Clinical validation of the tempus xT next-generation targeted oncology sequencing assay

Beaubier, Nike; Tell, Robert; Lau, Denise; Parsons, Jerod; Bush, Stephen J.; Perera, Jason; Sorrells, Shelly; Baker, Timothy; Chang, Alan L.; Michuda, Jackson; Iguartua, Catherine; MacNeil, Shelley M.; Shah, Kaanan P.; Ellis, Philip; Yeatts, Kimberly; Mahon, Brett; Taxter, Timothy J.; Bontrager, Martin; Khan, Aly A.; Huether, Robert; Lefkofsky, Eric; White, Kevin P.

doi:10.18632/oncotarget.26797

Cited by 152 publications

(150 citation statements)

References 40 publications

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“…TMB was determined by each clinical-grade test, as previously defined, and was denoted as the total number of somatic coding base substitutions and short insertions/deletions per megabase of genome examined (muts/Mb) (31)(32)(33). Frameshift mutation burden was calculated as number of frameshift mutations divided by length of genome examined, and was similarly reported as number of frameshift mutations per megabase (muts/Mb).…”

Section: Estimation Of Tumor Mutation Burden (Tmb) Frameshift Mutatimentioning

confidence: 99%

Tumor Frameshift Mutation Proportion Predicts Response to Immunotherapy in Mismatch Repair-Deficient Prostate Cancer

et al. 2020

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Background. Genomic biomarkers that predict response to anti-PD1 therapy in prostate cancer are needed. Frameshift mutations are predicted to generate more neoantigens than missense mutations; therefore, we hypothesized that the number or proportion of tumor frameshift mutations would correlate with response to anti-PD1 therapy in prostate cancer. Methods. To enrich for response to anti-PD1 therapy, we assembled a multi-center cohort of 65 men with mismatch repair-deficient (dMMR) prostate cancer. Patient characteristics and outcomes were determined by retrospective chart review. Clinical somatic DNA sequencing was used to determine tumor mutational burden (TMB), frameshift mutation burden (FSB) and frameshift mutation proportion (FSP), which were correlated to outcomes on anti-PD1 treatment. We subsequently utilized data from a clinical trial of pembrolizumab in patients with non-prostatic dMMR cancers of various histologies as a biomarker validation cohort. Results. Nineteen of 65 patients with dMMR metastatic castration-resistant prostate cancer were treated with anti-PD1 therapy and demonstrated a PSA 50 response rate of 65% and a median progression-free survival (PFS) of 24 (95%CI, 16-54) weeks. Tumor FSP, more than overall TMB, correlated most strongly with prolonged PFS and overall survival (OS) on anti-PD1 treatment, and with density of CD8+ tumor infiltrating lymphocytes. High FSP similarly identified patients with longer PFS as well as OS on anti-PD1 therapy in a validation cohort. Conclusions. Tumor FSP correlated with prolonged efficacy of anti-PD1 treatment among patients with dMMR cancers, and may represent a new biomarker of immune checkpoint inhibitor sensitivity. The Oncologist 2020;9999:• • Implications for Practice: Given the modest efficacy of immune checkpoint inhibition (ICI) in unselected patients with advanced prostate cancer, biomarkers of ICI sensitivity are needed. To facilitate biomarker discovery, we assembled a cohort of patients with DNA mismatch repair-deficient (dMMR) prostate cancer as these patients are enriched for responses to ICI. We observed a high response rate to anti-PD1 therapy in these patients, however these responses were not durable in most patients. Notably, we identified tumor frameshift mutation proportion (FSP) as a novel biomarker that was associated with prolonged response to anti-PD1 therapy in this cohort. This finding was validated in a separate cohort of patients with nonprostatic dMMR cancers of various primary histologies. This works suggests that FSP predicts response to anti-PD1 therapy in dMMR cancers, which should be validated prospectively in larger independent cohorts.

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Section: Estimation Of Tumor Mutation Burden (Tmb) Frameshift Mutatimentioning

confidence: 99%

Tumor Frameshift Mutation Proportion Predicts Response to Immunotherapy in Mismatch Repair-Deficient Prostate Cancer

et al. 2020

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“…RNA data were used for fusion validation and differential expression. 16,17 Paired normal samples were used when they were available. Through Tempus bioinformatic pipelines, single nucleotide polymorphism and indel mutations were called as low as 3% allele frequency.…”

Section: Genomic Sequencing and Analysismentioning

confidence: 99%

BRAF fusion Spitz neoplasms; clinical morphological, and genomic findings in six cases

et al. 2020

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Background: Fusions involving the BRAF gene are responsible for 5% of Spitz neoplasms. To better characterize them, we report the clinical, morphological, and genomic findings of six BRAF fusion Spitz tumors. Methods: The morphological, clinical, and molecular findings of six BRAF fusion Spitz neoplasms assessed by next generation sequencing (NGS) were compared to a control set of Spitz without BRAF fusions. Results: BRAF fusion Spitz tumors had frequent predominance of epithelioid morphology (4/6 cases), frequent high-grade nuclear atypia and pleomorphism (5/6 cases), and a frequent desmoplastic base (3/6 cases). Five of six cases were diagnosed as atypical Spitz tumor and one as Spitz nevus. All cases had uneventful clinical follow-up. There were five different fusion partners, with CLIP2 being the most frequent. Secondary pathogenic mutations were frequent and chromosomal copy number changes were seen in three of six cases by an NGS platform. Conclusions: BRAF fusions Spitz usually have epithelioid morphology, high-grade nuclear atypia, and desmoplasia. Chromosomal copy number changes are not infrequent. While our cases had uneventful follow-up, a meta-analysis of the literature suggests that among the fusion subtypes associated with Spitz tumors, they are among the subgroups more likely to develop distant metastasis.

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“…COSMIC also serves as an additional source of curated Drug Resistance gene category claims. Other new gene category sources include the Tempus xT ( 16 ) panel of actionable cancer therapy target genes, a list of the top priority genes from the Illuminating the Druggable Genome (IDG) ( 17 ) Initiative, the Human Protein Atlas ( 18 ), the Oncomine ( 19 ) clinical cancer biomarker assay, and understudied targets of the IDG program from Pharos ( 20 ) ( Supplementary Table S1 ). From these new sources, we have added 23 916 new drug-gene interaction claims and 8478 new druggable gene category claims (Figure 2 ).…”

Section: Introductionmentioning

confidence: 99%

Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

Freshour

Kiwala

Cotto

et al. 2020

Nucleic Acids Research

654

435

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The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.

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Clinical validation of the tempus xT next-generation targeted oncology sequencing assay

Cited by 152 publications

References 40 publications

Tumor Frameshift Mutation Proportion Predicts Response to Immunotherapy in Mismatch Repair-Deficient Prostate Cancer

Tumor Frameshift Mutation Proportion Predicts Response to Immunotherapy in Mismatch Repair-Deficient Prostate Cancer

BRAF fusion Spitz neoplasms; clinical morphological, and genomic findings in six cases

Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

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