  Motivation: Precision medicine is a major trend in the future of medicine. It
aims to provide tailored medical treatment and prevention strategies based on
an individual's unique characteristics and needs. Biomarker is the primary
source of patients' unique features used in precision medicine. We often need
to investigate many cutoff values of a continuous biomarker to find the optimal
one and test if it can help segment patients into two groups with significantly
different clinical outcomes. This requires multiple testing adjustments on
tests conducted on overlapped data. The permutation-based approach is often a
preferred solution, since it does not suffer the limitations of state-of-art
theoretical methods. However, permutation is computationally expensive and
limits its application scenarios, such as web applications requiring a fast
response or the analysis of genomic study requiring to repeat analysis many
times on tens of thousands of genes.
  Results: We proposed a novel method BOSS, Biomarker Optimal Segmentation
System, to solve this problem. In simulation studies, we found BOSS's
statistical power and type I error control are both non-inferior to the
permutation approach, and it is hundreds of times faster than permutation. To
illustrate our method, we applied BOSS to real data and revealed potentially
converging biomarkers that have referential importance in exploring synergy and
target-matched therapies in lung adenocarcinoma.
  Availability: An R package, boss, is being developed and will be available on
CRAN
