Genetic insights into the timing of metastasis: a secondary analysis of the Count Me In metastatic breast cancer cohort.

INTRODUCTION

Metastatic breast cancer is incurable, associated with poor outcomes, and expected to increase in both incidence and cost.

METHODS

We conducted a secondary analysis of retrospectively ascertained time-to-event outcomes within a cross-sectional genomic dataset linked to medical and self-reported patient data from the Count Me In: The Metastatic Breast Cancer Project (CMI-MBCProject), available through dbGaP (phs001709.v1.p1). Whole-exome sequencing data derived from saliva samples were bioinformatically processed, and single nucleotide polymorphisms (SNPs) within 50 previously identified metastasis-related genes were selected for analysis. Multivariable-adjusted gamma regression models were used to test associations between SNPs and time to metastasis.

RESULTS

The sample included 63 female participants, with the majority between the ages of 31 and 40 years (n = 29, 46.0%), non-Hispanic (n = 59, 93.7%), and White (n = 55, 87.3%). Time to metastasis was fully observed and right-skewed, with a mean of 732.6 ± 1054.3 days and median of 132.0 days (IQR: 6.0-1018.0). Twenty-three unique genes (58 variants) met the initial screening threshold of p < 0.10. Of these, CXCL12 rs266091 and GAPDH rs1803622 remained significant after false discovery rate correction (q < 0.05), with rs266091 also meeting the more stringent Bonferroni-adjusted threshold. Sensitivity analyses using accelerated failure time Weibull models confirmed the primary findings for both variants.

DISCUSSION: Our analysis identified CXCL12 and GAPDH as promising candidate genes associated with variation in metastatic progression timing. These findings support further investigation into whether genetic risk information could inform targeted surveillance and precision nursing care.