Whole-exome sequencing of FFPE samples reveals mutations associated with Luminal A breast cancer recurrence.

BACKGROUND

Early-stage Luminal A breast cancer generally has a favorable prognosis, yet some patients experience recurrence, presenting a challenge in understanding the underlying genetic factors. This study aimed to identify genetic mutations associated with recurrence in early-stage Luminal A breast cancer patients through whole-exome sequencing (WES).

METHODS

We collected formalin-fixed paraffin-embedded samples from 34 patients and divided them into two groups: 17 patients with recurrence within five years post-surgery (recurrence group) and 17 patients with no recurrence for over five years (control group). The extracted DNA went through library preparation and was subjected to WES. Sequencing data went through quality control, alignment, and mutation identification. Functional enrichment analyses were conducted to explore the biological implications of the mutations.

RESULTS

We generated on average ~ 11 Gb raw sequencing data for each sample and identified 7,066 nonsynonymous mutations. The recurrence group exhibited a higher mutation rate (11.48 mutations/Mb) compared with the control group (9.18 mutations/Mb, p < 0.05). A significant negative correlation was observed between disease-free survival time and the number of mutations (p < 0.05). Eight genes (MICALCL, G6PD, OR8U1, PCLO, OR8U8, ZCCHC18, CPED1, HMCN1) were significantly associated with early recurrence (p < 0.05). Functional enrichment analyses revealed that these genes were involved in pathways like mismatch repair and immune response.

CONCLUSIONS

This study identified specific genetic mutations linked to early recurrence in Luminal A breast cancer, highlighting potential biomarkers for predicting patient outcomes and personalizing cancer treatment. Our study also showed that state-of-the-art WES can extract biologically and clinically meaningful mutation signatures from routinely stored FFPE tissues, unlocking archived specimens for large-scale biomarker discovery.