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Melanoma is a highly aggressive tumour that can metastasize very early in disease progression. Notably, melanoma can disseminate using amoeboid invasive strategies. We show here that high Myosin II activity, high levels of ki-67 and high tumour-initiating abilities are characteristic of invasive amoeboid melanoma cells. Mechanistically, we find that WNT11-FZD7-DAAM1 activates Rho-ROCK1/2-Myosin II and plays a crucial role in regulating tumour-initiating potential, local invasion and distant metastasis formation. Importantly, amoeboid melanoma cells express both proliferative and invasive gene signatures. As such, invasive fronts of human and mouse melanomas are enriched in amoeboid cells that are also ki-67 positive. This pattern is further enhanced in metastatic lesions. We propose eradication of amoeboid melanoma cells after surgical removal as a therapeutic strategy. Amoeboid cells are associated with melanoma invasive capacity. Here, the authors show that the WNT11-FZD7-DAAM1 pathway regulates tumour-initiating potential, invasion and metastasis lead by amoeboid cells in the invasive front of melanoma tumours.

Cell migration is a key procedure involved in many biological processes including embryological development, tissue formation, immune defense or inflammation, and cancer progression. How physical, chemical, and molecular aspects can affect cell motility is a challenge to understand migratory cells behavior. assays are excellent approaches to extrapolate to situations and study live cells behavior. Here we present four protocols that describe step-by-step cell migration, invasion and adhesion strategies and their corresponding image data quantification. These current protocols are based on wound healing assays (comparing traditional pipette tip-scratch assay vs. culture insert assay), 2D individual cell-tracking experiments by live cell imaging and spreading and transwell assays. All together, they cover different phenotypes and hallmarks of cell motility and adhesion, providing orthogonal information that can be used either individually or collectively in many different experimental setups. These optimized protocols will facilitate physiological and cellular characterization of these processes, which may be used for fast screening of specific therapeutic cancer drugs for migratory function, novel strategies in cancer diagnosis, and for assaying new molecules involved in adhesion and invasion metastatic properties of cancer cells.

Cell migration is crucial for cancer dissemination. We find that AMP-activated protein kinase (AMPK) controls cell migration by acting as an adhesion sensing molecular hub. In 3-dimensional matrices, fast-migrating amoeboid cancer cells exert low adhesion/low traction linked to low ATP/AMP, leading to AMPK activation. In turn, AMPK plays a dual role controlling mitochondrial dynamics and cytoskeletal remodelling. High AMPK activity in low adhering migratory cells, induces mitochondrial fission, resulting in lower oxidative phosphorylation and lower mitochondrial ATP. Concurrently, AMPK inactivates Myosin Phosphatase, increasing Myosin II-dependent amoeboid migration. Reducing adhesion or mitochondrial fusion or activating AMPK induces efficient rounded-amoeboid migration. AMPK inhibition suppresses metastatic potential of amoeboid cancer cells in vivo, while a mitochondrial/AMPK-driven switch is observed in regions of human tumours where amoeboid cells are disseminating. We unveil how mitochondrial dynamics control cell migration and suggest that AMPK is a mechano-metabolic sensor linking energetics and the cytoskeleton. Cell metabolism must adapt to the energy needs of migrating cells. This study finds that fast amoeboid migrating cells harbor high AMPK activity, which controls both mitochondrial dynamics and cytoskeletal remodeling, enabling reduced energy needs.

Metastasis remains a leading cause of cancer-related mortality, irrespective of the primary tumour origin. However, the core gene regulatory program governing distinct stages of metastasis across cancers remains poorly understood. We investigate this through single-cell transcriptome analysis encompassing over two hundred patients with metastatic and non-metastatic tumours across six cancer types. Our analysis revealed a prognostic core gene signature that provides insights into the intricate cellular dynamics and gene regulatory networks driving metastasis progression at the pan-cancer and single-cell level. Notably, the dissection of transcription factor networks active across different stages of metastasis, combined with functional perturbation, identified SP1 and KLF5 as key regulators, acting as drivers and suppressors of metastasis, respectively, at critical steps of this transition across multiple cancer types. Through in vivo and in vitro loss of function of SP1 in cancer cells, we revealed its role in driving cancer cell survival, invasive growth, and metastatic colonisation. Furthermore, tumour cells and the microenvironment increasingly engage in communication through WNT signalling as metastasis progresses, driven by SP1. Further validating these observations, a drug repurposing analysis identified distinct FDA-approved drugs with anti-metastasis properties, including inhibitors of WNT signalling across various cancers.

Background Metastasis is a hallmark of cancer and responsible for most cancer deaths. Migrastatics were defined as drugs interfering with all modes of cancer cell invasion and thus cancers’ ability to metastasise. First anti-metastatic treatments have recently been approved. Methods We used bioinformatic analyses of publicly available melanoma databases. Experimentally, we performed in vitro target validation (including 2.5D cell morphology analysis and mass spectrometric analysis of RhoA binding partners), developed a new traceable spontaneously metastasising murine melanoma model for in vivo validation, and employed histology (haematoxylin/eosin and phospho-myosin II staining) to confirm drug action in harvested tumour tissues. Results Unbiased and targeted bioinformatic analyses identified the Rho kinase (ROCK)-myosin II pathway and its various components as potentially relevant targets in melanoma. In vitro validation demonstrated redundancy of several RhoGEFs upstream of RhoA and confirmed ROCK as a druggable target downstream of RhoA. The anti-metastatic effects of two ROCK inhibitors were demonstrated through in vivo melanoma metastasis tracking and inhibitor effects also confirmed ex vivo by digital pathology. Conclusions We proposed a migrastatic drug development pipeline. As part of the pipeline, we provide a new traceable spontaneous melanoma metastasis model for in vivo quantification of metastasis and anti-metastatic effects by non-invasive imaging.

Image analysis in clinical research has evolved at fast pace in the last decade. This review discusses basic concepts ranging from immunohistochemistry to advanced techniques such as multiplex imaging, digital pathology, flow cytometry and intravital microscopy. Tissue imaging ex vivo is still one of the gold-standards in the field due to feasibility. We describe here different protocols and applications of digital analysis providing basic and clinical researchers with an overview on how to analyse tissue images. In vivo imaging is not accessible to researchers; however, it provides invaluable dynamic information easily. Overall, we discuss a plethora of techniques that - when combined - constitute a powerful platform for basic and translational cancer research.

Acral lentiginous melanoma (ALM) is a rare melanoma subtype primarily located in acral regions. However, ALMs exhibit a distinctive genetic profile characterized by a high number of copy number variations (CNVs) and limited point mutations. Late diagnosis and restricted therapeutic efficacy contribute to its poor prognosis. The secretome within the tumor microenvironment (TME) influences immune modulation and plays a vital role in melanoma progression. We aim to analyze the role of ALM secretome and CNVs profile with prognosis in primary ALM patients. Here, we demonstrated that high CNV burden (CNVsHigh) was associated with worse clinicopathological characteristics and poor prognosis. Furthermore, our study also revealed that conditioned media (CM) of CNVsHigh genetic profile ALM cell line was associated with pro-tumoral, pro-angiogenic, and immunosuppressive secretome profiles. In addition, CM of CNVsHigh cell lines in vitro promotes macrophage polarization to immunosuppressive phenotype. Moreover, we observed an increased presence of immunosuppressive tumor-associated macrophages (TAMs) at the invasive front (IF) of CNVsHigh ALM biopsies. This research reveals the adverse prognostic impact of CNVsHigh in ALM patients, establishing a novel link with a pro-tumor secretome, offering potential biomarkers for prognosis and personalized treatment to enhanced disease monitoring in ALM patients.

Background The objective of this comprehensive pan-cancer study is to evaluate the potential of deep learning (DL) for molecular profiling of multi-omic biomarkers directly from hematoxylin and eosin (H&E)-stained whole slide images. Methods A total of 12,093 DL models predicting 4031 multi-omic biomarkers across 32 cancer types were trained and validated. The study included a broad range of genetic, transcriptomic, and proteomic biomarkers, as well as established prognostic markers, molecular subtypes, and clinical outcomes. Results Here we show that 50% of the models achieve an area under the curve (AUC) of 0.644 or higher. The observed AUC for 25% of the models is at least 0.719 and exceeds 0.834 for the top 5%. Molecular profiling with image-based histomorphological features is generally considered feasible for most of the investigated biomarkers and across different cancer types. The performance appears to be independent of tumor purity, sample size, and class ratio (prevalence), suggesting a degree of inherent predictability in histomorphology. Conclusions The results demonstrate that DL holds promise to predict a wide range of biomarkers across the omics spectrum using only H&E-stained histological slides of solid tumors. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients. Plain language summary Molecular profiling tests are used to check cancers for changes in certain genes, proteins, or other molecules. Results of such tests can be used to identify the most effective treatment for cancer patients. Faster and more accessible alternatives to standard tests are needed to improve cancer care. This study investigates whether deep learning (DL), a series of advanced computer techniques, can perform molecular profiling directly from routinely-collected images of tumor specimens used for diagnostic purposes. Over 12,000 DL models were utilized to evaluate thousands of biomarkers using statistical approaches. The results indicate that DL can effectively detect molecular changes in a tumor from these images, for many biomarkers and tumor types. The study shows that DL-based molecular profiling from images is possible. Introducing this type of approach into routine clinical workflows could potentially accelerate treatment decisions and improve outcomes. Arslan et al. present the results of a comprehensive pan-cancer study evaluating deep learning-based multi-omic biomarker profiling using H&E-stained whole slide images. They show that deep learning can predict a wide range of biomarkers across the omics spectrum and in different cancers directly from histomorphology.

Background: Cutaneous melanoma shows high variability regarding clinicopathological presentation, evolution and prognosis. Methods: Next generation sequencing was performed to analyze hotspot mutations in different areas of primary melanomas (MMp) and their paired metastases. Clinicopathological features were evaluated depending on the degree of variation of the BRAFV600E mutant allele frequency (MAF) in MMp. Results: In our cohort of 14 superficial spreading, 10 nodular melanomas and 52 metastases, 17/24 (71%) melanomas had a BRAFV600E mutation and 5/24 (21%) had a NRASQ61 mutation. We observed a high variation of BRAFV600E MAF (H-BRAFV600E) in 7/17 (41%) MMp. The H-BRAFV600E MMp were all located on the trunk, had lower Breslow and mitotic indexes and predominantly, a first nodal metastasis. Regions with spindled tumor cells (Spin) and high lymphocytic infiltrate (HInf) were more frequent in the H-BRAFV600E patients (4/7; 57%), whereas regions with epithelial tumor cells (Epit) and low lymphocytic infiltrate (LInf) were predominant (6/10; 60%) and exclusive in the low BRAFV600E MAF variation tumors (L-BRAFV600E). The H-BRAFV600E/Spin/HInf MMp patients had better prognostic features and nodal first metastasis. Conclusions: The H-BRAFV600E MMp were located on the trunk, had better prognostic characteristics, such as lower Breslow and mitotic indexes as well as high lymphocytic infiltrate.

PTEN is one of the most frequently mutated tumor suppressor genes in human cancers. The role of PTEN in carcinogenesis has been validated by knockout mouse models. PTEN heterozygous mice develop neoplasms in multiple organs. Unfortunately, the embryonic lethality of biallelic excision of PTEN has inhibited the study of complete PTEN deletion in the development and progression of cancer. By crossing PTEN conditional knockout mice with transgenic mice expressing a tamoxifen-inducible Cre-ER(T) under the control of a chicken actin promoter, we have generated a tamoxifen-inducible mouse model that allows temporal control of PTEN deletion. Interestingly, administration of a single dose of tamoxifen resulted in PTEN deletion mainly in epithelial cells, but not in stromal, mesenchymal or hematopoietic cells. Using the mT/mG double-fluorescent Cre reporter mice, we demonstrate that epithelial-specific PTEN excision was caused by differential Cre activity among tissues and cells types. Tamoxifen-induced deletion of PTEN resulted in extremely rapid and consistent formation of endometrial in situ adenocarcinoma, prostate intraepithelial neoplasia and thyroid hyperplasia. We also analyzed the role of PTEN ablation in other epithelial cells, such as the tubular cells of the kidney, hepatocytes, colonic epithelial cells or bronchiolar epithelium, but those tissues did not exhibit neoplastic growth. Finally, to validate this model as a tool to assay the efficacy of anti-tumor drugs in PTEN deficiency, we administered the mTOR inhibitor everolimus to mice with induced PTEN deletion. Everolimus dramatically reduced the progression of endometrial proliferations and significantly reduced thyroid hyperplasia. This model could be a valuable tool to study the cell-autonomous mechanisms involved in PTEN-loss-induced carcinogenesis and provides a good platform to study the effect of anti-neoplastic drugs on PTEN-negative tumors.