Methods for predicting risk of metastasis in cutaneous melanoma

a technology for cutaneous melanoma and risk prediction, which is applied in the direction of microbiological testing/measurement, biochemistry apparatus and processes, peptide/protein ingredients, etc., can solve the problems of inaccurate diagnosis, patient and doctor false sense of security about the extent of cancer in the patient's body, and inaccurate prognosis, etc., to achieve enhanced surveillance, accurate and objective, and low positivity rate

Inactive Publication Date: 2014-09-18
CASTLE BIOSCI
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  • Abstract
  • Description
  • Claims
  • Application Information

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Benefits of technology

[0008]There is a need in the art for a more accurate and objective method of predicting which tumors display aggressive metastatic activity. Development of an accurate molecular footprint, such as the gene expression profile assay encompassed by the invention disclosed herein, by which CM metastatic risk could be assessed from primary tumor tissue would be a significant advance forward for the field. Inaccurate prognosis for metastatic risk has profound effects upon patients including inappropriate exposure to over-treatment that includes enhanced surv

Problems solved by technology

Inaccurate prognosis for metastatic risk has profound effects upon patients including inappropriate exposure to over-treatment that includes enhanced surveillance, nodal surgery, and chemotherapy.
Patients with inaccurate diagnoses are also at risk of under-treatment; that is, cancer cells are not seen in the sentinel lymph node although they are present

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  • Methods for predicting risk of metastasis in cutaneous melanoma
  • Methods for predicting risk of metastasis in cutaneous melanoma
  • Methods for predicting risk of metastasis in cutaneous melanoma

Examples

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Effect test

example 1

Cutaneous Melanoma Metastatic Risk Genetic Signature and Biomarker Expression

[0064]Genetic expression of the discriminant genes in the signature (Table 1) was assessed in a cohort of 268 cutaneous melanoma samples using RT-PCR (FIG. 1). As shown in Table 2 below, of the 28 discriminating genes, 26 were significantly altered in metastatic melanoma tumors compared to nonmetastatic tumors (p<0.05, range 0.0366-6.08E-16), and 25 were down-regulated. Genes that were up-regulated in the metastatic tumors included SPP1, KRT6B, and EIF1B.

TABLE 1Genes included in the GEP signature able to predict metastatic risk from primary CM tumors.Gene SymbolGene NameAlternative Gene NamesBAP1_varABRCA1 associated protein-1TPDS, UCHL2, HUCEP-13, HUCEP-6,BAP_var1, BAP (a1)BAP1_varBBRCA1 associated protein-1UCHL2, HUCEP-13, HUCEP-6, BAP_var2,BAP (a2)MGPmatrix gamma-carboxyglutamicmatrix Gla protein, gamma-carboxyglutamicacidacid, GIG36, MGLAP, NTISPP1secreted phosphoprotein 1BNSP, BSPI, ETA-1, OPN, PSEC015...

example 2

Initial Training Set Development Studies and Comparison to Validation Cohort

[0065]Using JMP GENOMICS® software and clinical data analysis, a training set of 164 cutaneous melanoma samples was generated that could accurately predict the risk of metastasis based upon the 28 gene signature. The training set contained 15 stage 0 in situ melanomas, 61 stage I, 70 stage II, 17 stage III, and 1 stage IV melanomas. Metastatic risk was assessed using a radial basis machine predictive modeling algorithm, which reports class 1 (low risk of metastasis) or class 2 (high risk of metastasis). ΔCt values generated from RT-PCR analysis of the training set cohort were standardized to the mean for each gene, with a scale equivalent to the standard deviation. Analyses were also performed using KNN, PTA, and discriminant analysis to confirm the results from the RBM approach (as discussed below). The training set prediction algorithm was then validated using an independent cohort of 104 cutaneous melanom...

example 3

Analysis of 162 Sample Training Set with Multiple Predictive Modeling Methods

[0067]JMP GENOMICS® software allows for analysis using linear and non-linear predictive modeling methods. To assess whether accuracy of metastatic risk prediction for the validation cohort was limited to the RBM method, partition tree, K-nearest neighbor, logistic regression, and discriminant analysis were performed (FIGS. 2 and 3). Training set ROC, accuracy, sensitivity, specificity and K-M 5-year MFS for class 1 and class 2 cases were highly comparable to the RBM method. Highly accurate prediction of metastasis, and significantly different 5-year MFS between class 1 and class 2 cases was observed when using partition tree (FIG. 2A), K-nearest neighbor (FIG. 2B), logistic regression (FIG. 2C), or discriminant analysis (FIG. 2D). Significant differences in 5-year MFS were also observed for validation cohort class 1 and class 2 cases using partition tree (FIG. 3A), K-nearest neighbor (FIG. 3B), logistic reg...

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Abstract

The invention as disclosed herein in encompasses a method for predicting the risk of metastasis of a primary cutaneous melanoma tumor, the method encompassing measuring the gene-expression levels of at least eight genes selected from a specific gene set in a sample taken from the primary cutaneous melanoma tumor; determining a gene-expression profile signature from the gene expression levels of the at least eight genes; comparing the gene-expression profile to the gene-expression profile of a predictive training set; and providing an indication as to whether the primary cutaneous melanoma tumor is a certain class of metastasis or treatment risk when the gene expression profile indicates that expression levels of at least eight genes are altered in a predictive manner as compared to the gene expression profile of the predictive training set.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of U.S. Provisional Application No. 61 / 783,755, filed Mar. 14, 2013, the disclosure of which is explicitly incorporated by reference herein in its entirety.BACKGROUND OF THE INVENTION[0002]Cutaneous melanoma (CM) is an aggressive form of cancer presenting over 76,000 diagnosed cases in 2012.1 CM tumors develop through a number of discreet stages during the progression from a benign melanocytic nevus to a malignant metastatic tumor. Generally, benign nevi present as thin, pigmented lesions. After the acquisition of key genetic mutations and the initiation of cytoarchitectural modifications leading to shallow invasion of the skin, the lesions begin growing radially, a process referred to as the radial growth phase. Upon escape from growth control mediated by surrounding keratinocytes, stromal invasion to deeper regions of the dermis occurs, marking the progression to the vertical growth phase. The vertica...

Claims

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Application Information

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IPC IPC(8): C12Q1/68
CPCC12Q1/6886C12Q2600/158C12Q2600/118
Inventor COOK, ROBERT WILLISMAETZOLD, DEREKOESCHLAGER, KRISTEN
Owner CASTLE BIOSCI
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