Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach.

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually >50,000 individuals are estimated to die of CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (eg, tumor-infiltrating lymphocytes), proteomic, and transcriptomic expression patterns inside and around the tumor-the tumor immune microenvironment-can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of tumor-infiltrating lymphocytes and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler. In this study, machine learning and differential co-expression analyses helped identify biomarkers from Digital Spatial Profiler-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (eg, granzyme B and fibronectin), immune suppression (eg, forkhead box P3), exhaustion and cytotoxicity (eg, CD8), Programmed death ligand 1-expressing dendritic cells, and neutrophil proliferation, among other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays.

A novel method to assess copy number variations in melanocytic neoplasms: Droplet digital PCR for precise quantitation of MYC and MYB genes.

INTRODUCTION: While most melanocytic neoplasms can be classified as either benign or malignant by histopathology alone, ancillary molecular diagnostic tests can be necessary to establish the correct diagnosis in challenging cases. Currently, the detection of copy number variations (CNVs) by fluorescence in situ hybridization and chromosomal microarray (CMA) are the most popular methods, but remain expensive and inaccessible. We aim to develop a relatively inexpensive, fast, and accessible molecular assay to detect CNVs relevant to melanoma using droplet digital polymerase chain reaction (ddPCR) technology. METHODS: In this proof-of-concept study, we evaluated CNVs in MYC and MYB genes from 73 cases of benign nevi, borderline melanocytic lesions, and primary and metastatic melanoma at our institution from 2015 to 2022. A multiplexed ddPCR assay and CMA were performed on each sample, and the results were compared. RESULTS: Concordance analysis of ddPCR with CMA for quantification of MYC and MYB CNVs revealed a sensitivity and specificity of 89% and 86% for MYC and 83% and 74% for MYB, respectively. CONCLUSION: We demonstrate the first use of a multiplexed ddPCR assay to identify CNVs in melanocytic neoplasms. With further improvement and validation, ddPCR may represent a low-cost and rapid tool to aid in the diagnosis of histopathologically ambiguous melanocytic tumors.

A novel method to assess copy number variation in melanoma: Droplet digital PCR for precise quantitation of the RREB1 gene in formalin-fixed, paraffin-embedded melanocytic neoplasms, a proof-of-concept study.

BACKGROUND: Melanocytic neoplasms can be challenging to diagnose. One well-established diagnostic aid is the detection of copy number variation (CNV) in a few key genetic loci using conventional methods such as fluorescence in situ hybridization (FISH) and chromosomal microarray (CMA). Droplet digital polymerase chain reaction (ddPCR) is a novel, cost-effective, rapid, and automated method to detect CNV. METHODS: We perform the first investigation of ddPCR to assay Ras-responsive element-binding protein-1 (RREB1), the most common CNV in melanoma using formalin-fixed, paraffin-embedded (FFPE) melanocytic lesion samples; CMA data are used as the gold standard. Archival samples from 2013 to 2021 were analyzed, including 153 data points from 39 FFPE samples representing 34 patients. Benign, borderline, malignant, and metastatic melanocytic neoplasms were examined. RESULTS: ddPCR showed a sensitivity and specificity of 93.8% and 95.7% using one reference gene, and 87.5% and 100% using a different reference gene for RREB1 gain detection. CONCLUSIONS: Here we show that ddPCR can provide inexpensive, rapid, and robust data on the commonest copy number alteration in melanoma. Future development and validation could provide a useful ancillary tool in the diagnosis of challenging melanocytic lesions.

A Novel Method to Detect Copy Number Variation in Melanoma: Droplet Digital PCR for Quantitation of the CDKN2A Gene, a Proof-of-Concept Study.

ABSTRACT: A definitive diagnosis of nevus or melanoma is not always possible for histologically ambiguous melanocytic neoplasms. In such cases, ancillary molecular testing can support a diagnosis of melanoma if certain chromosomal aberrations are detected. Current technologies for copy number variation (CNV) detection include chromosomal microarray analysis (CMA) and fluorescence in situ hybridization. Although CMA and fluorescence in situ hybridization are effective, their utilization can be limited by cost, turnaround time, and inaccessibility outside of large reference laboratories. Droplet digital polymerase chain reaction (ddPCR) is a rapid, automated, and relatively inexpensive technology for CNV detection. We investigated the ability of ddPCR to quantify CNV in cyclin-dependent kinase inhibitor 2A ( CDKN2A ), the most commonly deleted tumor suppressor gene in melanoma. CMA data were used as the gold standard. We analyzed 57 skin samples from 52 patients diagnosed with benign nevi, borderline lesions, primary melanomas, and metastatic melanomas. In a training cohort comprising 29 randomly selected samples, receiver operator characteristic curve analysis revealed an optimal ddPCR cutoff value of 1.73 for calling CDKN2A loss. In a validation cohort comprising the remaining 28 samples, ddPCR detected CDKN2A loss with a sensitivity and specificity of 94% and 90%, respectively. Significantly, ddPCR could also identify whether CDKN2A losses were monoallelic or biallelic. These pilot data suggest that ddPCR can detect CDKN2A deletions in melanocytic tumors with accuracy comparable with CMA. With further validation, ddPCR could provide an additional CNV assay to aid in the diagnosis of challenging melanocytic neoplasms.

High-Throughput CRISPR-Cas13 SARS-CoV-2 Test.

BACKGROUND: The ability to control the spread of COVID-19 continues to be hampered by a lack of rapid, scalable, and easily deployable diagnostic solutions. METHODS: We developed a diagnostic method based on CRISPR (clustered regularly interspaced short palindromic repeats) that can deliver sensitive, specific, and high-throughput detection of Sudden Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). The assay utilizes SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing) for the qualitative detection of SARS-CoV-2 RNA and may be performed directly on a swab or saliva sample without nucleic acid extraction. The assay uses a 384-well format and provides results in <1 hour. RESULTS: Assay performance was evaluated with 105 (55 negative, 50 positive) remnant SARS-CoV-2 specimens previously tested using Food and Drug Administration emergency use authorized assays and retested with a modified version of the Centers for Disease Control and Prevention (CDC) quantitative PCR with reverse transcription (RT-qPCR) assay. When combined with magnetic bead-based extraction, the high-throughput SHERLOCK SARS-CoV-2 assay was 100% concordant (n = 60) with the CDC RT-qPCR. When used with direct sample addition the high-throughput assay was also 100% concordant with the CDC RT-qPCR direct method (n = 45). With direct saliva sample addition, the negative and positive percentage agreements were 100% (15/15, 95% CI: 81.8-100%) and 88% (15/17, 95% CI: 63.6-98.5%), respectively, compared with results from a collaborating clinical laboratory. CONCLUSIONS: This high-throughput assay identifies SARS-CoV-2 from patient samples with or without nucleic acid extraction with high concordance to RT-qPCR methods. This test enables high complexity laboratories to rapidly increase their testing capacities with simple equipment.

Analytical validation of a semi-automated methodology for quantitative measurement of SARS-CoV-2 RNA in wastewater collected in northern New England.

Wastewater-based epidemiology (WBE) can be used to monitor the community presence of infectious disease pathogens of public health concern such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Viral nucleic acid has been detected in the stool of SARS-CoV-2-infected individuals. Asymptomatic SARS-CoV-2 infections make community monitoring difficult without extensive and continuous population screening. In this study, we validated a procedure that includes manual pre-processing, automated SARS-CoV-2 RNA extraction and detection workflows using both reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) and reverse transcriptase droplet digital PCR (RT-ddPCR). Genomic RNA and calibration materials were used to create known concentrations of viral material to determine the linearity, accuracy, and precision of the wastewater extraction and SARS-CoV-2 RNA detection. Both RT-qPCR and RT-ddPCR perform similarly in all the validation experiments, with a limit of detection of 50 copies/mL. A wastewater sample from a care facility with a known outbreak was assessed for viral content in replicate, and we showed consistent results across both assays. Finally, in a 2-week survey of two New Hampshire cities, we assessed the suitability of our methods for daily surveillance. This paper describes the technical validation of a molecular assay that can be used for long-term monitoring of SARS-CoV-2 in wastewater as a potential tool for community surveillance to assist with public health efforts.IMPORTANCEThis paper describes the technical validation of a molecular assay that can be used for the long-term monitoring of SARS-CoV-2 in wastewater as a potential tool for community surveillance to assist with public health efforts.

Potential Impact of Pharmacogenomic Single Nucleotide Variants in a Rural Caucasian Population.

BACKGROUND: In the US adverse drug reactions (ADRs) are estimated to cause 100 000 fatalities and cost over $136 billion annually. A patient's genes play a significant role in their response to a drug. Pharmacogenomics aims to optimize drug choice and dose for individual patients by characterizing patients' pharmacologically relevant genes to identify variants of known impact. METHODS: DNA was extracted from randomly selected remnant whole blood samples from Caucasian patients with previously performed complete blood counts. Samples were genotyped by mass spectrometry using a customized pharmacogenomics panel. A third-party result interpretation service used genotypic results to predict likely individual responses to frequently prescribed drugs. RESULTS: Complete genotypic and phenotypic calls for all tested Cytochrome P450 isoenzymes and other genes were obtained from 152 DNA samples. Of these 152 unique genomic DNA samples, 140 had genetic variants suggesting dose adjustment for at least one drug. Cardiovascular and psychiatry drugs had the highest number of recommendations, which included United States Food and Drug Administration warnings for highly prescribed drugs metabolized by CYP2C19, CYP2C9, CYP2D6, HLA-A, and VKORC1. CONCLUSIONS: Risk for each drug:gene pairing primarily depends upon the degree of predicted enzyme impairment or activation, width of the therapeutic window, and whether parent compound or metabolite is pharmacologically active. The resulting metabolic variations range from risk of toxicity to therapeutic failure. Pharmacogenomic profiling likely reduces ADR potential by allowing up front drug/dose selection to fit a patient's unique drug-response profile.

Validation of a Droplet Digital PCR (ddPCR) Assay to Detect Cyanobacterial 16S rDNA in Human Lung Tissue.

Cyanobacteria produce a variety of secondary metabolites, including toxins that may contribute to the development of disease. Previous work was able to detect the presence of a cyanobacterial marker in human nasal and broncoalveolar lavage samples; however, it was not able to determine the quantification of the marker. To further research the relationship between cyanobacteria and human health, we validated a droplet digital polymerase chain reaction (ddPCR) assay to simultaneously detect the cyanobacterial 16S marker and a human housekeeping gene in human lung tissue samples. The ability to detect cyanobacteria in human samples will allow further research into the role cyanobacteria plays in human health and disease.

Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study.

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1-10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x-y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural networks to predict spatial RNA patterns at the Visium spots under the hypothesis that the transformer- and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the model's ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, the results indicate that the transformer- and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on understudied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications.

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma.

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically complex tumors, with a high degree of proteomic variability across both the tumor itself and its heterogenous microenvironment. The malignant potential of these tumors is enhanced by the dysregulation of proteins involved in several key pathways, including processes that maintain cellular stability and preserve the structural integrity of the microenvironment. Although there have been numerous bulk and single-cell glioma analyses, there is a relative paucity of spatial stratification of these proteomic data. Understanding differences in spatial distribution of tumorigenic factors and immune cell populations between the intrinsic tumor, invasive edge, and microenvironment offers valuable insight into the mechanisms underlying tumor proliferation and propagation. Digital spatial profiling (DSP) represents a powerful technology that can form the foundation for these important multilayer analyses. DSP is a method that efficiently quantifies protein expression within user-specified spatial regions in a tissue specimen. DSP is ideal for studying the differential expression of multiple proteins within and across regions of distinction, enabling multiple levels of quantitative and qualitative analysis. The DSP protocol is systematic and user-friendly, allowing for customized spatial analysis of proteomic data. In this experiment, tissue microarrays are constructed from archived glioblastoma core biopsies. Next, a panel of antibodies is selected, targeting proteins of interest within the sample. The antibodies, which are preconjugated to UV-photocleavable DNA oligonucleotides, are then incubated with the tissue sample overnight. Under fluorescence microscopy visualization of the antibodies, regions of interest (ROIs) within which to quantify protein expression are defined with the samples. UV light is then directed at each ROI, cleaving the DNA oligonucleotides. The oligonucleotides are microaspirated and counted within each ROI, quantifying the corresponding protein on a spatial basis.

Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction using Spatially Localized Immuno-Oncology Markers.

Spatially resolved characterization of the transcriptome and proteome promises to provide further clarity on cancer pathogenesis and etiology, which may inform future clinical practice through classifier development for clinical outcomes. However, batch effects may potentially obscure the ability of machine learning methods to derive complex associations within spatial omics data. Profiling thirty-five stage three colon cancer patients using the GeoMX Digital Spatial Profiler, we found that mixed-effects machine learning (MEML) methods† may provide utility for overcoming significant batch effects to communicate key and complex disease associations from spatial information. These results point to further exploration and application of MEML methods within the spatial omics algorithm development life cycle for clinical deployment.

Wastewater-Based SARS-CoV-2 Surveillance in Northern New England.

SARS-CoV-2 viral RNA is shed in the stool of 55-70% of infected individuals and can be detected in community wastewater up to 7 days before people present with COVID-19 symptoms. The detection of SARS-CoV-2 RNA in wastewater may serve as a lead indicator of increased community transmission. Here, we monitored viral concentrations in samples collected from nine municipal wastewater facilities in New Hampshire (NH) and Vermont (VT).Twenty-four-h composite primary influent wastewater samples were collected from nine municipal wastewater treatment facilities twice per week for 5 months (late September 2020 to early February 2021). Wastewater was centrifuged for 30 min at 4600 × g, then the supernatant was frozen until further analysis. Once thawed, samples were concentrated, extracted, and tested for SARS-CoV-2 RNA using reverse transcriptase-quantitative PCR (RT-qPCR) and reverse transcriptase-droplet digital PCR (RT-ddPCR) detection methods. Active case counts for each municipality were tracked from the NH and VT state COVID-19 dashboards. We received a total of 283 wastewater samples from all sites during the study period. Viral RNA was detected in 175/283 (61.8%) samples using RT-qPCR and in 195/283 (68.9%) samples using RT-ddPCR. All nine sites showed positivity in the wastewater, with 8/9 (88.8%) sites having over 50% of their samples test positive over the course of the study. Larger municipalities, such as Nashua, Concord, and Lebanon, NH, showed that SARS-CoV-2 positivity in the wastewater can precede spikes in active COVID-19 case counts by as much as 7 days. Smaller municipalities, such as Woodsville, NH and Hartford, VT, showed sporadic SARS-COV-2 detection and did not always precede a rise in active case counts. We detected SARS-CoV-2 RNA in samples from all 9 municipalities tested, including cities and small towns within this region, and showed wastewater positivity as an early indicator of active case count increases in some regions. Some of the smaller rural municipalities with low case counts may require more frequent sampling to detect SARS-CoV-2 in wastewater before a case surge. With timely collection and analysis of wastewater samples, a community could potentially respond to results by increasing public health initiatives, such as tightening mask mandates and banning large indoor gatherings, to mitigate community transmission of SARS-CoV-2. IMPORTANCE Despite vaccination efforts, the delta and omicron variants of SARS-CoV-2 have caused global surges of COVID-19. As the COVID-19 pandemic continues, it is important to find new ways of tracking early signs of SARS-CoV-2 outbreaks. The manuscript outlines how to collect wastewater from treatment facilities, concentrate the virus in a dilute wastewater sample, and detect it using two sensitive PCR-based methods. It also describes important trends in SARS-CoV-2 concentration in wastewater of a rural region of the United States from Fall 2020 - Winter 2021 and demonstrates the utility of wastewater monitoring as a leading indicator of active SARS-CoV-2 cases. Monitoring changes in concentration of SARS-CoV-2 virus in wastewater may offer an early indicator of increased case counts and enable appropriate public health actions to be taken.

CRISPR-cas13 enzymology rapidly detects SARS-CoV-2 fragments in a clinical setting.

BACKGROUND: The well-recognized genome editing ability of the CRISPR-Cas system has triggered significant advances in CRISPR diagnostics. This has prompted an interest in developing new biosensing applications for nucleic acid detection. Recently, such applications have been engineered for detection of SARS-CoV-2. Increased demand for testing and consumables of RT-PCR assays has led to the use of alternate testing options. Here we evaluate the accuracy and performance of a novel fluorescence-based assay that received EUA authorization for detecting SARS-CoV-2 in clinical samples. METHODS: The Specific High-Sensitivity Enzymatic Reporter UnLOCKing (SHERLOCK) technology forms the basis of the Sherlock CRISPR SARS-CoV-2 kit using the CRISPR-Cas13a system. Our experimental strategy included selection of COVID-19 patient samples from previously validated RT-PCR assays. Positive samples were selected based on a broad range of cycle thresholds. RESULTS: A total of 60 COVID-19 patient samples were correctly diagnosed with 100% detection accuracy (relative fluorescence ratios: N gene 95% CI 29.9-43.8, ORF1ab gene 95% CI 30.1-46.3). All controls, including RNase P, showed expected findings. Overall ratios were robustly distinct between positive and negative cases relative to the pre-established 5-fold change in fluorescence. CONCLUSIONS: We have evaluated the accuracy of detecting conserved targets of SARS-CoV-2 across a range of viral loads, including low titers, using SHERLOCK CRISPR collateral detection in a clinical setting. These findings demonstrate encouraging results, at a time when COVID-19 clinical diagnosis and screening protocols remain in demand; especially as new variants emerge and vaccine mandates evolve. This approach highlights new thinking in infectious disease identification and can be expanded to measure nucleic acids in other clinical isolates.