Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data.

The evolution and progression of multiple myeloma and its precursors over time is poorly understood. Here, we investigate the landscape and timing of mutational processes shaping multiple myeloma evolution in a large cohort of 89 whole genomes and 973 exomes. We identify eight processes, including a mutational signature caused by exposure to melphalan. Reconstructing the chronological activity of each mutational signature, we estimate that the initial transformation of a germinal center B-cell usually occurred during the first 2^nd-3^rd decades of life. We define four main patterns of activation-induced deaminase (AID) and apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) mutagenesis over time, including a subset of patients with evidence of prolonged AID activity during the pre-malignant phase, indicating antigen-responsiveness and germinal center reentry. Our findings provide a framework to study the etiology of multiple myeloma and explore strategies for prevention and early detection.

In multiple myeloma, novel treatments with proteasome inhibitors (PIs) and immunomodulatory agents (IMiDs) have prolonged survival but the disease remains incurable. At relapse, next-generation sequencing has shown occasional mutations of drug targets but has failed to identify unifying features that underlie chemotherapy resistance. We studied 42 patients refractory to both PIs and IMiDs. Whole-exome sequencing was performed in 40 patients, and RNA sequencing (RNA-seq) was performed in 27. We found more mutations than were reported at diagnosis and more subclonal mutations, which implies ongoing evolution of the genome of myeloma cells during treatment. The mutational landscape was different from that described in published studies on samples taken at diagnosis. The TP53 pathway was the most frequently inactivated (in 45% of patients). Conversely, point mutations of genes associated with resistance to IMiDs were rare and were always subclonal. Refractory patients were uniquely characterized by having a mutational signature linked to exposure to alkylating agents, whose role in chemotherapy resistance and disease progression remains to be elucidated. RNA-seq analysis showed that treatment or mutations had no influence on clustering, which was instead influenced by karyotypic events. We describe a cluster with both amp(1q) and del(13) characterized by CCND2 upregulation and also overexpression of MCL1, which represents a novel target for experimental treatments. Overall, high-risk features were found in 65% of patients. However, only amp(1q) predicted survival. Gene mutations of IMiD and PI targets are not a preferred mode of drug resistance in myeloma. Chemotherapy resistance of the bulk tumor population is likely attained through differential, yet converging evolution of subclones that are overall variable from patient to patient and within the same patient.

Personalized treatment is an attractive strategy that promises increased efficacy with reduced side effects in cancer. The feasibility of such an approach has been greatly boosted by next-generation sequencing (NGS) techniques, which can return detailed information on the genome and on the transcriptome of each patient's tumor, thus highlighting biomarkers of response or druggable targets that may differ from case to case. However, while the number of cancers sequenced is growing exponentially, much fewer cases are amenable to a molecularly-guided treatment outside of clinical trials to date. In multiple myeloma, genomic analysis shows a variety of gene mutations, aneuploidies, segmental copy-number changes, translocations that are extremely heterogeneous, and more numerous than other hematological malignancies. Currently, in routine clinical practice we employ reduced FISH panels that only capture three high-risk features as part of the R-ISS. On the contrary, recent advances have suggested that extending genomic analysis to the full spectrum of recurrent mutations and structural abnormalities in multiple myeloma may have biological and clinical implications. Furthermore, increased efficacy of novel treatments can now produce deeper responses, and standard methods do not have enough sensitivity to stratify patients in complete biochemical remission. Consequently, NGS techniques have been developed to monitor the size of the clone to a sensitivity of up to a cell in a million after treatment. However, even these techniques are not within reach of standard laboratories. In this review we will recapitulate recent advances in multiple myeloma genomics, with special focus on the ones that may have immediate translational impact. We will analyze the benefits and pitfalls of NGS-based diagnostics, highlighting crucial aspects that will need to be taken into account before this can be implemented in most laboratories. We will make the point that a new era in myeloma diagnostics and minimal residual disease monitoring is close and conventional genetic testing will not be able to return the required information. This will mandate that even in routine practice NGS should soon be adopted owing to a higher informative potential with increasing clinical benefits.

Genomic analysis could contribute to a better understanding of the biological determinants of the evolution of multiple myeloma (MM) precursor disease and an improved definition of high-risk patients. To assess the feasibility and value of next-generation sequencing approaches in an asymptomatic setting, we performed a targeted gene mutation analysis and a genome-wide assessment of copy number alterations (CNAs) by ultra-low-pass whole genome sequencing (ULP-WGS) in six patients with monoclonal gammopathy of undetermined significance and 25 patients with smoldering MM (SMM). Our comprehensive genomic characterization highlighted heterogeneous but substantial values of the tumor fraction, especially in SMM; a rather high degree of genomic complexity, in terms of both mutations and CNAs, and inter-patient variability; a higher incidence of gene mutations and CNAs in SMM, confirming ongoing evolution; intraclonal heterogeneity; and instances of convergent evolution. ULP-WGS of these patients proved effective in revealing the marked genome-wide level of their CNAs, most of which are not routinely investigated. Finally, the analysis of our small SMM cohort suggested that chr(8p) deletions, the DNA tumor fraction, and the number of alterations may have clinical relevance in the progression to overt MM. Although validation in larger series is mandatory, these findings highlight the promising impact of genomic approaches in the clinical management of SMM.

In Multiple myeloma (MM) no treatment has a curative potential and even complete response to novel agents such as proteasome inhibitors (PIs) and immunomodulatory agents (IMiDs) are followed by relapse over time. Next generation sequencing (NGS) has showed how MM at diagnosis is defined by several somatic mutations, but only few drivers, even fewer "druggable" mutations, and many found at a subclonal level. At relapse, targeted studies have shown occasional mutations in drug target genes but the genomic and transcriptomic determinants of chemoresistance in MM remains elusive. We selected 42 MM patients refractory to both lenalidomide and PIs. Whole exome sequencing was performed in 40 of them, and RNAseq in 27. Clinical annotation was available for all patients. Standard analysis pipelines where applied to analyze mutations, copy number alterations (CNAs), mutational signatures, gene expression and expressed mutations. Patients received a median of 3 lines of treatment, with median overall survival of 14.6 months from sampling. We found a median of 77.5 mutations per patient, which is more than what reported at diagnosis (Bolli et al, Nature Communications 2014;5:2997). 100% of samples showed evidence of subclonality, and 37% of them exhibited a higher number of subclonal than clonal variants. Therefore, even at this advanced stage the MM genome is evolving and is composed of different subclones that may display different chemosensitivity. The mutational landscape was also different. TP53 mutations were the second most common after KRAS (20% and 17.5%, respectively). Interestingly TP53 mutations all clustered in patients receiving bortezomib as the last line of treatment. Only 2 patients showed a CRBN mutation, both subclonal. Combining mutations and CNA analysis, the TP53 pathway was the most frequently inactivated (45% of patients). Altogether, mutations or deletions of genes in the CRBN E3 ubiquitin ligase complex were found in 32.5% of patients, while proteasomal subunit genes were infrequently hit. Refractory cases were also uniquely characterized by a novel signature linked to exposure to alkylating agents, whose activity was more pronounced after high-dose melphalan suggesting a mutagenic effect of the drug on residual cells at the time of transplant. Whether this has any pathogenetic role on the disease course remains to be elucidated. RNAseq analysis did not show any influence of treatment or mutational data on the clustering of samples, which was mainly influenced by karyotypic events. The main cluster was composed by non-hyperdiploid patients with both amp(1q) and del(13): these showed CCND2 and MCL1 upregulation, the latter representing a marker of venetoclax resistance and novel target of experimental treatments. Only 26.3% of mutations were expressed, and this correlated with the clonality level of the mutation. However, most mutations in driver genes were expressed, with the notable exception of those causing nonsense mediated decay. Overall, classical high-risk features or CRBN pathway mutations were found in 65% of the cohort. However, only amp(1q) predicted survival in our cohort. The lack of prognostic value of high-risk lesions is likely explained by a higher prevalence of such features in double-refractory stages. Our data suggest that gene mutation is not a preferred mode of evolution of drug resistance in MM. Chemoresistance of the bulk tumor population is likely attained though differential, yet converging evolution of different subclones that are overall highly variable from patient to patient and within the same patient.