Integrated transcriptomic and proteomic analysis of clonal plasma cells from patients with AL amyloidosis

Abstract Systemic AL amyloidosis is a rare disease caused by the abnormal extracellular deposition of misfolded immunoglobulin light chain fibrils that lead to organ dysfunction and failure. The plasma cell clones in AL amyloidosis are characterized by biased IgVL gene usage, frequent presence of translocation t(11;14) (in about 40-50% of cases) and low proliferative capacity; however, the biological and clinical implications of these features are not fully understood and latent mechanisms that provide unique properties to amyloidogenic plasma cell clones may exist. Aiming to identify transcript and protein features that would provide deeper insights into molecular mechanisms and regulatory processes of the amyloidogenic plasma cell clone, we performed an integrated transcriptomic and proteomic analysis in clonal plasma cells from patients with AL amyloidosis. We performed paired-end bulk-RNA-sequencing using Illumina technology and proteomic profiling using liquid chromatography tandem mass spectrometry (LC-MS/MS) on CD138+ selected cells from the bone marrow aspirates of 40 consecutive patients with AL amyloidosis. We used Principal Component Analysis (PCA) to visualize the high-dimensional transcriptomics and proteomics data and to identify patterns. We conducted gene expression and protein abundance analyses to characterize the transcriptomic and proteomic landscape of the clonal plasma cells in AL amyloidosis. Furthermore, we applied Multi-Omics Factor Analysis (MOFA) as an unsupervised method for multi-omics integration to identify latent factors driving clonal disease characteristics. In unsupervised PCA we observed a major cluster which contained the t(11;14) clones, whereas t(11;14)-negative patients displayed significant heterogeneity. Differential gene expression analysis showed that presence t(11;14), was associated with up-regulated genes (log2-fold-change >1 and adjusted p-value <0.05) involved in B-cell differentiation (PAX5, MS4A1, CD40, CD79A), endoplasmic reticulum (ER) stress (SLC7A11, PAQR3), oxidative stress (ALOX5, ALOX5AP), lipid metabolism (ACSL1, APOL3, PLD4), abnormal protein aggregates accumulation (APP, LRRK2, PRNP) and down-regulated genes (log2-fold-change <-1 and adjusted p-value <0.05) involved in mitochondrial apoptotic regulation (BAK1). Differential protein abundance analysis concordantly showed that t(11;14) clones exhibited up-regulated proteins (log2-fold-change >1 and adjusted p-value <0.05) involved in lymphocyte growth (LSP1), in oxidative stress (ALOX5), stress response (QKI, CAMK2D) and in epigenetic regulation (H1-3). Multi-omic analysis using MOFA identified the presence of t(11;14) as a major driver of molecular disease variation (Pearson correlation 0.76, adjusted p-value < 0.001). Patients harboring t(11;14) are characterized by CCND1-driven proliferation (feature weight 1, Pearson correlation 0.71, adjusted p-value <0.001), B-cell-like features, enhanced lipid metabolism and ER stress. On the other hand, t(11;14)-negative patients are characterized by CCND2-signaling (feature weight 0.84, Pearson correlation 0.66, adjusted p-value <0.001), mitochondrial metabolism and pro-inflammatory cytokine signaling. These findings suggest that the presence of t(11;14) in AL amyloidosis is associated with a B-cell-like phenotype and reduced apoptosis despite cellular stress, aligning with clinical observations that these patients respond to BCL-2 inhibitors. Moreover, transcriptomics data (up-regulation of genes typically associated with neurodegenerative diseases: APP, LRRK2, PRNP) suggest that t(11;14)-positive clones possibly undergo amyloidogenic neurodegenerative disease-like stress. On the other hand, t(11;14)-negative clones exhibit a more mature plasma cell phenotype. Interestingly, among the top transcriptomic features in t(11;14)-negative patients is GPRC5D (feature weight 0.54, Pearson correlation 0.61, adjusted p-value <0.001), an immunotherapeutic target in multiple myeloma, and PTP4A3, linked to cancer invasiveness and proliferation (feature weight 0.57, Pearson correlation 0.6, p-adjusted p-value <0.001). Overall, our findings highlight the molecular heterogeneity of AL amyloidosis and provide new mechanistic understanding of the transcriptomic and proteomic landscape shaped by the presence of t(11;14), with potential implications for the development of targeted therapeutic strategies.

Authors

Institutions

Publication Details

Journal
Blood
Published
2025-11-03
DOI
https://doi.org/10.1182/blood-2025-3931
Primary Topic
Amyloidosis: Diagnosis, Treatment, Outcomes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrated transcriptomic and proteomic analysis of clonal plasma cells from patients with AL amyloidosis

Asimina Papanikolaou, Tina Bagratuni, Efstathios Kastritis, Christine‐Ivy Liacos et al.
Blood
Amyloidosis: Diagnosis, Treatment, Outcomes
article

Integrated transcriptomic and proteomic analysis of clonal plasma cells from patients with AL amyloidosis

Asimina Papanikolaou, Tina Bagratuni, Efstathios Kastritis, Christine‐Ivy Liacos, Meletios Athanasios Dimopoulos, Julie Courraud, Evangelos Terpos, Eleni Kalafati, Ioannis Kollias, Alexandra Papadimou, Nefeli Mavrianou Koutsoukou, Maria Gavriatopoulou, Kostantina Taouxi, Ourania Theologi, Katerina Chrisostomidou, A Papadopoulos, Foteini Theodorakakou
article en

Abstract

Abstract Systemic AL amyloidosis is a rare disease caused by the abnormal extracellular deposition of misfolded immunoglobulin light chain fibrils that lead to organ dysfunction and failure. The plasma cell clones in AL amyloidosis are characterized by biased IgVL gene usage, frequent presence of translocation t(11;14) (in about 40-50% of cases) and low proliferative capacity; however, the biological and clinical implications of these features are not fully understood and latent mechanisms that provide unique properties to amyloidogenic plasma cell clones may exist. Aiming to identify transcript and protein features that would provide deeper insights into molecular mechanisms and regulatory processes of the amyloidogenic plasma cell clone, we performed an integrated transcriptomic and proteomic analysis in clonal plasma cells from patients with AL amyloidosis. We performed paired-end bulk-RNA-sequencing using Illumina technology and proteomic profiling using liquid chromatography tandem mass spectrometry (LC-MS/MS) on CD138+ selected cells from the bone marrow aspirates of 40 consecutive patients with AL amyloidosis. We used Principal Component Analysis (PCA) to visualize the high-dimensional transcriptomics and proteomics data and to identify patterns. We conducted gene expression and protein abundance analyses to characterize the transcriptomic and proteomic landscape of the clonal plasma cells in AL amyloidosis. Furthermore, we applied Multi-Omics Factor Analysis (MOFA) as an unsupervised method for multi-omics integration to identify latent factors driving clonal disease characteristics. In unsupervised PCA we observed a major cluster which contained the t(11;14) clones, whereas t(11;14)-negative patients displayed significant heterogeneity. Differential gene expression analysis showed that presence t(11;14), was associated with up-regulated genes (log2-fold-change >1 and adjusted p-value <0.05) involved in B-cell differentiation (PAX5, MS4A1, CD40, CD79A), endoplasmic reticulum (ER) stress (SLC7A11, PAQR3), oxidative stress (ALOX5, ALOX5AP), lipid metabolism (ACSL1, APOL3, PLD4), abnormal protein aggregates accumulation (APP, LRRK2, PRNP) and down-regulated genes (log2-fold-change <-1 and adjusted p-value <0.05) involved in mitochondrial apoptotic regulation (BAK1). Differential protein abundance analysis concordantly showed that t(11;14) clones exhibited up-regulated proteins (log2-fold-change >1 and adjusted p-value <0.05) involved in lymphocyte growth (LSP1), in oxidative stress (ALOX5), stress response (QKI, CAMK2D) and in epigenetic regulation (H1-3). Multi-omic analysis using MOFA identified the presence of t(11;14) as a major driver of molecular disease variation (Pearson correlation 0.76, adjusted p-value < 0.001). Patients harboring t(11;14) are characterized by CCND1-driven proliferation (feature weight 1, Pearson correlation 0.71, adjusted p-value <0.001), B-cell-like features, enhanced lipid metabolism and ER stress. On the other hand, t(11;14)-negative patients are characterized by CCND2-signaling (feature weight 0.84, Pearson correlation 0.66, adjusted p-value <0.001), mitochondrial metabolism and pro-inflammatory cytokine signaling. These findings suggest that the presence of t(11;14) in AL amyloidosis is associated with a B-cell-like phenotype and reduced apoptosis despite cellular stress, aligning with clinical observations that these patients respond to BCL-2 inhibitors. Moreover, transcriptomics data (up-regulation of genes typically associated with neurodegenerative diseases: APP, LRRK2, PRNP) suggest that t(11;14)-positive clones possibly undergo amyloidogenic neurodegenerative disease-like stress. On the other hand, t(11;14)-negative clones exhibit a more mature plasma cell phenotype. Interestingly, among the top transcriptomic features in t(11;14)-negative patients is GPRC5D (feature weight 0.54, Pearson correlation 0.61, adjusted p-value <0.001), an immunotherapeutic target in multiple myeloma, and PTP4A3, linked to cancer invasiveness and proliferation (feature weight 0.57, Pearson correlation 0.6, p-adjusted p-value <0.001). Overall, our findings highlight the molecular heterogeneity of AL amyloidosis and provide new mechanistic understanding of the transcriptomic and proteomic landscape shaped by the presence of t(11;14), with potential implications for the development of targeted therapeutic strategies.

BloodVol. 146(Supplement 1)
National and Kapodistrian University of Athens (GR), Alexandra Hospital (GR)
Openalex Percentile: Top 65%
Amyloidosis: Diagnosis, Treatment, Outcomes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.