the same tumour is an attractive approach that will provide a more sophisticated appreciation of disease biology and assist identification of molecular biomarkers.

TP53 is commonly mutated in STS and may influence response to pazopanib and other small molecule inhibitors.

TP53 is among the most commonly mutated gene in STS, occurring in over a third of cases included in the provisional 265 patient STS cohort reported by The Cancer Genome Atlas project. As is the case in other cancer types, there is no mutation hotspot seen within STS genomes, with described mutations either missense point mutations or in-frame deletions within exonic functional domains or intronic splice regions, or truncating frameshift indels occurring throughout the length of the gene. TP53 dysfunction has been implicated as an important mediator of angiogenic pathways that are targeted by pazopanib, and a recently reported 20 patient STS case series found significantly greater PFS with pazopanib treatment in tumours with TP53 mutations compared to wildtype cases.7 We identified missense mutations of predicted deleterious functional impact in 3 of our 5 fresh frozen tumours, with mutations demonstrable in 4 of 4 cores in 2 cases, and 3 of 4 cores in the other, suggesting an early driver event. Supervised clustering of matched gene expression signatures was used to identify annotated gene set pathways upregulated in TP53 mutant tumour cores which are currently under further investigation in our laboratory.

To further explore the potential role of TP53 in mediating pazopanib effect, we have performed Sanger sequencing of exonic TP53 on DNA derived from archival FFPE tumour samples taken from 35 STS patients subsequently treated with pazopanib at the Royal Marsden. This cohort will be expanded to provide greater statistical power to investigate for association with clinical outcome in attempt to replicate previous findings.


TP53 dysfunction is key to the biology of many cancers and has been demonstrated in a significant proportion of STS. Evidence exists that TP53 mutation may impact upon the likelihood of pazopanib effect. Our work will identify molecular profiles associated with TP53 mutation at gene expression and protein level and aims to further investigate its role in determining response to pazopanib.

Mass spectrometry proteomics

Report Figure 3: Gene ontology of 1261 unique protein IDs

Figure 3: Gene ontology of 1261 unique protein IDs

The demonstrated clinical effect of pazopanib across STS subtypes of distinct genomic and transcriptomic molecular profiles suggests that processes including epigenetic modification, microenvironment factors and post-translational protein modification play an important role in determining pazopanib response. Using mass-spectrometry assessment of protein extracted from tumour tissue, we have characterised the levels of protein expression that are targeted by pazopanib. The application of such techniques to archival FFPE tissue poses particular challenges related to the protein modifications and degradation that occur during tumour preservation and storage. Protein crosslinks resulting from formalin-induced methylene bridges between amino acid side chains must be reversed prior to tryptic digestion and mass spectrometry analysis. However, stochastic factors in this process result in unpredictable peptide modification that must be accounted for when mapping mass spectra to reference protein databases. We have now optimised in FFPE samples taken from STS patients prior to pazopanib therapy a method for analysis that has been able to identify over 1200 unique proteins expressed in more than half of which enriched into cellular or metabolic function gene ontology groups (Figure 3). This will provide a detailed overview of an additional level of STS biology that can be integrated with genomic and transcriptomic data.


Mass spectrometry-based analysis of protein expression and phosphorylation in archival tissues will provide novel insights into the driving molecular pathways that characterise STS subtypes and treatment responses. This data can be integrated with genomic and transcriptomic data in the development of more powerful biomarkers.

Establishment of pazopanib-resistant soft tissue tumour cell line

Report Figure 4: Colony formation assay of A204 cell line with pazopanib resistance

Figure 4: Colony formation assay of A204 cell line with pazopanib resistance

A large proportion of sarcoma cell lines are intrinsically resistant to pazopanib. However, in a screen of a panel of 15 soft tissue tumour cell lines, we have identified a pair of cell lines which are sensitive to this drug.  Colony formation assays demonstrate that while subjecting one of these lines (A204) with pazopanib leads to a reduction in colony numbers, a small number of pazopanib resistant colonies remain after two weeks of treatment (Figure 4). Through culture in presence of long term escalating dose of drug, we have developed a pazopanib-resistant cell line in order to investigate the key molecular pathways that confer pazopanib sensitivity and mechanisms by which tumour cells may develop resistance. Comparison of the proteome and phosphoproteome of sensitive and resistant cells is currently underway to discover potential resistance mechanisms.


In vitro models are important tools for the investigation and validation of molecular findings produce by translational tissue-based profiling. Our work comparing the molecular profiles of soft tissue tumour cell lines with differing pazopanib sensitivities will allow for the identification of novel resistance mechanisms that can then be taken on to validation in animal models as well as interrogating clinical samples for evidence of such resistance mechanisms emerging in patients.


Our work will add significantly to existing knowledge of aspects of STS biology that dictate disease response to pazopanib and influence the development of primary or secondary treatment resistance. Our ultimate goals are to develop molecular assays that can be used in the prospective identification of STS patients more likely to receive benefit from pazopanib treatment, and to provide biological rationale for therapeutic combinations that can be used to prevent or circumvent pazopanib resistance. The central role of TP53 dysfunction in coordinating a range of important aspects of cancer biology that are targeted by pazopanib is highlighted by the results of work by us and others. We plan to expand the size of our pazopanib-treated tumour cohort to further reinvestigate the association of TP53 mutation and TP53 dysfunction-related gene expression signatures with clinical outcome following pazopanib therapy. The likely complex interplay between these and other kinases will be further investigated by integrating proteomic data from mass spectrometry experiments with IHC and gene expression profiles. Changes in these profiles between matched pre- and post-treatment samples will provide additional information as to the changes in kinase signalling that influence the development of pazopanib resistance.

By Alex Lee, MBChB
The Royal Marsden in the United Kingdom

Ian Judson, MD
The Royal Marsden in the United Kingdom

and Paul Huang, PhD
The Institute of Cancer Research in the United Kingdom


1. Van der Graaf, W. T. et al. Pazopanib for metastatic soft-tissue sarcoma (PALETTE): a randomised, double-blind, placebo-controlled phase 3 trial. Lancet 379, 1879–1886 (2012).

2. Sleijfer, S. et al. Pazopanib, a multikinase angiogenesis inhibitor, in patients with relapsed or refractory advanced soft tissue sarcoma: a phase II study from the European organisation for research and treatment of cancer-soft tissue and bone sarcoma group (EORTC study 620. J. Clin. Oncol. 27, 3126–32 (2009).

3. Kasper, B. et al. Long-term responders and survivors on pazopanib for advanced soft tissue sarcomas: subanalysis of two European Organisation for Research and Treatment of Cancer (EORTC) clinical trials 62043 and 62072. Ann. Oncol. 25, 719–24 (2014).

4. Van Der Graaf, W. T. A. et al. PALETTE: Final overall survival (OS) data and predictive factorsfor OS of EORTC 62072/GSK VEG110727, a randomized double-blind phase III trial of pazopanib versus placebo in advanced soft tissue sarcoma (STS) patients. ASCO Meet. Abstr. 30, 10009 (2012).

5. NHS England. Cancer Drugs Fund Decision Summary January 2015: Pazopanib for the treatment of previously treated metastatic non-adipocytic soft tissue sarcomas. (2015). at <>

6. Hintzsche, J. et al. IMPACT: a whole-exome sequencing analysis pipeline for integrating molecular profiles with actionable therapeutics in clinical samples. J. Am. Med. Inform. Assoc. ocw022 (2016). doi:10.1093/jamia/ocw022

7. Koehler, K., Liebner, D. & Chen, J. L. TP53 mutational status is predictive of pazopanib response in advanced sarcomas. Ann. Oncol. 26, 2361–2362 (2015).

8. Kumar, R. et al. Pharmacokinetic-pharmacodynamic correlation from mouse to human with pazopanib, a multikinase angiogenesis inhibitor with potent antitumor and antiangiogenic activity. Mol. Cancer Ther. 6, 2012–21 (2007).

9. Mi, H., Poudel, S., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res. 44, D336–42 (2016).

Identifying the Molecular Determinants of Pazopanib Response in Advanced Soft Tissue Sarcoma


Pazopanib (Votrient) is a tyrosine kinase inhibitor (TKI) that increases progression free survival of most subsets of soft tissue sarcoma (STS) patients after failure of standard chemotherapy.1 A recent analysis of clinical trial data showed that a fifth of patients who were treated with pazopanib were both long term responders and long-term survivors.2 As a consequence of these data, pazopanib has been approved as a third line treatment for patients with metastatic non-adipocytic STS. Distinct STS subtypes show different response rates to pazopanib therapy. A higher survival for leiomyosarcomas and synovial sarcomas versus other STS types has been reported.3 Solitary fibrous tumours (SFT), which are fairly refractory to standard therapy, also respond to pazopanib treatment.4 The mechanisms of disease control for these three tumour subtypes are not well established and it is not currently possible to predict a priori which patients will benefit from this treatment.

Molecular profiling has the potential to define biomarkers that would enable patient stratification for response to pazopanib therapy. Significantly, while synovial sarcomas and leiomyosarcomas both display improved survival when treated with pazopanib, these two subtypes are driven by distinct genetic drivers with transcriptomic profiles that cluster into unique molecular groups.5 Collectively, these data suggest that transcriptomic analysis alone is insufficient to explain the clinical response to pazopanib. This lack of concordance may reflect genomic-independent factors such as epigenetic alterations, post-translational protein modifications or tumour microenvironmental influences. A recent computational analysis demonstrates that an integrated strategy incorporating transcriptomic and phosphoproteomic data can readily uncover novel mechanisms of tumourigenesis and candidate drug targets in kinase induced signalling.6 An integrated molecular profiling dataset may similarly improve patient stratification and therapeutic target identification in STS patients.

Purpose of Investigation

We have a poor understanding of the molecular factors that dictate sensitivity to pazopanib and cannot accurately predict which patients are likely to respond to this drug. This proposal seeks to address these challenges through the following specific aims:

1. To identify a phosphoproteomic signature for pazopanib response.

2. To investigate the utility of integrated molecular profiling analysis for the identification of predictive markers of pazopanib sensitivity.

3. To develop a defined assay for biomarker validation in future cohorts of STS patients.

Using a tool known as mass spectrometry, we propose to generate individualised high resolution signalling profiles of baseline tumours from an investigational study of STS patients that will be treated with pazopanib at the Royal Marsden Hospital (RMH) Sarcoma Unit. The tumours will additionally be subjected to transcriptomic analysis using RNA-Seq and genomic analysis using high-resolution Array-based Comparative Genomic Hybridisation (aCGH) technology. These molecular data will be integrated and correlated with patient survival to identify potential biomarkers of pazopanib response. In light of the small number of patients in this study, the cohort will be augmented by incorporating a retrospective analysis of pre-existing specimens from an additional 25 patients that have previously been treated with pazopanib at the RMH.4 At the end of the study period, we anticipate the identification of promising candidate markers for drug response which will be experimentally validated in future studies in STS cell line models and clinical cohorts.

Aim 1: Identification of a phosphoproteomic signature for pazopanib response.

The Sarcoma Unit at the RMH is conducting an open-label, non-randomised observational study of patients with advanced soft tissue sarcoma treated with pazopanib. Baseline biopsies will be collected two weeks prior to treatment for molecular profiling analysis. To determine the tumour cell signalling networks that are activated in the tissue, phosphoproteomic analysis will be performed using mass spectrometry assays developed in Dr Huang’s laboratory. This assay measures 200 phosphorylation sites across 100 phosphorylated proteins representing major components of tyrosine kinase signalling pathways. The major kinase targets of pazopanib are well represented in this assay (Table 1).

Table 1: A Subset of Kinase Targets Inhibited by Pazopanib

Figure 1

Figure 1

Tissues which are assessed to have >70% tumour cells will be subjected to mechanical disruption and cell lysis. Extracted proteins will be processed and subjected to mass spectrometry analysis as previously described.7 The individualised proteomic profiles will be quantified and a subset of the proteins identified from proteomic experiments will be independently confirmed with western blotting where commercial antibodies are available. We will subsequently perform a statistical comparative analysis of the proteomic data between different tumour subtypes which will enable the identification of unique molecular signatures between different STS subtypes and may shed light on candidate molecular signalling drivers in leiomyosarcoma, SFT and synovial sarcoma (Fig 1. Dataset A). To identify potential signalling markers of pazopanib response, we will apply computational analysis to visualize the correlations between our phosphoproteomic profiles and the clinical endpoint of progression free survival. This computational analysis will allow us to prioritize a phosphoproteomic signature that correlates with pazopanib response (Fig 1. Dataset B).

Aim 2: To investigate the utility of integrated molecular profiling analysis for the identification of predictive markers of pazopanib sensitivity.

As part of this study, we propose to evaluate the utility of unbiased integrated molecular profiling analysis of genomic, transcriptomic and phosphoproteomic data to identify candidate predictive markers of pazopanib response (A workflow of the proposed analysis is summarised in Figure 1). RNA will be extracted from tissue specimens and subjected to transcriptomic analysis by RNA-Seq while array-based CGH will be used for genomic level analysis with DNA extraction, labelling, array hybridisation and image acquisition being performed as described.8,9 Integration of the genomic and transcriptomic analysis will be implemented in a similar fashion as previously described to identify molecular drivers in distinct breast cancer subtypes.8,9

To identify the genes which are significantly associated with clinical endpoints of pazopanib response, two-class unpaired significance analysis of microarray (SAM) will be performed. Genes that are differentially expressed in pazopanib sensitive versus resistant tumours will then be integrated with the phosphoproteomic data using Ingenuity Pathway Analysis (IPA) and visualised using the Cytoscape software. This strategy will reveal the networks and pathways that have been enriched in genes/proteins whose expression or activation levels correlate with patient outcome. To that end, our proposed analysis will identify a proteogenomic signature of pazopanib response. Additionally, since components of these signalling networks/pathways may constitute therapeutic targets, our analysis will also reveal novel candidates for future drug development.

Due to the small number of patients in this prospective study, the cohort will be augmented by a retrospective analysis of pre-existing specimens from STS patients who have undergone pazopanib therapy at the RMH.H4 Genes and proteins that are identified to be correlated with pazopanib response in our integrated molecular profiling analysis will be measured using in-situ hybridisation (ISH) and immunohistochemistry (IHC) respectively. Univariate and multivariate Cox regression analysis will be used to identify relationships between abundance of genes and activated proteins to patient survival.

Aim 3. To develop a defined phosphoproteomic assay for biomarker validation in future cohorts of STS patients.

Multiplexed biomarker analysis of tumour specimens remains a major challenge in clinical research. This problem is compounded by the lack of good specific antibodies for measuring phosphorylated proteins in tissue sections. In this final aim, we propose to develop a targeted STS-specific phosphoproteomic assay based on the proteomic signatures defined in Aims 1 and 2. This assay will be deployed to complement standard tissue analysis in multiplexed biomarker measurements. A stable isotope-labelled “heavy” peptide library containing the critical proteins important for predicting pazopanib response in STS patients will be generated and optimised for the reproducible and robust analysis of tissue using mass spectrometry. We aim to develop an assay for pazopanib treatment in STS which can be readily implemented into clinical studies.

Impact Statement and Future Directions

Pazopanib has been approved for advanced and recurrent STS but a significant patient population remains resistant to this drug. This research has the potential to influence patient stratification and personalisation of pazopanib therapy. By identifying STS subtypes whose proteomic profiles are prognostic for drug resistance, it is likely that we will be able to better assess which patients are most likely to respond to targeted therapy and spare those who are unlikely to benefit from unnecessary chemotherapy. The development of a STS-specific phosphoproteomic assay will also result in the rapid assessment of patients which will aid clinical decisions on therapy. The proteomic characterisation of three distinct STS subtypes will generate an invaluable resource for the sarcoma research community to mine for additional targets. In the longer term, we anticipate that our integrated molecular profiling studies will facilitate the identification of signalling proteins and genes that mediate resistance and drive the development of improved combination therapeutic strategies to circumvent pazopanib resistance.

By Ian Judson, MD
The Royal Marsden in the United Kingdom

and Paul Huang, PhD
The Institute of Cancer Research in the United Kingdom


1. van der Graaf, W.T., et al., Pazopanib for metastatic soft-tissue sarcoma (PALETTE): a randomised, double-blind, placebo-controlled phase 3 trial. Lancet, 2012. 379(9829): p. 1879-86.

2. Kasper, B., et al., Long-term responders and survivors on pazopanib for advanced soft tissue sarcomas: subanalysis of two European Organisation for Research and Treatment of Cancer (EORTC) clinical trials 62043 and 62072. Ann Oncol, 2014. 25(3): p. 719-24.

3. Sleijfer, S., et al., Pazopanib, a multikinase angiogenesis inhibitor, in patients with relapsed or refractory advanced soft tissue sarcoma: a phase II study from the European organisation for research and treatment of cancer-soft tissue and bone sarcoma group (EORTC study 62043). J Clin Oncol, 2009. 27(19): p. 3126-32.

4. Maruzzo, M., et al., Pazopanib as first line treatment for solitary fibrous tumours: the Royal Marsden Hospital experience. Clin Sarcoma Res, 2015. 5: p. 5.

5. Nielsen, T.O., et al., Molecular characterisation of soft tissue tumours: a gene expression study. Lancet, 2002. 359(9314): p. 1301-7.

6. Huang, S.S., et al., Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling. PLoS Comput Biol, 2013. 9(2): p. e1002887.

7. Iwai, L.K., et al., Phosphoproteomics of collagen receptor networks reveals SHP-2 phosphorylation downstream of wild-type DDR2 and its lung cancer mutants. Biochem J, 2013. 454(3): p. 501-13.

8. Natrajan, R., et al., Functional characterization of the 19q12 amplicon in grade III breast cancers. Breast Cancer Res, 2012. 14(2): p. R53.

9. Natrajan, R., et al., An integrative genomic and transcriptomic analysis reveals molecular pathways and networks regulated by copy number aberrations in basal- like, HER2 and luminal cancers. Breast Cancer Res Treat, 2010. 121(3): p. 575-89.

Grant Funds Research on Pazopanib (Votrient) Response in Sarcoma

Pazopanib (Votrient)

April 28, 2015: The Liddy Shriver Sarcoma Initiative is pleased to announce the funding of a $50,000 grant for sarcoma research at the Institute of Cancer Research in the United Kingdom. In the study, Paul Huang, PhD and Ian Judson, MD will be working with the targeted therapy pazopanib (Votrient) in advanced soft tissue sarcomas. The investigators aim to better understand which patients are most likely to respond to paxopanib in order to spare those who are unlikely to benefit from unnecessary chemotherapy.

Sarcomas are rare cancers that are often aggressive and difficult to cure. Researchers continue to investigate new therapies that might improve outcomes for sarcoma patients. Pazopanib, a drug that targets a class of genes known as "kinases," was recently approved for the treatment of advanced sarcomas.

According to the Drs. Huang and Judson, some patients respond well to pazopanib, but the drug does not work in all cases. This means that there are subsets of patients who are exposed to the potential side effects of treatment for little or no benefit. Clinical trials have shown that leiomyosarcoma, synovial sarcoma and solitary fibrous tumour patients who are treated with pazopanib have a higher survival rate than those with other sarcoma subtypes. The reasons for these differences in drug response are not known, and are somewhat puzzling, considering that each of these sarcoma subtypes has a different genetic makeup.

At present, it is challenging to predict who will respond to pazopanib therapy. It would be helpful to identify tumour markers that enable doctors to choose the right patients for pazopanib treatment. This project should reveal the mechanisms of pazopanib resistance and will facilitate the identification of new candidates for prognostic biomarkers and combination therapy in leiomyosarcoma, synovial sarcoma and solitary fibrous tumour patients.

The Challenge of Pazopanib Resistance

By Dr. Paul Huang

There is currently a poor understanding of the determinants of pazopanib resistance, which hinders the development of predictive biomarkers for early patient stratification and the rational identification of new combination targets to overcome resistance. This study aims to address this challenge by identifying the molecular mechanisms of intrinsic pazopanib resistance in a panel of leiomyosarcoma, synovial sarcoma and solitary fibrous tumour specimens using an integrated proteomic, transcriptomic and genomic strategy.

We will employ phosphoproteomics, in combination with RNA Sequencing and Array-based Comparative Genomic Hybridisation technologies to characterise the proteins and genes in tumours from sarcoma patients who will undergo pazopanib therapy. A bioinformatics analysis of the molecular profiling data with patient outcome and drug response will enable the identification of biomarkers for pazopanib response. Significantly, this project will advance our understanding of the mechanisms of pazopanib resistance and will facilitate the identification of new candidates for prognostic biomarkers and combination therapy in leiomyosarcoma, synovial sarcoma and solitary fibrous tumour patients.

About the Investigators

Drs. Huang and Judson

This project leverages the combined expertise of the two principle investigators: Dr Huang, a group leader at the Institute of Cancer Research in London, who will lead the laboratory component of the project and Prof. Judson, Head of the Sarcoma Unit at the Royal Marsden Hospital (RMH) and Chair of Cancer Pharmacology at the ICR, who will provide the expertise to enable the translation of our laboratory findings into the clinical setting. The questions in this study require a multi-disciplinary approach - scientists (Dr. Huang) working closely with clinicians (Prof. Judson) to understand the molecular factors that drive pazopanib resistance in sarcoma patients.


The Liddy Shriver Sarcoma Initiative gratefully acknowledges a very generous donation from the Florence & Marshall Schwid Memorial Donor Advised Fund of the Jewish Community Foundation which was responsible for the funding of this research study.

  • Figure 1
  • Report Figure 1
    Figure 1. Tissue microarray of pre-treatment tumour tissue from pazopanib treated patients, with staining for receptor tyrosine kinase expression.
  • Report Figure 2
    Example of analysis of solitary fibrous tumour case. Fresh frozen core biopsies (A) were sectioned and stained with H&E (B) to confirm viable tumour material. Immunohistochemically staining for markers were performed on further sections. DNA and RNA were extracted and used for whole exome sequencing (D – genome alignment demonstrating missense mutation of TP53 in 4 of 4 tumour cores but absent in matched germline genome) and genome-wide gene expression microarray (E – gene expression profiles of 4 separate tumour cores from same SFT case).
  • Report Figure 3
    Gene ontology of 1261 unique protein IDs from mass spectrometry analysis of single case of undifferentiated pleomorphic sarcoma. Functional grouping performed using PANTHER (9).
  • Report Figure 4
    A204 cell lines incubated with increasing dose of pazopanib shows emergence of colonies able to grow despite presence of drug. These colonies were then selected and expanded in ongoing culture with pazopanib to develop resistant cell line.