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

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

Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis

Triple-negative breast cancer (TNBC) is a heterogeneous group of aggressive breast cancers for which no targeted treatment is available. Robust tools for TNBC classification are required, to improve the prediction of prognosis and to develop novel therapeutic interventions. We analyzed 3,247 primary human breast cancer samples from 21 publicly available datasets, using a five-step method: (1) selection of TNBC samples by bimodal filtering on ER-HER2 and PR, (2) normalization of the selected TNBC samples, (3) selection of the most variant genes, (4) identification of gene clusters and biological gene selection within gene clusters on the basis of String© database connections and gene-expression correlations, (5) summarization of each gene cluster in a metagene. We then assessed the ability of these metagenes to predict prognosis, on an external public dataset (METABRIC). Our analysis of gene expression (GE) in 557 TNBCs from 21 public datasets identified a six-metagene signature (167 genes) in which the metagenes were enriched in different gene ontologies. The gene clusters were named as follows: Immunity1, Immunity2, Proliferation/DNA damage, AR-like, Matrix/Invasion1 and Matrix2 clusters respectively. This signature was particularly robust for the identification of TNBC subtypes across many datasets (n = 1,125 samples), despite technology differences (Affymetrix© A, Plus2 and Illumina©). Weak Immunity two metagene expression was associated with a poor prognosis (disease-specific survival; HR = 2.68 [1.59–4.52], p = 0.0002). The six-metagene signature (167 genes) was validated over 1,125 TNBC samples. The Immunity two metagene had strong prognostic value. These findings open up interesting possibilities for the development of new therapeutic interventions

Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis

International audienceTriple-negative breast cancer (TNBC) is a heterogeneous group of aggressive breast cancers for which no targeted treatment is available. Robust tools for TNBC classification are required, to improve the prediction of prognosis and to develop novel therapeutic interventions. We analyzed 3,247 primary human breast cancer samples from 21 publicly available datasets, using a five-step method: (1) selection of TNBC samples by bimodal filtering on ER-HER2 and PR, (2) normalization of the selected TNBC samples, (3) selection of the most variant genes, (4) identification of gene clusters and biological gene selection within gene clusters on the basis of String (c) database connections and gene-expression correlations, (5) summarization of each gene cluster in a metagene. We then assessed the ability of these metagenes to predict prognosis, on an external public dataset (METABRIC). Our analysis of gene expression (GE) in 557 TNBCs from 21 public datasets identified a six-metagene signature (167 genes) in which the metagenes were enriched in different gene ontologies. The gene clusters were named as follows: Immunity1, Immunity2, Proliferation/DNA damage, AR-like, Matrix/Invasion1 and Matrix2 clusters respectively. This signature was particularly robust for the identification of TNBC subtypes across many datasets (n = 1,125 samples), despite technology differences (Affymetrix (c) A, Plus2 and Illumina (c)). Weak Immunity two metagene expression was associated with a poor prognosis (disease-specific survival; HR = 2.68 [1.59-4.52], p = 0.0002). The six-metagene signature (167 genes) was validated over 1,125 TNBC samples. The Immunity two metagene had strong prognostic value. These findings open up interesting possibilities for the development of new therapeutic interventions

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

Background: In multiple myeloma (MM), next generation sequencing (NGS) has expanded our knowledge of genomic lesions, and highlighted a dynamic and heterogeneous composition. Despite a growing number of cases sequenced, the full potential of NGS studies has not been exploited so far.Aims: We used a custom target pulldown (TPD) approach on a large cohort of MM samples at diagnosis, with homogeneous treatment and long follow-up, to further our understanding of the landscape of driver lesions in MM and how this can be used to improve prognostication and disease classification. Methods: We used a custom-designed SureSelect pulldown strategy (Agilent Biotechnologies) to target 246 genes implicated in MM or cancer in general; 2538 single nucleotide polymorphisms; the immunoglobulin heavy chain (IGH) locus. We sequenced unmatched DNA from CD138-purified plasma cells from 418 patients with a median follow-up of 5.4 years using Illumina Hiseq2000 machines. We applied algorithms developed in-house to detect driver genomic events, filtering out potential artifacts and germline variants. We then ranked each mutation on its likelihood of being oncogenic. Results: We identified 197 driver events including gene mutations, aneuploi- dies and IGH translocations (IGH-Tx), median of 6 per patient. Gene mutations where found in >99% of patients. At least one oncogenic mutation of a known driver gene previously identified (KRAS, NRAS, TP53, FAM46C, BRAF, DIS3, TRAF3, SP140, IRF4) was found in 64%, with a long tail of infrequently mutated genes with uncertain significance. Karyotypic class was assigned in 80% of patients, with 9% of hyperdiploid cases also showing an IGH-Tx (mostly t(4;14)). IGH-Tx and aneuploidies dominated the MM genomic landscape, KRAS and NRAS being the only point mutations present in the 15 most frequent driver events. Multivariate analysis by sparse Cox regression highlighted only four driver events with significant prognostic impact for both progression-free (PFS) and overall survival (OS): t(4;14) (HR 1.88, CI 1.25-1.84), amp(1q) (HR2.63, CI 1.92-3.59), del(17p) (HR2.55, CI 1.66-3.92), and rare mutations of ATP13A4 (HR 0.08, CI 0.01-0.65, mutated in 1.4% of patients). We found a significantly worse prognosis for increasing numbers of driver lesions in each patient (median OS 8.2 vs 3.5 years for 8 driver events, respectively). This was only partially explained by instances of additive effect or interactions between variables, which were very informative but not frequent. To better investigate these findings in the context of the genomic landscape of each case, we applied Bayesian clustering algorithms. The large number of driver events screened led to the identification of three groups: in the largest one, some hyperdiploid and IGH-Tx cases clustered together, suggesting that sec- ondary mutations and CNAs required for tumor progression are often shared between these two subgroups. We then identified two clusters both character- ized by significantly lower number of mutations, but with opposing features. One was enriched for IGH-Tx, had the highest number of CNAs overall, showed higher prevalence of amp(1q), del(13), del(17p), TP53 mutations, and had a shorter median OS of 5.3 years. The other was mostly composed of hyper- diploid cases and showed fewest CNAs and mutations, with a good prognosis (median OS not reached). Summary/Conclusions: We report on the first attempt towards the use of extended tumor genotype for a genomic classification of MM using innovative clustering algorithms. Despite the heterogeneity of the disease, we could iden- tify disease subgroups with a distinct spectrum and number of driver events carrying different prognosis, supporting the introduction of genomics in the clin- ical approach to MM

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

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. © 2018, The Author(s)