Treatment selection for lung cancer patients using mass spectrum of blood-based sample

A technology for patients and samples, applied in mass spectrometers, peptide/protein components, special data processing applications, etc.

Inactive Publication Date: 2016-04-20
BIODESIX
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

This unmet clinical need led to the development of a new

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  • Treatment selection for lung cancer patients using mass spectrum of blood-based sample
  • Treatment selection for lung cancer patients using mass spectrum of blood-based sample
  • Treatment selection for lung cancer patients using mass spectrum of blood-based sample

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Experimental program
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Embodiment Construction

[0058] The following description is divided into four general subsections:

[0059] Section I: Describe the new way of forming a classifier called CMC / D classifier herein (combination of mini classifiers with exit). This new approach is used to create classifiers that are useful for testing methods of this document.

[0060] Section II: Describe the specific CMC / D classifier (used in the predictive test described in this document) formed in accordance with Section 1 and demonstrate its results in predicting the beneficial effects of NSCLC patients from EGFRI-I compared with chemotherapy .

[0061] Section III: Describes the currently preferred test method for testing patient samples using the CMC / D classifier described in Section II.

[0062] Section IV: Describe the actual computing environment used to generate the classifier and perform the tests described in Section 1-III.

[0063] Section I General CMC / D classifier formation

[0064] This section describes CMC / D, its creation or fo...

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Abstract

A test for predicting whether a non-small-cell lung cancer patient is more likely to benefit from an EGFR-I as compared to chemotherapy uses a computer-implemented classifier operating on a mass spectrum of a blood-based sample obtained from the patient. The classifier makes use of a training set which includes mass spectral data from blood-based samples of other cancer patients who are members of a class of patients predicted to have overall survival benefit on EGFRI-Is, e.g., those patients testing VS Good under the test described in U.S. Pat. No.7,736,905. The class-labeled group is further subdivided into two subsets, i.e., those patients which exhibited early (class label 'early') and late (class label 'late') progression of disease after administration of the EGFR-I in treatment of cancer.

Description

[0001] Cross references to related applications [0002] This application claims the priority of U.S. Provisional Application Serial No. 61 / 975267 filed on April 4, 2014 according to 35 U.S.C. §119, which is incorporated herein by reference. Technical field [0003] The present invention relates to the field of biomarker discovery and personalized medicine, and more specifically, to a method for predicting before treatment whether non-small cell lung cancer (NSCLC) patients may be affected by epidermal growth factor compared with chemotherapy. EGFR-I, such as erlotinib or gefitinib. Background technique [0004] Non-small cell lung cancer is the leading cause of cancer deaths among men and women in the United States. There are at least four (4) different types of NSCLC, including adenocarcinoma, squamous cell, large cell, and bronchoaldeolarcarcinoma. Squamous cell (epidermal) carcinoma of the lung is the microscopic type of cancer most frequently associated with smoking. Adenoca...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F19/00G16B40/20G16B40/10
CPCG01N33/57423G01N33/6848G01N2800/52A61K38/179H01J49/26H01J49/0036G16B40/00G16B40/10G16B40/20G01N33/49
Inventor J.勒德H.勒德
Owner BIODESIX
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