Breast cancer is a heterogeneous disease comprising multiple molecular subtypes with distinct biological and clinical features. Among them, the basal-like subtype accounts for approximately 15–20% of cases and is associated with poor prognosis. Basal-like breast cancers (BLBC) share several histo-molecular characteristics with BRCA1-deficient tumors, including genomic instability and reduced BRCA1 expression beyond germline-mutated cases, suggesting potential alterations in homologous recombination (HR) DNA repair. These observations have led to the hypothesis that homologous recombination deficiency (HRD) may be enriched in a subset of BLBC. However, the extent, causes, and clinical implications of HRD in this subtype remain incompletely defined, as much of the available evidence is derived from triple-negative breast cancer (TNBC) cohorts, which only partially overlap with BLBC. In this review, we first summarize the transcriptomic definition of BLBC and its relationship to TNBC. We then provide a concise overview of current HRD detection methods, followed by a critical analysis of their application specifically to basal-like tumors. We next examine the molecular mechanisms that may contribute to HRD in BLBC, distinguishing well-supported alterations from emerging mechanisms. Finally, we discuss the therapeutic implications of HRD in BLBC, highlighting current limitations of clinical evidence and the challenges of translating a biologically defined subtype into a clinically actionable framework. Overall, while a BLBC-centered approach may offer a more biologically homogeneous context to study HRD, its clinical relevance remains to be established, and future studies specifically designed in molecularly defined cohorts will be required to clarify its diagnostic and therapeutic value.

Increasing levels of nanoplastics (NPLs) in the environment raise concerns about their effects on human health. We investigated the impact of the most prevalent NPLs, namely polyethylene terephthalate (PET) and polystyrene (PS), on stem cells (SCs), which persist for decades, support tissue function, and are often implicated in cancer development. Long-term exposure to both NPLs similarly affected mammary SC features with an enhanced self-renewal and altered 3D organization, without impairing differentiation capacity. Moreover, both NPLs significantly increased invasiveness and anchorage-independent growth, albeit molecular profiling revealed distinct signatures and mechanisms, indicating a shift towards a more aggressive phenotype. NPLs also synergized with BMP2 signaling, known to be disrupted by pollutants. These findings highlight how NPLs may contribute to early pre-neoplastic changes through distinct and cooperative mechanisms. ### Competing Interest Statement The authors have declared no competing interest.

Tunneling nanotubes (TNTs) are thin, actin-based structures allowing long-distance communication between cells by promoting the transfer of molecules and organelles. Well-known to contribute to cancer progression, we investigated their role in early tumor initiation using an in-house human breast cell model recapitulating early transformation. We observed that TNTs become more abundant and elongated during this process, and preferentially connect transformed donor cells to non-transformed acceptor cells. We show that this long-range, directional communication involves the transfer of the signaling receptor BMPR1b. Within days, this transfer induces gene expression changes in acceptor cells, consistent with early transformation programs. Functional analyses of these acceptor cells revealed metabolic rewiring and phenotypic changes, including anchorage-independent growth. Moreover, the transferred BMPR1b receptor sensitized acceptor cells to BMP2 signals present in the microenvironment, amplifying their transformation potential. Hence, by tracking the earliest molecular responses in acceptor cells, we deciphered the initial steps of a transformation cascade triggered by TNT-mediated transfer. These findings uncover a BMP-dependent mechanism by which transformed cells propagate a preneoplastic state to adjacent cells at the onset of transformation, offering new perspectives on how epithelial transformation arises and spreads to neighboring cells. ### Competing Interest Statement The authors have declared no competing interest. ANR, ANR-10-LABX-0061, ANR-CESA-018-04, ANR-17-CONV-0002 La Ligue Contre le Cancer, https://ror.org/00rkrv905 Fondation ARC, SFI20111203500, PJA20171206331 FRM, EQU202203014695 Symatese (France), https://ror.org/04xctba91 Ruban Rose Dechaine ton coeur Fondation MSD Avenir, DS-2018-0015

Basal-like breast cancer (BLBC) is an aggressive subtype frequently characterized by homologous recombination deficiency (HRD) and BRCAness, even in the absence of BRCA1 mutations. Here, we identify the BMP4-BMPR1A signaling axis as a novel regulator of BRCA1 expression and a driver of BRCAness in non-transformed immature mammary cells. Analyses of patient samples reveal that high BMPR1A expression correlates with low BRCA1 levels and poor prognosis in BLBC. We show that BMP4 exposure induces BRCA1 transcriptional repression via BMPR1A, promoting a basal differentiation and impairing homologous recombination. This results in increased sensitivity to PARP inhibitors (PARPi) and accumulation of genetically unstable, immature cells. In addition, long-term BMP4 stimulation or BMPR1A overexpression induces transformation. Our findings uncover a mechanism by which the tumor microenvironment could contributes to BLBC initiation through BMP4-induced suppression of BRCA1, and suggest BMP signaling and the resultant BRCAness as a therapeutic vulnerability in BRCA1 wild-type BLBCs. ### Competing Interest Statement The authors have declared no competing interest.

Micro/nanoplastics (MNPLs) are environmental contaminants originating mainly from plastic waste degradation that pose potential health risks. Inhalation is a major exposure route, as evidenced by their detection in human lungs, with polyethylene terephthalate (PET) among the most abundant particles in respiratory airways. However, the harmful effects of particle bioaccumulation remain unclear, as chronic effects are understudied. To assess long-term effects, specifically carcinogenic effects, BEAS-2B cells were exposed to PET-NPLs for 30 weeks. Genotoxicity, carcinogenic phenotypic hallmarks, and a panel of genes and pathways associated with cell transformation and lung cancer were examined and compared across three exposure durations. No significant effects were observed after 24 h or 15 weeks of exposure. However, a 30-week exposure led to increased genotoxic damage, anchorage-independent growth, and invasive potential. Transcriptomic analysis showed the upregulation of several oncogenes and lung cancer-associated genes at the end of the exposure. Further analysis revealed an increase in differentially expressed genes over time and a temporal gradient of lung cancer-related genes. Altogether, the data suggest PET-NPLs' potential carcinogenicity after extended exposure, highlighting serious long-term health risks of MNPLs. Assessing their carcinogenic risks under chronic scenarios of exposure is crucial to addressing knowledge gaps and eventually developing preventive policies.

Supplementary Figure S2 shows in A the percentage of indicated cells positive for CD10 staining by flow-cytometry and in B the mean fluorescence intensity of CD10-positive cells after infection with a vector expressing a control or anti-CD10 shRNA. In C the percentage of indicated cells positive for CD10 staining by flow-cytometry and in D the mean fluorescence intensity of CD10-positive cells after infection with a vector expressing the CD10 cDNA or an empty control vector are shown. In E the frequency of soft-agar colony forming cells is shown in the indicated cell lines after infection with a vector expressing the CD10 cDNA or an empty control vector. In F the frequency of soft-agar colony forming cells is shown in the indicated cell lines after infection with a vector expressing a control or anti-CD10 shRNA.

The ENI10 molecular signature is enriched in genes involved in chromosome segregation during mitosis. A, GO analysis on the CD10 signature genes using the “Biological Process” terms. The most downstream terms in the hierarchy with an FDR less than 0.01 are shown. B, Ratio of the percentage of CD10+ over CD10-negative cells in each phase of the cell cycle determined by flow-cytometry analysis of MCF10A-Fucci-CA cells stained with an anti-CD10 antibody. C, GSEA using RNA-seq experiments comparing MCF10A cells expressing a scramble (shCTRL) or CD10 specific shRNA (shCD10), the gene sets used are the ENI10 genes (top), ENI10 genes belonging to all enriched GO terms shown in A (middle) and ENI10 genes belonging to the “mitotic spindle assembly checkpoint signaling” GO term (bottom). D, ssGSEA quantification of the indicated gene sets in normal healthy breast tissue (H) or in DCIS (D) from GSE21422 series. E, Quantification of E-CFC from CD10+ MC26 or M1B26 cells infected with lentiviruses carrying a scramble (sh ctl) or sh CD10 vector. F, Quantification of spheres forming cells from CD10+ MC26 or M1B26 cells infected with lentiviruses carrying a scramble (sh ctl) or sh CD10 vector. G, Quantification of soft-agar clones from M1B26 CD10+ cells infected with lentiviruses carrying a scramble (sh ctl) or sh CD10 vector.

Supplementary Table S1 shows the full Gene Ontology terms enrichment analysis of the indicated cell lines transcriptome.

BMP2 induces early transformation of a model of breast SCs and increases CD10 expression. A, Schematic representation of the experimental protocol used to obtain the MCF10-CT, MC26, and M1B26 cell lines from the MCF10A cells. B, Quantification of soft-agar colony formation [error bars represent the SD (n = 7), significance measured using the Mann–Whitney test is indicated on the graph by the P values. ns for P > 0.05]. C, Xenografts of the indicated number cells of MCF10A-derived models injected into nude mice presented as the number of successful grafts after 4 weeks/mouse (n = 10). D, GSEA of transcriptomic data comparing MC26 cells (top row) or M1B26 cells (bottom row) with MCF10A-CT cells. Data represent enrichment plots analyzed using public gene sets (41) of upregulated (left) or downregulated (right) genes in human primary ductal carcinoma compared with healthy tissues. E, GSEA of transcriptomic data comparing MC26 (first column) or M1B26 (second column) with CT cells and M1B26 with MC26 cells (third column). The “hallmarks” gene sets from the MSigDB were used and the NES (normalized enrichment score) with P values inferior to 0.05 are shown. F, E-CFC Progenitor content from MCF10A-CT cells (n = 5), MC26 cells (n = 8) or M1B26 cells (n = 6) quantified after 6 days by scoring colonies numbers and presented/10,000 cells. G, Number of spheres per 100 seeded cells after 1 week from MCF10A-CT cells (n = 4), MC26 cells (n = 6), and M1B26 cells (n = 6). H, TDLU, 3D structures from primary human breast cells (top) and MCF10A cell line (bottom). I, Images at day 21 of 3D structures in the TDLU assay from MCF10A-CT, MC26, or M1B26 cells. A representative TDLU section from MCF10A-CT, stained with H&E, is shown on the top right. J, Flow cytometry analysis of CD10 expression on MCF10A-CT (n = 4), MC26 (n = 7), and M1B26 (n = 6) presented as the percentage of positive cells (left) and mean fluorescence intensity (right).

ENI10 predict pan-cancer survival independently of the cell cycle and more efficiently than other SC–derived signatures. A, ssGSEA score of the ENI10 signature in all tumors and normal samples from TCGA database. B, Correlation of CD10 signature score and survival in TCGA's Pan-Cancer Atlas. All tumor samples were pooled and the effect of the CD10-signature score discretized by deciles on survival outcome was evaluated from Cox models stratified on cancer types, using unadjusted (black marks), adjusted on age alone (blue marks, modeled with a 3-degree polynomial spline) or with a supplemental stratification term for stage (I/II/III/IV; green marks) or grade (1/2/3/4; red marks) pathologic scoring systems. Dots show the HR for PFI and adjacent bars the 95% confidence interval. C, Left: Overall Pan-Cancer analysis of the correlation between the ENI10 score and survival in TCGA's Pan-Cancer Atlas. All tumor samples were pooled and the effect of the ENI10 score discretized by deciles on survival outcome was evaluated from Cox models stratified on cancer types. Right: Same analysis with a reduced ENI10 signature were genes known to be regulated during the cell cycle were removed. D and E, Overall Pan-Cancer analysis of the effect of CD10 score on survival in TCGA's Pan-Cancer atlas. All tumor samples were pooled and the effect of the CD10 enrichment score discretized by deciles on survival outcome was evaluated using a multivariable Cox model including as covariables both the CD10 score and the Smith and colleagues (D) or Pece and colleagues (E) signatures. Dots show the hazard ratio for PFI and adjacent bars the 95% confidence interval.

Supplementary Figure S3 shows in A the CD10 transcript expression level in various cancer types from the TCGA cohorts. B shows the expression level of the CD10 protein in various cancer types from the human protein atlas. C shows the progression free intervals in function of the ENI10 score of various cancer types from the TCGA cohorts. D shows the ENI10 score of cohorts of benign melanocytic nevi or primary melanoma.

An increased ENI10 score predicts patient's survival in several cancer types. A, ssGSEA ENI10 score in pairs of normal and tumor samples from TCGA's Pan-Cancer atlas linked by gray lines and the difference are color coded on the dots representing the tumor samples. ACC: adrenocortical carcinoma, BLCA: bladder urothelial carcinoma, BRCA: breast invasive carcinoma, CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma, CHOL: cholangiocarcinoma, COAD: colon adenocarcinoma, DLBC: lymphoid neoplasm diffuse large B-cell lymphoma, ESCA: esophageal carcinoma, HNSC: head and neck squamous cell carcinoma, KICH: kidney chromophobe, KIRC: kidney renal clear cell carcinoma, KIRP: kidney renal papillary cell carcinoma, LAML: acute myeloid leukemia, LGG: low-grade glioma, LIHC: liver hepatocellular carcinoma, LUAD: lung adenocarcinoma, LUSC: lung squamous cell carcinoma, MESO: mesothelioma, OV: ovarian cancer, PCPG: pheochromocytoma and paraganglioma, PRAD: prostate adenocarcinoma, READ: rectum adenocarcinoma, SARC: sarcoma, SKCM: skin cutaneous melanoma, STAD: stomach adenocarcinoma, TGCT: testicular germ cell tumor, THCA: thyroid carcinoma, THYM: thymoma, UCEC: uterine corpus endometrial carcinoma, UCS: uterine carcinosarcoma, UVM: uveal melanoma. B, Correlation of the ENI10 score and survival outcome for each type of cancer of TCGA Pan-Cancer atlas estimated by HRs of PFS corresponding to one SD of the score taken as a continuous variable. Dots show the HR and adjacent bars the 95% confidence interval. C, Examples of PFS curves from TCGA Pan-Cancer atlas in the whole cohort and as a function of stage estimated using the Kaplan–Meier method and compared with the log-rank test between groups of patients defined by the median of the ENI10 score (low scores in blue and high scores in red). D, ssGSEA ENI10 score in transcriptomic data from nevus or melanoma at different stage of clinically defined transformation. E, Using transcriptomic data from the “Genomics of drug sensitivity in cancer” project from the Sanger Institute, ENI10 score of all human cancers cell lines available were correlated with their IC50 to 441 drugs. Targets of the drugs with a significative negative correlation between the ENI10 ssGSEA score and IC50 (indicating a sensitivity to the drug when the ENI10 score increase) are shown. According to the GDSC guidelines, red dots show drugs with a significative IC50 correlation with the ENI10 score with a P value inferior to 0.001 and a Benjamini–Hochberg FDR inferior to 0.25. Black dots show suggestive correlations with a P value inferior to 0.005 and a nonparametric P value inferior to 0.1.

One important environmental/health challenge is to determine, in a feasible way, the potential carcinogenic risk associated with environmental agents/exposures. Since a significant proportion of tumors have an environmental origin, detecting the potential carcinogenic risk of environmental agents is mandatory, as regulated by national and international agencies. The challenge mainly implies finding a way of how to overcome the inefficiencies of long-term trials with rodents when thousands of agents/exposures need to be tested. To such an end, the use of in vitro cell transformation assays (CTAs) was proposed, but the existing prevalidated CTAs do not cover the complexity associated with carcinogenesis processes and present serious limitations. To overcome such limitations, we propose to use a battery of assays covering most of the hallmarks of the carcinogenesis process. For the first time, we grouped such assays as early, intermediate, or advanced biomarkers which allow for the identification of the cells in the initiation, promotion or aggressive stages of tumorigenesis. Our proposal, as a novelty, points out that using a battery containing assays from all three groups can identify if a certain agent/exposure can pose a carcinogenic risk; furthermore, it can gather mechanistic insights into the mode of the action of a specific carcinogen. This structured battery could be very useful for any type of in vitro study, containing human cell lines aiming to detect the potential carcinogenic risks of environmental agents/exposures. In fact, here, we include examples in which these approaches were successfully applied. Finally, we provide a series of advantages that, we believe, contribute to the suitability of our proposed approach for the evaluation of exposure-induced carcinogenic effects and for the development of an alternative strategy for conducting an exposure risk assessment.

Supplementary Table S5 shows the drugs corresponding to the targets indicated in Figures 2K and 5E of the main text.

Supplementary Table S4 shows the correlation between the ENI10 score and the IC50 of the indicated drugs in breast cancer cell lines from the "Genomics of Drug Sensitivity in Cancer Project".

Supplementary Table S6 shows the correlation between the ENI10 score and the IC50 of the indicated drugs in cancer cell lines from the "Genomics of Drug Sensitivity in Cancer Project".

An accurate estimate of patient survival at diagnosis is critical to plan efficient therapeutic options. A simple and multiapplication tool is needed to move forward the precision medicine era. Taking advantage of the broad and high CD10 expression in stem and cancers cells, we evaluated the molecular identity of aggressive cancer cells. We used epithelial primary cells and developed a breast cancer stem cell–based progressive model. The superiority of the early-transformed isolated molecular index was evaluated by large-scale analysis in solid cancers. BMP2-driven cell transformation increases CD10 expression which preserves stemness properties. Our model identified a unique set of 159 genes enriched in G2–M cell-cycle phases and spindle assembly complex. Using samples predisposed to transformation, we confirmed the value of an early neoplasia index associated to CD10 (ENI10) to discriminate premalignant status of a human tissue. Using a stratified Cox model, a large-scale analysis (>10,000 samples, The Cancer Genome Atlas Pan-Cancer) validated a strong risk gradient (HRs reaching HR = 5.15; 95% confidence interval: 4.00–6.64) for high ENI10 levels. Through different databases, Cox regression model analyses highlighted an association between ENI10 and poor progression-free intervals for more than 50% of cancer subtypes tested, and the potential of ENI10 to predict drug efficacy. The ENI10 index constitutes a robust tool to detect pretransformed tissues and identify high-risk patients at diagnosis. Owing to its biological link with refractory cancer stem cells, the ENI10 index constitutes a unique way of identifying effective treatments to improve clinical care.SIGNIFICANCE:We identified a molecular signature called ENI10 which, owing to its biological link with stem cell properties, predicts patient outcome and drugs efficiency in breast and several other cancers. ENI10 should allow early and optimized clinical management of a broad number of cancers, regardless of the stage of tumor progression.

Supplementary Figure S1 shows the doubling time of MCF10A-CT, MC26 and M1B26 cell lines in A; the expression of the CD10 mRNA in the METABRIC cohort in B; the probability of survival of patients from the METABRIC cohort in function of CD10 expression in C; the ENI10 score of patients form the METABRIC cohort in D; the relapse-free interval of patients from the METABRIC cohort in function of the ENI10 score in E and the progression free interval in F or overall survival in G of patients form the METABRIC cohort in function of the ENI10 score and of the molecular subtype of the breast tumor.

Prostate cancer is a major public health concern and one of the most prevalent forms of cancer worldwide. The definition of altered signaling pathways implicated in this complex disease is thus essential. In this context, abnormal expression of the receptor of Macrophage Colony-Stimulating Factor-1 (M-CSF or CSF-1) has been described in prostate cancer cells. Yet, outcomes of this expression remain unknown. Using mouse and human prostate cancer cell lines, this study has investigated the functionality of the wild-type CSF-1 receptor in prostate tumor cells and identified molecular mechanisms underlying its ligand-induced activation. Here, we showed that upon CSF-1 binding, the receptor autophosphorylates and activates multiple signaling pathways in prostate tumor cells. Biological experiments demonstrated that the CSF-1R/CSF-1 axis conferred significant advantages in cell growth and cell invasion in vitro. Mouse xenograft experiments showed that CSF-1R expression promoted the aggressiveness of prostate tumor cells. In particular, we demonstrated that the ligand-activated CSF-1R increased the expression of spp1 transcript encoding for osteopontin, a key player in cancer development and metastasis. Therefore, this study highlights that the CSF-1 receptor is fully functional in a prostate cancer cell and may be a potential therapeutic target for the treatment of prostate cancer.

Understanding mechanisms of cancer development is mandatory for disease prevention and management. In healthy tissue, the microenvironment or niche governs stem cell fate by regulating the availability of soluble molecules, cell-cell contacts, cell-matrix interactions, and physical constraints. Gaining insight into the biology of the stem cell microenvironment is of utmost importance, since it plays a role at all stages of tumorigenesis, from (stem) cell transformation to tumor escape. In this context, BMPs (Bone Morphogenetic Proteins), are key mediators of stem cell regulation in both embryonic and adult organs such as hematopoietic, neural and epithelial tissues. BMPs directly regulate the niche and stem cells residing within. Among them, BMP2 and BMP4 emerged as master regulators of normal and tumorigenic processes. Recently, a number of studies unraveled important mechanisms that sustain cell transformation related to dysregulations of the BMP pathway in stem cells and their niche (including exposure to pollutants such as bisphenols). Furthermore, a direct link between BMP2/BMP4 binding to BMP type 1 receptors and the emergence and expansion of cancer stem cells was unveiled. In addition, a chronic exposure of normal stem cells to abnormal BMP signals contributes to the emergence of cancer stem cells, or to disease progression independently of the initial transforming event. In this review, we will illustrate how the regulation of stem cells and their microenvironment becomes dysfunctional in cancer via the hijacking of BMP signaling with main examples in myeloid leukemia and breast cancers.