Molecular Evolution of Classical Hodgkin Lymphoma Revealed Though Whole Genome Sequencing of Hodgkin and Reed-Sternberg Cells

Abstract Introduction: Classical Hodgkin lymphoma (cHL) is characterized by a small fraction of Hodgkin and Reed-Sternberg (HRS) tumor cells (~1%) which are surrounded by an extensive immune infiltrate. The rare nature of HRS cells limits the ability to study the genomics of cHL using standard platforms. To circumvent this, our group has optimized fluorescence-activated cell sorting to isolate HRS cells and intratumor B- and T- cells and to perform whole exome sequencing (WES; Reichel, Blood 2015). To date, however, there have been no reports on whole genome sequencing (WGS) of cHL. Methods: We performed flow-sorting of HRS cells and WGS to define the genomic landscape of cHL including: i) mutational processes involved in pathogenesis, ii) large and focal copy number variants, iii) structural variants including complex events, iv) the sequence and evolution of molecular events in cHL. We interrogated WGS from 25 cases of cHL: 10 pediatric patients (age<18), 9 adolescents and young adults (AYA, age 18-40), and 6 older adults (age>40). Intra-tumoral T-cells were used as germline control. An additional 36 cHL cases were evaluated by WES. Results: The average depth of coverage among the 25 WGS cases was 27.5x. After having identified and removed amplification-based palindromic sequencing artifacts, we observed a median of 5006 single base substitutions (SBS; range 1763-18436). Pediatric and AYA patients had a higher SBS burden compared to older adults (median 5279 vs. 2945, p=0.009). Five main SBS signatures were identified: SBS1 and SBS5 (aging), SBS2 and SBS13 (APOBEC), and SBS25 (chemotherapy, in a relapsed case). A dNdScv driver discovery analysis performed on the combined WES and WGS cases identified 24 driver genes including BCL7A and CISH which had not been previously reported as drivers in cHL. An investigation of copy number alterations (CNAs) confirmed high ploidy in cHL (median 2.95, range 1.66-5.33). Whole genome duplication was identified in 64% cases. We also observed clear evidence of complex events such as chromothripsis (n=4), double minutes (dm, n=2), breakage-fusion-bridge (bfb; n=4). Some of these events were responsible for the acquisition of distinct drivers. For example, we observed one dm and one bfb responsible for CD274 and REL gains, respectively (>10 copies). Leveraging the high prevalence of large chromosomal gains, we performed an investigation of the relative timing of acquisition of driver mutations. Clonal mutations within chromosomal gains can be defined as duplicated (VAF~66%; acquired before the gain) or non-duplicated (VAF~33%; acquired before or after the gain). Sixty-one percent (152/249) of driver genes were duplicated suggesting that they were acquired prior to large chromosomal gains. Next, we used the corrected ratio between duplicated and non-duplicated mutations within large chromosomal gains to estimate the molecular time of each duplicated segment (Rustad, Nat Comm 2020). In 11/22 genomes the final CNA profile was acquired through at least two temporally distinct events. To convert these relative estimations into absolute timing (i.e., the age at which events occurred), we leveraged the clock-like mutation signatures (SBS1, SBS5). We first confirmed that the SBS1 and SBS5 mutation rate were constant over time (R 2=0.84; p<0.0001 in Peds/AYA; R 2 =0.82; p=0.002 in older adults). We observed a higher mutation rate in Pediatric/AYA cases compared to older adults (p=0.01), which is consistent with the higher mutational burden observed in this age group. By estimating the SBS1- and SBS5-based molecular time for large chromosomal gains and converting relative estimates to absolute time, we are able to estimate the age in years at the time of the first multi-chromosomal gain event. We observed that the first multi-chromosomal gain in cHL is often acquired several years before the diagnosis/sample collection: median latency of 19.5 (range 12-27) and 5.6 (range 1.8-16) years in older adults and pediatric/AYA patients respectively. Conclusion: Here we report the first WGS in cHL. We identify novel drivers and genomic mechanisms involved in cHL pathogenesis. We found that mutations in driver genes are often acquired earlier then chromosomal gains, potentially preceding the cHL diagnosis by several years. In addition, we observed key differences in biology of cHL across age groups including accelerated mutagenesis and increased mutational burden among younger patients. Disclosures Maura: OncLive: Honoraria; Medscape: Consultancy, Honoraria. Oberley: Caris LIfe Science: Current Employment. Lim: EUSA Pharma: Honoraria. Landgren: Janssen: Other: IDMC; Celgene: Research Funding; Janssen: Honoraria; Amgen: Honoraria; Janssen: Research Funding; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. Moskowitz: Merck & Co., Inc.: Research Funding. Roshal: Celgene: Other: Provision of services; Auron Therapeutics: Other: Ownership / Equity interests; Provision of services; Physicians' Education Resource: Other: Provision of services. Elemento: Owkin: Consultancy, Other: Current equity holder; AstraZeneca: Research Funding; Champions Oncology: Consultancy; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; One Three Biotech: Consultancy, Other: Current equity holder; Eli Lilly: Research Funding; Johnson and Johnson: Research Funding; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Janssen: Research Funding. Roth: Janssen: Consultancy; Merck: Consultancy.

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Whole Genome Copy Number Variation Analysis of Chronic Lymphocytic Leukemia (CLL) Cells From Early-Intermediate Stage, High Risk CLL Patients Prior to First Treatment Reveals New Loss of Heterozygosity and Duplication Events in the CLL Genome.

Blood ◽

10.1182/blood.v114.22.1265.1265 ◽

2009 ◽

Vol 114 (22) ◽

pp. 1265-1265

Author(s):

Steven A. Schichman ◽

Annjanette Stone ◽

Maria Winters ◽

Weleetka Carter ◽

Lori Frederick ◽

...

Keyword(s):

Board Of Directors ◽

Copy Number ◽

Research Funding ◽

Homozygous Deletion ◽

Chromosome 12 ◽

Whole Genome ◽

Advisory Committees ◽

Hemizygous Deletion ◽

Number Variation ◽

Trisomy 12

Abstract Abstract 1265 Poster Board I-287 Introduction Fluorescence in situ hybridization (FISH), in combination with other markers, is used as a prognostic tool for CLL patients at diagnosis. The presence or absence of trisomy 12 and deletions at 13q, 11q, and 17p helps to predict disease progression and to stratify patients for therapeutic decisions. We hypothesized that whole genome single nucleotide polymorphism (SNP)-based copy number variation (CNV) analysis would capture all of the information in current CLL FISH panels and would reveal new CNV features in the CLL genome. Patients and Methods Nineteen early-intermediate clinical stage, untreated CLL patients aged 29 to 77 were determined to be at high risk for disease progression by FISH, IgVH mutation status, ZAP-70, and CD38 prognostic markers. CLL cells and normal cells were separated by magnetic bead selection from patient peripheral blood samples with absolute lymphocyte counts that ranged from 7.4 to 162 × 109/L. CNV analysis was performed on purified genomic DNA from the CLL cells and from normal cells for each patient in order to distinguish acquired CNVs in malignant cells from polymorphic CNVs in the human genome. We used the Illumina human660w-quad beadchip, a SNP-based microarray for whole-genome genotyping and CNV analysis that contains more than 550,000 tag SNPs and approximately 100,000 additional markers that target regions of common CNV. CNV data was analyzed using CNV partition (Illumina Genome Studio software) and PennCNV. Results 100% concordance is found between del(13q), del(11q), and del(17p) FISH abnormalities and loss of heterozygosity (LOH) at 13q, 11q, and 17p by CNV analysis. All three patients with trisomy 12 by FISH show copy number(CN)=3 of chromosome 12 by CNV analysis. Of 15 patients with del(13q) by FISH, 12 out of 15 have regions of hemizygous deletion on 13q that vary from ∼830 Kb to ∼38 Mb. The smallest region of LOH is located within 13q14.3. Three out of 15 patients show homozygous deletion within 13q14.3. One of these 3 patients has copy-neutral LOH of the entire 13q arm with an embedded 835 Kb segment of homozygous deletion at 13q14.3. Two patients have large discontinuous segments of LOH on 13q, indicating complex interstitial deletion events. Two out of 5 patients with del(13q) as a sole FISH abnormality show additional CNV events in the CLL genome. One of these patients has copy neutral LOH at 2q33.1-telomere(tel). One other patient with sole del(13q) FISH shows LOH events at 10q23.31-23.33 and at 15q15.1. Five out of six patients with del(11q) by FISH have either 13q LOH (n=4) or chromosome 12 CN=3 (n=1) without any other CNV events detected in the CLL genome. One patient with trisomy 12 and del(11q) by FISH has three additional acquired CNV abnormalities in the CLL genome: LOH at 7p15.2-tel, LOH at 11p13, and CN=3 at 3q24-tel. In contrast to patients with del(11q), del(13q), and trisomy 12, patients with del(17p) by FISH have numerous acquired CNV abnormalities in the CLL genome. These include LOH events at 1p34.3-p34.2, 2q34-q36.3, 3p21.31-tel, 4p13, 4p15.1-tel, 15q11.2-q14 and 15q14-q15.3, 16p13.3-tel, 16p13.11, 16p13.2, 18p11.21-tel, 20p11.21-tel, and 20q13.2-q13.31. CN=3 at 2p12-tel is detected in 2 out of 5 patients with 17p hemizygous deletion. One out of 5 patients with 17p hemizygous deletion shows CN=3 at 10q22.2-tel. One other patient also with 17p hemizygous deletion shows CN=3 at 22q12.2-tel. Conclusions Whole genome CNV analysis by SNP-based microarrays greatly expands our ability to detect acquired genomic events in CLL cells. These events include hemizygous deletion, homozygous deletion, copy-neutral LOH, and CN=3 duplication. Detection of copy-neutral LOH is not possible by FISH or array comparative genomic hybridization technology. The current study reveals a high number of acquired CNV events in earlier stage, untreated CLL patients with 17p hemizygous deletion. This observation, indicative of genomic instability, is consistent with the known poor prognosis of del(17p) patients. The new somatic CNV abnormalities detected in CLL cells may help to discover additional genes or signaling pathways involved in CLL initiation and progression. In addition, the new CNV markers may be used in larger clinical studies to improve CLL prognosis and patient stratification for therapy. Disclosures Shanafelt: Genentech: Research Funding; Hospira: Membership on an entity's Board of Directors or advisory committees, Research Funding; Polyphenon E International: Research Funding; Celgene: Research Funding; Cephalon: Research Funding; Bayer Health Care Pharmaceuticals: Research Funding. Kay:Genentech, Celgene, Hospira, Polyphenon Pharma, Sanofi-Aventis: Research Funding; Biogenc-Idec, Celgene, Genentech, genmab: Membership on an entity's Board of Directors or advisory committees. Zent:Genentech, Bayer, Genzyme, Novartis: Research Funding.

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Whole-Exome Sequencing and Targeted Deep Sequencing of cfDNA Enables a Comprehensive Mutational Profiling of Multiple Myeloma

Blood ◽

10.1182/blood.v128.22.197.197 ◽

2016 ◽

Vol 128 (22) ◽

pp. 197-197 ◽

Cited By ~ 4

Author(s):

Salomon Manier ◽

Jihye Park ◽

Samuel Freeman ◽

Gavin Ha ◽

Marzia Capelletti ◽

...

Keyword(s):

Deep Sequencing ◽

Research Funding ◽

Clonal Evolution ◽

Target Coverage ◽

Whole Genome ◽

Driver Genes ◽

Whole Exome ◽

Low Pass ◽

Targeted Deep Sequencing ◽

Tumor Dna

Abstract Background . Cell-free DNA (cfDNA) sequencing enables serial temporal sampling, which offers the possibility of following the dynamics of biomarkers and clonal evolution in Multiple Myeloma (MM) over time. The use of cfDNA in clinical practice as a molecular biomarker and for monitoring response/resistance is dependent on a comprehensive profile of matched cfDNA and tumor DNA (tDNA) samples. Here we performed Ultra-Low Pass Whole Genome Sequencing (ULP-WGS) followed by whole-exome sequencing (WES) and targeted deep sequencing of matched cfDNA/tDNA samples from MM patients. Methods. We performed next generation sequencing of matched cfDNA/tDNA samples for 63 patients with newly diagnosed or relapsed MM, SMM, or MGUS. Libraries were constructed using the Kappa Hyper kit and sequenced by ultra-low-pass whole-genome sequencing (ULP-WGS, 0.1x coverage) to quantify tumor fraction within cfDNA. WES was performed on 30 matched samples cfDNA/tDNA/germline DNA from 10 patients with more than 5% of tumor fraction. Libraries were hybridized to the Nextera Rapid Capture Exome kit (Illumina) and then sequenced on HiSeq 4000 (Illumina). Targeted deep sequencing was performed on 32 matched cfDNA/tDNA samples from 16 patients using the HaloPlex HS technology (Agilent), allowing for molecular barcoding. Libraries were constructed according to the manufacturer's instructions and sequenced on HiSeq 2500 (Illumina). Sequencing data were analyzed using the Firehose pipelines, including MuTect, ABSOLUTE, ReCapSeg, GISTIC and MutSig. Results. We first used a cost-effective approach to establish the tumor content of cfDNA in a large-scale manner by ULP-WGS. Among 63 tested samples (53 MM, 6 SMM and 4 MGUS patient samples), the tumor fraction within cfDNA ranged from 0 to 81% with a mean of 10%. About 43% of these samples had tumor fraction greater than 5% within cfDNA. To assess whether cfDNA can capture the genetic diversity of MM and inform clinical management, we performed WES of matched cfDNA/tDNA/germline DNA samples for 10 patients (mean target coverage 194x). Copy number alterations (CNAs) assessed by WES (ReCapSeg) were consistent between cfDNA and tumor DNA. Similarly, focal CNAs assessed by GISTIC were consistent between tDNA and cfDNA. We then examined the overlap of somatic single nucleotide variants (SSNVs) between WES of cfDNA and matched tDNA. We found, on average, 100% of the clonal and 96% of the subclonal (range 54-100%) SSNVs that were detected in the tumor were confirmed to be present in cfDNA. Similarly, for mutations detected in the cfDNA, we found, on average, 100% of the clonal and 99% of the subclonal (range 98-100%) SSNVs were confirmed in the tumor. To assess whether targeted deep sequencing of cfDNA could be a good proxy for tumor biopsy we used a targeted deep sequencing approach of known MM driver genes. Libraries were prepared using unique molecular barcodes to avoid duplication rates, for 32 matched cfDNA/tDNA samples from 16 patients with MM. The mean target coverage was 596x. We found similar frequencies of altered MM driver genes in both cfDNA and tDNA, including KRAS, NRAS, and TP53, indicating that cfDNA can be used for precision medicine. Conclusions. Our study demonstrates that both WES and targeted deep sequencing of cfDNA are consistently representative of tumor DNA alterations in terms of CNAs, focal CNAs and SSNVs. This approach could therefore be used to longitudinally follow clonal evolution across the course of the disease and precision medicine in patients with MM. Disclosures Palumbo: Takeda: Employment, Honoraria; Janssen Cilag: Honoraria. Kumar:Noxxon Pharma: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Kesios: Consultancy; Glycomimetics: Consultancy; BMS: Consultancy; Array BioPharma: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; AbbVie: Research Funding; Onyx: Consultancy, Research Funding. Roccaro:Takeda Pharmaceutical Company Limited: Honoraria. Facon:Amgen: Consultancy, Speakers Bureau; Novartis: Consultancy; Janssen: Consultancy, Speakers Bureau; Bristol: Consultancy; Millenium/Takeda: Consultancy; Celgene: Consultancy, Speakers Bureau; Karyopharm: Consultancy. Ghobrial:Celgene: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Noxxon: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Amgen: Honoraria.

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Profiling the Genomic Landscape at the Early Stages of CLL: Low Genomic Complexity and Paucity of Driver Mutations in MBL and Indolent CLL

Blood ◽

10.1182/blood.v128.22.3214.3214 ◽

2016 ◽

Vol 128 (22) ◽

pp. 3214-3214 ◽

Cited By ~ 1

Author(s):

Andreas Agathangelidis ◽

Viktor Ljungström ◽

Lydia Scarfò ◽

Claudia Fazi ◽

Maria Gounari ◽

...

Keyword(s):

Deep Sequencing ◽

Research Funding ◽

Somatic Mutations ◽

Driver Mutations ◽

Whole Genome ◽

Sequencing Analysis ◽

Driver Genes ◽

Hematopoietic Stem ◽

Genomic Complexity ◽

Targeted Deep Sequencing

Abstract Chronic lymphocytic leukemia (CLL) is preceded by monoclonal B cell lymphocytosis (MBL), characterized by the presence of monoclonal CLL-like B cells in the peripheral blood, yet at lower numbers than those required for the diagnosis of CLL. MBL is distinguished into low-count (LC-MBL) and high-count (HC-MBL), based on the number of circulating CLL-like cells. While the former does not virtually progress into a clinically relevant disease, the latter may evolve into CLL at a rate of 1% per year. In CLL, genomic studies have led to the discovery of recurrent gene mutations that drive disease progression. These driver mutations may be detected in HC-MBL and even in multipotent hematopoietic progenitor cells from CLL patients, suggesting that they may be essential for CLL onset. Using whole-genome sequencing (WGS) we profiled LC-MBL and HC-MBL cases but also CLL patients with stable lymphocytosis (range: 39.8-81.8*109 CLL cells/l) for >10 years (hereafter termed indolent CLL). This would refine our understanding of the type of genetic aberrations that may be involved in the initial transformation rather than linked to clinical progression as is the case for most, if not all, CLL driver mutations. To this end, we whole-genome sequenced CD19+CD5+CD20dim cells from 6 LC-MBL, 5 HC-MBL and 5 indolent CLL cases; buccal control DNA and polymorphonuclear (PMN) cells were analysed in all cases. We also performed targeted deep-sequencing on 11 known driver genes (ATM, BIRC3, MYD88, NOTCH1, SF3B1, TP53, EGR2, POT1, NFKBIE, XPO1, FBXW7) in 8 LC-MBL, 13 HC-MBL and 7 indolent CLL cases and paired PMN samples. Overall similar mutation signatures/frequencies were observed for LC/HC-MBL and CLL concerning i) the entire genome; with an average of 2040 somatic mutations observed for LC-MBL, 2558 for HC-MBL and 2400 for CLL (186 for PMN samples), as well as ii) in the exome; with an average of non-synonymous mutations of 8.9 for LC-MBL, 14.6 for HC-MBL, 11.6 for indolent CLL (0.9 for PMN samples). Regarding putative CLL driver genes, WGS analysis revealed only 2 somatic mutations within NOTCH1, and FBXW7 in one HC-MBL case each. After stringent filtering, 106 non-coding variants (NCVs) of potential relevance to CLL were identified in all MBL/CLL samples and 4 NCVs in 2/24 PMN samples. Seventy-two of 110 NCVs (65.5%) caused a potential breaking event in transcription factor binding motifs (TFBM). Of these, 29 concerned cancer-associated genes, including BTG2, BCL6 and BIRC3 (4, 2 and 2 samples, respectively), while 16 concerned genes implicated in pathways critical for CLL e.g. the NF-κB and spliceosome pathways. Shared mutations between MBL/CLL and their paired PMN samples were identified in all cases: 2 mutations were located within exons, whereas an average of 15.8 mutations/case for LC-MBL, 8.2 for HC-MBL and 9 for CLL, respectively, concerned the non-coding part. Finally, 16 sCNAs were identified in 9 MBL/CLL samples; of the Döhner model aberrations, only del(13q) was detected in 7/9 cases bearing sCNAs (2 LC-MBL, 3 HC-MBL, 2 indolent CLL). Targeted deep-sequencing analysis (coverage 3000x) confirmed the 2 variants detected by WGS, i.e. in NOTCH1 (n=1) and FBXW7 (n=1), while 4 subclonal likely damaging variants were detected with a VAF <10% in POT1 (n=2), TP53 (n=1), and SF3B1 (n=1) in 4 HC-MBL samples. In conclusion, LC-MBL and CLL with stable lymphocytosis for >10 years display similar low genomic complexity and absence of exonic driver mutations, assessed both with WGS and deep-sequencing, underscoring their common low propensity to progress. On the other hand, HC-MBL comprising cases that may ultimately evolve into clinically relevant CLL can acquire exonic driver mutations associated with more dismal prognosis, as exemplified by subclonal driver mutations detected by deep-sequenicng. The existence of NCVs in TFBMs targeting pathways critical for CLL prompts further investigation into their actual relevance to the clinical behavior. Shared mutations between CLL and PMN cells indicate that some somatic mutations may occur before CLL onset, likely at the hematopoietic stem-cell level. Their potential oncogenic role likely depends on the cellular context and/or microenvironmental stimuli to which the affected cells are exposed. Disclosures Stamatopoulos: Novartis: Honoraria, Research Funding; Janssen: Honoraria, Other: Travel expenses, Research Funding; Gilead: Consultancy, Honoraria, Research Funding; Abbvie: Honoraria, Other: Travel expenses. Ghia:Adaptive: Consultancy; Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; Roche: Honoraria, Research Funding.

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A study of real-time CLIA-enabled whole genome tumor sequencing: Results for testicular cancer and sarcomatoid RCC.

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.4_suppl.463 ◽

2014 ◽

Vol 32 (4_suppl) ◽

pp. 463-463

Author(s):

Alan Haruo Bryce ◽

Mitesh J. Borad ◽

Rachel M. Condjella ◽

Jan B. Egan ◽

Mia D. Champion ◽

...

Keyword(s):

Testicular Cancer ◽

Copy Number ◽

Thyroid Hormone Receptor ◽

Papillary Renal Cell Carcinoma ◽

Copy Number Gain ◽

Whole Genome ◽

Sequencing Data ◽

Driver Genes ◽

Whole Genome Analysis ◽

Initial Report

463 Background: The genomic assessment of cancer has been revolutionized by next-generation sequencing and is increasingly being applied in the clinic to guide therapeutic decision-making. Time to reporting of results, specimen quantity, and analyte quality have constrained initial clinical applications to gene panels and whole exome based strategies. Methods: Patients underwent surgical resection, excisional or core biopsies, or bone marrow biopsy. Samples were analyzed by whole genome or exome sequencing and RNA sequencing, bioinformatics analysis, and therapeutic target prioritization by a multi-disciplinary Clinical Genomics Board. All prioritized targets were CLIA validated using Sanger sequencing, RT-qPCR, FISH, or IHC as appropriate. Treatment was delivered using off-label FDA approved drugs, clinical trials, or single patient INDs. Results: We have enrolled 40 patients with advanced, treatment-refractory cancers of whom sequencing data is available on 33. The initial 6 patients were evaluated in a non-CLIA pilot phase and 27 in the CLIA enabled phase. Upon availability of the initial report, identified targets of putative therapeutic relevance were then prioritized by the CGB in 22/27 patients (81%) for subsequent CLIA validation. Eleven patients have been treated with genomically selected therapy with partial response in 3/10 assessed patients. A testicular cancer patient had aberrations in two testes specific genes, a copy number gain in TSSK6 and a novel gene fusion between thyroid hormone receptor associated protein 3 (THRAP3) and Tektin 2 (TEKT2). Additionally, a case of papillary renal cell carcinoma had an amplification of YAP1 and a previously unreported P287T mutation in CCND1, suggesting potential benefit with a CDK4/6 inhibitor. Treatment is ongoing and results will be reported. Conclusions: Integrated whole genome analysis in a CLIA setting is feasible. Integration of SNV, copy number and transcriptional data may allow for selection of putative driver genes to enhance targeted therapy decisions. Barriers for future broader implementation include the need for reduced time from biopsy to report and availability of therapies.

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Non-Genetic Determinants of Clonotypic T Cell Expansion Following Allogeneic Stem Cell Transplant

Blood ◽

10.1182/blood-2021-146026 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 646-646

Author(s):

Albert Yeh ◽

Motoko Koyama ◽

Simone A Minnie ◽

Julie Boiko ◽

Kathleen S Ensbey ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Copy Number ◽

Research Funding ◽

Molecular Transport ◽

Cell Transplant ◽

Antigen Specificity ◽

Donor Pool ◽

Post Transplant ◽

Low Copy Number

Abstract Background: The immunologic basis of acute GVHD fundamentally involves alloreactive donor T cells that recognize foreign major histocompatibility complex (MHC)-peptide structures derived from both major and minor antigen mismatches with the host. Within this paradigm, the relationship between the donor and recipient genetics represents a closed system that dictates the potential ability of any given T cell receptor (TCR) to expand, raising the question of whether there are predictable aspects of TCR reconstitution at a clonal level. Take a hypothetical example - if genetically identical twins were to receive allogeneic grafts from the same donor and both recipients develop GVHD, would one expect similar TCRs to be clonally expanded? It has been challenging to rigorously explore this phenomenon, however, because of the vast combinatorial diversity of αβ TCRs, the high prevalence of low copy number TCRs, and sampling constraints - all of which render tracking and comparing TCR expansion between the donor and host difficult. Methods: We address these challenges in order to better understand the predictability TCR clonal dynamics through an analysis platform utilizing 1) a series of matched and mismatched murine transplant experiments where genetically identical littermates receive T cells from the same polyclonal donor pool, thus creating multiple transplant replicates simulating the twin transplant system describe above (Fig 1), and 2) probabilistic modeling of individual TCR frequencies to account for partitioning stochasticity (variation in how low copy number TCRs are distributed from donor to recipient). We conduct high-throughput DNA-based TCR amplicon sequencing for both donor and post-transplant recipient samples to generate over 20 million TCRs and model the expansion rates of all identifiable TCRs in each transplant system using a Bayesian approach. Results: While overall V and J gene usage were similar amongst identical recipients (Fig 2), we find that a small fraction of TCR clonotypes appears to have widely disparate clone counts amongst identical recipients receiving the same donor T cell pool. For example, we saw 9,739, 129 and 0 copies of a particular TCR in 3 different recipients in our B6->B6D2F1 system (Fig 3). In order to distinguish whether TCR count discrepancies seen across identical recipients is simply a reflection of donor partitioning stochasticity or true differential expansion (Fig 4), we apply a Bayesian algorithm to identify differential expanders, which represent TCRs that are asymmetrically expanded between recipients of a genetically identical pair (Fig 5). These TCRs can be generated from both memory and naïve T cell compartments. The presence of these differentially expanded clones amongst identical recipients suggests that non-genetic dependent mechanisms may influence which TCRs expand post-transplant. We next show that broad gut decontamination of microbiota with peri-transplant vancomycin, gentamicin, cefoxitin and metronidazole dramatically reduced the fraction of differential expanders (p<0.0001). However, the change in inflammation from microbiome depletion did not appear to drive this difference, as 1) MyD88/TRIF double knockout recipients (deficient TLR signaling) did not show a reduction in differential expanders, and 2) altering conditioning intensity (900cGy to 1300 cGy TBI) also did not change the fraction of differential expanders. Rather, the difference is likely antigenically driven, as differential expanders are enriched in antigen specificity compared to other TCR sequences (p<0.0001) based on published algorithm that identify TCRs with similar amino acid sequence overlap. Conclusions: These results refine our current understanding of clonal T cell selection and expansion after allogeneic BMT and suggests that for a given transplant system, individual TCR selection is not solely dictated by genetic donor and recipient major and/or minor histocompatibility disparities. Rather, microbiota-derived molecules appear to behave as minor antigens to direct systemic clonal TCR selection. These data suggest a novel mechanism by which the microbiome may modulate transplant outcome, challenging current paradigms suggesting the microbiota primarily drive inflammation via their PAMP activities. Figure 1 Figure 1. Disclosures Hill: Applied Molecular Transport: Research Funding; Syndax Pharmaceuticals: Research Funding; Compass Therapeutics: Research Funding; NapaJun Pharma: Consultancy; Generon corporation: Consultancy; iTeos Therapeutics: Consultancy, Research Funding; Neoleukin Therapeutics: Consultancy; Roche: Research Funding.

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The Tumor Microenvironment of Nodular Lymphocyte Predominant Hodgkin Lymphoma Is a Unique Immunobiological Entity Distinct from Classical Hodgkin Lymphoma

Blood ◽

10.1182/blood-2018-99-115836 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 4123-4123

Author(s):

Jay Gunawardana ◽

Karolina Bednarska ◽

Soi C Law ◽

Justina Lee ◽

Muhammed Bilal Sabdia ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Board Of Directors ◽

Hodgkin Lymphoma ◽

Research Funding ◽

Host Immunity ◽

Immune Checkpoints ◽

Classical Hodgkin Lymphoma ◽

Advisory Committees ◽

Immune Effector

Abstract There is proven pre-clinical and clinical efficacy of mono or combinatorial immune strategies to boost host anti-lymphoma immunity, with classical Hodgkin Lymphoma (cHL) seen as the 'poster child'. Approaches include blockade of immune-checkpoints on exhausted tumor-specific T-cells (via mAb blockade of PD-1, TIM3, LAG3, TIGIT or their ligands), activation of T-cells via mAbs agonistic to CD137, and finally modulation of FOXP3, CTLA-4 and/or LAG3 regulatory T-cells (Tregs) or immunosuppressive tumor-associated macrophages (TAMs). In contrast, studies characterizing the circulating and intra-tumoral microenvironment (TME) of the distinct but rare CD20+ Hodgkin Lymphoma entity (5-8% of HL), Nodular Lymphocyte Predominant Hodgkin Lymphoma (NLPHL), are minimal. Furthermore, to our knowledge no functional profiling studies comparing the host immunity of NLPHL with cHL has been performed. We compared host immunity in 29 NLPHL patients, 30 cHL patients and 10 healthy individuals, with a focus on pertinent and clinically actionable immune parameters. Paraffin-embedded tissue and paired (pre- and post-therapy) peripheral blood mononuclear cells samples were interrogated by digital multiplex hybridization (Nanostring Cancer Immune Profiling Panel) and flow cytometry. Although cytotoxic T-cell gene counts (CD8a, CD8b) were similar, compared to cHL there were higher levels of the immune effector activation marker CD137 (gene counts 439 vs. 287; P<0.01). Consistent with this, CD4 and the Treg markers LAG3, FOXP3 and CTLA-4 were lower in NLPHL (2-4 fold lower, all P<0.05), with no difference in T-helper cell activation markers CD40L and CD30L seen between tumors. TAMs and dendritic cell markers MARCO, CD36, CD68, CD163, COLEC12 and CD11b were all lower in NLPHL than cHL (all P<0.05). In line with the known 'rossette' formed around LP cells by PD-1+ T-lymphocytes, we observed strikingly elevated PD-1 and the other T-cell checkpoints TIM3 and TIGIT in NLPHL (all 2-3 fold, P<0.001). However, in line with the known gene amplification of PD-L1 on HRS cells and its presence on TAMs, gene counts of this checkpoint ligand were 2-fold higher in cHL (P<0.001). Flow cytometry profiling of immune subsets in peripheral blood showed findings consistent with findings in the TME. Specifically, there was elevation of multiple exhaustion markers within CD4, CD8, and NK immune effector cells, with a striking proportion of highly anergic dual-LAG3/PD-1 positive CD8+ T-cells. Also there was elevation of immune-suppressive monocyte/macrophages in cHL relative to NLPHL. Relative to healthy lymph nodes, there was prominent up-regulation of a range of T-cell associated exhaustion markers in both NLPHL and cHL, indicating dysregulated priming of effector immune responses and host immune homeostasis. Comparison between NLPHL and cHL illustrated that NLPHL had a myriad of features that marked its intratumoral TME as a unique immunobiological entity typified by elevated immune checkpoint markers and T-cells with a highly anergic phenotype. Put together, these findings indicate that distinct immune evasion mechanisms are operative within the TME of NLPHL, including markedly higher levels of multiple immune-checkpoints relative to cHL. In contrast, Treg subsets and immune-suppressive monocyte/macrophages were relatively lower than that seen in cHL. T-cells frequently had dual immune-checkpoint expression. The findings from this study provides a compelling pre-clinical rationale for targeting PD-1 or combinatory checkpoint inhibition in NLPHL and sets the basis for future 'chemo-free' rituximab + checkpoint inhibitor clinical trials. Disclosures Tobin: Amgen: Other: Educational Travel; Celgene: Research Funding. Birch:Medadvance: Equity Ownership. Keane:Takeda: Other: Educational Meeting; BMS: Research Funding; Roche: Other: Education Support, Speakers Bureau; Celgene: Consultancy, Research Funding; Merck: Consultancy. Gandhi:BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Takeda: Honoraria; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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Genome-Wide Analysis of Non-Coding Alterations in Pan-Myeloid Cancers Using Whole Genome Sequencing

Blood ◽

10.1182/blood-2018-99-116250 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 103-103

Author(s):

Yasuhito Nannya ◽

Kenichi Yoshida ◽

Lanying Zhao ◽

June Takeda ◽

Hiroo Ueno ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Research Funding ◽

Large Cohort ◽

Whole Genome ◽

Structural Variations ◽

Driver Genes ◽

Complex Structural ◽

Short Indels ◽

Tandem Duplications

Abstract Background Intensive efforts of genome sequencing studies during the past decade identified >100 driver genes recurrently mutated in one or more subtypes of myeloid neoplasms, which collectively account for the pathogenesis of >90% of the cases. However, approximately 10% of the cases have no alterations in known drivers and their pathogenesis is still unclear. A possible explanation might be the presence of alterations in non-coding regions that are not detected by conventional exome/panel sequencing; mutations and complex structural variations (SVs) affecting these regions have been shown to deregulate expression of relevant genes in a variety of solid cancers. Unfortunately, however, no large studies have ever been performed, in which a large cohort of myeloid malignancies were analyzed using whole genome sequencing (WGS) in an attempt to identify a full spectrum of non-coding alterations, even though its efficacy have been demonstrated in many solid cancers. In this study, we performed WGS in a large cohort of pan-myeloid cancers, in which both coding and non-coding lesions were comprehensively analyzed. Patients and methods A total of 338 cases of myeloid malignancies, including 212 with MDS, 70 with AML, 17 with MDS/MPN, 23 with t-AML/MDS, and 16 with MPN were analyzed with WGS, of which 173 were also analyzed by transcriptome sequencing. Tumor samples were obtained from patients' bone marrow (N=269) or peripheral blood (N=69), while normal controls were derived from buccal smear (N=263) or peripheral T cells (N=75). Sequencing of target panel of 86 genes were performed for all samples. Sequencing data were processed using in-house pipelines, which were optimized for detection of complex structural variations (SVs) and abnormalities in non-coding sequences. Results WGS identified a median of 586,612 single nucleotide variants (SNVs) and 124,863 short indels per genome. NMF-based decomposition of the variants disclosed three major mutational signatures, which were characterized by age-related C>T transitions at CpG sites (Sig. A), C>T transitions at CpT sites (Sig. B), and T>C transitions at ApTpN context (Sig. C). Among these, Sig. C showed a prominent strand bias and corresponds to COSMIC signature 16, which has recently been implicated in alcohol drinking. Significant clustering of SNVs and short indels were interrogated across the genome divided into different window sizes (1Kbp, 10Kbp, 100Kbp) or confining the targets to coding exons and known regulatory regions, such as promoters, enhancers/super enhances, and DNase I hypersensitive sites. Recapitulating previous findings, SNVs in the coding exons were significantly enriched in known drivers, including TP53, TET2, ASXL1, DNMT3A, SF3B1, RUNX1, EZH2, and STAG2. We detected significant enrichment of SNVs in CpG islands, and promoters/enhancers. We also detected a total of 8,242 SVs with a median of 15 SVs/sample, which is more prevalent than expected from conventional karyotype analysis. Focal clusters of complex rearrangements compatible with chromothripsis were found in 8 cases, of which 7 carried biallelic TP53 alterations. NMF-based signature analysis of SVs revealed that large (>1Mb) deletions, inversions, and tandem duplications and translocations are clustered together and were strongly associated with TP53 mutations, while smaller deletions and tandem duplications, but not inversions, constitute another cluster. As expected, FLT3-ITD (N=15) and MLL-PTD (N=12) were among the most frequent SVs. Unexpectedly, in addition to known SVs associated with t(8;21) (RUNX1-RUNX1T1) (N=6) and t(3;21) (RUNX1-MECOM) (n=1) as well as non-synonymous SNVs within the coding exons (N=30), we detected frequent non-coding alterations affecting RUNX1, including SVs (N=15) and SNVs around splicing acceptor sites (N=5), suggesting that RUNX1 was affected by multiple mechanism, where as many as 38% of RUNX1 lesions were explained by non-coding alterations. Other recurrent targets of non-coding lesions included ASXL1, NF1, and ETV6. Conclusions WGS was successfully used to reveal a comprehensive registry of genetic alterations in pan-myeloid cancers. Non-coding alterations affecting known driver genes were more common than expected, suggesting the importance of detecting non-coding abnormalities in diagnostic sequencing. Disclosures Nakagawa: Sumitomo Dainippon Pharma Co., Ltd.: Research Funding. Usuki:Mochida Pharmaceutical: Speakers Bureau; Astellas Pharma Inc.: Research Funding; Sanofi K.K.: Research Funding; GlaxoSmithKline K.K.: Research Funding; Otsuka Pharmaceutical Co., Ltd.: Research Funding; Kyowa Hakko Kirin Co., Ltd.: Research Funding; Daiichi Sankyo: Research Funding; Celgene Corporation: Research Funding, Speakers Bureau; SymBio Pharmaceuticals Limited.: Research Funding; Shire Japan: Research Funding; Janssen Pharmaceutical K.K: Research Funding; Boehringer-Ingelheim Japan: Research Funding; Sumitomo Dainippon Pharma: Research Funding, Speakers Bureau; Pfizer Japan: Research Funding, Speakers Bureau; Novartis: Speakers Bureau; Nippon Shinyaku: Speakers Bureau; Chugai Pharmaceutical: Speakers Bureau; Takeda Pharmaceutical: Speakers Bureau; Ono Pharmaceutical: Speakers Bureau; MSD K.K.: Speakers Bureau. Chiba:Bristol Myers Squibb, Astellas Pharma, Kyowa Hakko Kirin: Research Funding. Miyawaki:Otsuka Pharmaceutical Co., Ltd.: Consultancy; Novartis Pharma KK: Consultancy; Astellas Pharma Inc.: Consultancy.

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Diverging Clonal Evolution during Sequential Therapy with Chemoimmunotherapy Followed By BTK Inhibitors

Blood ◽

10.1182/blood-2019-122142 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 850-850

Author(s):

Inhye E Ahn ◽

Yun-Ching Chen ◽

Chingiz Underbayev ◽

Erika M Gaglione ◽

Clare Sun ◽

...

Keyword(s):

Copy Number ◽

Research Funding ◽

Clonal Selection ◽

Clonal Evolution ◽

Growth Potential ◽

Driver Genes ◽

Parent Clone ◽

Richter’S Transformation ◽

Richter's Transformation ◽

Clonal Composition

Background: Treatment confers an evolutionary bottleneck for cancer. Little data exists on the genomic evolution of CLL under different treatment pressure and disease compartments. Methods: We identified 13 patients uniformly treated with chemoimmunotherapy (CIT) as first-line therapy and a BTK inhibitor (BTKi) as second-line. We collected peripheral blood (PB) samples before CIT, at relapse after CIT (which was also before starting a BTKi), and 6 months on a BTKi. Five patients on a long-term BTKi were additionally tested at two years on BTKi. Two patients had samples from bone marrow (BM) and/or lymph node (LN) compartments at 6 months on a BTKi. In total, 48 tumor samples paired with saliva serving as a germline control were tested with whole exome sequencing (WES). We used high confidence variants identified by at least two variant callers for somatic single nucleotide polymorphisms and insertion-deletion variants, and TitanCNA for copy number variations. Clonal composition was reconstructed using PhyloWGS. Results: The median age of the cohort was 60 years at the start of CIT. The median duration of follow-up was 7 years. Median time between the start of CIT and a BTKi was 43 months (range 10-135). To date, eleven patients are alive and remain in response to a BTKi. Two patients died, one due to hepatitis B virus reactivation, and one with Richter's transformation. The median tumor mutational burden (TMB), defined as the number of nonsynonymous coding mutations per Mb, was 1.9 before CIT, 2.1 after CIT, and 1.9 after 6 months on a BTKi, consistent with previous reports in CLL. TMB was loosely associated with time since CIT exposure (R2=0.12, P=0.014). All patients had at least one mutation in known driver genes, most commonly NOTCH1 and SF3B1 (31% each; Figure A). Most of these mutations were subclonal and remained subclonal throughout the treatment course. The number of known driver gene mutations per patient remained relatively stable during the treatment course (median: 2 before CIT, 3 after CIT, 3 at 6 months on a BTKi; all P>0.05). We recomposed clonal structures and estimated the cancer cell fraction (CCF) of each clone. Phylogenetic analysis showed branching evolution in most patients with a median of three child clones originating from a parent clone. In these patients, multiple clonal branches and its descendants were simultaneously being selected during therapy. In addition, we observed linear evolution in three (23%) patients, characterized by a repetitive selection of one parent clone and its leading progeny per generation. Treatment with CIT and a BTKi selected for distinct sets of clones. BTKi therapy effectively downsized clones which grew to dominance after CIT, except in one patient whose subclone with concurrent NRAS and TP53 mutations remained dominant throughout CIT and BTKi therapy. The median CCF decrease of CIT-selected clones was 18% during BTKi therapy (range 1-52). In all patients with SF3B1 mutation, clonal fractions of SF3B1 mutated clones were higher at relapse after CIT and lower during BTKi therapy. There was no consistent pattern of clonal selection for NOTCH1 mutation. We compared patterns of clonal evolution in different compartments in two patients who had PB, LN and/or BM samples available. Each compartment had a distinct clonal composition. Notably, one patient who progressed with Richter's transformation after 6 months on a BTKi showed three main clones shared among all compartments and a set of compartment-restricted clones (Figure B). The LN was preferentially enriched with clones carrying HIST1H1E mutation and complex copy number changes, reflecting transformed clones. The NOTCH1 mutated clone and its progeny, reflecting CLL clones, were dominant in PB and BM, but not in LN. Conclusion: The number and clonality of somatic mutations affecting known driver genes, remained relatively stable over the course of treatment with CIT followed by a BTKi at relapse. Distinct sets of clones evolved under each line of therapy. Clonal composition is shaped by the growth potential of individual clones, the selective pressure of the therapy used, and the tumor microenvironment. Figure Disclosures Wiestner: Pharmayclics: Research Funding; Acerta: Research Funding; Nurix: Research Funding; Merck: Research Funding.

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Identification of LAG3+ T Cell Populations in the Tumor Microenvironment of Classical Hodgkin Lymphoma and B-Cell Non-Hodgkin Lymphoma

Blood ◽

10.1182/blood-2020-141462 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 19-19

Author(s):

Katsuyoshi Takata ◽

Tomohiro Aoki ◽

Lauren C. Chong ◽

Katy Milne ◽

Tomoko Miyata-Takata ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

B Cell ◽

Hodgkin Lymphoma ◽

Research Funding ◽

Cell Lymphoma ◽

Expression Patterns ◽

B Cell Lymphoma ◽

Cell Populations ◽

Classical Hodgkin Lymphoma

Background: LAG3 is one of the immune check point receptors that are expressed on activated cytotoxic T-cells and regulatory T cells. Physiologically, T-cell proliferation and memory T-cell differentiation is negatively regulated by LAG3-MHC interaction. In cancer tissues, T-cells that are chronically exposed to tumor antigens might upregulate LAG3 and receive inhibitory stimuli to enter an exhaustion state limiting anti-tumor immune responses. Currently, clinical trials using double blockade of LAG3/PD1 are active in several solid tumours, but there are only a small number of clinical trials using LAG3 monoclonal antibodies in lymphoma. Recently, we published a characteristic LAG3+ T-cell population as a mediator of immune suppression in classical Hodgkin lymphoma (Aoki & Chong et al. Cancer Discovery 2020). However, the abundance and variability of LAG3 positive T-cell populations across a spectrum of B-cell lymphoma has not been well studied and it remains an open question if LAG3 expression is associated with treatment outcome under standard-of-care conditions. Methods: We performed a LAG3 immunohistochemical (IHC) screen in a large cohort of B-cell Non-Hodgkin lymphoma (diffuse large B-cell lymphoma (DLBCL); N=341, follicular lymphoma (FL); N=198 (grade 1-3A), transformed FL to aggressive lymphoma (tFL); N=120, mantle cell lymphoma (MCL); N=179, primary mediastinal large B-cell lymphoma (PMBCL); N=61) and classical Hodgkin lymphoma (HL; N=459) to assess LAG3 expression in the tumor microenvironment (TME). Moreover, we characterized LAG3+ T-cell populations using multi-color immmunohistochemistry (IHC) (LAG3, PD1, CD4, CD8, FOXP3, CD20) in various lymphoma subtypes. Clinical parameters including treatment outcome were correlated with the abundance of LAG3+ T-cell populations in the TME. Results: On average, HL (7%) and PMBCL (6%) showed higher LAG3+ cellular frequency than the other B-cell lymphoma subtypes studied (DLBCL and FL: 2%, MCL: 0.8%). Comparing the frequency of LAG3+ cells according to MHC class I/II status, DLBCL showed a significant correlation with MHC class I status, and LAG3 expression correlated with MHC class II status in HL. Next, we performed multi-color IHC to describe subtype-specific expression patterns of LAG3 in T cell subsets. LAG3+PD1- T-cells were predominantly found in HL and PMBCL with only rare LAG3+PD1+ cells in HL. The majority of LAG3+ T-cells co-expressed CD4 in HL, in contrast to CD8 in PMBCL. DLBCL showed a mixed population pattern with a 1:1 ratio of LAG3+PD1- and LAG3+PD1+ T-cells. In FL, the majority of LAG3+ T-cells were CD4+PD1+, suggesting a more exhausted TME phenotype in FL than in other lymphoma subtypes. Cellular distance analysis showed that LAG3+CD4+ T-cells were in close vicinity to CD20+ lymphoma cells in FL, while in DLBCL and PMBCL, the nearest neighbors of malignant cells were LAG3+CD8+. Triple-positive LAG3+PD1+CD8+ T-cells significantly correlated with high infiltrating M2 macrophage (Pearson's correlation test, P < 0.001) content and the ABC cell-of-origin subtype (Pearson's correlation test, P = 0.002) in DLBCL. The abundance of LAG3+CD8+PD1- cells correlated with a high FLIPI score (Pearson's correlation test, P = 0.033), disease specific survival (HR = 2.8, 95% CI = 1.3-5.9, P = 0.006), time to progression (HR = 2.8, 95% CI = 1.6-5.0, P = 0.001) and transformation (HR = 4.0, 95%CI = 1.7-9.6, P = 0.002) in FL treated with R-CVP (N = 135). Assessing LAG3 expression by single color IHC in FL (cut-off at 5%), patients with LAG3-positive samples showed significantly higher FL transformation rates (P = 0.023) and tFL samples showed higher abundance of LAG3+ cells than the corresponding primary pretreatment FL samples (primary FL: 1.5±1.7% vs. tFL: 4.2±3.8%, t-test, P = 0.01). The increased transformation risk was validated in an independent FL cohort treated with R-CHOP/CVP (N=97, HR = 6.2, 95% CI = 2.8-13.9, P < 0.001). Conclusion: The highest abundance of LAG3+ T-cells in the TME was found in HL and its related entity PMBCL. The differential outcome correlations and co-expression patterns in LAG3+ T cells across B-cell lymphoma subtypes indicate heterogeneity in TME composition and related pathogenic mechanisms. Our results suggest that LAG3 expression patterns will be important in the interpretation of ongoing studies and highlight populations that may benefit from LAG3 checkpoint inhibition. Disclosures Sehn: AstraZeneca: Consultancy, Honoraria; Genentech, Inc.: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Chugai: Consultancy, Honoraria; TG therapeutics: Consultancy, Honoraria; Verastem Oncology: Consultancy, Honoraria; Teva: Consultancy, Honoraria, Research Funding; Servier: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Apobiologix: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Acerta: Consultancy, Honoraria. Savage:Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy; BeiGene: Other: Steering Committee; Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria. Scott:Celgene: Consultancy; Abbvie: Consultancy; AstraZeneca: Consultancy; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; Janssen: Consultancy, Research Funding. Steidl:Bayer: Consultancy; Juno Therapeutics: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding; AbbVie: Consultancy; Curis Inc: Consultancy.

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Whole-Genome Sequencing Reveals Evidence of Two Biologically and Clinically Distinct Entities: Progressive Versus Stable Myeloma Precursor Disease

Blood ◽

10.1182/blood-2020-136403 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 47-48

Author(s):

Bénedith Oben ◽

Guy Froyen ◽

Kylee H Maclachlan ◽

Binbin Zheng-Lin ◽

Venkata Yellapantula ◽

...

Keyword(s):

Multiple Myeloma ◽

Clinical Trials ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Research Funding ◽

Data Monitoring ◽

Whole Genome ◽

Independent Data ◽

Genetics Research ◽

Complex Events

Introduction Multiple myeloma (MM) is consistently preceded by an asymptomatic expansion of clonal plasma cells, clinically recognized as monoclonal gammopathy of undetermined significance (MGUS) or smoldering multiple myeloma (SMM). Here, we present the first comprehensive whole-genome sequencing (WGS) analysis of patients with MGUS and SMM. Methods To characterize the genomic landscape of myeloma precursor disease (i.e. SMM and MGUS) we performed WGS of CD138-positive bone marrow mononuclear samples from 32 patients with MGUS (N=18) and SMM (N=14), respectively. For cases with low cellularity resulting in low amounts of extracted DNA (N=15), we used the low-input enzymatic fragmentation-based library preparation method (Lee-Six et al, Nature 2019). Myeloma precursor disease samples were compared with 80 WGS of patients with MM. All WGSs (N=112) were investigated using computational tools available at the Wellcome Sanger Institute. Results After a median follow up of 29 months (range: 2-177), 17 (53%) patients with myeloma precursor disease progressed to MM (13 SMM and 4 MGUS). To interrogate the genomic differences between progressive versus stable myeloma precursor disease we first characterized the single base substitution (SBS) signature landscape. Across the entire cohort of plasma cell disorders, all main MM mutational signatures were identified: aging (SBS1 and SBS5), AID (SBS9), SBS8, SBS18, and APOBEC (SBS2 and SBS13). Interestingly, only 2/15 (13%) stable myeloma precursor disease cases showed evidence of APOBEC activity, while 14/17 (82%) and 68/80 (85%) patients with progressive myeloma precursor disease (p=0.0058) and MM (p=0.004), respectively, had APOBEC mutational activity. The two stable cases with detectable APOBEC were characterized by a high APOBEC3A:3B ratio, a feature which defines a group of MAF-translocated MM patients whose pathogenesis is characterized by intense and early APOBEC activity (Rustad et al Nat Comm 2020) and is distinct from the canonical ~1:1 APOBEC3A:3B mutational activity observed in most cases. When exploring the cytogenetic landscape, no differences were found between progressive myeloma precursor disease and MM cases. Compared to progressors and to MM, patients with stable myeloma precursor disease were characterized by a significantly lower prevalence of known recurrent MM aneuploidies (i.e. gain1q, del6q, del8p, gain 8q24, del16q) (p<0.001). This observation was validated using SNP array copy number data from 78 and 161 stable myeloma precursor disease and MM patients, respectively. To further characterize differences between progressive versus stable myeloma precursor disease, we leveraged the comprehensive WGS resolution to explore the distribution and prevalence of structural variants (SV). Interestingly, stable cases were characterized by low prevalence of SV, SV hotspots, and complex events, in particular chromothripsis and templated insertions (both p<0.01). In contrast, progressors showed a genome wide distribution and high prevalence of SV and complex events similar to the one observed in MM. To rule out that the absence of key WGS-MM defining events among stable cases would reflect a sample collection time bias, we leveraged our recently developed molecular-clock approach (Rustad et al. Nat Comm 2020). Notably, this approach is based on pre- and post-chromosomal gain SBS5 and SBS1 mutational burden, designed to estimate the time of cancer initiation. Stable myeloma precursor disease showed a significantly different temporal pattern, where multi-gain events were acquired later in life compared to progressive myeloma precursor disease and MM cases. Conclusions In summary, we were able to comprehensively interrogate for the first time the whole genome landscape of myeloma precursor disease. We provide novel evidence of two biologically and clinically distinct entities: (1) progressive myeloma precursor disease, which represents a clonal entity where most of the genomic drivers have been already acquired, conferring an extremely high risk of progression to MM; and (2) stable myeloma precursor disease, which does not harbor most of the key genomic MM hallmarks and follows an indolent clinical outcome. Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Dogan:Roche: Consultancy, Research Funding; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; EUSA Pharma: Consultancy; National Cancer Institute: Research Funding; AbbVie: Consultancy. Landgren:Pfizer: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Merck: Other; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Merck: Other; Seattle Genetics: Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria. Bolli:Celgene: Honoraria; Janssen: Honoraria.

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