Methods for assessing the risk of disease occurrence or recurrence using expression level and sequence variant information

A disease occurrence and expression level technology, applied in biochemical equipment and methods, informatics, sequence analysis, etc., can solve problems such as inaccurate assessment, and achieve the effect of improving risk prediction

Pending Publication Date: 2018-01-26
VERACYTE INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although this anatomical grading system has proven clinically useful, it cannot be accurately assessed prior to invasive thyroidectomy and does not include any molecular predictors of disease outcome

Method used

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  • Methods for assessing the risk of disease occurrence or recurrence using expression level and sequence variant information
  • Methods for assessing the risk of disease occurrence or recurrence using expression level and sequence variant information
  • Methods for assessing the risk of disease occurrence or recurrence using expression level and sequence variant information

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0154] Example 1: Risk stratification of samples using a risk classifier

[0155] The current risk-adjusted approach to the initial management of thyroid cancer is based on postoperative classification using the 2009 American Thyroid Association (ATA) grading system to classify subjects as intermediate-high or low-risk. Although this anatomical grading system can be used clinically, it cannot be accurately assessed prior to thyroidectomy and cannot include any molecular predictors of subject outcome. This study determined whether transcriptional data obtained during diagnostic fine-needle aspiration (FNA) of malignant thyroid nodules could be used to improve risk stratification prior to thyroid surgery.

[0156] FNA material from samples collected preoperatively (n = 79) and postoperatively diagnosed by an expert panel as papillary thyroid carcinoma (PTC), including classical histological subtypes ( figure 1 and figure 2 ). Each patient was classified as "low risk" or "...

Embodiment 2

[0158] Example 2: Cross Validation Model

[0159] Indeterminate thyroid nodules were tested with a gene expression classifier (GEC) with mutation panels to determine whether preoperative risk stratification was improved by the use of machine learning. Figure 10 is a flowchart showing training marker determination. AfirmaGEC version 1 training markers were used to distinguish histologically benign from histologically malignant samples. Histologically malignant samples were further differentiated into low risk and intermediate / high risk using the American Thyroid Association (ATA) risk training markers. Intermediate / high risk features include lymph node metastasis, vascular invasion, extrathyroidal extension, or any combination thereof. The risk training sample group is in figure 1 shown in . Percentage of samples with intermediate / high risk of developing histological features in figure 2 shown in . 10-fold cross-validation was performed to evaluate the area under the...

Embodiment 3

[0160] Example 3: Mutation Analysis

[0161] Fine needle aspiration (FNA) samples (n = 81) were collected and diagnosed as malignant (papillary thyroid carcinoma (PTC), multifocal papillary thyroid carcinoma (mPTC), papillary thyroid carcinoma Follicular variant (FVPTC), papillary thyroid carcinoma with hypercellular features (PTC-TCV), medullary thyroid carcinoma (MTC), well-differentiated carcinoma-not otherwise specified (WDC) -NOS), hepatocellular carcinoma (HCC), follicular carcinoma (FC)) or benign (benign familial neutropenia (BFN), fibroadenoma (FA), hepatocellular adenoma (HCA ), hyalinizing beam adenoma (HTA), Leydig cell tumor (LCT)). Histopathologically plausible surgical tissue samples (n=57) were also analyzed. Serial series of indeterminate FNAs (n=101 ) from Clinical Laboratory Improvement Amendment (CLIA) laboratories without histopathology were also analyzed. Next-generation sequencing (NGS) was performed on the samples, and 14 genes were assessed with i...

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Abstract

Provided herein are methods, systems and kits for stratification of risk of disease occurrence of a sample obtained from a subject by combining two or more feature spaces to improve individualizationof subject management.

Description

[0001] cross reference [0002] This application claims U.S. Provisional Application 62 / 128,463, filed March 4, 2015, U.S. Provisional Application 62 / 128,469, filed March 4, 2015, and U.S. Provisional Application 62 / 238,893, filed October 8, 2015 priority of each application is hereby incorporated by reference in its entirety. Background technique [0003] Risk-adjusted approaches to disease treatment, such as thyroid cancer treatment, minimize disease risk in addition to improving disease-specific survival. Currently, this risk-adjusted approach for initial subject management is largely based on surgical classification of subjects as high, intermediate, or low risk of disease recurrence using the 2009 American Thyroid Association (ATA) grading system. post classification. Although this anatomical grading system has proven clinically useful, it cannot be accurately assessed prior to invasive thyroidectomy and does not include any molecular predictors of disease outcome. C...

Claims

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Application Information

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IPC IPC(8): C12Q1/6886G06F19/18G16B20/00G16B20/20G16B25/10
CPCC12Q1/6886G16B30/10G16H50/30C12Q2600/156C12Q2600/158C12Q2600/118G16B20/00G16B25/00G16B20/20G16B25/10Y02A90/10
Inventor G·C·肯尼迪M·帕甘林竹芳黄静P·肖恩·沃尔什松崎新凯文·特拉弗斯金洙延
Owner VERACYTE INC
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