Risk Stratification in Newly Diagnosed Transplant-Eligible Multiple Myeloma View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

Megan H. Jagosky , Alankrita Taneja , Manisha Bhutani

ABSTRACT

Over the past two decades, there has been a major shift in how we understand and treat multiple myeloma (MM). As a result, molecular complete response, prolonged remission duration, and extended survival have become realistic goals with a possibility of cure in a small but increasing number of patients. Triplet induction using a proteasome inhibitor and an immunomodulatory drug, followed by early high-dose melphalan-autologous stem cell transplant, remains the preferred treatment strategy for transplant-eligible patients. Interestingly, not all patients derive the same benefit with this strategy, underscoring the fact that MM is a mixed bag of molecular subtypes, each with a distinct pathogenesis and clinical course. Numerous prognostic markers have been described, some of which form the basis of existing risk prediction models. No prognostic marker has solely been found to completely explain the heterogeneity in this disease. Important prognostic markers in MM are based on staging, chromosomal abnormalities, and gene expression profile classifiers. Patients can be classified into three distinct prognosis groups: standard, intermediate, and high. Risk classification assists in patient counseling, clinical trial design and outcome reporting, and, to some extent, in therapeutic decision making. In light of recent advances, the risk classification systems as well as risk-based therapeutic strategies for transplant-eligible MM patients are reviewed here. More... »

PAGES

15-36

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  • Book

    TITLE

    Personalized Therapy for Multiple Myeloma

    ISBN

    978-3-319-61871-5
    978-3-319-61872-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-61872-2_2

    DOI

    http://dx.doi.org/10.1007/978-3-319-61872-2_2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092458759


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