Prognosis and Genomic Landscape of Liver Metastasis in Patients With Breast Cancer

Abstract

Objective: The prognosis of breast cancer liver metastasis (BCLM) is poor, and its molecular mechanism is unclear. We aimed to determine the factors that affect the prognosis of patients with BCLM and investigate the genomic landscape of liver metastasis (LM).

Methods: We described the prognosis of patients with BCLM and focused on prognosis prediction for these patients based on clinicopathological factors. Nomogram models were constructed for progression-free survival (PFS) and overall survival (OS) by using a cohort of 231 patients with BCLM who underwent treatment at Shandong Cancer Hospital and Institute (SCHI). We explored the molecular mechanism of LM and constructed driver genes, mutation signatures by using a targeted sequencing dataset of 217 samples of LM and 479 unpaired samples of primary breast cancer (pBC) from Memorial Sloan Kettering Cancer Center (MSKCC).

Results: The median follow-up time for 231 patients with BCLM in the SCHI cohort was 46 months. The cumulative incidence of LM at 1, 2, and 5 years was 17.5%, 45.0%, and 86.8%, respectively. The median PFS and OS were 7 months (95% CI, 6-8) and 22 months (95% CI, 19-25), respectively. The independent factors that increased the progression risk of patients with LM were Karnofsky performance status (KPS) ≤ 80, TNBC subtype, grade III, increasing trend of CA153, and disease-free interval (DFS) ≤ 1 year. Simultaneously, the independent factors that increased the mortality risk of patients with LM were Ki-67 ≥ 30%, grade III, increasing trend of CA153, pain with initial LM, diabetes, and DFI ≤ 1 year. In the MSKCC dataset, the LM driver genes were ESR1, AKT1, ERBB2, and FGFR4, and LM matched three prominent mutation signatures: APOBEC cytidine deaminase, ultraviolet exposure, and defective DNA mismatch repair.

Conclusion: This study systematically describes the survival prognosis and characteristics of LM from the clinicopathological factors to the genetic level. These results not only enable clinicians to assess the risk of disease progression in patients with BCLM to optimize treatment options, but also help us better understand the underlying mechanisms of tumor metastasis and evolution and provide new therapeutic targets with potential benefits for drug-resistant patients.

Keywords: breast cancer; genomic landscape; liver metastasis; nomogram model; prognosis.

Figure 1

Article structure frame diagram. SCHI, Shandong Cancer Hospital and Institute; MSKCC, Memorial Sloan Kettering Cancer Center; POTUAM, Predictive Oncology team of the University of Aix-Marseille; NGS, next-generation sequencing; WES, whole-exome sequencing.

Figure 2

Survival of patients with LM. (A) Kaplan–Meier PFS curve of 231 patients with LM. (B) Kaplan–Meier OS curve of 231 patients with LM. (C) Kaplan-Meier plots illustrating PFS of patients with “only LM initially”, “LM with other organs initially”, and “subsequently recurrent LM”, respectively. No significant difference in PFS among groups (8 vs. 7 vs. 7 months, p = 0.0813). (D) Kaplan-Meier plots illustrating OS of patients with “only LM initially”, “LM with other organs initially”, and “subsequently recurrent LM”, respectively. Patients with only LM initial presence were associated with longer OS (26 vs. 22 vs. 18 months, p = 0.0036). (E) Kaplan-Meier plots illustrating PFS of patients with different molecular subtypes. Patients with TNBC were associated with shorter PFS (14 vs. 8 vs. 10 vs. 6 months, p = 0.000179). (F) Kaplan-Meier plots illustrating OS of patients with different molecular subtypes. Patients with TNBC were associated with shorter OS (34 vs. 23 vs. 21 vs. 15 months, p = 0.00053). (G) Kaplan-Meier plots illustrating time from pBC to LM (TTLM) of patients with different molecular subtypes. Patients with TNBC were associated with shorter TTLM (43 vs. 24 vs. 31 vs. 15 months, p = 0.00005).

Figure 3

Nomograms of prognosis for patients with LM. (A) The nomogram of prognosis for patients with PFS (C-index = 0.743). (B) The nomogram of prognosis for patients with OS (C-index = 0.718).

Figure 4

Comparison of molecular subtypes between LMs and paired pBCs (p = 1.92 × 10⁻¹¹). (A) Components of molecular subtypes of pBC. (B) Components of molecular subtypes of LM.

Figure 5

Somatic mutations in pBCs and LMs. (A, B) Top 20 SMGs in pBCs and LMs, respectively. (C) Comparison of SMGs between pBCs and LMs. (D) Comparison of the top 10 SMGs between pBCs and LMs. (The genes in the red box represent the driver genes, *p < 0.05, **p < 0.01, ***p < 0.001) (E) Comparison of TMB between pBCs and LMs (p < 0.001). SMG, significantly mutanted gene; TMB, Tumor Mutation Burden.

Figure 6

Mutation patterns in patients with LM. (A) Mutually exclusive or co-occurring set of genes. (B) Lolliplot of ESR1 mutation rate in the patients with pBC and LM. (C, D) Enrichment of known Oncogenic Signaling Pathways (Tumor suppressor genes are in red, and oncogenes are in blue font).

Figure 7

MSs in patients with LM. (A) Correlation between the MSs derived from the patients with LM and previously defined signatures from COSMIC. A pair-wise cosine correlation was performed between the COSMIC and LM signatures. The most correlated COSMIC signatures were used to determine the identity of each LM signature. (B) Three MSs identified in patients with LM.