A practical guide for mutational signature analysis in hematological malignancies

Germline predisposition in multiple myeloma

We present a study of rare germline predisposition variants in 954 unrelated individuals with multiple myeloma (MM) and 82 MM families. Using a candidate gene approach, we identified such variants across all age groups in 9.1% of sporadic and 18% of familial cases. Implicated genes included genes suggested in other MM risk studies as potential risk genes (DIS3, EP300, KDM1A, and USP45); genes involved in predisposition to other cancers (ATM, BRCA1/2, CHEK2, PMS2, POT1, PRF1, and TP53); and BRIP1, EP300, and FANCM in individuals of African ancestry. Variants were characterized using loss of heterozygosity (LOH), biallelic events, and gene expression analyses, revealing 31 variants in 3.25% of sporadic cases for which pathogenicity was supported by multiple lines of evidence. Our results suggest that the disruption of DNA damage repair pathways may play a role in MM susceptibility. These results will inform improved surveillance in high-risk groups and potential therapeutic strategies.

The Genetic and Molecular Drivers of Multiple Myeloma: Current Insights, Clinical Implications, and the Path Forward

Background

Multiple myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of malignant plasma cells within the bone marrow. The disease's complexity is underpinned by a variety of genetic and molecular abnormalities that drive its progression.

Methods

This review was conducted through a state-of-The-art literature search, primarily utilizing PubMed to gather peer-reviewed articles. We focused on the most comprehensive and cited studies to ensure a thorough understanding of the genetic and molecular landscapes of MM.

Results

We detail primary and secondary alterations such as translocations, hyperdiploidy, single nucleotide variants (SNVs), copy number alterations (CNAs), gene fusions, epigenetic modifications, non-coding RNAs, germline predisposing variants, and the influence of the tumor microenvironment (TME). Our analysis highlights the heterogeneity of MM and the challenges it poses in treatment and prognosis, emphasizing the distinction between driver mutations, which actively contribute to oncogenesis, and passenger mutations, which arise due to genomic instability and do not contribute to disease progression.

Conclusion & Future Perspectives

We report key controversies and challenges in defining the genetic drivers of MM, and examine their implications for future therapeutic strategies. We discuss the importance of systems biology approaches in understanding the dependencies and interactions among these alterations, particularly highlighting the impact of double and triple-hit scenarios on disease outcomes. By advancing our understanding of the molecular drivers and their interactions, this review sets the stage for novel therapeutic targets and strategies, ultimately aiming to improve clinical outcomes in MM patients.

Whole-Exome Sequencing, Mutational Signature Analysis, and Outcome in Multiple Myeloma-A Pilot Study

The complex and heterogeneous genomic landscape of multiple myeloma (MM) and many of its clinical and prognostic implications remains to be understood. In other cancers, such as breast cancer, using whole-exome sequencing (WES) and molecular signatures in clinical practice has revolutionized classification, prognostic prediction, and patient management. However, such integration is still in its early stages in MM. In this study, we analyzed WES data from 35 MM patients to identify potential mutational signatures and driver mutations correlated with clinical and cytogenetic characteristics. Our findings confirm the complex mutational spectrum and its impact on previously described ontogenetic and epigenetic pathways. They show TYW1 as a possible new potential driver gene and find no significant associations of mutational signatures with clinical findings. Further studies are needed to strengthen the role of mutational signatures in the clinical context of patients with MM to improve patient management.

New horizons in our understanding of precursor multiple myeloma and early interception

Multiple myeloma is an incurable plasma cell malignancy that evolves over decades through the selection and malignant transformation of monoclonal plasma cells. The evolution from precursor states to symptomatic disease is characterized by an increasing complexity of genomic alterations within the plasma cells and a remodelling of the microenvironment towards an immunosuppressive state. Notably, in patients with advanced disease, similar mechanisms of tumour escape and immune dysfunction mediate resistance to modern T cell-based therapies, such as T cell-engaging bispecific antibodies and chimeric antigen receptor (CAR)-T cells. Thus, an increasing number of clinical trials are assessing the efficiency and safety of these therapies in individuals with newly diagnosed multiple myeloma and high-risk smoldering multiple myeloma. In this Review, we summarize the current knowledge about tumour intrinsic and extrinsic processes underlying progression from precursor states to symptomatic myeloma and discuss the rationale for early interception including the use of T cell-redirecting therapies.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Mutagenic impact and evolutionary influence of radiotherapy in hematologic malignancies

Ionizing radiotherapy (RT) is a widely used palliative and curative treatment strategy for malignancies. In solid tumors, RT-induced double strand breaks lead to the accumulation of indels, and their repair by non-homologous end-joining has been linked to the ID8 mutational signature in resistant cells. However, the extent of RT-induced DNA damage in hematologic malignancies and its impact on their evolution and interplay with commonly used chemotherapies has not yet been explored. Here, we interrogated 580 whole genome sequencing (WGS) from patients with large B-cell lymphoma, multiple myeloma, and myeloid neoplasms and identified ID8 only in relapsed disease. Yet, it was detected after exposure to both RT and mutagenic chemotherapy (i.e., platinum). Using WGS of single-cell colonies derived from treated lymphoma cells, we revealed a dose-response relationship between RT and platinum and ID8. Finally, using ID8 as a genomic barcode we demonstrate that a single RT-resistant cell may seed systemic relapse.

Advancements in Multiple Myeloma Research: High-Throughput Sequencing Technologies, Omics, and the Role of Artificial Intelligence

Multiple myeloma is a complex and challenging type of blood cancer that affects plasma cells in the bone marrow. In recent years, the development of advanced research techniques, such as omics approaches-which involve studying large sets of biological data like genes and proteins-and high-throughput sequencing technologies, has allowed researchers to analyze vast amounts of genetic information rapidly and gain new insights into the disease. Additionally, the advent of artificial intelligence tools has accelerated data analysis, enabling more accurate predictions and improved treatment strategies. This review aims to highlight recent research advances in multiple myeloma made possible by these novel techniques and to provide guidance for researchers seeking effective approaches in this field.

A comprehensive comparison of tools for fitting mutational signatures

Mutational signatures connect characteristic mutational patterns in the genome with biological or chemical processes that take place in cancers. Analysis of mutational signatures can help elucidate tumor evolution, prognosis, and therapeutic strategies. Although tools for extracting mutational signatures de novo have been extensively benchmarked, a similar effort is lacking for tools that fit known mutational signatures to a given catalog of mutations. We fill this gap by comprehensively evaluating twelve signature fitting tools on synthetic mutational catalogs with empirically driven signature weights corresponding to eight cancer types. On average, SigProfilerSingleSample and SigProfilerAssignment/MuSiCal perform best for small and large numbers of mutations per sample, respectively. We further show that ad hoc constraining the list of reference signatures is likely to produce inferior results. Evaluation of real mutational catalogs suggests that the activity of signatures that are absent in the reference catalog poses considerable problems to all evaluated tools.

Genomic Profiling to Contextualize the Results of Intervention for Smoldering Multiple Myeloma

PURPOSE

Early intervention for high-risk smoldering multiple myeloma (HR-SMM) achieves deep and prolonged responses. It is unclear if beneficial outcomes are due to the treatment of less complex, susceptible disease or inaccuracy in clinical definition of cases entered.

EXPERIMENTAL DESIGN

In this study, we interrogated whole-genome and whole-exome sequencing for 54 patients across two HR-SMM interventional studies (NCT01572480 and NCT02279394).

RESULTS

We reveal that the genomic landscape of treated HR-SMM is generally simple as compared with newly diagnosed multiple myeloma counterparts with less inactivation of tumor suppressor genes, RAS pathway mutations, MYC disruption, and APOBEC contribution. The absence of these events parallels that of indolent precursor conditions, possibly explaining overall excellent outcomes. However, some patients harboring genomic complexity fail to sustain response and experience resistant, progressive disease. Overall, clinical risk scores do not effectively discriminate between genomically indolent and aggressive disease.

CONCLUSIONS

Genomic profiling can contextualize the advantage of early intervention in SMM and guide personalization of therapy. See related commentary by Weinhold and Rasche, p. 4263.

Predictive value of homologous recombination deficiency status for survival outcomes in primary tubo-ovarian high-grade serous carcinoma

OBJECTIVES

This is a protocol for a Cochrane Review (prognosis). The objectives are as follows: To evaluate the predictive value of the prognostic factor HRD status, as determined by various clinically validated HRD assays at the time of staging laparotomy, compared to BRCA1/2 mutation status for progression-free survival and overall survival in patients with tubo-ovarian high-grade serous carcinoma treated in the first-line setting with a combination of surgery and platinum-based chemotherapy and/or maintenance with PARP inhibitors.

Mutational Signatures in Colorectal Cancer: Translational Insights, Clinical Applications, and Limitations

A multitude of exogenous and endogenous processes have the potential to result in DNA damage. While the repair mechanisms are typically capable of correcting this damage, errors in the repair process can result in mutations. The findings of research conducted in 2012 indicate that mutations do not occur randomly but rather follow specific patterns that can be attributed to known or inferred mutational processes. The process of mutational signature analysis allows for the inference of the predominant mutational process for a given cancer sample, with significant potential for clinical applications. A deeper comprehension of these mutational signatures in CRC could facilitate enhanced prevention strategies, facilitate the comprehension of genotoxic drug activity, predict responses to personalized treatments, and, in the future, inform the development of targeted therapies in the context of precision oncology. The efforts of numerous researchers have led to the identification of several mutational signatures, which can be categorized into different mutational signature references. In CRC, distinct mutational signatures are identified as correlating with mismatch repair deficiency, polymerase mutations, and chemotherapy treatment. In this context, a mutational signature analysis offers considerable potential for enhancing minimal residual disease (MRD) tests in stage II (high-risk) and stage III CRC post-surgery, stratifying CRC based on the impacts of genetic and epigenetic alterations for precision oncology, identifying potential therapeutic vulnerabilities, and evaluating drug efficacy and guiding therapy, as illustrated in a proof-of-concept clinical trial.

Pan-cancer mutational signature analysis of 111,711 targeted sequenced tumors using SATS

Tumor mutational signatures are informative for cancer diagnosis and treatment. However, targeted sequencing, commonly used in clinical settings, lacks specialized analytical tools and a dedicated catalogue of mutational signatures. Here, we introduce SATS, a scalable mutational signature analyzer for targeted sequencing data. SATS leverages tumor mutational burdens to identify and quantify signatures in individual tumors, overcoming the challenges of sparse mutations and variable gene panels. Validations across simulated data, pseudo-targeted sequencing data, and matched whole-genome and targeted sequencing samples show that SATS can accurately detect common mutational signatures and estimate their burdens. Applying SATS to 111,711 tumors from the AACR Project GENIE, we created a pan-cancer mutational signature catalogue specific to targeted sequencing. We further validated signatures in lung, breast and colorectal cancers using an additional 16,774 independent samples. This signature catalogue is a valuable resource for estimating signature burdens in individual targeted sequenced tumors, facilitating the integration of mutational signatures with clinical data.

Genomic and immune determinants of resistance to daratumumab-based therapy in relapsed refractory multiple myeloma

Targeted immunotherapy combinations, including the anti-CD38 monoclonal antibody (MoAb) daratumumab, have shown promising results in patients with relapsed/refractory multiple myeloma (RRMM), leading to a considerable increase in progression-free survival. However, a large fraction of patients inevitably relapse. To understand this, we investigated 32 relapsed MM patients treated with daratumumab, lenalidomide, and dexamethasone (Dara-Rd; NCT03848676). We conducted an integrated analysis using whole-genome sequencing (WGS) and flow cytometry in patients with RRMM. WGS before and after treatment pinpointed genomic drivers associated with early progression, including RPL5 loss, APOBEC mutagenesis, and gain of function structural variants involving MYC and chromothripsis. Flow cytometry on 202 blood samples, collected every 3 months until progression for 31 patients, revealed distinct immune changes significantly impacting clinical outcomes. Progressing patients exhibited significant depletion of CD38-positive NK cells, persistence of T-cell exhaustion, and reduced depletion of regulatory T cells over time. These findings underscore the influence of immune composition and daratumumab-induced immune changes in promoting MM resistance. Integrating genomics and flow cytometry unveiled associations between adverse genomic features and immune patterns. Overall, this study sheds light on the intricate interplay between genomic complexity and the immune microenvironment driving resistance to Dara-Rd in patients with RRMM.

Cancer mutational signatures identification in clinical assays using neural embedding-based representations

While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation associations with signatures, such as SBS5 with TP53 and APOBEC with FGFR3S249C. In a clinical use case, APOBEC signature is a robust and specific predictor for resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Our model provides an easy-to-use way to detect signatures in clinical setting assays with many possible clinical implications for an unprecedented number of cancer patients.

Progression of monoclonal gammopathy of undetermined significance to multiple myeloma is associated with enhanced translational quality control and overall loss of surface antigens

BACKGROUND

Despite significant advancements in treatment strategies, multiple myeloma remains incurable. Additionally, there is a distinct lack of reliable biomarkers that can guide initial treatment decisions and help determine suitable replacement or adjuvant therapies when relapse ensues due to acquired drug resistance.

METHODS

To define specific proteins and pathways involved in the progression of monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM), we have applied super-SILAC quantitative proteomic analysis to CD138 + plasma cells from 9 individuals with MGUS and 37 with MM.

RESULTS

Unsupervised hierarchical clustering defined three groups: MGUS, MM, and MM with an MGUS-like proteome profile (ML) that may represent a group that has recently transformed to MM. Statistical analysis identified 866 differentially expressed proteins between MM and MGUS, and 189 between MM and ML, 177 of which were common between MGUS and ML. Progression from MGUS to MM is accompanied by upregulated EIF2 signaling, DNA repair, and proteins involved in translational quality control, whereas integrin- and actin cytoskeletal signaling and cell surface markers are downregulated.

CONCLUSION

Compared to the premalignant plasma cells in MGUS, malignant MM cells apparently have mobilized several pathways that collectively contribute to ensure translational fidelity and to avoid proteotoxic stress, especially in the ER. The overall reduced expression of immunoglobulins and surface antigens contribute to this and may additionally mediate evasion from recognition by the immune apparatus. Our analyses identified a range of novel biomarkers with potential prognostic and therapeutic value, which will undergo further evaluation to determine their clinical significance.

Mutational signatures of colorectal cancers according to distinct computational workflows

Tumor mutational signatures have gained prominence in cancer research, yet the lack of standardized methods hinders reproducibility and robustness. Leveraging colorectal cancer (CRC) as a model, we explored the influence of computational parameters on mutational signature analyses across 230 CRC cell lines and 152 CRC patients. Results were validated in three independent datasets: 483 endometrial cancer patients stratified by mismatch repair (MMR) status, 35 lung cancer patients by smoking status and 12 patient-derived organoids (PDOs) annotated for colibactin exposure. Assessing various bioinformatic tools, reference datasets and input data sizes including whole genome sequencing, whole exome sequencing and a pan-cancer gene panel, we demonstrated significant variability in the results. We report that the use of distinct algorithms and references led to statistically different results, highlighting how arbitrary choices may induce variability in the mutational signature contributions. Furthermore, we found a differential contribution of mutational signatures between coding and intergenic regions and defined the minimum number of somatic variants required for reliable mutational signature assignment. To facilitate the identification of the most suitable workflows, we developed Comparative Mutational Signature analysis on Coding and Extragenic Regions (CoMSCER), a bioinformatic tool which allows researchers to easily perform comparative mutational signature analysis by coupling the results from several tools and public reference datasets and to assess mutational signature contributions in coding and non-coding genomic regions. In conclusion, our study provides a comparative framework to elucidate the impact of distinct computational workflows on mutational signatures.

The genomic landscape of Vk*MYC myeloma highlights shared pathways of transformation between mice and humans

Multiple myeloma (MM) is a heterogeneous disease characterized by frequent MYC translocations. Sporadic MYC activation in the germinal center of genetically engineered Vk*MYC mice is sufficient to induce plasma cell tumors in which a variety of secondary mutations are spontaneously acquired and selected over time. Analysis of 119 Vk*MYC myeloma reveals recurrent copy number alterations, structural variations, chromothripsis, driver mutations, apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC) mutational activity, and a progressive decrease in immunoglobulin transcription that inversely correlates with proliferation. Moreover, we identify frequent insertional mutagenesis by endogenous retro-elements as a murine specific mechanism to activate NF-kB and IL6 signaling pathways shared with human MM. Despite the increased genomic complexity associated with progression, advanced tumors remain dependent on MYC. In summary, here we credential the Vk*MYC mouse as a unique resource to explore MM genomic evolution and describe a fully annotated collection of diverse and immortalized murine MM tumors.

MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification

MOTIVATION

Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis, and treatment of cancer patients.

RESULTS

We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics.

AVAILABILITY AND IMPLEMENTATION

MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE.

Childhood cancer mutagenesis caused by transposase-derived PGBD5

Genomic rearrangements are a hallmark of most childhood tumors, including medulloblastoma, one of the most common brain tumors in children, but their causes remain largely unknown. Here, we show that PiggyBac transposable element derived 5 (Pgbd5) promotes tumor development in multiple developmentally accurate mouse models of Sonic Hedgehog (SHH) medulloblastoma. Most Pgbd5-deficient mice do not develop tumors, while maintaining normal cerebellar development. Ectopic activation of SHH signaling is sufficient to enforce cerebellar granule cell progenitor-like cell states, which exhibit Pgbd5-dependent expression of distinct DNA repair and neurodevelopmental factors. Mouse medulloblastomas expressing Pgbd5 have increased numbers of somatic structural DNA rearrangements, some of which carry PGBD5-specific sequences at their breakpoints. Similar sequence breakpoints recurrently affect somatic DNA rearrangements of known tumor suppressors and oncogenes in medulloblastomas in 329 children. This identifies PGBD5 as a medulloblastoma mutator and provides a genetic mechanism for the generation of oncogenic DNA rearrangements in childhood cancer.

Whole-exome sequencing is feasible on a fresh-frozen skin sample of intravascular large B cell lymphoma

Intravascular large B-cell lymphoma (IVLBCL) is a rare aggressive extranodal non-Hodgkin lymphoma. The predominant, if not exclusive, growth of neoplastic cells within the lumina of small-sized vessels represents the hallmark of the disease. Diagnosis is challenging due to the absence of marked lymphadenopathy, the highly heterogeneous clinical presentation, and the rarity of the condition. Clinical presentation is characterized by variable combinations of nonspecific signs and symptoms (such as fever and weight loss), organ-specific focal manifestations due to altered perfusion, and hemophagocytic syndrome. The rarity of this entity and the paucity of neoplastic cells in biopsy samples hamper the study of recurrent molecular abnormalities. The purpose of this study was to explore the feasibility of a different approach to recover a sufficient amount of DNA of acceptable quality to perform next-generation sequencing studies. Here, we report the findings of whole-exome next-generation sequencing performed on a fresh-frozen cutaneous sample of IVLBCL, paired with the patient saliva used as germline DNA. To increase the cancer cell fraction, only the subcutaneous tissue was selected. With this approach, we obtained high-quality DNA and were able to identify oncogenic mutations specific for this entity and recapitulating its post-germinal center origin, even if the tumor fraction was low. Molecular studies performed on fresh-frozen cutaneous sample are feasible in IVLBCL, especially when analysis is restricted to the subcutaneous tissue. Wide adoption of this reproducible and cost-effective approach may foster further studies, which may be of help in supporting diagnosis, providing pathogenetic insights, and guiding treatment decisions.

Clonal origin and genomic diversity in Lynch syndrome-associated endometrial cancer with multiple synchronous tumors: Identification of the pathogenicity of MLH1 p.L582H

Lynch syndrome-associated endometrial cancer patients often present multiple synchronous tumors and this assessment can affect treatment strategies. We present a case of a 27-year-old woman with tumors in the uterine corpus, cervix, and ovaries who was diagnosed with endometrial cancer and exhibited cervical invasion and ovarian metastasis. Her family history suggested Lynch syndrome, and genetic testing identified a variant of uncertain significance, MLH1 p.L582H. We conducted immunohistochemical staining, microsatellite instability analysis, and Sanger sequencing for Lynch syndrome-associated cancers in three generations of the family and identified consistent MLH1 loss. Whole-exome sequencing for the corpus, cervical, and ovarian tumors of the proband identified a copy-neutral loss of heterozygosity (LOH) occurring at the MLH1 position in all tumors. This indicated that the germline variant and the copy-neutral LOH led to biallelic loss of MLH1 and was the cause of cancer initiation. All tumors shared a portion of somatic mutations with high mutant allele frequencies, suggesting a common clonal origin. There were no mutations shared only between the cervix and ovary samples. The profiles of mutant allele frequencies shared between the corpus and cervix or ovary indicated that two different subclones originating from the corpus independently metastasized to the cervix or ovary. Additionally, all tumors presented unique mutations in endometrial cancer-associated genes such as ARID1A and PIK3CA. In conclusion, we demonstrated clonal origin and genomic diversity in a Lynch syndrome-associated endometrial cancer, suggesting the importance of evaluating multiple sites in Lynch syndrome patients with synchronous tumors.

Accurate and sensitive mutational signature analysis with MuSiCal

Mutational signature analysis is a recent computational approach for interpreting somatic mutations in the genome. Its application to cancer data has enhanced our understanding of mutational forces driving tumorigenesis and demonstrated its potential to inform prognosis and treatment decisions. However, methodological challenges remain for discovering new signatures and assigning proper weights to existing signatures, thereby hindering broader clinical applications. Here we present Mutational Signature Calculator (MuSiCal), a rigorous analytical framework with algorithms that solve major problems in the standard workflow. Our simulation studies demonstrate that MuSiCal outperforms state-of-the-art algorithms for both signature discovery and assignment. By reanalyzing more than 2,700 cancer genomes, we provide an improved catalog of signatures and their assignments, discover nine indel signatures absent in the current catalog, resolve long-standing issues with the ambiguous 'flat' signatures and give insights into signatures with unknown etiologies. We expect MuSiCal and the improved catalog to be a step towards establishing best practices for mutational signature analysis.

Predicting response to immune checkpoint blockade therapy among mismatch repair-deficient patients using mutational signatures

Despite the overall efficacy of immune checkpoint blockade (ICB) for mismatch repair deficiency (MMRD) across tumor types, a sizable fraction of patients with MMRD still do not respond to ICB. We performed mutational signature analysis of panel sequencing data (n = 95) from MMRD cases treated with ICB. We discover that T>C-rich single base substitution (SBS) signatures-SBS26 and SBS54 from the COSMIC Mutational Signatures catalog-identify MMRD patients with significantly shorter overall survival. Tumors with a high burden of SBS26 show over-expression and enriched mutations of genes involved in double-strand break repair and other DNA repair pathways. They also display chromosomal instability (CIN), likely related to replication fork instability, leading to copy number losses that trigger immune evasion. SBS54 is associated with transcriptional activity and not with CIN, defining a distinct subtype. Consistently, cancer cell lines with a high burden of SBS26 and SBS54 are sensitive to treatments targeting pathways related to their proposed etiology. Together, our analysis offers an explanation for the heterogeneous responses to ICB among MMRD patients and supports an SBS signature-based predictor as a prognostic biomarker for differential ICB response.

Genomic and immune determinants of resistance to anti-CD38 monoclonal antibody-based therapy in relapsed refractory multiple myeloma

Anti-CD38 antibody therapies have transformed multiple myeloma (MM) treatment. However, a large fraction of patients inevitably relapses. To understand this, we investigated 32 relapsed MM patients treated with daratumumab, lenalidomide, and dexamethasone (Dara-Rd; NCT03848676 ). Whole genome sequencing (WGS) before and after treatment pinpointed genomic drivers associated with early progression, including RPL5 loss and APOBEC mutagenesis. Flow cytometry on 202 blood samples, collected every three months until progression for 31 patients, revealed distinct immune changes significantly impacting clinical outcomes. Progressing patients exhibited significant depletion of CD38+ NK cells, persistence of T cell exhaustion, and reduced depletion of T-reg cells over time. These findings underscore the influence of immune composition and daratumumab-induced immune changes in promoting MM resistance. Integrating genomics and flow cytometry unveiled associations between adverse genomic features and immune patterns. Overall, this study sheds light on the intricate interplay between genomic complexity and the immune microenvironment driving resistance to Dara-Rd.

Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens

Despite improving outcomes, 40% of patients with newly diagnosed multiple myeloma treated with regimens containing daratumumab, a CD38-targeted monoclonal antibody, progress prematurely. By integrating tumor whole-genome and microenvironment single-cell RNA sequencing from upfront phase 2 trials using carfilzomib, lenalidomide and dexamethasone with daratumumab ( NCT03290950 ), we show how distinct genomic drivers including high APOBEC mutational activity, IKZF3 and RPL5 deletions and 8q gain affect clinical outcomes. Furthermore, evaluation of paired bone marrow profiles, taken before and after eight cycles of carfilzomib, lenalidomide and dexamethasone with daratumumab, shows that numbers of natural killer cells before treatment, high T cell receptor diversity before treatment, the disappearance of sustained immune activation (that is, B cells and T cells) and monocyte expansion over time are all predictive of sustained minimal residual disease negativity. Overall, this study provides strong evidence of a complex interplay between tumor cells and the immune microenvironment that is predictive of clinical outcome and depth of treatment response in patients with newly diagnosed multiple myeloma treated with highly effective combinations containing anti-CD38 antibodies.

Ultra-Deep Sequencing Reveals the Mutational Landscape of Classical Hodgkin Lymphoma

The malignant Hodgkin and Reed Sternberg (HRS) cells of classical Hodgkin lymphoma (cHL) are scarce in affected lymph nodes, creating a challenge to detect driver somatic mutations. As an alternative to cell purification techniques, we hypothesized that ultra-deep exome sequencing would allow genomic study of HRS cells, thereby streamlining analysis and avoiding technical pitfalls. To test this, 31 cHL tumor/normal pairs were exome sequenced to approximately 1,000× median depth of coverage. An orthogonal error-corrected sequencing approach verified >95% of the discovered mutations. We identified mutations in genes novel to cHL including: CDH5 and PCDH7, novel stop gain mutations in IL4R, and a novel pattern of recurrent mutations in pathways regulating Hippo signaling. As a further application of our exome sequencing, we attempted to identify expressed somatic single-nucleotide variants (SNV) in single-nuclei RNA sequencing (snRNA-seq) data generated from a patient in our cohort. Our snRNA analysis identified a clear cluster of cells containing a somatic SNV identified in our deep exome data. This cluster has differentially expressed genes that are consistent with genes known to be dysregulated in HRS cells (e.g., PIM1 and PIM3). The cluster also contains cells with an expanded B-cell clonotype further supporting a malignant phenotype. This study provides proof-of-principle that ultra-deep exome sequencing can be utilized to identify recurrent mutations in HRS cells and demonstrates the feasibility of snRNA-seq in the context of cHL. These studies provide the foundation for the further analysis of genomic variants in large cohorts of patients with cHL.

SIGNIFICANCE

Our data demonstrate the utility of ultra-deep exome sequencing in uncovering somatic variants in Hodgkin lymphoma, creating new opportunities to define the genes that are recurrently mutated in this disease. We also show for the first time the successful application of snRNA-seq in Hodgkin lymphoma and describe the expression profile of a putative cluster of HRS cells in a single patient.

Enhanced clinical assessment of hematologic malignancies through routine paired tumor and normal sequencing

Genomic profiling of hematologic malignancies has augmented our understanding of variants that contribute to disease pathogenesis and supported development of prognostic models that inform disease management in the clinic. Tumor only sequencing assays are limited in their ability to identify definitive somatic variants, which can lead to ambiguity in clinical reporting and patient management. Here, we describe the MSK-IMPACT Heme cohort, a comprehensive data set of somatic alterations from paired tumor and normal DNA using a hybridization capture-based next generation sequencing platform. We highlight patterns of mutations, copy number alterations, and mutation signatures in a broad set of myeloid and lymphoid neoplasms. We also demonstrate the power of appropriate matching to make definitive somatic calls, including in patients who have undergone allogeneic stem cell transplant. We expect that this resource will further spur research into the pathobiology and clinical utility of clinical sequencing for patients with hematologic neoplasms.

Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches

Single-base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard index = 0.38-0.83) and quantitatively (Kendall tau-b = 0.18-0.76). This phenomenon was exacerbated for 'flat' signatures such as the homologous recombination deficiency signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2-deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9-24.7% on average using Catalogue of Somatic Mutations In Cancer and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution.

Understanding DNA Damage Response and DNA Repair in Multiple Myeloma

Multiple myeloma (MM) is a plasma cell malignancy characterized by several genetic abnormalities, including chromosomal translocations, genomic deletions and gains, and point mutations. DNA damage response (DDR) and DNA repair mechanisms are altered in MM to allow for tumor development, progression, and resistance to therapies. Damaged DNA rarely induces an apoptotic response, given the presence of ataxia-telangiectasia mutated (ATM) loss-of-function or mutations, as well as deletions, mutations, or downregulation of tumor protein p53 (TP53) and tumor protein p73 (TP73). Moreover, DNA repair mechanisms are either hyperactive or defective to allow for rapid correction of the damage or permissive survival. Medications used to treat patients with MM can induce DNA damage, by either direct effects (mono-adducts induced by melphalan), or as a result of reactive oxygen species (ROS) production by proteasome inhibitors such as bortezomib. In this review, we will describe the mechanisms of DDR and DNA repair in normal tissues, the contribution of these pathways to MM disease progression and other phenotypes, and the potential therapeutic opportunities for patients with MM.

Sequence dependencies and mutation rates of localized mutational processes in cancer

BACKGROUND

Cancer mutations accumulate through replication errors and DNA damage coupled with incomplete repair. Individual mutational processes often show nucleotide sequence and functional region preferences. As a result, some sequence contexts mutate at much higher rates than others, with additional variation found between functional regions. Mutational hotspots, with recurrent mutations across cancer samples, represent genomic positions with elevated mutation rates, often caused by highly localized mutational processes.

METHODS

We count the 11-mer genomic sequences across the genome, and using the PCAWG set of 2583 pan-cancer whole genomes, we associate 11-mers with mutational signatures, hotspots of single nucleotide variants, and specific genomic regions. We evaluate the mutation rates of individual and combined sets of 11-mers and derive mutational sequence motifs.

RESULTS

We show that hotspots generally identify highly mutable sequence contexts. Using these, we show that some mutational signatures are enriched in hotspot sequence contexts, corresponding to well-defined sequence preferences for the underlying localized mutational processes. This includes signature 17b (of unknown etiology) and signatures 62 (POLE deficiency), 7a (UV), and 72 (linked to lymphomas). In some cases, the mutation rate and sequence preference increase further when focusing on certain genomic regions, such as signature 62 in transcribed regions, where the mutation rate is increased up to 9-folds over cancer type and mutational signature average.

CONCLUSIONS

We summarize our findings in a catalog of localized mutational processes, their sequence preferences, and their estimated mutation rates.

The Vk*MYC Mouse Model recapitulates human multiple myeloma evolution and genomic diversity

Despite advancements in profiling multiple myeloma (MM) and its precursor conditions, there is limited information on mechanisms underlying disease progression. Clincal efforts designed to deconvolute such mechanisms are challenged by the long lead time between monoclonal gammopathy and its transformation to MM. MM mouse models represent an opportunity to overcome this temporal limitation. Here, we profile the genomic landscape of 118 genetically engineered Vk*MYC MM and reveal that it recapitulates the genomic heterogenenity and life history of human MM. We observed recurrent copy number alterations, structural variations, chromothripsis, driver mutations, APOBEC mutational activity, and a progressive decrease in immunoglobulin transcription that inversely correlates with proliferation. Moreover, we identified frequent insertional mutagenesis by endogenous retro-elements as a murine specific mechanism to activate NF-kB and IL6 signaling pathways shared with human MM. Despite the increased genomic complexity associated with progression, advanced tumors remain dependent on MYC expression, that drives the progression of monoclonal gammopathy to MM.

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

BACKGROUND

Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown.

METHODS

We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. In contrast to many previous methods, MuAt utilizes the attention mechanism on individual mutations instead of aggregated mutation counts.

RESULTS

We trained MuAt models on 2587 whole cancer genomes (24 tumour types) from the Pan-Cancer Analysis of Whole Genomes (PCAWG) and 7352 cancer exomes (20 types) from the Cancer Genome Atlas (TCGA). MuAt achieved prediction accuracy of 89% for whole genomes and 64% for whole exomes, and a top-5 accuracy of 97% and 90%, respectively. MuAt models were found to be well-calibrated and perform well in three independent whole cancer genome cohorts with 10,361 tumours in total. We show MuAt to be able to learn clinically and biologically relevant tumour entities including acral melanoma, SHH-activated medulloblastoma, SPOP-associated prostate cancer, microsatellite instability, POLE proofreading deficiency, and MUTYH-associated pancreatic endocrine tumours without these tumour subtypes and subgroups being provided as training labels. Finally, scrunity of MuAt attention matrices revealed both ubiquitous and tumour-type specific patterns of simple and complex somatic mutations.

CONCLUSIONS

Integrated representations of somatic alterations learnt by MuAt were able to accurately identify histological tumour types and identify tumour entities, with potential to impact precision cancer medicine.

The landscape of somatic mutations in lymphoblastoid cell lines

Somatic mutations have important biological ramifications while exerting substantial rate, type, and genomic location heterogeneity. Yet, their sporadic occurrence makes them difficult to study at scale and across individuals. Lymphoblastoid cell lines (LCLs), a model system for human population and functional genomics, harbor large numbers of somatic mutations and have been extensively genotyped. By comparing 1,662 LCLs, we report that the mutational landscape of the genome varies across individuals in terms of the number of mutations, their genomic locations, and their spectra; this variation may itself be modulated by somatic trans-acting mutations. Mutations attributed to the translesion DNA polymerase η follow two different modes of formation, with one mode accounting for the hypermutability of the inactive X chromosome. Nonetheless, the distribution of mutations along the inactive X chromosome appears to follow an epigenetic memory of the active form.

Cell-type specificity of the human mutation landscape with respect to DNA replication dynamics

The patterns of genomic mutations are associated with various genomic features, most notably late replication timing, yet it remains contested which mutation types and signatures relate to DNA replication dynamics and to what extent. Here, we perform high-resolution comparisons of mutational landscapes between lymphoblastoid cell lines, chronic lymphocytic leukemia tumors, and three colon adenocarcinoma cell lines, including two with mismatch repair deficiency. Using cell-type-matched replication timing profiles, we demonstrate that mutation rates exhibit heterogeneous replication timing associations among cell types. This cell-type heterogeneity extends to the underlying mutational pathways, as mutational signatures show inconsistent replication timing bias between cell types. Moreover, replicative strand asymmetries exhibit similar cell-type specificity, albeit with different relationships to replication timing than mutation rates. Overall, we reveal an underappreciated complexity and cell-type specificity of mutational pathways and their relationship to replication timing.

Comparative analyses define differences between BHD-associated renal tumour and sporadic chromophobe renal cell carcinoma

BACKGROUND

Birt-Hogg-Dubé (BHD) syndrome, caused by germline alteration of folliculin (FLCN) gene, develops hybrid oncocytic/chromophobe tumour (HOCT) and chromophobe renal cell carcinoma (ChRCC), whereas sporadic ChRCC does not harbor FLCN alteration. To date, molecular characteristics of these similar histological types of tumours have been incompletely elucidated.

METHODS

To elucidate renal tumourigenesis of BHD-associated renal tumours and sporadic renal tumours, we conducted whole genome sequencing (WGS) and RNA-sequencing (RNA-seq) of sixteen BHD-associated renal tumours from nine unrelated BHD patients, twenty-one sporadic ChRCCs and seven sporadic oncocytomas. We then compared somatic mutation profiles with FLCN variants and RNA expression profiles between BHD-associated renal tumours and sporadic renal tumours.

FINDINGS

RNA-seq analysis revealed that BHD-associated renal tumours and sporadic renal tumours have totally different expression profiles. Sporadic ChRCCs were clustered into two distinct clusters characterized by L1CAM and FOXI1 expressions, molecular markers for renal tubule subclasses. Increased mitochondrial DNA (mtDNA) copy number with fewer variants was observed in BHD-associated renal tumours compared to sporadic ChRCCs. Cell-of-origin analysis using WGS data demonstrated that BHD-associated renal tumours and sporadic ChRCCs may arise from different cells of origin and second hit FLCN alterations may occur in early third decade of life in BHD patients.

INTERPRETATION

These data further our understanding of renal tumourigenesis of these two different types of renal tumours with similar histology.

FUNDING

This study was supported by JSPS KAKENHI Grants, RIKEN internal grant, and the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), Center for Cancer Research.

Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases

A biomarker is any measurable biological moiety that can be assessed and measured as a potential index of either normal or abnormal pathophysiology or pharmacological responses to some treatment regimen. Every tissue in the body has a distinct biomolecular make-up, which is known as its biomarkers, which possess particular features, viz., the levels or activities (the ability of a gene or protein to carry out a particular body function) of a gene, protein, or other biomolecules. A biomarker refers to some feature that can be objectively quantified by various biochemical samples and evaluates the exposure of an organism to normal or pathological procedures or their response to some drug interventions. An in-depth and comprehensive realization of the significance of these biomarkers becomes quite important for the efficient diagnosis of diseases and for providing the appropriate directions in case of multiple drug choices being presently available, which can benefit any patient. Presently, advancements in omics technologies have opened up new possibilities to obtain novel biomarkers of different types, employing genomic strategies, epigenetics, metabolomics, transcriptomics, lipid-based analysis, protein studies, etc. Particular biomarkers for specific diseases, their prognostic capabilities, and responses to therapeutic paradigms have been applied for screening of various normal healthy, as well as diseased, tissue or serum samples, and act as appreciable tools in pharmacology and therapeutics, etc. In this review, we have summarized various biomarker types, their classification, and monitoring and detection methods and strategies. Various analytical techniques and approaches of biomarkers have also been described along with various clinically applicable biomarker sensing techniques which have been developed in the recent past. A section has also been dedicated to the latest trends in the formulation and designing of nanotechnology-based biomarker sensing and detection developments in this field.

Tracking the evolution of therapy-related myeloid neoplasms using chemotherapy signatures

Patients treated with cytotoxic therapies, including autologous stem cell transplantation, are at risk for developing therapy-related myeloid neoplasms (tMN). Preleukemic clones (ie, clonal hematopoiesis [CH]) are detectable years before the development of these aggressive malignancies, although the genomic events leading to transformation and expansion are not well defined. Here, by leveraging distinctive chemotherapy-associated mutational signatures from whole-genome sequencing data and targeted sequencing of prechemotherapy samples, we reconstructed the evolutionary life-history of 39 therapy-related myeloid malignancies. A dichotomy was revealed, in which neoplasms with evidence of chemotherapy-induced mutagenesis from platinum and melphalan were hypermutated and enriched for complex structural variants (ie, chromothripsis), whereas neoplasms with nonmutagenic chemotherapy exposures were genomically similar to de novo acute myeloid leukemia. Using chemotherapy-associated mutational signatures as temporal barcodes linked to discrete clinical exposure in each patient's life, we estimated that several complex events and genomic drivers were acquired after chemotherapy was administered. For patients with prior multiple myeloma who were treated with high-dose melphalan and autologous stem cell transplantation, we demonstrate that tMN can develop from either a reinfused CH clone that escapes melphalan exposure and is selected after reinfusion, or from TP53-mutant CH that survives direct myeloablative conditioning and acquires melphalan-induced DNA damage. Overall, we revealed a novel mode of tMN progression that is not reliant on direct mutagenesis or even exposure to chemotherapy. Conversely, for tMN that evolve under the influence of chemotherapy-induced mutagenesis, distinct chemotherapies not only select preexisting CH but also promote the acquisition of recurrent genomic drivers.

Molecular Evolution of Classic Hodgkin Lymphoma Revealed Through Whole-Genome Sequencing of Hodgkin and Reed Sternberg Cells

The rarity of malignant Hodgkin and Reed Sternberg (HRS) cells in classic Hodgkin lymphoma (cHL) limits the ability to study the genomics of cHL. To circumvent this, our group has previously optimized fluorescence-activated cell sorting to purify HRS cells. Using this approach, we now report the whole-genome sequencing landscape of HRS cells and reconstruct the chronology and likely etiology of pathogenic events leading to cHL. We identified alterations in driver genes not previously described in cHL, APOBEC mutational activity, and the presence of complex structural variants including chromothripsis. We found that high ploidy in cHL is often acquired through multiple, independent chromosomal gains events including whole-genome duplication. Evolutionary timing analyses revealed that structural variants enriched for RAG motifs, driver mutations in B2M, BCL7A, GNA13, and PTPN1, and the onset of AID-driven mutagenesis usually preceded large chromosomal gains. This study provides a temporal reconstruction of cHL pathogenesis.

SIGNIFICANCE

Previous studies in cHL were limited to coding sequences and therefore not able to comprehensively decipher the tumor complexity. Here, leveraging cHL whole-genome characterization, we identify driver events and reconstruct the tumor evolution, finding that structural variants, driver mutations, and AID mutagenesis precede chromosomal gains. This article is highlighted in the In This Issue feature, p. 171.

High-dose melphalan treatment significantly increases mutational burden at relapse in multiple myeloma

High-dose melphalan (HDM) improves progression-free survival in multiple myeloma (MM), yet melphalan is a DNA-damaging alkylating agent; therefore, we assessed its mutational effect on surviving myeloma cells by analyzing paired MM samples collected at diagnosis and relapse in the IFM 2009 study. We performed deep whole-genome sequencing on samples from 68 patients, 43 of whom were treated with RVD (lenalidomide, bortezomib, and dexamethasone) and 25 with RVD + HDM. Although the number of mutations was similar at diagnosis in both groups (7137 vs 7230; P = .67), the HDM group had significantly more mutations at relapse (9242 vs 13 383, P = .005). No change in the frequency of copy number alterations or structural variants was observed. The newly acquired mutations were typically associated with DNA damage and double-stranded breaks and were predominantly on the transcribed strand. A machine learning model, using this unique pattern, predicted patients who would receive HDM with high sensitivity, specificity, and positive prediction value. Clonal evolution analysis showed that all patients treated with HDM had clonal selection, whereas a static progression was observed with RVD. A significantly higher percentage of mutations were subclonal in the HDM cohort. Intriguingly, patients treated with HDM who achieved complete remission (CR) had significantly more mutations at relapse yet had similar survival rates as those treated with RVD who achieved CR. This similarity could have been due to HDM relapse samples having significantly more neoantigens. Overall, our study identifies increased genomic changes associated with HDM and provides rationale to further understand clonal complexity.

Deciphering gene contributions and etiologies of somatic mutational signatures of cancer

Somatic mutational signatures (MSs) identified by genome sequencing play important roles in exploring the cause and development of cancer. Thus far, many such signatures have been identified, and some of them do imply causes of cancer. However, a major bottleneck is that we do not know the potential meanings (i.e. carcinogenesis or biological functions) and contributing genes for most of them. Here, we presented a computational framework, Gene Somatic Genome Pattern (GSGP), which can decipher the molecular mechanisms of the MSs. More importantly, it is the first time that the GSGP is able to process MSs from ribonucleic acid (RNA) sequencing, which greatly extended the applications of both MS analysis and RNA sequencing (RNAseq). As a result, GSGP analyses match consistently with previous reports and identify the etiologies for a number of novel signatures. Notably, we applied GSGP to RNAseq data and revealed an RNA-derived MS involved in deficient deoxyribonucleic acid mismatch repair and microsatellite instability in colorectal cancer. Researchers can perform customized GSGP analysis using the web tools or scripts we provide.

Spatial genomic diversity associated with APOBEC mutagenesis in squamous cell carcinoma arising from ovarian teratoma

Although the gross and microscopic features of squamous cell carcinoma arising from ovarian mature cystic teratoma (MCT-SCC) vary from case to case, the spatial spreading of genomic alterations within the tumor remains unclear. To clarify the spatial genomic diversity in MCT-SCCs, we performed whole-exome sequencing by collecting 16 samples from histologically different parts of two MCT-SCCs. Both cases showed histological diversity within the tumors (case 1: nonkeratinizing and keratinizing SCC and case 2: nonkeratinizing SCC and anaplastic carcinoma) and had different somatic mutation profiles by histological findings. Mutation signature analysis revealed a significantly enriched apolipoprotein B mRNA editing enzyme catalytic subunit (APOBEC) signature at all sites. Intriguingly, the spread of genomic alterations within the tumor and the clonal evolution patterns from nonmalignant epithelium to cancer sites differed between cases. TP53 mutation and copy number alterations were widespread at all sites, including the nonmalignant epithelium, in case 1. Keratinizing and nonkeratinizing SCCs were differentiated by the occurrence of unique somatic mutations from a common ancestral clone. In contrast, the nonmalignant epithelium showed almost no somatic mutations in case 2. TP53 mutation and the copy number alteration similarities were observed only in nonkeratinizing SCC samples. Nonkeratinizing SCC and anaplastic carcinoma shared almost no somatic mutations, suggesting that each locally and independently arose in the MCT. We demonstrated that two MCT-SCCs with different histologic findings were highly heterogeneous tumors with clearly different clones associated with APOBEC-mediated mutagenesis, suggesting the importance of evaluating intratumor histological and genetic heterogeneity among multiple sites of MCT-SCC.

Whole-genome analysis identifies novel drivers and high-risk double-hit events in relapsed/refractory myeloma

Large-scale analyses of genomic data from patients with newly diagnosed multiple myeloma (ndMM) have been undertaken, however, large-scale analysis of relapsed/refractory MM (rrMM) has not been performed. We hypothesize that somatic variants chronicle the therapeutic exposures and clonal structure of myeloma from ndMM to rrMM stages. We generated whole-genome sequencing (WGS) data from 418 tumors (386 patients) derived from 6 rrMM clinical trials and compared them with WGS from 198 unrelated patients with ndMM in a population-based case-control fashion. We identified significantly enriched events at the rrMM stage, including drivers (DUOX2, EZH2, TP53), biallelic inactivation (TP53), noncoding mutations in bona fide drivers (TP53BP1, BLM), copy number aberrations (CNAs; 1qGain, 17pLOH), and double-hit events (Amp1q-ISS3, 1qGain-17p loss-of-heterozygosity). Mutational signature analysis identified a subclonal defective mismatch repair signature enriched in rrMM and highly active in high mutation burden tumors, a likely feature of therapy-associated expanding subclones. Further analysis focused on the association of genomic aberrations enriched at different stages of resistance to immunomodulatory agent (IMiD)-based therapy. This analysis revealed that TP53, DUOX2, 1qGain, and 17p loss-of-heterozygosity increased in prevalence from ndMM to lenalidomide resistant (LENR) to pomalidomide resistant (POMR) stages, whereas enrichment of MAML3 along with immunoglobulin lambda (IGL) and MYC translocations distinguished POM from the LEN subgroup. Genomic drivers associated with rrMM are those that confer clonal selective advantage under therapeutic pressure. Their role in therapy evasion should be further evaluated in longitudinal patient samples, to confirm these associations with the evolution of clinical resistance and to identify molecular subsets of rrMM for the development of targeted therapies.

Unravelling the instability of mutational signatures extraction via archetypal analysis

The high cosine similarity between some single-base substitution mutational signatures and their characteristic flat profiles could suggest the presence of overfitting and mathematical artefacts. The newest version (v3.3) of the signature database available in the Catalogue Of Somatic Mutations In Cancer (COSMIC) provides a collection of 79 mutational signatures, which has more than doubled with respect to previous version (30 profiles available in COSMIC signatures v2), making more critical the associations between signatures and specific mutagenic processes. This study both provides a systematic assessment of the de novo extraction task through simulation scenarios based on the latest version of the COSMIC signatures and highlights, through a novel approach using archetypal analysis, which COSMIC signatures are redundant and more likely to be considered as mathematical artefacts. 29 archetypes were able to reconstruct the profile of all the COSMIC signatures with cosine similarity > 0.8. Interestingly, these archetypes tend to group similar original signatures sharing either the same aetiology or similar biological processes. We believe that these findings will be useful to encourage the development of new de novo extraction methods avoiding the redundancy of information among the signatures while preserving the biological interpretation.

Clonal Hematopoiesis: Role in Hematologic and Non-Hematologic Malignancies

Hematopoietic stem cells (HSCs) ensure the coordinated and balanced production of all hematopoietic cell types throughout life. Aging is associated with a gradual decline of the self-renewal and regenerative potential of HSCs and with the development of clonal hematopoiesis. Clonal hematopoiesis of indeterminate potential (CHIP) defines the clonal expansion of genetically variant hematopoietic cells bearing one or more gene mutations and/or structural variants (such as copy number alterations). CHIP increases exponentially with age and is associated with cancers, including hematologic neoplasia, cardiovascular and other diseases. The presence of CHIP consistently increases the risk of hematologic malignancy, particularly in individuals who have CHIP in association with peripheral blood cytopenia.

Homologous Recombination Deficiency Scar: Mutations and Beyond-Implications for Precision Oncology

Homologous recombination deficiency (HRD) is a prevalent in approximately 17% of tumors and is associated with enhanced sensitivity to anticancer therapies inducing double-strand DNA breaks. Accurate detection of HRD would therefore allow improved patient selection and outcome of conventional and targeted anticancer therapies. However, current clinical assessment of HRD mainly relies on determining germline BRCA1/2 mutational status and is insufficient for adequate patient stratification as mechanisms of HRD occurrence extend beyond functional BRCA1/2 loss. HRD, regardless of BRCA1/2 status, is associated with specific forms of genomic and mutational signatures termed HRD scar. Detection of this HRD scar might therefore be a more reliable biomarker for HRD. This review discusses and compares different methods of assessing HRD and HRD scar, their advances into the clinic, and their potential implications for precision oncology.

Detection of early seeding of Richter transformation in chronic lymphocytic leukemia

Richter transformation (RT) is a paradigmatic evolution of chronic lymphocytic leukemia (CLL) into a very aggressive large B cell lymphoma conferring a dismal prognosis. The mechanisms driving RT remain largely unknown. We characterized the whole genome, epigenome and transcriptome, combined with single-cell DNA/RNA-sequencing analyses and functional experiments, of 19 cases of CLL developing RT. Studying 54 longitudinal samples covering up to 19 years of disease course, we uncovered minute subclones carrying genomic, immunogenetic and transcriptomic features of RT cells already at CLL diagnosis, which were dormant for up to 19 years before transformation. We also identified new driver alterations, discovered a new mutational signature (SBS-RT), recognized an oxidative phosphorylation (OXPHOS)^high–B cell receptor (BCR)^low-signaling transcriptional axis in RT and showed that OXPHOS inhibition reduces the proliferation of RT cells. These findings demonstrate the early seeding of subclones driving advanced stages of cancer evolution and uncover potential therapeutic targets for RT.

Single-cell genomic and transcriptomic analyses of longitudinal samples of patients with Richter syndrome reveal the presence and dynamics of clones driving transformation from chronic lymphocytic leukemia years before clinical manifestation

Elevated Mutational Age in Blood of Children Treated for Cancer Contributes to Therapy-Related Myeloid Neoplasms

Childhood cancer survivors are confronted with various chronic health conditions like therapy-related malignancies. However, it is unclear how exposure to chemotherapy contributes to the mutation burden and clonal composition of healthy tissues early in life. Here, we studied mutation accumulation in hematopoietic stem and progenitor cells (HSPC) before and after cancer treatment of 24 children. Of these children, 19 developed therapy-related myeloid neoplasms (t-MN). Posttreatment HSPCs had an average mutation burden increase comparable to what treatment-naïve cells accumulate during 16 years of life, with excesses up to 80 years. In most children, these additional mutations were induced by clock-like processes, which are also active during healthy aging. Other patients harbored mutations that could be directly attributed to treatments like platinum-based drugs and thiopurines. Using phylogenetic inference, we demonstrate that most t-MN in children originate after the start of treatment and that leukemic clones become dominant during or directly after chemotherapy exposure.

SIGNIFICANCE

Our study shows that chemotherapy increases the mutation burden of normal blood cells in cancer survivors. Only few drugs damage the DNA directly, whereas in most patients, chemotherapy-induced mutations are caused by processes similar to those present during normal aging. This article is highlighted in the In This Issue feature, p. 1825.