Primary plasma cell leukemia (PCL) has a consistently ominous prognosis, even after progress in the last decades. PCL deserves a prompt identification to start the most effective treatment for this ultra-high-risk disease. The aim of this position paper is to revisit the diagnosis of PCL according to the presence of circulating plasma cells in patients otherwise meeting diagnostic criteria of multiple myeloma. We could identify two retrospective series where the question about what number of circulating plasma cells in peripheral blood should be used for defining PCL. The presence of ≥5% circulating plasma cells in patients with MM had a similar adverse prognostic impact as the previously defined PCL. Therefore, PCL should be defined by the presence of 5% or more circulating plasma cells in peripheral blood smears in patients otherwise diagnosed with symptomatic multiple myeloma.

Purpose To define the biology driving the aggressive nature of acute myeloid leukemia (AML) in elderly patients. Patients and Methods Clinically annotated microarray data from 425 patients with newly diagnosed de novo AML from two publicly available data sets were analyzed after age-specific cohorts (young ≤ 45 years, n = 175; elderly ≥ 55 years; n = 144) were prospectively identified. Gene expression analysis was conducted utilizing gene set enrichment analysis, and by applying previously defined and tested signature profiles reflecting dysregulation of oncogenic signaling pathways and altered tumor environment. Results Elderly AML patients as expected had worse overall survival and event-free survival compared with younger patients. Analysis of oncogenic pathways revealed that older patients had higher probability of RAS, Src, and tumor necrosis factor (TNF) pathway activation (all P < .0001). Hierarchical clustering revealed that younger patients with AML in cluster 2 had clinically worse survival, with high RAS, Src, and TNF pathway activation compared with patients in cluster 1. However, among elderly patients with AML, those in cluster 1 also demonstrated high RAS, Src, and TNF pathway activation but this did not translate into differences in survival. Conclusion AML in the elderly represents a distinct biologic entity characterized by unique patterns of deregulated signaling pathway variations that contributes to poor survival. These insights should enable development and adjustments of clinically meaningful treatment strategies in the older patient population.

Gene expression profiling may be useful for prognostic and therapeutic strategies in breast carcinoma. To demonstrate the value in integrating genomic information with clinical and pathological risk factors, to refine prognosis, and to improve therapeutic strategies for early stage breast cancer. Retrospective study of patients with early stage breast carcinoma who were candidates for adjuvant chemotherapy; 964 clinically annotated breast tumor samples (573 in the initial discovery set and 391 in the validation cohort) with corresponding microarray data were used. All patients were assigned relapse risk scores based on their respective clinicopathological features. Signatures representing oncogenic pathway activation and tumor biology/microenvironment status were applied to these samples to obtain patterns of deregulation that correspond with relapse risk scores to refine prognosis with the clinicopathological prognostic model alone. Predictors of chemotherapeutic response were also applied to further characterize clinically relevant heterogeneity in early stage breast cancer. Gene expression signatures and clinicopathological variables in early stage breast cancer to determine a refined estimation of relapse-free survival and sensitivity to chemotherapy. In the initial data set of 573 patients, prognostically significant clusters representing patterns of oncogenic pathway activation and tumor biology/microenvironment states were identified within the low-risk (log-rank P = .004), intermediate-risk (log-rank P = .01), and high-risk (log-rank P = .003) model cohorts, representing clinically important genomic subphenotypes of breast cancer. As an example, in the low-risk cohort, of 6 prognostically significant clusters, patients in cluster 4 had an inferior relapse-free survival vs patients in cluster 1 (log-rank P = .004) and cluster 5 (log-rank P = .03). Median relapse-free survival for patients in cluster 4 was 16 months less than for patients in cluster 1 (95% CI, 7.5-24.5 months) and 19 months less than for patients in cluster 5 (95% CI, 10.5-27.5 months). Multivariate analyses confirmed the independent prognostic value of the genomic clusters (low risk, P = .05; high risk, P = .02). The reproducibility and validity of these patterns of pathway deregulation in predicting relapse risk was established using related but not identical clusters in the independent validation cohort. The prognostic clinicogenomic clusters also have unique sensitivity patterns to commonly used cytotoxic therapies. These results provide preliminary evidence that incorporation of gene expression signatures into clinical risk stratification can refine prognosis. Prospective studies are needed to determine the value of this approach for individualizing therapeutic strategies.

Purpose: Monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM) comprise heterogeneous disorders with incompletely understood molecular defects and variable clinical features. We performed gene expression profiling (GEP) with microarray data to better dissect the molecular phenotypes and prognoses of these diseases.Methods: Using gene expression and clinical data from 877 patients ranging from normal plasma cells (NPC) to relapsed MM (RMM), we applied gene expression signatures reflecting deregulation of oncogenic pathways and tumor microenvironment to highlight molecular changes that occur as NPCs transition to MM, create a high-risk MGUS gene signature, and subgroup International Staging System (ISS) stages into more prognostically accurate clusters of patients.Results: Myc upregulation and increasing chromosomal instability (CIN) characterized the evolution from NPC to RMM (P < .0001 for both). Studies of MGUS revealed that some samples shared biologic features with RMM, which comprised the basis for a high-risk MGUS signature. Regarding MM, we subclassified ISS stages into clusters based on shared features of tumor biology. These clusters differentiated themselves based on predictions for prognosis (eg, in ISS stage I, one cluster was characterized by increased CIN and a poor prognosis).Conclusion: GEP provides insight into the molecular defects underlying plasma cell dyscrasias that may explain their clinical heterogeneity. GEP also may also refine current prognostic and therapeutic models for MGUS and MM.

Multiple myeloma (MM) is the second most common hematologic malignancy in the US. It is typically characterized by production of large amounts of defective immunoglobulin (Ig). Diagnosing MM and monitoring treatment response, including eventual relapse, are largely based on sequential measurements of Ig. However, a small subset of MM called non-secretory multiple myeloma (NSMM) produces no detectable Ig. This subset of true NSMM has become even smaller over time, as the advent of the serum free light chain assay has resulted in the majority of NSMM patients being recategorized as light-chain MM – that is, MM cells that produce only the light-chain component of Ig. True forms of NSMM, meaning MM that secretes no monoclonal proteins whatsoever, constitute a distinct entity that is reviewed; definition of NSMM using current detection methods, discuss the biology underpinning NSMM development, and share recommendations for how NSMM should be managed clinically with respect to detection, treatment, and monitoring.