Company: BLLN
Filing Date: 2025-10-17
Form Type: S-1/A
Source: 0001193125-25-242632
Chunk: 198

Company: BillionToOne, Inc.
Filing Date: 2025-10-17
Form: S-1/A
Chunk 198
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 Bower, X., Wignall, J., Varga, M. G., Zhu, J., O’Sullivan, M., Searle, N. E., Hong, L. K., Dogruluk, T., Li, Z., Farmer, T. E., Rosas-Linhard, E., Luong, J., Lin, E., Simon, M. E., Tsao, D. S., Bosch, J. R. T., 
 Palmer, G., Gajra, A., Huynh, C., & Zhou, W. (2025). Validation of a liquid biopsy assay with increased sensitivity for clinical comprehensive genomic profiling. The Journal of Liquid Biopsy, 100322.            |

| 45 |     | See “Management’s discussion and analysis of financial condition and results of operations—Key factors affecting our results of operations and performance” for more information regarding how 
 these total addressable markets are calculated, including material assumptions.                                                                                                                |

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Response and MRD is a necessary precursor for early detection development. In particular, we believe that a sensitive tissue-free MRD test, which we have already de-risked by the recent launch of an ultra-sensitive response monitoring assay (Northstar Response v2), which can detect tumor DNA in blood down to a limit of detection (LOD) of 0.01%, can be technically equivalent to an early detection test for cancer. 46We estimate the annual United States market opportunity for early detection to be over $50 billion. 45 Our vision of powering AI-enabledpersonalized medicine for all Healthcare today stands at an inflection point, poised for transformation through the convergence of unprecedented molecular insights and AI. Despite significant advances in precision medicine, particularly in oncology where treatments have evolved from being organ-based to increasingly being mutation-based, response rates for many marketed targeted therapies can be as low as single-digit percentages in their indicated patient populations. Similarly, pregnancy care follows one-size-fits-allstandardized protocols despite unique patient biology. Even with broader technological advances, clinicians still cannot reliably predict or prevent majorcomplications like preterm birth, preeclampsia, and gestational diabetes. This sobering reality underscores a fundamental challenge: many current approaches to precision medicine often rely on single biomarkers that fail to capture the full complexity of disease biology and individual patient variation. We believe that these important problems can be addressed in the future with the combination of AI and smNGS technology. We believe AI-enabledpersonalized medicine can revolutionize