Fig S2 from Dynamics of Sequence and Structural Cell-Free DNA Landscapes in Small-Cell Lung Cancer

Lavanya Sivapalan, Wade T. Iams, Zineb Belcaid,Susan C. Scott, Noushin Niknafs, Archana Balan, James R. White, Prasad Kopparapu, Christopher Cann, Blair V. Landon, Gavin Pereira,Victor E. Velculescu, Christine L. Hann, Christine M. Lovly,Valsamo Anagnostou

crossref(2023)

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Abstract
(A) PA scores were assessed across sequenced plasma (n=139) and matched white blood cell (n=32) samples from patients analyzed in the study cohort to determine a threshold for determination of aneuploidy in plasma. A PA score of 4.90, which was the maximum score observed in WBC DNA samples, was selected as the threshold for determination of aneuploidy in plasma. (B) Distribution of PA scores in the panel of 32 matched normal WBC samples analyzed for this study and an independent held out set of 56 WBC normal samples that were processed and analyzed using the same targeted panel and sequencing approach (Methods). The maximum PA score observed for both normal panels was 4.90, supporting the use of this value as the threshold for determination of aneuploidy in plasma. No significant differences were observed in the median PA score (indicated by dotted lines) for each normal panel. (C) Comparison between plasma aneuploidy scores and tumor fraction scores determined using an orthogonal method (ichorCNA) in matched normal DNA (blue) and plasma samples from all timepoints (yellow), plasma samples with detectable tumor-derived sequence mutations (orange) and plasma timepoints with undetectable sequence alterations (grey). Non-zero ichorCNA tumor fraction values were assigned to most samples resulting in significant overlap between the distributions of tumor fractions in plasma and matched normal DNA samples. In contrast, comparisons performed using plasma aneuploidy scores revealed significant differences in the distribution of assigned values across matched normal DNA samples (reflecting normal ploidy) and plasma samples (reflecting tumor aneuploidy). (D-I) Statistical modelling of MAF dynamics was performed using a set of known tumor-specific mutations previously characterized in Phallen et al, Science Transl Med, 2017 from a mixture of DNA from tumor cell lines spiked into unrelated wild-type DNA at dilutions ranging from 0.1-100%. Mutations in this set with replicate data were evaluated for (D, G) variability in MAF estimate followed by (E, H) calculation of the coefficient of variation (CV), and (F, I) finally projection of the relative percentage uncertainty in the MAF estimate. Analyses of mutation data from Phallen et al (2017) are shown in (D-F) and results from a simulation study of 30 mutations ranging in MAF from 0.1% to 80% using a simple statistical model of variability are shown in (G-I). Both analyses of mutation data from Phallen et al (2017) and simulation data showed a significant increase in CV as MAF decreases. For example, given a maximal MAF of 1%, the real 95% uncertainty in MAF estimate was shown to range from -50% to 50% (0.5% MAF to 1.5% MAF). Thus, a greater relative reduction is required to determine elimination of ctDNA for lower maximal MAF values. Based on these results, complete elimination of cell-free tumor load (to either 0% max MAF or undetectable PA) was deemed to be the most appropriate threshold for precise determination of molecular response.
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