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Background Breast cancer cell lines are frequently used as model systems to study the cellular properties and biology of breast cancer. Our objective was to characterize a large, commonly employed panel of breast cancer cell lines obtained from the American Type Culture Collection (ATCC 30-4500 K) to enable researchers to make more informed decisions in selecting cell lines for specific studies. Information about these cell lines was obtained from a wide variety of sources. In addition, new information about cellular pathways that are activated within each cell line was generated. Methods We determined key protein expression data using immunoblot analyses. In addition, two analyses on serum-starved cells were carried out to identify cellular proteins and pathways that are activated in these cells. These analyses were performed using a commercial PathScan array and a novel and more extensive phosphopeptide-based kinome analysis that queries 1290 phosphorylation events in major signaling pathways. Data about this panel of breast cancer cell lines was also accessed from several online sources, compiled and summarized for the following areas: molecular classification, mRNA expression, mutational status of key proteins and other possible cancer-associated mutations, and the tumorigenic and metastatic capacity in mouse xenograft models of breast cancer. Results The cell lines that were characterized included 10 estrogen receptor (ER)-positive, 12 human epidermal growth factor receptor 2 (HER2)-amplified and 18 triple negative breast cancer cell lines, in addition to 4 non-tumorigenic breast cell lines. Within each subtype, there was significant genetic heterogeneity that could impact both the selection of model cell lines and the interpretation of the results obtained. To capture the net activation of key signaling pathways as a result of these mutational combinations, profiled pathway activation status was examined. This provided further clarity for which cell lines were particularly deregulated in common or unique ways. Conclusions These two new kinase or “Kin-OMIC” analyses add another dimension of important data about these frequently used breast cancer cell lines. This will assist researchers in selecting the most appropriate cell lines to use for breast cancer studies and provide context for the interpretation of the emerging results.

The lack of targeted therapies for triple-negative breast cancer (TNBC) contributes to their high mortality rates and high risk of relapse compared to other subtypes of breast cancer. Most TNBCs (75%) have downregulated the expression of CREB3L1 (cAMP-responsive element binding protein 3 like 1), a transcription factor and metastasis suppressor that represses genes that promote cancer progression and metastasis. In this report, we screened an FDA-approved drug library and identified four drugs that were highly cytotoxic towards HCC1806 CREB3L1-deficient TNBC cells. These four drugs were: (1) palbociclib isethionate, a CDK4/6 inhibitor, (2) lanatocide C (also named isolanid), a Na+/K+-ATPase inhibitor, (3) cladribine, a nucleoside analog, and (4) homoharringtonine (also named omacetaxine mepesuccinate), a protein translation inhibitor. Homoharringtonine consistently showed the most cytotoxicity towards an additional six TNBC cell lines (BT549, HCC1395, HCC38, Hs578T, MDA-MB-157, MDA-MB-436), and several luminal A breast cancer cell lines (HCC1428, MCF7, T47D, ZR-75-1). All four drugs were then separately evaluated for possible synergy with the chemotherapy agents, doxorubicin (an anthracycline) and paclitaxel (a microtubule stabilizing agent). A strong synergy was observed using the combination of homoharringtonine and paclitaxel, with high cytotoxicity towards TNBC cells at lower concentrations than when each was used separately.

Next generation sequencing is becoming the method of choice for functional genomic studies that use pooled shRNA or CRISPR libraries. A key challenge in sequencing these mixed-oligo libraries is that they are highly susceptible to hairpin and/or heteroduplex formation. This results in polyclonal, low quality, and incomplete reads and reduces sequencing throughput. Unfortunately, this challenge is significantly magnified in low-to-medium throughput bench-top sequencers as failed reads significantly perturb the maximization of sequence coverage and multiplexing capabilities. Here, we report a methodology that can be adapted to maximize the coverage on a bench-top, Ion PGM System for smaller shRNA libraries with high efficiency. This ligation-based, half-shRNA sequencing strategy minimizes failed sequences and is also equally amenable to high-throughput sequencers for increased multiplexing. Towards this, we also demonstrate that our strategy to reduce heteroduplex formation improves multiplexing capabilities of pooled CRISPR screens using Illumina NextSeq 500. Overall, our method will facilitate sequencing of pooled shRNA or CRISPR libraries from genomic DNA and maximize sequence coverage.

Metabolic alterations play an important role in cancer and yet, few metabolic cancer driver genes are known. Here we perform a combined genomic and metabolic modeling analysis searching for metabolic drivers of colorectal cancer. Our analysis predicts FUT9, which catalyzes the biosynthesis of Ley glycolipids, as a driver of advanced‐stage colon cancer. Experimental testing reveals FUT9's complex dual role; while its knockdown enhances proliferation and migration in monolayers, it suppresses colon cancer cells expansion in tumorspheres and inhibits tumor development in a mouse xenograft models. These results suggest that FUT9's inhibition may attenuate tumor‐initiating cells (TICs) that are known to dominate tumorspheres and early tumor growth, but promote bulk tumor cells. In agreement, we find that FUT9 silencing decreases the expression of the colorectal cancer TIC marker CD44 and the level of the OCT4 transcription factor, which is known to support cancer stemness. Beyond its current application, this work presents a novel genomic and metabolic modeling computational approach that can facilitate the systematic discovery of metabolic driver genes in other types of cancer. Synopsis A combined computational and experimental analysis reveals FUT9 as a new, context‐dependent driver of colon cancer. FUT9 expression is required in tumor initiating cells while its loss favors bulk tumor growth and supports tumor aggressiveness. A combined genomic and metabolic modeling analysis is performed to identify metabolic drivers of colorectal cancer. FUT9, which catalyzes the biosynthesis of Ley glycolipids in the Golgi compartment, emerges as a driver of advanced stage colon cancer. FUT9 activity has different effects on tumor initiating cells vs. bulk tumor cells, supporting the former but attenuating the latter. The presented combined computational and experimental approach can be applied for the systematic discovery of metabolic driver genes in other cancer types. Graphical Abstract A combined computational and experimental analysis reveals FUT9 as a new, context‐dependent driver of colon cancer. FUT9 expression is required in tumor initiating cells while its loss favors bulk tumor growth and supports tumor aggressiveness.

Gastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.

The global COVID-19 pandemic continues with continued cases worldwide and the emergence of new SARS-CoV-2 variants. In our study, we have developed novel tools with applications for screening antivirals, identifying virus–host dependencies, and characterizing viral variants. Using reverse genetics, we rescued SARS-CoV-2 Wuhan1 (D614G variant) wild type (WTFL) and reporter virus (NLucFL) using molecular BAC clones. The replication kinetics, plaque morphology, and titers were comparable between viruses rescued from molecular clones and a clinical isolate (VIDO-01 strain). Furthermore, the reporter SARS-CoV-2 NLucFL virus exhibited robust luciferase values over the time course of infection and was used to develop a rapid antiviral assay using remdesivir as proof-of-principle. In addition, as a tool to study lung-relevant virus–host interactions, we established novel human lung cell lines that support SARS-CoV-2 infection with high virus-induced cytopathology. Six lung cell lines (NCI-H23, A549, NCI-H1703, NCI-H520, NCI-H226, and HCC827) and HEK293T cells were transduced to stably express ACE2 and tested for their ability to support virus infection. A549ACE2 B1 and HEK293TACE2 A2 cell lines exhibited more than 70% virus-induced cell death, and a novel lung cell line, NCI-H23ACE2 A3, showed about ~99% cell death post-infection. These cell lines are ideal for assays relying on live–dead selection, such as CRISPR knockout and activation screens.

Background BRK is, a non-receptor tyrosine kinase, overexpressed in approximately 85% of human invasive ductal breast tumors. It is not clear whether BRK expression correlates with breast cancer subtypes, or the expression has prognostic or diagnostic significance. Herein, we investigated the correlation of BRK with any breast cancer subtypes and clinicopathological significance of BRK expression in breast cancer. Methods In this study, we examined BRK expression in 120 breast tumor samples and 29 breast cancer cell lines to explore the positive correlation between BRK and the expression of ERα. We used immunohistochemistry, RT-PCR, and immunoblotting to analyse our experimental samples. Result We demonstrate that estrogen induces BRK gene and protein expression in ER+ breast cancer cells. Over-expression of ERα in the ER-negative breast cancer cell line increased BRK expression, and knock-down of ESR1 in MCF7 cells reduced BRK levels. Further, we provide evidence that BRK is regulated by ERα signaling and the presence of ER antagonists (tamoxifen and fulvestrant) reduce the expression of BRK in ER-positive breast cancer cells. Finally, we demonstrate that the overall survival of ER-positive breast cancer patients is poor when their cancers express high levels of BRK. Conclusion Our data indicate that BRK is a prognostic marker for ER+ breast cancers and provide a strong rationale for targeting BRK to improve patients’ survival.

Purpose To investigate whether individually targeting the outer mitochondrial membrane fission receptors FIS1 and MFF rather than the universally essential fission GTPase DRP1 is sufficient to suppress tumor initiating cells (TICs) without causing general mitochondrial dysfunction. Methods FIS1 or MFF were silenced or knocked out in triple-negative breast cancer (TNBC) cells to investigate their essentiality for maintaining TICs in cell culture and xenograft models. We further investigate the impact of FIS1 deficiency on several functional properties of mitochondria including morphology, membrane potential and ROS production. Results We demonstrate that FIS1 absence consistently suppressed TIC populations in cultured TNBC cells, and reduced tumor initiating activity in TNBC xenografts. Remarkably, we found that this phenotypic effect occurred in the absence of significant changes in ROS production, mitochondrial membrane potential and oxidative phosphorylation complex abundance even though FIS1-deficient TICs harbored a more reticular mitochondrial network. Finally, our i n silico analyses established that all four DRP1 receptors (FIS1, MFF, MID49 and MID51) are ubiquitously expressed in healthy human tissues, and FIS1 is the most highly expressed DRP1 receptor in mammary gland. Conclusion Our data collectively suggest that FIS1 targeting should allow for the suppression of TICs in TNBC tumors without compromising mitochondrial functionality or causing major, systemic toxicity. We believe our findings have the potential to facilitate the development of TIC suppressing therapies for TNBC patients, which is of considerable clinical relevance given that this malignancy has very limited targeted treatment options and is associated with a high mortality rate. Clinical trial number Not applicable.

Mediator of ERBB2-driven cell motility 1 (MEMO1) is an evolutionary conserved protein implicated in many biological processes; however, its primary molecular function remains unknown. Importantly, MEMO1 is overexpressed in many types of cancer and was shown to modulate breast cancer metastasis through altered cell motility. To better understand the function of MEMO1 in cancer cells, we analyzed genetic interactions of MEMO1 using gene essentiality data from 1028 cancer cell lines and found multiple iron-related genes exhibiting genetic relationships with MEMO1. We experimentally confirmed several interactions between MEMO1 and iron-related proteins in living cells, most notably, transferrin receptor 2 ( TFR 2), mitoferrin-2 ( SLC25A28 ), and the global iron response regulator IRP1 ( ACO1 ). These interactions indicate that cells with high-MEMO1 expression levels are hypersensitive to the disruptions in iron distribution. Our data also indicate that MEMO1 is involved in ferroptosis and is linked to iron supply to mitochondria. We have found that purified MEMO1 binds iron with high affinity under redox conditions mimicking intracellular environment and solved MEMO1 structures in complex with iron and copper. Our work reveals that the iron coordination mode in MEMO1 is very similar to that of iron-containing extradiol dioxygenases, which also display a similar structural fold. We conclude that MEMO1 is an iron-binding protein that modulates iron homeostasis in cancer cells.

A survey of 1,590 putative integral, peripheral and lipid-anchored membrane proteins from Saccharomyces cerevisiae reveals unexpected physical associations underlying the membrane biology of eukaryotes and delineates the global topological landscape of the membrane interactome. Mapping membrane protein interactions Affinity purification procedures have been successfully used to characterize soluble protein complexes, but complexes involving membrane proteins are more difficult to purify due to their hydrophobic nature. Here, Andrew Emili and colleagues have affinity purified membrane proteins from the yeast Saccharomyces cerevisiae in the presence of three different non-denaturing detergents, and identified the co-purifying proteins by mass spectrometry. They generate an extensive physical interaction map of membrane protein interactions, most of which have not been previously reported. Macromolecular assemblies involving membrane proteins (MPs) serve vital biological roles and are prime drug targets in a variety of diseases 1 . Large-scale affinity purification studies of soluble-protein complexes have been accomplished for diverse model organisms, but no global characterization of MP-complex membership has been described so far. Here we report a complete survey of 1,590 putative integral, peripheral and lipid-anchored MPs from Saccharomyces cerevisiae , which were affinity purified in the presence of non-denaturing detergents. The identities of the co-purifying proteins were determined by tandem mass spectrometry and subsequently used to derive a high-confidence physical interaction map encompassing 1,726 membrane protein–protein interactions and 501 putative heteromeric complexes associated with the various cellular membrane systems. Our analysis reveals unexpected physical associations underlying the membrane biology of eukaryotes and delineates the global topological landscape of the membrane interactome.