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AML genetic survival prediction research

Overview

Researchers at the Centre for Genomic Regulation (CRG) in Barcelona have identified a six-gene signature to predict survival outcomes for patients with acute myeloid leukemia (AML). Published in Science Advances, the study utilizes a scoring system called ‘PHFScore’ to classify patients into groups with distinct survival prospects.

The research identified that higher activity in the genes CARMIL1, HOPX, and ALDH1A1 is associated with poor clinical outcomes, while lower activity in CALR, STAR, and PHF19 correlates with different prognostic scenarios. Senior author Luciano Di Croce noted that combining these genes provides a “dramatic improvement in prognostic accuracy” compared to single markers. The researchers suggest that measuring these six genes could be a simpler and more cost-effective alternative to broad genetic profiling, potentially facilitating new diagnostic tests and therapeutic targets using existing bone marrow samples.

Entities

Center for Genomic Regulation · UK Biobank · Gianni Gamarra · European Society for Medical Oncology · Technical University of Dresden

Timeline

  1. [HEALTH] 4 sources
    Researchers identify genetic and machine learning tools for cancer risk prediction

    New research highlights the use of a six-gene signature to predict AML survival and machine learning models to identify risks for 20 common cancer types using routine clinical data.

  2. [HEALTH] 2 sources
    Barcelona researchers identify six genes to predict AML survival

    Researchers at Barcelona's CRG developed the ‘PHFScore’, a six-gene system to more accurately predict survival rates in acute myeloid leukemia patients.

Sources

cablecarver.com · ecuadoruniversitario.com · isanidad.com · lacronicadebadajoz.elperiodicoextremadura.com · laopinioncoruna.es · news-medical.net · noticias24hrs.com.ve