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A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer
Open accessAbstract
Immune-checkpoint blockade (ICB) combined with neoadjuvant chemotherapy improves pathological complete response in breast cancer. To understand why only a subset of tumors respond to ICB, patients with hormone receptor-positive or triple-negative breast cancer were treated with anti-PD1 before surgery. Paired pre- versus on-treatment biopsies from treatment-naive patients receiving anti-PD1 (n = 29) or patients receiving neoadjuvant chemotherapy before anti-PD1 (n = 11) were subjected to single-cell transcriptome, T cell receptor and proteome profiling. One-third of tumors contained PD1-expressing T cells, which clonally expanded upon anti-PD1 treatment, irrespective of tumor subtype. Expansion mainly involved CD8+ T cells with pronounced expression of cytotoxic-activity (PRF1, GZMB), immune-cell homing (CXCL13) and exhaustion markers (HAVCR2, LAG3), and CD4+ T cells characterized by expression of T-helper-1 (IFNG) and follicular-helper (BCL6, CXCR5) markers. In pre-treatment biopsies, the relative frequency of immunoregulatory dendritic cells (PD-L1+), specific macrophage phenotypes (CCR2+ or MMP9+) and cancer cells exhibiting major histocompatibility complex class I/II expression correlated positively with T cell expansion. Conversely, undifferentiated pre-effector/memory T cells (TCF7+, GZMK+) or inhibitory macrophages (CX3CR1+, C3+) were inversely correlated with T cell expansion. Collectively, our data identify various immunophenotypes and associated gene sets that are positively or negatively correlated with T cell expansion following anti-PD1 treatment. We shed light on the heterogeneity in treatment response to anti-PD1 in breast cancer.
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“These cell subsets show different responses to anti-PD(L)-1 therapies,”
From Contrasting cytotoxic and regulatory T cell responses underlying distinct clinical outcomes to anti-PD-1 plus lenvatinib therapy in cancer (2025) · cites this paper as [17]
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“Applying ICB and combination therapies as neoadjuvant treatments provides an opportunity to observe immune cell dynamics in tumor microenvironment (TME) with temporal samples.”
From Contrasting cytotoxic and regulatory T cell responses underlying distinct clinical outcomes to anti-PD-1 plus lenvatinib therapy in cancer (2025) · cites this paper as [17]
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Cited by
Sorted by citations
Items 601–700 of 707
Time-, tissue- and treatment-associated heterogeneity in tumour-residing migratory DCs
Lee, C. Q., Kennedy, B. C., Richoz, N., Dean, I., Tuong, Z., Gaspal, F., Li, Z., Willis, C., Hasegawa, T., Whiteside, S. K., et al. (2023) · bioRxiv · doi:10.1101/2023.07.03.547454 · Cited by 1
Mechanism of breast cancer immune microenvironment in prognosis of heart failure
Wang, L.-D., Ding, X.-L., & Qiu, X. (2023) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2023.107339 · Cited by 1
Bhat-Nakshatri, P., Khatpe, A., Chen, D., Batic, K., Mang, H. E., Herodotou, C., McGuire, P. C., Xuei, X., Gao, H.-Y., Liu, Y.-L., et al. (2023) · bioRxiv · doi:10.1101/2023.07.26.550741 · Cited by 1
“For example, breast tissues from BRCA1 and BRCA2 mutation carriers are enriched for Interleukin 17 receptorpositive (IL17R+), Granulysin-positive (GNLY+) effector T cells, and Granzyme K-positive (GZMK+) cytotoxic T cells, which are enriched in the microenvironment of Triple Negative Breast Cancer (TNBCs) (34-36), and…”
BackgroundLi, J., Zhang, H., Song, K., Wu, J., Li, L., Liu, Z., Xu, B., & Song, Q. (2023) · Cancer Biomarkers · doi:10.3233/cbm-230036 · Cited by 8
Machine learning reveals genetic modifiers of the immune microenvironment of cancer
Riley-Gillis, B. M., Tsaih, S., King, E. A., Wollenhaupt, S., Reeb, J., Peck, A. R., Wackman, K. K., Lemke, A., Rui, H., Dezső, Z., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107576 · Cited by 2
“…LSP1 expression in treatment-naïve tumor specimens from BrCa patients was largely restricted to the infiltrating leukocytes (Qian et al. 2020; Bassez et al. 2021) (Figure 2C, 2D) and high leukocyte-specific expression of LSP1 correlated with shorter progression-free survival in univariate…”
BackgroundFacts and challenges of immunotherapy in triple-negative breast cancer
Wang, X.-H., Wang, F.-X., Xia, W., Deng, S., Zhang, H., & Zhao, X.-Y. (2022) · Tumor Discovery · doi:10.36922/td.v1i2.196 · Cited by 1
“LAR represents TNBC tumors with the lowest genomic complexity, with mutations in phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), AKT serine/threonine kinase 1 (AKT1), neurofibromatosis type 1 (NF1), GATA binding protein 3 (GATA3), and cadherin-1 (CDH1)[12-15].”
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“CX3CR1 was reported to be a marker of T cell differentiation, which indicated a predictive correlate of response to immune checkpoint inhibitor therapy, and CX3CR1 + inhibitory macrophages were negatively correlated with T-cell expansion (Bassez et al., 2021; Yamauchi et al., 2021).”
Innate immunity as a target for novel therapeutics in triple negative breast cancer
Borgovan, T., Yanamandra, N., & Schmidt, H. (2022) · Expert Opinion on Investigational Drugs · doi:10.1080/13543784.2022.2096005 · Cited by 3
Al‐Eryani, G., Bartoniček, N., Chan, C., Anderson, A., Harvey, K., Wu, S. Z., Roden, D., Wang, T., Reeves, J., Yeung, B. Z., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.31.494081 · Cited by 1
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“We collated public and proprietary data from tumour-infiltrating immune cells from one study we generated internally and four public studies (Zhang et al., 2021; Bassez et al., 2021; Yost et al., 2019; Qian et al., 2020), comprising of 125 patients across five tumor types.”
Johri, S., Bi, K., Titchen, B. M., Fu, J., Conway, J. R., Crowdis, J. P., Vokes, N. I., Fan, Z., Fong, L. H., Park, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57377-6 · Cited by 3
“Raw data was downloaded (.rds objects and.csv metadata fi les) from https://lambrechtslab.sites.vib.be/en/ single-cell (Bassez et al. 2021).”
InfluentialPaterson, K., Paterson, S., Mulholland, T., Coffelt, S., & Zagnoni, M. (2021) · bioRxiv · doi:10.1101/2021.09.14.458168 · Cited by 1
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“…], naive CD8 T cells ( Ccr7 and Sell ) [ 65 ], exhausted CD8 + T cells (Tex; Pdcd1, Lag3 and Havcr2 ) [ 69 ], Gzmk effector CD8 + T cells ( Gzmk, Ccl3, Ccr1 and Cxcr2 ) [ 66 ], mitotic CD8 + T cells ( Mki67 and Stmn1 ) [ 65 ], and NK cells ( Gzma, Nkg7, Klra7, Ccl5 and Xcl1 ) [ 65 ] (Figure 6A,B ).”
The HLA-E–NKG2A axis in breast cancer: an emerging NK-cell checkpoint and therapeutic target
Li, L., Han, X., Lu, L., Liu, K., Li, Y., & Su, L. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.2001812
A knowledge graph-guided framework for multitask single-cell analysisPreprint
Mathew, B., Maity, A., Ganguly, R., Halder, A., Sabu, J. T., Gupta, N., Peela, S. C. M., Samanta, S., Kumari, S., Bhattacharjee, N., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.25.720773
“Bassez profiled breast tumors before and during anti-PD-1 treatment, distinguishing patients with subsequent T-cell clonotype expansion (E) from those without expansion (NE) 2 .”
Xu, L., & Lim, B. (2026) · Cancer Metastasis Review · doi:10.1007/s10555-026-10387-2
“Dynamic profiling is essential because the immune landscape shifts with tumor-intrinsic changes such as clonal evolution and with each line of treatment [67].”
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“Serial sampling matters because anti-PD-1 treatment remodels myeloid–lymphoid states, whereas SPP1 ⁺ macrophage–CD44 ⁺ tumor-cell communication has been implicated in chemotherapy resistance [32, 64].”
InfluentialCXCR5 regulates CD8+/GZMB+ T cell infiltration and apoptotic signaling in breast cancer
Feng, W., Yan, J., Gong, X., Hu, J.-D., Jin, M.-L., Ma, C.-H., & Li, F.-G. (2026) · iScience · doi:10.1016/j.isci.2026.117874
Mesrizadeh, Z., Mukund, K., Yang, L.-X., Pillai, R., Frankhouser, D., Schmolze, D., Neuhausen, S., Zabaleta, J., Valle, L. D., Tomsic, J., et al. (2026) · bioRxiv · doi:10.64898/2026.10.10.758236
“The CC1-CC3 framework for TNBC subtyping was validated for robustness and reproducibility using the TCGA TNBC cohort (n = 156) and an independent single-cell TNBC dataset (n = 47) 19,20 , defining three distinct, reproducible ecosystems with unique immune and stromal characteristics.”
Mitochondrial regulation of tumor-associated macrophage function
Xu, J., Liu, Y., Zhang, H., Yuan, Z., Sheng, Y., Wang, X., Liu, H., Zhong, F., Zhang, H., Xiong, H., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1971517
“These fi ndings support measuring cell location and interactions alongside macrophage markers (45 – 47).”
Udden, S. N., Obaid, M., Pop, L. M., & Hannan, R. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27198854
“Single-cell and spatial studies in breast cancer have since shown why baseline lymphocyte abundance alone is insufficient to characterize the immune microenvironment: cellular states, spatial organization, and interactions also matter [6–8].”
InfluentialLi, T., Lin, Y.-X., Gang, Q., Zhu, X.-D., Hong, T., Su, L., & Hao, Y.-H. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218861
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McRae, H. M., Nguyen, K. M., Vallmajó-Martín, Q., Rivera, S. A., Stevenson, B. T., Evensen, K. G., Burns, M. J., Bell, J. C., Mandias, H. C. R., Chick, B. Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.25.754528
“TAMs exhibiting pro-tumor and anti-tumor properties coexist in mouse and human tumors and subtypes can be distinguished in single cell analyses 15–18 .”
InfluentialUncovering Immune Cell–Specific lncRNAs as Anti-PD-1 Response-Associated Markers in Breast Cancer
Voidelo, C., Nascimento, T. M. D., da Cunha, R. D., de Oliveira, J. D., & Mathias, C. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27198606
“In this study, we used a publicly available single-cell RNA-seq dataset generated in the BioKey clinical study (NCT03197389), first reported by Bassez et al. (2021) [13].”
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Zeng, L., Lin, W., Xue, S., Chen, M., & Wu, H. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1899098 · Cited by 1
Hu, X.-Y., Ma, Y.-H., Ming, R., Zhang, H.-P., & Jiang, H.-J. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag476
“Here, we apply DeepVaris to CD8 + T cells from breast cancer (BC) patients before (pre-) and during (on) anti-PD1 immunotherapy [ 33] to identify critical genes and synergistic pathways driving treatment response ( Fig.”
Li, F., Luo, D., Kuang, X., Wen, Y., & Yang, X. (2026) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2026.09.015
Meng, X.-Y., Liu, Z.-L., Hao, Y.-Q., Zhu, Y., Gu, J.-F., Zhong, H.-Z., He, Y., & Fu, J. (2026) · Bioactive Materials · doi:10.1016/j.bioactmat.2026.08.034
“Our previous work has reported that macrophages exhibited high carbohydrate metabolism activities [13], which is also confirmed in three scRNA-seq datasets (BRCA: EGAS00001004809, COAD: E_MTAB_8410, HNSC) [31 – 33] (Fig.”
Xiang, T., He, Z.-Y., Hu, X., Wang, X.-Y., Zhang, S.-S., Zhu, S., Yang, J., Yi, H., Liu, Q.-Y., Tan, W., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-76940-3
“…ICB treatment, were signi fi cantly higher in ICB non-responders than in ICBrespondersacrossalmostallcancertypesincludingmelanoma 50 – 53 , non-small cell lung cancer 54 – 56 , head and neck squamous cell carcinoma 57 , gastric carcinoma 58,59 , colorectal cancer 60 , and breast cancer 61 (Figs.”
Li, Z.-Q., Tawawalla, A., Foidart, P., Seehawer, M., Nishida, J., Jimenez, E., Bui, T. M., Huang, X.-Y., Rees, M., Ronan, M. M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-76773-0
“Furthermore, we observed a positive correlation between these two pathways in TNBC clinical samples, both in merged cancer cells and in individual TNBC tumors (18 out of 19), across two published scRNA-seq datasets 40,41 (Fig.”
Garland, B., Delisle, S., Abou-Hamad, J., de Souza, C. D., Zuccarini, R., Cook, D. P., Auer, R. C., & Sabourin, L. A. (2026) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2520063123
Zheng, C., Pan, Y., Zheng, D., & Pan, Y. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1934879
Kashiwagi, S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1895475
“Single-cell, spatial transcriptomic, and high-dimensional imaging studies have shifted TIME interpretation from cell abundance to spatial organization and functional state (26 – 29, 45, 46).”
Single cell mapping of B and T cell dynamics in breast cancer lymph node metastasis
Khan, S., Jamil, S., Hamza, M., Attique, Z., & Zhang, S. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1833416
“Post-therapy immune profiling shows a predominance of cytotoxic CD8 + and regulatory CD4 + T cells across compartments, with reduced B-cell representation (Bassez et al., 2021; Koh and Ellisen, 2021).”
Xie, M.-Y., Wang, C., Zhang, J., Jing, X.-M., Ma, P., & Shu, Y.-Q. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1870840
T cell exhaustion in cancer: from functional decline to adaptive immune regulation
Wang, Y., Yu, Y., Zhuang, J., Liu, R., & Sun, C. (2026) · Molecular Cancer · doi:10.1186/s12943-026-02767-7
“…expansion after anti–PD-1 therapy are precisely those that simultaneously carry exhaustion markers and are enriched for genes associated with cytotoxic activity [139]; moreover, in the TME with high CD8 + Tex infiltration, the IFN-γ signaling pathway usually exhibits a highly active state [138].”
Zhang, B., Zhang, P., Zhang, Z., Xie, J., Cui, X., Chen, C., Zhang, Q., Cui, Y., Sun, L., Wang, S. S., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102964
“While several studies have demonstrated distinctive T cell responses in different cancer contexts, 36,41 our findings provide additional evidence supporting the potential role of the microenvironment in modulating T cell state transitions in human cancers.”
Wouters, J., Bertorello, J., Gaillard, M., Simon, B., Gastineau, S., Roehrig, A., Dupont-Roc, M., Amblard, E., Pupo, A., Yu, H., et al. (2026) · bioRxiv · doi:10.64898/2026.07.23.740343
“Across multiple cancer types, single-cell studies have identified in patients clinically relevant tumor states 5–7 , mechanisms of resistance 8,9 , and microenvironmental determinants of therapeutic response 10–13 that are not captured by conventional approaches.”
Butz, H., Pongor, L., Vereczki, V., Dávid, C., Szakács, K., Kovács, V., Pósa, S. P., Svajda, L., Tóvári, J., Bozsik, A., et al. (2026) · Cell Communication and Signaling · doi:10.1186/s12964-026-03104-7
“…proficient for BRCA1 were obtained from Pal et al. [20] (GSE161529), and Wu et al. 2021 [22] (GSE176078) through the NCBI GEO database, while the data of Bassez et al. 2021 [21] was obtained from h t t p s : / / l a m b r e c h t s l a b . s i t e s . v i b . b e / e n / s i n g l e - c e l l .”
D'Amato, M., Morritti, M., Morelli, F., Ciuffreda, L., Castelvetere, M., Mangiacotti, F., Pasculli, B., & Parrella, P. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1842762
“Bassez et al. (91) applied single-cell RNA sequencing to paired pre-and on-treatment biopsies from breast cancer patients receiving neoadjuvant pembrolizumab, demonstrating that clonotypic expansion of CD8 + T cells with cytotoxic effector features during treatment was associated with pathological response.”
Liu, X.-L., Huang, Z.-X., Wang, H.-J., Wang, J.-L., Kang, H.-Y., Lu, J.-H., & Yan, H.-M. (2026) · Translational Cancer Research · doi:10.21037/tcr-2026-0604
Tian, Z., He, L., & Zhang, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1870393
“Some macrophages localize near tumor nests and express antigen-presentation or interferon-re-sponse genes, whereas others accumulate in hypoxic, necrotic, perivascular, or stromal regions and show programs related to immunosuppression, angiogenesis, tissue repair, or matrix remodeling (17, 18).”
Tumor innervation drives cancer cell plasticity and immune evasion through the SLPI-GZMB axis
Zhang, H., Wu, Q., Wang, L., Hu, X., Li, S., Tang, M., Ma, X., Wang, J., & Hu, Q. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218731 · Cited by 1
Landberg, G., Ståhlberg, M., Frisk, E., Ruiz-Martínez, S., Kaya, M., Rydholm, M., Martner, A., Helou, K., Ståhlberg, A., & Garre, E. (2026) · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · doi:10.1016/j.biopha.2026.119649
Tiamiyu, Z., Czabala, P., Worthy, J., Zarate, L. V., Zheng, M., Poschel, D. B., Fick, K., Yang, D.-F., Monnig, H. R., Rashmi, R., et al. (2026) · bioRxiv · doi:10.64898/2026.06.26.734809
“…state toward an ISG-high monocyte state directly parallels the myeloid phenotype that distinguishes pembrolizumab responders from non-responders in human breast cancer 66 , reinforcing the myeloid compartment as a primary, rather than bystander, effector of IFNα2 -driven tumor growth control.”
Wright, S. J., Kang, S., An, M., Heo, Y. J., Parikh, M., Bi, L., Lee, H., Moorhead, G., Haradhvala, N. J., Lim, S. H., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102910 · Cited by 1
“12,33 To benchmark the putative tumor-reactivity signature, we utilized a scRNA-seq dataset with paired scTCR-seq of CD8 + T cells from a cohort of melanoma patients in which anti-tumor specificity had been experimentally validated and annotated.”
Xia, Z., & He, J. (2026) · International Journal of Women's Health · doi:10.2147/ijwh.s615081
“13,14 Nevertheless, the specific dendritic cell populations associated with immunotherapy response remain incompletely characterized.”
Chen, S., Wang, Y., Chen, J., Xie, W., Chen, J., Liao, G., Ren, X., Lei, K., Zhang, Y., Wang, S., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.06.010 · Cited by 1
Aberrant methylated and expressed genes as potential biomarkers for breast cancer in saudi patients
Mustafa, N., Omar, A., Alhazmi, S., Alsaleem, M., Alkhatabi, H., Sindi, S., Hamdi, N., Bakhsh, T., Al-saud, N., Aljumaiah, L., et al. (2026) · Gene Reports · doi:10.1016/j.genrep.2026.102584
Zhang, J.-X., Qiu, J.-M., Wang, N.-N., Wu, D.-Y., Guo, Y., Li, X.-Y., Yu, J., Li, X.-H., Ning, F.-L., Wang, P.-Y., et al. (2026) · Biochemical and Biophysical Research Communications - BBRC · doi:10.1016/j.bbrc.2026.154381
Perampalam, S., Lam, E., Rankin, G., O'Keeffe, C. J., Sey, A., Luxford, C., Sywak, M., Clifton-Bligh, R., & Bullock, M. (2026) · Thyroid Research · doi:10.1186/s13044-026-00306-6
Benedetti, V. V., Conte, F., Santoni, D., Pentimalli, T., Bei, R., Cifaldi, L., Barillari, G., Spallotta, F., Cencioni, C., & Melaiu, O. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08446-x
“For the Nature Medicine dataset [29], only treatment-naïve triple-negative breast cancer samples collected at baseline were retained.”
InfluentialPapakyriacou, I., Lu, Y., Jarvius, M., Lapins, M., Puigvert, J. C., & Mao, Y. (2026) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-25-1309
“Although these datasets establish an encyclo-pedia of tumor-infiltrating immune cells, illustrations of the spatial– temporal features of TNBC tumors were needed to reveal novel biological insights (40).”
InfluentialZheng, L.-W., Liu, Y.-M., Yu, K.-D., & Liu, C.-C. (2026) · PLoS ONE · doi:10.1371/journal.pone.0349410
“Molecular classification based on genomic, transcriptomic, and proteomic subtypes, combined with gene expression profiling, has enhanced diagnostic accuracy and facilitated individualized treatment [10,13,14].”
Chowdhury, K. (2026) · Clinical Cancer Bulletin · doi:10.1007/s44272-026-00063-5
“The next generation of this tool would ingest labelled immuno-therapy response data — such as from the anti-PD-1 TNBC cohort [23] — to learn optimal feature weights and decision boundaries through logistic regression or gradient boosting.”
InfluentialGeospatial genetic evolution and phenotypic plasticity in triple-negative breast cancer
Hong, R., Wu, P., Qiu, Y., Dong, S., Huang, Y., Lee, K., Wang, H., Li, M., Zhang, C., Ye, F., et al. (2026) · Genome Medicine · doi:10.1186/s13073-026-01684-0
“The same approach was used for TNBC samples from the Bulk RNA-seq dataset (iSPY-2 TNBC cohort ( n = 346) [19], BrighTNess TNBC cohort ( n = 482) [20], SCAN-B cohort ( n = 874) [21]), and tumor cells from the two TNBC single cell cohorts [22, 23].”
InfluentialChai, Y.-Y., Wang, K.-Y., Fang, J.-L., Jia, S., Shi, Y., Gao, W.-F., Liu, X.-P., Li, J.-Q., Cui, Z., Qian, Y., et al. (2026) · Advancement of science · doi:10.1002/advs.76003
“Firstly, we verified the specificity of Lands‘ cycle-related geneset in public single-cell RNA sequencing (scRNA-seq) datasets of human breast cancers and lung cancers [ 29–31 ].”
Heterogeneity of macrophages in PD-1/PD-L1 inhibitor therapy: a single-cell perspective
Wu, J., Zhou, D., Jiang, K., Li, Z., Yao, Y., Rao, S., Yang, X., & Luo, D. (2026) · Cellular & Molecular Biology Letters · doi:10.1186/s11658-026-00956-2
“This shows that the immunotherapy response to PD-1 inhibitor is closely related to the heterogeneity of macrophages [97].”
Luo, J., Lu, Q., Zhang, X., & Wang, X. (2026) · Human Genetics · doi:10.1007/s00439-026-02841-6
“This pattern was further corroborated at the cellular level by analysis of the breast cancer dataset from Bassez et al. (Bassez et al. 2021), which demonstrated that cancer cells within TNBC tumors displayed greater transcriptomic heterogeneity compared to those in non-TNBC tumors ( Supplementary Fig.”
Chen, B., Ma, K., Wang, Y.-J., Zhang, L., Feng, X., Long, C., Tan, X., & Wang, K. (2026) · Clinical and Translational Medicine · doi:10.1002/ctm2.70686
Vermare, A., Ventura, A., Rouault, A., Simula, L., Seradj, M., Lhuillier, L., Weber-Delacroix, E., Mulder, K., Vimeux, L., Espie, D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.28.728475
“For example, some cancer types, including breast cancer, treated with immune checkpoints blockade, a modification of the myeloid compartment has been reported, in addition to the directly targeted T cell populations (Bassez et al. 2021; Gubin et al. 2018).”
QeITH: Quantifies Tumor Ecosystem Heterogeneity to Predict Cancer Progression and Treatment Benefit
Lu, Q., Luo, J., Wang, J., Zhao, B., Wang, X., & You, L. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0061
“In addition, we sourced 1 lung cancer scRNA-seq dataset [ 36 ], 2 breast cancer scRNA-seq datasets [ 37 , 38 ], 1 pancreatic cancer scRNA-seq dataset [ 39 ], 1 hepatocellular carcinoma scRNA-seq dataset [ 40 ], and 1 kidney cancer scRNA-seq dataset [ 41 ] from their respective publications.”
InfluentialExploring drug mechanisms through single‐cell proteomics
Sun, Y., Liu, R., Mu, J., Zhang, L., & Wang, Y. (2026) · British Journal of Pharmacology · doi:10.1111/bph.70487
Djerroudi, L., Mhaidly, R., Kieffer, Y., Damei, I., Croizer, H., Selvarasa, V., Gentric, G., Fuhrmann, L., Goncalves, A., Caly, M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72844-4
CD39 defines a cytotoxic tumor-associated NK cell state responsive to NKG2A blockade in lung cancerPreprint
Serger, C., Rebuffet, L., Sandholzer, M., Rackwitz, W., Fusi, I., Herzig, P., Brüggemann, A., Pawlow, C., Chichelnitskiy, E., Hajnal, D., et al. (2026) · bioRxiv · doi:10.1101/2025.08.26.672316
Chen, H., Yue, Z., Zhang, Y., Zhou, Y., Su, Y., Xu, Y., Sun, Y., Yu, W.-X., & Shen, Z. (2026) · International journal of pharmaceutics · doi:10.1016/j.ijpharm.2026.126954
He, Z.-M., Xu, Y.-S., & Wu, C. (2026) · Translational Cancer Research · doi:10.21037/tcr-2026-1-0221
“A BRCA single-cell RNA sequencing (scRNA-seq) dataset (EGAS00001004809) was applied to assess CHPF levels in specific cell types and to validate the correlation of CHPF with ICB response (15).”
Turep: Detecting cross-cancer tumor-reactive T cells in single-cell and spatial transcriptomics dataPreprint
Liu, W.-D., Tung, C.-H., Sevick-Muraca, E. M., & Zhao, Z.-M. (2026) · bioRxiv · doi:10.64898/2026.04.21.719961
Li, J., Gao, Y., Zhang, S.-R., Guo, S., Hou, Q., Zhang, S.-S., Yu, W.-X., & Liu, K. (2026) · Communications Medicine · doi:10.1038/s43856-026-01548-z
Elephant-derived Bacillus licheniformis modulates immune cells shedding light on cancer resistance
Wang, X.-Y., Wei, M., Yi, Y., An, J., Wang, J., Zhao, R., Su, Z., Ji, G., Zhang, X., & Liu, X. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1753410
Mu, Y., Zeng, X., Yang, Z., Duan, Y., Su, H., Zhu, L., Liu, H., Xu, M., Wang, B., Hong, W., et al. (2026) · ACS Omega · doi:10.1021/acsomega.5c11680
Tissue-Resident Macrophage PIEZO1 Transduces Mechanical Stress into Inflammatory Pain in Acute Gout
Shen, T., Wang, T., Wu, L., Wang, Z., Gan, L., Luo, Y., Liu, C., Xu, Y., Guo, D., Bi, Q., et al. (2026) · Neuroscience Bulletin · doi:10.1007/s12264-026-01592-8 · Cited by 1
Sutcliffe, M. D., Mott, K., Yilmaz-Swenson, T., Felsheim, B., Lobanov, A. V., Michmerhuizen, A., Rädler, P. D., Okumu, D., He, X.-P., Pfefferle, A., et al. (2026) · bioRxiv · doi:10.64898/2026.02.28.707759
“To further evaluate this signature’s performance, we again utilized Bassez et al. 2021 and Gide et (C) Immune signature score calculated for median expression profiles of untreated and ICI-treated tumor models.”
InfluentialQiu, A., Zhang, H., Ramsey, J., Andrews, B., Sun, B., Ren, S., Lu, M., Zhang, K., Cooper, G. F., Lu, B., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag139
“Given that TCR clonotype expansion serves as a biomarker of anti-PD-1 responsiveness [ 4], our findings suggest that the CCC patterns from CD4/CD8 EX T cells to M1 TAMs are highly enriched in E tumors, providing mechanistic insights into anti-PD-1 efficacy.”
InfluentialGarland, B., Delisle, S., Abou-Hamad, J., de Souza, C. D., Zuccarini, R., Cook, D. P., Auer, R., & Sabourin, L. A. (2026) · bioRxiv · doi:10.1101/2025.08.26.672423
Bridging cancer cell-intrinsic driver genes and -extrinsic cell-cell communication with Driver2Comm
Xie, R., Li, J., Hu, Y., & Gao, L. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013973
YAP/TEAD drives treatment-induced adaptive immunosuppression in EGFR-mutant lung cancerPreprint
Honkanen, M., Baumgarten, J., Dibus, N., Rannikko, J. H., Gojković, M., Cejas, P., Xie, Y., Ortutay, Z., Merilahti, J. A. M., Tienhaara, M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.22.701073
Gu, J.-Y., Qi, Q., Mao, X., Cong, R., Sun, Y., Tang, X.-Y., Guan, X., Wang, J., Yu, M.-X., Wang, S., et al. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-02205-4
“Moreover, high expression of IFN response genes which might be related with T cell clono-type expansion [41], as well as oxidative phosphorylation gene signature and cytokine/cytokine receptors (such as IL2RA, IL2RG, IL32) were also observed (Fig.”
Beckmann, H. M., Tong, M., Chang, G., & Steif, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.14.692655
“For the breast cancer cohort 38 , non-proliferating tumour cells were divided into two dormancy pseudotime bins: deep dormancy ( − 1 ≤ ϕ − 0.6), and mid/light dormancy ( − 0.6 ≤ ϕ 0).”
InfluentialDynamic Tumor Immune Microenvironment Remodeling Predicts Response of Checkpoint Inhibitor TherapyPreprint
Lin, Z., Chandra, M., Srinivas, N., Ugurel, S., Becker, J. C., & Aran, D. (2025) · bioRxiv · doi:10.1101/2024.06.30.601413
“For reference preparation, reference data for T-cells, NK cells, myeloid cells, and CAFs were retrieved from the pan-cancer atlas studies 3,4,6,8,20,21 reference at the fine-granular level.”
Hu, J., Yuan, N., Huang, Z., Liu, Y., Tu, B., Yu, X., Hui, T., & Zuo, G. (2025) · Functional & Integrative Genomics · doi:10.1007/s10142-025-01729-4
“Of these immune checkpoints, CD276 (Zhou and Jin 2021) is pivotal in the spread and growth of malignancies, HAVCR2 (Bassez et al. 2021) acts as an indicator of cell exhaustion, and NRP1(Wang et al. 2023), found in numerous tumors, bolsters the tumor cells' stem cell properties, indicating a…”
Toward Machine Learning Prediction of Immunotherapy to Cope With Breast Cancer
Rivas, J., Vásquez, Y., & Alvarado-Mentado, M. (2025) · International Conference on Electrical Engineering, Computing Science and Automatic Control · doi:10.1109/cce67728.2025.11272022
“Data from public Gene Expression Om-nibus (GEO) repository are related to the use of ICis in patients with breast cancer, from the project in [7], and in [8] of TNBC patients.”
Schachter, L. Z., Pinhasi, A., & Yizhak, K. (2025) · bioRxiv · doi:10.1101/2025.09.26.678647
“Finally, to further contextualize these findings, we examined an independent dataset from Bassez et al., 2021.”
Shorer, O., Amit, R., & Yizhak, K. (2025) · bioRxiv · doi:10.1016/j.xgen.2026.101328
“More-over, a subsampling experiment considering different sizes of the training set showed significantly improved performance over the test datasets when using 100% of the training set, demonstrating the added value of the large-scale database used for training (Figures S11A and S11B; STAR Methods).”
Calcium Electrochemotherapy and Challenges in Combined Treatment with Dendritic Cell Vaccination
Radzevičiūtė-Valčiukė, E., Balevičiūtė, A., Želvys, A., Suveizdė, K., Zinkevičienė, A., Kašėta, V., Malyško-Ptašinskė, V., Dobrovolskienė, N., Pašukonienė, V., Novickij, J., et al. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17070804
“Although PD-L1 expression is typically low in these cells, inflammatory factors, such as IFNg, IFNa, IFNb, LPS, IL-4, and GM-CSF [82] as well as immunosuppressive factors [83] from tumors, induce PD-L1 expression.”
Single cell analysis of pan-cancer tumor microenvironment in immunotherapy
Li, F., Xue, K., Pei, L., Sun, Y., Chen, Z., Zhang, Y., Song, K., Liu, W., Quan, Q., Gong, X., et al. (2025) · Human Molecular Genetics · doi:10.1093/hmg/ddaf101
Basto, E., Wajid, B., Read, J. F., Armitage, J. D., Waithman, J., Small, M., & Bosco, A. (2025) · bioRxiv · doi:10.1101/2024.05.01.591681
“…molecular responses to a perturbation, we applied Decipher to twelve publicly available scRNA-seq datasets (Methods) focused on immune responses to viral and bacterial infections 28–31 , vaccination 32,33 , autoimmune conditions 34,35 , genetic disorders 36 , chronic disease 37 , and cancer 38,39 .”
Xiao, F., Xie, D., Zhou, D., Yang, L., & Liu, F. (2025) · Medicine · doi:10.1097/md.0000000000042571 · Cited by 1
“[9] Moreover, in LADA, autoreactive T-cells identify and attack β -cell antigens, resulting in inflammation and damage to islet cells.”
Han, Y., Hu, M., Wang, Y.-Z., Xu, S., Jiang, F., Wang, Y.-J., & Liu, Z.-W. (2025) · Discover Oncology · doi:10.1007/s12672-025-02316-6
“Thirdly, the predictive model primarily relied on transcriptomic data, lacking integration of complementary omics layers (e.g., proteomics, epigenomics), which may limit comprehensive biological interpretation.”
Huang, J., Zhang, H., Lin, X., Wu, X., Chen, X., Chen, W., Liang, S., Chen, Y., Luo, Q., Xu, C., et al. (2025) · Journal of Gastrointestinal Cancer · doi:10.1007/s12029-025-01174-3 · Cited by 1
TNIP1 Impacts Prognosis by Modulating the Immune Microenvironment in BRCA
Liao, D.-J., Liu, W., Jiang, Y.-H., Zhao, P., & Yao, Y. (2025) · Biochemical Genetics · doi:10.1007/s10528-025-11034-1
“Mapping the neoadjuvant anti-PD1 response with T-cell proliferation in the pre-treatment immune environment of BRCA patients, a research observed significant intertumoral changes in patients receiving ICB (Bassez et al. 2021; Rayson et al. 2024).”
InfluentialPRF1 as a prognostic gene for lymphatic metastasis in skin melanoma.
Lin, Y., Liu, J., Chen, S.-Z., Wu, Q.-Q., Shen, F., & Gan, L. (2024) · Biochemical and Biophysical Research Communications - BBRC · doi:10.1016/j.bbrc.2024.150338
Exploring the role of CD8 + T cells in clear renal cell carcinoma metastasis
Chen, Y., Shen, J., Ling, C., Liang, Z.-F., Huang, S., Lin, W., Qin, Y., Meng, L.-Z., & Luo, Y. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13819
“6A,B) is especially important in inhibiting cellular immunity, which is crucial for immune surveillance against cancer cells [22,24].”
BackgroundOncogenic composite mutations can be predicted by co‐mutations and their chromosomal location
Küçükosmanoglu, A., van der Borden, C. L., de Boer, L. E. A., Verhaak, R., Noske, D., Wurdinger, T., Radonic, T., & Westerman, B. A. (2024) · Molecular Oncology · doi:10.1002/1878-0261.13636
Psoríase de novo ou exacerbação por inibidores de checkpoint PD‐1
Wan, Z., Huang, J., Ou, X.-J., Lou, S., Wan, J.-J., & Shen, Z. (2024) · Anais Brasileiros de Dermatologia (Versão em Português) · doi:10.1016/j.abdp.2024.02.008
Metadata
- Authors
- Bassez, A., Vos, H., Van Dyck, L., Floris, G., Arijs, I., Desmedt, C., Boeckx, B., Vanden Bempt, M., Nevelsteen, I., Lambein, K., Punie, K., Neven, P., Garg, A. D., Wildiers, H., Qian, J., Smeets, A., & Lambrechts, D.
- Affiliations
- VIB-KU Leuven Center for Cancer Biology (BE)
- KU Leuven (BE)
- Universitair Ziekenhuis Leuven (BE)
- Women's Hospital, School of Medicine, Zhejiang University (CN)
- Corresponding author
- Qian, Junbin
- Smeets, Ann
- Lambrechts, Diether
- ORCID
- Venue
- Nature Medicine
- Published
- 2021-05
- Volume
- 27
- Issue
- 5
- Pages
- 820-832
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3157384750
- Cited by
- 867
- References
- 74
- Citations by year
- 2026: 156 · 2025: 168 · 2024: 234 · 2023: 159 · 2022: 116 · 2021: 33
- FWCI
- 52.64
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Funders
- Vlaams Supercomputer Centrum
- European Commission (847912)
- Fonds Wetenschappelijk Onderzoek (KOTK/2018/11509/1, C14/18/092, POR/16/040, G0B4620N, C14/19/098, 30837538, G0B6120N)
- KU Leuven (C14/19/098, C14/18/092, POR/16/040, C14/19, 30837538, KOTK/2018/11509/1)
- Kom op tegen Kanker (C14/19/098, 30837538, POR/16/040, KOTK/2018/11509/1)
- Vlaamse regering (VR 2016 2312 Doc.1521/4, 30837538, C14/19/098)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- breast cancer, anti-PD-1 therapy, immune checkpoint blockade, neoadjuvant chemotherapy, tumor immune microenvironment, T cell clonal expansion, CD8+ T cells, CD4+ T cells, T cell exhaustion, tumor-infiltrating macrophages, treatment response heterogeneity, triple-negative breast cancer, hormone receptor-positive breast cancer, single-cell transcriptomics, proteomics
- MeSH terms
- Immune Checkpoint Inhibitors, Dendritic Cells, Female, Humans, Macrophages, Receptors, Antigen, T-Cell, CD4-Positive T-Lymphocytes, CD8-Positive T-Lymphocytes, Neoadjuvant Therapy, Single-Cell Analysis, Programmed Cell Death 1 Receptor, Antibodies, Monoclonal, Humanized, Triple Negative Breast Neoplasms
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