Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages

Breast cancer is a heterogeneous disease, and treatment is guided by biomarker profiles representing distinct molecular subtypes. Breast cancer arises from the breast ductal epithelium, and experimental data suggests breast cancer subtypes have different cells of origin within that lineage. The precise cells of origin for each subtype and the transcriptional networks that characterize these tumor-normal lineages are not established. In this work, we applied bulk, single-cell (sc), and single-nucleus (sn) multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 breast cancer patients to show characteristic links in gene expression and chromatin accessibility between breast cancer subtypes and their putative cells of origin. We applied the PAM50 subtyping algorithm in tandem with bulk RNA-seq and snRNA-seq to reliably subtype even low-purity tumor samples and confirm promoter accessibility using snATAC. Trajectory analysis of chromatin accessibility and differentially accessible motifs clearly connected progenitor populations with breast cancer subtypes supporting the cell of origin for basal-like and luminal A and B tumors. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal breast cancer and luminal mature cells, and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like ( PRKCA , SOX6 , RGS6 , KCNQ3 ) and luminal A/B ( FAM155A , LRP1B ) lineages, with expression in both precursor and cancer cells and further upregulation in tumors. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like breast cancer, suggesting altered means of immune dysfunction among breast cancer subtypes. We used spatial transcriptomics and multiplex imaging to provide spatial detail for key markers of benign and malignant cell types and immune cell colocation. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single cell level is a powerful tool for investigating breast cancer lineage development and highlight transcriptional networks that define basal and luminal breast cancer lineages.

Epigenetic regulation during cancer transitions across 11 tumour types

Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis1-4. Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial-mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions.

Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin

DNA methylation plays a critical role in establishing and maintaining cellular identity. However, it is frequently dysregulated during tumor development and is closely intertwined with other genetic alterations. Here, we leveraged multi-omic profiling of 687 tumors and matched non-involved adjacent tissues from the kidney, brain, pancreas, lung, head and neck, and endometrium to identify aberrant methylation associated with RNA and protein abundance changes and build a Pan-Cancer catalog. We uncovered lineage-specific epigenetic drivers including hypomethylated FGFR2 in endometrial cancer. We showed that hypermethylated STAT5A is associated with pervasive regulon downregulation and immune cell depletion, suggesting that epigenetic regulation of STAT5A expression constitutes a molecular switch for immunosuppression in squamous tumors. We further demonstrated that methylation subtype-enrichment information can explain cell-of-origin, intra-tumor heterogeneity, and tumor phenotypes. Overall, we identified cis-acting DNA methylation events that drive transcriptional and translational changes, shedding light on the tumor's epigenetic landscape and the role of its cell-of-origin.

Baricitinib with cyclosporine eliminates acute graft rejection in fully mismatched skin and heart transplant models

Solid organ transplant represents a potentially lifesaving procedure for patients suffering from end-stage heart, lung, liver, and kidney failure. However, rejection remains a significant source of morbidity and immunosuppressive medications have significant toxicities. Janus kinase (JAK) inhibitors are effective immunosuppressants in autoimmune diseases and graft versus host disease after allogeneic hematopoietic cell transplantation. Here we examine the role of JAK inhibition in preclinical fully major histocompatibility mismatched skin and heart allograft models. Baricitinib combined with cyclosporine A (CsA) preserved fully major histocompatibility mismatched skin grafts for the entirety of a 111-day experimental period. In baricitinib plus CsA treated mice, circulating CD4+T-bet+ T cells, CD8+T-bet+ T cells, and CD4+FOXP3+ regulatory T cells were reduced. Single cell RNA sequencing revealed a unique expression profile in immune cells in the skin of baricitinib plus CsA treated mice, including decreased inflammatory neutrophils and increased CCR2- macrophages. In a fully major histocompatibility mismatched mismatched heart allograft model, baricitinib plus CsA prevented graft rejection for the entire 28-day treatment period compared with 9 days in controls. Our findings establish that the combination of baricitinib and CsA prevents rejection in allogeneic skin and heart graft models and supports the study of JAK inhibitors in human solid organ transplantation.

Single-Cell Discovery and Multiomic Characterization of Therapeutic Targets in Multiple Myeloma

Multiple myeloma (MM) is a highly refractory hematologic cancer. Targeted immunotherapy has shown promise in MM but remains hindered by the challenge of identifying specific yet broadly representative tumor markers. We analyzed 53 bone marrow (BM) aspirates from 41 MM patients using an unbiased, high-throughput pipeline for therapeutic target discovery via single-cell transcriptomic profiling, yielding 38 MM marker genes encoding cell-surface proteins and 15 encoding intracellular proteins. Of these, 20 candidate genes were highlighted that are not yet under clinical study, 11 of which were previously uncharacterized as therapeutic targets. The findings were cross-validated using bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry of MM cell lines and patient BM, demonstrating high overall concordance across data types. Independent discovery using bulk RNA sequencing reiterated top candidates, further affirming the ability of single-cell transcriptomics to accurately capture marker expression despite limitations in sample size or sequencing depth. Target dynamics and heterogeneity were further examined using both transcriptomic and immuno-imaging methods. In summary, this study presents a robust and broadly applicable strategy for identifying tumor markers to better inform the development of targeted cancer therapy.

SIGNIFICANCE

Single-cell transcriptomic profiling and multiomic cross-validation to uncover therapeutic targets identifies 38 myeloma marker genes, including 11 transcribing surface proteins with previously uncharacterized potential for targeted antitumor therapy.

Motixafortide and G-CSF to mobilize hematopoietic stem cells for autologous transplantation in multiple myeloma: a randomized phase 3 trial

Autologous hematopoietic stem cell transplantation (ASCT) improves survival in multiple myeloma (MM). However, many individuals are unable to collect optimal CD34+ hematopoietic stem and progenitor cell (HSPC) numbers with granulocyte colony-stimulating factor (G-CSF) mobilization. Motixafortide is a novel cyclic-peptide CXCR4 inhibitor with extended in vivo activity. The GENESIS trial was a prospective, phase 3, double-blind, placebo-controlled, multicenter study with the objective of assessing the superiority of motixafortide + G-CSF over placebo + G-CSF to mobilize HSPCs for ASCT in MM. The primary endpoint was the proportion of patients collecting ≥6 × 106 CD34+ cells kg-1 within two apheresis procedures; the secondary endpoint was to achieve this goal in one apheresis. A total of 122 adult patients with MM undergoing ASCT were enrolled at 18 sites across five countries and randomized (2:1) to motixafortide + G-CSF or placebo + G-CSF for HSPC mobilization. Motixafortide + G-CSF enabled 92.5% to successfully meet the primary endpoint versus 26.2% with placebo + G-CSF (odds ratio (OR) 53.3, 95% confidence interval (CI) 14.12-201.33, P < 0.0001). Motixafortide + G-CSF also enabled 88.8% to meet the secondary endpoint versus 9.5% with placebo + G-CSF (OR 118.0, 95% CI 25.36-549.35, P < 0.0001). Motixafortide + G-CSF was safe and well tolerated, with the most common treatment-emergent adverse events observed being transient, grade 1/2 injection site reactions (pain, 50%; erythema, 27.5%; pruritis, 21.3%). In conclusion, motixafortide + G-CSF mobilized significantly greater CD34+ HSPC numbers within two apheresis procedures versus placebo + G-CSF while preferentially mobilizing increased numbers of immunophenotypically and transcriptionally primitive HSPCs. Trial Registration: ClinicalTrials.gov , NCT03246529.

Epigenetic and transcriptomic characterization reveals progression markers and essential pathways in clear cell renal cell carcinoma

Identifying tumor-cell-specific markers and elucidating their epigenetic regulation and spatial heterogeneity provides mechanistic insights into cancer etiology. Here, we perform snRNA-seq and snATAC-seq in 34 and 28 human clear cell renal cell carcinoma (ccRCC) specimens, respectively, with matched bulk proteogenomics data. By identifying 20 tumor-specific markers through a multi-omics tiered approach, we reveal an association between higher ceruloplasmin (CP) expression and reduced survival. CP knockdown, combined with spatial transcriptomics, suggests a role for CP in regulating hyalinized stroma and tumor-stroma interactions in ccRCC. Intratumoral heterogeneity analysis portrays tumor cell-intrinsic inflammation and epithelial-mesenchymal transition (EMT) as two distinguishing features of tumor subpopulations. Finally, BAP1 mutations are associated with widespread reduction of chromatin accessibility, while PBRM1 mutations generally increase accessibility, with the former affecting five times more accessible peaks than the latter. These integrated analyses reveal the cellular architecture of ccRCC, providing insights into key markers and pathways in ccRCC tumorigenesis.

Cross center single-cell RNA sequencing study of the immune microenvironment in rapid progressing multiple myeloma

Despite advancements in understanding the pathophysiology of Multiple Myeloma (MM), the cause of rapid progressing disease in a subset of patients is still unclear. MM's progression is facilitated by complex interactions with the surrounding bone marrow (BM) cells, forming a microenvironment that supports tumor growth and drug resistance. Understanding the immune microenvironment is key to identifying factors that promote rapid progression of MM. To accomplish this, we performed a multi-center single-cell RNA sequencing (scRNA-seq) study on 102,207 cells from 48 CD138- BM samples collected at the time of disease diagnosis from 18 patients with either rapid progressing (progression-free survival (PFS) < 18 months) or non-progressing (PFS > 4 years) disease. Comparative analysis of data from three centers demonstrated similar transcriptome profiles and cell type distributions, indicating subtle technical variation in scRNA-seq, opening avenues for an expanded multicenter trial. Rapid progressors depicted significantly higher enrichment of GZMK+ and TIGIT+ exhausted CD8+ T-cells (P = 0.022) along with decreased expression of cytolytic markers (PRF1, GZMB, GNLY). We also observed a significantly higher enrichment of M2 tolerogenic macrophages in rapid progressors and activation of pro-proliferative signaling pathways, such as BAFF, CCL, and IL16. On the other hand, non-progressive patients depicted higher enrichment for immature B Cells (i.e., Pre/Pro B cells), with elevated expression for markers of B cell development (IGLL1, SOX4, DNTT). This multi-center study identifies the enrichment of various pro-tumorigenic cell populations and pathways in those with rapid progressing disease and further validates the robustness of scRNA-seq data generated at different study centers.

Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis

As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4+ T/CD8+ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project.

Significance

scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.

Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease.

Abstract 2025: Spatial transcriptomic profiling of progression markers in clear cell renal cell carcinoma

Next-generation sequencing enabled molecular landscaping and the discovery of novel tumor markers in ccRCC. However, the spatial relationships and histological features of the tumor microenvironment were lost during this process. Combining snRNA-seq, snATAC-seq, Spatial Transcriptomics (ST) technology, and immunofluorescence labeling, we discovered novel ccRCC progression tumor markers, and uncovered their spatial dependent expression pattern in both human ccRCC tumors and patient-derived xenograft (PDX). Using snRNA-seq and snATAC-seq, novel tumor progression markers such as NDRG1, MGST1, ABCC3 and PCSK6 were discovered, and their expressions were validated in ST. We found that one of the key novel tumor markers, CP, exhibited specific spatial expression patterns on tissue slides. The elevation of CP expression region is associated with a higher degree of hyalinization in ccRCC human tumor samples. Similarly, using immunofluorescence labeling, we validated the co-expression of CP and PCSK6 canonical ccRCC marker CA9. Overall, this study revealed novel ccRCC progression markers and linked their spatial expression pattern with histological features.

Citation Format: Chia-Kuei Mo, Yige Wu, Terekhanova Nadezhda, Caravan Wagma, Preet Lal, Siqi Chen, Nataly Naser AL Deen, Ruiyang Liu, Yanyan Zhao, Kazuhito Sato, Lijun Yao, Mamatha Serasanambati, Andrew Shinkle, Reyka G. Jayasinghe, Li Ding, Feng Chen. Spatial transcriptomic profiling of progression markers in clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2025.

Abstract 1932: Pollock: Fishing for cell states

The use of single-cell methods is expanding at an ever-increasing rate. While multiple algorithms address the task of cell classification, they are limited in terms of cross platform compatibility, reliance on the availability of a reference dataset, and classification interpretability. Here, we introduce Pollock, a suite of algorithms for cell type identification that is compatible with popular single cell methods and analysis platforms, provides a series of pretrained human cancer reference models, and reports interpretability scores that identify the genes that drive cell type classifications. Our model combines two important approaches, one each from machine learning and deep learning: a variational autoencoder (VAE) and random forest classifier, to make cell type predictions. Pollock is highly versatile, being available as a command line tool, Python library (with scanpy integration), or R library (with Seurat integration), and can be installed as a conda package, or in containerized form via Docker. To allow for easier pan-disease and pan-tissue analyses, Pollock also ships with a library of pretrained cancer type specific and agnostic modules that were trained on expertly-curated single cell data that are ready to “plug and play” with no additional annotation or training required. Conversely, Pollock also allows for the training of custom classification modules, if an annotated reference single cell dataset is available. These pretrained models were fitted on manually curated and annotated single cell data from eight different cancer types spanning three single cell technologies (scRNA-seq, snRNA-seq, and snATAC-seq). Pollock also provides feature importance scores that allow for cell type classifications to be traced back to the genes influencing a particular cell type classification, further promoting biological interpretability. These scores could allow for new, technology-specific biomarker discovery. We also demonstrate the utility of Pollock by applying it in a pan-cancer single cell immune analysis.

Citation Format: Erik Storrs, Daniel Cui Zhou, Michael C. Wendl, Matthew A. Wyczalkowski, Alla Karpova, Liang-Bo Wang, Yize Li, Austin Southard-Smith, Reyka G. Jayasinghe, Lijun Yao, Ruiyang Liu, Yige Wu, Nadezhda V. Terekhanova, Houxiang Zhu, John M. Herndon, Feng Chen, William E. Gillanders, Ryan C. Fields, Li Ding. Pollock: Fishing for cell states [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1932.

Abstract 2936: Multi-omic characterization of transitional cell populations in breast cancer

Breast cancer is a heterogeneous collection of diseases grouped by hormone receptor status or by expression of key subtype-determining genes. Breast cancer subtypes, in particular basal-like breast cancer and the luminal breast cancer subtypes, differ by hormonal receptor status, proliferation, genomic instability and mutational signatures, treatment response, and prognosis. The highly distinct signatures of basal-like and luminal breast cancer suggest that they may have different cells of origin within the breast duct. As part of the Washington University Human Tumor Atlas Network (WU-HTAN) program, we generated multi-omic data for 53 samples from 37 breast cancer tumors and 4 normal adjacent tissues. Genomic subtyping was applied to both bulk and single-nucleus RNA sequencing. Analysis of single-nucleus gene expression and chromatin accessibility in epithelial cells underscores similarities between basal-like breast cancer and luminal progenitor cells within the breast duct, and between luminal breast cancer and mature luminal ductal cells. This study links distinct breast cancer subtypes to normal breast cell populations and suggests distinct cells of origin for these cancer types.

Citation Format: Michael D. Iglesia, Reyka G. Jayasinghe, Daniel Cui Zhou, Nadezhda V. Terekhanova, John Herndon, Alla Karpova, Siqi Chen, Nataly Naser Al Deen, Kazuhito Sato, Feng Chen, Deborah J. Veis, Ryan C. Fields, William E. Gillanders, Li Ding. Multi-omic characterization of transitional cell populations in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2936.

CS1 CAR-T targeting the distal domain of CS1 (SLAMF7) shows efficacy in high tumor burden myeloma model despite fratricide of CD8+CS1 expressing CAR-T cells

Despite improvement in treatment options for myeloma patients, including targeted immunotherapies, multiple myeloma remains a mostly incurable malignancy. High CS1 (SLAMF7) expression on myeloma cells and limited expression on normal cells makes it a promising target for CAR-T therapy. The CS1 protein has two extracellular domains - the distal Variable (V) domain and the proximal Constant 2 (C2) domain. We generated and tested CS1-CAR-T targeting the V domain of CS1 (Luc90-CS1-CAR-T) and demonstrated anti-myeloma killing in vitro and in vivo using two mouse models. Since fratricide of CD8 + cells occurred during production, we generated fratricide resistant CS1 deficient Luc90- CS1- CAR-T (ΔCS1-Luc90- CS1- CAR-T). This led to protection of CD8 + cells in the CAR-T cultures, but had no impact on efficacy. Our data demonstrate targeting the distal V domain of CS1 could be an effective CAR-T treatment for myeloma patients and deletion of CS1 in clinical production did not provide an added benefit using in vivo immunodeficient NSG preclinical models.

Pollock: fishing for cell states

Motivation

The use of single-cell methods is expanding at an ever-increasing rate. While there are established algorithms that address cell classification, they are limited in terms of cross platform compatibility, reliance on the availability of a reference dataset and classification interpretability. Here, we introduce Pollock, a suite of algorithms for cell type identification that is compatible with popular single-cell methods and analysis platforms, provides a set of pretrained human cancer reference models, and reports interpretability scores that identify the genes that drive cell type classifications.

Results

Pollock performs comparably to existing classification methods, while offering easily deployable pretrained classification models across a wide variety of tissue and data types. Additionally, it demonstrates utility in immune pan-cancer analysis.

Availability and implementation

Source code and documentation are available at https://github.com/ding-lab/pollock. Pretrained models and datasets are available for download at https://zenodo.org/record/5895221.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

LINC00355 regulates p27KIP expression by binding to MENIN to induce proliferation in late-stage relapse breast cancer

Late-stage relapse (LSR) in patients with breast cancer (BC) occurs more than five years and up to 10 years after initial treatment and has less than 30% 5-year relative survival rate. Long non-coding RNAs (lncRNAs) play important roles in BC yet have not been studied in LSR BC. Here, we identify 1127 lncRNAs differentially expressed in LSR BC via transcriptome sequencing and analysis of 72 early-stage and 24 LSR BC patient tumors. Decreasing expression of the most up-regulated lncRNA, LINC00355, in BC and MCF7 long-term estrogen deprived cell lines decreases cellular invasion and proliferation. Subsequent mechanistic studies show that LINC00355 binds to MENIN and changes occupancy at the CDKN1B promoter to decrease p27Kip. In summary, this is a key study discovering lncRNAs in LSR BC and LINC00355 association with epigenetic regulation and proliferation in BC.

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.

Discovery of driver non-coding splice-site-creating mutations in cancer

Non-coding mutations can create splice sites, however the true extent of how such somatic non-coding mutations affect RNA splicing are largely unexplored. Here we use the MiSplice pipeline to analyze 783 cancer cases with WGS data and 9494 cases with WES data, discovering 562 non-coding mutations that lead to splicing alterations. Notably, most of these mutations create new exons. Introns associated with new exon creation are significantly larger than the genome-wide average intron size. We find that some mutation-induced splicing alterations are located in genes important in tumorigenesis (ATRX, BCOR, CDKN2B, MAP3K1, MAP3K4, MDM2, SMAD4, STK11, TP53 etc.), often leading to truncated proteins and affecting gene expression. The pattern emerging from these exon-creating mutations suggests that splice sites created by non-coding mutations interact with pre-existing potential splice sites that originally lacked a suitable splicing pair to induce new exon formation. Our study suggests the importance of investigating biological and clinical consequences of noncoding splice-inducing mutations that were previously neglected by conventional annotation pipelines. MiSplice will be useful for automatically annotating the splicing impact of coding and non-coding mutations in future large-scale analyses.

Systematic Analysis of Splice-Site-Creating Mutations in Cancer

For the past decade, cancer genomic studies have focused on mutations leading to splice-site disruption, overlooking those having splice-creating potential. Here, we applied a bioinformatic tool, MiSplice, for the large-scale discovery of splice-site-creating mutations (SCMs) across 8,656 TCGA tumors. We report 1,964 originally mis-annotated mutations having clear evidence of creating alternative splice junctions. TP53 and GATA3 have 26 and 18 SCMs, respectively, and ATRX has 5 from lower-grade gliomas. Mutations in 11 genes, including PARP1, BRCA1, and BAP1, were experimentally validated for splice-site-creating function. Notably, we found that neoantigens induced by SCMs are likely several folds more immunogenic compared to missense mutations, exemplified by the recurrent GATA3 SCM. Further, high expression of PD-1 and PD-L1 was observed in tumors with SCMs, suggesting candidates for immune blockade therapy. Our work highlights the importance of integrating DNA and RNA data for understanding the functional and the clinical implications of mutations in human diseases.

Abstract 2362: Comprehensive portrait of canonical and non-canonical splicing in cancer

Current annotation methods typically classify mutations as disruptors of splicing if they fall on either the consensus intronic dinucleotide splice donor, GT, or the splice acceptor, AG. As a group, splice site mutations have been presumed to be invariably deleterious because of their disruption of the conserved sequences that are used to identify exon-intron boundaries. While this classification method has been useful, increasing evidence suggests that mutations outside of the canonical splice site can lead to transcriptional changes beyond disruption of the nearest junction. In this study, we have developed the MiSplice pipeline to determine the local and global effects of genomic mutations on splicing across 33 cancer types. To evaluate the local effects of mutations on splicing, we applied MiSplice to identify splice-disrupting mutations (SDMs) and splice-creating mutations (SCMs), genome-wide. We identified 1,964 novel SCMs, of which 26% and 11% were mis-annotated as missense and silent mutations and validated 10 of 11 genes in a mini-gene splicing assay. SDM identification predicted complex splicing patterns associated with canonical splice site mutations and identified a handful of mutations in proximity to the canonical junction that disrupt splicing factor binding sites. Interestingly, further investigation of the novel neoantigens produced by SCMs and SDMs are likely several folds more immunogenic than missense mutations. To explore the global impact of mutations on splicing we focused on recurrent and adjacent mutations disrupting the spliceosomal complex and related splicing factor genes from over 400 curated splice related genes. In addition, we applied HotSpot3D to identify splice-factor mutations (SFMs) that are significantly proximal to one another. Our analysis has identified novel SFMs that disrupt the spliceosomal complex and globally impact downstream splicing targets creating novel peptide sequences and alter key cancer genes. The current study has greatly extended the insight into the transcriptional ramifications of genomic alterations by integrating DNA and RNA sequencing data and painting the portrait of alternative splicing across cancer genomes.

Citation Format: Reyka G. Jayasinghe, Song Cao, Qingsong Gao, Matthew A. Wyczalkowski, Sohini Sengupta, Matthew J. Walter, Christopher Maher, Michael C. Wendl, Feng Chen, Eduardo Eyras, Alexander J. Lazar, Ken Chen, Ilya Shmulevich, Li Ding, The Splicing Analysis Working Group. Comprehensive portrait of canonical and non-canonical splicing in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2362.

Abstract 2357: Utilizing biological and protein structure-guided features to improve driver mutation discovery

Distinguishing between driver and passenger somatic mutations to pinpoint genetic alterations leading to oncogenesis still presents significant challenges. To meet these challenges, computational tools have been developed as effective filters, pruning most of the somatic mutations to a shortlist of high-priority, functional candidates for experimental validation. Most tools include searching for genes or pathways having mutation rates higher than explained by chance, mutations in conserved regions, or genes with neighboring mutations on the linear DNA or protein sequence. Recently, there has been a shift to utilize tertiary/quaternary protein structures to identify mutations clustering proximal to each other in 3D space. Such enrichment of mutations can indicate specific domains critical to normal protein function and when mutated, can drive tumor initiation and progression. HotSpot3D, a protein structure-based tool, identifies clusters enriched with proximal mutations within proteins. Though HotSpot3D has been valuable in identifying clusters of residues that are important to cancer, it does not distinguish the driving potential or structural impact of different mutations within a cluster nor does it consider the physical impact of different amino acid substitutions at the same site. The prediction power of HotSpot3D in distinguishing driver mutations from passenger mutations can be improved if spatial clustering considers physical/biological features proximal to mutations in significant clusters as well as the specific amino acid substitutions of mutations. We have created a machine learning algorithm that further prioritizes putative driver mutations found in HotSpot3D clusters by incorporating structural/biological features such as proximity of mutations to functional sites (active sites, phosphorylation sites, disulfide bonds, etc.), solvent accessibility, physiochemical property change of mutations, free energy change of mutations, conservation of residue sites, secondary structure state of residue sites, and expression/phosphorylation changes of samples containing mutations. We have curated experimentally validated mutations identified as neutral or oncogenic from various databases to serve as our training sets. This algorithm can be trained on the curated mutations in various protein subclasses such as homologous proteins, oncogenes, tumor suppressors, etc. to identify distinct structural feature signatures per subclass specific to driver mutations. This tool will aid in revealing putative driver mutations in genes not previously linked with cancer and help pinpoint mutations in known cancer genes that are driving cancer. Specifically, we are interested in applying the algorithm to druggable protein families such as G-Protein Coupled Receptors, Kinases, and Nuclear Hormone Receptors to better understand their role in tumor initiation and progression.

Citation Format: Sohini Sengupta, Adam Scott, Amila Weerasinghe, Dan C. Zhou, Matthew A. Wyczalkowski, Reyka G. Jayasinghe, Ken Chen, Gordon Mills, Mike C. Wendl, John Dipersio, Li Ding. Utilizing biological and protein structure-guided features to improve driver mutation discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2357.

Current theoretical models fail to predict the topological complexity of the human genome

Understanding the folding of the human genome is a key challenge of modern structural biology. The emergence of chromatin conformation capture assays (e.g., Hi-C) has revolutionized chromosome biology and provided new insights into the three dimensional structure of the genome. The experimental data are highly complex and need to be analyzed with quantitative tools. It has been argued that the data obtained from Hi-C assays are consistent with a fractal organization of the genome. A key characteristic of the fractal globule is the lack of topological complexity (knotting or inter-linking). However, the absence of topological complexity contradicts results from polymer physics showing that the entanglement of long linear polymers in a confined volume increases rapidly with the length and with decreasing volume. In vivo and in vitro assays support this claim in some biological systems. We simulate knotted lattice polygons confined inside a sphere and demonstrate that their contact frequencies agree with the human Hi-C data. We conclude that the topological complexity of the human genome cannot be inferred from current Hi-C data.

Abstract 1929: Pan-Cancer analysis of the effects of splice-altering variants on mRNA splicing and stability

Sequences surrounding splice junctions function to recruit splicing elements to promote and suppress the inclusion of an adjacent exon. Mutations disrupting motifs around splice regions and the creation of de novo splice sites have not been functionally characterized genome-wide. Furthermore, many variants that alter the mRNA isoform have been shown to introduce splicing defects, such as premature termination codons (PTCs), which are degraded via nonsense-mediated decay (NMD). Using cancer samples across multiple cancer types, we have the unique opportunity to study the effects of somatic and germline events introduced throughout the genome and with the large dataset provided by The Cancer Genome Atlas (TCGA), we have the power to identify significant events that are influencing mRNA splicing and stability.

In this study we aim to characterize splice altering variants (SAVs) and nonsense mutations that directly effect exon inclusion and mRNA degradation, respectively, utilizing the TCGA dataset consisting of both DNA-Seq (whole genome and/or exome sequencing) and RNA-seq from approximately 8,000 tumors representing 24 major cancer types. We have identified 23,615 somatic splice site mutations and 71,009 somatic nonsense mutations in 24 cancer types. For each variant, we have collected the following expression data derived from the RNA-seq: exon, splice junction, isoform, and gene; defining the expression signature of each variant. Expression signatures of known SAVs will facilitate the identification of novel and mis-classified missense and silent mutations affecting splicing. Exploring the distribution of mutations and their correlation with the aforementioned expression data will allow us to establish commonalities and differences that may be indicative of cancer type, molecular subtype, or clinical characteristics. Furthermore our position dependent analysis of nonsense mutations and their correlation with degradation will enhance our ability to determine significant sites influencing mRNA isoform presence. We hypothesize that some intronic/exonic variants outside the canonical splice site will influence exon inclusion and alter the ratio of mRNA isoforms present.

Our analysis has identified a number of SAVs, including variants that were mis-classified as missense mutations such as c.190 in BRCA1. The germline coding mutation was found to strengthen a cryptic splice site and discovered to have a higher variant allele fraction in the tumor relative to the normal tissue in two ovarian samples. In conclusion our findings and the continued development of our project will contribute to improving current annotation methods and broaden our understanding of variants that affect splicing and their biological contribution to mRNA isoforms selection in the cell.

Citation Format: Reyka G. Jayasinghe, Kuan-lin Huang, Jie Ning, Matthew Wyczalkowski, Charles Lu, Mingchao Xie, Michael Wendl, Michael McLellan, Kai Ye, Li Ding. Pan-Cancer analysis of the effects of splice-altering variants on mRNA splicing and stability. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1929. doi:10.1158/1538-7445.AM2015-1929