0000000000020211
AUTHOR
Ibai Goicoechea
Transcriptional profiling of circulating tumor cells in multiple myeloma: a new model to understand disease dissemination
The reason why a few myeloma cells egress from the bone marrow (BM) into peripheral blood (PB) remains unknown. Here, we investigated molecular hallmarks of circulating tumor cells (CTCs) to identify the events leading to myeloma trafficking into the bloodstream. After using next-generation flow to isolate matched CTCs and BM tumor cells from 32 patients, we found high correlation in gene expression at single-cell and bulk levels (r ≥ 0.94, P = 10−16), with only 55 genes differentially expressed between CTCs and BM tumor cells. CTCs overexpressed genes involved in inflammation, hypoxia, or epithelial–mesenchymal transition, whereas genes related with proliferation were downregulated in CTCs…
Single-Cell Characterization of the Multiple Myeloma (MM) Immune Microenvironment Identifies CD27-Negative T Cells As Potential Source of Tumor-Reactive Lymphocytes
Background: The broad use of immunomodulatory drugs (IMiDs) and the breakthrough of novel immunotherapies in MM, urge the optimization of immune monitoring to help tailoring treatment based on better prediction of patients' response according to their immune status. For example, current T cells immune monitoring is of limited value because the phenotype of tumor-reactive T cells is uncertain. Aims: To characterize the MM immune microenvironment at the single-cell level and to identify clinically relevant subsets for effective immune monitoring. Methods: We used a semi-automated pipeline to unveil full cellular diversity based on unbiased clustering, in a large flow cytometry dataset of 86 n…
Biological and clinical significance of dysplastic hematopoiesis in patients with newly diagnosed multiple myeloma
On behalf of the PETHEMA/GEM Cooperative Group.
Deep MRD profiling defines outcome and unveils different modes of treatment resistance in standard- and high-risk myeloma
PETHEMA/GEM Cooperative Group.
Preclinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma
AbstractThe historical lack of preclinical models reflecting the genetic heterogeneity of multiple myeloma (MM) hampers the advance of therapeutic discoveries. To circumvent this limitation, we screened mice engineered to carry eight MM lesions (NF-κB, KRAS, MYC, TP53, BCL2, cyclin D1, MMSET/NSD2 and c-MAF) combinatorially activated in B lymphocytes following T cell-driven immunization. Fifteen genetically diverse models developed bone marrow (BM) tumors fulfilling MM pathogenesis. Integrative analyses of ∼500 mice and ∼1,000 patients revealed a common MAPK–MYC genetic pathway that accelerated time to progression from precursor states across genetically heterogeneous MM. MYC-dependent time …
Preneoplastic somatic mutations including MYD88(L265P) in lymphoplasmacytic lymphoma
Normal cell counterparts of solid and myeloid tumors accumulate mutations years before disease onset; whether this occurs in B lymphocytes before lymphoma remains uncertain. We sequenced multiple stages of the B lineage in elderly individuals and patients with lymphoplasmacytic lymphoma, a singular disease for studying lymphomagenesis because of the high prevalence of mutated MYD88 . We observed similar accumulation of random mutations in B lineages from both cohorts and unexpectedly found MYD88 L265P in normal precursor and mature B lymphocytes from patients with lymphoma. We uncovered genetic and transcriptional pathways driving malignant transformation and leveraged these to model lymph…
A machine learning model based on tumor and immune biomarkers to predict undetectable MRD and survival outcomes in multiple myeloma
Abstract Purpose: Undetectable measurable residual disease (MRD) is a surrogate of prolonged survival in multiple myeloma. Thus, treatment individualization based on the probability of a patient achieving undetectable MRD with a singular regimen could represent a new concept toward personalized treatment, with fast assessment of its success. This has never been investigated; therefore, we sought to define a machine learning model to predict undetectable MRD at the onset of multiple myeloma. Experimental Design: This study included 487 newly diagnosed patients with multiple myeloma. The training (n = 152) and internal validation cohorts (n = 149) consisted of 301 transplant-eligible patients…