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URL: https://pubmed.ncbi.nlm.nih.gov/40465842/

⇱ Elevated ctDNA Tumor Fraction Is Associated with Improved Mutation Detection but Worse Overall Survival in Advanced Non-Small Cell Lung Cancer: A Lung-MAP Study - PubMed

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Abstract

Purpose: ctDNA is a powerful diagnostic companion to tissue profiling. Tumor fraction (TF) is a global assessment of an individual's ctDNA burden. We evaluated the impact of plasma TF on mutation detection and clinical outcomes in patients with previously treated, advanced non-small cell lung cancer on the Lung Master Protocol (Lung-MAP).

Experimental design: Paired tumor tissue and plasma were collected prospectively from patients on the Lung-MAP study. Plasma was collected within 30 days of a new biopsy with no intervening therapies. Tissue and ctDNA genomic profiling and ctDNA TF levels were assessed by Foundation Medicine. TF was primarily calculated from tumor aneuploidy, defaulting to fragmentomics and maximum somatic allele frequencies when aneuploidy was not detectable. The effect of TF on tissue-plasma mutation concordance, overall survival, and its relation to variant allele frequencies was assessed using linear regression, Lin's coefficient, and Cox modeling/log-rank testing.

Results: A total of 194 patients were eligible for analysis. TF ≥1% was significantly associated with improved positive percent agreement between ctDNA and tissue across multiple alteration types with the exception of copy-number gains. For short variants, positive percent agreement improved from 51% when TF <1% to 95% when TF ≥1%. TF showed a significant robust correlation with variant allele frequency for KRAS, STK11, and TP53-the three most common mutations. TF <1% was significantly associated with improved patient overall survival compared with TF ≥1% or TF ≥10%.

Conclusions: TF provides an accurate, clinically useful assessment of ctDNA plasma levels from patients with refractory, advanced non-small cell lung cancer. TF levels ≥1% are associated with significantly worse overall survival but improved mutation detection in liquid biopsies.

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Conflict of interest statement

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HT, KAT, RWM, and RB are employees of Foundation Medicine Inc. All other authors have no conflicts that pertain directly to this manuscript. No tobacco company funding was received. While companies from the pharmaceutical industry are involved in the Lung-MAP study, they had zero influence over the study presented here.

Figures

👁 Figure 1.
Figure 1.
Graphical representation of the TF algorithm. The primary method of TF assessment is based on the presence of tumor aneuploidy, quantified by variation in sequencing coverage and SNP allele frequencies. A. An example copy number plot of a sample with detectable tumor aneuploidy. B. An example copy number plot where TF cannot be reliably estimated from tumor-specific aneuploidy, in which case, estimates are derived from: C. cell-free tumor DNA fragmentomics (fragment length) and/or D. the presence of known somatic short variants (such as PIK3CA activating mutations). E. Variants at or above 1% have improved concordance with tumor tissue CGP.
👁 Figure 2.
Figure 2.
Effect of TF level on tissue/plasma concordance. Forest plots for PPA (red left side) and PPV (blue right side), showing total TF verses TF ≥ 1% and TF < 1%. Each series is accompanied by a table showing values. Error bars = 95% CI. A. Driver genes (KRAS, EGFR, MET, ERRB2, RET). B. Cancer Genes of Interest (CGoI) (TP53, STK11, ATM, PTEN, RB1, BRCA1 and BRCA2).
👁 Figure 3.
Figure 3.
Overall distribution of PAA and PPV by mutation grouping with 95% CI, subdivided by TF level and further subdivided by mutation type. A. PPA for all patients; B. PPA for TF ≥1%; C. PPA for TF <1%; D. PPV for all patients; E. PPV for TF ≥1%; F. PPV for TF <1%; For mutation subtypes. All = all mutations, SNV: single nucleotide variants (and other small variants); CNA: copy number abnormalities; REA: genomic rearrangements (oncogenic fusions).
👁 Figure 4.
Figure 4.
Scatter plot showing relationship between PPA and PPV for each gene with representation above 5%, color coded by mutation group (red: CGoI; green: Drivers: blue: all others, including non-actionable EGFR and KRAS mutations). Panel A: candidates with TF ≥ 1%; Panel B: candidates with TF < 1%.
👁 Figure 5.
Figure 5.
Correlation between individual gene VAF and global TF for all patients with ctDNA-detected KRAS, STK11 or TP53 mutations, color-coded by whether the mutation was also present in matched tissue.
👁 Figure 6.
Figure 6.
KM curves showing the all-cause overall survival for the analysis cohort (n = 194) with patients subdivided by TF level. TF <1 [blue]: median (95% CI) = 16.2 (12.1–19.6); TF 1–9.9 [Red] = 7.6 (5.7–9.8); TF ≥ 10 [orange] = 5.5 (3.4 – 7.1). HRs(95% CI) comparing TF < 1% to the two higher groups are: TF 1–9.9%: 2.45 (2.66–3.61), p<0.001; and TF ≥ 10%: 2.75 (1.89–4.03) p < 0.001.

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