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Lyskjaer, I.

Publications and source records attributed to Lyskjaer, I..

2 recordsLinked to original sources

Preoperative thymic output and T cell repertoire diversity are associated with recurrence after nephrectomy for localized renal cell carcinoma

Radiographic thymic health and peripheral T-cell repertoire features have been linked to outcomes in renal cell carcinoma (RCC), but their prognostic value after curative-intent surgery is unclear. We quantified preoperative blood signal-joint T-cell receptor excision circles (sjTRECs), a marker of thymic output, in 218 patients with localized RCC undergoing nephrectomy and characterized T-cell receptor {beta} (TCR{beta}) repertoires in 201 patients. We assessed overall survival (OS) and recurrence-free survival (RFS) using Kaplan-Meier and Cox models adjusted for age, sex, and Leibovich risk group. High sjTREC levels were associated with longer RFS after adjustment for age, sex, and Leibovich risk group (hazard ratio [HR] 0.50; 95% confidence interval [CI], 0.29-0.87; p=0.015), with a concordant OS estimate (HR 0.62; 95% CI, 0.38-1.02; p=0.058). sjTREC provided additional prognostic information beyond Leibovich risk stratification for recurrence overall and among intermediate/high-risk patients (likelihood-ratio test, p=0.024 and p=0.034, respectively). Higher TCR{beta} diversity was associated with longer RFS overall (HR 0.76; 95% CI, 0.60-0.96; p=0.024) and among intermediate/high-risk patients with longer RFS (HR 0.71; 95% CI, 0.55-0.92; p=0.009) and OS (HR 0.80; 95% CI, 0.65-0.98; p=0.031). Preoperative sjTREC provided prognostic information beyond clinicopathological risk, with higher TCR{beta} diversity associated with favorable outcomes.

cancer biology↗

DREAMS: Deep Read-level Error Model for Sequencing data applied to low-frequency variant calling and circulating tumor DNA detection

Circulating tumor DNA detection using Next-Generation Sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we developed statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generated deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performed better than state-of-the-art methods for variant calling and cancer detection.

bioinformatics↗