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Mechanical proprieties determine many cellular functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors governing cell mechanical phenotype is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between mechanical phenotype changes and the genes involved in driving them. We employ a machine learning-based discriminative network analysis method termed PC-corr to associate cell mechanical states, measured by real-time deformability cytometry (RT-DC), with large scale transcriptome datasets ranging from stem cell development to cancer progression, and originating from different murine and human tissues. By intersecting the discriminative networks inferred from two selected datasets, we identify a conserved module of five genes with putative roles in the regulation of cell mechanics. We validate the power of the individual genes to discriminate between soft and stiff cell states in silico, and demonstrate experimentally that the top scoring gene, CAV1, changes the mechanical phenotype of cells when silenced or overexpressed. The data-driven approach presented here has the power of de novo identification of genes involved in cell mechanics regulation and paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

Late relapse of disseminated cancer cells is a common feature of breast and prostate tumors. Several intrinsic and extrinsic factors have been shown to affect quiescence and reawakening of disseminated dormant cancer cells (DDCCs), however, the signals and processes sustaining the survival of DDCCs in a foreign environment are still poorly understood. We have recently shown that crosstalk with lung epithelial cells promotes survival of DDCCs of estrogen receptor-positive (ER+) breast tumors. By using a lung organotypic system and in vivo dissemination assays, here we show that the TFEB-lysosomal axis is activated in DDCCs and that it is modulated by the pro-survival ephrin receptor EphB6. TFEB lysosomal direct targets are enriched in DDCCs in vivo and correlate with relapse in ER+ breast cancer patients. Direct coculture of DDCCs with alveolar type I-like lung epithelial cells and dissemination in the lung drive lysosomal accumulation and EphB6 induction. EphB6 contributes to survival, TFEB transcriptional activity, and lysosome formation in DDCCs in vitro and in vivo. Furthermore, signaling from EphB6 promotes the proliferation of surrounding lung parenchymal cells in vivo. Our data provide evidence that EphB6 is a key factor in the crosstalk between disseminated dormant cancer cells and the lung parenchyma and that the TFEB-lysosomal pathway plays an important role in the persistence of DDCCs.

Late relapse of disseminated cancer cells is a common feature of some types of tumors. Several intrinsic and extrinsic factors have been shown to affect reawakening of disseminated dormant cancer cells (DDCCs); however, the signals and processes sustaining survival of DDCCs in a foreign environment are still poorly understood. We have recently shown that crosstalk with lung epithelial cells promotes persistence of DDCCs from estrogen receptor positive (ER+) breast tumors. Here we show that TFEB-lysosomal axis is activated in DDCCs and that it is modulated by the pro-survival ephrin receptor EphB6. TFEB lysosomal direct targets are enriched in DDCCs in vivo and correlate with relapse in ER+ breast cancer patients. Direct contact of DDCCs with alveolar type I-like lung epithelial cells drives lysosomal accumulation and EphB6 induction. EphB6 contributes to TFEB transcriptional activity and lysosome formation in DDCCs in vitro and in vivo, and supports survival of DDCCs in coculture and in vivo. Furthermore, signaling from EphB6 promotes the proliferative response of surrounding lung parenchymal cells in vivo.

Acute myeloid leukemia (AML) is a rare disease with an estimated incidence of 14 500 and 2500 cases/year in the USA and Italy, respectively. AML is subdivided into groups according to the presence/...