David Soave

Outcome-dependent two-phase sampling designs permit cost-effective measurement of expensive covariates in large prospective cohorts. Weighted Cox regressio

Pretreatment diagnostic tumor somatic variants and transcriptional signatures associated with PFS. A, Frequency of variants across breast cancer driver genes in pretreatment tumor biopsies. Colors indicate variant classification (B) VAF of detected single-nucleotide variants across samples. C, HRs of PFS associated with the presence of somatic variants in driver genes. D–F, Kaplan–Meier curves of genetic variants associated with shorter PFS time in (D) TP53 and (E) CDH1. F–I, Kaplan–Meier curves of transcriptional signatures stratified by TP53 variant status associated with shorter PFS time for (F) HRD, (G) hypoxia, (H) androgen receptor, and (I) claudin low.

Figure S9. Association between tumour-fraction, short to long fragment length ratios and progression-free survival time. A) Hazard ratio between individuals above and below each tumour fraction cut-off at baseline (green), W12 (red) and W24 (yellow). Filled in points indicate significant hazard ratios. B) Hazard ratios of tumour fraction change between baseline and W12 cfDNA associated with progression-free survival at various cut-offs. Filled in points indicate significant hazard ratios. C) Cell-free DNA fragment length distribution between individuals with shorter and longer progression free survival time of three months. D) Hazard ratio of short to long fragment length ratio on progression free survival time using various cut-offs to divide short and long. Colours indicate cfDNA sampling time at baseline (green), W12 (red) and at W24 (yellow). Filled in points are significant log-rank p-values fragment length cut-offs.

Supplementary Table 1. Breast Cancer 360 Gene Signatures and Description

Somatic variants detected in cfDNA associated with PFS time at baseline and first follow-up at week 12. A and B, Summary of genetic variants captured at (A) baseline prior to palbociclib treatment and at (B) week 12. Blue squares indicate the detection of a somatic variant in cfDNA for individuals. Barplot colors indicate variant classification. C and D, HR associated with PFS time for genetic variants detected at (C) baseline and at (D) week 12. Red points indicate gene variants significantly associated with PFS. E–I, Kaplan–Meier curves of mutations significantly associated with poor PFS time at (E–G) baseline in (E) SETD2, (F) DDR2, and (G) FGFR4 and (H and I) at week 12 in (H) TP53 and (I) ESR1. J and K, Kaplan–Meier curves of mutations associated with poor PFS in groups of genes in the (J) DNA repair pathway and (K) MAPK cascade.

Figure S7. Summary of top mutated genes in solid tissue and longitudinal liquid biopsy samples.

Table 2 from Continuous versus Standard Palbociclib Treatment and Molecular Profiling of Solid Tissues and Liquid Biopsies in the CCTG MA.38 Trial in Advanced Breast Cancer

Supplementary Figure S6. Prognostic transcriptional signatures stratified by TP53 mutation and BC360 HRD expression status in solid tissues.

Figure S8. Summary of biological processes associated with PFS in baseline and W12 cfDNA plasma based on mutational profiles. (A-B) Forest plots summarize HR of mutations in genes among biological processes significantly associated with PFS among (A) baseline and (B) W12 samples.

Association among tumor fraction, short/long fragment length, and PFS time. A and B, Kaplan–Meier curves of cfDNA tumor fraction cutoffs most significantly associated with poor PFS time for (A) baseline samples at 2% tumor fraction and (B) week 24 samples at 6% tumor fraction. C, Change in tumor fraction between baseline and week 12 associated with PFS. D and E, Kaplan–Meier curves of fragment length ratio cutoffs (D) at baseline and (E) week 12. F, Correlation between tumor fraction and short/long fragment length ratio at baseline.