Landscape Of Somatic Mutations Research Articles

Abstract 3934: Smoking's influence on divergent paths to lung cancer

Abstract Lung cancer is the leading cause of cancer mortality in the world. Many studies have highlighted how mutagens from cigarette smoke promote specific mutations such as G12 mutations in KRAS, contributing to clonal evolution that leads to lung cancer. Despite the indisputable correlation between smoking and lung cancer, people who have never smoked account for the 7th leading cause of cancer mortality. Given the differences in predominant exposures, we hypothesize that the clonal evolution between people with and without smoking histories are markedly different. Therefore, we analyzed lung samples from people with different cigarette smoking exposure to elucidate smoking’s effect on the somatic mutational landscape in the progression to lung cancer. To observe somatic mutations subjected to different exposures of smoking, we used a rare-mutation detection technique called DuplexSeq to analyze somatic variants from 200+ lung samples. We acquired DNA from cells from bronchoscopy brushings and lung punches of histologically normal tissue from people with and without lung cancer and who currently, formerly, or never smoked cigarettes. Through collaborations, we characterized mutations using bioinformatic pipelines, various databases, and statistical methods. In samples from people who smoke, we observe an enhancement of mutations with higher variant allele frequencies and pervasive positive selection acting on mutations in many cancer-associated genes. From our bronchoscopy brushings, NOTCH1 mutations show the most definitive increase of protein-altering variants, while NF1 and KRAS show a predominance of driver mutation in lung punches. Unexpectedly, people without lung cancer who never smoked had a notable presence of pathogenic mutations in PTEN and PIK3CA. Furthermore, multiple punches from the same individual highlight how expansions of mutations have stronger similarity within an individual than across individuals regardless of smoking status. This association points to the importance of the inherent lung microenvironment in influencing the mutational landscapes in lung cancer progression. Altogether, these results show how smoking increases the oncogenicity of observable mutations in the lung but does not preclude the presence of oncogenic clones, emphasizing the value of precision diagnoses and prevention. Ongoing research will be centered around functional assays to determine the impact of observed mutations on tissue function and longitudinal studies using mutational landscapes as a proxy for lung cancer risk. Citation Format: Edward James Evans, Fabio Marongiu, Emilia Lim, Oriol Pich, Yonghua Zhuang, Dexiang Gao, William Hill, Cian Murphy, Ryan Turner, Moumita Ghosh, York Miller, Mariam Jamal-Hanjani, Charles Swanton, James DeGregori. Smoking's influence on divergent paths to lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3934.

Identification of Somatic Mutations in Thai Patients with Renal Cell Carcinoma Using Whole Exome Sequencing Analysis

Background: Whole exome sequencing (WES) is increasingly used to identify genetic alterations of renal cell carcinoma (RCC). However, in Thailand, there is no report that clarifies the common somatic mutations and tumor mutational burden (TMB) in RCC. Therefore, the understanding of the tumor somatic mutational landscape could improve RCC management in the authors’ country. Objective: To perform a descriptive study to identify common somatic mutated genes and TMB in clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and clear cell papillary renal cell carcinoma (ccpRCC). Materials and Methods: The authors enrolled 13 patients, which consisted of 10 cases of ccRCC, two cases of pRCC, and one case of ccpRCC. DNA was isolated from peripheral blood mononuclear cells and tumor tissues for WES to identify tumor somatic mutations. The results were analyzed for correlations with tumor-aggressive features and compared with the public database. Results: The authors identified common somatic mutations in VHL, SVIL, MUC16, CSMD3, CSMD1, and BAP1 in the study patients. In ccRCC cases, VHL mutation was detected in 90% of the cases corresponding to its high frequency in the TCGA’s ccRCC database. In pRCC cases, KDM6A was the only common mutated gene that overlapped with the top-ten most common genes in the TCGA’s pRCC database. Somatic mutations in BAP1, SETD2, and PBRM1 were significantly associated with tumor-aggressive features in the present study. The mean TMB of ccRCC, pRCC, and ccpRCC were 2.017, 2.143, and 6.61 mutations per megabase, respectively. Conclusion: Identification of common somatic mutations and TMB in all subtypes of RCC from the present study showed the diversity of genetic alterations between Thai patients and the public database. This leads to specific therapeutic approaches for Thai patients. Moreover, the authors also detected similar associations between significant mutations reported in prior studies with the tumor-aggressive features in the present cases. Keywords: Renal cell carcinoma; Somatic mutation; Tumor mutational burden; Whole exome sequencing

Somatic Copy Number Alterations and Mutation Landscape in Before and Post-treatment Malignant Rhabdoid Tumor

Somatic Copy Number Alterations and Mutation Landscape in Before and Post-treatment Malignant Rhabdoid Tumor

Undifferentiated Carcinoma with Osteoclast-like Giant Cells of the Pancreas: Molecular Genetic Analysis of 13 Cases.

Undifferentiated carcinoma with osteoclast-like giant cells (UCOGC) of the pancreas is a rare malignancy regarded as a subvariant of pancreatic ductal carcinoma (PDAC) characterized by variable prognosis. UCOGC shows a strikingly similar spectrum of oncogenic DNA mutations to PDAC. In the current work, we analyzed the landscape of somatic mutations in a set of 13 UCOGC cases via next-generation sequencing (NGS). We detected a spectrum of pathogenic or likely pathogenic mutations similar to those observed in PDAC following previously published results (10 KRAS, 9 TP53, 4 CDKN2A, and 1 SMAD4, CIC, GNAS, APC, ATM, NF1, FBXW7, ATR, and FGFR3). Our results support the theory that UCOGC is a variant of PDAC, despite its unique morphology; however, a UCOGC-specific genomic signature as well as predictive markers remain mainly unknown. Programmed death ligand 1 (PD-L1) status remains an important predictive marker based on previous studies.

Genomic drivers in craniopharyngiomas: Analysis of the AACR project GENIE database.

Craniopharyngiomas are rare tumors originating in the sellar region, with limited information on their somatic mutational landscape. In this study, we utilized a publicly available genomic database to profile the somatic mutational landscape of craniopharyngioma patients and interrogate differences based on histologic subtype. We utilized the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE)® database accessed from cBioPortal (v13.1-public) to query all patients with craniopharyngiomas. Of the 336 patients with sellar tumors, 51 (15.2%) had craniopharyngiomas. Of these 51 patients, 42 (82.4%) were adamantinomatous subtype and 9 (17.6%) were papillary subtype. In this cohort, 32 (62.7%) patients were pediatric, while 19 (37.3%) were adult. The top mutations in the cohort were: CTNNB1 (n = 37; 73%), BRAF (n = 7; 14%), ARID1B (n = 5; 10%), KMT2D (n = 4; 8%), FANCA (n = 4; 8%), ATM (n = 4; 8%), and TERT (n = 3; 8%). Of the 37 patients with CTNNB1 mutations, 8 (21.6%) had S33X, 9 (24.3%) had S37X, 7 (18.9%) had T41X, and 5 (13.5%) had D32X. In this cohort, CTNNB1 mutations tended to co-occur with ATM (n = 4; 10.8%), KMT2C (n = 4; 10.8%), TERT (n = 3; 8.1%), BLM (n = 3; 8.1%), and ERBB2/3 (n = 3; 8.1%), suggesting CTNNB1 mutations tended to co-occur with mutations in genes important in cell growth and survival, chromatin accessibility, and DNA damage response pathways. CTNNB1 mutations account for a large proportion of somatic mutations in craniopharyngiomas. Identification of specific point mutations and secondary drivers may advance development of novel craniopharyngioma preclinical models for targeted therapy testing.

Clinical sequencing defines the somatic and germline mutation landscapes of Chinese HER2-Low Breast Cancer

More than half of the breast cancer initially labeled as human epidermal growth factor receptor 2 (HER2)-negative actually exhibited low HER2 levels (IHC 1+ or IHC 2+/FISH-) and were classified as HER2-low breast cancer. Previous research emphasized the significant biological heterogeneity in HER2-low breast cancer, highlighting the importance of accurately characterizing HER2-low tumors to promote the precise management of antibody‒drug conjugates. In this study, we established a large-scale targeted sequencing cohort (N = 1907) representing Chinese HER2-low breast cancer patients with detailed clinical annotation. Our research findings revealed that HER2-low breast cancer demonstrated distinct clinical pathological characteristics and mutation landscapes compared to HER2-zero group. When compared to HER2-zero tumors, HER2-low tumors exhibited a higher proportion of Luminal B subtypes and better disease-free survival. In hormone receptor (HR)-positive breast cancer, HER2-low group showed a higher frequency of GATA3 somatic mutations, BRCA2 germline mutations, and mutations in the DNA damage repair pathway. In contrast, in HR-negative breast cancer, the HER2-low group displayed a higher frequency of PIK3CA mutations and PI3K pathway alterations. These findings offered valuable insights for the precise targeted treatment of HER2-low breast cancer in different HR statuses.

Genomic Landscape of Lynch Syndrome Colorectal Neoplasia Identifies Shared Mutated Neoantigens for Immunoprevention

Background & AimsLynch syndrome (LS) carriers develop mismatch repair deficient (MMRd) neoplasia with high neoantigen (neoAg) rates. No detailed information on targetable neoAgs from LS pre-cancers exists, which is crucial for vaccine development and immune-interception strategies. We report a focused somatic mutation and frameshift-neoAg landscape of microsatellite loci from colorectal polyps without malignant potential (PWOMPs), pre-cancers and early-stage cancers in LS carriers. MethodsWe generated paired whole-exome and transcriptomic sequencing data from 8 colorectal PWOMPs, 41 pre-cancers, 8 advanced pre-cancers, and 12 early-stage cancers of 43 LS carriers. A computational pipeline was developed to predict, rank, and prioritize the top 100 detected mutated neoAgs that were validated in vitro using ELISpot and tetramer assays. ResultsMutation calling revealed >10 mut/Mb in 83% of cancers, 63% of advanced pre-cancers, and 20% of pre-cancers. Cancers displayed an average of 616 MHC-I neoAgs/sample, 294 in advanced pre-cancers, and 107 in pre-cancers. No neoAgs were detected in PWOMPs. A total of 65% of our top 100 predicted neoAgs were immunogenic in vitro, and were present in 92% of cancers, 50% of advanced pre-cancers, and 29% of pre-cancers. We observed increased levels of naïve CD8+ and memory CD4+ T-cells in MMRd cancers and pre-cancers via transcriptomics analysis. ConclusionsShared frameshift-neoAgs are generated within unstable microsatellite loci at initial stages of LS carcinogenesis and can induce T-cell responses, generating opportunities for vaccine development, targeting LS pre-cancers and early-stage cancers.

Multiomics profiling reveals the benefits of gamma-delta (γδ) T lymphocytes for improving the tumor microenvironment, immunotherapy efficacy and prognosis in cervical cancer.

As an unconventional subpopulation of T lymphocytes, γδ T cells can recognize antigens independently of major histocompatibility complex restrictions. Recent studies have indicated that γδ T cells play contrasting roles in tumor microenvironments-promoting tumor progression in some cancers (eg, gallbladder and leukemia) while suppressing it in others (eg, lung and gastric). γδ T cells are mainly enriched in peripheral mucosal tissues. As the cervix is a mucosa-rich tissue, the role of γδ T cells in cervical cancer warrants further investigation. We employed a multiomics strategy that integrated abundant data from single-cell and bulk transcriptome sequencing, whole exome sequencing, genotyping array, immunohistochemistry, and MRI. Heterogeneity was observed in the level of γδ T-cell infiltration in cervical cancer tissues, mainly associated with the tumor somatic mutational landscape. Definitely, γδ T cells play a beneficial role in the prognosis of patients with cervical cancer. First, γδ T cells exert direct cytotoxic effects in the tumor microenvironment of cervical cancer through the dynamic evolution of cellular states at both poles. Second, higher levels of γδ T-cell infiltration also shape the microenvironment of immune activation with cancer-suppressive properties. We found that these intricate features can be observed by MRI-based radiomics models to non-invasively assess γδ T-cell proportions in tumor tissues in patients. Importantly, patients with high infiltration levels of γδ T cells may be more amenable to immunotherapies including immune checkpoint inhibitors and autologous tumor-infiltrating lymphocyte therapies, than to chemoradiotherapy. γδ T cells play a beneficial role in antitumor immunity in cervical cancer. The abundance of γδ T cells in cervical cancerous tissue is associated with higher response rates to immunotherapy.

Prognosis and therapy in thyroid cancer by gene signatures related to natural killer cells.

Natural killer (NK) cells are crucial to cancer development and prognosis. However, the role of NK cell-related genes in immunotherapy and the tumor immune microenvironment (TIME) is not well understood. This study aimed to develop reliable risk signatures associated with NK cell-related genes for predicting thyroid cancer (THCA). The single-cell RNA sequencing (scRNA-seq) data from seven THCA samples (GSE184362) and bulk-RNA-seq data of 502 THCA patients (TCGA-THCA) were included. The scRNA-seq data was analyzed using the "Seurat" R package to identify differentially expressed genes in NK cells. The clustering analysis was carried out using the R package "ConsensusClusterPlus". The gene set variation analysis (GSVA) algorithm was applied to assess the variations in biological pathways among subtypes. The ESTIMATE algorithm was utilized to calculate the scores for stromal, immune and estimate variables. In addition, we used the single sample Gene Set Enrichment Analysis and CIBERSORT algorithms to assess the degree to which immune cells and pathways related to immunity were enriched based on the meta-cohort. In the TCGA-THCA cohort, the "glmnet" R package was used for the gene selection, and LASSO Cox analysis was used to construct prognostic features. The "maftools" R package was used to examine the somatic mutation landscape of THCA in both low- and high-risk groups. One-hundred and eighty-five NK cell marker genes were screened, and nine genes were associated with the THCA prognosis. KLF2, OSTF1 and TAPBP were finally identified and constructed a risk signature with significant prognostic value. KLF2 and OSTF1 were protective genes, and TAPBP was a risk gene. Patients at high risk had a considerably lower overall survival compared with those at low risk. Mutations in the TCGA-THCA cohort were predominantly C > T. Increased tumor mutation burden (TMB) levels were linked to overall survival. The low-risk H-TMB+ group had a better prognosis, while the high-risk L-TMB+ group had the worst prognosis. Natural killer cell-related genes KLF2, OSTF1 and TAPBP were used to develop a novel prognostic risk signature, offering a new perspective on the prognosis and treatment of THCA.

Phased genomics reveals hidden somatic mutations and provides insight into fruit development in sweet orange.

Although revisiting the discoveries and implications of genetic variations using phased genomics is critical, such efforts are still lacking. Somatic mutations represent a crucial source of genetic diversity for breeding and are especially remarkable in heterozygous perennial and asexual crops. In this study, we focused on a diploid sweet orange (Citrus sinensis) and constructed a haplotype-resolved genome using high fidelity (HiFi) reads, which revealed 10.6% new sequences. Based on the phased genome, we elucidate significant genetic admixtures and haplotype differences. We developed a somatic detection strategy that reveals hidden somatic mutations overlooked in a single reference genome. We generated a phased somatic variation map by combining high-depth whole-genome sequencing (WGS) data from 87 sweet orange somatic varieties. Notably, we found twice as many somatic mutations relative to a single reference genome. Using these hidden somatic mutations, we separated sweet oranges into seven major clades and provide insight into unprecedented genetic mosaicism and strong positive selection. Furthermore, these phased genomics data indicate that genomic heterozygous variations contribute to allele-specific expression during fruit development. By integrating allelic expression differences and somatic mutations, we identified a somatic mutation that induces increases in fruit size. Applications of phased genomics will lead to powerful approaches for discovering genetic variations and uncovering their effects in highly heterozygous plants. Our data provide insight into the hidden somatic mutation landscape in the sweet orange genome, which will facilitate citrus breeding.

An Integrated Multi-Omics Analysis Identifying Immune Subtypes of Pancreatic Cancer.

Limited studies have explored novel pancreatic cancer (PC) subtypes or prognostic biomarkers based on the altered activity of relevant signaling pathway gene sets. Here, we employed non-negative matrix factorization (NMF) to identify three immune subtypes of PC based on C7 immunologic signature gene set activity in PC and normal samples. Cluster 1, the immune-inflamed subtype, showed a higher response rate to immune checkpoint blockade (ICB) and had the lowest tumor immune dysfunction and exclusion (TIDE) scores. Cluster 2, the immune-excluded subtype, exhibited strong associations with stromal activation, characterized by elevated expression levels of transforming growth factor (TGF)-β, cell adhesion, extracellular matrix remodeling, and epithelial-to-mesenchymal transition (EMT) related genes. Cluster 3, the immune-desert subtype, displayed limited immune activity. For prognostic prediction, we developed an immune-related prognostic risk model (IRPM) based on four immune-related prognostic genes in pancreatic cancer, RHOF, CEP250, TSC1, and KIF20B. The IRPM demonstrated excellent prognostic efficacy and successful validation in an external cohort. Notably, the key gene in the prognostic model, RHOF, exerted significant influence on the proliferation, migration, and invasion of pancreatic cancer cells through in vitro experiments. Furthermore, we conducted a comprehensive analysis of somatic mutational landscapes and immune landscapes in PC patients with different IRPM risk scores. Our findings accurately stratified patients based on their immune microenvironment and predicted immunotherapy responses, offering valuable insights for clinicians in developing more targeted clinical strategies.

Lymphopenia Confers Poorer Prognosis in Myelodysplastic Neoplasms (MDS) with Very Low and Low IPSS-M

Introduction: In addition to MDS cell-intrinsic genetic lesions, we and others found increasing evidence of dynamic alterations in both adaptive and innate immunity in the disease genesis and/or progression (Boy et al., Nat Commun 2023;14:588). Although a few reports have suggested a prognostic impact of absolute lymphocyte count (ALC) in both del(5q) and non-del(5q) MDS, lymphopenia is not captured by current prognostic scores. Moreover, the association between lymphopenia and somatic mutations has not been reported. In this work, we describe the prevalence of lymphopenia in MDS patients, the interaction with molecular features, and the impact of lymphopenia on patient outcomes. Methods: We retrospectively included all MDS patients followed in our center (Saint-Louis Hospital, Paris, France) between January 2015 and January 2022 with a molecular evaluation. According to the local laboratory threshold, lymphopenia was defined as ALC <1.5 G/L. Low (LR), intermediate (IR), and high-risk (HR) MDS were defined according to IPSS-M (LR: IPSS-M Low and Very Low, scores < -0.5; IR: IPSS-M Moderate Low and Moderate High, scores -0.5 to 0.5; and HR: IPSS-M High and Very High, scores >0.5 respectively).Survival analyses were estimated between MDS diagnosis until death from any cause (OS) or last follow-up, or until AML transformation (LFS) or last follow-up. Allogeneic stem cell transplantation was considered a censoring event. Results: Our cohort included 372 MDS patients (45% females, median age 72 IQR[65-79]) with 230 (62%), 70 (19%), and 72 (19%) patients with LR, IR and HR-MDS respectively. Compared to IPSS-R, molecular IPSS reclassified 175 patients (47%, with 97 (26%) upstaged and 78 (21%) downstaged). The median ALC in the whole cohort was 1.20 IQR[0.87-1.60] G/L. Lymphopenia was highly prevalent in all MDS subgroups (68%, 79% and 75% in LR, IR and HR-MDS respectively), and ALC inversely correlated with higher IPSS-M (Spearman rho=-0.13, p=0.01). Lymphopenia was associated with lower ANC (median 1.90 IQR[1.1-3.4] versus 2.70 IQR[1.6-4.2], p=0.01) and monocyte count (median 0.31 IQR[0.17-0.46] versus 0.42 IQR[0.25-0.78], p=0.04). Lymphopenia was not significantly associated with an excess of marrow blasts (78/267, 29% versus 21/105, 21% in patients with or without lymphopenia respectively, p=0.09). However, its prevalence was significantly lower in MDS with ringed sideroblasts (RS) (27/49 (55%) versus 240/267 (90%), p<0.01). Consistently, SF3B1 mutations were more prevalent in MDS patients without lymphopenia (29/107 (27%) versus 27/265 (10%), p<0.01), while the rest of the somatic mutational landscape did not differ between the 2 groups. The median follow-up of the entire cohort was 4.1 IQR[3.7-4.6] years. Among IPSS-M categories, lymphopenia adversely impacted OS and LFS in LR-MDS patients (median 8.8 years vs not reached for OS, p=0.02; median 12.3 years versus not reached, p=0.02 for LFS) but not in IR (p=0.34 and p=0.49 for OS and LFS respectively) or in HR-MDS (p=0.82 and p=0.83) patients ( Figure 1). In SF3B1 mutated LR-MDS patients (n=46), the adverse prognostic impact of lymphopenia at diagnosis was still significant for both OS (p=0.03) and LFS (p=0.03). Conclusion: In this single-center retrospective analysis, lymphopenia was highly prevalent at MDS diagnosis in all IPSS-M subgroups and ALC negatively correlated with disease severity. The presence of lymphopenia adversely impacted OS and LFS in the LR-MDS subgroup, potentially suggesting a significant role of host immunity in early disease phase and possibly adding a prognostic parameter to IPSS-M.

Expanded Assessments of Germline Genetic Variants Identify Additional Risk Loci for Langerhans Cell Histiocytosis

Introduction: Langerhans cell histiocytosis (LCH) is an inflammatory myeloid neoplasia characterized by lesions including pathogenic CD207+ dendritic cells among an inflammatory infiltrate. Sequencing studies have identified recurrent, mutually exclusive somatic activating mutations in MAPK pathway genes in ~85% of LCH lesions, including BRAF V600E in 50-65%. Despite advances to elucidate the somatic mutational landscape underlying LCH pathogenesis, germline risk factors remain largely unknown. We previously conducted the first genome-wide association study (GWAS) of LCH and identified and validated a risk variant in SMAD6 (rs12438941, G->A) associated with a 3-fold increase in LCH risk. Our current objective is to further elucidate the role of SMAD6 variation on LCH risk and identify novel LCH susceptibility loci. Methods: Independent of our discovery GWAS, we sequenced SMAD6 in 466 additional LCH cases and compared them to aggregate-level SMAD6 data from ~15,694 non-cancer controls in gnomAD v2.1.1. Targeted sequencing of SMAD6 was conducted among these cases with ~200x coverage at the Avera Institute of Human Genetics. In parallel, we conducted a GWAS of 319 of those cases and leveraged controlled-access data from 9,921 controls in the ADDHealth study. Genotyping of the 319 cases was also performed at the Avera Institute for Human Genetics on the Illumina Infinium Global Screening Array. We evaluated the role of common variants (minor allele frequency >5%) on LCH risk and applied principal components analysis to account for potential population stratification. A genome-wide threshold of significance was applied at P-value<5.0x10 -8, and threshold of suggestive significance at P-value<1.0x10 -6. Analyses were conducted in Plink and STATAv17.1. Results: The case-control study of targeted SMAD6 identified novel SMAD6 variants associated with LCH risk ( P-values ranging from 4.54x10 -28 to 2.9x10 -4), including our original GWAS locus. All cases with two copies of the alternate alleles in the top 2 SNPs had BRAF V600E + lesions. In terms of the new GWAS, 31 novel loci were associated with LCH at P-value=5x10 -8, including 2 separate loci on Chr 9, with the top hit being intergenic between KCNV2 and VLDLR on Chr 9 and the second being intergenic between OLFM1 and LOC401557. Other identified top hits suggest putative biologic significance in terms of LCH pathogenesis. Conclusion: We identified additional support that inherited genetic variation impacts LCH risk. We are concurrently assessing the role of ancestry-specific loci on LCH risk and analyzing newly generated SMAD6 targeted sequence and GWAS data in an independent set of case-parent trios (n=185) to integrate with the findings reported here. Next steps include a meta-analysis of our discovery GWAS, the new GWAS reported here, and the recently genotyped new set of 185 case-parent trios. We also aim to determine the role of variants on clinical characteristics (e.g., ancestry, age at onset, BRAF V600E). Our work suggests an important and complex role of germline variants and ancestry in LCH pathogenesis.

Clonal selection of hematopoietic stem cells after gene therapy for sickle cell disease

Gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD); however, the occurrence of myeloid malignancies in GT clinical trials has prompted concern, with several postulated mechanisms. Here, we used whole-genome sequencing to track hematopoietic stem cells (HSCs) from six patients with SCD at pre- and post-GT time points to map the somatic mutation and clonal landscape of gene-modified and unmodified HSCs. Pre-GT, phylogenetic trees were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified among gene-modified or unmodified cells; however, an increased frequency of potential driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) was observed in both genetically modified and unmodified cells, suggesting positive selection of mutant clones during GT. This work sheds light on HSC clonal dynamics and the mutational landscape after GT in SCD, highlighting the enhanced fitness of some HSCs harboring pre-existing driver mutations. Future studies should define the long-term fate of mutant clones, including any contribution to expansions associated with myeloid neoplasms.

CNSC-40. HARNESSING EVOLUTIONARY CONSTRAINT IDENTIFIES POTENTIAL NON-CODING DRIVERS IN BRAIN TUMORS

Abstract The somatic mutational landscape of brain tumors has primarily focused on coding mutations for driver gene discovery. Non-coding mutations can drive cancer by targeting regulatory elements and be valuable biomarkers and therapeutic targets. However, their identification and functional impact have been elusive. Here, we apply evolutionary constraint (phyloP scores, a constraint metric from sequence alignment of 241 mammals) to whole genome sequencing data from three different brain tumors; pilocytic astrocytoma (PA), medulloblastoma (MB), and glioblastoma (GBM) to identify non-coding constraint mutations (NCCMs) with regulatory potential. NCCM rates (phyloP ≥ 1.2) differ significantly between these cancers, with low rates of NCCMs in PA reflecting its benign feature and high rates in MB and GBM, in support of their aggressivity and heterogeneity. Many of the NCCMs affect genes that are developmentally regulated and expressed in the brain, hence they could be potential drivers. In PA, a high NCCM frequency only affects the BRAF locus, while in MB, over 500 genes have high levels of NCCMs. Intriguingly, many of these genes are associated with different age of onset, such as NUAK1 in adult patients and HOXB1 in young patients, pointing to different molecular pathways in patient groups. NCCMs in the HOXB locus altered the expression of multiple genes of the cluster in MB cells. In GBM NCCMs with regulatory potential cluster as hotspots in PTEN, LRFN5 and MIR99AHG. We observe an enrichment of NCCMs in the regulatory regions of transcription factors including FOXA1, HOXA9 and EBF1. We conclude that since evolutionary constraint is agnostic to cell type or disease stage, it is a powerful predictor of function. Understanding changes in the non-coding cancer genomes will lead to more robust patient stratification of future treatment.

Somatic Mutational Landscape and Transcriptomic Profiles of Lymphoproliferative Disorders with Epstein-Barr Virus Infection in Activated PI3K Delta Syndrome

Introduction: Germline gain-of-function variants in PIK3CD and PIK3R1 cause activated PI3K delta syndrome (APDS) types 1 and 2, respectively. Lymphoproliferative disorder (LPD) and susceptibility to various infections, including Epstein-Barr virus (EBV), are well-known features of APDS. EBV-associated LPDs are frequently observed in patients with APDS, and oligoclonal LPD might transform malignant lymphoma (ML). However, somatic molecular alterations in LPD and ML are not fully elucidated. Here, we report clinical characteristics and somatic mutational landscape and transcriptomic profiles of LPDs with EBV infection in APDS. Methods: LPD and ML in APDS diagnosed during 2011-2022 were recruited all over Japan. Clinical characteristics and outcomes were analyzed. EBV infection status was analyzed by EBER ISH and RNA sequencing (RNA-seq). Whole-exome sequencing (WES), RNA-seq, and single cell RNA-seq (scRNA-seq) were also performed. Results: Five LPD and 5 ML (borderline ML: n = 2, diffuse large B-cell lymphoma (DLBCL): n = 2, classical Hodgkin lymphoma: n = 1) cases were enrolled. Seven cases were APDS1 and 3 cases were APDS2. The median follow-up period was 2.9 (0.5-8.4) years. LPD cases were treated with rituximab monotherapy (n = 1), prednisolone (n = 3), and hematopoietic cell transplantation (HCT) (n = 1). Rituximab-combined chemotherapy was performed in borderline ML and DLBCL (n = 4) or ABVD regimen in Hodgkin lymphoma cases (n = 1). Sirolimus was used in two ML cases. HCT was performed in 9 cases. Two-year overall survival and event-free survival (events: relapse and death) were 78.8% and 70.0% in total LPDs, respectively, and 60% and 40% in ML cases. EBV was negative in 3 cases and positive in 2 cases of LPD cases, and positive in 4 cases, and negative in 1 case of ML cases. The median of 18 (10-48) and 42 (18-189) somatic variants were detected by tumor/germline pair WES in LPD (n = 5) and ML (n = 7, 2 samples were from the same patient at different time points), respectively. The recurrent variants were identified in ZFHX3 (n = 4), TRPM2 (n = 3), BEND3, CDH23, UBR4, ARAF, TET2, FBXO10, MAP4K1, KMT2C, KMT2D and PIK3R1 (n = 2) in LPD and ML cases. In transcriptome analysis, genes associated with lymphocyte activation including B-cell receptor signaling were upregulated in LPD, cell-cell adhesion, cellular response to TNF, and IL-18 signaling pathway were upregulated in ML samples. We performed correlation analysis to elucidate a relationship between EBV infection status and host transcriptomic profiles using RNA-seq data. Interestingly, in addition to cell-cell adhesion and inflammatory machinery, the upregulated molecules of the p53 pathway and negative regulation of the immune system were correlated with transcripts associated with EBV latency, such as EBNA, LMP-1, and LMP-2. In single-cell transcriptome analysis, LPD sample consisted of two large clusters of T cells and B cells with 10 subsets, and EBV-positive DLBCL sample consisted of one independent large cluster with 9 subsets. In LPD sample, components indicative of the normal lymphocyte differentiation process, including the germinal center, were preserved. The molecules involved in lymphocyte activation were upregulated in LPD subsets. In DLBCL sample, the genes associated with mitotic G2-M phases, chromatin organization and RNA metabolism were upregulated in the subsets in which EBV transcripts were more actively transcribed. Discussion and Conclusions: Our retrospective analysis demonstrated that the outcome of LPD and ML in APDS is not satisfactory. scRNA-seq data revealed that cells in LPD still retain the signature of normal lymph node. However, once transformed with EBV, EBV infection may affect epigenetic machineries including chromatin organizer followed by active RNA transcription involved in cell-cell adhesion, inflammatory machineries, molecules of p53 pathway and negative regulation of immune system in the host genome. As the outcome of ML is inferior to LPD, preemptive HCT would be an option for LPD cases.

High Frequency of HLA-Class II B Cell Receptor Neoantigens in IGHV Mutated-CLL

Introduction: Chronic lymphocytic leukemia (CLL) is the most common hematological malignancy and remains incurable. IGHV mutational status is an independent predictor of outcome with chemoimmunotherapy, however the mechanism(s) underpinning the superior prognosis of M-CLL are still to be elucidated. Clonal T cell expansions are well described in CLL. Neoantigens are immunogenic peptides unique to malignant cells, which are presented to host T cells via HLA-I/II, and if harnessed may have potential as a novel immunotherapeutic strategy. It is believed CLL has a low tumor mutational burden (TMB) due to few somatic mutations (~1/Mb) and hence few somatic neoantigens. However, traditional concepts of TMB fail to recognize that the IGH and IGκ/λ chains of the B cell receptor (BCR) also become highly mutated during B cell maturation. BCR mutations in B cell malignancies can create ‘BCR neoantigens’ (Khodadoust, Nature 2017 / Blood 2019). There is no large-scale analysis of HLA-I/II somatic and BCR neoantigens in CLL. Methods: Pre-therapy PBMCs from 23 patients with CLL (M17:F6, median 69 yrs, M-CLL=10, U-CLL=13) were tested. Cytogenetics and patient characteristics were similar between M-CLL and U-CLL. Samples underwent paired WES and RNAseq. Somatic mutations were called by Strelka2 and somatic neoantigens by pVACseq (TESLA guidelines; Wells, Cell 2020). BCR neoantigens were called from full-length BCR sequences by pVACbind. Neoantigen transcriptome expression was confirmed in all cases. Results: Importantly, expression of genes important for CLL antigen presentation was almost universally preserved. Furthermore, consistent with T cells controlling disease progression, central memory/effector T cells were expanded in both M-CLL and U-CLL by CIBERSORTx. Next, the somatic mutational landscape was characterized. WES revealed a median 1.06 somatic mutations/Mb, with frequency of driver mutations as expected (Knisbacher, Nature 2022). Only a median 22% of mutations/case were expressed, with expression of driver mutations only modestly increased in U-CLL vs M-CLL (1 vs 0, p=0.024), suggesting that enrichment of driver mutations in U-CLL only partially explains its inferior prognosis. Strikingly, unlike somatic mutations, all BCR mutations were expressed, with 12x > BCR mutations expressed in M-CLL vs U-CLL (37 vs 3, p<0.0001). Furthermore, in M-CLL expressed BCR mutations were 3x > somatic mutations, whereas BCR mutations were 3x < somatic mutations in U-CLL. Neoantigens were then assessed (Figure 1). Combining somatic + BCR neoantigens, there were a median 24 neoantigens/case. Somatic neoantigens included those found in driver mutations (ATM, XPO1, SF3B1, TP53, NOTCH1). Expression of driver neoantigens were retained in sequential patient samples and new driver neoantigens also acquired. Importantly, public neoantigens (i.e., neoantigens shared across CLL patients) in TP53 and XPO1 were detected. Notably, the frequency of BCR neoantigens was higher than somatic neoantigens, with differences most marked and enriched for HLA-II (HLA-I: 5 vs 2, p<0.0001 and HLA-II: 17 vs 3, p<0.0001). Strikingly, there were 2x > BCR neoantigens in M-CLL vs U-CLL (HLA-I: 6 vs 2, p<0.001; HLA-II: 23 vs 15, p=0.032). Interestingly, the nature of BCR neoantigens between M-CLL and U-CLL was different. Consistent with M-CLL B cells undergoing somatic hypermutation, IGHV mutations strongly positively correlated with numbers of V region BCR neoantigens (R 2=0.784, p<0.0001), and weakly negatively correlated with V(D)J recombination region BCR neoantigens (R 2=0.3992, p<0.005). As expected, there was no correlation between the number of IGHV mutations and somatic neoantigens. Conclusions: The reported low TMB in CLL does not reflect the true frequency of neoantigens, as it fails to account for BCR neoantigens. These contribute ~80% of the neoantigen pool and are predominantly presented by HLA-II. The majority of BCR neoantigens in M-CLL are within the V region, whereas in U-CLL, BCR neoantigens are largely restricted to the V(D)J recombination site. The known enrichment of driver mutations in U-CLL coupled with the increase of BCR neoantigens in M-CLL, are the likely mechanisms behind the different survival outcomes between M-CLL and U-CLL. The presence of public neoantigens in driver mutations opens up the possibility of off-the-shelf neoantigen specific immunotherapy and warrants further investigation.

Somatic Mutations in Cancer-Related Genes Were Observed More Frequently in AYA CML Patients Compared to Elderly at Diagnosis, Whereas the Frequency Was Markedly Higher in Elderly Patients during TKI Treatment

CONCLUSIONS Several works showed that patients with chronic myeloid leukemia (CML) diagnosed at the age of 18 to 39 years (adolescent and young adults, AYAs), had a worse outcome of tyrosine kinase inhibitors (TKI) therapy compared to elderly patients. Only limited data on the molecular background differing AYA from the other age groups are available. To characterize the somatic mutation landscape of AYA CML patients in chronic phase at the time of diagnosis and follow-up in comparison to pediatric and elderly patients, and to follow their clonal evolution. Ph-positive acute lymphoblastic leukemia (Ph+ ALL) patients were included for comparison. Samples from the time of diagnosis (CML children N=18; AYA N=69; elderly N=70; Ph+ ALL children N=32, AYA N=13, elderly N=39) and TKI treatment follow-up (CML AYA N=69, elderly N=70) were analyzed with custom panel (22 whole genes, 40 selected exons). To follow the clonal evolution vital cells available from 10 CML patients were FACS sorted into populations of neutrophils, monocytes, T- and B-lymphocytes. Genomic BCR::ABL1 (g BCR::ABL1) and mutations were quantified using ddPCR in the sorted cells. At the time of diagnosis, a slightly higher frequency of somatic mutations was detected in AYA CML patients (28%, 19/69) compared to elderly (20%, 14/70) and children (11%, 2/18). The similar trend with markedly higher differences was observed in Ph+ ALL patients (62% 8/13 of AYA, 31% 12/39 of elderly, 22% 7/32 of pediatric). The most frequently mutated genes in AYA and elderly CML patients were identically ASXL1, DNMT3A, and TET2. Other mutated genes in AYA were FBXW7, IDH2, IKZF1 PHF6 and KDM6A, SETD2, SF3B1 in elderly. Considering the treatment response, a higher number of mutations at diagnosis were found in the AYA patients that responded optimally in follow-up (21%, 7/33) than in elderly 4% (1/26) and a slightly higher frequency of mutations in AYA patients who later failed on TKI (35%, 8/23) than in elderly 30% (13/43). In contrary, the frequency of acquired mutations or persisted mutations from diagnosis detected during TKI therapy was higher in elderly that failed on TKI therapy (67%, 29/43) than in AYA (52%, 12/23). The treatment failure was associated with de novo mutations in ABL1, RUNX1, TET2, ASXL1 in AYA and elderly CML patients, and in addition in IKZF1, and in DNMT3A, WT1, JAK2, respectively. Sorted cells were analyzed from the time of optimal response in 7 CML patients (+ diagnostic sample of 2 patients) with mutated ASXL1 in 6 patients, FBXW7 in 1 patient, DNMT3A in 1 patient and IDH2 in 1 patient. Samples from the time of therapy failure were available for sorting of 3 patients (+ diagnostic sample of 1 patient). All mutations were detected in g BCR::ABL1 positive neutrophiles and monocytes. The g BCR::ABL1 levels matched exactly with the levels of mutations except for 1 patient with ASXL1-mutated subclone (VAF 5%), which was found in g BCR::ABL1 positive cells. Similar data were found in patients with therapy failure. However, in one patient with secondary failure, the ASXL1 mutation detected at diagnosis disappeared. In all 10 CML patients, g BCR::ABL1 and mutations were not detected in T- and B- lymphocytes under the therapy. In diagnostic samples some g BCR::ABL1 and mutations positive T- and B- lymphocytes were found, but the levels were 3-4 log lower compared to levels in neutrophils and monocytes, which likely correspond to the contamination (purity check was done after sorting) than to the presence of a very small subset of physiological lymphoid cells. Of all age groups, AYAs had the highest number of mutations detected at the time of diagnosis with differences in the spectrum between CML and Ph+ ALL. A markedly higher de novo acquisition of mutations was observed in elderly CML patients during TKI treatment. Moreover, therapy failures were observed more frequently in elderly than in AYA CML patients. Clonal analysis of 10 CML patients outlined that myeloid, but not lymphoid stem/progenitor cells were mutated. Supported by MZCR NU21-07-00225

Multi-Omic Insights into the Mutational Landscape and Dysregulated Transcriptional Programs of Autoimmune B-Cells in Systemic Lupus Erythematosus

Systemic Lupus Erythematosus (SLE) is an autoimmune disease with diverse manifestations, from mild and sporadic to severe and life-threatening. SLE heterogeneity arises from various factors, including genetic, epigenetic, and immune niche-related features (Kaul et al., 2016; Jenks et al., 2018). However, the exact interactions and hierarchy between these features remain unclear. Recent studies have found somatic mutations in genes typically mutated in B-cell lymphomas, in subsets of B-cells that are associated with SLE pathogenesis (Piotrowski et al., 2015; Pullabhatla et al., 2018; Singh et al., 2020; Brown et al., 2022). These mutations may support B-cell clonal bursts, but direct information from clinical specimens on the impact of these mutations on B-cell lineages and their influence on SLE progression or malignant transformation is missing. To define the somatic mutational landscape of B-cells in SLE, we first profiled peripheral blood mononuclear cells obtained from a pilot cohort of four SLE patients using whole-exome sequencing (WES; 200X depth). Following GATK best practices (Van der Auwera and O'Connor, 2020), we identified somatic alterations in DDX11 [variant allele frequency (VAF): 10.2%], DNMT3A [VAF: 3.4%]; PMS2 [VAF: 47.9%], and BICD1 [VAF: 47.3%]; these genes have been linked to lymphoid malignancies and SLE pathogenesis (Piotrowski et al., 2015; Pullabhatla et al., 2018; Niroula et al., 2021; Saeed et al., 2021). We validated the presence of the top-identified mutations using droplet digital PCR technology on sorted B-cell subsets (naïve, memory, and double-negative [DN; CD19 hiIgD loCD27 lo]) from these SLE patients. DN cells are a heterogeneous subtype of autoreactive memory B-cells that play a crucial role in SLE pathogenesis being the precursors of autoantibody producing plasma cells (Wei et al., 2007; Jenks et al., 2018). To investigate the transcriptional and epigenetic alterations in the mutation-harboring B-cells, we then conducted single-cell multi-omic sequencing. We profiled sorted activated B-cells (CD19 hiIgD lo) from the four SLE cases, to largely exclude non-autoreactive naïve B-cells. To maintain a comparison with naïve B cells, we included a small proportion of these cells in the input. In total, we analyzed 35,339 cells, and identified different B-cell subsets with distinct gene expression patterns, including DN cells ( n = 1,437 cells; ZEB2 hiTRAF5 lo, Jenks et al., 2018). To uncover, in an unbiased way, transcriptional differences between B-cell subsets, we catalogued variation in transcriptomic signatures derived from the scRNA data using consensus non-negative matrix factorization. We found a unique gene expression program exclusively present in DN cells ( Panel A), which includes key genes implicated in immune regulation and the development of autoimmunity, such as PTPN22 (Chung & Criswell, 2007; Tizaoui et al., 2021) . Pathway analysis of the DN-specific program revealed an enrichment of complement system genes (FDR = 0.074; Hallmark), which are crucial for immune regulation and tolerance, such as CBLB (Tang et al., 2019). Notably, in DN cells from one of the SLE samples carrying lymphoma/SLE-associated mutations in BICD1, PMS2, DDX11, we observed an altered expression of this gene program compared to DN cells from other samples ( P-value = 3.6x10 -11; Panel A, inset), suggesting that a malfunctioning complement system in DN cells may lead to an overly active immune response (Weinstein et al., 2021). In line with this notion, using chromatin accessibility profiles as a measure of transcription factor (TF) regulatory activities, we showed increased activity of TFs involved in interferon-dependent innate immune response and in B-cell activation and differentiation (RFX and OCT-1, respectively; FDR < 0.05), in the DN B-cells of this sample ( Panel B). In summary, we uncovered a unique transcriptional program associated with autoimmune DN B-cells. This program showed enrichment in key genes and pathways involved in immune response regulation. The disruption of this program, accompanied by the somatic SLE-associated mutations, may lead to overactivation of DN B cells, ultimately contributing to autoimmunity. Further analyses involving a larger cohort of clinical specimens are needed to directly link B-cell genotypes with their transcriptional and epigenetic programs and to elucidate the role of the identified mutations in SLE pathobiology.

Effects of psoriasis and psoralen exposure on the somatic mutation landscape of the skin

Somatic mutations are hypothesized to play a role in many non-neoplastic diseases. We performed whole-exome sequencing of 1,182 microbiopsies dissected from lesional and nonlesional epidermis from 111 patients with psoriasis to search for evidence that somatic mutations in keratinocytes may influence the disease process. Lesional skin remained highly polyclonal, showing no evidence of large-scale spread of clones carrying potentially pathogenic mutations. The mutation rate of keratinocytes was similarly only modestly affected by the disease. We found evidence of positive selection in previously reported driver genes NOTCH1, NOTCH2, TP53, FAT1 and PPM1D and also identified mutations in four genes (GXYLT1, CHEK2, ZFP36L2 and EEF1A1) that we hypothesize are selected for in squamous epithelium irrespective of disease status. Finally, we describe a mutational signature of psoralens—a class of chemicals previously found in some sunscreens and which are used as part of PUVA (psoralens and ultraviolet-A) photochemotherapy treatment for psoriasis.