Abstract 2239: Comprehensive validation of RNA sequencing for clinical NGS fusion genes and RNA expression reporting

Hu, Jun; Parsons, Jerod; Mineo, Brittany; Bell, Josh Sk; Malinauskas, Jenna; Drews, Joshua; Michuda, Jack; McCarter, Calvin; Dhond, Rasika M.; Tyndall, Jack C.; Sreenivasan, Nithya; Hanjra, Sheeri; Gallagher, Shannon; Jambusaria, Ankit; Zhou, Naihui; Beaubier, Nike; Blidner, Richard A.; Tell, Robert

doi:10.1158/1538-7445.am2021-2239

Cited by 6 publications

(2 citation statements)

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“…The Tempus xT RNA-seq protocol uses exome-capture via IDT xGen probes (> 415,000) spanning > 19,000 genes, with at least 50 ng of extracted RNA required before proceeding to library preparation and a minimum depth of 30 million reads (also sequenced on a NovaSeq 6000). Transcript level abundances are derived from Kallisto (v.0.44.0) [ 41 , 42 ] pseudoalignments to the Ensembl GRCh37 (Release 75) and normalized for transcript length, GC content, and library size; batch correction is performed for samples sequenced with different probe designs [ 43 ]. Gene-level abundances are obtained by summing only transcripts labeled as protein coding by Ensembl.…”

Section: Methodsmentioning

confidence: 99%

Validation of genomic and transcriptomic models of homologous recombination deficiency in a real-world pan-cancer cohort

et al. 2022

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Background With the introduction of DNA-damaging therapies into standard of care cancer treatment, there is a growing need for predictive diagnostics assessing homologous recombination deficiency (HRD) status across tumor types. Following the strong clinical evidence for the utility of DNA-sequencing-based HRD testing in ovarian cancer, and growing evidence in breast cancer, we present analytical validation of the Tempus HRD-DNA test. We further developed, validated, and explored the Tempus HRD-RNA model, which uses gene expression data from 16,750 RNA-seq samples to predict HRD status from formalin-fixed paraffin-embedded tumor samples across numerous cancer types. Methods Genomic and transcriptomic profiling was performed using next-generation sequencing from Tempus xT, Tempus xO, Tempus xE, Tempus RS, and Tempus RS.v2 assays on 48,843 samples. Samples were labeled based on their BRCA1, BRCA2 and selected Homologous Recombination Repair pathway gene (CDK12, PALB2, RAD51B, RAD51C, RAD51D) mutational status to train and validate HRD-DNA, a genome-wide loss-of-heterozygosity biomarker, and HRD-RNA, a logistic regression model trained on gene expression. Results In a sample of 2058 breast and 1216 ovarian tumors, BRCA status was predicted by HRD-DNA with F1-scores of 0.98 and 0.96, respectively. Across an independent set of 1363 samples across solid tumor types, the HRD-RNA model was predictive of BRCA status in prostate, pancreatic, and non-small cell lung cancer, with F1-scores of 0.88, 0.69, and 0.62, respectively. Conclusions We predict HRD-positive patients across many cancer types and believe both HRD models may generalize to other mechanisms of HRD outside of BRCA loss. HRD-RNA complements DNA-based HRD detection methods, especially for indications with low prevalence of BRCA alterations.

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Section: Methodsmentioning

confidence: 99%

Validation of genomic and transcriptomic models of homologous recombination deficiency in a real-world pan-cancer cohort

et al. 2022

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“…Gene expression features were analytically validated 32 to ensure that the model relied on accurate and reproducible inputs. Universal Human Reference (UHR) RNA 33 was sequenced on the Tempus RNA-seq assay to establish linearity against an orthogonal method.…”

Section: Methodsmentioning

confidence: 99%

Validation of a transcriptome-based assay for classifying cancers of unknown primary origin

Michuda

Breschi

Kapilivsky

et al. 2022

Preprint

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Cancers assume a variety of distinct histologies and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision making based on consensus guidelines such as NCCN is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings—in addition to ambiguous clinical presentations such as recurrence versus new primary—a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for CUP patients, with a median survival of 8-11 months. Here we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-seq-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. We show that the Tempus TO model is 91% accurate when assessed on retrospectively and prospectively held out cohorts of containing 9,210 samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype. Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.

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Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin

et al. 2023

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Introduction Cancers assume a variety of distinct histologies, and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision-making based on consensus guidelines such as the National Comprehensive Cancer Network (NCCN) is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings—in addition to ambiguous clinical presentations such as recurrence versus new primary—a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for patients with CUP, with a median survival of 8–11 months. Methods Here, we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-sequencing-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. Model accuracy was assessed using primary and/or metastatic samples with known subtype. Results We show that the Tempus TO model is 91% accurate when assessed on both a retrospectively held out cohort and a set of samples sequenced after model freeze that collectively contained 9210 total samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype. Discussion Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology. Supplementary Information The online version contains supplementary material available at 10.1007/s40291-023-00650-5.

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Abstract 2239: Comprehensive validation of RNA sequencing for clinical NGS fusion genes and RNA expression reporting

Cited by 6 publications

References 0 publications

Validation of genomic and transcriptomic models of homologous recombination deficiency in a real-world pan-cancer cohort

Validation of genomic and transcriptomic models of homologous recombination deficiency in a real-world pan-cancer cohort

Validation of a transcriptome-based assay for classifying cancers of unknown primary origin

Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin

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