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.

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.

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.

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.

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.

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.

Abstract 2126: Characterization of colorectal liver metastasis at single-cell resolution reveals dynamic interplay in the tumor microenvironment

Colorectal cancer (CRC) metastasizes to the liver in over half of patients. Compared to primary tumors, the biology of CRC liver metastasis (CLM) is poorly characterized. We performed high-throughput microfluidics-based single-cell RNA sequencing (scRNA-Seq) to define the epithelial and stromal components of CLMs. Using the 10X Genomics platform, scRNA-Seq was performed on single-cell suspensions generated from dissociation of fresh surgical excisions. We detected 1,551 cells from 2 CLMs and 5,407 cells from paired normal liver. Cells were sequenced at an average depth of 1 million reads/cell using Illumina sequencing. Graph-based clustering and differential expression analysis was performed using the Seurat algorithm. Tumor epithelial cells (EPCAM+, CDH1+, TFF3+) consisted of heterogeneous subpopulations with diverse transcriptomes and activation of varying signaling pathways. The stroma consisted of myofibroblasts, endothelial cells and immune cells. Myofibroblasts consisted of multiple subtypes indicative of a range of differentiation between smooth muscle cells and fibroblasts (ACTA2+, THY1+, COLA1+). These cells were absent in normal liver and could be confirmed as a desmoplastic stroma on histology. The immune infiltrate was rich in monocyte-derived macrophages, with few dendritic cells, CD4 and CD8 T cells. We identified 2 macrophage subpopulations in both CLMs. These cells expressed markers belonging to both the M1 and M2 classes (IL1A+, IL1B+, TNF+, MARCO+, MSR1+, CD68+) suggestive of a spectrum of polarization states. Macrophages showed increased expression of the immune checkpoint inhibitor TIM3, while CD4 and CD8 cells showed upregulation of TIGIT. Immune cell populations also expressed a variety of cytokines such as chemokines and interleukins capable of regulating functional effector states. Tumor epithelial cells expressed receptors for various growth factors of the EGF, VEGF, PDGF, FGF and TGFGB families. Stromal cells expressed several heterologous growth factors that can act as receptors ligands and influence tumor cell growth. This included BMPs and INHA expressed in myofibroblasts that can modulate TGFB signaling, as well as FGFs, HGF and VEGF. Endothelial cells secreted PDGF and macrophages expressed AREG and EREG that can activate EGF signaling. These features of the cellular landscape of CLMs can be used for devising novel therapeutic strategies for patients unfit for surgery or for those with unresectable tumors. We will test inhibition of the identified heterologous growth factors and improving the antitumor potential of macrophages in an ex vivo air liquid interphase organoid system from a cohort of CLMs.

Citation Format: Anuja Sathe, Jiamin Chen, Christina Wood-Bouwens, Alison Almeda, Billy Lau, Sue M. Grimes, George A. Poultsides, Hanlee Ji. Characterization of colorectal liver metastasis at single-cell resolution reveals dynamic interplay in the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2126.

Measurement of the proton-air cross section at √s=57 TeV with the Pierre Auger Observatory

We report a measurement of the proton-air cross section for particle production at the center-of-mass energy per nucleon of 57 TeV. This is derived from the distribution of the depths of shower maxima observed with the Pierre Auger Observatory: systematic uncertainties are studied in detail. Analyzing the tail of the distribution of the shower maxima, a proton-air cross section of [505±22(stat)(-36)(+28)(syst)] mb is found.