Risk Stratification in Multiple Myeloma in Indian Settings

Abstract

Multiple myeloma (MM) constitutes 10% of all hematological malignancies. The last one decade has seen a phenomenal progress in the therapeutic options available for the management. Although it still remains incurable, with the advent of newer therapies, the median survival in many risk groups is now around 10 years. Conventional karyotyping of bone marrow samples has a positivity rate of 20-30% at diagnosis in patients of Multiple Myeloma. However, array Comparative Genomic Hybridisation (aCGH) has revealed that almost all MM patients have cytogenetic abnormalities which may affect the pathophysiology, selection of therapy and outcomes of the disease. The progress in the field of exploring the genetic landscape of multiple myeloma with multiple tools like Fluorescent in-situ hybridization, aCGH, Next Generation Sequencing, Flow cytometry, etc., combined with the traditional risk stratification markers like albumin, β2 microglobulin and LDH, is gradually leading towards a risk-adapted therapy. The recent R-ISS risk stratification has combined these two group of information to validate a prognostic score which is an improvement over the past tools like DSS and ISS. In view of the plethora of information available on the multitude of cytogenetic markers there is a tendency to evaluate for all of them at diagnosis, especially in research centers. This leads to a significant increase in the cost of therapy of Multiple Myeloma in day-to-day clinical practice and an increased out-of-pocket spending to the patient, especially in resource-limited settings like India. Also, there is a variable approach to pre-therapy cytogenetic evaluation and risk stratification at different Hematology centres in the country, often dictated by financial constraints and availability of specialized tests. This review discusses the risk stratification markers and tools available in MM in 2019 and how it can be adapted in the resource constraint settings so as to derive the maximum prognostic information from a minimal prognostic panel, as well as lead to standardization of the prognostic protocols in resource limited settings across various Hematology centres in India.

Keywords: Cytogenetics; Myeloma; Prognosis; Risk stratification.

Fig. 1

Simplified algorithm towards initial risk stratification in patients of multiple myeloma in resource poor setting. aCGH: array comparative genomic hybridization