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. 2022 Feb 15;28(4):697-707.
doi: 10.1158/1078-0432.CCR-21-3231.

Persistence of ctDNA in Patients with Breast Cancer During Neoadjuvant Treatment Is a Significant Predictor of Poor Tumor Response

Affiliations

Persistence of ctDNA in Patients with Breast Cancer During Neoadjuvant Treatment Is a Significant Predictor of Poor Tumor Response

Qing Zhou et al. Clin Cancer Res. .

Abstract

Purpose: Accurate response assessment during neoadjuvant systemic treatment (NST) poses a clinical challenge. Therefore, a minimally invasive assessment of tumor response based on cell-free circulating tumor DNA (ctDNA) may be beneficial to guide treatment decisions.

Experimental design: We profiled 93 genes in tissue from 193 patients with early breast cancer. Patient-specific assays were designed for 145 patients to track ctDNA during NST in plasma. ctDNA presence and levels were correlated with complete pathological response (pCR) and residual cancer burden (RCB) as well as clinicopathologic characteristics of the tumor to identify potential proxies for ctDNA release.

Results: At baseline, ctDNA could be detected in 63/145 (43.4%) patients and persisted in 25/63 (39.7%) patients at mid-therapy (MT) and 15/63 (23.8%) patients at the end of treatment. ctDNA detection at MT was significantly associated with higher RCB (OR = 0.062; 95% CI, 0.01-0.48; P = 0.0077). Of 31 patients with detectable ctDNA at MT, 30 patients (96.8%) were nonresponders (RCB II, n = 8; RCB III, n = 22) and only one patient responded to the treatment (RCB I). Considering all 145 patients with baseline (BL) plasma, none of the patients with RCB 0 and only 6.7% of patients with RCB I had ctDNA detectable at MT, whereas 30.6% and 29.6% of patients with RCB II/III, respectively, had a positive ctDNA result.

Conclusions: Overall, our results demonstrate that the detection and persistence of ctDNA at MT may have the potential to negatively predict response to neoadjuvant treatment and identify patients who will not achieve pCR or be classified with RCB II/III.

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Figures

Figure 1. Study design and flow chart and molecular profiling results. A, Shown is an overview of the study design. Patients with early breast cancer were treated with standard of care (SoC) NCT or NET. After mutation analysis of the primary tumors using a 93-gene panel, patient-specific high-resolution assays were designed to track ctDNA during treatment. B, Flow chart showing patients and samples evaluated in the study. NA, not available; QC, quality control; UV, variants with unknown clinical significance. Created with BioRender.com.
Figure 1.
Study design and flow chart and molecular profiling results. A, Shown is an overview of the study design. Patients with early breast cancer were treated with standard of care (SoC) NCT or NET. After mutation analysis of the primary tumors using a 93-gene panel, patient-specific high-resolution assays were designed to track ctDNA during treatment. B, Flow chart showing patients and samples evaluated in the study. NA, not available; QC, quality control; UV, variants with unknown clinical significance. Created with BioRender.com.
Figure 2. Somatic driver alterations in tumor and plasma. A, Heat map representing the landscape of somatic driver alterations, including mutations and CNAs in tumor samples. B, Heat map representing number of mutations detected by SiMSen-seq in all plasma samples (All), at BL, MT, and EOT. C, Time courses of exemplary patients with various numbers of screened mutations in plasma. Shown are the VAF in plasma at BL, MT, and EOT (left y-axis) and the VAFs of the mutations detected in matched tissue samples (right y-axis). LOD, limit of detection.
Figure 2.
Somatic driver alterations in tumor and plasma. A, Heat map representing the landscape of somatic driver alterations, including mutations and CNAs in tumor samples. B, Heat map representing number of mutations detected by SiMSen-seq in all plasma samples (All), at BL, MT, and EOT. C, Time courses of exemplary patients with various numbers of screened mutations in plasma. Shown are the VAF in plasma at BL, MT, and EOT (left y-axis) and the VAFs of the mutations detected in matched tissue samples (right y-axis). LOD, limit of detection.
Figure 3. Predictive value of ctDNA. A, Flow chart depicting patients with (ctDNA+) and without (ctDNA−) detectable ctDNA at BL, MT, and EOT. NA, no plasma available. B, OR with 95% confidence intervals (CI) and P values for ctDNA detection at BL, MT, EOT, or all time points to predict tumor response (RCB 0/I vs. RCB II/III) calculated from univariate logistic regression models. C, Plotted are the fractions of patients stratified by response (RCB 0–III) with (ctDNA+) and without (ctDNA−) detectable ctDNA at MT. D, Detection rates of ctDNA at BL, MT, and EOT stratified by RCB scores (E) same as in B but for pCR. NA, not analyzable.
Figure 3.
Predictive value of ctDNA. A, Flow chart depicting patients with (ctDNA+) and without (ctDNA−) detectable ctDNA at BL, MT, and EOT. NA, no plasma available. B, OR with 95% confidence intervals (CI) and P values for ctDNA detection at BL, MT, EOT, or all time points to predict tumor response (RCB 0/I vs. RCB II/III) calculated from univariate logistic regression models. C, Plotted are the fractions of patients stratified by response (RCB 0–III) with (ctDNA+) and without (ctDNA−) detectable ctDNA at MT. D, Detection rates of ctDNA at BL, MT, and EOT stratified by RCB scores (E) same as in B but for pCR. NA, not analyzable.
Figure 4. ctDNA levels and dynamics over the course of neoadjuvant therapy. A, Plotted are VAF distributions for three time points stratified by RCB (left plot). At BL, ctDNA levels were significantly elevated in patients with poor tumor responses (RCB II/III) compared with responders (RCB 0/I). For MT and the EOT, no association could be established due to a small number of positive samples for responders (right plot). B, Shown are patients with ctDNA data at BL and at least one additional time point stratified by tumor response. Top plot denotes responders, lower plot represents nonresponders. C, Waterfall plot of ctDNA changes (in percent) from BL to MT. *, Clearance of ctDNA at MT was considered as a 100% decrease; #, ctDNA detection at MT but not at BL was considered as a 100% increase.
Figure 4.
ctDNA levels and dynamics over the course of neoadjuvant therapy. A, Plotted are VAF distributions for three time points stratified by RCB (left plot). At BL, ctDNA levels were significantly elevated in patients with poor tumor responses (RCB II/III) compared with responders (RCB 0/I). For MT and the EOT, no association could be established due to a small number of positive samples for responders (right plot). B, Shown are patients with ctDNA data at BL and at least one additional time point stratified by tumor response. Top plot denotes responders, lower plot represents nonresponders. C, Waterfall plot of ctDNA changes (in percent) from BL to MT. *, Clearance of ctDNA at MT was considered as a 100% decrease; #, ctDNA detection at MT but not at BL was considered as a 100% increase.
Figure 5. Association between circulating tumor DNA and clinicopathologic characteristics. A, Association between ctDNA presence (ctDNA+) and absence (ctDNA−) at baseline and clinicopathologic characteristics. P values were calculated using Fisher exact test. B, Distribution of tumor size, Ki-67 scores, EndoPredict scores (MS, 12-gene molecular score; EPclin, combined clinic-molecular score), and stromal and intratumoral TILs in patients with (ctDNA+) and without (ctDNA−) detectable ctDNA at baseline.
Figure 5.
Association between circulating tumor DNA and clinicopathologic characteristics. A, Association between ctDNA presence (ctDNA+) and absence (ctDNA−) at baseline and clinicopathologic characteristics. P values were calculated using Fisher exact test. B, Distribution of tumor size, Ki-67 scores, EndoPredict scores (MS, 12-gene molecular score; EPclin, combined clinic-molecular score), and stromal and intratumoral TILs in patients with (ctDNA+) and without (ctDNA−) detectable ctDNA at baseline.

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