Tunneling Nanotubes: Implications for Chemoresistance.

Tunneling nanotubes (TNTs) are thin, membranous protrusions that connect cells and allow for the transfer of various molecules, including proteins, organelles, and genetic material. TNTs have been implicated in a wide range of biological processes, including intercellular communication, drug resistance, and viral transmission. In cancer, they have been investigated more deeply over the past decade for their potentially pivotal role in tumor progression and metastasis. TNTs, as cell contact-dependent protrusions that form at short and long distances, enable the exchange of signaling molecules and cargo between cancer cells, facilitating communication and coordination of their actions. This coordination induces a synchronization that is believed to mediate the TNT-directed evolution of drug resistance by allowing cancer cells to coordinate, including through direct expulsion of chemotherapeutic drugs to neighboring cells. Despite advances in the overall field of TNT biology since the first published report of their existence in 2004 (Rustom A, Saffrich R, Markovic I, Walther P, Gerdes HH, Science. 303:1007-10, 2004), the mechanisms of formation and components vital for the function of TNTs are complex and not yet fully understood. However, several factors have been implicated in their regulation, including actin polymerization, microtubule dynamics, and signaling pathways. The discovery of TNT-specific components that are necessary and sufficient for their formation, maintenance, and action opens a new potential avenue for drug discovery in cancer. Thus, targeting TNTs may offer a promising therapeutic strategy for cancer treatment. By disrupting TNT formation or function, it may be possible to inhibit tumor growth and metastasis and overcome drug resistance.

Treatment with tumor-treating fields (TTFields) suppresses intercellular tunneling nanotube formation in vitro and upregulates immuno-oncologic biomarkers in vivo in malignant mesothelioma.

Disruption of intercellular communication within tumors is emerging as a novel potential strategy for cancer-directed therapy. Tumor-Treating Fields (TTFields) therapy is a treatment modality that has itself emerged over the past decade in active clinical use for patients with glioblastoma and malignant mesothelioma, based on the principle of using low-intensity alternating electric fields to disrupt microtubules in cancer cells undergoing mitosis. There is a need to identify other cellular and molecular effects of this treatment approach that could explain reported increased overall survival when TTFields are added to standard systemic agents. Tunneling nanotube (TNTs) are cell-contact-dependent filamentous-actin-based cellular protrusions that can connect two or more cells at long-range. They are upregulated in cancer, facilitating cell growth, differentiation, and in the case of invasive cancer phenotypes, a more chemoresistant phenotype. To determine whether TNTs present a potential therapeutic target for TTFields, we applied TTFields to malignant pleural mesothelioma (MPM) cells forming TNTs in vitro. TTFields at 1.0 V/cm significantly suppressed TNT formation in biphasic subtype MPM, but not sarcomatoid MPM, independent of effects on cell number. TTFields did not significantly affect function of TNTs assessed by measuring intercellular transport of mitochondrial cargo via intact TNTs. We further leveraged a spatial transcriptomic approach to characterize TTFields-induced changes to molecular profiles in vivo using an animal model of MPM. We discovered TTFields induced upregulation of immuno-oncologic biomarkers with simultaneous downregulation of pathways associated with cell hyperproliferation, invasion, and other critical regulators of oncogenic growth. Several molecular classes and pathways coincide with markers that we and others have found to be differentially expressed in cancer cell TNTs, including MPM specifically. We visualized short TNTs in the dense stromatous tumor material selected as regions of interest for spatial genomic assessment. Superimposing these regions of interest from spatial genomics over the plane of TNT clusters imaged in intact tissue is a new method that we designate Spatial Profiling of Tunneling nanoTubes (SPOTT). In sum, these results position TNTs as potential therapeutic targets for TTFields-directed cancer treatment strategies. We also identified the ability of TTFields to remodel the tumor microenvironment landscape at the molecular level, thereby presenting a potential novel strategy for converting tumors at the cellular level from 'cold' to 'hot' for potential response to immunotherapeutic drugs.

TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images.

BACKGROUND: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. METHODS: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. RESULTS: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). CONCLUSIONS: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.

The Mechanisms of Action of Tumor Treating Fields.

Tumor treating fields (TTFields), a new modality of cancer treatment, are electric fields transmitted transdermally to tumors. The FDA has approved TTFields for the treatment of glioblastoma multiforme and mesothelioma, and they are currently under study in many other cancer types. While antimitotic effects were the first recognized biological anticancer activity of TTFields, data have shown that tumor treating fields achieve their anticancer effects through multiple mechanisms of action. TTFields therefore have the ability to be useful for many cancer types in combination with many different treatment modalities. Here, we review the current understanding of TTFields and their mechanisms of action.

Molecular alterations associated with improved outcome in patients with glioblastoma treated with Tumor-Treating Fields.

Background: The genomic and overall biologic landscape of glioblastoma (GB) has become clearer over the past 2 decades, as predictive and prognostic biomarkers of both de novo and transformed forms of GB have been identified. The oral chemotherapeutic agent temozolomide (TMZ) has been integral to standard-of-care treatment for nearly 2 decades. More recently, the use of non-pharmacologic interventions, such as application of alternating electric fields, called Tumor-Treating Fields (TTFields), has emerged as a complementary treatment option that increases overall survival (OS) in patients with newly diagnosed GB. The genomic factors associated with improved or lack of response to TTFields are unknown. Methods: We performed comprehensive genomic analysis of GB tumors resected from 55 patients who went on to receive treatment using TTFields, and compared results to 57 patients who received standard treatment without TTFields. Results: We found that molecular driver alterations in NF1, and wild-type PIK3CA and epidermal growth factor receptor (EGFR), were associated with increased benefit from TTFields as measured by progression-free survival (PFS) and OS. There were no differences when stratified by TP53 status. When NF1, PIK3CA, and EGFR status were combined as a Molecular Survival Score, the combination of the 3 factors significantly correlated with improved OS and PFS in TTFields-treated patients compared to patients not treated with TTFields. Conclusions: These results shed light on potential driver and passenger mutations in GB that can be validated as predictive biomarkers of response to TTFields treatment, and provide an objective and testable genomic-based approach to assessing response.