Pangenome graph construction from genome alignments with Minigraph-Cactus

Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but advances in long-read sequencing are leading to widely available, high-quality phased assemblies. Constructing a pangenome graph directly from assemblies, as opposed to variant calls, leverages the graph's ability to represent variation at different scales. Here we present the Minigraph-Cactus pangenome pipeline, which creates pangenomes directly from whole-genome alignments, and demonstrate its ability to scale to 90 human haplotypes from the Human Pangenome Reference Consortium. The method builds graphs containing all forms of genetic variation while allowing use of current mapping and genotyping tools. We measure the effect of the quality and completeness of reference genomes used for analysis within the pangenomes and show that using the CHM13 reference from the Telomere-to-Telomere Consortium improves the accuracy of our methods. We also demonstrate construction of a Drosophila melanogaster pangenome.

Tumor-associated microbiome features of metastatic colorectal cancer and clinical implications

Background

Colon microbiome composition contributes to the pathogenesis of colorectal cancer (CRC) and prognosis. We analyzed 16S rRNA sequencing data from tumor samples of patients with metastatic CRC and determined the clinical implications.

Materials and methods

We enrolled 133 patients with metastatic CRC at St. Vincent Hospital in Korea. The V3-V4 regions of the 16S rRNA gene from the tumor DNA were amplified, sequenced on an Illumina MiSeq, and analyzed using the DADA2 package.

Results

After excluding samples that retained <5% of the total reads after merging, 120 samples were analyzed. The median age of patients was 63 years (range, 34-82 years), and 76 patients (63.3%) were male. The primary cancer sites were the right colon (27.5%), left colon (30.8%), and rectum (41.7%). All subjects received 5-fluouracil-based systemic chemotherapy. After removing genera with <1% of the total reads in each patient, 523 genera were identified. Rectal origin, high CEA level (≥10 ng/mL), and presence of lung metastasis showed higher richness. Survival analysis revealed that the presence of Prevotella (p = 0.052), Fusobacterium (p = 0.002), Selenomonas (p<0.001), Fretibacterium (p = 0.001), Porphyromonas (p = 0.007), Peptostreptococcus (p = 0.002), and Leptotrichia (p = 0.003) were associated with short overall survival (OS, <24 months), while the presence of Sphingomonas was associated with long OS (p = 0.070). From the multivariate analysis, the presence of Selenomonas (hazard ratio [HR], 6.35; 95% confidence interval [CI], 2.38-16.97; p<0.001) was associated with poor prognosis along with high CEA level.

Conclusion

Tumor microbiome features may be useful prognostic biomarkers for metastatic CRC.

Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions

Existing methods for analysis of spatial transcriptomic data focus on delineating the global gene expression variations of cell types across the tissue, rather than local gene expression changes driven by cell-cell interactions. We propose a new statistical procedure called niche-differential expression (niche-DE) analysis that identifies cell-type-specific niche-associated genes, which are differentially expressed within a specific cell type in the context of specific spatial niches. We further develop niche-LR, a method to reveal ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. Niche-DE and niche-LR are applicable to low-resolution spot-based spatial transcriptomics data and data that is single-cell or subcellular in resolution.

A clinical trial of therapeutic vaccination in lymphoma with serial tumor sampling and single-cell analysis

ABSTRACT

In situ vaccination (ISV) triggers an immune response to tumor-associated antigens at 1 tumor site, which can then tackle the disease throughout the body. Here, we report clinical and biological results of a phase 1/2 ISV trial in patients with low-grade lymphoma, combining an intratumoral toll-like receptor 9 (TLR9) agonist with local low-dose radiation and ibrutinib (an inhibitor of B- and T-cell kinases). Adverse events were predominately low grade. The overall response rate was 50%, including 1 complete response. All patients experienced tumor reduction at distant sites. Single-cell analyses of serial fine needle aspirates from injected and uninjected tumors revealed correlates of clinical response, such as lower CD47 and higher major histocompatibility complex class II expression on tumor cells, enhanced T-cell and natural killer cell effector function, and reduced immune suppression from transforming growth factor β and inhibitory T regulatory 1 cells. Although changes at the local injected site were more pronounced, changes at distant uninjected sites were more often associated with clinical responses. Functional immune response assays and tracking of T-cell receptor sequences provided evidence of treatment-induced tumor-specific T-cell responses. Induction of immune effectors and reversal of negative regulators were both important in producing clinically meaningful tumor responses. The trial was registered at www.clinicaltrials.gov as #NCT02927964.

Follicular lymphoma evolves with a surmountable dependency on acquired glycosylation motifs in the B-cell receptor

ABSTRACT

An early event in the genesis of follicular lymphoma (FL) is the acquisition of new glycosylation motifs in the B-cell receptor (BCR) due to gene rearrangement and/or somatic hypermutation. These N-linked glycosylation motifs (N-motifs) contain mannose-terminated glycans and can interact with lectins in the tumor microenvironment, activating the tumor BCR pathway. N-motifs are stable during FL evolution, suggesting that FL tumor cells are dependent on them for their survival. Here, we investigated the dynamics and potential impact of N-motif prevalence in FL at the single-cell level across distinct tumor sites and over time in 17 patients. Although most patients had acquired at least 1 N-motif as an early event, we also found (1) cases without N-motifs in the heavy or light chains at any tumor site or time point and (2) cases with discordant N-motif patterns across different tumor sites. Inferring phylogenetic trees of the patients with discordant patterns, we observed that both N-motif-positive and N-motif-negative tumor subclones could be selected and expanded during tumor evolution. Comparing N-motif-positive with N-motif-negative tumor cells within a patient revealed higher expression of genes involved in the BCR pathway and inflammatory response, whereas tumor cells without N-motifs had higher activity of pathways involved in energy metabolism. In conclusion, although acquired N-motifs likely support FL pathogenesis through antigen-independent BCR signaling in most patients with FL, N-motif-negative tumor cells can also be selected and expanded and may depend more heavily on altered metabolism for competitive survival.

GITR and TIGIT immunotherapy provokes divergent multicellular responses in the tumor microenvironment of gastrointestinal cancers

BACKGROUND

Understanding the mechanistic effects of novel immunotherapy agents is critical to improving their successful clinical translation. These effects need to be studied in preclinical models that maintain the heterogenous tumor microenvironment (TME) and dysfunctional cell states found in a patient's tumor. We investigated immunotherapy perturbations targeting co-stimulatory molecule GITR and co-inhibitory immune checkpoint TIGIT in a patient-derived ex vivo system that maintains the TME in its near-native state. Leveraging single-cell genomics, we identified cell type-specific transcriptional reprogramming in response to immunotherapy perturbations.

METHODS

We generated ex vivo tumor slice cultures from fresh surgical resections of gastric and colon cancer and treated them with GITR agonist or TIGIT antagonist antibodies. We applied paired single-cell RNA and TCR sequencing to the original surgical resections, control, and treated ex vivo tumor slice cultures. We additionally confirmed target expression using multiplex immunofluorescence and validated our findings with RNA in situ hybridization.

RESULTS

We confirmed that tumor slice cultures maintained the cell types, transcriptional cell states and proportions of the original surgical resection. The GITR agonist was limited to increasing effector gene expression only in cytotoxic CD8 T cells. Dysfunctional exhausted CD8 T cells did not respond to GITR agonist. In contrast, the TIGIT antagonist increased TCR signaling and activated both cytotoxic and dysfunctional CD8 T cells. This included cells corresponding to TCR clonotypes with features indicative of potential tumor antigen reactivity. The TIGIT antagonist also activated T follicular helper-like cells and dendritic cells, and reduced markers of immunosuppression in regulatory T cells.

CONCLUSIONS

We identified novel cellular mechanisms of action of GITR and TIGIT immunotherapy in the patients' TME. Unlike the GITR agonist that generated a limited transcriptional response, TIGIT antagonist orchestrated a multicellular response involving CD8 T cells, T follicular helper-like cells, dendritic cells, and regulatory T cells. Our experimental strategy combining single-cell genomics with preclinical models can successfully identify mechanisms of action of novel immunotherapy agents. Understanding the cellular and transcriptional mechanisms of response or resistance will aid in prioritization of targets and their clinical translation.

A spatially mapped gene expression signature for intestinal stem-like cells identifies high-risk precursors of gastric cancer

Objective

Gastric intestinal metaplasia (GIM) is a precancerous lesion that increases gastric cancer (GC) risk. The Operative Link on GIM (OLGIM) is a combined clinical-histopathologic system to risk-stratify patients with GIM. The identification of molecular biomarkers that are indicators for advanced OLGIM lesions may improve cancer prevention efforts.

Methods

This study was based on clinical and genomic data from four cohorts: 1) GAPS, a GIM cohort with detailed OLGIM severity scoring (N=303 samples); 2) the Cancer Genome Atlas (N=198); 3) a collation of in-house and publicly available scRNA-seq data (N=40), and 4) a spatial validation cohort (N=5) consisting of annotated histology slides of patients with either GC or advanced GIM. We used a multi-omics pipeline to identify, validate and sequentially parse a highly-refined signature of 26 genes which characterize high-risk GIM.

Results

Using standard RNA-seq, we analyzed two separate, non-overlapping discovery (N=88) and validation (N=215) sets of GIM. In the discovery phase, we identified 105 upregulated genes specific for high-risk GIM (defined as OLGIM III-IV), of which 100 genes were independently confirmed in the validation set. Spatial transcriptomic profiling revealed 36 of these 100 genes to be expressed in metaplastic foci in GIM. Comparison with bulk GC sequencing data revealed 26 of these genes to be expressed in intestinal-type GC. Single-cell profiling resolved the 26-gene signature to both mature intestinal lineages (goblet cells, enterocytes) and immature intestinal lineages (stem-like cells). A subset of these genes was further validated using single-molecule multiplex fluorescence in situ hybridization. We found certain genes (TFF3 and ANPEP) to mark differentiated intestinal lineages, whereas others (OLFM4 and CPS1) localized to immature cells in the isthmic/crypt region of metaplastic glands, consistent with the findings from scRNAseq analysis.

Conclusions

using an integrated multi-omics approach, we identified a novel 26-gene expression signature for high-OLGIM precursors at increased risk for GC. We found this signature localizes to aberrant intestinal stem-like cells within the metaplastic microenvironment. These findings hold important translational significance for future prevention and early detection efforts.

Direct measurement of engineered cancer mutations and their transcriptional phenotypes in single cells

Genome sequencing studies have identified numerous cancer mutations across a wide spectrum of tumor types, but determining the phenotypic consequence of these mutations remains a challenge. Here, we developed a high-throughput, multiplexed single-cell technology called TISCC-seq to engineer predesignated mutations in cells using CRISPR base editors, directly delineate their genotype among individual cells and determine each mutation's transcriptional phenotype. Long-read sequencing of the target gene's transcript identifies the engineered mutations, and the transcriptome profile from the same set of cells is simultaneously analyzed by short-read sequencing. Through integration, we determine the mutations' genotype and expression phenotype at single-cell resolution. Using cell lines, we engineer and evaluate the impact of >100 TP53 mutations on gene expression. Based on the single-cell gene expression, we classify the mutations as having a functionally significant phenotype.

Pan-conserved segment tags identify ultra-conserved sequences across assemblies in the human pangenome

The human pangenome, a new reference sequence, addresses many limitations of the current GRCh38 reference. The first release is based on 94 high-quality haploid assemblies from individuals with diverse backgrounds. We employed a k-mer indexing strategy for comparative analysis across multiple assemblies, including the pangenome reference, GRCh38, and CHM13, a telomere-to-telomere reference assembly. Our k-mer indexing approach enabled us to identify a valuable collection of universally conserved sequences across all assemblies, referred to as "pan-conserved segment tags" (PSTs). By examining intervals between these segments, we discerned highly conserved genomic segments and those with structurally related polymorphisms. We found 60,764 polymorphic intervals with unique geo-ethnic features in the pangenome reference. In this study, we utilized ultra-conserved sequences (PSTs) to forge a link between human pangenome assemblies and reference genomes. This methodology enables the examination of any sequence of interest within the pangenome, using the reference genome as a comparative framework.

Magnetic DNA random access memory with nanopore readouts and exponentially-scaled combinatorial addressing

The storage of data in DNA typically involves encoding and synthesizing data into short oligonucleotides, followed by reading with a sequencing instrument. Major challenges include the molecular consumption of synthesized DNA, basecalling errors, and limitations with scaling up read operations for individual data elements. Addressing these challenges, we describe a DNA storage system called MDRAM (Magnetic DNA-based Random Access Memory) that enables repetitive and efficient readouts of targeted files with nanopore-based sequencing. By conjugating synthesized DNA to magnetic agarose beads, we enabled repeated data readouts while preserving the original DNA analyte and maintaining data readout quality. MDRAM utilizes an efficient convolutional coding scheme that leverages soft information in raw nanopore sequencing signals to achieve information reading costs comparable to Illumina sequencing despite higher error rates. Finally, we demonstrate a proof-of-concept DNA-based proto-filesystem that enables an exponentially-scalable data address space using only small numbers of targeting primers for assembly and readout.

Short Tandem Repeat DNA Profiling Using Perylene-Oligonucleotide Fluorescence Assay

We report an amplification-free genotyping method to determine the number of human short tandem repeats (STRs). DNA-based STR profiling is a robust method for genetic identification purposes such as forensics and biobanking and for identifying specific molecular subtypes of cancer. STR detection requires polymerase amplification, which introduces errors that obscure the correct genotype. We developed a new method that requires no polymerase. First, we synthesized perylene-nucleoside reagents and incorporated them into oligonucleotide probes that recognize five common human STRs. Using these probes and a bead-based hybridization approach, accurate STR detection was achieved in only 1.5 h, including DNA preparation steps, with up to a 1000-fold target DNA enrichment. This method was comparable to PCR-based assays. Using standard fluorometry, the limit of detection was 2.00 ± 0.07 pM for a given target. We used this assay to accurately identify STRs from 50 human subjects, achieving >98% consensus with sequencing data for STR genotyping.

Single-molecule methylation profiles of cell-free DNA in cancer with nanopore sequencing

Epigenetic characterization of cell-free DNA (cfDNA) is an emerging approach for detecting and characterizing diseases such as cancer. We developed a strategy using nanopore-based single-molecule sequencing to measure cfDNA methylomes. This approach generated up to 200 million reads for a single cfDNA sample from cancer patients, an order of magnitude improvement over existing nanopore sequencing methods. We developed a single-molecule classifier to determine whether individual reads originated from a tumor or immune cells. Leveraging methylomes of matched tumors and immune cells, we characterized cfDNA methylomes of cancer patients for longitudinal monitoring during treatment.

A draft human pangenome reference

Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals1. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample.

Abstract 3116: Spatial single-cell atlas of stage III colorectal cancer

Background: Spatial heterogeneity and multicellular interactions within the tumor microenvironment drive tumor progression and response to therapy, yet, tumor molecular profiling often requires dissociation of cells, losing the spatial context. Spatial proteomic technologies enable capturing single-cell information with the spatial context allowing us to link cancer-causing mutations to outcomes in cell signaling, responses and survival that will aid in diagnosis, prognostication, and treatment of cancer. We aim to capture single cells in intact tissue sections of stage III colorectal tumors from 52 patients to spatially characterize cell types, interactions, and organization of the tumor microenvironment in relation to genetic alterations and clinical outcomes.

Methods: Using Hyperion Imaging Mass Cytometry (IMC), we profiled 16 protein markers across 170 tissue regions, capturing molecular signatures of tissue architecture, tumor cells, and immune cells. We developed an analysis pipeline to quantify single-cell protein expression and identify cell phenotypes. We preserved spatial information to characterize the interactions between neighboring cells and identify multicellular communities. We also generated whole exome sequencing data, RNAseq data and histopathological images from sections of the same tissue blocks. We compared cellular and spatial features between tumors harboring prognostic genomic evens such as TP53 deletion and MSI and between patients stratified by clinical outcomes such as survival and recurrence.

Results: We spatially profiled over 800,000 cells to identify 10 tumor, immune and stromal cell phenotypes, and validated identified cell types against matched histopathology and CIBERSORTx deconvoluted RNAseq. To quantify cell-cell interactions, we measured the distance between neighboring cells and performed neighborhood enrichment analysis. We characterized the multicellular organization of the tumor microenvironment to identify 7 consistent cell neighborhoods of up to 10 adjacent cells. We quantified p53 expression in individual tumor cells and found that TP53 deletion (all heterozygous) had no significant association with p53 expression or p53+ tumor cell abundance, suggesting spatial proteomics reveals additional information absent in traditional sequencing. We find that MSI is exclusive of TP53 deletion and that MSI positive tumors had few p53+ tumor cells, increased CD8+ T cell composition and B cells that were further away from p53+ tumor cells compared to MSI negative tumors.

Conclusions: Using Hyperion IMC to acquire a spatial single-cell atlas of stage III colorectal cancer enabled us to characterize the tumor microenvironment by defining cell types, quantifying cell interactions, and identifying multicellular communities. These features enable the linkage of prognostic genetic alternations and clinical outcomes with changes in the tumor microenvironment.

Citation Format: Andrew Su, Minh Tran, HoJoon Lee, Anuja Sathe, Xiangqi Bai, Richard Cruz, Lance Pflieger, Quan Nguyen, Hanlee P. Ji, Terence Rhodes. Spatial single-cell atlas of stage III colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3116.

Single cell and spatial alternative splicing analysis with long read sequencing

Long-read sequencing has become a powerful tool for alternative splicing analysis. However, technical and computational challenges have limited our ability to explore alternative splicing at single cell and spatial resolution. The higher sequencing error of long reads, especially high indel rates, have limited the accuracy of cell barcode and unique molecular identifier (UMI) recovery. Read truncation and mapping errors, the latter exacerbated by the higher sequencing error rates, can cause the false detection of spurious new isoforms. Downstream, there is yet no rigorous statistical framework to quantify splicing variation within and between cells/spots. In light of these challenges, we developed Longcell, a statistical framework and computational pipeline for accurate isoform quantification for single cell and spatial spot barcoded long read sequencing data. Longcell performs computationally efficient cell/spot barcode extraction, UMI recovery, and UMI-based truncation- and mapping-error correction. Through a statistical model that accounts for varying read coverage across cells/spots, Longcell rigorously quantifies the level of inter-cell/spot versus intra-cell/ spot diversity in exon-usage and detects changes in splicing distributions between cell populations. Applying Longcell to single cell long-read data from multiple contexts, we found that intra-cell splicing heterogeneity, where multiple isoforms co-exist within the same cell, is ubiquitous for highly expressed genes. On matched single cell and Visium long read sequencing for a tissue of colorectal cancer metastasis to the liver, Longcell found concordant signals between the two data modalities. Finally, on a perturbation experiment for 9 splicing factors, Longcell identified regulatory targets that are validated by targeted sequencing.

GITR and TIGIT immunotherapy provokes divergent multi-cellular responses in the tumor microenvironment of gastrointestinal cancers

Understanding the cellular mechanisms of novel immunotherapy agents in the human tumor microenvironment (TME) is critical to their clinical success. We examined GITR and TIGIT immunotherapy in gastric and colon cancer patients using ex vivo slice tumor slice cultures derived from cancer surgical resections. This primary culture system maintains the original TME in a near-native state. We applied paired single-cell RNA and TCR sequencing to identify cell type specific transcriptional reprogramming. The GITR agonist was limited to increasing effector gene expression only in cytotoxic CD8 T cells. The TIGIT antagonist increased TCR signaling and activated both cytotoxic and dysfunctional CD8 T cells, including clonotypes indicative of potential tumor antigen reactivity. The TIGIT antagonist also activated T follicular helper-like cells and dendritic cells, and reduced markers of immunosuppression in regulatory T cells. Overall, we identified cellular mechanisms of action of these two immunotherapy targets in the patients' TME.

The Gastric Cancer Registry Genome Explorer: A tool for genomic discovery

434

Background: The Gastric Cancer Registry (GCR) collects clinical questionnaire data and biospecimens from gastric cancer (GC) patients and individuals at high risk for GC (through family history of GC or a germline CDH1 mutation). Its purpose is to facilitate research into better detection and treatment strategies for GC. In 2020, the GCR Genome Explorer (GCR-GE), a publicly accessible and interactive database of clinical and genomic data, was launched to meet this goal. Methods: We generated genomic datasets from participants when GC status and archival gastric tumor tissue were available. We processed the tumor tissue (and paired normal tissue when possible) for whole genome, whole exome, and bulk RNA sequencing. The following genomic features were identified: copy number variations, gene mutations, gene expression, microbiome composition, estimation of tumor-infiltrating immune cells, and tumor neoantigens. We populated (1) all genomic alterations identified from GCR sequencing data, and (2) demographic and pathologic data regarding the tumor that were compiled from participant questionnaires and medical records into the GCR-GE. In addition, sequencing files from the Cancer Genome Atlas (TCGA) were similarly analyzed and uploaded as external datasets. Features of the GCR-GE include cross-study comparisons, study summaries, and queries down to the individual gene, neoantigen, and patient level. Results: In total, 243 GC patients donated tumor samples. The new GCR-GE release contains genomic datasets for 214 of these tumors, as well as datasets for 443 gastric tumors and 185 esophageal tumors from TCGA. Data generation was possible thanks to 756 subjects who enrolled in the GCR from 2011-2022. Most subjects were diagnosed with GC only (N=487), but interestingly, some met multiple criteria. For instance, 10 GC patients had a family history; 10 had a germline CDH1 mutation; and 21 patients had GC, a family history, and a germline CDH1 mutation. Efforts to upload additional datasets are ongoing. All data in the GCR-GE is downloadable. Conclusions: The GCR-GE is a comprehensive resource of genomic and clinical data. Given its accessibility, ease of use, and large cohort sizes, the GCR-GE presents a highly valuable tool for accelerating GC research.

Colorectal Cancer Metastases in the Liver Establish Immunosuppressive Spatial Networking between Tumor-Associated SPP1+ Macrophages and Fibroblasts

PURPOSE

The liver is the most frequent metastatic site for colorectal cancer. Its microenvironment is modified to provide a niche that is conducive for colorectal cancer cell growth. This study focused on characterizing the cellular changes in the metastatic colorectal cancer (mCRC) liver tumor microenvironment (TME).

EXPERIMENTAL DESIGN

We analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue, and peripheral blood mononuclear cells using single-cell RNA sequencing (scRNA-seq). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution.

RESULTS

We identified TME-specific SPP1-expressing macrophages with altered metabolism features, foam cell characteristics, and increased activity in extracellular matrix (ECM) organization. SPP1+ macrophages and fibroblasts expressed complementary ligand-receptor pairs with the potential to mutually influence their gene-expression programs. TME lacked dysfunctional CD8 T cells and contained regulatory T cells, indicative of immunosuppression. Spatial imaging validated these cell states in the TME. Moreover, TME macrophages and fibroblasts had close spatial proximity, which is a requirement for intercellular communication and networking. In an independent cohort of mCRCs in the liver, we confirmed the presence of SPP1+ macrophages and fibroblasts using gene-expression data. An increased proportion of TME fibroblasts was associated with the worst prognosis in these patients.

CONCLUSIONS

We demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports colorectal cancer growth in the immunosuppressed metastatic niche in the liver. These features can be used to target immune-checkpoint-resistant MSS tumors.

Exploratory genomic analysis of high-grade neuroendocrine neoplasms across diverse primary sites

High-grade (grade 3) neuroendocrine neoplasms (G3 NENs) have poor survival outcomes. From a clinical standpoint, G3 NENs are usually grouped regardless of primary site and treated similarly. Little is known regarding the underlying genomics of these rare tumors, especially when compared across different primary sites. We performed whole transcriptome (n = 46), whole exome (n = 40), and gene copy number (n = 43) sequencing on G3 NEN formalin-fixed, paraffin-embedded samples from diverse organs (in total, 17 were lung, 16 were gastroenteropancreatic, and 13 other). G3 NENs despite arising from diverse primary sites did not have gene expression profiles that were easily segregated by organ of origin. Across all G3 NENs, TP53, APC, RB1, and CDKN2A were significantly mutated. The CDK4/6 cell cycling pathway was mutated in 95% of cases, with upregulation of oncogenes within this pathway. G3 NENs had high tumor mutation burden (mean 7.09 mutations/MB), with 20% having >10 mutations/MB. Two somatic copy number alterations were significantly associated with worse prognosis across tissue types: focal deletion 22q13.31 (HR, 7.82; P = 0.034) and arm amplification 19q (HR, 4.82; P = 0.032). This study is among the most diverse genomic study of high-grade neuroendocrine neoplasms. We uncovered genomic features previously unrecognized for this rapidly fatal and rare cancer type that could have potential prognostic and therapeutic implications.

The Gastric Cancer Registry: A Genomic Translational Resource for Multidisciplinary Research in Gastric Cancer

BACKGROUND

Gastric cancer is a leading cause of cancer morbidity and mortality. Developing information systems which integrate clinical and genomic data may accelerate discoveries to improve cancer prevention, detection, and treatment. To support translational research in gastric cancer, we developed the Gastric Cancer Registry (GCR), a North American repository of clinical and cancer genomics data.

METHODS

Participants self-enrolled online. Entry criteria into the GCR included the following: (i) diagnosis of gastric cancer, (ii) history of gastric cancer in a first- or second-degree relative, or (iii) known germline mutation in the gene CDH1. Participants provided demographic and clinical information through a detailed survey. Some participants provided specimens of saliva and tumor samples. Tumor samples underwent exome sequencing, whole-genome sequencing, and transcriptome sequencing.

RESULTS

From 2011 to 2021, 567 individuals registered and returned the clinical questionnaire. For this cohort 65% had a personal history of gastric cancer, 36% reported a family history of gastric cancer, and 14% had a germline CDH1 mutation. 89 patients with gastric cancer provided tumor samples. For the initial study, 41 tumors were sequenced using next-generation sequencing. The data was analyzed for cancer mutations, copy-number variations, gene expression, microbiome, neoantigens, immune infiltrates, and other features. We developed a searchable, web-based interface (the GCR Genome Explorer) to enable researchers' access to these datasets.

CONCLUSIONS

The GCR is a unique, North American gastric cancer registry which integrates clinical and genomic annotation.

IMPACT

Available for researchers through an open access, web-based explorer, the GCR Genome Explorer will accelerate collaborative gastric cancer research across the United States and world.

ALTEN: A High-Fidelity Primary Tissue-Engineering Platform to Assess Cellular Responses Ex Vivo

To fully investigate cellular responses to stimuli and perturbations within tissues, it is essential to replicate the complex molecular interactions within the local microenvironment of cellular niches. Here, the authors introduce Alginate-based tissue engineering (ALTEN), a biomimetic tissue platform that allows ex vivo analysis of explanted tissue biopsies. This method preserves the original characteristics of the source tissue's cellular milieu, allowing multiple and diverse cell types to be maintained over an extended period of time. As a result, ALTEN enables rapid and faithful characterization of perturbations across specific cell types within a tissue. Importantly, using single-cell genomics, this approach provides integrated cellular responses at the resolution of individual cells. ALTEN is a powerful tool for the analysis of cellular responses upon exposure to cytotoxic agents and immunomodulators. Additionally, ALTEN's scalability using automated microfluidic devices for tissue encapsulation and subsequent transport, to enable centralized high-throughput analysis of samples gathered by large-scale multicenter studies, is shown.

Germline variants of ATG7 in familial cholangiocarcinoma alter autophagy and p62

Autophagy is a housekeeping mechanism tasked with eliminating misfolded proteins and damaged organelles to maintain cellular homeostasis. Autophagy deficiency results in increased oxidative stress, DNA damage and chronic cellular injury. Among the core genes in the autophagy machinery, ATG7 is required for autophagy initiation and autophagosome formation. Based on the analysis of an extended pedigree of familial cholangiocarcinoma, we determined that all affected family members had a novel germline mutation (c.2000C>T p.Arg659* (p.R659*)) in ATG7. Somatic deletions of ATG7 were identified in the tumors of affected individuals. We applied linked-read sequencing to one tumor sample and demonstrated that the ATG7 somatic deletion and germline mutation were located on distinct alleles, resulting in two hits to ATG7. From a parallel population genetic study, we identified a germline polymorphism of ATG7 (c.1591C>G p.Asp522Glu (p.D522E)) associated with increased risk of cholangiocarcinoma. To characterize the impact of these germline ATG7 variants on autophagy activity, we developed an ATG7-null cell line derived from the human bile duct. The mutant p.R659* ATG7 protein lacked the ability to lipidate its LC3 substrate, leading to complete loss of autophagy and increased p62 levels. Our findings indicate that germline ATG7 variants have the potential to impact autophagy function with implications for cholangiocarcinoma development.

Abstract 5042: Reconstructing the spatial evolution of cancer through subclone detection on copy number profiles in tumor sequencing data

Cancer results from somatic mutations such as copy number alterations (CNAs) that continue to accumulate during disease progression. These mutations can lead to functional heterogeneity within tumors and can influence the efficacy of cancer therapy. Therefore, studying the functional characteristics and spatial distribution of genetically distinct subclones is crucial to the understanding of tumor evolution and the design of cancer treatment. Here, we present Spatioscope, a method for subclone detection using copy number profiles that can be applied to spatial transcriptomics (ST) data and data from single-cell sequencing platforms such as scRNA-seq, scATAC-seq and scDNA-seq. Spatioscope implements a nested Chinese restaurant process, which mimics the tumor evolutionary process, to identify de novo subclones within one or multiple samples from the same patient. Spatioscope incorporates prior information from paired whole-genome or whole-exome sequencing (WGS/WES) data to achieve more reliable subclone detection and malignant cell labeling. We first applied Spatioscope on ST data from breast cancer, colorectal cancer and squamous cell carcinoma, as well as on scRNA-seq from three primary and metastatic gastrointestinal tumor samples. Spatioscope successfully distinguished malignant cells from stromal cells, and identified genetically distinct subclones which were validated using matched WGS/WES data, pathology annotations, or scDNA-seq data. On ST data, we show that Spatioscope accurately delineates the tumor’s invasive front, allowing for detailed characterization of interactions between stromal cells and malignant cells of different subclonal origins. In previous work, we showed the pervasive occurrence of highly complex subclonal allele-specific copy number alterations, and thus, we extended Spatioscope to identify subclones with different allele-specific copy number profiles. On three gastrointestinal tumor samples with scDNA-seq and two additional scATAC-seq datasets from a basal cell carcinoma and a gastric cancer cell line, Spatioscope successfully reconstructed complex recurrently mutated subclonal copy number regions. This is especially useful for data with sparse signals such as scATAC-seq when matched scDNA-seq data are unavailable. Using Spatioscope, we detected subclones based on copy number profiles in spatial and single cell tumor sequencing, enabling the investigation of the interplay between genome, transcriptome, and spatial environment during tumor evolution.

Citation Format: Chi-Yun Wu, Paul R. Hess, Anuja Sathe, Jiazhen Rong, Billy T. Lau, Susan M. Grimes, Hanlee P. Ji, Nancy R. Zhang. Reconstructing the spatial evolution of cancer through subclone detection on copy number profiles in tumor sequencing data [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 5042.

Analysis of 16S rRNA sequencing in advanced colorectal cancer tissue samples

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Background: Changes in the colon’s microbiota composition are contributors to the pathogenesis of colorectal cancer (CRC). Features of this microbiome may have prognostic significance. For this study, we analyzed 16S rRNA sequencing data from tumor tissue samples of advanced CRC patients and determined if there were potential correlations between microbiome composition and clinical outcomes. Methods: One hundred and thirty three advanced CRC patients in St. Vincent’s Hospital in Korea were enrolled. DNA was extracted from collected tissue samples, the V3-V4 regions were amplified, and a 16S rRNA gene amplicon library was prepared using an Illumina protocol. DNA was sequenced on an Illumina MiSeq instrument. We used three different bioinformatic packages to process the sequence data and evaluate the microbiome composition of each tumor. Results: The classification performances of three different analytic pipelines (Kraken2, QIIME2, and DADA2) were compared in a microbiome control sample. Among these, DADA2 and QIIME2 were chosen for use in subsequent analysis, due to their lower Chi-square (χ2) test statistic values on the control data. After excluding samples that retained less than 5% of total reads after merging, 120 samples were analyzed. The median age of the cohort was 63 years, 63.3% were male, and all were Korean. The distribution over primary sites was 27.5% from the right-side colon, 30.8% from the left-side colon, and 41.7% from the rectum. All subjects received the first line of systemic treatment. The median progression-free survival time was 8.9 months. Twenty-nine patients (24.2%) survived more than 24 months. When examining the results from the two bioinformatic packages, pairwise comparisons showed positive correlations in the relative abundances of the top 20 genera. Fusobacterium, a microbe known to relate to pathogenesis and prognosis of CRC, was not detected by QIIME2. Stratifying by primary site, rectal cancers showed higher alpha diversity than left- or right-side colon cancers. Separately, a higher serum CEA level (≥8) at diagnosis, and the presence of lung metastasis were both found to be related to higher alpha-diversity, a global indicator of microbiome composition. When excluding minimally abundant (< 1% per patient) genera, beta-diversity was found to be differentiable by T stage, the presence of lung metastasis, and the presence of liver metastasis. Most notably, beta-diversity differed between patients who survived more than two years and patients who died within 2 years. Using the DADA2 results, we confirmed that the presence of Fusobacterium nucleatum in CRC tissue was found to be a strong predictor of poor overall survival (OS), along with old age and liver metastasis. Conclusions: This study suggests potential associations between microbiome composition and clinical parameters of advanced CRC. Microbial biomarkers may be a valuable prognostic tool in this population.

Characterization of the consensus mucosal microbiome of colorectal cancer

Dysbioisis is an imbalance of an organ's microbiome and plays a role in colorectal cancer pathogenesis. Characterizing the bacteria in the microenvironment of a cancer through genome sequencing has advantages compared to culture-based profiling. However, there are notable technical and analytical challenges in characterizing universal features of tumor microbiomes. Colorectal tumors demonstrate microbiome variation among different studies and across individual patients. To address these issues, we conducted a computational study to determine a consensus microbiome for colorectal cancer, analyzing 924 tumors from eight independent RNA-Seq data sets. A standardized meta-transcriptomic analysis pipeline was established with quality control metrics. Microbiome profiles across different cohorts were compared and recurrently altered microbial shifts specific to colorectal cancer were determined. We identified cancer-specific set of 114 microbial species associated with tumors that were found among all investigated studies. Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria were among the four most abundant phyla for the colorectal cancer microbiome. Member species of Clostridia were depleted and Fusobacterium nucleatum was one of the most enriched bacterial species in tumors. Associations between the consensus species and specific immune cell types were noted. Our results are available as a web data resource for other researchers to explore (https://crc-microbiome.stanford.edu).

New approaches to moderate CRISPR-Cas9 activity: Addressing issues of cellular uptake and endosomal escape

CRISPR-Cas9 is rapidly entering molecular biology and biomedicine as a promising gene-editing tool. A unique feature of CRISPR-Cas9 is a single-guide RNA directing a Cas9 nuclease toward its genomic target. Herein, we highlight new approaches for improving cellular uptake and endosomal escape of CRISPR-Cas9. As opposed to other recently published works, this review is focused on non-viral carriers as a means to facilitate the cellular uptake of CRISPR-Cas9 through endocytosis. The majority of non-viral carriers, such as gold nanoparticles, polymer nanoparticles, lipid nanoparticles, and nanoscale zeolitic imidazole frameworks, is developed with a focus toward optimizing the endosomal escape of CRISPR-Cas9 by taking advantage of the acidic environment in the late endosomes. Among the most broadly used methods for in vitro and ex vivo ribonucleotide protein transfection are electroporation and microinjection. Thus, other delivery formats are warranted for in vivo delivery of CRISPR-Cas9. Herein, we specifically revise the use of peptide and nanoparticle-based systems as platforms for CRISPR-Cas9 delivery in vivo. Finally, we highlight future perspectives of the CRISPR-Cas9 gene-editing tool and the prospects of using non-viral vectors to improve its bioavailability and therapeutic potential.

Single-cell characterization of CRISPR-modified transcript isoforms with nanopore sequencing

We developed a single-cell approach to detect CRISPR-modified mRNA transcript structures. This method assesses how genetic variants at splicing sites and splicing factors contribute to alternative mRNA isoforms. We determine how alternative splicing is regulated by editing target exon-intron segments or splicing factors by CRISPR-Cas9 and their consequences on transcriptome profile. Our method combines long-read sequencing to characterize the transcript structure and short-read sequencing to match the single-cell gene expression profiles and gRNA sequence and therefore provides targeted genomic edits and transcript isoform structure detection at single-cell resolution.

Profiling diverse sequence tandem repeats in colorectal cancer reveals co-occurrence of microsatellite and chromosomal instability involving Chromosome 8

We developed a sensitive sequencing approach that simultaneously profiles microsatellite instability, chromosomal instability, and subclonal structure in cancer. We assessed diverse repeat motifs across 225 microsatellites on colorectal carcinomas. Our study identified elevated alterations at both selected tetranucleotide and conventional mononucleotide repeats. Many colorectal carcinomas had a mix of genomic instability states that are normally considered exclusive. An MSH3 mutation may have contributed to the mixed states. Increased copy number of chromosome arm 8q was most prevalent among tumors with microsatellite instability, including a case of translocation involving 8q. Subclonal analysis identified co-occurring driver mutations previously known to be exclusive.

Abstract 1680: Patient-derived ex vivo TME-models and single-cell sequencing reveal transcriptional responses to immunotherapy

The tumor microenvironment (TME) contains diverse cell phenotypes. This heterogeneity influences the cancer immunity cycle and response to immunotherapy. We examined the effects of immunotherapy in a unique patient-derived ex vivo system that maintains the TME in its near-native state. We leveraged single-cell RNA sequencing (scRNA-seq) to conduct an unbiased analysis of transcriptional responses in heterogenous TME cell types. We established slice cultures using a vibratome from five surgical resections of colorectal or gastric cancer. Ex vivo slice cultures (‘TME-models') were treated with isotype control antibody, GITR agonist, TIGIT inhibitor or PMA/Ionomycin for 24 hours. TME-models and original surgical resection (‘T0') were dissociated into single-cell suspensions and subjected to scRNA-seq. Following quality control, we performed dimensionality reduction and differential expression analysis. We sequenced 219000 single cells detecting an average of 841 median genes per cell. All cell lineages identified in the T0 resection including tumor epithelium, CD4, CD8, Treg, NK, B, plasma, mast, dendritic cells, macrophages, fibroblasts and endothelial cells were maintained in the corresponding ex vivo TME-model. No consistent differences in proportions or transcriptional profiles were observed, indicating that original TME is preserved in the near-native state. PMA/ionomycin led to an activation phenotype in CD8, CD4 and regulatory T cells including a significant increase in expression of effector cytokines (GZMB, IFNG, PRF1, CCL3, etc.) , activation markers (IL2RA, CD69), immune checkpoints (PDCD1, TIGIT, LAG3, etc.) and co-stimulatory molecules (TNFRSF18, TNFRSF9, etc.). PMA/Ionomycin also led to upregulation of chemokines (CXCL9, CXCL5, etc.) and interferon response genes (ISG20, IRF1, etc.) in fibroblasts and tumor epithelial cells potentially indicative of indirect T-cell mediated effects. GITR agonist and TIGIT inhibitor led to an increase in cytotoxic genes including IFNG in effector CD8 T cells but not in naïve cells. Moreover, the extent of transcriptional response varied across tumors. Experiments on additional tumors are ongoing. Ex vivo TME-models derived from surgical resections maintain all TME components in their near native state. In combination with scRNA-seq, this system can be utilized to test targets in the TME and provide insights into their mechanisms of action and resistance. Using this approach, we successfully identified heterogenous patient responses to GITR stimulation and TIGIT inhibition in gastrointestinal cancers.

Citation Format: Anuja Sathe, Jiamin Chen, Susan M. Grimes, Carlos I. Ayala, George Poultsides, Hanlee P. Ji. Patient-derived ex vivo TME-models and single-cell sequencing reveal transcriptional responses to immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1680.

Single-cell analysis can define distinct evolution of tumor sites in follicular lymphoma

Tumor heterogeneity complicates biomarker development and fosters drug resistance in solid malignancies. In lymphoma, our knowledge of site-to-site heterogeneity and its clinical implications is still limited. Here, we profiled 2 nodal, synchronously acquired tumor samples from 10 patients with follicular lymphoma (FL) using single-cell RNA, B-cell receptor (BCR) and T-cell receptor sequencing, and flow cytometry. By following the rapidly mutating tumor immunoglobulin genes, we discovered that BCR subclones were shared between the 2 tumor sites in some patients, but in many patients, the disease had evolved separately with limited tumor cell migration between the sites. Patients exhibiting divergent BCR evolution also exhibited divergent tumor gene-expression and cell-surface protein profiles. While the overall composition of the tumor microenvironment did not differ significantly between sites, we did detect a specific correlation between site-to-site tumor heterogeneity and T follicular helper (Tfh) cell abundance. We further observed enrichment of particular ligand-receptor pairs between tumor and Tfh cells, including CD40 and CD40LG, and a significant correlation between tumor CD40 expression and Tfh proliferation. Our study may explain discordant responses to systemic therapies, underscores the difficulty of capturing a patient's disease with a single biopsy, and furthers our understanding of tumor-immune networks in FL.

Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer

Cancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single-cell DNA- and/or transposase-accessible chromatin-sequencing (scDNA-seq, ATAC-seq) data, enabling combined analysis of allele-specific copy number and chromatin accessibility. On scDNA-seq data from gastric, colorectal and breast cancer samples, with validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multiallelic CNAs, in which cells that carry varying allelic configurations adding to the same total copy number coevolve within a tumor. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detected multiallelic copy number events and copy-neutral loss-of-heterozygosity, enabling dissection of the contributions of chromosomal instability and chromatin remodeling to tumor evolution.

Ultra-fast detection and quantification of nucleic acids by amplification-free fluorescence assay

Two types of clinically important nucleic acid biomarkers, microRNA (miRNA) and circulating tumor DNA (ctDNA) were detected and quantified from human serum using an amplification-free fluorescence hybridization assay. Specifically, miRNAs hsa-miR-223-3p and hsa-miR-486-5p with relevance for rheumatoid arthritis and cancer related mutations BRAF and KRAS of ctDNA were directly measured. The required oligonucleotide probes for the assay were rationally designed and synthesized through a novel "clickable" approach which is time and cost-effective. With no need for isolating nucleic acid components from serum, the fluoresence-based assay took only 1 hour. Detection and absolute quantification of targets was successfully achieved despite their notoriously low abundance, with a precision down to individual nucleotides. Obtained miRNA and ctDNA amounts showed overall a good correlation with current techniques. With appropriate probes, our novel assay and signal boosting approach could become a useful tool for point-of-care measuring other low abundance nucleic acid biomarkers.

Profiling SARS-CoV-2 mutation fingerprints that range from the viral pangenome to individual infection quasispecies

Background

The genome of SARS-CoV-2 is susceptible to mutations during viral replication due to the errors generated by RNA-dependent RNA polymerases. These mutations enable the SARS-CoV-2 to evolve into new strains. Viral quasispecies emerge from de novo mutations that occur in individual patients. In combination, these sets of viral mutations provide distinct genetic fingerprints that reveal the patterns of transmission and have utility in contact tracing.

Methods

Leveraging thousands of sequenced SARS-CoV-2 genomes, we performed a viral pangenome analysis to identify conserved genomic sequences. We used a rapid and highly efficient computational approach that relies on k-mers, short tracts of sequence, instead of conventional sequence alignment. Using this method, we annotated viral mutation signatures that were associated with specific strains. Based on these highly conserved viral sequences, we developed a rapid and highly scalable targeted sequencing assay to identify mutations, detect quasispecies variants, and identify mutation signatures from patients. These results were compared to the pangenome genetic fingerprints.

Results

We built a k-mer index for thousands of SARS-CoV-2 genomes and identified conserved genomics regions and landscape of mutations across thousands of virus genomes. We delineated mutation profiles spanning common genetic fingerprints (the combination of mutations in a viral assembly) and a combination of mutations that appear in only a small number of patients. We developed a targeted sequencing assay by selecting primers from the conserved viral genome regions to flank frequent mutations. Using a cohort of 100 SARS-CoV-2 clinical samples, we identified genetic fingerprints consisting of strain-specific mutations seen across populations and de novo quasispecies mutations localized to individual infections. We compared the mutation profiles of viral samples undergoing analysis with the features of the pangenome.

Conclusions

We conducted an analysis for viral mutation profiles that provide the basis of genetic fingerprints. Our study linked pangenome analysis with targeted deep sequenced SARS-CoV-2 clinical samples. We identified quasispecies mutations occurring within individual patients and determined their general prevalence when compared to over 70,000 other strains. Analysis of these genetic fingerprints may provide a way of conducting molecular contact tracing.

Unique k-mer sequences for validating cancer-related substitution, insertion and deletion mutations

Cancer genome sequencing has led to important discoveries such as the identification of cancer genes. However, challenges remain in the analysis of cancer genome sequencing. One significant issue is that mutations identified by multiple variant callers are frequently discordant even when using the same genome sequencing data. For insertion and deletion mutations, oftentimes there is no agreement among different callers. Identifying somatic mutations involves read mapping and variant calling, a complicated process that uses many parameters and model tuning. To validate the identification of true mutations, we developed a method using k-mer sequences. First, we characterized the landscape of unique versus non-unique k-mers in the human genome. Second, we developed a software package, KmerVC, to validate the given somatic mutations from sequencing data. Our program validates the occurrence of a mutation based on statistically significant difference in frequency of k-mers with and without a mutation from matched normal and tumor sequences. Third, we tested our method on both simulated and cancer genome sequencing data. Counting k-mer involving mutations effectively validated true positive mutations including insertions and deletions across different individual samples in a reproducible manner. Thus, we demonstrated a straightforward approach for rapidly validating mutations from cancer genome sequencing data.

Profiling SARS-CoV-2 mutation fingerprints that range from the viral pangenome to individual infection quasispecies

Background

The genome of SARS-CoV-2 is susceptible to mutations during viral replication due to the errors generated by RNA-dependent RNA polymerases. These mutations enable the SARS-CoV-2 to evolve into new strains. Viral quasispecies emerge from de novo mutations that occur in individual patients. In combination, these sets of viral mutations provide distinct genetic fingerprints that reveal the patterns of transmission and have utility in contract tracing.

Methods

Leveraging thousands of sequenced SARS-CoV-2 genomes, we performed a viral pangenome analysis to identify conserved genomic sequences. We used a rapid and highly efficient computational approach that relies on k-mers, short tracts of sequence, instead of conventional sequence alignment. Using this method, we annotated viral mutation signatures that were associated with specific strains. Based on these highly conserved viral sequences, we developed a rapid and highly scalable targeted sequencing assay to identify mutations, detect quasispecies and identify mutation signatures from patients. These results were compared to the pangenome genetic fingerprints.

Results

We built a k-mer index for thousands of SARS-CoV-2 genomes and identified conserved genomics regions and landscape of mutations across thousands of virus genomes. We delineated mutation profiles spanning common genetic fingerprints (the combination of mutations in a viral assembly) and rare ones that occur in only small fraction of patients. We developed a targeted sequencing assay by selecting primers from the conserved viral genome regions to flank frequent mutations. Using a cohort of SARS-CoV-2 clinical samples, we identified genetic fingerprints consisting of strain-specific mutations seen across populations and de novo quasispecies mutations localized to individual infections. We compared the mutation profiles of viral samples undergoing analysis with the features of the pangenome.

Conclusions

We conducted an analysis for viral mutation profiles that provide the basis of genetic fingerprints. Our study linked pangenome analysis with targeted deep sequenced SARS-CoV-2 clinical samples. We identified quasispecies mutations occurring within individual patients, mutations demarcating dominant species and the prevalence of mutation signatures, of which a significant number were relatively unique. Analysis of these genetic fingerprints may provide a way of conducting molecular contact tracing.

A Summary of the 2020 Gastric Cancer Summit at Stanford University

There exists no coherent national strategy for the early detection or prevention of gastric cancer in the United States (US), even among identified high-risk groups such as Asian Americans, African Americans, Hispanic Americans, and Alaska Native/American Indian peoples. As a result, patients with gastric cancer in the US are diagnosed at later stages and demonstrate worse overall survival compared to nations of East Asia with established screening programs (Table 1). The under-recognition of gastric cancer risk within minority communities is a significant unaddressed healthcare disparity.

Structural variant analysis for linked-read sequencing data with gemtools

SUMMARY

Linked-read sequencing generates synthetic long reads which are useful for the detection and analysis of structural variants (SVs). The software associated with 10× Genomics linked-read sequencing, Long Ranger, generates the essential output files (BAM, VCF, SV BEDPE) necessary for downstream analyses. However, to perform downstream analyses requires the user to customize their own tools to handle the unique features of linked-read sequencing data. Here, we describe gemtools, a collection of tools for the downstream and in-depth analysis of SVs from linked-read data. Gemtools uses the barcoded aligned reads and the Megabase-scale phase blocks to determine haplotypes of SV breakpoints and delineate complex breakpoint configurations at the resolution of single DNA molecules. The gemtools package is a suite of tools that provides the user with the flexibility to perform basic functions on their linked-read sequencing output in order to address even more questions.

AVAILABILITY AND IMPLEMENTATION

The gemtools package is freely available for download at: https://github.com/sgreer77/gemtools.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution

Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.

CRISPRpic: fast and precise analysis for CRISPR-induced mutations via prefixed index counting

Analysis of CRISPR-induced mutations at targeted locus can be achieved by polymerase chain reaction amplification followed by parallel massive sequencing. We developed a novel algorithm, named as CRISPRpic, to analyze the sequencing reads for the CRISPR experiments via counting exact-matching and pattern-searching. Compare to the other methods based on sequence alignment, CRISPRpic provides precise mutation calling and ultrafast analysis of the sequencing results. Python script of CRISPRpic is available at https://github.com/compbio/CRISPRpic.

Strain-resolved microbiome sequencing reveals mobile elements that drive bacterial competition on a clinical timescale

BACKGROUND

Populations of closely related microbial strains can be simultaneously present in bacterial communities such as the human gut microbiome. We recently developed a de novo genome assembly approach that uses read cloud sequencing to provide more complete microbial genome drafts, enabling precise differentiation and tracking of strain-level dynamics across metagenomic samples. In this case study, we present a proof-of-concept using read cloud sequencing to describe bacterial strain diversity in the gut microbiome of one hematopoietic cell transplantation patient over a 2-month time course and highlight temporal strain variation of gut microbes during therapy. The treatment was accompanied by diet changes and administration of multiple immunosuppressants and antimicrobials.

METHODS

We conducted short-read and read cloud metagenomic sequencing of DNA extracted from four longitudinal stool samples collected during the course of treatment of one hematopoietic cell transplantation (HCT) patient. After applying read cloud metagenomic assembly to discover strain-level sequence variants in these complex microbiome samples, we performed metatranscriptomic analysis to investigate differential expression of antibiotic resistance genes. Finally, we validated predictions from the genomic and metatranscriptomic findings through in vitro antibiotic susceptibility testing and whole genome sequencing of isolates derived from the patient stool samples.

RESULTS

During the 56-day longitudinal time course that was studied, the patient's microbiome was profoundly disrupted and eventually dominated by Bacteroides caccae. Comparative analysis of B. caccae genomes obtained using read cloud sequencing together with metagenomic RNA sequencing allowed us to identify differences in substrain populations over time. Based on this, we predicted that particular mobile element integrations likely resulted in increased antibiotic resistance, which we further supported using in vitro antibiotic susceptibility testing.

CONCLUSIONS

We find read cloud assembly to be useful in identifying key structural genomic strain variants within a metagenomic sample. These strains have fluctuating relative abundance over relatively short time periods in human microbiomes. We also find specific structural genomic variations that are associated with increased antibiotic resistance over the course of clinical treatment.

One Size Does Not Fit All: Marked Heterogeneity in Incidence of and Survival from Gastric Cancer among Asian American Subgroups

BACKGROUND

Asian Americans are at higher risk for noncardia gastric cancers (NCGC) relative to non-Hispanic Whites (NHW). Asian Americans are genetically, linguistically, and culturally heterogeneous, yet have mostly been treated as a single population in prior studies. This aggregation may obscure important subgroup-specific cancer patterns.

METHODS

We utilized data from 13 regional United States cancer registries from 1990 to 2014 to determine secular trends in incidence and survivorship from NCGC. Data were analyzed for NHWs and the six largest Asian American subgroups: Chinese, Japanese, Filipino, Korean, Vietnamese, and South Asian (Indian/Pakistani).

RESULTS

There exists substantial heterogeneity in NCGC incidence between Asian subgroups, with Koreans (48.6 per 100,000 person-years) having seven-fold higher age-adjusted incidence than South Asians (7.4 per 100,000 person-years). Asians had generally earlier stages of diagnosis and higher rates of surgical resection compared with NHWs. All Asian subgroups also demonstrated higher 5-year observed survival compared with NHWs, with Koreans (41.3%) and South Asians (42.8%) having survival double that of NHWs (20.1%, P < 0.001). In multivariable regression, differences in stage of diagnosis and rates of resection partially explained the difference in survivorship between Asian subgroups.

CONCLUSIONS

We find substantial differences in incidence, staging, histology, treatment, and survivorship from NCGC between Asian subgroups, data which challenge our traditional perceptions about gastric cancer in Asians. Both biological heterogeneity and cultural/environmental differences may underlie these findings.

IMPACT

These data are relevant to the national discourse regarding the appropriate role of gastric cancer screening, and identifies high-risk racial/ethnic subgroups who many benefit from customized risk attenuation programs.

Single-Cell Genomic Characterization Reveals the Cellular Reprogramming of the Gastric Tumor Microenvironment

PURPOSE

The tumor microenvironment (TME) consists of a heterogenous cellular milieu that can influence cancer cell behavior. Its characteristics have an impact on treatments such as immunotherapy. These features can be revealed with single-cell RNA sequencing (scRNA-seq). We hypothesized that scRNA-seq analysis of gastric cancer together with paired normal tissue and peripheral blood mononuclear cells (PBMC) would identify critical elements of cellular deregulation not apparent with other approaches.

EXPERIMENTAL DESIGN

scRNA-seq was conducted on seven patients with gastric cancer and one patient with intestinal metaplasia. We sequenced 56,167 cells comprising gastric cancer (32,407 cells), paired normal tissue (18,657 cells), and PBMCs (5,103 cells). Protein expression was validated by multiplex immunofluorescence.

RESULTS

Tumor epithelium had copy number alterations, a distinct gene expression program from normal, with intratumor heterogeneity. Gastric cancer TME was significantly enriched for stromal cells, macrophages, dendritic cells (DC), and Tregs. TME-exclusive stromal cells expressed distinct extracellular matrix components than normal. Macrophages were transcriptionally heterogenous and did not conform to a binary M1/M2 paradigm. Tumor DCs had a unique gene expression program compared to PBMC DCs. TME-specific cytotoxic T cells were exhausted with two heterogenous subsets. Helper, cytotoxic T, Treg, and NK cells expressed multiple immune checkpoint or co-stimulatory molecules. Receptor-ligand analysis revealed TME-exclusive intercellular communication.

CONCLUSIONS

Single-cell gene expression studies revealed widespread reprogramming across multiple cellular elements in the gastric cancer TME. Cellular remodeling was delineated by changes in cell numbers, transcriptional states, and intercellular interactions. This characterization facilitates understanding of tumor biology and enables identification of novel targets including for immunotherapy.

Therapeutic Monitoring of Circulating DNA Mutations in Metastatic Cancer with Personalized Digital PCR

As a high-performance solution for longitudinal monitoring of patients being treated for metastatic cancer, a single-color digital PCR (dPCR) assay that detects and quantifies specific cancer mutations present in circulating tumor DNA (ctDNA) was developed. This customizable assay has a high sensitivity of detection. One can detect a mutation allelic fraction of 0.1%, equivalent to three mutation-bearing DNA molecules among 3000 genome equivalents. The objective of this study was to validate the use of personalized dPCR mutation assays to monitor patients with metastatic cancer. The dPCR results were compared with serum biomarkers indicating disease progression or response. Patients had metastatic colorectal, biliary, breast, lung, and melanoma cancers. Mutations occurred in essential cancer drivers such as BRAF, KRAS, and PIK3CA. Patients were monitored over multiple cycles of treatment for up to a year. All patients had detectable ctDNA mutations. The results correlated with serum markers of metastatic cancer burden, including carcinoembryonic antigen, CA-19-9, and CA-15-3, and qualitatively corresponding to imaging studies. Corresponding trends were observed among these patients receiving active treatment with chemotherapy or targeted agents. For example, in one patient under active treatment, increasing quantities of ctDNA molecules were detected over time, indicating recurrence of tumor. This study demonstrates that personalized dPCR enables longitudinal monitoring of patients with metastatic cancer and may be a useful indicator for treatment response.

scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/.

Targeted short read sequencing and assembly of re-arrangements and candidate gene loci provide megabase diplotypes

The human genome is composed of two haplotypes, otherwise called diplotypes, which denote phased polymorphisms and structural variations (SVs) that are derived from both parents. Diplotypes place genetic variants in the context of cis-related variants from a diploid genome. As a result, they provide valuable information about hereditary transmission, context of SV, regulation of gene expression and other features which are informative for understanding human genetics. Successful diplotyping with short read whole genome sequencing generally requires either a large population or parent-child trio samples. To overcome these limitations, we developed a targeted sequencing method for generating megabase (Mb)-scale haplotypes with short reads. One selects specific 0.1-0.2 Mb high molecular weight DNA targets with custom-designed Cas9-guide RNA complexes followed by sequencing with barcoded linked reads. To test this approach, we designed three assays, targeting the BRCA1 gene, the entire 4-Mb major histocompatibility complex locus and 18 well-characterized SVs, respectively. Using an integrated alignment- and assembly-based approach, we generated comprehensive variant diplotypes spanning the entirety of the targeted loci and characterized SVs with exact breakpoints. Our results were comparable in quality to long read sequencing.

Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2

HepG2 is one of the most widely used human cancer cell lines in biomedical research and one of the main cell lines of ENCODE. Although the functional genomic and epigenomic characteristics of HepG2 are extensively studied, its genome sequence has never been comprehensively analyzed and higher order genomic structural features are largely unknown. The high degree of aneuploidy in HepG2 renders traditional genome variant analysis methods challenging and partially ineffective. Correct and complete interpretation of the extensive functional genomics data from HepG2 requires an understanding of the cell line’s genome sequence and genome structure. Using a variety of sequencing and analysis methods, we identified a wide spectrum of genome characteristics in HepG2: copy numbers of chromosomal segments at high resolution, SNVs and Indels (corrected for aneuploidy), regions with loss of heterozygosity, phased haplotypes extending to entire chromosome arms, retrotransposon insertions and structural variants (SVs) including complex and somatic genomic rearrangements. A large number of SVs were phased, sequence assembled and experimentally validated. We re-analyzed published HepG2 datasets for allele-specific expression and DNA methylation and assembled an allele-specific CRISPR/Cas9 targeting map. We demonstrate how deeper insights into genomic regulatory complexity are gained by adopting a genome-integrated framework.

Identification of large rearrangements in cancer genomes with barcode linked reads

Large genomic rearrangements involve inversions, deletions and other structural changes that span Megabase segments of the human genome. This category of genetic aberration is the cause of many hereditary genetic disorders and contributes to pathogenesis of diseases like cancer. We developed a new algorithm called ZoomX for analysing barcode-linked sequence reads—these sequences can be traced to individual high molecular weight DNA molecules (>50 kb). To generate barcode linked sequence reads, we employ a library preparation technology (10X Genomics) that uses droplets to partition and barcode DNA molecules. Using linked read data from whole genome sequencing, we identify large genomic rearrangements, typically greater than 200kb, even when they are only present in low allelic fractions. Our algorithm uses a Poisson scan statistic to identify genomic rearrangement junctions, determine counts of junction-spanning molecules and calculate a Fisher's exact test for determining statistical significance for somatic aberrations. Utilizing a well-characterized human genome, we benchmarked this approach to accurately identify large rearrangement. Subsequently, we demonstrated that our algorithm identifies somatic rearrangements when present in lower allelic fractions as occurs in tumors. We characterized a set of complex cancer rearrangements with multiple classes of structural aberrations and with possible roles in oncogenesis.

Abstract 2278: A high throughput method for the optimization of digital PCR assays for personalized circulating tumor DNA detection

Single color digital PCR (sc-dPCR) is a robust approach for the quantitation of low allelic fraction mutations in clinical oncology samples. More recently this technology has been employed to identify mutations from circulating tumor DNA (ctDNA) that has been extracted from the blood samples of cancer patients. The use of digital PCR has great potential for non-invasive longitudinal monitoring via liquid biopsies. However, this application requires low input DNA volumes and relies on a single nucleotide variant (SNV) to distinguish between normal and ctDNA, necessitating that sc-dPCR primer binding is both highly efficient and specific. These stringent requirements make assay optimization a tedious process that greatly limits the rate at which personalized detection panels can be generated. We have developed a high throughput method to optimize sc-dPCR assays utilizing Next Generation Sequencing (NGS) technology to assess amplification more quickly and with more flexibility than traditional gel based analysis.

Using our assay optimization approach, a segment of each gene containing a tumor specific SNV was incorporated into the genome of Saccharomyces cerevisiae. These renewable positive control colonies were cultured in a 96 well plate format and pooled to mimic the low allelic frequency conditions of ctDNA. The presence of each tumor specific SNV was confirmed by preparing and sequencing a library containing the unique barcode region of each colony. Using bulk PCR, up to 96 primer sets were tested at one annealing temperature in a singleplex format. Alternatively, we multiplexed up to 11 primers in each well, greatly increasing the number of assays that can be developed per plate. Using this multiplexed format, we introduced a thermal gradient across the plate to identify the optimal annealing temperature of each primer set in a single run. A parallel experiment with identical PCR conditions was run using NA18507 human DNA to act as a negative control for primer specificity.

All amplicons in each PCR condition were uniquely indexed and sequenced using an NGS platform. Using a ratio of the number of reads associated with on target and non-mutation specific amplicon sequences for each primer set, the success of each assay was determined. This method was also used to identify specific mismatches incorporated in the primer sequence that increased binding specificity. Using a sequencing based analysis method, we have observed that sc-dPCR assays can be optimized rapidly across multiple mutations, making them more accessible for personalized monitoring.

Citation Format: Maya M. Arce, Christina Wood-Bouwens, Derrick Haslem, Billy T. Lau, John Bell, Alison Almeda, Matt Kubit, Bryce Moulton, Robin Romero, Robert P. St. Onge, Lincoln Nadauld, Hanlee P. Ji. A high throughput method for the optimization of digital PCR assays for personalized circulating tumor DNA detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2278.

A functional CRISPR/Cas9 screen identifies kinases that modulate FGFR inhibitor response in gastric cancer

Some gastric cancers have FGFR2 amplifications, making them sensitive to FGFR inhibitors. However, cancer cells inevitably develop resistance despite initial response. The underlying resistance mechanism to FGFR inhibition is unclear. In this study, we applied a kinome-wide CRISPR/Cas9 screen to systematically identify kinases that are determinants of sensitivity to a potent FGFR inhibitor AZD4547 in KatoIII cells, a gastric cancer cell line with FGFR2 amplification. In total, we identified 20 kinases, involved in ILK, SRC, and EGFR signaling pathways, as determinants that alter cell sensitivity to FGFR inhibition. We functionally validated the top negatively selected and positively selected kinases, ILK and CSK, from the CRISPR/Cas9 screen using RNA interference. We observed synergistic effects on KatoIII cells as well as three additional gastric cancer cell lines with FGFR2 amplification when AZD4547 was combined with small molecular inhibitors Cpd22 and lapatinib targeting ILK and EGFR/HER2, respectively. Furthermore, we demonstrated that GSK3b is one of the downstream effectors of ILK upon FGFR inhibition. In summary, our study systematically evaluated the kinases and associated signaling pathways modulating cell response to FGFR inhibition, and for the first time, demonstrated that targeting ILK would enhance the effectiveness of AZD4547 treatment of gastric tumors with amplifications of FGFR2.

Single-cell transcriptome analysis identifies distinct cell types and niche signaling in a primary gastric organoid model

The diverse cellular milieu of the gastric tissue microenvironment plays a critical role in normal tissue homeostasis and tumor development. However, few cell culture model can recapitulate the tissue microenvironment and intercellular signaling in vitro. We used a primary tissue culture system to generate a murine p53 null gastric tissue model containing both epithelium and mesenchymal stroma. To characterize the microenvironment and niche signaling, we used single cell RNA sequencing (scRNA-Seq) to determine the transcriptomes of 4,391 individual cells. Based on specific markers, we identified epithelial cells, fibroblasts and macrophages in initial tissue explants during organoid formation. The majority of macrophages were polarized towards wound healing and tumor promotion M2-type. During the course of time, the organoids maintained both epithelial and fibroblast lineages with the features of immature mouse gastric stomach. We detected a subset of cells in both lineages expressing Lgr5, one of the stem cell markers. We examined the lineage-specific Wnt signaling activation, and identified that Rspo3 was specifically expressed in the fibroblast lineage, providing an endogenous source of the R-spondin to activate Wnt signaling. Our studies demonstrate that this primary tissue culture system enables one to study gastric tissue niche signaling and immune response in vitro.