System for predicting prognosis of patients with lung adenocarcinoma and judging benefit of adjuvant chemotherapy

A technique for lung adenocarcinoma, patients, applied in the field of biomedicine

Active Publication Date: 2017-02-22
BIOMEDICAL ANALYSIS CENT OF ACADEMY OF MILITARY MEDICAL SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, so far no gene expression profiling has been applied to the prediction of clinical prognosis in NSCLC
[0004] In addition, since postoperative adjuvant chemotherapy has only about 5.4% five-year survival b

Method used

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  • System for predicting prognosis of patients with lung adenocarcinoma and judging benefit of adjuvant chemotherapy
  • System for predicting prognosis of patients with lung adenocarcinoma and judging benefit of adjuvant chemotherapy
  • System for predicting prognosis of patients with lung adenocarcinoma and judging benefit of adjuvant chemotherapy

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0069] Example 1. Discovery of adenocarcinoma-associated protein profile, combined molecular marker model, non-small cell lung cancer prognosis judgment, P6-ADC method for prognosis of adenocarcinoma and validation of its effectiveness

[0070] 1. Discovery of adenocarcinoma-associated protein signatures

[0071] 1.1. Cases and samples

[0072] The formalin-fixed and paraffin-embedded human normal lung tissue samples were provided by Peking University People's Hospital and Southwest Hospital Tissue Bank. The formalin-fixed and paraffin-embedded lung tumor tissue samples were provided by the tissue banks of the Pathology Department of Peking University People's Hospital and the Pathology Department of Chongqing Southwest Hospital. The provider of the lung tumor tissue (ie, the patient) received radical surgery for lung cancer in the above-mentioned hospital from 2004 to 2010 and systematically. The present invention does not include the following cases: cases with malignant t...

Embodiment 2

[0132] Example 2, P6-ADC Prognosis Adenocarcinoma Method for Further Prognosis of TNM Graded Adenocarcinoma Patients

[0133] The method for further prognosing the adenocarcinoma patients graded by TNM is as follows: for any grade of adenocarcinoma patients graded by TNM (such as patients with stage IB adenocarcinoma), the expression score of P6-ADC is obtained according to the method in Example 1, Then the expression score is obtained according to the method in step 2.1 of Example 1 to obtain the prognosis score; and each prognosis score is determined according to the method in step 2.2 of Example 1 to determine whether the prognosis of the adenocarcinoma patient is good or poor.

[0134] The total adenocarcinoma sample was obtained by integrating the samples of the BJ group and the CQ group. The P6-ADC prognostic adenocarcinoma method can further divide each patient in the total adenocarcinoma sample into a poor prognosis group or a good prognosis group (stage IB P figure 2 ...

Embodiment 3

[0135] Example 3, P6-ADC prognostic adenocarcinoma method to predict whether patients with lung adenocarcinoma benefit from adjuvant chemotherapy

[0136] According to the guidelines issued by the American Society of Clinical Oncology (ASCO), postoperative adjuvant chemotherapy is recommended for patients with stage IB, II or IIIA non-small cell lung cancer, but data show that only a very small proportion of patients can benefit from chemotherapy.

[0137] Experiments have shown that the P6-ADC prognostic adenocarcinoma approach helps to identify those TNMIB and TNM II and IIIA patients who can really benefit from adjuvant chemotherapy. In the poor prognosis group, adjuvant chemotherapy significantly improved overall survival (hazard ratio 0.51, confidence interval 0.24-0.86; P=0.018; image 3 Middle A). In the P6-ADC prognostic adenocarcinoma group, adjuvant chemotherapy did not have any significant effect on prolonging the prognosis (hazard ratio 0.99, confidence interval 0...

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Abstract

The invention discloses a system for predicting prognosis of patients with lung adenocarcinoma and judging benefit of an adjuvant chemotherapy. The system comprises a system used for detecting expression quantities of six proteins, i.e., c-Src, Cyclin E1, TTF1, p65, CHK1 and JNK1, and a protein expression quantity data processing system. The system used for detecting the expression quantities of the six proteins can determine the expression quantities of the proteins by using an immunohistochemistry staining method; the protein expression quantity data processing system converts the expression quantities of the six proteins in lung adenocarcinoma tissues, separated from to-be-predicted patients with the lung adenocarcinoma, into a prognosis score, and predicting the prognosis of the to-be-predicted patients with the lung adenocarcinoma according to the prognosis score, and/or predicting whether the to-be-predicted patients with the lung adenocarcinoma are benefited from the adjuvant chemotherapy or not according to the prognosis score.

Description

technical field [0001] The invention relates to the field of biomedicine, in particular to a system for predicting the prognosis of lung adenocarcinoma patients and judging the benefits of adjuvant chemotherapy. Background technique [0002] Lung cancer is the most common cause of cancer-related death in the world today, and 80% of it is non-small cell lung cancer (NSCLC). TNM staging is a generally accepted clinical staging system, which is used to predict the prognosis and guide the treatment of patients with non-small cell lung cancer. However, the current TNM staging system is far from adequate to accurately predict the prognosis of NSCLC patients. For example, for lung cancer patients, even in clinical stage I, the recurrence rate of lung cancer is as high as 35-50%. In addition, a considerable number of patients can be cured only by surgery, and these patients should be able to avoid the extremely strong side effects of adjuvant chemotherapy based on the current TNM ...

Claims

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Application Information

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IPC IPC(8): G01N33/574
CPCG01N33/57423G01N33/57484
Inventor 张学敏周涛靳宝锋
Owner BIOMEDICAL ANALYSIS CENT OF ACADEMY OF MILITARY MEDICAL SCI
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