Back

Multiomic dissection of HR+/HER2- invasive lobular breast carcinoma reveals mobilized yet dysfunctional anti-tumor immunity shaped by tumor-stroma crosstalk and impaired antigen presentation

Picard, M.; Finetti, P.; Guille, A.; Lumet, G.; Mescam, L.; Boudin, L.; Goncalves, A.; Bertucci, F.; Mamessier, E.

2026-05-29 cancer biology
10.64898/2026.05.28.728418 bioRxiv
Show abstract

ContextImmunotherapy based on immune checkpoint inhibitors (ICI) revolutionized the treatment of triple-negative (TN) breast carcinomas (BC), but remains more challenging in HR+/HER2- BCs. Because invasive lobular carcinomas (ILC) generally exhibit low immune infiltration, ICIs were largely overlooked in this pathological type. The only clinical trial of ICIs dedicated to ILCs showed disappointing results, notably in HR+/HER2- cases. The immune landscape of HR+/HER2- ILCs has been poorly described. High level of tumor-infiltrating lymphocytes (TIL) was associated with worse prognosis in HR+/HER2- ILCs. A better characterization of the immune landscape of HR+/HER2- ILCs could clarify the poor efficiency of ICIs and the negative prognostic value of TILs, and reveal complementary targets able to increase immunotherapy efficiency. MethodWe comprehensively characterized the immune landscape of HR+/HER2- ILCs, comparatively to HR+/HER2- invasive ductal carcinomas (IDC), by applying multi-omics and multi-scale analysis (gene expression at the bulk and single-cell levels, and protein-based spatial analysis) to clinical samples. ResultsWhile the overall level of immune infiltration was comparable between both pathological types, the quality of immune infiltrate differed markedly. Comparatively to HR+/HER2- IDCs, HR+/HER2- ILCs were enriched in immune cells and tertiary lymphoid structures with anti-tumor potential, presented more spatial proximity between cancer cells and CD8+ cytotoxic T cells, and stronger theorical vulnerability to ICIs. However, in HR+/HER2- ILCs, anti-tumor response was defective; CD8+ cytotoxic T cells failed to fully unleash their cytotoxic function and CD4+ helper T cells evidenced a pro-tumoral and naive phenotype. Furthermore, antigen-presenting compartment was defective, altogether embedded in a stronger immunosuppressive environment, enriched in immunoregulatory cancer-associated fibroblasts (iCAF). ConclusionThis study contributes to explain the lesser efficiency of PD-1/PD-L1-based ICIs in HR+/HER2-ILCs by comparison with HR+/HER2- IDCs, by shedding light on a complex ecosystem where tumor cells shape a distinctive stroma that contribute to prevent anti-tumor immune response activation. Altogether, our findings further support the rationale for combining iCAF-targeting strategy with an ad hoc immunotherapy (such as an anti-VTCN1/B7-H4 antibody-drug conjugates for example). Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/728418v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c62294org.highwire.dtl.DTLVardef@86392org.highwire.dtl.DTLVardef@c10748org.highwire.dtl.DTLVardef@c543da_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_ST_ABSWHAT IS ALREADY KNOWN ON THIS TOPICC_ST_ABSO_LIImmune cells infiltrate both HR+/HER2- IDC and HR+/HER2- ILC tumors, but current ICIs are less effective in HR+/HER2- ILCs than HR+/HER2- IDCs. C_LI WHAT THIS STUDY ADDSO_LIThe anti-tumor immune response is mobilized but not effective in HR+/HER2- ILCs. C_LIO_LIA complex ecosystem - composed of immunoregulatory cancer-associated fibroblasts, high levels of TGFa, prostaglandin, acidosis, and a lack of antigen-presenting cells - prevents anti-tumor CD8+ cytotoxic T cell activation in HR+/HER2- ILCs. C_LI HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICYO_LITargeting the PD-1/PD-L1 axis is not the appropriate therapeutic strategy for HR+/HER2- ILCs. A more complex approach should be considered, notably those combining other immune-based strategies and iCAF targeting, which may offer a better chance to eradicate HR+/HER2- ILC tumor cells. C_LI

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

1
Cancers
200 papers in training set
Top 0.8%
6.4%
2
Frontiers in Oncology
95 papers in training set
Top 0.5%
6.4%
3
Journal for ImmunoTherapy of Cancer
64 papers in training set
Top 0.2%
6.3%
4
OncoImmunology
22 papers in training set
Top 0.1%
4.8%
5
Cell Reports Medicine
140 papers in training set
Top 0.7%
4.8%
6
Journal of Experimental & Clinical Cancer Research
25 papers in training set
Top 0.1%
4.0%
7
Cancer Cell
38 papers in training set
Top 0.4%
4.0%
8
Frontiers in Immunology
586 papers in training set
Top 2%
3.9%
9
Cancer Immunology, Immunotherapy
11 papers in training set
Top 0.1%
3.6%
10
Journal of Translational Medicine
46 papers in training set
Top 0.3%
3.1%
11
eBioMedicine
130 papers in training set
Top 0.6%
2.6%
12
Molecular Oncology
50 papers in training set
Top 0.2%
2.6%
50% of probability mass above
13
Antibody Therapeutics
16 papers in training set
Top 0.2%
2.1%
14
Theranostics
33 papers in training set
Top 0.4%
2.1%
15
Cell Communication and Signaling
35 papers in training set
Top 0.4%
1.8%
16
Signal Transduction and Targeted Therapy
29 papers in training set
Top 0.8%
1.5%
17
Cancer Medicine
24 papers in training set
Top 0.8%
1.5%
18
Cancer Research Communications
46 papers in training set
Top 0.5%
1.5%
19
Cancer Immunology Research
34 papers in training set
Top 0.3%
1.5%
20
Computational and Structural Biotechnology Journal
216 papers in training set
Top 6%
1.3%
21
Breast Cancer Research
32 papers in training set
Top 0.4%
1.3%
22
Biomedicine & Pharmacotherapy
43 papers in training set
Top 0.6%
1.2%
23
PeerJ
261 papers in training set
Top 10%
1.2%
24
International Journal of Cancer
42 papers in training set
Top 0.9%
1.1%
25
PLOS ONE
4510 papers in training set
Top 62%
0.9%
26
The Journal of Pathology
22 papers in training set
Top 0.3%
0.9%
27
eLife
5422 papers in training set
Top 54%
0.9%
28
British Journal of Cancer
42 papers in training set
Top 1%
0.9%
29
Advanced Science
249 papers in training set
Top 18%
0.8%
30
Translational Oncology
18 papers in training set
Top 0.3%
0.8%