The role of activated androgen receptor in cofilin phospho-regulation depends on the molecular subtype of TNBC cell line and actin assembly dynamics.

Triple negative breast cancer (TNBC) is highly metastatic and of poor prognosis. Metastasis involves coordinated actin filament dynamics mediated by cofilin and associated proteins. Activated androgen receptor (AR) is believed to contribute to TNBC tumorigenesis. Our current work studied roles of activated AR and cofilin phospho-regulation during migration of three AR+ TNBC cell lines to determine if altered cofilin regulation can explain their migratory differences. Untreated or AR agonist-treated BT549, MDA-MB-453, and SUM159PT cells were compared to cells silenced for cofilin (KD) or AR expression/function (bicalutamide). Cofilin-1 was found to be the only ADF/cofilin isoform expressed in each TNBC line. Despite a significant increase in cofilin kinase caused by androgens, the ratio of cofilin:p-cofilin (1:1) did not change in SUM159PT cells. BT549 and MDA-MB-453 cells contain high p-cofilin levels which underwent androgen-induced dephosphorylation through increased cofilin phosphatase expression, but surprisingly maintain a leading-edge with high p-cofilin/total cofilin not found in SUM159PT cells. Androgens enhanced cell polarization in all lines, stimulated wound healing and transwell migration rates and increased N/E-cadherin mRNA ratios while reducing cell adhesion in BT549 and MDA-MB-453 cells. Cofilin KD negated androgen effects in MDA-MB-453 except for cell adhesion, while in BT549 cells it abrogated androgen-reduced cell adhesion. In SUM159PT cells, cofilin KD with and without androgens had similar effects in almost all processes studied. AR dependency of the processes were confirmed. In conclusion, cofilin regulation downstream of active AR is dependent on which actin-mediated process is being examined in addition to being cell line-specific. Although MDA-MB-453 cells demonstrated some control of cofilin through an AR-dependent mechanism, other AR-dependent pathways need to be further studied. Non-cofilin-dependent mechanisms that modulate migration of SUM159PT cells need to be investigated. Categorizing TNBC behavior as AR responsive and/or cofilin dependent can inform on decisions for therapeutic treatment.

Morphological signatures of actin organization in single cells accurately classify genetic perturbations using CNNs with transfer learning.

The actin cytoskeleton plays essential roles in countless cell processes, from cell division to migration to signaling. In cancer cells, cytoskeletal dynamics, cytoskeletal filament organization, and overall cell morphology are known to be altered substantially. We hypothesize that actin fiber organization and cell shape may carry specific signatures of genetic or signaling perturbations. We used convolutional neural networks (CNNs) on a small fluorescence microscopy image dataset of retinal pigment epithelial (RPE) cells and triple-negative breast cancer (TNBC) cells for identifying morphological signatures in cancer cells. Using a transfer learning approach, CNNs could be trained to accurately distinguish between normal and oncogenically transformed RPE cells with an accuracy of about 95% or better at the single cell level. Furthermore, CNNs could distinguish transformed cell lines differing by an oncogenic mutation from each other and could also detect knockdown of cofilin in TNBC cells, indicating that each single oncogenic mutation or cytoskeletal perturbation produces a unique signature in actin morphology. Application of the Local Interpretable Model-Agnostic Explanations (LIME) method for visually interpreting the CNN results revealed features of the global actin structure relevant for some cells and classification tasks. Interestingly, many of these features were supported by previous biological observation. Actin fiber organization is thus a sensitive marker for cell identity, and identification of its perturbations could be very useful for assaying cell phenotypes, including disease states.

Correction: Morphological signatures of actin organization in single cells accurately classify genetic perturbations using CNNs with transfer learning.

Correction for 'Morphological signatures of actin organization in single cells accurately classify genetic perturbations using CNNs with transfer learning' by Sydney Alderfer et al., Soft Matter, 2022, https://doi.org/10.1039/d2sm01000c.