Inactivation Of Tumor Suppressor Genes Research Articles

Introduction Multiple myeloma (MM) is shaped by interactions between immune, stromal and tumor cells in the bone marrow (BM) microenvironment (BMME). Our understanding of these processes is based on high parametric flow cytometry and single cell transcriptomics, which lack spatial resolution. Thus, crucial aspects of the in situ tumor ecosystem, including cell-cell interactions, can only be predicted. Novel high dimensional, multiplex tissue imaging methods are able to capture similar granularity while also preserving spatial organization. By optimizing antibody selection, as well as staining, imaging and data processing protocols we have established a BMME-targeted CODEX workflow based on a 60 marker panel. Here, we conducted the first comprehensive multiplex protein imaging survey of the BMME across the spectrum of MM and precursor stages and leveraged paired FISH and whole genome sequencing (WGS) to elucidate the interplay of tumor intrinsic features and the BMME. Methods We imaged 489 BM trephine biopsies in tissue microarray format on the CODEX (Akoya Biosciences) platform. After quality control (≥750 cells per core, &lt;25% unclassifiable cells) we retained data from 12 non-malignant controls, 13 MGUS, 20 smoldering MM, 349 uniformly treated newly diagnosed MM (NDMM) patients from a phase III clinical trial, as well as 22 relapsed MM samples with a median of 3 (range 1-6) tissue cores per sample, comprising a total of 3.1 million single cells. CODEX data was processed and analyzed using custom scripts. Cellular neighborhoods were computed by clustering the cell type composition of sliding windows (n=10 nearest neighboring cells) across the tissue. WGS (n=212) and FISH (n=309) were performed on CD138-purified BM mononuclear cells and analyzed using in-house pipelines. Results We identified 40 cell types, encompassing the relevant lineages (myeloid, lymphoid, stromal, endothelial, tumor), subtypes (e.g. T, B, NK cells) and functional states (e.g GRZB, Ki67, and PD1) of the BMME. The degree of plasma cell infiltration determined by CODEX correlated with the clinical pathologist's quantification based on classical immunohistology (r=0.77, p&lt;0.0001), and the kappa/lambda status of MM cells matched the reported light chain restriction, supporting the validity of our data. At the compositional level, we observed shifts in relative cell type abundance from precursor to MM stages. Major changes in the BMME included enriched stromal, endothelial and CD8+ T cells as well as depleted polymorphonuclear (PMN), erythropoietic and CD4+ T cells in advanced stages. The highest proportion of monocytoid dendritic cells, monocytes, mast cells and cytotoxic CD8+ T cells was seen in relapsed MM. Leveraging the spatial resolution of the CODEX data, we identified multiple distinct cellular neighborhoods (CNs) defined by recurrent local cell type composition. CNs enriched for plasma cells in combination with different immune cell subsets including exhausted T cells increased towards the MM stage, while CNs dominated by PMNs, erythropoietic cells and CD206 negative macrophages decreased. To investigate links between MM subtypes and BMME patterns, we correlated CODEX data with MM-initiating and driver events. While patients with t(4;14) showed a significant increase in eosinophils, patients with gain1q had higher levels of endothelial and stromal subsets colocalizing in the same CN. Distinct types of stromal cells were also enriched in patients with double hits (≥2 FISH high risk features). RAS mutations and biallelic inactivation of tumor suppressor genes were associated with altered T cell subset composition. Further corroborating the importance of BMME architecture, a CN with colocalization of plasma and exhausted CD8 T cells was associated with unfavorable prognosis in NDMM patients. Conclusions High dimensional, multiplex tissue imaging of the BMME enables interrogation of cellular interactions and tissue architecture in situ in clinical samples at single cell resolution. To our knowledge this is the first highly multiplexed spatial study of the human BMME in a large clinical cohort and the first such study in multiple myeloma. We track characteristic shifts in tissue composition associated with disease progression, reveal genotype-phenotype associations in the BMME as a correlate of tumor-microenvironment co-evolution and detect prognostically relevant tissue architectural features.

INTRODUCTION: Multiple myeloma (MM) evolution is complex and heterogeneous. Hyperdiploidy (HRD) and translocations affecting the immunoglobulin heavy chain (IGH) locus are historically considered initiating genomic events of MM. The potential impact of genomic events acquired before known MM initiating events has never been addressed. METHODS: To investigate whether genomic deletions are acquired before and after known MM initiating events we interrogated whole-genome sequencing (WGS) data from patients with newly diagnosed MM (NDMM, n=319) and relapsed MM (RRMM, n=59). Our newly developed analytical and chronological workflow integrating single nucleotide variants (SNV), structural variants (SV), and copy number variants (CNV) comprises two main steps. First, we estimated the molecular time (i.e. corrected ratio between duplicated and non-duplicated clonal SNV) of large clonal chromosomal duplications and identified the earliest set of chromosomal gains in each patient. Second, within each early large chromosomal gain we identified clonal SV mediating CNV loss. A deletion on a gain can generate three possible scenarios: 1) one of the duplicated alleles is lost after the gain (i.e. post-gain) causing a CNV jump from 3:1 to 2:1 (total alleles : minor alleles); 2) there is a deletion before the duplication (i.e., pre-gain) causing a CNV jump from 3:1 to 1:0. 3) the deletion occurs on the minor, non-duplicated allele, causing a CNV jump from 3:1 to 2:0. Timing the deletion in relation with the chromosomal duplication is impossible in this scenario. RESULTS: Molecular time data were successfully generated for 249/319 (78%) NDMM and 51/59 (86%) RRMM patients. Restricting our analysis to NDMM with HRD without canonical IGH translocations, 16/170 (9%) of patients acquired deletions before the earliest multi-chromosomal gains, suggesting MM precursors can acquire deletions before HRD. Investigating the entire series, post-gain deletions were observed in 126/417 (30%) samples considering both early and late time windows. Leveraging a background model, we demonstrated that pre-gain events involved more tumor-suppressor genes (TSG) than expected by chance, including TCF3, ATM and TRAF3. In contrast, oncogenes were involved less than expected. In post-gain deletions, both oncogenes and TSG were involved more than expected by chance . To validate and assess the impact of deletions on driver gene expression we investigated WGS and RNAsequencing data from the MMRF CoMMpass study (n=752 NDMM). Because of the low coverage not allowing for molecular time estimation, we limited our analysis to HRD without IGH translocations. Pre-gain and post-gain deletions were observed in 47/431 (11%) and 225/431 (52%) of patients, respectively. We defined loss and gain of function events based on whether the expression level of a gene affected by the deletion was in the first or fourth quartile, respectively. Pre-gain deletions were mostly associated with downregulation of TSG expression [n=31 events in 13/431 (3%) patients]. In contrast,post-gain deletions had a more heterogenous impact with loss- and gain-of-function events. Gain-of-function events were driven by two main mechanisms: 1) the deletion joined an oncogene with a distal regulatory region inducing its overexpression [n=44 events in 6/431 (1.3%) patients]; 2) the deletion caused a new and expressed fusion [n=231 events in 109/431 (25%) patients], resulting in either loss- or gain-of-function. Surprisingly, post-gain deletions had also a major impact on TSG expression. In 60 patients (14%), we observed 146 post-gain deletions where the affected tumor suppressor gene (TSG) expression was downregulated to the level of cases with monoallelic and biallelic deletions, even though two alleles were retained. Finally, to validate these findings, we investigated 16 RRMM patients with available WGS, scATAC-seq and scRNA-seq and observed additional evidence of TSG downregulation after both pre- and post-gain deletions. CONCLUSION: Leveraging a large cohort of NDMM we show that somatic deletions can be acquired before HRD trisomies that are assumed to be initiating events. Furthermore, post-gain deletions emerged as a new mechanism inducing TSG down-regulation, despite an apparently diploid gene status.

Inactivation Of Tumor Suppressor Genes Research Articles

Related Topics

Articles published on Inactivation Of Tumor Suppressor Genes

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

Genome-wide DNA methylation-analysis of blastic plasmacytoid dendritic cell neoplasm identifies distinct molecular features

Abstract 4895: Optical genome mapping analysis across multiple solid tumor types in Bionano VIATM software

Abstract 2887: Impact of ionizing radiation on the energy metabolism of normal and tumor cells in the brain

Abstract 5004: Oncolytic Jurona-driven systemic and intratumoral immunotherapy combined with immune checkpoint blockade boost immune response and survival in hepatocellular carcinoma models

Abstract 1202: Signatures of selection for biallelic inactivation in tumor suppressor genes across cancer types

Label-free plasmonic spectral profiling of serum DNA

EPIGENETIC INACTIVATION OF TUMOR SUPPRESSOR GENES- MARKER FOR MONITORING THE EFFECTIVENESS OF THERAPY

Abstract B010: Response to treatment with nab-sirolimus among patients with primary uterine tumors: A subgroup analysis from AMPECT

The Role of the PTEN Tumor Suppressor Gene and Its Anti-Angiogenic Activity in Melanoma and Other Cancers.

Methylation Status of CDH1 Gene in Gastric Biopsy Specimens and Its Association with H. Pylori Infection.

Is vestibular schwannoma really a benign tumor? Case report and review

Spatial Dissection of the Bone Marrow Microenvironment in Multiple Myeloma By High Dimensional Multiplex Tissue Imaging

Revealing Novel Mechanisms Underlying Inactivation of Tumor Suppressor Genes on Duplicated Chromosomes in Multiple Myeloma

Whole-Genome Sequencing Identifies Structural Variation As a Key Driver of Disease Relapse and Aggressive Clinical Behavior in Multiple Myeloma

Biallelic inactivation of the NF1 tumour suppressor gene in juvenile myelomonocytic leukaemia: Genetic evidence of driver function and implications for diagnostic workup.

Dysregulation of Non-Coding RNAs: Roles of miRNAs and lncRNAs in the Pathogenesis of Multiple Myeloma.

Design, In Silico Molecular Docking, and ADMET Prediction of Amide Derivatives of Chalcone Nucleus as EGFR Inhibitors for the Treatment of Cancer.

The Special Role of JAK/STAT, and Notch Signaling Pathways in Cancer Pathogenesis

FRI222 Large Non-functional Adrenocortical Carcinoma In A Patient With History Of Occupational Radiation Exposure

Lead the way for us