Tumor-Intrinsic Enhancer of Zeste Homolog 2 Controls Immune Cell Infiltration, Tumor Growth, and Lung Metastasis in a Triple-Negative Breast Cancer Model.

Triple-negative breast cancer (TNBC) is an aggressive and highly metastatic type of tumor. TNBC is often enriched in tumor-infiltrating neutrophils (TINs), which support cancer growth in part by counteracting tumor-infiltrating lymphocytes (TILs). Prior studies identified the enhancer of zeste homolog 2 (EZH2) as a pro-tumor methyltransferase in primary and metastatic TNBCs. We hypothesized that EZH2 inhibition in TNBC cells per se would exert antitumor activity by altering the tumor immune microenvironment. To test this hypothesis, we used CRISPR to generate EZH2 gene knockout (KO) and overexpressing (OE) lines from parent (wild-type-WT) 4T1 cells, an established murine TNBC model, resulting in EZH2 protein KO and OE, respectively. In vitro, EZH2 KO and OE cells showed early, transient changes in replicative capacity and invasiveness, and marked changes in surface marker profile and cytokine/chemokine secretion compared to WT cells. In vivo, EZH2 KO cells showed significantly reduced primary tumor growth and a 10-fold decrease in lung metastasis compared to WT cells, while EZH2 OE cells were unchanged. Compared to WT tumors, TIN:TIL ratios were greatly reduced in EZH2 KO tumors but unchanged in EZH2 OE tumors. Thus, EZH2 is key to 4T1 aggressiveness as its tumor-intrinsic knockout alters their in vitro secretome and in vivo primary tumor growth, TIN/TIL poise, and metastasis.

Bisbiguanide analogs induce mitochondrial stress to inhibit lung cancer cell invasion.

Targeting cancer metabolism to limit cellular energy and metabolite production is an attractive therapeutic approach. Here, we developed analogs of the bisbiguanide, alexidine, to target lung cancer cell metabolism and assess a structure-activity relationship (SAR). The SAR led to the identification of two analogs, AX-4 and AX-7, that limit cell growth via G1/G0 cell-cycle arrest and are tolerated in vivo with favorable pharmacokinetics. Mechanistic evaluation revealed that AX-4 and AX-7 induce potent mitochondrial defects; mitochondrial cristae were deformed and the mitochondrial membrane potential was depolarized. Additionally, cell metabolism was rewired, as indicated by reduced oxygen consumption and mitochondrial ATP production, with an increase in extracellular lactate. Importantly, AX-4 and AX-7 impacted overall cell behavior, as these compounds reduced collective cell invasion. Taken together, our study establishes a class of bisbiguanides as effective mitochondria and cell invasion disrupters, and proposes bisbiguanides as promising approaches to limiting cancer metastasis.

Subpopulation commensalism promotes Rac1-dependent invasion of single cells via laminin-332.

Phenotypic heterogeneity poses a significant hurdle for cancer treatment but is under-characterized in the context of tumor invasion. Amidst the range of phenotypic heterogeneity across solid tumor types, collectively invading cells and single cells have been extensively characterized as independent modes of invasion, but their intercellular interactions have rarely been explored. Here, we isolated collectively invading cells and single cells from the heterogeneous 4T1 cell line and observed extensive transcriptional and epigenetic diversity across these subpopulations. By integrating these datasets, we identified laminin-332 as a protein complex exclusively secreted by collectively invading cells. Live-cell imaging revealed that laminin-332 derived from collectively invading cells increased the velocity and directionality of single cells. Despite collectively invading and single cells having similar expression of the integrin α6ß4 dimer, single cells demonstrated higher Rac1 activation upon laminin-332 binding to integrin α6ß4. This mechanism suggests a novel commensal relationship between collectively invading and single cells, wherein collectively invading cells promote the invasive potential of single cells through a laminin-332/Rac1 axis.

A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling.

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype-such as invasiveness, cell:cell interactions, and changes in spatial positioning-with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations.

Live-cell invasive phenotyping uncovers the ALK2/BMP6 iron homeostasis pathway as a therapeutic vulnerability in LKB1-mutant lung cancer.

The acquisition of invasive properties is a prerequisite for tumor progression and metastasis. Molecular subtypes of KRAS-driven lung cancer exhibit distinct modes of invasion that likely contribute to unique growth properties and therapeutic susceptibilities. Despite this, pre-clinical discovery strategies designed to exploit invasive phenotypes are lacking. To address this, we designed an experimental system to screen for targetable signaling pathways linked to active early invasion phenotypes in the two most prominent molecular subtypes, TP53 and LKB1, of KRAS-driven lung adenocarcinoma (LUAD). By combining live-cell imaging of human bronchial epithelial cells in a 3D invasion matrix with RNA transcriptome profiling, we identified the LKB1-specific upregulation of bone morphogenetic protein 6 (BMP6). Examination of early-stage lung cancer patients confirmed upregulation of BMP6 in LKB1-mutant lung tumors. At the molecular level, we find that the canonical iron regulatory hormone Hepcidin is induced via BMP6 signaling upon LKB1 loss, where intact LKB1 kinase activity is necessary to maintain signaling homeostasis. Furthermore, pre-clinical studies in a novel Kras/Lkb1-mutant syngeneic mouse model show that potent growth suppression was achieved by inhibiting the ALK2/BMP6 signaling axis with single agents that are currently in clinical trials. We show that alterations in the iron homeostasis pathway are accompanied by simultaneous upregulation of ferroptosis protection proteins. Thus, LKB1 is sufficient to regulate both the 'gas' and 'breaks' to finely tune iron-regulated tumor progression.

Concurrent Ischemic Lesion Age Estimation and Segmentation of CT Brain Using a Transformer-Based Network.

The cornerstone of stroke care is expedient management that varies depending on the time since stroke onset. Consequently, clinical decision making is centered on accurate knowledge of timing and often requires a radiologist to interpret Computed Tomography (CT) of the brain to confirm the occurrence and age of an event. These tasks are particularly challenging due to the subtle expression of acute ischemic lesions and the dynamic nature of their appearance. Automation efforts have not yet applied deep learning to estimate lesion age and treated these two tasks independently, so, have overlooked their inherent complementary relationship. To leverage this, we propose a novel end-to-end multi-task transformer-based network optimized for concurrent segmentation and age estimation of cerebral ischemic lesions. By utilizing gated positional self-attention and CT-specific data augmentation, the proposed method can capture long-range spatial dependencies while maintaining its ability to be trained from scratch under low-data regimes commonly found in medical imaging. Furthermore, to better combine multiple predictions, we incorporate uncertainty by utilizing quantile loss to facilitate estimating a probability density function of lesion age. The effectiveness of our model is then extensively evaluated on a clinical dataset consisting of 776 CT images from two medical centers. Experimental results demonstrate that our method obtains promising performance, with an area under the curve (AUC) of 0.933 for classifying lesion ages ≤ 4.5 hours compared to 0.858 using a conventional approach, and outperforms task-specific state-of-the-art algorithms.

A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling.

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state, with molecular profiles. This inability to integrate a historical live-cell phenotype, such as invasiveness, cell:cell interactions, and changes in spatial positioning, with multi-omic data, creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomics and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live-cells. We begin with cells stably expressing a photoconvertible fluorescent protein and employ live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulation for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live-cell phenotype and multi-omic heterogeneity within normal and diseased cellular populations.

Loss of the endocytic tumor suppressor HD-PTP phenocopies LKB1 and promotes RAS-driven oncogenesis.

Oncogenic RAS mutations drive aggressive cancers that are difficult to treat in the clinic, and while direct inhibition of the most common KRAS variant in lung adenocarcinoma (G12C) is undergoing clinical evaluation, a wide spectrum of oncogenic RAS variants together make up a large percentage of untargetable lung and GI cancers. Here we report that loss-of-function alterations (mutations and deep deletions) in the gene that encodes HD-PTP (PTPN23) occur in up to 14% of lung cancers in the ORIEN Avatar lung cancer cohort, associate with adenosquamous histology, and occur alongside an altered spectrum of KRAS alleles. Furthermore, we show that in publicly available early-stage NSCLC studies loss of HD-PTP is mutually exclusive with loss of LKB1, which suggests they restrict a common oncogenic pathway in early lung tumorigenesis. In support of this, knockdown of HD-PTP in RAS-transformed lung cancer cells is sufficient to promote FAK-dependent invasion. Lastly, knockdown of the Drosophila homolog of HD-PTP (dHD-PTP/Myopic) synergizes to promote RAS-dependent neoplastic progression. Our findings highlight a novel tumor suppressor that can restrict RAS-driven lung cancer oncogenesis and identify a targetable pathway for personalized therapeutic approaches for adenosquamous lung cancer.

Orientational self-sorting in cuboctahedral Pd cages.

Cuboctahedral coordination cages of the general formula [Pd12L24]24+ (L = low-symmetry ligand) were analyzed theoretically and experimentally. With 350 696 potential isomers, the structural space of these assemblies is vast. Orientational self-sorting refers to the preferential formation of particular isomers within the pool of potential structures. Geometric and computational analyses predict the preferred formation of cages with a cis arrangement at the metal centers. This prediction was corroborated experimentally by synthesizing a [Pd12L24]24+ cage with a bridging 3-(4-(pyridin-4-yl)phenyl)pyridine ligand. A crystallographic analysis of this assembly showed exclusive cis coordination of the 3- and the 4-pyridyl donor groups at the Pd2+ ions.

Finite free convolutions of polynomials.

We study three convolutions of polynomials in the context of free probability theory. We prove that these convolutions can be written as the expected characteristic polynomials of sums and products of unitarily invariant random matrices. The symmetric additive and multiplicative convolutions were introduced by Walsh and Szegö in different contexts, and have been studied for a century. The asymmetric additive convolution, and the connection of all of them with random matrices, is new. By developing the analogy with free probability, we prove that these convolutions produce real rooted polynomials and provide strong bounds on the locations of the roots of these polynomials.

Improved prediction of postoperative pediatric cerebellar mutism syndrome using an artificial neural network.

BACKGROUND: Postoperative pediatric cerebellar mutism syndrome (pCMS) is a common but severe complication that may arise following the resection of posterior fossa tumors in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalization of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN). METHODS: An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset was collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumors. The ANN was trained on this dataset and its performance was evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. The area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed-rank test, with P < .05 considered statistically significant. RESULTS: Two hundred and four children were included, of whom 80 developed pCMS. The performance of the ANN (AUC 0.949; accuracy 90.9%) exceeded that of logistic regression (P < .05) and both external models (P < .001). CONCLUSION: Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods.

Orientational self-sorting: formation of structurally defined Pd4L8 and Pd6L12 cages from low-symmetry dipyridyl ligands.

Tetra- and hexanuclear coordination cages were obtained in reactions of [Pd(CH3CN)4](BF4)2 with low-symmetry dipyridyl ligands. In both cases, only one structurally defined complex was formed out of a vast pool of potential isomers.

Leflunomide Suppresses the Growth of LKB1-Inactivated Tumors in the Immune-Competent Host and Attenuates Distant Cancer Metastasis.

Liver kinase B1 (LKB1)-inactivated tumors are vulnerable to the disruption of pyrimidine metabolism, and leflunomide emerges as a therapeutic candidate because its active metabolite, A77-1726, inhibits dihydroorotate dehydrogenase, which is essential for de novo pyrimidine biosynthesis. However, it is unclear whether leflunomide inhibits LKB1-inactivated tumors in vivo, and whether its inhibitory effect on the immune system will promote tumor growth. Here, we carried out a comprehensive analysis of leflunomide treatment in various LKB1-inactivated murine xenografts, patient-derived xenografts, and genetically engineered mouse models. We also generated a mouse tumor-derived cancer cell line, WRJ388, that could metastasize to the lung within a month after subcutaneous implantation in all animals. This model was used to assess the ability of leflunomide to control distant metastasis. Leflunomide treatment shrank a HeLa xenograft and attenuated the growth of an H460 xenograft, a patient-derived xenograft, and lung adenocarcinoma in the immune-competent genetically engineered mouse models. Interestingly, leflunomide suppressed tumor growth through at least three different mechanisms. It caused apoptosis in HeLa cells, induced G1 cell-cycle arrest in H460 cells, and promoted S-phase cell-cycle arrest in WRJ388 cells. Finally, leflunomide treatment prevented lung metastasis in 78% of the animals in our novel lung cancer metastasis model. In combination, these results demonstrated that leflunomide utilizes different pathways to suppress the growth of LKB1-inactivated tumors, and it also prevents cancer metastasis at distant sites. Therefore, leflunomide should be evaluated as a therapeutic agent for tumors with LKB1 inactivation.

An "alarming" and "exceptionally high" rate of COVID-19 retractions?

More than 20 papers about COVID-19 have been retracted at the time of this writing. It is premature, however, to conclude that such work is being retracted at higher rates than the rest of the literature.

Epigenetically heterogeneous tumor cells direct collective invasion through filopodia-driven fibronectin micropatterning.

Tumor heterogeneity drives disease progression, treatment resistance, and patient relapse, yet remains largely underexplored in invasion and metastasis. Here, we investigated heterogeneity within collective cancer invasion by integrating DNA methylation and gene expression analysis in rare purified lung cancer leader and follower cells. Our results showed global DNA methylation rewiring in leader cells and revealed the filopodial motor MYO10 as a critical gene at the intersection of epigenetic heterogeneity and three-dimensional (3D) collective invasion. We further identified JAG1 signaling as a previously unknown upstream activator of MYO10 expression in leader cells. Using live-cell imaging, we found that MYO10 drives filopodial persistence necessary for micropatterning extracellular fibronectin into linear tracks at the edge of 3D collective invasion exclusively in leaders. Our data fit a model where epigenetic heterogeneity and JAG1 signaling jointly drive collective cancer invasion through MYO10 up-regulation in epigenetically permissive leader cells, which induces filopodia dynamics necessary for linearized fibronectin micropatterning.

Prognostic significance of an invasive leader cell-derived mutation cluster on chromosome 16q.

BACKGROUND: Intratumoral heterogeneity is defined by subpopulations with varying genotypes and phenotypes. Specialized, highly invasive leader cells and less invasive follower cells are phenotypically distinct subpopulations that cooperate during collective cancer invasion. Because leader cells are a rare subpopulation that would be missed by bulk sequencing, a novel image-guided genomics platform was used to precisely select this subpopulation. This study identified a novel leader cell mutation signature and tested its ability to predict prognosis in non-small cell lung cancer (NSCLC) patient cohorts. METHODS: Spatiotemporal genomic and cellular analysis was used to isolate and perform RNA sequencing on leader and follower populations from the H1299 NSCLC cell line, and it revealed a leader-specific mutation cluster on chromosome 16q. Genomic data from patients with lung squamous cell carcinoma (LUSC; n = 475) and lung adenocarcinoma (LUAD; n = 501) from The Cancer Genome Atlas were stratified by 16q mutation cluster (16qMC) status (16qMC+ vs 16qMC-) and compared for overall survival (OS), progression-free survival (PFS), and gene set enrichment analysis (GSEA). RESULTS: Poorer OS, poorer PFS, or both were found across all stages and among early-stage patients with 16qMC+ tumors within the LUSC and LUAD cohorts. GSEA revealed 16qMC+ tumors to be enriched for the expression of metastasis- and survival-associated gene sets. CONCLUSIONS: This represents the first leader cell mutation signature identified in patients and has the potential to better stratify high-risk NSCLC and ultimately improve patient outcomes.