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.

Circulating perturbation of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) is associated to cardiac remodeling and NLRP3 inflammasome in cardiovascular patients with insulin resistance risk

Lipidome perturbation occurring during meta-inflammation is associated to left ventricle (LV) remodeling though the activation of the NLRP3 inflammasome, a key regulator of chronic inflammation in obesity-related disorders. Little is known about phosphatidylcholine (PC) and phosphatidylethanolamine (PE) as DAMP-induced NLRP3 inflammasome. Our study is aimed to evaluate if a systemic reduction of PC/PE molar ratio can affect NLRP3 plasma levels in cardiovascular disease (CVD) patients with insulin resistance (IR) risk. Forty patients from IRCCS Policlinico San Donato were enrolled, and their blood samples were drawn before heart surgery. LV geometry measurements were evaluated by echocardiography and clinical data associated to IR risk were collected. PC and PE were quantified by ESI-MS/MS. Circulating NLRP3 was quantified by an ELISA assay. Our results have shown that CVD patients with IR risk presented systemic lipid impairment of PC and PE species and their ratio in plasma was inversely associated to NLRP3 levels. Interestingly, CVD patients with IR risk presented LV changes directly associated to increased levels of NLRP3 and a decrease in PC/PE ratio in plasma, highlighting the systemic effect of meta-inflammation in cardiac response. In summary, PC and PE can be considered bioactive mediators associated to both the NLRP3 and LV changes in CVD patients with IR risk.

Clinical Trial Protocol for "Replace Cysto": Replacing Invasive Cystoscopy with Urine Testing for Non-muscle-invasive Bladder Cancer Surveillance-A Multicenter, Randomized, Phase 2 Healthcare Delivery Trial Comparing Quality of Life During Cancer Surveillance with Xpert Bladder Cancer Monitor or Bladder EpiCheck Urine Testing Versus Frequent Cystoscopy

"Replace Cysto" is a multisite randomized phase 2 trial including 240 participants with low-grade intermediate-risk non-muscle-invasive bladder cancer, in which participants will be randomized 1:1:1 to one of two urine marker-based approaches alternating a urine marker test (Xpert Bladder Cancer Monitor or Bladder EpiCheck) with cystoscopy or to frequent scheduled cystoscopy. The primary objective is to determine whether urinary quality of life after surveillance is significantly improved in the urine marker arms. The primary outcome will be the patient-reported urinary quality of life domain score of the validated QLQ-NMIBC24 instrument, measured 1-3 d after surveillance. Exploratory outcomes include discomfort after surveillance, the number of invasive procedures that participants undergo per 1000 person years, complications from these procedures per 1000 person years, nonurinary quality of life, acceptability of surveillance, and bladder cancer recurrence and progression. Comparators include surveillance using (1) the Xpert Bladder Cancer Monitor test, (2) the Bladder EpiCheck urinary marker, or (3) frequent cystoscopy alone. After a negative cystoscopy ≤4 mo following bladder tumor resection, all the participants will undergo surveillance at 6, 12, 18, and 24 mo (with time zero defined as the date of the most recent bladder tumor resection). In the urine marker arms, surveillance at 6 and 18 mo will be performed with the marker. Regardless of the arm, participants will undergo cystoscopy at 12 and 24 mo. End of study for each participant will be their 24-mo cystoscopy. Overall trial duration is estimated at 5 yr from when the study opens to enrollment until completion of data analyses. The trial is registered at clinicaltrials.gov (NCT05796375).

Providing Diagnostic Pathology Services in Low and Middle-Income Countries

Diagnostic pathology services in low and middle-income countries are often hindered by lack of expertise, equipment, and reagents. However, there are also educational, cultural, and political decisions, which must be addressed in order to provide these services successfully. In this review, we describe some of the infrastructure barriers that must be overcome and provide 3 examples of implementing molecular testing in Rwanda and Honduras despite initial lack of resources.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

Utility of Retrospective Molecular Analysis in Diagnostically Challenging Mesenchymal Neoplasms

Introduction: Molecular analysis plays a growing role in the diagnosis of mesenchymal neoplasms. The aim of this study was to retrospectively apply broad, multiplex molecular assays (a solid tumor targeted next-generation sequencing [NGS]) assay and single nucleotide polymorphism [SNP] microarray) to selected tumors, exploring the current utility and limitations. Methods: We searched our database (2010-2020) for diagnostically challenging mesenchymal neoplasms. After histologic review of available slides, tissue blocks were selected for NGS, SNP microarray, or both. DNA and RNA were extracted using the AllPrep DNA/RNA FFPE Kit Protocol on the QIAcube instrument. The NGS platform used was the TruSight Tumor 170 (TST-170). For SNP array, copy number variant (CNV) analysis was performed using the OncoScanTM CNV Plus Assay. Results: DNA/RNA was successfully extracted from 50% of tumors (n = 10/20). Specimens not successfully extracted included 6 core biopsies, 3 incisional biopsies, and 1 resection; 4 were decalcified (3 hydrochloric acid, 1 ethylenediaminetetraacetic acid). Higher tumor proportion and number of tumor cells were parameters positively associated with sufficient DNA/RNA extraction whereas necrosis and decalcification were negatively associated with sufficient extraction. Molecular testing helped reach a definitive diagnosis in 50% of tumors (n = 5/10). Conclusions: Although the overall utility of this approach is limited, these molecular panels can be helpful in detecting a specific "driver" alteration.

A rapid, multiplex digital PCR assay to detect gene variants and fusions in non-small cell lung cancer

Digital PCR (dPCR) is emerging as an ideal platform for the detection and tracking of genomic variants in cancer due to its high sensitivity and simple workflow. The growing number of clinically actionable cancer biomarkers creates a need for fast, accessible methods that allow for dense information content and high accuracy. Here, we describe a proof-of-concept amplitude modulation-based multiplex dPCR assay capable of detecting 12 single-nucleotide and insertion/deletion (indel) variants in EGFR, KRAS, BRAF, and ERBB2, 14 gene fusions in ALK, RET, ROS1, and NTRK1, and MET exon 14 skipping present in non-small cell lung cancer (NSCLC). We also demonstrate the use of multi-spectral target-signal encoding to improve the specificity of variant detection by reducing background noise by up to an order of magnitude. The assay reported an overall 100% positive percent agreement (PPA) and 98.5% negative percent agreement (NPA) compared with a sequencing-based assay in a cohort of 62 human formalin-fixed paraffin-embedded (FFPE) samples. In addition, the dPCR assay rescued actionable information in 10 samples that failed to sequence, highlighting the utility of a multiplexed dPCR assay as a potential reflex solution for challenging NSCLC samples.

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

Background

Spatial transcriptomics involves studying the spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. While spatial gene expression is commonly amalgamated from 1-10 cells across 50-micron spots, recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. However, elucidating such information from histology alone presents a significant challenge but if solved can better permit spatial molecular analysis at cellular resolution for instances where Visium data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in whole slide images collected from a cohort of stage pT3 colorectal cancer patients. A cell graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA patterns through optimal transport methods, facilitating the analysis of cellular groupings and gene relationships. This approach leveraged spot-level expression as an intermediary to co-map histological and transcriptomic information at the single-cell level.

Results

Our study demonstrated that single-cell transcriptional heterogeneity within a spot could be predicted from histological markers extracted from cells detected within a spot. Furthermore, our model exhibited proficiency in delineating overarching gene expression patterns across whole-slide images. This approach compared favorably to traditional patch-based computer vision methods as well as other methods which did not incorporate single cell expression during the model fitting procedures. Topological nuances of single-cell expression within a Visium spot were preserved using the developed methodology.

Conclusion

This innovative approach augments the resolution of spatial molecular assays utilizing histology as a sole input through synergistic co-mapping of histological and transcriptomic datasets at the single-cell level, anchored by spatial transcriptomics. While initial results are promising, they warrant rigorous validation. This includes collaborating with pathologists for precise spatial identification of distinct cell types and utilizing sophisticated assays, such as Xenium, to attain deeper subcellular insights.

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows

The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides. However, current methods utilizing these techniques are plagued by substantial variability in tissue preparation and characteristics, which can hinder the broader adoption of these tools. Furthermore, training deep learning models using spatial transcriptomics on small study cohorts remains a costly endeavor. Necessitating novel tissue preparation processes enhance assay reliability, resolution, and scalability. This study investigated the impact of an enhanced specimen processing workflow for facilitating a deep learning-based spatial transcriptomics assessment. The enhanced workflow leveraged the flexibility of the Visium CytAssist assay to permit automated H&E staining (e.g., Leica Bond) of tissue slides, whole-slide imaging at 40x-resolution, and multiplexing of tissue sections from multiple patients within individual capture areas for spatial transcriptomics profiling. Using a cohort of thirteen pT3 stage colorectal cancer (CRC) patients, we compared the efficacy of deep learning models trained on slide prepared using an enhanced workflow as compared to the traditional workflow which leverages manual tissue staining and standard imaging of tissue slides. Leveraging Inceptionv3 neural networks, we aimed to predict gene expression patterns across matched serial tissue sections, each stemming from a distinct workflow but aligned based on persistent histological structures. Findings indicate that the enhanced workflow considerably outperformed the traditional spatial transcriptomics workflow. Gene expression profiles predicted from enhanced tissue slides also yielded expression patterns more topologically consistent with the ground truth. This led to enhanced statistical precision in pinpointing biomarkers associated with distinct spatial structures. These insights can potentially elevate diagnostic and prognostic biomarker detection by broadening the range of spatial molecular markers linked to metastasis and recurrence. Future endeavors will further explore these findings to enrich our comprehension of various diseases and uncover molecular pathways with greater nuance. Combining deep learning with spatial transcriptomics provides a compelling avenue to enrich our understanding of tumor biology and improve clinical outcomes. For results of the highest fidelity, however, effective specimen processing is crucial, and fostering collaboration between histotechnicians, pathologists, and genomics specialists is essential to herald this new era in spatial transcriptomics-driven cancer research.

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.

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.

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.

Gain of CCND1 May Occur Too Infrequently in Cutaneous Melanoma, and Too Late in Melanomagenesis, to Be Diagnostically Useful: Genomic Analysis of 88 Cases

ABSTRACT

Genomic analysis is an important tool in the diagnosis of histologically ambiguous melanocytic neoplasms. Melanomas, in contrast to nevi, are characterized by the presence of multiple copy number alterations. One such alteration is gain of the proto-oncogene CCND1 at 11q13. In melanoma, gain of CCND1 has been reported in approximately one-fifth of cases. Exact frequencies of CCND1 gain vary by melanoma subtype, ranging from 15.8% for lentigo maligna to 25.1% for acral melanoma. We present a cohort of 72 cutaneous melanomas from 2017-2022 in which only 6 (8.3%) showed evidence of CCND1 gain by chromosomal microarray. This CCND1 upregulation frequency falls well below those previously published and is significantly lower than estimated in the literature ( P < 0.05). In addition, all 6 melanomas with CCND1 gain had copy number alterations at other loci (most commonly CDKN2A loss, followed by RREB1 gain), and 5 were either thick or metastatic lesions. This suggests that CCND1 gene amplification may be a later event in melanomagenesis, long after a lesion would be borderline or equivocal by histology. Data from fluorescence in situ hybridization, performed on 16 additional cutaneous melanomas, further corroborate our findings. CCND1 gain may not be a common alteration in melanoma and likely occurs too late in melanomagenesis to be diagnostically useful. We present the largest chromosomal microarray analysis of CCND1 upregulation frequencies in cutaneous melanoma, conjecture 3 hypotheses to explain our novel observation, and discuss implications for the inclusion or exclusion of CCND1 probes in future melanoma gene panels.

Validation of a rapid HLA-DQA1*05 pharmacogenomics assay to identify at-risk resistance to anti-tumor necrosis factor therapy among patients with inflammatory bowel disease

OBJECTIVES

The HLA-DQA1*05 variant (rs2097432) is associated with increased risk of immunogenicity to tumor necrosis factor antagonists, with subsequent resistance to therapy in patients with inflammatory bowel disease. Identification of these patients would optimize personalized therapeutic selection.

METHODS

Genomic DNA was extracted from 80 deidentified samples in an unselected patient population with an unknown rs2097432 genotype. Split sample analysis was performed using a reference laboratory. Primer probes for a TaqMan quantitative polymerase chain reaction (qPCR) assay (Thermo Fisher Scientific) were custom designed. Synthesized genomic-block fragments were used as controls. All qPCR reactions were performed using a TaqMan GTXpress Master Mix (Thermo Fisher Scientific) on the Applied Biosystems 7500 system under fast cycling conditions.

RESULTS

Of 80 samples, 50% were wild-type reference genotypes, 22.5% were heterozygous, and 27.5% were homozygous variant calls, comparable to population data. Split analysis samples between 2 independent laboratories were 100% concordant. The detection limit tested across genomic-block controls processed in duplicate was reproducible on sample input from 10 ng titrated down to 1.25 ng across 2 independent runs. Further, analytical specificity assessed with previous wild-type reference and homozygous variant DNA spiked into genomic-block controls produced appropriate heterozygous genotypes.

CONCLUSIONS

Here we present validation of a lab-developed test for a rapid HLA-DQA1*05 (rs2097432) pharmacogenomics assay targeting a hotspot identified by genome-wide association studies. Targeted genotyping employed here will allow for expeditious personalized therapeutic selection.

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.

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.

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.

High-definition PCR (HDPCR) detection of DNA and RNA variants in non-small cell lung cancer (NSCLC) samples

e20603

Background: Digital PCR (dPCR) is an emerging technology platform for detecting genomic variants in cancer genomes due to its high sensitivity and fast time to results compared to sequencing. However, translational oncology applications often require the measurement of more biomarkers than there are color channels available on dPCR platforms. One approach to address this limitation with dPCR is to split a sample across many wells and profile a subset of variants in each well. For input-limited samples, however, this results in fewer molecules being profiled in each dPCR well, resulting in a reduction in sensitivity and fewer patient samples processed per instrument run. ChromaCode has developed a research use only (RUO) digital High Definition PCR (HDPCR) NSCLC assay, for multiplexed detection of 14 DNA variants and 15 RNA fusion variants relevant in non-small cell lung cancer samples. The assay is constructed using both amplitude modulation and multi-channel resilient signal encoding methods. Amplitude modulation enables different variants to generate a distinguishable signal at different intensity levels in a single color channel, allowing for multiple targets to be detected within that single-color channel. In addition, resilient encoding generates a signal in more than one color channel to create a form of error detection in the assay design. Methods: Assay benchmarking was performed using over 500 contrived human biological FFPE samples, consisting of synthetic DNA or RNA variants spiked into a background matrix of FFPE-extracted DNA or RNA; over 500 contrived human biological plasma samples, consisting of synthetic DNA or RNA variants spiked into a background matrix of plasma-extracted cell free DNA or RNA; and residual human biological FFPE and plasma NSCLC samples that were previously characterized using a targeted sequencing workflow. The samples were tested using the HDPCR NSCLC assay on the QuantStudio Absolute Q Digital PCR system, and data analysis was performed with custom analysis algorithms. Results: For the more than 500 contrived FFPE and plasma samples, the HDPCR NSCLC assay had high overall agreement with expectation across a range of mutant allele fractions for both DNA and RNA analytes (≥99% PPA and ≥99% NPA). For a set of N = 25 residual human biological FFPE samples, the assay was also highly concordant (100% PPA and 99% NPA) with a targeted panel sequencing comparator. The hands-on workflow time from isolation start to analysis complete was < 24 hours. Conclusions: The HDPCR NSCLC assay is a robust RUO tool for the sensitive and rapid detection of commonly targeted variants relevant to NSCLC samples. This technology could complement sequencing assays when there is a need for a rapid turnaround time or there are limited amounts of isolated nucleic acid.

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.

Actionable Tumor Alterations and Treatment Protocol Enrollment of Pediatric and Young Adult Patients With Refractory Cancers in the National Cancer Institute-Children's Oncology Group Pediatric MATCH Trial

PURPOSE

The National Cancer Institute-Children's Oncology Group Pediatric MATCH trial aimed to facilitate evaluation of molecular-targeted therapies in biomarker-selected cohorts of childhood and young adult patients with cancer by screening tumors for actionable alterations.

PATIENTS AND METHODS

Tumors from patients age 1-21 years with refractory solid tumors, lymphomas, or histiocytic disorders were subjected to cancer gene panel sequencing and limited immunohistochemistry to identify actionable alterations for assignment to phase II treatment arms. The rates of treatment arm assignment and enrollment were compared between clinical and demographic groups.

RESULTS

Testing was completed for 94.7% of tumors submitted. Actionable alterations were detected in 31.5% of the first 1,000 tumors screened, with treatment arm assignment and enrollment occurring in 28.4% and 13.1% of patients, respectively. Assignment rates varied by tumor histology and were higher for patients with CNS tumors or enrolled at Pediatric Early Phase Clinical Trials Network sites. A reported history of prior clinical molecular testing was associated with higher assignment and enrollment rates. Actionable alterations in the mitogen-activated protein kinase signaling pathway were most frequent (11.2%). The most common reasons provided for not enrolling on treatment arms were patients receiving other treatment or poor clinical status.

CONCLUSION

The Pediatric MATCH trial has proven the feasibility of a nationwide screening Protocol for identification of actionable genetic alterations and assignment of pediatric and young adult patients with refractory cancers to trials of molecularly targeted therapies. These data support the early use of tumor molecular screening for childhood patients with cancer whose tumors have not responded to standard treatments.