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- Creators: Compton, Carolyn
- All Subjects: Cancer
- All Subjects: RT-qPCR
In the middle of the COVID-19 epidemic, flaws in the SARS-CoV-2 diagnostic
test were identified by the impending supply shortages of nasopharyngeal swabs and nucleic acid isolation and purification kits. The ASU Biodesign Clinical Testing Lab (ABCTL), which converted from a research lab to SARS-CoV-2 testing lab, was not an exception to these shortages, but the consequences were greater due to its significant testing load in the state of Arizona. In response to the shortages, researchers at The Department of Epidemiology of Microbial Diseases, at the Yale School of Public Health created SalivaDirect method, which is an epidemic effective test, that accounts for limitations of materials, accessibility to specialized lab equipment, time per test, and cost per test. SalivaDirect simplified the diagnostic process by collecting samples via saliva and skipping the nucleic acid extraction and purification, and did it in a way that resulted in a highly sensitive limit of detection of 6-12 SARS-CoV-2 copies/μL with a minimal decrease in positive test agreement.
In mid-March of 2020, Arizona State University transformed one of its research labs into ASU Biodesign Clinical Testing Laboratory (ABCTL) to meet the testing needs of the surrounding community during the COVID-19 pandemic. The lab uses RT-qPCR, or reverse transcription polymerase chain reaction, to match the components of a biosample to a portion of the SARS-CoV-2 genome. The ABCTL uses the TaqPath™ COVID-19 Combo Kit, which has undergone many different types of efficacy and efficiency tests and can successfully denote saliva samples as positive even when an individual is infected with various emerging strains of the SARS-CoV-2. Samples are collected by volunteers at testing sites with stringent biosafety precautions and processed in the lab using specific guidelines. As the pandemic eventually becomes less demanding, the ABCTL plans to utilize the Devil’s Drop-off program at various school districts around Arizona to increase testing availability, transfer to the SalivaDirect method, and provide other forms of pathogen testing to distinguish COVID-19 from other types of infections in the ASU community.
The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case.
The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%.
Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02).