Clinical impact of chromothriptic complex chromosomal rearrangements in newly diagnosed multiple myeloma

Correlation of changes in subclonal architecture with progression in the MMRF CoMMpass study

Multiple myeloma (MM) is a heterogeneous plasma cell proliferative disorder that arises from its premalignant precursor stages through a complex cascade of interactions between clonal mutations and co-evolving microenvironment. The temporo-spatial evolutionary trajectories of MM are established early during myelomatogenesis in precursor stages and retained in MM. Such molecular events impact subsequent disease progression and clinical outcomes. Identification of clonal sweeps of actionable gene targets in MM could reveal potential vulnerabilities that may exist in early stages and thus potentiate prognostication and customization of early therapeutic interventions. We have evaluated clonal evolution at multiple time points in 76 MM patients enrolled in the MMRF CoMMpass study. The major findings of this study are (a) MM progresses predominantly through branching evolution, (b) there is a heterogeneous spectrum of mutational landscapes that include unique actionable gene targets at diagnosis compared to progression, (c) unique clonal gains/ losses of mutant driver genes can be identified in patients with different cytogenetic aberrations, (d) there is a significant correlation between co-occurring oncogenic mutations/ co-occurring subclones e.g., with mutated TP53+SYNE1, NRAS+MAGI3, and anticorrelative dependencies between FAT3+FCGBP gene pairs. Such co-trajectories may synchronize molecular events of drug response, myelomatogenesis and warrant future studies to explore their potential for early prognostication and development of risk stratified personalized therapies in MM.

Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data

Introduction

Current risk predictors of multiple myeloma do not integrate ethnicity-specific information. However, the impact of ethnicity on disease biology cannot be overlooked. In this study, we have investigated the impact of ethnicity in multiple myeloma risk prediction. In addition, an efficient and robust artificial intelligence (AI)-enabled risk-stratification system is developed for newly diagnosed multiple myeloma (NDMM) patients that utilizes ethnicity-specific cutoffs of key prognostic parameters.

Methods

K-adaptive partitioning is used to propose new cutoffs of parameters for two different datasets—the MMIn (MM Indian dataset) dataset and the MMRF (Multiple Myeloma Research Foundation) dataset belonging to two different ethnicities. The Consensus-based Risk-Stratification System (CRSS) is designed using the Gaussian mixture model (GMM) and agglomerative clustering. CRSS is validated via Cox hazard proportional methods, Kaplan–Meier analysis, and log-rank tests on progression-free survival (PFS) and overall survival (OS). SHAP (SHapley Additive exPlanations) is utilized to establish the biological relevance of the risk prediction by CRSS.

Results

There is a significant variation in the key prognostic parameters of the two datasets belonging to two different ethnicities. CRSS demonstrates superior performance as compared with the R-ISS in terms of C-index and hazard ratios on both the MMIn and MMRF datasets. An online calculator has been built that can predict the risk stage of a multiple myeloma (MM) patient based on the values of parameters and ethnicity.

Conclusion

Our methodology discovers changes in the cutoffs with ethnicities from the established cutoffs of prognostic features. The best predictor model for both cohorts was obtained with the new ethnicity-specific cutoffs of clinical parameters. Our study also revealed the efficacy of AI in building a deployable risk prediction system for MM. In the future, it is suggested to use the CRSS risk calculator on a large dataset as the cohort size of the present study is 25% of the cohort used in the R-ISS reported in 2015.

Genome Instability in Multiple Myeloma: Facts and Factors

Multiple myeloma (MM) is a malignant neoplasm of terminally differentiated immunoglobulin-producing B lymphocytes called plasma cells. MM is the second most common hematologic malignancy, and it poses a heavy economic and social burden because it remains incurable and confers a profound disability to patients. Despite current progress in MM treatment, the disease invariably recurs, even after the transplantation of autologous hematopoietic stem cells (ASCT). Biological processes leading to a pathological myeloma clone and the mechanisms of further evolution of the disease are far from complete understanding. Genetically, MM is a complex disease that demonstrates a high level of heterogeneity. Myeloma genomes carry numerous genetic changes, including structural genome variations and chromosomal gains and losses, and these changes occur in combinations with point mutations affecting various cellular pathways, including genome maintenance. MM genome instability in its extreme is manifested in mutation kataegis and complex genomic rearrangements: chromothripsis, templated insertions, and chromoplexy. Chemotherapeutic agents used to treat MM add another level of complexity because many of them exacerbate genome instability. Genome abnormalities are driver events and deciphering their mechanisms will help understand the causes of MM and play a pivotal role in developing new therapies.

AI-supported modified risk staging for multiple myeloma cancer useful in real-world scenario

•

An AI-enabled risk staging method, MRS, is developed using easy-to-acquire parameters.

•

Genomic tests cannot be performed owing to economical or geographical constraints.

•

MRS does not use cytogenetic abnormalities for risk stage prediction unlike RISS.

•

K-adaptive partitioning (KAP) used to find new thresholds for the parameters.

Introduction

: An efficient readily employable risk prognostication method is desirable for MM in settings where genomics tests cannot be performed owing to geographical/economical constraints. In this work, a new Modified Risk Staging (MRS) has been proposed for newly diagnosed Multiple Myeloma (NDMM) that exploits six easy-to-acquire clinical parameters i.e. age, albumin, β2-microglobulin (β2M), calcium, estimated glomerular filtration rate (eGFR) and hemoglobin.

Materials and Methods

: MRS was designed using a training cohort of 716 NDMM patients of our inhouse MM Indian (MMIn) cohort and validated on MMIn (n=354) cohort and MMRF (n=900) cohort. K-adaptive partitioning (KAP) was used to find new thresholds for the parameters. Risk staging rules, obtained via training a J48 classifier, were used to build MRS.

Results

: New thresholds were identified for albumin (3.6 g/dL), β2M (4.8 mg/L), calcium (11.13 mg/dL), eGFR (48.1 mL/min), and hemoglobin (12.3 g/dL) using KAP on the MMIn dataset. On the MMIn dataset, MRS outperformed ISS for OS prediction in terms of C-index, hazard ratios, and its corresponding p-values, but performs comparable in prediction of PFS. On both MMIn and MMRF datasets, MRS performed better than RISS in terms of C-index and p-values. A simple online tool was also designed to allow automated calculation of MRS based on the values of the parameters.

Discussion

: Our proposed ML-derived yet simple staging system, MRS, although does not employ genetic features, outperforms RISS as confirmed by better separability in KM survival curves and higher values of C-index on both MMIn and MMRF datasets.

Funding

: Grant: BT/MED/30/SP11006/2015 (Department of Biotechnology, Govt. of India), Grant: DST/ICPS/CPS-Individual/2018/279(G) (Department of Science and Technology, Govt. of India), UGC-Senior Research Fellowship.

Genetic Abnormalities in Multiple Myeloma: Prognostic and Therapeutic Implications

Some genetic abnormalities of multiple myeloma (MM) detected more than two decades ago remain major prognostic factors. In recent years, the introduction of cutting-edge genomic methodologies has enabled the extensive deciphering of genomic events in MM. Although none of the alterations newly discovered have significantly improved the stratification of the outcome of patients with MM, some of them, point mutations in particular, are promising targets for the development of personalized medicine. This review summarizes the main genetic abnormalities described in MM together with their prognostic impact, and the therapeutic approaches potentially aimed at abrogating the undesirable pathogenic effect of each alteration.

A Tangle of Genomic Aberrations Drives Multiple Myeloma and Correlates with Clinical Aggressiveness of the Disease: A Comprehensive Review from a Biological Perspective to Clinical Trial Results

Multiple myeloma (MM) is a genetically heterogeneous disease, in which the process of tumorigenesis begins and progresses through the appearance and accumulation of a tangle of genomic aberrations. Several are the mechanisms of DNA damage in MM, varying from single nucleotide substitutions to complex genomic events. The timing of appearance of aberrations is well studied due to the natural history of the disease, that usually progress from pre-malignant to malignant phase. Different kinds of aberrations carry different prognostic significance and have been associated with drug resistance in some studies. Certain genetic events are well known to be associated with prognosis and are incorporated in risk evaluation in MM at diagnosis in the revised International Scoring System (R-ISS). The significance of some other aberrations needs to be further explained. Since now, few phase 3 randomized trials included analysis on patient's outcomes according to genetic risk, and further studies are needed to obtain useful data to stratify the choice of initial and subsequent treatment in MM.

Germline Risk Contribution to Genomic Instability in Multiple Myeloma

Genomic instability, a well-established hallmark of human cancer, is also a driving force in the natural history of multiple myeloma (MM) - a difficult to treat and in most cases fatal neoplasm of immunoglobulin producing plasma cells that reside in the hematopoietic bone marrow. Long recognized manifestations of genomic instability in myeloma at the cytogenetic level include abnormal chromosome numbers (aneuploidy) caused by trisomy of odd-numbered chromosomes; recurrent oncogene-activating chromosomal translocations that involve immunoglobulin loci; and large-scale amplifications, inversions, and insertions/deletions (indels) of genetic material. Catastrophic genetic rearrangements that either shatter and illegitimately reassemble a single chromosome (chromotripsis) or lead to disordered segmental rearrangements of multiple chromosomes (chromoplexy) also occur. Genomic instability at the nucleotide level results in base substitution mutations and small indels that affect both the coding and non-coding genome. Sometimes this generates a distinctive signature of somatic mutations that can be attributed to defects in DNA repair pathways, the DNA damage response (DDR) or aberrant activity of mutator genes including members of the APOBEC family. In addition to myeloma development and progression, genomic instability promotes acquisition of drug resistance in patients with myeloma. Here we review recent findings on the genetic predisposition to myeloma, including newly identified candidate genes suggesting linkage of germline risk and compromised genomic stability control. The role of ethnic and familial risk factors for myeloma is highlighted. We address current research gaps that concern the lack of studies on the mechanism by which germline risk alleles promote genomic instability in myeloma, including the open question whether genetic modifiers of myeloma development act in tumor cells, the tumor microenvironment (TME), or in both. We conclude with a brief proposition for future research directions, which concentrate on the biological function of myeloma risk and genetic instability alleles, the potential links between the germline genome and somatic changes in myeloma, and the need to elucidate genetic modifiers in the TME.

Prostate Cancer Genomic Subtypes

Over the last decade, advancements in massively-parallel DNA sequencing and computational biology have allowed for unprecedented insights into the fundamental mutational processes that underlie virtually every major cancer type. Two major cancer genomics consortia-The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC)-have produced rich databases of mutational, pathological, and clinical data that can be mined through web-based portals, allowing for correlative studies and testing of novel hypotheses on well-powered patient cohorts.In this chapter, we will review the impact of these technological developments on the understanding of molecular subtypes that promote prostate cancer initiation, progression, metastasis, and clinical aggression. In particular, we will focus on molecular subtypes that define clinically-relevant patient cohorts and assess how a better understanding of how these subtypes-in both somatic and germline genomes-may influence the clinical course for individual men diagnosed with prostate cancer.