Usage Information
Citation Information: JCI Insight. 2018;3(21):e122109. https://doi.org/10.1172/jci.insight.122109.
Abstract
BACKGROUND. Tumor content in circulating cell-free DNA (cfDNA) is a promising biomarker, but longitudinal dynamics of tumor-derived and non–tumor-derived cfDNA through multiple courses of therapy have not been well described. METHODS. CfDNA from 663 plasma samples from 140 patients with castration-resistant prostate cancer (CRPC) was subject to sparse whole genome sequencing. Tumor fraction (TFx) estimated using the computational tool ichorCNA was correlated with clinical features and responses to therapy. RESULTS. TFx associated with the number of bone metastases (median TFx = 0.014 with no bone metastases, 0.047 with 1–3 bone metastases, 0.190 for 4+ bone metastases; P < 0.0001) and with visceral metastases (P < 0.0001). In multivariable analysis, TFx remained associated with metastasis location (P = 0.042); TFx was positively correlated with alkaline phosphatase (P = 0.0227) and negatively correlated with hemoglobin (Hgb) (P < 0.001), but it was not correlated with prostate specific antigen (PSA) (P = 0.75). Tumor-derived and non–tumor-derived cfDNA track together and do not increase with generalized tissue damage from chemotherapy or radiation at the time scales examined. All new treatments that led to ≥30% PSA decline at 6 weeks were associated with TFx decline when baseline TFx was >7%; however, TFx in patients being subsequently maintained on secondary hormonal therapy was quite dynamic. CONCLUSION. TFx correlates with clinical features associated with overall survival in CRPC, and TFx decline is a promising biomarker for initial therapeutic response. TRIAL REGISTRATION. Dana-Farber/Harvard Cancer Center (DF/HCC) protocol no. 18-135. FUNDING. Wong Family Award in Translational Oncology, Dana Farber Cancer Institute Medical Oncology grant, Gerstner Family Foundation, Janssen Pharmaceuticals Inc., and Koch Institute Support (core) grant P30-CA14051 from the National Cancer Institute (NCI).
Authors
Atish D. Choudhury, Lillian Werner, Edoardo Francini, Xiao X. Wei, Gavin Ha, Samuel S. Freeman, Justin Rhoades, Sarah C. Reed, Gregory Gydush, Denisse Rotem, Christopher Lo, Mary-Ellen Taplin, Lauren C. Harshman, Zhenwei Zhang, Edward P. O’Connor, Daniel G. Stover, Heather A. Parsons, Gad Getz, Matthew Meyerson, J. Christopher Love, William C. Hahn, Viktor A. Adalsteinsson
Usage data is cumulative from May 2019 through May 2020.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 1,878 | 0 |
| 274 | 0 | |
| Figure | 208 | 0 |
| Table | 60 | 0 |
| Supplemental data | 75 | 0 |
| Citation downloads | 30 | 0 |
| Totals | 2,525 | 0 |
| Total Views | 2,525 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
