Biology subjects

Grimes, S. M.

Publications and source records attributed to Grimes, S. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Aneuploidy profiling among different types of brain metastases reveals a colon cancer-specific chromosomal signature

Brain metastasis is a significant cause of patient morbidity and mortality. There is increasing evidence that genomic instability features such as aneuploidy are associated with metastases to the brain and other organs. We analyzed the genomic instability of brain metastases among different cancer types. This included brain metastases from non-small cell lung, breast, melanoma and colorectal cancer. Our study identified a significantly higher degree of genomic instability in colorectal brain metastases compared to the other types of brain metastases. Nex, we examined the brain metastases for the types of aneuploidy events, namely chromosomal arm imbalances that involved gains or losses. Colorectal cancer brain metastases had specific chromosomal arm imbalance profiles that included gains of 13q and 20q which were significantly more frequent compared to the other cancer types. We examined other colorectal cancer metastatic sites in the lung, liver, lymph nodes and the primary colon. Examining colorectal cancer metastases more closely, aneuploidy gains across either arms for chromosomes 7 and 16 were also higher among brain metastases compared to liver, lung and lymph node metastases. Overall, this study shows that colorectal cancer brain metastasis has specific aneuploidy profiles different than other cancer types as well as being different among other metastatic organ sites.

cancer biology↗

bioRxiv2025

Colorectal cancer relies on an immunosuppressive cellular topography and genomic adaptations for establishing brain metastases

Colorectal cancer (CRC) brain metastases have a poor prognosis and limited treatment options, including resistance to radiation therapy. Little is known about the molecular and cellular mechanisms that enable CRC tumor cells to adapt to the brain and establish a supportive tumor microenvironment. To address this gap we used spatial transcriptomics to analyze 51 CRC brain metastases. A subset had matched primary colon tumors and longitudinally paired metastatic resections before and after radiation treatment. We identified the critical spatial cellular features of the tumor epithelium and the surrounding tumor microenvironment that support metastatic growth in the brain. CRC brain metastases developed a stromal microenvironment with abundant fibroblasts and tumor-associated macrophages. A fibroblast-macrophage cellular neighborhood promoted angiogenesis, extracellular matrix remodeling, and immune suppression. Tumor cells showed local adaptations. In endothelial-rich regions, they were proliferative whereas in macrophage-rich regions, they were more differentiated and immune evasive. Compared with paired primary tumors, CRC brain metastases showed increased chromosomal instability, with activation of RNA-processing, stress response, and junctional remodeling pathways. After radiation treatment, resistant clones had increased epithelial-mesenchymal transition, while the immunosuppressive stroma remained intact. We identified tumor-derived MIF, GDF15, PRSS3 and SEMA3C ligands and macrophage-derived SPP1 that have the potential to affect multiple cell types in the metastatic niche. These ligand-receptor interactions drive angiogenesis, stromal activation and immune suppression. In a macrophage-tumor-fibroblast co-culture model, knockout of SPP1 in macrophages led to reduced expression of lipid-metabolism related genes and disrupted tumor-promoting interactions. Together, these results indicate that CRC growth in the brain is sustained by a specific cellular organization with immunosuppressive multicellular interactions.

cancer biology↗

bioRxiv2023

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

ObjectiveGastric 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. MethodsThis 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. ResultsUsing 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. Conclusionsusing 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.

cancer biology↗

bioRxiv2023

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.

cancer biology↗

bioRxiv2023

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.

bioinformatics↗

bioRxiv2022

Adaptive nanopore sequencing for single cell characterization of cancer mutations and gene rearrangements

In this proof-of-concept study, we developed a single cell method that identifies somatic alterations found in coding regions of mRNAs and integrates these mutation genotypes with their matching cell transcriptomes. We used nanopore adaptive sampling on single cell cDNA libraries, generated long read sequences from target gene transcripts and identified coding variants among individual cells. Short-read single cell transcriptomes characterized the cell types with mutations. We delineated CRISPR edits from a cancer cell line. From primary cancer samples, we targeted hundreds of cancer genes, identified somatic coding mutations and a gene rearrangement among individual tumor cells.

genomics↗

bioRxiv2022

Single cell CRISPR base editor engineering and transcriptional characterization of cancer mutations

We developed a multiplexed single cell technology to genome engineer mutations, directly delineate their genotype among individual cells and determine each mutations transcriptional phenotype. This approach uses CRISPR base editors to introduce predesignated variants into a target gene. Long-read sequencing of the target genes transcript identifies the engineered mutations among individual cells. Simultaneously, we analyzed the transcriptome profile from the same set of cells by short-read sequencing. By integrating the two types of data, we determined the mutations genotype and expression phenotype at single cell resolution. Using cell lines, we engineered and evaluated the phenotype of more than 100 TP53 mutations. Based on the single cell gene expression, we classified the mutations as having a functionally significant phenotype versus the wild-type state. We validated these results on a subset of mutations using isolated clones analyzed with RNA-seq. Overall, we successfully demonstrated single cell mutation engineering and phenotypic assessment.

genomics↗

bioRxiv2022

Cancer subclone detection based on DNA copy number in single cell and spatial omic sequencing data

In cancer, somatic mutations such as copy number alterations (CNAs) accumulate during disease progression and lead to functional intra-tumor heterogeneity that 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 Clonalscope, 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 and scATAC-seq. Clonalscope implements a nested Chinese restaurant process to identify de novo subclones within one or multiple samples from the same patient. Clonalscope incorporates prior information from paired whole-genome or whole-exome sequencing (WGS/WES) data to achieve more reliable subclone detection and malignant cell labeling. On scRNA-seq and scATAC-seq data from four gastrointestinal tumor samples, Clonalscope successfully labeled malignant cells and identified genetically different subclones, which were validated in detail using matched scDNA-seq data. On ST data from a squamous cell carcinoma and two invasive ductal carcinoma samples, Clonalscope successfully labelled malignant spots, traced subclones between associated datasets, and identified spatially segregated subclones expressing genes associated with drug resistance and survival.

bioinformatics↗

bioRxiv2022

Single cell multi-omic mapping of subclonal architecture and pathway phenotype in primary gastric and metastatic colon cancers

Single cell genomics provides a high-resolution profile of intratumoral heterogeneity and subclonal structure in primary and metastatic tumors. Notably, metastases and therapeutic resistant tumors often originate from distinct subclones. These distinct cellular populations are an important contributor to adaptation and resistance to ongoing therapy. Single cell DNA-sequencing (scDNA-seq) defines subclones but does not provide biological information about cell types. Single cell RNA-sequencing (scRNA-seq) provides biological information but is less useful for identifying different subclones. The integrated scDNA-seq and scRNA-seq data from the same tumor cell population provides both subclone structure and biology. To understand the cellular genomic variation of different subclones in primary and metastatic cancers, we developed an approach to integrate multi-omics data from both types of single cells. This joint data set represented thousands of normal and tumor cells derived from a set of primary gastric and metastatic colorectal cancers. The extensive cellular sampling provided robust characterization of the subclonal architecture of gastric and colorectal cancers. We reconstructed the subclonal architecture using the cells in G0/G1 phase. The scDNA-seq provided a ground truth for copy number-based subclones. From the scRNA-seq data, the epithelial cells in G0/G1 were identified and assigned to specific subclones by a correlation algorithm based on gene dosage. The inferred CNV profiles from the scRNA-seq epithelial cells were assigned subclones identified from the scDNA-seq. Afterward, we determined the biological pathway activities of specific clones. Overall, integrative multi-omics analysis of single-cell datasets is more informative than any individual genomic modality, provides deep insights into the intratumoral heterogeneity, and reveals subclonal biology.

genomics↗

bioRxiv2022

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 characterizing diseases. We developed a strategy using nanopore-based single-molecule sequencing to measure cfDNA methylomes. This approach generated up to hundreds of millions of reads per cfDNA sample from cancer patients, an order of magnitude improvement over existing nanopore methods. Leveraging methylomes of matched tumors and immune cells, we characterized cfDNA methylomes of cancer patients for longitudinal monitoring during treatment.

genomics↗

bioRxiv2022

Distinct cell states define the developmental trajectories of mucinous appendiceal neoplasms towards pseudomyxoma metastases

Appendiceal mucinous neoplasms (AMN) are rare tumors of the gastrointestinal tract. They metastasize with widespread abdominal dissemination leading to pseudomyxoma peritonei (PMP), a disease with poor prognosis. The cellular features of origin, differentiation and progression in AMN and PMP remain largely unknown. We characterized the distinct cell states of AMN and PMP using single-cell RNA-sequencing in 31 samples including AMNs, PMP metastases and matched normal tissues. We identified previously undescribed cellular features and heterogeneity in AMN and PMP. We discovered developmental cell states in tumor epithelial cells that ranged from progenitor phase to goblet cell differentiation, which was accompanied by elevated mucin gene expression. Metastatic PMP cells had a distinct cell state with increased mTOR and RAS signaling pathways and a series of upregulated cancer genes. We observed heterogeneity in a single PMP tumor as well as PMP metastases from the same patient. We validated our findings with immunohistochemistry, mass spectrometry on malignant ascites from PMP patients and gene expression data from an independent set of 63 PMPs.

cancer biology↗

bioRxiv2021

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

Transcript isoforms are mRNAs that arise from alternative splicing events. During RNA processing, different combinations of a gene's exons lead to a diverse set of isoforms. Polymorphisms or mutations at splice junctions can generate alternative splicing events. Various splicing factors also impact the representation of a gene's transcript isoforms. To assess how these two features contribute to alternative splicing, we developed a single cell approach to introduce CRISPR edits that modify mRNA transcript structure. Our method combines (1) long-read sequencing to characterize the expressed transcripts and identify the edit at single cell resolution; (2) short-read sequencing to match the single cell gene expression profiles of the cells with the altered isoform. First, we modify target exon-intron segments with CRISPR-Cas9. Second, using cDNAs with cell barcodes, we use long read sequencing to directly identify the changes in transcript isoforms from the targeted CRISPR edits. As a variation on this approach, we also determined how modifying specific splicing factors influence isoform expression and structure. Overall, we demonstrate how the integration of single cell long read analysis and CRISPR engineering can be used to directly confirm transcript isoform and target genomic edits at single cell resolution. This approach will improve our understanding of the role of alternative splicing in transcriptional regulation.

genomics↗

bioRxiv2021

Characterization of the consensus mucosal microbiome of colorectal cancer

Recent evidence suggests that dysbiosis, an imbalance of microbiota, is associated with increased risk of colorectal cancer. Diverse microbial organisms are physically associated with the cells found in tumor biopsies. Characterizing this mucosa-associated microbiome through genome sequencing has advantages compared to culture-based profiling. However, there are notable challenges in accurately characterizing the features of tumor microbiomes with methods like transcriptome sequencing. Most sequence reads originate from the host. Moreover, there is a high likelihood of bacterial contaminants being introduced. Another major challenge is the microbiome diversity among different studies. Colorectal tumors demonstrate a significant extent of microbiome variation among individuals from different geographic and ethnic origins. To address these challenges, we identified a consensus microbiome for colorectal cancer through analyzing 924 tumors from eight independent RNA-Seq data sets. A standardized meta-transcriptomic analysis pipeline was established and applied to the complete CRC cohort. Common contaminants were filtered out. Our study involved taxonomic investigation of non-human sequences, linked microbial signatures to phenotypes and the association of microbiome with tumor microenvironment components. Microbiome profiles across different CRC cohorts were compared, and recurrently altered microbial shifts specific to CRC were determined. We identified cancer-specific set of 114 microbial species associated with tumors that were found among all investigated studies. Validating our approach, we found that Fusobacterium nucleatum was one of the most enriched bacterial species in CRC. Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria were among the four most abundant phyla for CRC microbiome. Signficant 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).

genomics↗

bioRxiv2021

KmerKeys: a web resource for searching indexed genome assemblies and variants

K-mers are short DNA sequences that are used for genome sequence analysis. Applications that use k-mers include genome assembly and alignment. Despite these current applications, the wider bioinformatic use of k-mers in has challenges related to the massive scale of genomic sequence data. A single human genome assembly has billions of these short sequences. The sheer amount of computation for effective use of k-mer information is enormous, particularly when involving multiple genome assemblies. To address these issues, we developed a new k-mer indexing data structure based on a hash table tuned for the lookup of k-mer keys. This web application, referred to as KmerKeys (https://kmerkeys.dgi-stanford.org/), provides performant, rapid query speeds for cloud computation on genome assemblies. We enable fuzzy as well as exact k-mer-based searches of assemblies. To enable robust and speedy performance, the website implements cache-friendly hash tables, memory mapping and massive parallel processing. Our method employs a scalable and efficient data structure that can be used to jointly index and search a large collection of human genome assembly information. One can include variant databases and their associated metadata such as the gnomAD population variant catalog. This feature enables the incorporation of future genomic information into sequencing analysis.

bioinformatics↗

bioRxiv2020

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

Colorectal carcinomas (CRCs) which have lost DNA mismatch repair display hypermutability evident in a molecular phenotype called microsatellite instability (MSI). These mismatch repair deficient tumors are thought to lack widespread genomic instability features, such as copy number changes and rearrangements. To identify MSI for clinical diagnosis, current molecular testing looks for changes in mononucleotide or dinucleotide repeats. However, microsatellites have other types of sequence tandem repeats such as tri- and tetranucleotide motifs. These additional classes of microsatellites are generally not examined for MSI but are known to be unstable in a phenotype known as elevated microsatellite alterations at selected tetranucleotide repeats, or EMAST. We developed a sequencing approach that provides ultra-high coverage (>2500X) of microsatellite targets and cancer genes for profiling genomic instability. We assessed the diverse repeat motifs across 200 microsatellites. Our approach provides highly sensitive detection of MSI with high specificity, evaluates copy number alterations with high accuracy, delineates chromosomal instability (CIN) classification and deconvolutes subclonal architecture. By examining both MSI and CIN, we discovered mutations and copy number alterations that defined mixed genomic instability states of CIN and MSI, which are normally considered exclusive. An increase in copy number of chromosome arm 8q was prevalent among MSI tumors. Moreover, we identified an inter-chromosomal translocation event from a CRC with co-occurrence of MSI. Subclonal analysis demonstrated that mutations which are typically considered to be exclusive in MSI, shows mutual occurrence in MSI tumors with more sensitive characterization. Our approach revealed that MSH3 mutations are a potential source of mixed genomic instability features. Overall, our study demonstrates that some colorectal cancers have features of both microsatellite and chromosomal instability. This result may have implications for immunotherapy treatment.

genomics↗

bioRxiv2020

Alleloscope: Integrative analysis of single cell haplotype-divergent copy number alterations and chromatin accessibility changes reveals novel clonal architecture of cancers

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 ATAC sequencing data, either separately or in combination. This approach allows for integrative multi-omic analysis of allele-specific copy number and chromatin accessibility on the same cell. On scDNA-seq data from gastric, colorectal, and breast cancer samples, with extensive validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multi-allelic copy number aberrations, where cells that carry varying allelic configurations adding to the same total copy number co-evolve within a tumor. The contributions of such allele-specific events to intratumor heterogeneity have been under-reported and under-studied due to the lack of methods for their detection. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detects multi-allelic copy number events and copy neutral loss-of-heterozygosity, enabling the dissection of the contributions of chromosomal instability and chromatin remodeling in tumor evolution.

bioinformatics↗

bioRxiv2020

The cellular genomic diversity, regulatory states and networking of the metastatic colorectal cancer microenvironment

PurposeThe liver is the most frequent metastatic site for colorectal cancer (CRC). Its microenvironment is modified to provide a niche that allows CRC cell growth. This study focused on characterizing the cellular changes in the metastatic CRC (mCRC) liver tumor microenvironment (TME). Experimental DesignWe analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue and peripheral blood mononuclear cells using single cell RNA-seq (scRNA-seq). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution. ResultsWe identified TME-specific SPP1-expressing macrophages with altered metabolism features, foam cell characteristics and increased activity for 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, 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 worst prognosis in these patients. ConclusionsWe demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports CRC growth in the immunosuppressed metastatic niche in the liver. These features can be used to target these immune checkpoint resistant MSS tumors. TRANSLATIONAL RELEVANCEThe liver is the commonest site for metastatic colorectal cancer (mCRC). Alterations in the tumor microenvironment (TME) allow metastatic cells to seed the distant liver site and grow. Leveraging single-cell RNA sequencing, we discovered a distinct SPP1+ macrophage cell state with pro-fibrogenic gene expression and altered metabolism. These SPP1+ macrophages communicated with fibroblasts, mutually influencing each others gene expression program. Using spatial imaging, we confirmed proximal colocalization between macrophages and fibroblasts in the mCRC TME, which is required for intercellular communication. These states and intercellular communication promoted immunosuppression in the TME, with a lack of dysfunctional anti-tumor CD8 T cells and prevalence of regulatory T cells. Increased fibroblasts were associated with worst prognosis in an independent patient cohort. Our results identified novel TME features that result in reshaping of the metastatic niche that allows progression of mCRC. These features can be potential targets for mCRC treatment, which is microsatellite stable and resistant to immune checkpoint blockade.

cancer biology↗

bioRxiv2019

Whole genome analysis identifies the association of TP53 genomic deletions with lower survival in Stage III colorectal cancer

DNA copy number aberrations (CNA) were frequently observed in colorectal cancers (CRC). There is an urgent call for CNA-based biomarkers in clinics, in particular for Stage III CRC, if combined with imaging or pathologic evidence, promise more precise care at the timing. We conducted this Stage III specific biomarker discovery with a cohort of 134 CRCs, and with a newly developed high-efficiency CNA profiling protocol. Specifically, we developed the profiling protocol for tumor-normal matched tissue samples based on low-coverage clinical whole-genome sequencing (WGS). We demonstrated the protocols accuracy and robustness by a systematic benchmark with microarray, high-coverage whole-exome and -genome approaches, where the low-coverage WGS-derived CNA segments were highly accordant (PCC>0.95) with those derived from microarray, and they were substantially less variable if compared to exome-derived segments. A lasso-based model and multivariate cox regression analysis identified a chromosome 17p loss, containing the TP53 tumor suppressor gene, that was significantly associated with reduced survival (P=0.0139, HR=1.688, 95% CI = [1.112-2.562]), which was validated by an independent cohort of 187 Stage III CRCs. In summary, the new low-coverage WGS protocol has high sensitivity, high resolution and low cost and the identified 17p-loss is an effective poor prognosis marker for Stage III patients.

genomics↗