Project description:We recently defined a gene expression-based signature of high-risk multiple myeloma; this predictive signature was developed with and independently validated for newly diagnosed patients treated with high dose therapy and stem cell rescue. Here we use Phase 3 clinical trial data to show that this signature also predicts short survival in relapsed disease treated with single agent bortezomib or high dose dexamethasone. In addition, a survival signature derived with relapsed myeloma samples identified newly diagnosed patients with short survival. Taken together these data suggest that a similar biology underlies poor outcome in both newly diagnosed and relapsed myeloma and provide strong evidence that the high-risk signature is a powerful tool to identify patients who are candidates for new therapeutic regimens. Keywords: Model validation See above (Series_summary)
Project description:We recently defined a gene expression-based signature of high-risk multiple myeloma; this predictive signature was developed with and independently validated for newly diagnosed patients treated with high dose therapy and stem cell rescue. Here we use Phase 3 clinical trial data to show that this signature also predicts short survival in relapsed disease treated with single agent bortezomib or high dose dexamethasone. In addition, a survival signature derived with relapsed myeloma samples identified newly diagnosed patients with short survival. Taken together these data suggest that a similar biology underlies poor outcome in both newly diagnosed and relapsed myeloma and provide strong evidence that the high-risk signature is a powerful tool to identify patients who are candidates for new therapeutic regimens. Keywords: Model validation
Project description:Multiple Myeloma (MM) is a hematologic malignancy characterized by a wide clinical and biological heterogeneity leading to different patient outcomes. Various prognostic tools to stratify newly diagnosed (ND)MM patients into different risk groups have been proposed. At baseline, the standard-of-care prognostic score is the Revised International Staging System (R-ISS), which stratifies patients according to widely available serum markers (i.e., albumin, β 2-microglobulin, lactate dehydrogenase) and high-risk cytogenetic abnormalities detected by fluorescence in situ hybridization. Though this score clearly identifies a low-risk and a high-risk population, the majority of patients are categorized as at "intermediate risk". Although new prognostic factors identified through molecular assays (e.g., gene expression profiling, next-generation sequencing) are now available and may improve risk stratification, the majority of them need specialized centers and bioinformatic expertise that may preclude their broad application in the real-world setting. In the last years, new tools to monitor response and measurable residual disease (MRD) with very high sensitivity after the start of treatment have been developed. MRD analyses both inside and outside the bone marrow have a strong prognostic impact, and the achievement of MRD negativity may counterbalance the high-risk behavior identified at baseline. All these techniques have been developed in clinical trials. However, their efficient application in real-world clinical practice and their potential role to guide treatment-decision making are still open issues. This mini review will cover currently known prognostic factors identified before and during first-line treatment, with a particular focus on their potential applications in real-world clinical practice.
Project description:Metaphase cytogenetic abnormalities, plasma cell proliferation index (PCPro), and gain 1q by fluorescence in situ hybridization (FISH) are associated with inferior survival in newly diagnosed multiple myeloma (MM) treated with novel agents; however, their role in risk stratification is unclear in the era of the revised International Staging System (R-ISS). The objective of this study was to determine if these predictors improve risk stratification in newly diagnosed MM when accounting for R-ISS and age. We studied a retrospective cohort of 483 patients with newly diagnosed MM treated with proteasome inhibitors and/or immunomodulators. On multivariable analysis, R-ISS, age, metaphase cytogenetic abnormalities (both in aggregate and for specific abnormalities), PCPro, and FISH gain 1q were associated with inferior progression-free (PFS) and overall survival (OS). We devised a risk scoring system based on hazard ratios from multivariable analyses and assigned patients to low-, intermediate-, and high-risk groups based on their cumulative scores. The addition of metaphase cytogenetic abnormalities, PCPro, and FISH gain 1q to a risk scoring system accounting for R-ISS and age did not improve risk discrimination of Kaplan-Meier estimates for PFS or OS. Moreover, they did not improve prognostic performance when evaluated by Uno's censoring-adjusted C-statistic. Lastly, we performed a paired analysis of metaphase cytogenetic and interphase FISH abnormalities, which revealed the former to be insensitive for the detection of prognostic chromosomal abnormalities. Ultimately, metaphase cytogenetics lack sensitivity for important chromosomal aberrations and, along with PCPro and FISH gain 1q, do not improve risk stratification in MM when accounting for R-ISS and age.
Project description:The International Staging System (ISS) and the Revised International Staging System (R-ISS) are commonly used prognostic scores in multiple myeloma (MM). These methods have significant gaps, particularly among intermediate-risk groups. The aim of this study was to improve risk stratification in newly diagnosed MM patients using data from three different trials developed by the Spanish Myeloma Group. For this, we applied an unsupervised machine learning clusterization technique on a set of clinical, biochemical and cytogenetic variables, and we identified two novel clusters of patients with significantly different survival. The prognostic precision of this clusterization was superior to those of ISS and R-ISS scores, and appeared to be particularly useful to improve risk stratification among R-ISS 2 patients. Additionally, patients assigned to the low-risk cluster in the GEM05 over 65 years trial had a significant survival benefit when treated with VMP as compared with VTD. In conclusion, we describe a simple prognostic model for newly diagnosed MM whose predictions are independent of the ISS and R-ISS scores. Notably, the model is particularly useful in order to re-classify R-ISS score 2 patients in 2 different prognostic subgroups. The combination of ISS, R-ISS and unsupervised machine learning clusterization brings a promising approximation to improve MM risk stratification.
Project description:Multiple myeloma is a malignant plasma cell neoplasm that affects more than 20,000 people each year and is the second most common hematologic malignancy. It is part of a spectrum of monoclonal plasma cell disorders, many of which do not require active therapy. During the past decade, considerable progress has been made in our understanding of the disease process and factors that influence outcome, along with development of new drugs that are highly effective in controlling the disease and prolonging survival without compromising quality of life. Identification of well-defined and reproducible prognostic factors and introduction of new therapies with unique modes of action and impact on disease outcome have for the first time opened up the opportunity to develop risk-adapted strategies for managing this disease. Although these risk-adapted strategies have not been prospectively validated, enough evidence can be gathered from existing randomized trials, subgroup analyses, and retrospective studies to develop a working framework. This set of recommendations represents such an effort-the development of a set of consensus guidelines by a group of experts to manage patients with newly diagnosed disease based on an interpretation of the best available evidence.
Project description:PurposeThe GMMG-CONCEPT trial investigated isatuximab, carfilzomib, lenalidomide, and dexamethasone (Isa-KRd) in transplant-eligible (TE) and transplant-noneligible (TNE) patients with newly diagnosed multiple myeloma (NDMM) with exclusively high-risk disease for whom prospective trials are limited, aiming to induce minimal residual disease (MRD) negativity.MethodsThis academic, investigator-initiated, multicenter, phase II trial enrolled patients with high-risk NDMM (HRNDMM) defined by mandatory International Staging System stage II/III combined with del17p, t(4;14), t(14;16), or more than three 1q21 copies as high-risk cytogenetic aberrations (HRCAs). Patients received Isa-KRd induction/consolidation and Isa-KR maintenance. TE patients received high-dose melphalan. TNE patients received two additional Isa-KRd cycles postinduction. This prespecified interim analysis (IA) reports the primary end point, MRD negativity (<10-5, next-generation flow), at the end of consolidation. The secondary end point was progression-free survival (PFS).ResultsAmong 125 patients with HRNDMM (TE-intention-to-treat [ITT]-IA, 99; TNE-ITT, 26) of the IA population for the primary end point, the median age was 58 (TE-ITT-IA) and 74 (TNE-ITT) years. Del17p was the most common HRCA (TE, 44.4%; TNE, 42.3%); about one third of evaluable TE/TNE patients presented two or more HRCAs, respectively. The trial met its primary end point with MRD negativity rates after consolidation of 67.7% (TE) and 54.2% (TNE) of patients. Eighty-one of 99 TE-ITT-IA patients reached MRD negativity at any time point (81.8%). MRD negativity was sustained for ≥1 year in 62.6% of patients. With a median follow-up of 44 (TE) and 33 (TNE) months, median PFS was not reached in either arm.ConclusionIsa-KRd effectively induces high rates of sustainable MRD negativity in the difficult-to-treat HRNDMM population, regardless of transplant status, translating into a median PFS that was not yet reached after 44/33 months.
Project description:Smoldering multiple myeloma is a heterogeneous asymptomatic precursor to multiple myeloma. Since its identification in 1980, risk stratification models have been developed using two main stratification methods: clinical measurement-based and genetics-based. Clinical measurement models can be subdivided in three types: baseline measurements (performed at diagnosis), evolving measurements (performed over time during follow-up appointments), and imaging (for example, magnetic resonance imaging). Genetic approaches include gene expression profiling, DNA/RNA sequencing, and cytogenetics. It is important to accurately distinguish patients with indolent disease from those with aggressive disease, as clinical trials have shown that patients designated as "high-risk of progression" have improved outcomes when treated early. The risk stratification models, and clinical trials are discussed in this review.
Project description:The overall survival of patients with multiple myeloma (MM) has been improved greatly over the last 2 decades with the broader use of novel drugs and autologous tandem transplantation. However, more than one tenth of myeloma patients still die shortly after diagnosis. We therefore aim to investigate the risk factors of early mortality (death within 60 days after diagnosis) in patients with MM. We included in this study 451 consecutive patients with MM, newly diagnosed at an Asian tertiary medical center between January 1, 2002 and April 30, 2015. A total of 57 subjects who experienced early mortality were identified. Risk factors for early mortality in myeloma patients were collected and analyzed. Early mortality occurred in 57 (12.6%) of the myeloma patients. In the multivariate analysis, being male (adjusted OR 2.93, 95% CI 1.17-7.31), serum albumin < 3.5 g/dL (adjusted OR 2.71, 95% CI 1.09-6.74), primary plasma cell leukemia (adjusted OR 17.61, 95% CI 1.01-306.05), serum albumin (adjusted OR 2.70, 95% CI 1.15-6.38), corrected serum calcium ≥ 12 mg/dL (adjusted OR 2.94, 95% CI 1.21-7.14), and LDH ≥ 250 U/L (adjusted OR 3.07, 95% CI 1.50-6.27) were identified as independent risk factors of early mortality. Pneumonia with other infections contributed most to early mortality (n = 36, 65%), followed by renal failure and cardiac failure. The early mortality rate is high (12.6%) in patients with MM. Patients who are male and those with primary plasma cell leukemia, low serum albumin, high-corrected serum calcium, or LDH are at risk of early mortality. Nearly two thirds of the myeloma patients who experienced early mortality in our study (37 of 57, 65%) died of infection. Once a high-risk group is identified, much effort is required to target new approaches for prevention, early detection, and treatment of infections.