Clonal and subclonal TP53 molecular impairment is associated with prognosis and progression in multiple myeloma

ctDNA improves prognostic prediction for patients with relapsed/refractory MM receiving ixazomib, lenalidomide, and dexamethasone

ABSTRACT

It remains elusive how driver mutations, including those detected in circulating tumor DNA (ctDNA), affect prognosis in relapsed/refractory multiple myeloma (RRMM). Here, we performed targeted-capture sequencing using bone marrow plasma cells (BMPCs) and ctDNA of 261 RRMM cases uniformly treated with ixazomib, lenalidomide, and dexamethasone in a multicenter, prospective, observational study. We detected 24 and 47 recurrently mutated genes in BMPC and ctDNA, respectively. In addition to clonal hematopoiesis-associated mutations, varying proportion of driver mutations, particularly TP53 mutations (59.2% of mutated cases), were present in only ctDNA, suggesting their subclonal origin. In univariable analyses, ctDNA mutations of KRAS, TP53, DIS3, BRAF, NRAS, and ATM were associated with worse progression-free survival (PFS). BMPC mutations of TP53 and KRAS were associated with inferior PFS, whereas KRAS mutations were prognostically relevant only when detected in both BMPC and ctDNA. A total number of ctDNA mutations in the 6 relevant genes was a strong prognostic predictor (2-year PFS rates: 57.3%, 22.7%, and 0% for 0, 1, and ≥2 mutations, respectively) and independent of clinical factors and plasma DNA concentration. Using the number of ctDNA mutations, plasma DNA concentration, and clinical factors, we developed a prognostic index, classifying patients into 3 categories with 2-year PFS rates of 57.9%, 28.6%, and 0%. Serial analysis of ctDNA mutations in 94 cases revealed that TP53 and KRAS mutations frequently emerge after therapy. Thus, we clarify the genetic characteristics and clonal architecture of ctDNA mutations and demonstrate their superiority over BMPC mutations for prognostic prediction in RRMM. This study is a part of the C16042 study, which is registered at www.clinicaltrials.gov as #NCT03433001.

Approach to High-Risk Multiple Myeloma

Improving the outcome of high-risk myeloma (HRMM) is a key therapeutic aim for the next decade. To achieve this aim, it is necessary to understand in detail the genetic drivers underlying this clinical behavior and to target its biology therapeutically. Advances have already been made, with a focus on consensus guidance and the application of novel immunotherapeutic approaches. Cases of HRMM are likely to have impaired prognosis even with novel strategies. However, if disease eradication and minimal disease states are achieved, then cure may be possible.

Multi-dimensional scaling techniques unveiled gain1q&loss13q co-occurrence in Multiple Myeloma patients with specific genomic, transcriptional and adverse clinical features

The complexity of Multiple Myeloma (MM) is driven by several genomic aberrations, interacting with disease-related and/or -unrelated factors and conditioning patients' clinical outcome. Patient's prognosis is hardly predictable, as commonly employed MM risk models do not precisely partition high- from low-risk patients, preventing the reliable recognition of early relapsing/refractory patients. By a dimensionality reduction approach, here we dissect the genomic landscape of a large cohort of newly diagnosed MM patients, modelling all the possible interactions between any MM chromosomal alterations. We highlight the presence of a distinguished cluster of patients in the low-dimensionality space, with unfavorable clinical behavior, whose biology was driven by the co-occurrence of chromosomes 1q CN gain and 13 CN loss. Presence or absence of these alterations define MM patients overexpressing either CCND2 or CCND1, fostering the implementation of biology-based patients' classification models to describe the different MM clinical behaviors.

A rational approach to functional high-risk myeloma

Multiple myeloma is a clinically and biologically highly heterogeneous disease, as the overall survival can vary from more than a decade in patients with standard risk disease treated with intensive chemotherapy to 2-3 years in patients with high-risk features. The current staging systems, which rely on baseline biological risk factors to stratify patients into groups with differing risks of progression or death, are sometimes suboptimal tools for identifying high-risk patients. This is particularly evident when considering the so-called functional high-risk patients-patients who do not necessarily display baseline high-risk features but typically show a suboptimal response to induction therapy or relapse early after treatment initiation: the survival of these patients is particularly poor even in the context of newer therapies. The prompt identification, as well as a consistent definition, of this subset of patients, as well as their management, currently represents an unmet medical need. In this review we explore the main characteristics of functional high-risk patients, the available known risk factors and scoring systems, and the possible management.

Association of Chromosome 17 Aneuploidy, TP53 Deletion, Expression and Its rs1042522 Variant with Multiple Myeloma Risk and Response to Thalidomide/Bortezomib Treatment

Multiple myeloma (MM) is a multifactorial genetic disorder caused by interactive effects of environmental and genetic factors. The proper locus of the TP53 gene (17p13.1) and its protein is essential in genomic stability. The most common variant of the TP53 gene-p.P72R (rs1042522)-shows functional variation. The aim of our study was a complex analysis of the TP53 p.P72R variant and TP53 gene expression in relation to chromosomal changes of the TP53 gene locus, as well as MM risk and outcome. Genomic DNA from 129 newly diagnosed MM patients was analyzed by methods of automated DNA sequencing (for TP53 variant analysis) and cIg-FISH (for chromosomal aberrations analysis). RNA was used in real-time PCR to determine the TP53 expression. In MM patients, the TP53 variant was not in Hardy-Weinberg equilibrium. The RR genotype was associated with lower MM risk (OR = 0.44, p = 0.004). A higher number of plasma cells was found in patients with RR genotype in comparison to those with PP + PR genotypes (36.74% vs. 28.30%, p = 0.02). A higher expression of the TP53 gene was observed in PP + PR genotypes vs. RR homozygote (p < 0.001), in smokers vs. non-smokers (p = 0.02). A positive Pearson's correlation was found between the TP53 expression level and the number of plasma cells (r = 0.26, p = 0.04). The presence of chromosome 17 aberrations with or without TP53 locus did not affect the MM risk and outcome. Similar results were observed in the case of TP53 gene expression and the p.P72R variant.

Optical Genome Mapping Reveals the Complex Genetic Landscape of Myeloma

Fluorescence in situ hybridization (FISH) on enriched CD138 plasma cells is the standard method for identification of clinically relevant genetic abnormalities in multiple myeloma. However, FISH is a targeted analysis that can be challenging due to the genetic complexity of myeloma. The aim of this study was to evaluate the potential of optical genome mapping (OGM) to detect clinically significant cytogenetic abnormalities in myeloma and to provide larger pangenomic information. OGM and FISH analyses were performed on CD138-purified cells of 20 myeloma patients. OGM successfully detected structural variants (SVs) (IGH and MYC rearrangements), copy number variants (CNVs) (17p/TP53 deletion, 1p deletion and 1q gain/amplification) and aneuploidy (gains of odd-numbered chromosomes, monosomy 13) classically expected with myeloma and led to a 30% increase in prognosis yield at our institution when compared to FISH. Despite challenges in the interpretation of OGM calls for CNV and aneuploidy losses in non-diploid genomes, OGM has the potential to replace FISH as the standard of care analysis in clinical settings and to efficiently change how we identify prognostic and predictive markers for therapies in the future. To our knowledge, this is the first study highlighting the feasibility and clinical utility of OGM in myeloma.

TP53 mutations identify high-risk events for peripheral T-cell lymphoma treated with CHOP-based chemotherapy

Nodal peripheral T-cell lymphomas (PTCL), the most common PTCLs, are generally treated with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP)-based curative-intent chemotherapy. Recent molecular data have assisted in prognosticating these PTCLs, but most reports lack detailed baseline clinical characteristics and treatment courses. We retrospectively evaluated cases of PTCL treated with CHOP-based chemotherapy that had tumors sequenced by the Memorial Sloan Kettering Integrated Mutational Profiling of Actionable Cancer Targets next-generation sequencing panel to identify variables correlating with inferior survival. We identified 132 patients who met these criteria. Clinical factors correlating with an increased risk of progression (by multivariate analysis) included advanced-stage disease and bone marrow involvement. The only somatic genetic aberrancies correlating with inferior progression-free survival (PFS) were TP53 mutations and TP53/17p deletions. PFS remained inferior when stratifying by TP53 mutation status, with a median PFS of 4.5 months for PTCL with a TP53 mutation (n = 21) vs 10.5 months for PTCL without a TP53 mutation (n = 111). No TP53 aberrancy correlated with inferior overall survival (OS). Although rare (n = 9), CDKN2A-deleted PTCL correlated with inferior OS, with a median of 17.6 months vs 56.7 months for patients without CDKN2A deletions. This retrospective study suggests that patients with PTCL with TP53 mutations experience inferior PFS when treated with curative-intent chemotherapy, warranting prospective confirmation.

Loss of p53 enhances the tumor-initiating potential and drug resistance of clonogenic multiple myeloma cells

Tumor relapse and drug resistance are major factors that limit the curability of multiple myeloma (MM). New regimens have improved overall MM survival rates, but patients with high-risk features continue to have inferior outcomes. Chromosome 17p13 deletion (del17p) that includes the loss of the TP53 gene is a high-risk cytogenetic abnormality and is associated with poor clinical outcomes owing to relatively short remissions and the development of pan-drug resistant disease. Increased relapse rates suggest that del17p enhances clonogenic growth, and we found that the loss of p53 increased both the frequency and drug resistance of tumor-initiating MM cells (TICs). Subsequent RNA sequencing (RNA-seq) studies demonstrated significant activation of the Notch signaling pathway and upregulation of inhibitor of DNA binding (ID1/ID2) genes in p53-knock out (p53-KO) cells. We found that the loss of ID1 or HES-1 expression or treatment with a gamma-secretase inhibitor (GSI) significantly decreased the clonogenic growth of p53-KO but not p53 wild-type cells. GSI treatment in a small set of MM specimens also reduced the clonogenic growth in del17p samples but not in non-del17p samples. This effect was specific as overexpression of the Notch intracellular domain (NICD) rescued the effects of GSI treatment. Our study demonstrates that the Notch signaling and ID1 expression are required for TIC expansion in p53-KO MM cells. These findings also suggest that GSI may be specifically active in patients with p53 mutant MM.

Integrative Analysis of Proteomics and Transcriptomics Reveals Endothelin Receptor B as Novel Single Target and Identifies New Combinatorial Targets for Multiple Myeloma

Despite the recent introduction of next-generation immunotherapeutic agents, multiple myeloma (MM) remains incurable. New strategies targeting MM-specific antigens may result in a more effective therapy by preventing antigen escape, clonal evolution, and tumor resistance. In this work, we adapted an algorithm that integrates proteomic and transcriptomic results of myeloma cells to identify new antigens and possible antigen combinations. We performed cell surface proteomics on 6 myeloma cell lines based and combined these results with gene expression studies. Our algorithm identified 209 overexpressed surface proteins from which 23 proteins could be selected for combinatorial pairing. Flow cytometry analysis of 20 primary samples confirmed the expression of FCRL5, BCMA, and ICAM2 in all samples and IL6R, endothelin receptor B (ET_B), and SLCO5A1 in >60% of myeloma cases. Analyzing possible combinations, we found 6 combinatorial pairs that can target myeloma cells and avoid toxicity on other organs. In addition, our studies identified ET_B as a tumor-associated antigen that is overexpressed on myeloma cells. This antigen can be targeted with a new monoclonal antibody RB49 that recognizes an epitope located in a region that becomes highly accessible after activation of ET_B by its ligand. In conclusion, our algorithm identified several candidate antigens that can be used for either single-antigen targeting approaches or for combinatorial targeting in new immunotherapeutic approaches in MM.

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.

Genetics of Transformed Follicular Lymphoma

Histological transformation (HT) to a more aggressive disease–mostly diffuse large B-cell lymphoma–is considered one of the most dismal events in the clinical course of follicular lymphoma (FL). Current knowledge has not found a single biological event specific for HT, although different studies have highlighted common genetic alterations, such as TP53 and CDKN2A/B loss, and MYC translocations, among others. Together, they increase genomic complexity and mutational burden at HT. A better knowledge of HT pathogenesis would presumably help to find diagnostic biomarkers allowing the identification of patients at high-risk of transformation, as well as the discrimination from patients with FL recurrence, and those who remain in remission. This would also help to identify new drug targets and the design of clinical trials for the treatment of transformation. In the present review we provide a comprehensive overview of the genetic events frequently identified in transformed FL contributing to the switch towards aggressive behaviour, and we will discuss current open questions in the field of HT.

Triplet Therapy, Transplantation, and Maintenance until Progression in Myeloma

BACKGROUND

In patients with newly diagnosed multiple myeloma, the effect of adding autologous stem-cell transplantation (ASCT) to triplet therapy (lenalidomide, bortezomib, and dexamethasone [RVD]), followed by lenalidomide maintenance therapy until disease progression, is unknown.

METHODS

In this phase 3 trial, adults (18 to 65 years of age) with symptomatic myeloma received one cycle of RVD. We randomly assigned these patients, in a 1:1 ratio, to receive two additional RVD cycles plus stem-cell mobilization, followed by either five additional RVD cycles (the RVD-alone group) or high-dose melphalan plus ASCT followed by two additional RVD cycles (the transplantation group). Both groups received lenalidomide until disease progression, unacceptable side effects, or both. The primary end point was progression-free survival.

RESULTS

Among 357 patients in the RVD-alone group and 365 in the transplantation group, at a median follow-up of 76.0 months, 328 events of disease progression or death occurred; the risk was 53% higher in the RVD-alone group than in the transplantation group (hazard ratio, 1.53; 95% confidence interval [CI], 1.23 to 1.91; P<0.001); median progression-free survival was 46.2 months and 67.5 months. The percentage of patients with a partial response or better was 95.0% in the RVD-alone group and 97.5% in the transplantation group (P = 0.55); 42.0% and 46.8%, respectively, had a complete response or better (P = 0.99). Treatment-related adverse events of grade 3 or higher occurred in 78.2% and 94.2%, respectively; 5-year survival was 79.2% and 80.7% (hazard ratio for death, 1.10; 95% CI, 0.73 to 1.65).

CONCLUSIONS

Among adults with multiple myeloma, RVD plus ASCT was associated with longer progression-free survival than RVD alone. No overall survival benefit was observed. (Funded by the National Heart, Lung, and Blood Institute and others; DETERMINATION ClinicalTrials.gov number, NCT01208662.).

Perspectives on the Risk-Stratified Treatment of Multiple Myeloma

The multiple myeloma treatment landscape has changed dramatically. This change, paralleled by an increase in scientific knowledge, has resulted in significant improvement in survival. However, heterogeneity remains in clinical outcomes, with a proportion of patients not benefiting from current approaches and continuing to have a poor prognosis. A significant proportion of the variability in outcome can be predicted on the basis of clinical and biochemical parameters and tumor-acquired genetic variants, allowing for risk stratification and a more personalized approach to therapy. This article discusses the principles that can enable the rational and effective development of therapeutic approaches for high-risk multiple myeloma.

BoBafit: a copy number clustering tool designed to refit and recalibrate the baseline region of tumors’ profiles

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Tools generating CN profiles derive the baseline region from samples’ median CN signal.

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This canonical approach might cause wrongly estimated CN profile in complex karyotypes.

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CNAs call is crucial for patients’ risk stratification aimed at personalized treatment.

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BoBafit computes the correct baseline region and the CN profile, taking into account tumor genomic complexity and samples-specific alterations.

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BoBafit should be implemented within CN analysis pipelines especially for clinical aims.

Human cancer arises from a population of cells that have acquired a wide range of genetic alterations, most of which are targets of therapeutic treatments or are used as prognostic factors for patient’s risk stratification. Among these, copy number alterations (CNAs) are quite frequent. Currently, several molecular biology technologies, such as microarrays, NGS and single-cell approaches are used to define the genomic profile of tumor samples. Output data need to be analyzed with bioinformatic approaches and particularly by employing computational algorithms.

Molecular biology tools estimate the baseline region by comparing either the mean probe signals, or the number of reads to the reference genome. However, when tumors display complex karyotypes, this type of approach could fail the baseline region estimation and consequently cause errors in the CNAs call. To overcome this issue, we designed an R-package, BoBafit, able to check and, eventually, to adjust the baseline region, according to both the tumor-specific alterations’ context and the sample-specific clustered genomic lesions.

Several databases have been chosen to set up and validate the designed package, thus demonstrating the potential of BoBafit to adjust copy number (CN) data from different tumors and analysis techniques.

Relevantly, the analysis highlighted that up to 25% of samples need a baseline region adjustment and a redefinition of CNAs calls, thus causing a change in the prognostic risk classification of the patients.

We support the implementation of BoBafit within CN analysis bioinformatics pipelines to ensure a correct patient’s stratification in risk categories, regardless of the tumor type.