In multiple myeloma malignant plasma cells expand within the bone marrow. Since this site is well-perfused, a rapid dissemination of “fitter” clones may be anticipated. However, an imbalanced distribution of multiple myeloma is frequently observed in medical imaging. Here, we perform multi-region sequencing, including iliac crest and radiology-guided focal lesion specimens from 51 patients to gain insight into the spatial clonal architecture. We demonstrate spatial genomic heterogeneity in more than 75% of patients, including inactivation of CDKN2C and TP53, and mutations affecting mitogen-activated protein kinase genes. We show that the extent of spatial heterogeneity is positively associated with the size of biopsied focal lesions consistent with regional outgrowth of advanced clones. The results support a model for multiple myeloma progression with clonal sweeps in the early phase and regional evolution in advanced disease. We suggest that multi-region investigations are critical to understanding intra-patient heterogeneity and the evolutionary processes in multiple myeloma. In multiple myeloma, malignant cells expand within bone marrow. Here, the authors use multi-region sequencing in patient samples to analyse spatial clonal architecture and heterogeneity, providing novel insight into multiple myeloma progression and evolution.

Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing

Multiple myeloma, a plasma cell malignancy, is the second most common blood cancer. Despite extensive research, disease heterogeneity is poorly characterized, hampering efforts for early diagnosis and improved treatments. Here, we apply single cell RNA sequencing to study the heterogeneity of 40 individuals along the multiple myeloma progression spectrum, including 11 healthy controls, demonstrating high interindividual variability that can be explained by expression of known multiple myeloma drivers and additional putative factors. We identify extensive subclonal structures for 10 of 29 individuals with multiple myeloma. In asymptomatic individuals with early disease and in those with minimal residual disease post-treatment, we detect rare tumor plasma cells with molecular characteristics similar to those of active myeloma, with possible implications for personalized therapies. Single cell analysis of rare circulating tumor cells allows for accurate liquid biopsy and detection of malignant plasma cells, which reflect bone marrow disease. Our work establishes single cell RNA sequencing for dissecting blood malignancies and devising detailed molecular characterization of tumor cells in symptomatic and asymptomatic patients. Single cell dissection of plasma cell heterogeneity in myeloma patients reveals new insights into disease that may inform early diagnosis and clinical management.

/pdf/single-cell-dissection-of-plasma-cell-heterogeneity-in-50n18x7cnj.pdf

Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma

We analyzed whole genomes of unique paired samples from smoldering multiple myeloma (SMM) patients progressing to multiple myeloma (MM). We report that the genomic landscape, including mutational profile and structural rearrangements at the smoldering stage is very similar to MM. Paired sample analysis shows two different patterns of progression: a “static progression model”, where the subclonal architecture is retained as the disease progressed to MM suggesting that progression solely reflects the time needed to accumulate a sufficient disease burden; and a “spontaneous evolution model”, where a change in the subclonal composition is observed. We also observe that activation-induced cytidine deaminase plays a major role in shaping the mutational landscape of early subclinical phases, while progression is driven by APOBEC cytidine deaminases. These results provide a unique insight into myelomagenesis with potential implications for the definition of smoldering disease and timing of treatment initiation. Smoldering MM (SMM) is a premalignant stage of multiple myeloma (MM). Here the authors perform whole genome sequencing of unique paired samples of SMM progressing to MM, and show that the genomic landscape at the SMM stage is very similar to MM, but trajectories of evolution can vary from patient to patient.

/pdf/genomic-patterns-of-progression-in-smoldering-multiple-2jezqh19m3.pdf

Genomic patterns of progression in smoldering multiple myeloma

In multiple myeloma, next-generation sequencing (NGS) has expanded our knowledge of genomic lesions, and highlighted a dynamic and heterogeneous composition of the tumor. Here we used NGS to characterize the genomic landscape of 418 multiple myeloma cases at diagnosis and correlate this with prognosis and classification. Translocations and copy number abnormalities (CNAs) had a preponderant contribution over gene mutations in defining the genotype and prognosis of each case. Known and novel independent prognostic markers were identified in our cohort of proteasome inhibitor and immunomodulatory drug-treated patients with long follow-up, including events with context-specific prognostic value, such as deletions of the PRDM1 gene. Taking advantage of the comprehensive genomic annotation of each case, we used innovative statistical approaches to identify potential novel myeloma subgroups. We observed clusters of patients stratified based on the overall number of mutations and number/type of CNAs, with distinct effects on survival, suggesting that extended genotype of multiple myeloma at diagnosis may lead to improved disease classification and prognostication.

/pdf/analysis-of-the-genomic-landscape-of-multiple-myeloma-2ygf8yrvzr.pdf

Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups

Clonal evolution drives tumor progression, chemoresistance and relapse in cancer. Little is known about clonal selection induced by therapeutic pressure in multiple myeloma. To address this issue, we performed large targeted sequencing of bone marrow plasma cells in 43 multiple myeloma patients at diagnosis and at relapse from exactly the same intensive treatment. The most frequently mutated genes at diagnosis were KRAS (35%), NRAS (28%), DIS3 (16%), BRAF, and LRP1B (12% each). At relapse, the mutational burden was unchanged. Many of the mutations were present at the subclonal level at both time points, including driver ones. According to patients and mutations, we observed different scenarios: selection of a very rare subclone present at diagnosis, appearance, or disappearance of mutations, but also stability. Our data highlight that chemoresistance and relapse could be induced by newly acquired mutations in myeloma drivers but also by (sub)clonal mutations preexisting to the treatment. Importantly, no specific mutation or rearrangement was observed at relapse, demonstrating that intensive treatment has a nonspecific effect on clonal selection in multiple myeloma. Finally, we identified 22 cases of biallelic event, including a double event deletion 17p/TP53mut.

Multiple myeloma clonal evolution in homogeneously treated patients

Genomic lesions are not investigated during routine diagnostic workup for multiple myeloma (MM). Cytogenetic studies are performed to assess prognosis but with limited impact on therapeutic decisions. Recently, several recurrently mutated genes have been described, but their clinical value remains to be defined. Therefore, clinical-grade strategies to investigate the genomic landscape of myeloma samples are needed to integrate new and old prognostic markers. We developed a target-enrichment strategy followed by next-generation sequencing (NGS) to streamline simultaneous analysis of gene mutations, copy number changes and immunoglobulin heavy chain (IGH) translocations in MM in a high-throughput manner, and validated it in a panel of cell lines. We identified 548 likely oncogenic mutations in 182 genes. By integrating published data sets of NGS in MM, we retrieved a list of genes with significant relevance to myeloma and found that the mutational spectrum of primary samples and MM cell lines is partially overlapping. Gains and losses of chromosomes, chromosomal segments and gene loci were identified with accuracy comparable to conventional arrays, allowing identification of lesions with known prognostic significance. Furthermore, we identified IGH translocations with high positive and negative predictive value. Our approach could allow the identification of novel biomarkers with clinical relevance in myeloma.

/pdf/a-dna-target-enrichment-approach-to-detect-mutations-copy-2q96mogid2.pdf

A DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma

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A DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma