Back

A stress-NRF2 response axis polarises tumor macrophages and undermines immunotherapy

Schaer, D. J.; Schulthess-Lutz, N.; Baselgia, L.; Kunasingam, K.; Humar, R.; Hansen, K.; Vallelian, F.

2025-07-07 immunology
10.1101/2025.07.02.662726 bioRxiv
Show abstract

Tumor-associated macrophages (TAMs) can switch between immune-activating and cancer-promoting states; yet, the stress pathways that lock them into pro-cancerous states remain obscure. In MC38 colon tumors, repeated anti-CD40 or radiotherapy created necrosis that split TAMs into peripheral Cxcl9+ and peri-necrotic Spp1+ subsets. Spatial transcriptomics, single-cell RNA-seq, and Keap1-deficient mice showed that the latter are NRF2high "stress-TAMs", with immunosuppressive and tumor-promoting activity. The same NRF2 activation gradient separates pro-inflammatory CXCL9+ and anti-inflammatory SPP1+ TAMs across diverse human cancers. NRF2high TAMs silence IFN-STAT1 programmes, lose MHC-II and chemokine expression, fail to expand T cells, drive tumor cell invasion in 3D co-cultures, and foster metastasis. Constitutive hematopoietic NRF2 activation accelerated the growth of therapy-naive MMTV-PyMT breast tumors and markedly impaired anti-CD40 efficacy in MC38 subcutaneous and lung-metastasis models. Conversely, macrophage-specific Nrf2 deletion restored immunogenic TAMs and potentiated anti-CD40 and anti-PD-1 treatments. Thus, NRF2 constitutes a stress-response axis that fixes TAMs in a pro-cancer, therapy-resistant state; inhibiting it could revive the macrophage-T-cell amplification loop and broaden immunotherapy responses. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/662726v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@728b03org.highwire.dtl.DTLVardef@4a473org.highwire.dtl.DTLVardef@c8a209org.highwire.dtl.DTLVardef@60681_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.