Gene expression profiling for the investigation of soft tissue sarcoma pathogenesis and the identification of diagnostic, prognostic, and predictive biomarkers

8593 Background: Treatment with ipilimumab, a fully human anti-CTLA-4 antibody approved for treatment of metastatic melanoma (MM), is associated with a number of immune-mediated Adverse Events (imAEs) such as colitis and skin rash. Predictive biomarkers that can help identify patients (pts) who might experience these imAEs could enhance the management of these toxicities. Methods: Gene expression profiling (using Affymetrix gene chip HT-HG-U133A) was performed on the whole blood samples from 162 MM pts at baseline, 3 and 11 weeks after the start of ipilimumab treatment in two phase II clinical trials (CA184004 and -007). Overall, 49 pts experienced Grade 2 or higher GI-imAE (G2+) during the course of treatment. A repeated measures ANOVA was used to evaluate the differences in mean expression levels between the two groups and at the three time points. Uncorrected p-value of 0.05 was used as a cutoff for this analysis. Results: In baseline samples, 27 probe sets showed differential mean expression (≥ 1.5 fold, p < 0.05) between G2+ pts and others. Most of these genes belonged to three functional categories: immune system, cell cycle or intracellular trafficking. Changes in gene expression over time were also characterized. In the G2+ pts, 58 and 247 genes had a ≥ 1.5 fold (p < 0.05) change in expression from baseline to week 3 and 11 post-treatment, respectively, compared to 17 and 73 in other pts. In particular, the on-treatment increases of the expression of CD177 and CEACAM1, two neutrophil activation markers, were closely associated with G2+ GI-imAE, suggesting a possible role of neutrophils in ipilimumab associated GI-imAEs. In addition, the expression of several Ig genes increased over time, with higher increases in the G2+ pts. These observations were reproduced in another ipilimumab monotherapy study in MM (CA184078). Conclusions: Gene expression profiling of peripheral blood resulted in the identification of a set of potential biomarkers that may be predictive of severe GI-imAEs before, or early in the course of treatment with ipilimumab. Further validation of these biomarkers in a larger patient cohort is warranted.

Download Full-text

Predictive and prognostic transcriptomic biomarkers in soft tissue sarcomas

npj Precision Oncology ◽

10.1038/s41698-021-00157-4 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Eve Merry ◽

Khin Thway ◽

Robin L. Jones ◽

Paul H. Huang

Keyword(s):

Gene Expression ◽

Clinical Practice ◽

Soft Tissue ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Cancer Biology ◽

Treatment Strategies ◽

Soft Tissue Sarcomas ◽

Selective Treatment ◽

Genomic Grade Index

AbstractSoft tissue sarcomas (STS) are rare and heterogeneous tumours comprising over 80 different histological subtypes. Treatment options remain limited in advanced STS with high rates of recurrence following resection of localised disease. Prognostication in clinical practice relies predominantly on histological grading systems as well as sarcoma nomograms. Rapid developments in gene expression profiling technologies presented opportunities for applications in sarcoma. Molecular profiling of sarcomas has improved our understanding of the cancer biology of these rare cancers and identified potential novel therapeutic targets. In particular, transcriptomic signatures could play a role in risk classification in sarcoma to aid prognostication. Unlike other solid and haematological malignancies, transcriptomic signatures have not yet reached routine clinical use in sarcomas. Herein, we evaluate early developments in gene expression profiling in sarcomas that laid the foundations for transcriptomic signature development. We discuss the development and clinical evaluation of key transcriptomic biomarker signatures in sarcomas, including Complexity INdex in SARComas (CINSARC), Genomic Grade Index, and hypoxia-associated signatures. Prospective validation of these transcriptomic signatures is required, and prospective trials are in progress to evaluate reliability for clinical application. We anticipate that integration of these gene expression signatures alongside existing prognosticators and other Omics methodologies, including proteomics and DNA methylation analysis, could improve the identification of ‘high-risk’ patients who would benefit from more aggressive or selective treatment strategies. Moving forward, the incorporation of these transcriptomic prognostication signatures in clinical practice will undoubtedly advance precision medicine in the routine clinical management of sarcoma patients.

Download Full-text

Chemo-response biomarker discovery via expression profiling using soft-tissue sarcoma xenografts

Journal of Clinical Oncology ◽

10.1200/jco.2006.24.18_suppl.9569 ◽

2006 ◽

Vol 24 (18_suppl) ◽

pp. 9569-9569

Author(s):

S. Bruheim ◽

Y. Xi ◽

J. Ju ◽

O. Fodstad

Keyword(s):

Gene Expression ◽

Soft Tissue ◽

Soft Tissue Sarcoma ◽

Expression Profiling ◽

Biomarker Discovery ◽

Expression Profiles ◽

Clinical Samples ◽

Marker Genes ◽

Candidate Marker ◽

General Response

9569 Background: Soft-tissue sarcoma (STS) constitute a heterogeneous group of tumours of mesenchymal origin. Whereas the mainstay of treatment has been surgery and radiation, these tumours are generally considered as quite chemoresistant. However, it is well known that subgroups of patients benefit from chemotherapy. Markers that could predict drug response would therefore be beneficial for the management of this malignancy. We have previously established panel of 17 unique human soft tissue xenografts, representing 7 different histological subgroups and assessed their responsiveness to doxorubicin, ifosfamide, etoposide, and cisplatin. We wanted to utilize these xenografts as a model system to discover for novel candidate marker genes for STS chemo-response. Methods: GE Uniset Human 20K microarrays were used to obtain gene expression profiles from the each xenografts. One-way ANOVA test with a Benjamini-Hochberg multiple test correction allowing a false discovery rate of 5% was used to identify genes with significantly differential expression. Results: Doxorubicin, ifosfamide, etoposide and cisplatin were efficient in 6/17, 10/17, 1/17 and 7/17 xenografts respectively. However, in the expression profiles obtained none of the genes showed significantly correlation with chemo-responsiveness to any of the drugs. Two of the xenografts, TAX 1 and TAX 2, both originate from a malignant fibrous histiocytoma (MFH) in the same patient, but show strikingly different sensitivity to ifosfamide (TAX1 resistant, TAX2 sensitive). When triplicate hybridizations of TAX1 and 2 were compared, 294 genes met the above criteria. In addition we identified a subset of 122 genes that were flagged absent in one of the specimens, present in the other. Among genes with an already described role in mediating drug resistance are GST-pi and glutathione peroxidase. Taken together, these results indicate that discovery of general response markers in STSs may be difficult due to the heterogeneity of the different subgroups constituting this malignancy. Conclusions: Gene expression profiling of the TAX 1 and TAX 2 xenografts revealed a number of interesting candidate marker genes for ifosfamide sensitivity of MFH. This list of genes will be further refined by validation in clinical samples. No significant financial relationships to disclose.

Download Full-text

Custom microarray for glycobiologists: considerations for glycosyltransferase gene expression profiling

Biochemical Society Symposium ◽

10.1042/bss0690135 ◽

2002 ◽

Vol 69 ◽

pp. 135-142 ◽

Cited By ~ 12

Author(s):

Elena M. Comelli ◽

Margarida Amado ◽

Steven R. Head ◽

James C. Paulson

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Affymetrix Genechip ◽

Microarray Technology ◽

Gene Arrays ◽

Glycosyltransferase Gene

The development of microarray technology offers the unprecedented possibility of studying the expression of thousands of genes in one experiment. Its exploitation in the glycobiology field will eventually allow the parallel investigation of the expression of many glycosyltransferases, which will ultimately lead to an understanding of the regulation of glycoconjugate synthesis. While numerous gene arrays are available on the market, e.g. the Affymetrix GeneChip® arrays, glycosyltransferases are not adequately represented, which makes comprehensive surveys of their gene expression difficult. This chapter describes the main issues related to the establishment of a custom glycogenes array.

Download Full-text