Cancer Res Treat.  2023 Apr;55(2):671-683. 10.4143/crt.2022.251.

Comprehensive Molecular Characterization of Soft Tissue Sarcoma for Prediction of Pazopanib-Based Treatment Response

• Hong JY
• Cho HJ
• Yun KH
• Lee YH
• Kim SH
• Baek W
• Kim SK
• Lee Y
• Choi YL
• Kwon M
• Kim HS
• Lee J

• KMID: 2541254
• DOI: http://doi.org/10.4143/crt.2022.251

Abstract

Purpose
Even though pazopanib, a multitargeted tyrosine kinase inhibitor, has been approved for refractory soft tissue sarcoma (STS), little is known about the molecular determinants of the response to pazopanib. We performed integrative molecular characterization to identify potential predictors of pazopanib efficacy.
Materials and Methods
We obtained fresh pre-treatment tumor tissue from 35 patients with advanced STS receiving pazopanib-based treatment. Among those, 18 (51.4%) received pazopanib monotherapy, and the remaining 17 (48.6%) received pazopanib in combination with durvalumab, programmed death-ligand 1 blockade. Whole-exome and transcriptome sequencing were performed for each tumor and patient germline DNA.
Results
Of the 35 patients receiving pazopanib-based treatment, nine achieved a partial response (PR), resulting in an objective response rate (ORR) of 27.3%, and the median progression-free survival (PFS) was 6.0 months. Patients with CDK4 amplification (copy ratio tumor to normal > 2) exhibited shorter PFS (3.7 vs. 7.9 months, p=2.09×10–4) and a poorer response (ORR; 0% vs. 33.3%) compared to those without a gene amplification (copy ratio ≤ 2). Moreover, non-responders demonstrated transcriptional activation of CDK4 via DNA amplification, resulting in cell cycle activation. In the durvalumab combination cohort, seven of the 17 patients (41.2%) achieved a PR, and gene expression analysis revealed that durvalumab responders exhibited high immune/stromal cell infiltration, mainly comprising natural killer cells, compared to non-responders as well as increased expression of CD19, a B-cell marker.
Conclusion
Despite the limitation of heterogeneity in the study population and treatment, we identified possible molecular predictors of pazopanib efficacy that can be employed in future clinical trials aimed at evaluating therapeutic strategies.

Keyword

Soft tissue sarcoma; Pazopanib; Immune checkpoint inhibitors; Whole exome sequencings; Whole transcriptome sequencing

Figure

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  Fig. 1 Molecular landscape and response to pazopanib-based treatment. Integrated plot of clinical and molecular features. From top to bottom, panels indicate: Waterfall plot represents percentage of maximum tumor reduction as assessed according to Response Evaluation Criteria in Solid Tumor 1.1 criteria; the number of mutations; clinical characteristics including primary site, histological subtype, best response, treatment (pazopanib vs. pazopanib and durvalumab combination), and percentage of alterations (fusions, mutations, and somatic copy number alterations). Patient identity number is provided in S2 Table. CN, copy number; DDLPS, dedifferentiated liposarcoma; DSRCT, desmoplastic small round cell tumor; G-LMS, gynecological leiomyosarcomas; MPNST, malignant peripheral nerve sheath tumors; NA, not available; SS, synovial sarcoma; ST-LMS, soft tissue leiomyosarcomas; TMB, tumor mutation burden; UPS, undifferentiated pleomorphic sarcoma.

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  Fig. 2 Response to pazopanib or pazopanib and durvalumab combination. (A) Waterfall plot represents percentage of maximum tumor reduction after treatment, as assessed according to Response Evaluation Criteria in Solid Tumor 1.1 criteria. (B) Swimmer plot. Each lane represents a single patient’s data. X-axis represents the duration of treatment for each patient. (C) Spider plots of the percentage change in the sum of target lesions by subject. CSS, clear cell sarcoma; DDLPS, dedifferentiated liposarcoma; DSRCT, desmoplastic small round cell tumor; ESS, endometrial stromal cell sarcoma; LMS, leiomyosarcomas; MFS, myxofibrosarcoma; MPNST, malignant peripheral nerve sheath tumors; SFT, solitary fibrous tumor; SS, synovial sarcoma; UPS, undifferentiated pleomorphic sarcoma.

• 

  Fig. 3 CDK4, FRS2, and HDLBP copy number alterations are genomic determinants of pazopanib resistance. (A) Prevalence of tumors harboring CDK4 gains (red) and amplifications (bule) in responders and non-responders. PD, progressive disease; PR, partial response; SD, stable disease. (B) Univariate Cox regression analysis with genetic variables. Error bars represent the 95% confidence interval. X-axis indicates log10-transformed hazard ratio. AMP, amplification/gain; DEL, deletion/loss. *p < 0.05, ***p < 0.001. (C) Kaplan-Meier curve of progression-free survival (months) based on CDK4 amplification. (D) Univariate Cox regression analysis among histologic variants. Error bars represent the 95% confidence intervals. X-axis indicates hazard ratio (log10-scaled). AIC, Akaike information criterion; DDLPS, dedifferentiated liposarcoma; LMS, leiomyosarcomas; MPNST, malignant peripheral nerve sheath tumors; Others, other sarcoma subtypes; SS, synovial sarcoma; UPS, undifferentiated pleomorphic sarcoma. *p < 0.05.

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  Fig. 4 Transcriptomic correlates of clinical response to pazopanib. (A) Gene Set Enrichment Analysis between pazopanib-sensitive and resistant patients. Significantly enriched gene sets (false discovery rate < 0.05) were clustered based on their similarity. (B) Volcano plot representation of genes differentially expressed between pazopanib-responders and non-responders. Genes with > 1 log2 fold change and an adjusted p < 0.05 are colored in red (responder DEGs), and those with < −1 log2 fold change and an adjusted p < 0.05 are colored in blue (non-responder DEGs). (C) The association between mRNA expression levels (log2(FPKM+1)) and copy number variations of CDK4. p-values were calculated using the two-sided Student t test. DEG, differentially expressed genes; FPKM, fragments per kilobase of transcripts per million mapped reads; TCR, T cell receptor.

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  Fig. 5 Transcriptomic correlates of clinical response to the pazopanib-durvalumab combination. (A) Heat map and unsupervised hierarchical clustering describing tumor microenvironment cell infiltration. Color scale indicates Z-normalized MCP scores of each type of microenvironment cell across samples. Color bar above the heatmap indicates responders (blue) and non-responders (red) to the pazopanib-durvalumab combination. (B) Immune cell fraction analysis of responding and non-responding patients receiving the pazopanib-durvalumab combination. Immune cell fractions were estimated using MCPcounter scores. *p < 0.05. p-value is from Student’s t test and not adjusted for multiple tests. (C) Gene Set Enrichment Analysis (GSEA) between sensitive and resistant patients to the pazopanib-durvalumab combination. Significantly enriched gene sets (FDR < 0.05) were clustered based on their similarity. (D, E) GSEA plot showing BIOCARTA natural killer (NK) cell pathway and Kyoto Encyclopedia of Genes and Genomes (KEGG) NK cell–mediated cytotoxicity pathway enrichment in the responder group. FDR, false discovery rate; NES, normalized enriched score. (F) Volcano plot representation of differentially expressed gene analysis between responders and non-responders to the pazopanib-durvalumab combination. Genes with > 1 log2 fold change and an adjusted p < 0.05 are colored in red, and those with < −1 log2 fold change and an adjusted p < 0.05 are colored in blue.

Reference

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