TRI solves the drug resistance in acute myeloid leukemia

TRI solves the drug resistance in acute myeloid leukemia.  Leukemia may be the most common cancer heard by ordinary people, and acute myeloid leukemia is the most common type.

Acute myeloid leukemia (AML) is one of the most common types of leukemia. It mostly occurs in middle-aged and elderly people over 65. The disease progresses rapidly. It is one of the most aggressive and difficult to treat blood cancers. “Nightmare”.

Although the existing therapies have made great progress, the 5-year survival rate of patients diagnosed with AML is still less than 30%.

At present, AML is mainly based on strong induction chemotherapy, but AML is easy to be resistant, and the condition will deteriorate rapidly, and due to factors such as age or complications, many patients are not eligible for hematopoietic stem cell transplantation, and the treatment options available are very limited.

▌Solve the problem of drug resistance in acute myeloid leukemia

Recently, at the 62nd Annual American Society of Hematology Virtual Annual Meeting, Cellworks, a leading company in the field of oncology precision medicine in the United States, announced the results of a blockbuster clinical study (myCare-021-02).

The study found that the use of TRI for genomic biologic simulation can improve the prognosis by combining patient biomarkers and recommend new treatment options for AML patients who have developed drug resistance.

TRI relies on multi-omics input from tumor cells to predict and identify potential treatment options for the unique molecular mechanism of each patient’s tumor.

The discovery of the specific drug resistance mechanism of AML patients provides a new breakthrough for the treatment based on deep molecular diagnosis, which can assist clinical treatment decision-making, reduce the risk of ineffective treatment, improve the efficacy of medication, and reduce the cost of treatment.

“AML is currently the main cause of death from leukemia, but the use of TRI personalized treatment plans can improve patient prognosis.”

Dr. Michael Castro, an expert on oncology precision medicine and the principal investigator of the myCare-021-02 clinical trial (who has been rated as “Top Doctor in the United States” many times) said:

“Induction chemotherapy response and disease remission vary depending on the biological subtype of the tumor and the drugs used for induction therapy, but even in a specific biological subgroup, the response is unpredictable. The TRI biological simulation platform provides a way for patients Individualized medical methods for specific disease biomarkers and resistance mechanisms.”

“It is not uncommon for AML induction chemotherapy to fail, even in those biological subtypes that are sensitive to treatment.”

Dr. Scott Howard, a professor at the University of Tennessee Health Sciences Center, said, “TRI can identify the reasons for the failure of standard induction programs and provide alternative treatments based on the genomic abnormalities shared in drug-resistant patients.”

▌About myCare-021-02 clinical research

The study aims to predict the response of AML patients to induction chemotherapy regimens, and to determine the genomic characteristics, resistance mechanisms of refractory patients, and to predict personalized treatment options.

This study selected 57 patients with AML who were known to respond to treatment from PubMed publications. These patients were divided into 3 groups: CBFB-MYH11 fusion group, RUNX1-RUNX1T1 fusion group and CEBPα fusion group.

NCCN recommends specific treatment recommendations for patients with acute myeloid leukemia with these mutations. After induction of these mutations, a high remission rate has been achieved.

Researchers use the TRI biological simulation platform to analyze the available genomic data of each map, to use public information from PubMed and other online resources to generate AML subtype-specific protein network maps, and to analyze drug sensitivity and resistance based on patient-specific biomarkers Drug pathway.

For each patient’s tumor molecular characteristics in the three cohorts, the response of drugs screened from the digital drug library was simulated. The clinical benefit was evaluated by testing the effect of each drug on the cell growth score (combined proliferation, viability and apoptosis index).

The results of the study found that of 57 high-risk AML patients, 6 (11%) failed induction therapy; 1/19 patients carrying the CBFB-MYH11 fusion protein did not respond to induction therapy.

TRI identified common genetic abnormalities that lead to drug resistance in patients with drug resistance: these patients carry chromosome 6 trisomy, and have GSTA1, GSTA2, GSTA4, DEK, TFAP2A, NFYA, and EHMT2 gene amplification, which is caused by abnormal signal pathways. The treatment failed.

TRI also identified three prospective treatment options for the patient-specific biomarkers.

Similarly, 2/10 patients with the RUNX1-RUNX1T1 gene fusion did not respond to induction therapy. TRI found that loss of function mutations in EZH2 lead to elevated levels of HOXA5 and HOXA9, which is a key factor in treatment failure.

In addition, TRI also identified three prospective treatment options for the patient’s specific resistance mechanisms.

In the CEBPα cohort, 3/28 patients did not respond to induction therapy. For one of the patients, TRI found that DNMT3A loss-of-function mutations and elevated levels of HOXA5 and HOXA9 were key factors for treatment failure.

Similarly, TRI also identified three new treatment options for this patient with specific biomarkers.

▌TRI “sand table deduction”, let tumor treatment “knowledge

Tumor Therapy Response Index (TRI) relies on computer biological simulation and machine learning artificial intelligence technology, breakthrough use of patient’s entire exome information to establish personalized disease models, conduct biological simulations, and analyze genome, proteome, transcriptome and epigenome mutations The influence of information on the upstream and downstream of tumor cell signaling pathways is calculated for specific drugs or drug combinations.

This is equivalent to a rigorous “sand table deduction” before treatment, so as to predict the best personalized medication plan for patients, and at the same time provide decision support based on molecular and genetic-level pathogenesis for clinicians who are faced with treatment options.

(source:internet, reference only)

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