Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Feb 8;11(1):12.
doi: 10.1038/s41523-025-00718-x.

Molecular characterization of pregnancy-associated breast cancer and insights on timing from GEICAM-EMBARCAM study

Regina Peña-Enríquez  1 , Begoña Bermejo  2   3   4 , Marina Pollán  2   5   6 , Alejandra Díaz-Chacón  1 , Yolanda Jerez Gilarranz  2   7 , José J Ponce Lorenzo  2   8 , Antonio Fernández Aramburo  2   9 , Blanca Cantos Sánchez de Ibargüen  2   10 , Ana Santaballa Bertrán  2   11 , Elena Galve-Calvo  2   12 , Álvaro Jiménez-Arranz  1 , Yolanda Fernández  2   13 , María Eva Pérez  2   14 , Susana De La Cruz  2   15 , Antonio Anton-Torres  2   16 , Fernando Moreno  2   17 , María Jesús Vidal-Losada  2   18 , María Helena López-Ceballos  2   19 , Isabel Blancas  2   20   21   22 , María José Echarri  2   23 , Raúl Rincón  2 , Rosalía Caballero  2 , Ángel Guerrero-Zotano  2   24 , Silvia Guil-Luna  25   26   27 , Juan de la Haba-Rodríguez  28   29   30
Affiliations

Molecular characterization of pregnancy-associated breast cancer and insights on timing from GEICAM-EMBARCAM study

Regina Peña-Enríquez et al. NPJ Breast Cancer. .

Abstract

Pregnancy-associated breast cancer (PABC), diagnosed during or shortly after pregnancy, is a challenging entity with an aggressive biology and poor prognosis. This study analyzed the clinicopathological characteristics and gene expression profile of 33 PABC and 26 non-PABC patients using the nCounter BC360 Panel (NanoString). Notably, PABC showed a higher prevalence of basal-like tumors than non-PABC (48.48% vs 15.38%, p = 0.012) and displayed 73 differentially expressed genes (e.g., DEPDC1, CCNA2, PSAT1, CDKN3, and FAM83D), enriched in DNA repair and cell proliferation pathways. Through the PPI network, we also identified a cluster of cell-cycle regulation genes like MYC, FOXM1, or PTEN. Interestingly, differences emerged when comparing patients diagnosed during gestation (PABC-GS) and the postpartum period (PABC-PP), with PABC-PP showing increased expression of immune-related genes, including PD-1, and greater immune cell infiltration (Tregs, macrophages, neutrophils, B-cells). These findings suggest an enhanced proliferative capacity and impaired DNA repair in PABC, and underscore the role of immune infiltration in postpartum cases; providing insights into its aggressive nature and potential targets.

PubMed Disclaimer

Conflict of interest statement

Competing interests: B.B. declares having received fees for medical education in a consulting or advisory role with Lilly, Pfizer, MSD, Pierr Fabre, Astra Zeneca, and Gilead; participated in a speakers’ bureau with Roche, MSD, Daichii, Sankio Astra Zeneca, Novartis, Lilly, Gilead, Seagen; travel accommodation by Pfizer and Gilead. Y.J.G. declares having received speakers´ honoraria from Roche, Novartis, Lilly, Daichii, and AstraZeneca; travel and training grants from Roche, Novartis, Pfizer, Daichii, Gilead, and Lilly. A.F.A. declares Honoraria or consultation fees from GSK, MSD, AstraZeneca, Pharma & Go, EISAI, Lilly, Pfizer, Novartis, and Pierre Fabre; travel/accommodation expenses from GSK, MSD, AstraZeneca, and Pfizer; speakers bureau participation with GSK, MSD, AstraZeneca, EISAI, Novartis and Pierre Fabre. F.M. declares consulting/advisory role with Novartis, Pfizer, AstraZeneca, MSD, Daiichi Sankyo, and Seagen; speakers’ bureau from Pfizer and Novartis; research funding from Pfizer; travel/accommodations expenses by Pfizer, Novartis and Gilead. M.H.L.-C. declares having received speaker honoraria from Daichii, Novartis & Pierre Fabre; and travel and training grants from Roche & Novartis. I.B. declares having research funding from Agendia, AstraZeneca, Lilly, Pfizer, and Roche; honoraria as a medical monitor from Medical Scientia Innovation Research (MEDSIR); honoraria and advisor collaboration from AstraZeneca, Bristol-Myers Squibb, Celgene, Daiichi-Sankyo, Eisai, Gilead, Grünenthal, GSK, Jazz Pharmaceutical, Lilly, MSD, Novartis, Pfizer, Pierre-Fabre, Roche, Seagen and Veracyte; travel and meeting attendance grants from AstraZeneca, Bristol-Myers Squibb, Daiichi-Sankyo, Gilead, Lilly, Novartis, Pfizer, Pierre-Fabre and Roche. A.G.-Z. declares institutional grant from Pfizer; advisory role honoraria from AstraZeneca, Novartis, MSD, Pierre-Fabre, Exact Science, Menarini-Stemline and Daichy Sankyo; travel grants from Roche, Novartis, Pfizer, Gilead and Pierre Fabre; speaker Bureau/Expert testimony with Roche, AstraZeneca, Daichi-Sankyo, Novartis, MSD, Pfizer and Menarini-Stemline. J.H.-R. declares having received research grants from Roche and Pfizer; consulting/advisory fees from AstraZeneca, Amgen, Roche/Genentech, Novartis, Eli Lilly, and Pfizer; speakers’ honoraria from AstraZeneca, Lilly, Amgen, Roche/Genentech, Novartis, and Pfizer. All remaining authors have declared no conflicts of interest.

Figures

Fig. 1
Fig. 1. Intrinsic subtypes classification of samples.
a Distribution of BC molecular subtypes in PABC and non-PABC groups of tumors using the PAM50 prediction algorithm. b Distribution of BC molecular subtypes in PABC-GS and PABC-PP subgroups of tumors. c Principal component analysis (PCA) of the gene expression data of samples, on the left classified by group (PABC and non-PABC), and on the right classified by intrinsic subtype (LumA, Basal-like, HER2-E, and LumB). *p-value < 0.05.
Fig. 2
Fig. 2. DEGs between PABC and non-PABC.
a Volcano plot showing the log10 (p-value) and log2 FC of the 776 genes from nCounter BC360™ Panel in PABC (n = 33) compared to non-PABC (n = 26). Several FDR thresholds were indicated by horizontal lines. b Heatmap for the 73 DEGs with |FC| ≥ 1.5 and FDR < 0.05 in PABC (red) and non-PABC (green) samples. The red through blue color indicates high to low expression levels. c Comparative box plots displaying the normalized expression levels of the top ten most significant DEGs (FDR < 0.01) for the 59 samples classified into patient subgroups (PABC-GS and PABC-PP) and control (non-PABC) (*FDR < 0.05; **FDR < 0.01; ***FDR < 0.001 compared with control). d STRING clustering analysis of the DEGs in PABC patients. Network represented the most significant DEGs in PABC (nodes, n = 73) and their interactions (edges, n = 306). Hub genes, nodes with a degree of connectivity ≥ 15, are listed in the table on the right. Genes involved in the cell cycle regulation process, according to gene ontology (GO) term enrichment analysis, are shown as red nodes.
Fig. 3
Fig. 3. PSA.
a Graph showing the average scores of the 23 biological pathways/processes from the nCounter BC360 panel according to the gene expression profile across patient (PABC-GS and PABC-PP) and control (non-PABC) samples. b Comparative box plots of pathways score for DNA damage repair, proliferation, and subtype signatures across patient (PABC-GS and PABC-PP) and control (non-PABC) samples.
Fig. 4
Fig. 4. DEGs for the most relevant signatures in PABC.
a Volcano plot of DE in PABC vs non-PABC with the DNA damage repair genes highlighted in orange. b Heat map for the most DEGs of the DNA damage repair signature (|FC| ≥ 1.5 and FDR < 0.05) in PABC (red) and non-PABC (green). c Volcano plot of DE in PABC vs non-PABC with the proliferation genes highlighted in orange. d Heat map for the most DEGs of the proliferation signature (|FC| ≥ 1.5 and FDR < 0.05) in PABC (red) and non-PABC (green). e Volcano plot of the DE in PABC vs non-PABC with the subtypes-related genes highlighted in orange. f Heat map for the most DEGs of the subtypes signature (|FC| ≥ 1.5 and FDR < 0.05) in PABC (red) and non-PABC (green).
Fig. 5
Fig. 5. Specific DEGs for each subgroup of PABC vs non-PABC tumors.
a Venn diagram displays the number of specific genes from each subgroup of PABC tumors (PABC-GS, brown; PABC-PP, blue) compared to tumors from controls (non-PABC). The intersection indicates the overlap of ten common genes in both subgroups. b Volcano plot of the DE in PABC-GS (n = 15) vs non-PABC (n = 26). c Heatmap for the 60 DEGs with an |FC| ≥ 1.5 and FDR < 0.05 in PABC-GS (blue) and non-PABC (green) samples. d Volcano plot of the DE in PABC-PP (n = 18) vs non-PABC (n = 26). e Heatmap for the 30 DEGs with an |FC| ≥ 1.5 and FDR < 0.05 in PABC-PP (blue) and non-PABC (green) samples.
Fig. 6
Fig. 6. DEGs between PABC-PP and PABC-GS.
a Volcano plot showing the log10 (p-value) and log2 FC of the 776 genes from the nCounter Breast Cancer 360 panel in PABC-PP (n = 18) compared to PABC-GS (n = 15). b Heatmap for the 71 DEGs with an |FC| ≥ 1.5 and p-value < 0.05 in PABC-PP (blue) and PABC-GS (brown) samples. The red through blue color indicates high to low expression levels. c Comparative box plots displaying the normalized expression levels of the top ten most significant DEGs (p-value < 0.01) for the 33 samples classified into subgroups (PABC-GS, PABC-PP). d STRING clustering analysis displays a network comprising the DEGs in PABC-PP vs PABC-GS (nodes, n = 71) and their interactions (edges, n = 238). Hub genes, nodes with a degree of connectivity ≥ 10, are listed in the table on the right. Genes involved in the immune response process, according to gene ontology (GO) term enrichment analysis, are shown as red nodes.
Fig. 7
Fig. 7. Pathway and cell-type profiling analysis between subgroups.
a Graph showing the average scores of the 23 biological pathways/processes from the nCounter BC360 panel according to the gene expression profile between PABC-GS and PABC-PP. b Volcano plot of the DE in PABC-PP vs PABC-GS with the immune infiltration genes highlighted in orange, and on the right a heat map for the 33 genes from the immune infiltration signature in PABC-PP (blue) and PABC-GS (brown), marking with an *those genes with a |FC| ≥ 1.5 and p-value < 0.05. c The graph shows immune cell-type abundance measurements across subgroups of patients (PABC-GS and PABC-PP), according to their gene expression profiles. Each cell type score was centered to zero value; on the right comparative box plot displayed the cell abundance score for the most enrichment cell types in PABC-PP compared to PABC-GS.
See this image and copyright information in PMC

References

    1. Peccatori, F. A. et al. Cancer, pregnancy and fertility: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann.Oncol. 10.1093/ANNONC/MDT199 (2013). - PubMed
    1. Vinatier, E., Merlot, B., Poncelet, E., Collinet, P. & Vinatier, D. Breast cancer during pregnancy. Eur. J. Obstet. Gynecol. Reprod. Biol.147, 9–14 (2009). - PubMed
    1. Johansson, A. L. V. & Stensheim, H. Epidemiology of pregnancy-associated breast cancer. Adv. Exp. Med. Biol.1252, 75–79 (2020). - PubMed
    1. Amant, F. et al. The definition of pregnancy-associated breast cancer is outdated and should no longer be used. Lancet Oncol.22, 753 (2021). - PMC - PubMed
    1. Andersson, T. M. L., Johansson, A. L. V., Hsieh, C. C., Cnattingius, S. & Lambe, M. Increasing incidence of pregnancy-associated breast cancer in Sweden. Obstet. Gynecol.114, 568–572 (2009). - PubMed

LinkOut - more resources