Breast cancer is a highly heterogenous disease, both phenotypically and genetically. Importantly a correlation between intra‐tumoral heterogeneity, drug resistance and negative clinical outcome has been established. Previously, quantity or subcellular location of protein biomarkers have been compared to identify tumor tissue types. To‐date, mass cytometry and single‐cell sequencing studies have been used to assess spatial intra‐tumor heterogeneity by identifying a range of phenotypic and genetic alterations across different regions of a single tumor. Recently, cell morphology has been indicated as a direct readout of the functional phenotypic state of an individual cancer cell. Here, we hypothesize that the spatial context of organelles within cancer cells, specifically their subcellular location and inter‐organelle relationships (topology), can be used to inform breast cancer cell classification. Since numerous correlations between biologic behaviors and pathologic findings have been well‐established, we propose that organelle topological heterogeneity reveals the long‐term adaptation of organelles and cytoskeletal networks to match breast cancer cell type status. Thus, this study aims to investigate the heterogeneity of organelle topology and morphology in breast cancer cells to increase our basic understanding of breast cancer biology on a subcellular level. We have introduced a novel approach that quantifies, for the first time, the feature of organelles in breast cancer cells, removing the bias of visual interpretation to classify different cell lines based on organelle morphology and topology. This method was tested on three different organelle datasets: mitochondria, early endosomes and recycling endosomes in a panel of human breast cancer cells, including T47D (estrogen receptor‐positive), MDAMB231, MDAMB436 and MDAMB468 (triple negative) and AU565 (HER2 positive), as well as non‐cancerous mammary epithelial MCF10A cells. High resolution Airyscan microscopy was used to collect z‐stacks across cells labeled with fluorescently labeled‐transferrin and immunostained with anti‐Tom20 and anti‐EEA1 to label the recycling, early endosomal and mitochondria networks, respectively. Subsequently, 3D surface rendering of organelle objects was performed using IMARIS image analysis software. A morphometric evaluation of mitochondrial and endosomal compartments resulted in 34 topology and morphology parameters. Application of Random Forest machine learning based classification to 18 of these 34 parameters generated the highest accuracy in breast cancer cell classification. We systematically evaluated how different parameter combinations affected the machine learning‐based cancer cell classification and discovered that topology parameters were crucial to achieve the highest accuracy. Based on organelle spatial distribution and their interaction with neighbor organelles (topology) a classification accuracy over 95% was achieved to distinguish between a variety of human breast cancer cell lines of differing subtype and aggr...

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Mitochondria‐early endosome (EE) interactions have been shown to facilitate the translocation of iron into mitochondria. Here we show that Divalent Metal Transporter 1 (DMT1) modulates iron exit from endosomes and transport into mitochondria via regulation of EE‐mitochondria interactions. In cancer cells, mitochondria are the ultimate cellular iron sink, where iron can be either stored or used for example to shift cellular metabolism towards glycolysis (Warburg effect), a key adaptive mechanism of cancer cells. Moreover, a gene signature associated with reduced intracellular iron content, including low transferrin receptor (TfR) (anti‐import) and high ferroportin (FPN) (pro‐export) expression levels, has been related to favorable breast cancer prognosis. Similarly, reduced DMT1 expression associates with improved breast cancer patient survival. We evaluated the role of DMT1 in two distinct breast cancer cell lines: estrogen receptor positive T47D and triple‐negative MDAMB231. In both cell lines, we demonstrate colocalization between EE, DMT1 and mitochondria. Interestingly, DMT1 is localized to the surface contact area between endosomes and mitochondria. To demonstrate that DMT1 plays a role in endosome‐mitochondria interactions and Mitochondrial Iron Translocation (MIT), we have generated MDAMB231 as well as T47D CRISPR/Cas9 based DMT1 knockout (KO) stable cell lines. Several lines of evidence show that DMT1 regulates MIT and labile iron pool (LIP) levels via modulation of EE‐mitochondria interactions in MDAMB231 cells. MIT decrease via DMT1 silencing was partially rescued by re‐expression of DMT1 in MDAMB231, but not in T47D cells. MDAMB231 DMT1 KO cells showed increased Ferro‐Orange staining, indicating higher LIP levels, as well as decreased TfR and increased FPN protein levels. Importantly, DMT1 silencing significantly reduced EE‐mitochondria interactions and EE speed in MDAMB231 but not in T47D. Thus, DMT1 regulates MIT and LIP levels via EE‐mitochondria interactions in MDAMB231. These results are in agreement with previous results showing that MDAMB231 display a delay in iron release in comparison to T47D, making them more sensitive to disruptions in MIT. Since mitophagy has been shown to act as a tumor suppressor in breast cancer, we tested whether it could be modulated by DMT1‐mediated MIT. We found that DMT1 silencing increases mitochondrial ferritin, global autophagy marker LC3B and PINK1/Parkin‐dependent mitophagy markers in MDAMB231; levels of all proteins evaluated were rescued to basal levels upon re‐expression of DMT1 in DMT KO cells. Moreover, DMT1 silencing decreases Tom 20 (outer mitochondrial membrane marker) with PMPCB, a known DMT1 interactor that is required for PINK1 turnover. Concurring with the role of DMT1 in mitophagy and iron metabolism, both mitochondrial metabolism and invasive cell migration are significantly impaired by DMT1 silencing and are partially rescued by re‐expression of DMT1. Overall, our results implicate DMT1 in the regulation of EE dynamics and EE‐mitochondria interac...

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