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Clinical Utilization Pattern of Liquid Biopsies (LB) to Detect Actionable Driver Mutations, Guide Treatment Decisions and Monitor Disease Burden During Treatment of 33 Metastatic Colorectal Cancer (mCRC) Patients (pts) at a Fox Chase Cancer Center GI Oncology Subspecialty Clinic
Front Oncol. 2019 Jan;8 :652
PMID: 30705875    PMCID: PMC6344461    URL: https://www.ncbi.nlm.nih.gov/pubmed/30705875
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Background: Liquid biopsy (LB) captures dynamic genomic alterations (alts) across metastatic colorectal cancer (mCRC) therapy and may complement tissue biopsy (TB). We sought to describe the utility of LB and better understand mCRC biology during therapy. Methods: Thirty-three patients (pts) with mCRC underwent LB. We used permutation-based t-tests to assess associations between alts, and clinical variables and used Kendall's tau to measure correlations. Results: Of 33 pts, 15 were women; 22 had colon, and the rest rectal cancer. Pts received a median of two lines of therapy before LB. Nineteen pts had limited testing on TB (RAS/RAF/TP53/APC), 11 extended NGS, and 3 no TB. Maxpct and alts correlated with CEA (p < 0.001, respectively). In 3/5 pts with serial LB, CEA correlated with maxpct trend, and CT tumor burden. In 6 pts, mutant RAS was seen in LB and not TB; 5/6 had received anti-EGFR therapy prior to LB, suggesting RAS alts developed post-therapy. In two pts RAS-mutated by TB, no RAS alts were detected on LB; these pts had low disease burden on CT at time of LB that also did not reveal APC or TP53 alts. In six patients who were KRAS wt based on TB, post anti-EGFR LB revealed subclonal KRAS mutations, likely a treatment effect. The median number of alts was higher post anti-EGFR LB (n = 12) vs. anti-EGFR naive LB (n = 22) (9.5 vs. 5.5, p = 0.059) but not statistically significant. More alts were also noted in post anti-EGFR therapy LB vs. KRAS wt anti-EGFR-naive LB (n = 6) (9.5 vs. 5) among patients with KRAS wild-type tumors, although the difference was not significant (p = 0.182). Conclusions: LB across mCRC therapy detects driver mutations, monitors disease burden, and identifies sub-clonal alts that reflect drug resistance, tumor evolution, and heterogeneity. Interpretation of LB results is impacted by clinical context.
Ghatalia, Pooja Smith, Chad H. Winer, Arthur Gou, Jiangtao Kiedrowski, Lesli A. Slifker, Michael Saltzberg, Patricia D. Bubes, Nicole Anari, Fern M. Kasireddy, Vineela Varshaysky, Asya Liu, Yang Ross, Eric A. El-Deiry, Wafik S.\ P30 CA006927/CA/NCI NIH HHS/ Front Oncol. 2019 Jan 17;8:652. doi: 10.3389/fonc.2018.00652. eCollection 2018.