Precision medical methods for cancer immunotherapy

A technology for immunotherapy and cancer, applied in the field of precision medicine for cancer immunotherapy, which can solve the problems of decreased neutrophil/lymphocyte ratio and increased number

Pending Publication Date: 2022-03-25
艾迪菲斯健康有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

For example, patients more likely to respond to anti-CTLA-4 therapy had increased numbers of

Method used

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  • Precision medical methods for cancer immunotherapy
  • Precision medical methods for cancer immunotherapy
  • Precision medical methods for cancer immunotherapy

Examples

Experimental program
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example 1

[0090] Example 1: iAge is associated with naïve CD8(+) T cells and ex vivo Jak-STAT signaling responses to stimulation

[0091] Frequency of circulating naive CD8(+) T cells decreases with high iAge (A), which predicts poor response to stimulated ex vivo Jak-STAT signaling (B and C). A total of 96 cytokine-cell-STAT combinations were analyzed with respect to subjects' iAge. These include eight cell types: B cells, CD4(+) T cells (and their CD45(+) and (-) subsets), CD8(+) T cells (and their CD45(+) and (-) ) and monocytes; four cytokines: interleukin-6 (IL-6), IL-10, IL-21, and interferon-α; and three STAT proteins (STAT1, 3, and 5). Figure 1B : Volcano plot, results of multiple regression analysis with permutation test estimating the false discovery rate (Benjamini-Hochberg FDR) (y-axis) as a function of regression coefficients obtained for iAge, after adjusting for age, sex, and CMV status. Figure 1C : Normalized ex vivo CD8(+) T cell phospho-STAT-1 response to interle...

example 2

[0093] Example 2: Stratification of cancer patients using iAge and CRS

[0094] Before immunotherapy treatment, a blood sample is obtained from the patient. Serum and immune cells were isolated by standard methods. Serum samples were used to measure protein concentrations to determine inflammatory age (iAge); cells were stimulated ex vivo with cytokines to measure phosphorylation of intracellular signal transducers and activators of transcription (STAT) proteins, resulting in a cytokine response score ( CRS). iAge and CRS can independently predict patient response to immunotherapy treatment. figure 2 A flowchart of the process is shown.

[0095] iAge and CRS can be used to stratify cancer patients into immunotherapy responders and non-responders prior to treatment.

example 3

[0096] Example 3: Using iAge to Stratify Cancer Patients

[0097]iAge can be used to classify cancer patients into responders and non-responders to immunotherapy treatment (A), and derive iAge individual inflammatory protein signatures / signatures (barcodes), which can be fed into the iAge personalized recommendation engine to create targeted Individualized initial therapy that lowers iAge to inform medical decision-making, thereby converting non-responders to responders (suitable for immunotherapy) (B). image 3 A flowchart of the process is shown.

[0098] iAge is being used to stratify patients for cancer immunotherapy and to help convert non-responders to responders to immunotherapy.

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Abstract

Cancer immunotherapy has obtained a huge clinical success, and even the most difficult-to-treat cancer also has a relatively long lifetime. However, this effect is observed only in a small number of persons and no biomarkers of this reaction. The methods described herein use two independent systemic chronic inflammation metrics (inflammation age-iAge-and cytokine response score-CRS) to stratify cancer patients into responders and non-responders to cancer immunotherapy, thereby improving the outcome of cancer immunotherapy. The iAge personalized immunoproteomic markers/features create an individualized initial therapy to reduce the iAge and convert unresponsive patients to reactants prior to treatment. By treating a patient to reduce its iAge and improve its CRS, unresponsive persons can be converted into reactants.

Description

Background technique [0001] Over the past five years, cancer immunotherapy treatments have achieved tremendous clinical success across multiple cancer types, often extending disease-free survival to more than 10 years. Examples of successful immunotherapies are immune checkpoint inhibitors, which have demonstrated unprecedented durable response rates in many difficult-to-treat cancers. However, only a limited percentage of patients (~20%) benefit from these approaches, regardless of the affected organ and cancer type. Therefore, there is a growing need to identify biomarkers to improve the selection of patients who will respond to therapy. [0002] Biomarkers are needed both before and during treatment to enable identification of patients likely to respond to immunotherapy treatment to reduce inappropriate drug use. Objective clinical response was defined as a reduction in tumor size during treatment. Multiple baseline factors related to disease prognosis were associated wi...

Claims

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Application Information

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IPC IPC(8): G01N33/68G01N33/50A61K45/06A61K39/395
CPCA61K45/06G01N33/57488G01N2800/60G01N2800/52G01N33/574C07K16/2887C07K16/32C07K16/22C07K16/2875C07K16/2863G16B40/00G16B40/10G16B40/30
Inventor D·福尔曼
Owner 艾迪菲斯健康有限公司
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