Gene expression signatures predictive of subject response to a multi-kinase inhibitor and methods of using the same

A multi-kinase inhibitor and subject technology, applied in biological testing, biochemical equipment and methods, microbial measurement/testing, etc., can solve problems such as instabilities

Active Publication Date: 2016-06-08
CROWN BIOSCI INC TAICANG
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The initial single-arm phase II study on sorafenib reported that baseline levels of phosphorylated ERK (pERK) might be a relevant marker, and some reports published serum markers (e.g., soluble c-KIT, HGF, AFP, VEGF, etc.) and sorafenib treatment outcomes, but these results are very preliminary or not constant across multiple studies

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  • Gene expression signatures predictive of subject response to a multi-kinase inhibitor and methods of using the same
  • Gene expression signatures predictive of subject response to a multi-kinase inhibitor and methods of using the same
  • Gene expression signatures predictive of subject response to a multi-kinase inhibitor and methods of using the same

Examples

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Embodiment 1

[0113] Example 1. Bulk HCC Models were sensitive or partially sensitive to sorafenib.

[0114] Experimental models that capture the oncogenic mechanisms of human HCC are critical for evaluating HCC therapy and discovering biomarkers that predict patient response. Patient-derived xenograft (PDX) models reflect the patient's histopathology and genetic profile (2-7). Currently, a large group of HCC-PDX models (named or patient avatar). In a clinical trial-like study, sorafenib was tested on a randomized cohort of HCC-HuPrime. The study identified "responders and non-responders." Next, the expression of these models was analyzed using microarray GeneChip technology. By applying statistical analysis, specific molecular markers, or The gene expression signature, consisting of only a few genes, could predict the response of HCC patients to sorafenib and was also suitable for developing companion diagnostics for patient staging in the clinic. This signature can be used a...

Embodiment 2

[0117] Example 2. HCC-PDX can be divided into three main classes based on global gene expression profiling do not

[0118] HCC is a disease with different types. Whole-gene expression profiling of patient tumor samples has revealed that HCC can be divided into three major subtypes. Recently, transcriptome analysis divided HCC into three major categories using different clinical parameters as well as cell differentiation 1 . These categories are S1 (stem-like group, presence of WNT pathway and activation of TGF-β), S2 (activation of MYC and AKT) and S3 (hepatocyte differentiation). HCC-PDX is believed to be a predictive experimental model due to the maintenance of the original patient histopathology and genetic profile 2，3 . This study attempts to test this hypothesis by illustrating that HCC avatars have similar genomic profiles to patient tumors in terms of classification and biological properties.

[0119] First, global gene expression profiling was performed for al...

Embodiment 3

[0125] Example 3. Identification of HCC gene expression signatures predictive of response to sorafenib.

[0126]Using a statistical approach based on linear regression, global gene expression levels and the effect of Sorafenib treatment on HCC-PDX as measured by %ΔT / ΔC as described above were used to identify predictive biomarkers (i.e., gene signatures). ). Based on pre-set statistical criteria, including p-value 4-fold for all tested HCC-PDXs, and no outliers in linear regression analysis, it can be concluded that the composition of 8 genes exhibited good predictive properties signs (eg, Figure 1 and Table 3). It is worth noting that markers with more genes will yield better associations and smaller p-values, while having less practical value in clinical applications. The stringency of the preset criteria will determine the number of marker genes.

[0127] Although these marker genes were only identified using statistical analysis without any filtering before and after ...

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Abstract

Gene expression signature predictive of cancer patient response to multi-kinase inhibitor is disclosed. Also disclosed are methods predicting the efficacy of the multi-kinase inhibitor for treating cancer in a patient. Also disclosed are methods for distinguishing responders from non-responders to a multi-kinase inhibitor in treating cancer. Also disclosed are methods for treating a cancer patient with a multi-kinase inhibitor.

Description

[0001] Cross References to Related Applications [0002] This application claims priority and benefit from International Patent Application Serial No. PCT / CN2013 / 082487 filed on August 28, 2013, which is hereby incorporated by reference in its entirety for all purposes. field of invention [0003] The present invention relates to the treatment of subjects and for use with multikinase inhibitors, such as Sorafenib, Sunitinib, Axitinib, Vandetanib, Identification and selection of subjects treated with Pazopanib, Cabozantinib, etc., or their salts, solvates, or physiologically functional derivatives, or their mixtures. Methods, reagents and tools for prediction, diagnosis, prognosis and treatment of diseases such as cancer are provided. Background of the invention [0004] Hepatocellular carcinoma (HCC) is an aggressive tumor and the fifth most deadly cancer in the world. It is especially prevalent in East Asia and men (ParkinDM, BrayF, FerlayJ, PisaniP.Globalcancerstatistics...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/48
CPCC12Q1/6886C12Q2600/106C12Q2600/158A61P1/16A61P35/00A61P43/00C12Q1/6883
Inventor 郭盛D·陈J·张蔡杰H·Q·李
Owner CROWN BIOSCI INC TAICANG
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