Tumor genome sequencing has emerged as a powerful tool for identifying biomarkers for targeted cancer therapies. While DNA sequencing is a well-established method and considered a gold standard, RNA sequencing (RNA-seq) can identify anomalies in gene transcription, regulation of gene expression, and gene fusions, which have critical diagnostic and therapeutic impacts. Tempus Labs is CLIA-certified, CAP-accredited and offers several clinically validated NGS assays for solid tumor and hematological malignancy testing, including the xT targeted 648-gene oncology panel and the xE whole-exome ~20,000 gene panel. The Tempus whole-transcriptome assay by hybrid-capture NGS has been enhanced to improve coverage, focusing on fusion genes and RNA expression calls. We clinically and analytically validated fusion calls for gene rearrangement detection and clinical reporting, and analytically validated RNA expression counts for research purposes only. For RNA fusion (translocation) calling, we sequenced 96 tumor samples, including formalin-fixed, paraffin-embedded (FFPE) tissue, whole blood, and bone marrow samples from 12 cancer types; 82 of these samples contained reportable fusions. Of these, 86 samples were positive for RNA fusions, including 62 positive samples with targeted fusion breakpoints (i.e., utilizing custom probes designed to the breakpoints and spiked-in at hybridization) and 24 positive samples with untargeted fusion breakpoints (i.e., utilizing no custom probes). Targeted fusion accuracy was evaluated against the first version of the RNA sequencing assay. The first version of this assay was evaluated with cell line controls. In addition, validation included a specificity test and a rolling validation of fusions via DNA sequencing (positive controls). The concordance for targeted fusions was 100%, with 99.9% sensitivity and 99.9% specificity. For untargeted fusions, the overall concordance was 97%, with 97% sensitivity. For RNA expression calling, we conducted a clinical linearity study using 88 samples and testing 18 genes to measure concordance between qPCR ΔCT values and normalized gene expression levels. Of these, 15/18 genes met the established acceptance criteria of R > 0.75. In conclusion, the whole-transcriptome by hybrid-capture NGS assay offers clinically and analytically validated unbiased detection of common and novel gene rearrangements, as well as analytically validated gene expression data for comprehensive research analyses. Citation Format: Jun Hu, Jerod Parsons, Brittany Mineo, Josh SK Bell, Jenna Malinauskas, Joshua Drews, Jack Michuda, Calvin McCarter, Rasika Dhond, Jack C. Tyndall, Nithya Sreenivasan, Sheeri Hanjra, Shannon Gallagher, Ankit Jambusaria, Naihui Zhou, Catherine Igartua, Nike Beaubier, Richard A. Blidner, Robert Tell. Comprehensive validation of RNA sequencing for clinical NGS fusion genes and RNA expression reporting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2239.
Acute respiratory distress syndrome (ARDS) is a fulminant inflammatory lung injury that develops in patients with critical illnesses including sepsis, pneumonia, and trauma. However, many patients with ARDS are not recognized when they develop this syndrome nor given outcome-improving treatments. Because ARDS is a clinical syndrome, physicians may not be certain about a patient's diagnosis (label uncertainty). In addition, the diagnosis requires a chest xray, which may not be always be available in a clinical setting (privileged information). For this paper, we implemented the Learning Using Label Uncertainty and Partially Available Privileged Information (LULUPAPI) paradigm, built on classical SVM, to detect ARDS using Electronic Health Record (EHR) data and chest radiography. In comparison to SVM, this resulted in a 3.55 percent improvement of test AUC. I. INTRODUCTION200,000 patients in the United States each year suffer from Acute Respiratory Distress Syndrome (ARDS), a fulminant lung injury. Patients with ARDS have a mortality rate of 30-40% [1]. Simple interventions such as reducing ventilator tidal volume have been shown to improve patient outcomes [2]. However, physician recognition of ARDS ranges from 50 to 80% depending on the severity of condition; as a consequence, many patients do not receive these life-saving treatments [3]. One potentially effective, yet underutilized, method of assisting physicians in recognition of ARDS is the analysis of electronic health record (EHR) data. Algorithms that process information provided by EHR data and bedside
Laboratories conducting high volumes of RNA sequencing must be extremely wary of technical batch effects if samples are to be compared across extended time periods, which is imperative for the most well-powered analyses of cancer transcriptomes. Changes in reagents, protocols, or technologies used in nucleic acid extraction, library preparation, and sequencing can alter transcriptomes in ways that invalidate or complicate comparisons of samples from different batches, necessitating continuous monitoring. This monitoring can be particularly difficult when analyzing samples from distinct tissue sites as tumor type is the major biological determinant of transcriptome variance in cancer. Brain and liver cancer transcriptomes, for example, are expected to differ so drastically that their comparison is not informative for batch effect detection. Detection methods must also be robust to disparate batch effects that can manifest as minor changes in expression among many genes or major changes in a subset of genes making ad hoc detection unfeasible. To overcome these challenges, we developed MaCoBED (matched cohort batch effect detection), a novel method that evaluates technical batch effects in a set of transcriptome samples (e.g., a flow cell) by pooling them with a set of validated reference samples matched by cancer type and tissue site. This pooled set of transcriptomes is then subjected to low-dimensional embedding using Uniform Manifold Approximation and Projection (UMAP), and each component is tested for deviation from the reference set using a Wilcox test. Matching new and legacy samples by cancer type and tissue site ensures that any differences in UMAP clustering are not driven by known biological contributions. We found that UMAP was preferable to Principal Components Analysis (PCA). UMAP can capture variability in just two dimensions, accentuating modest but consistent transcriptome differences among batches that would otherwise be manifested among multiple minor principal components, making batch effects more obvious and readily detectable. This approach was able to detect a number of simulated batch effects with high specificity and sensitivity relative to randomly sampled validated legacy samples. Thus, we propose MaCoBED as a simple and rapid approach for batch effect monitoring of high-throughput RNA sequencing datasets that is versatile in detecting distinct kinds of batch effects, easily automatable, readily interpretable upon visualization, and extensible to small or large batch sizes. Citation Format: Joshua Drews, Joshua Bell, Wesley Munson, Saksham Saini, Benjamin Leibowitz, Jackson Michuda, Calvin McCarter, Lee Langer, Catherine Igartua, Kevin White. Robust detection of sequencing batch effects in RNA through low dimensional embedding with subtype-matched reference samples [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5466.
Background: Next-generation sequencing of circulating tumor DNA (ctDNA) and solid-tissue can identify clinically actionable genomic variants that may be used for both treatment selection and disease surveillance. Due to differences in tumor biology and assay design, ctDNA and solid biopsies may identify unique variants. Here, we investigate a real-world dataset of breast cancer patients to determine whether clinically actionable variant detection is enhanced by dual ctDNA and solid tissue testing. Methods: We used the deidentified Tempus Lens database to retrospectively analyze stage IV breast cancer patients with known hormonal subtype. Each patient had dual testing defined as Tempus xF (ctDNA) and Tempus xT (tumor tissue)—which resulted in clinical reports for both tests. Patients were further stratified according to the timing of ctDNA biopsy relative to tissue biopsy. Concurrent dual testing was defined as samples collected ≤30 days apart and longitudinal dual testing was defined as liquid >30 days after solid. Variants were included in analyses if they met the limit of detection criteria of both assays. Clinical actionability was defined by indication-matched OncoKB Level 1-3. Fisher exact test was used to calculate significance. Results: Of the 1,341 breast cancer patients with dual ctDNA and tissue sequencing, at least one actionable variant was identified in 61% (n=823) of patients. In the subset of concurrent tested patients (n=782), 60% (n=473) had one or more actionable findings: 54% (n=257/473) of patients with actionable variants had perfectly concordant variants, 29% (n=136/473) had at least one unique variant detected only by solid tumor testing, and 20% (n=93/473) had at least one unique variant detected only by ctDNA testing. Similarly, in the longitudinal set (n=559), 63% (n=350) had one or more actionable findings: 34% (n=118/350) were concordant, 43% (n=150/350) were unique to solid, and 27% (n=96/350) were unique to ctDNA. When stratifying concurrent patients by OncoKB levels of evidence, 72% (n=98/136) of patients with variants unique in solid had at least one level 1-2 variant, while 39% (n=53/136) contained unique level 3 variants. Level 1-2 variants in PIK3CA were the most frequent variants seen uniquely in solid tumors, occurring in 54% (n=73/136) of patients. In contrast, in patients with unique ctDNA variants, 37% (n=34/93) of patients had at least one level 1-2 variants and 72% (n=67/93) had level 3 variants. Level 3 variants in ESR1 were the most frequent variants seen uniquely in ctDNA, occurring in 57% (n=53/93) of patients. The proportion of concurrent patients with actionable variants found exclusively in ctDNA significantly differed by subtype (p=0.04): Luminal A (22%) and Luminal B (23%) contained the most patients with unique ctDNA variants. This ability to detect additional variants in ctDNA remained true even if profiling occurred over time. Indeed, in patients with ESR1 variants tested with ctDNA > 1 year after tissue, 78% (n=43/55) had ESR1 variants only detected in blood. Conclusions: We show that dual testing in breast cancer patients improves the identification of clinically actionable variants which may be missed by either ctDNA or solid tissue biopsy alone. Adoption of dual testing should be considered as standard practice to provide a comprehensive view of actionable molecular alterations. Citation Format: Matthew Mackay, Kabir Manghnani, Adam Hockenberry, Joshua Drews, James Chen, Rotem Ben-Shachar, Justin Guinney. Dual ctDNA and tissue sequencing improves detection of actionable variants in breast cancer patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-05-08.
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