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Risk Stratification in Multiple Myeloma in Indian Settings.

Rajan Kapoor1, Rajiv Kumar2, A P Dubey3

  • 1Medicine and Clinical Hematology, Command Hospital (EC), Kolkata, India.

Indian Journal of Hematology & Blood Transfusion : an Official Journal of Indian Society of Hematology and Blood Transfusion
|July 11, 2020
PubMed
Summary
This summary is machine-generated.

Multiple myeloma (MM) management has advanced, but cost-effective risk stratification is crucial. This review explores adapting genetic and traditional markers for minimal prognostic panels in resource-limited settings.

Keywords:
CytogeneticsMyelomaPrognosisRisk stratification

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Area of Science:

  • Hematology
  • Oncology
  • Genetics

Background:

  • Multiple myeloma (MM) is a significant hematological malignancy with evolving therapeutic options.
  • While incurable, median survival has improved to approximately 10 years with newer therapies.
  • Conventional karyotyping detects abnormalities in 20-30% of MM patients, whereas array Comparative Genomic Hybridization (aCGH) reveals widespread cytogenetic abnormalities.

Purpose of the Study:

  • To review current risk stratification markers and tools for multiple myeloma (MM).
  • To discuss adapting these tools for resource-limited settings, focusing on minimal prognostic panels.
  • To promote standardization of prognostic protocols in Indian Hematology centers.

Main Methods:

  • Review of existing literature on MM risk stratification tools, including cytogenetic analyses (Fluorescent in-situ hybridization, aCGH, Next Generation Sequencing) and traditional markers (albumin, β2 microglobulin, LDH).
  • Analysis of the Revised International Staging System (R-ISS) for prognostic scoring.
  • Discussion on adapting these methods for cost-effectiveness in resource-constrained environments.

Main Results:

  • Array Comparative Genomic Hybridization (aCGH) identifies cytogenetic abnormalities in nearly all MM patients, impacting pathophysiology and outcomes.
  • The R-ISS integrates cytogenetic and traditional markers, offering improved prognostic accuracy over previous tools.
  • There's a trend towards comprehensive genetic evaluation, increasing costs and patient expenditure, particularly in resource-limited settings.

Conclusions:

  • Optimizing risk stratification in MM requires balancing comprehensive genetic information with cost-effectiveness.
  • Adapting prognostic panels for resource-limited settings is essential for equitable and standardized patient care.
  • Minimal prognostic panels can maximize prognostic information, guiding risk-adapted therapy in diverse clinical settings.