Quantitative Proteomics of the Cancer Cell Line Encyclopedia.

Proteins are essential agents of biological processes. To date, large-scale profiling of cell line collections including the Cancer Cell Line Encyclopedia (CCLE) has focused primarily on genetic information whereas deep interrogation of the proteome has remained out of reach. Here, we expand the CCLE through quantitative profiling of thousands of proteins by mass spectrometry across 375 cell lines from diverse lineages to reveal information undiscovered by DNA and RNA methods. We observe unexpected correlations within and between pathways that are largely absent from RNA. An analysis of microsatellite instable (MSI) cell lines reveals the dysregulation of specific protein complexes associated with surveillance of mutation and translation. These and other protein complexes were associated with sensitivity to knockdown of several different genes. These data in conjunction with the wider CCLE are a broad resource to explore cellular behavior and facilitate cancer research.

Characterizing Mutational Signatures in Human Cancer Cell Lines Reveals Episodic APOBEC Mutagenesis.

Multiple signatures of somatic mutations have been identified in cancer genomes. Exome sequences of 1,001 human cancer cell lines and 577 xenografts revealed most common mutational signatures, indicating past activity of the underlying processes, usually in appropriate cancer types. To investigate ongoing patterns of mutational-signature generation, cell lines were cultured for extended periods and subsequently DNA sequenced. Signatures of discontinued exposures, including tobacco smoke and ultraviolet light, were not generated in vitro. Signatures of normal and defective DNA repair and replication continued to be generated at roughly stable mutation rates. Signatures of APOBEC cytidine deaminase DNA-editing exhibited substantial fluctuations in mutation rate over time with episodic bursts of mutations. The initiating factors for the bursts are unclear, although retrotransposon mobilization may contribute. The examined cell lines constitute a resource of live experimental models of mutational processes, which potentially retain patterns of activity and regulation operative in primary human cancers.

A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.

A major challenge of precision oncology is the identification and prioritization of suitable treatment options based on molecular biomarkers of the considered tumor. In pursuit of this goal, large cancer cell line panels have successfully been studied to elucidate the relationship between cellular features and treatment response. Due to the high dimensionality of these datasets, machine learning (ML) is commonly used for their analysis. However, choosing a suitable algorithm and set of input features can be challenging. We performed a comprehensive benchmarking of ML methods and dimension reduction (DR) techniques for predicting drug response metrics. Using the Genomics of Drug Sensitivity in Cancer cell line panel, we trained random forests, neural networks, boosting trees and elastic nets for 179 anti-cancer compounds with feature sets derived from nine DR approaches. We compare the results regarding statistical performance, runtime and interpretability. Additionally, we provide strategies for assessing model performance compared with a simple baseline model and measuring the trade-off between models of different complexity. Lastly, we show that complex ML models benefit from using an optimized DR strategy, and that standard models-even when using considerably fewer features-can still be superior in performance.

Incorporation of 53BP1 into phase-separated bodies in cancer cells during aberrant mitosis.

53BP1 (also known as TP53BP1) is a key mediator of the non-homologous end joining (NHEJ) DNA repair pathway, which is the primary repair pathway in interphase cells. However, the mitotic functions of 53BP1 are less well understood. Here, we describe 53BP1 mitotic stress bodies (MSBs) formed in cancer cell lines in response to delayed mitosis. These bodies displayed liquid-liquid phase separation characteristics, were close to centromeres, and included lamin A/C and the DNA repair protein RIF1. After release from mitotic arrest, 53BP1 MSBs decreased in number and moved away from the chromatin. Using GFP fusion constructs, we found that the 53BP1 oligomerization domain region was required for MSB formation, and that inclusion of the 53BP1 N terminus increased MSB size. Exogenous expression of 53BP1 did not increase MSB size or number but did increase levels of MSB-free 53BP1. This was associated with slower mitotic progression, elevated levels of DNA damage and increased apoptosis, which is consistent with MSBs suppressing a mitotic surveillance by 53BP1 through sequestration. The 53BP1 MSBs, which were also found spontaneously in a subset of normally dividing cancer cells but not in non-transformed cells (ARPE-19), might facilitate the survival of cancer cells following aberrant mitoses. This article has an associated First Person interview with the first author of the paper.

CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.

Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much needed for narrowing down this space, especially when examining new cellular contexts. Here, we thus introduce CCSynergy, a flexible, context aware and integrative deep-learning framework that we have established to unleash the potential of the Chemical Checker extended drug bioactivity profiles for the purpose of drug synergy prediction. We have shown that CCSynergy enables predictions of superior accuracy, remarkable robustness and improved context generalizability as compared to the state-of-the-art methods in the field. Having established the potential of CCSynergy for generating experimentally validated predictions, we next exhaustively explored the untested drug combination space. This resulted in a compendium of potentially synergistic drug combinations on hundreds of cancer cell lines, which can guide future experimental screens.

Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems.

Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.

O-Glycome Profiling of Breast Cancer Cell Lines to Understand Breast Cancer Brain Metastasis.

Breast cancer is the second leading cause of cancer-related death among women and a major source of brain metastases. Despite the increasing incidence of brain metastasis from breast cancer, the underlying mechanisms remain poorly understood. Altered glycosylation is known to play a role in various diseases including cancer metastasis. However, profiling studies of O-glycans and their isomers in breast cancer brain metastasis (BCBM) are scarce. This study analyzed the expression of O-glycans and their isomers in human breast cancer cell lines (MDA-MB-231, MDA-MB-361, HTB131, and HTB22), a brain cancer cell line (CRL-1620), and a brain metastatic breast cancer cell line (MDA-MB-231BR) using nanoLC-MS/MS, identifying 27 O-glycan compositions. We observed significant upregulation in the expression of HexNAc1Hex1NeuAc2 and HexNAc2Hex3, whereas the expression of HexNAc1Hex1NeuAc1 was downregulated in MDA-MB-231BR compared to other cell lines. In our isomeric analysis, we observed notable alterations in the isomeric forms of the O-glycan structure HexNAc1Hex1NeuAc1 in a comparison of different cell lines. Our analysis of O-glycans and their isomers in cancer cells demonstrated that changes in their distribution can be related to the metastatic process. We believe that our investigation will contribute to an enhanced comprehension of the significance of O-glycans and their isomers in BCBM.

Targeting cholesterol impairs cell invasion of all breast cancer types.

BACKGROUND: Breast cancer clinical outcome relies on its intrinsic molecular subtype and mortality is almost exclusively due to metastasis, whose mechanism remains unclear. We recently revealed the specific contribution of plasma membrane cholesterol to the invasion of malignant MCF10CAIa but not premalignant MCF10AT and normal MCF10A cell lines in 2D, through invadopodia formation and extracellular matrix (ECM) degradation. In the present study, we address the impact of breast cancer subtypes, mutations and aggressiveness on cholesterol implication in breast cancer cell invasion and 3D spheroid invasion and growth. METHODS: We used nine breast cancer cell lines grouped in four subtypes matching breast tumor classification. Four of these cell lines were also used to generate 3D spheroids. These cell lines were compared for cell invasion in 2D and 3D, spheroid growth in 3D, gelatin degradation, cortactin expression, activation and subcellular distribution as well as cell surface cholesterol distribution and lipid droplets. The effect of plasma membrane cholesterol depletion on all these parameters was determined in parallel and systematically compared with the impact of global matrix metalloproteinase (MMP) inhibition. RESULTS: The six invasive cell lines in 2D were sensitive to partial cholesterol depletion, independently of their subtype, aggressiveness or mutation. Nevertheless, the effect was stronger in the three cell lines able to degrade gelatin. 3D spheroid invasion was also reduced after cholesterol depletion in all breast cancer subtypes tested. Notably, targeting cholesterol was more powerful than MMP inhibition in reducing invasion in both 2D and 3D culture models. Moreover, cholesterol depletion in the six invasive cell lines impaired cortactin distribution in the perinuclear region where invadopodia localized. Breast cancer cell line aggressiveness relied on cholesterol-enriched domains at the ECM-free side and intracellular lipid droplets. Furthermore, the three gelatin-degrading cell lines were characterized by increased cholesterol-enriched submicrometric domains at their ECM-contact side. CONCLUSION: Together, our data suggest cell surface cholesterol combined with lipid droplet labeling as a breast cancer cell aggressiveness marker. They also open the way to test other cholesterol-targeting drugs in more complex models to further evaluate whether cholesterol could represent a strategy in breast cancer therapy.

Investigation of antimicrobial and anti-cancer activity of thermally sensitive SnO2 nanostructures with green-synthesized cauliflower morphology at ambient weather conditions.

A tin oxide (SnO2) nanostructure was prepared using Matricaria recutita leaf extract to investigate its anticancer activity against SK-MEL-28 cells. The tetragonal crystal structure of tin oxide nanoparticles with an average crystal size of 27 nm was confirmed by X-ray diffraction (XRD) analysis. The tetragonal crystal structure of the tin oxide nanoparticles, with an average crystallite size of 27 nm, was confirmed by XRD an absorbance peak at 365 nm was identified by UV-visible spectroscopy analysis as belonging to the bio-mediated synthesis of SnO2 nanoparticles. The SnO2 NPs are capped and stabilized with diverse functional groups derived from bioactive molecules, including aldehydes, benzene rings, amines, alcohols, and carbonyl stretch protein molecules. Fourier transform infrared spectroscopy (FTIR) analysis validated the presence of these capping and stabilizing chemical bonds. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) studies revealed the cauliflower-shaped morphology of the SnO2 nanoparticles with an average particle size of 28 nm. The antimicrobial activity of both prepared and encapsulated samples confirmed their biological activities. Furthermore, both prepared and encapsulated tin oxide samples exhibited excellent anticancer activity against SK-MEL-28 human cancer cells. The present study introduces a reliable and uncomplicated approach to produce SnO2 nanoparticles and demonstrates their effectiveness in various applications, including cancer therapy, drug administration, and disinfectant.

Establishment of primary prostate epithelial and tumorigenic cell lines using a non-viral immortalization approach.

BACKGROUND: Research on prostate cancer is mostly performed using cell lines derived from metastatic disease, not reflecting stages of tumor initiation or early progression. Establishment of cancer cell lines derived from the primary tumor site has not been described so far. By definition, cancer cells are able to be cultured indefinitely, whereas normal epithelial cells undergo senescence in vitro. Epithelial cells can be immortalized, accomplished by using viral integration of immortalization factors. Viral approaches, however, might be impaired by regulatory and safety issues as well as random integration into regulatory genetic elements, modifying precise gene expression. We intend to use surgical specimen of prostate cancer patients to (i) prove for establishment of cancer cell lines, and (ii) perform non-viral, Sleeping Beauty (SB) transposase-based immortalization of prostate epithelial cells. METHODS: Radical prostatectomy samples of prostate cancer patients (n = 4) were dissociated and cultured in vitro. Cells were cultivated either without or after non-viral, Sleeping-Beauty transposase-based stable transfection with immortalization factors SV40LT and hTERT. Established cell lines were analyzed in vitro and in vivo for characteristics of prostate (cancer) cells. RESULTS: Initial cell cultures without genetic manipulation underwent senescence within ≤ 15 passages, demonstrating inability to successfully derive primary prostate cancer cell lines. By using SB transposase-based integration of immortalization factors, we were able to establish primary prostate cell lines. Three out of four cell lines displayed epithelial characteristics, however without expression of prostate (cancer) characteristics, e.g., androgen receptor. In vivo, one cell line exhibited tumorigenic potential, yet characteristics of prostate adenocarcinoma were absent. CONCLUSION: Whereas no primary prostate cancer cell line could be established, we provide for the first-time immortalization of primary prostate cells using the SB transposase system, thereby preventing regulatory and molecular issues based on viral immortalization approaches. Although, none of the newly derived cell lines demonstrated prostate cancer characteristics, tumor formation was observed in one cell line. Given the non-prostate adenocarcinoma properties of the tumor, cells have presumably undergone oncogenic transformation rather than prostate cancer differentiation. Still, these cell lines might be used as a tool for research on prostate cancer initiation and early cancer progression.

Identification and Cytotoxic Evaluation of Pregnane Saponins from the Twigs and Leaves of Dregea volubilis.

Four new polyhydroxy pregnane glycosides, named volubilosides G-K (3, 5-7), along with three known secondary metabolites, dregeoside Da1 (1), dregeoside Ka1 (2), and volubiloside E (4) were isolated from the twigs and leaves of Dregea volubilis (DV). The chemical structures of these compounds (1-7) were elucidated using spectroscopic techniques (1D and 2D NMR and HR-ESI-MS analyses) and compared with those in the published literature. Compounds (1-7) were evaluated for cytotoxicity against eight cancer cell lines (MB49, K562, MKN-7, HT29, A549, MCF-7, MDA-MB-231, and HepG2), revealing varying levels of cytotoxic effects with IC50 values ranging from 4.29 to 21.05 µM. The results indicated that compounds 1-7 may serve as potential lead compounds for the discovery and development of novel anti-cancer drugs.

α-Umpolung/Michael Addition/Quaternization Tandem Reaction to provide α-Imido-ß-phosphonium Propanoates with Broad Spectrum of Biological Activity.

This paper has been supported by the Kazan Federal University Strategic Academic Leadership Program ('PRIORITY-2030'). HRMS data were obtained in the CSF-SAC FRC KSC RAS by support of the State Assignment of the Federal Research Center "Kazan Scientific Center", Russian Academy of Sciences. A.D.V, conducted studies of anticancer activity with financial support form the government assignment for FRC Kazan Scientific Center of RAS.

Radiotherapy Metastatic Prostate Cancer Cell Lines Treated with Gold Nanorods Modulate miRNA Signatures.

MicroRNA (miRNA) modulation has been identified as a promising strategy for improving the response of human prostate cancer (PCa) to radiotherapy (RT). Studies have shown that mimics or inhibitors of miRNAs could modulate the sensitivity of PCa cells to RT. In addition, pegylated gold nanoparticles have been studied as a therapeutic approach to treat PCa cells and/or vehicles for carrying miRNAs to the inside of cells. Therefore, we evaluated the capacity of hypofractionated RT and pegylated gold nanorods (AuNPr-PEG) to modulate the miRNA signature on PCa cells. Thus, RT-qPCR was used to analyze miRNA-95, miRNA-106-5p, miRNA-145-5p, and miRNA-541-3p on three human metastatic prostate cell lines (PC3, DU145, and LNCaP) and one human prostate epithelial cell line (HprEpiC, a non-tumor cell line) with and without treatment. Our results showed that miRNA expression levels depend on cell type and the treatment combination applied using RT and AuNPr-PEG. In addition, cells pre-treated with AuNPr-PEG and submitted to 2.5 Gy per day for 3 days decreased the expression levels of miRNA-95, miRNA-106, miRNA-145, and miRNA-541-3p. In conclusion, PCa patients submitted to hypofractionated RT could receive personalized treatment based on their metastatic cellular miRNA signature, and AuNPr-PEG could be used to increase metastatic cell radiosensitivity.

Promising Role of Alkaloids in the Prevention and Treatment of Thyroid Cancer and Autoimmune Thyroid Disease: A Comprehensive Review of the Current Evidence.

Thyroid cancer (TC) and thyroid autoimmune disorders (AITD) are among the most common diseases in the general population, with higher incidence in women. Chronic inflammation and autoimmunity play a pivotal role in carcinogenesis. Some studies, indeed, have pointed out the presence of AITD as a risk factor for TC, although this issue remains controversial. Prevention of autoimmune disease and cancer is the ultimate goal for clinicians and scientists, but it is not always feasible. Thus, new treatments, that overcome the current barriers to prevention and treatment of TC and AITD are needed. Alkaloids are secondary plant metabolites endowed with several biological activities including anticancer and immunomodulatory properties. In this perspective, alkaloids may represent a promising source of prophylactic and therapeutic agents for TC and AITD. This review encompasses the current published literature on alkaloids effects on TC and AITD, with a specific focus on the pathways involved in TC and AITD development and progression.

A Prognostic Activity of Glutaredoxin 1 Protein (Grx1) in Colon Cancer.

Glutaredoxin 1 (Grx1) is an essential enzyme that regulates redox signal transduction and repairs protein oxidation by reversing S-glutathionylation, an oxidative modification of protein cysteine residues. Grx1 removes glutathione from proteins to restore their reduced state (protein-SH) and regulate protein-SSG levels in redox signaling networks. Thus, it can exert an influence on the development of cancer. To further investigate this problem, we performed an analysis of Grx1 expression in colon adenocarcinoma samples from the Polish population of patients with primary colon adenocarcinoma (stages I and II of colon cancer) and those with regional lymph node metastasis (stage III of colon cancer). Our study revealed a significant correlation between the expression of Grx1 protein through immunohistochemical analysis and various clinical characteristics of patients, such as histological grade, depth of invasion, angioinvasion, staging, regional lymph node invasion, and PCNA expression. It was found that almost 88% of patients with stage I had high levels of Grx1 expression, while only 1% of patients with stage III exhibited high levels of Grx1 protein expression. Furthermore, the study discovered that high levels of Grx1 expression were present in samples of colon mucosa without any pathological changes. These results were supported by in vitro analysis conducted on colorectal cancer cell lines that corresponded to stages I, II, and III of colorectal cancer, using qRT-PCR and Western blot.

FEN1 Inhibition as a Potential Novel Targeted Therapy against Breast Cancer and the Prognostic Relevance of FEN1.

To improve breast cancer treatment and to enable new strategies for therapeutic resistance, therapeutic targets are constantly being studied. Potential targets are proteins of DNA repair and replication and genomic integrity, such as Flap Endonuclease 1 (FEN1). This study investigated the effects of FEN1 inhibitor FEN1-IN-4 in combination with ionizing radiation on cell death, clonogenic survival, the cell cycle, senescence, doubling time, DNA double-strand breaks and micronuclei in breast cancer cells, breast cells and healthy skin fibroblasts. Furthermore, the variation in the baseline FEN1 level and its influence on treatment prognosis was investigated. The cell lines show specific response patterns in the aspects studied and have heterogeneous baseline FEN1 levels. FEN1-IN-4 has cytotoxic, cytostatic and radiosensitizing effects, expressed through increasing cell death by apoptosis and necrosis, G2M share, senescence, double-strand breaks and a reduced survival fraction. Nevertheless, some cells are less affected by the cytotoxicity and fibroblasts show a rather limited response. In vivo, high FEN1 mRNA expression worsens the prognosis of breast cancer patients. Due to the increased expression in breast cancer tissue, FEN1 could represent a new tumor and prognosis marker and FEN1-IN-4 may serve as a new potent agent in personalized medicine and targeted breast cancer therapy.

The characterization of the sensitive ovarian cancer cell lines A2780 and W1 in response to ovarian CAFs.

PURPOSE: The cancer-associated fibroblasts (CAFs) are one of the most abundant components of the tumor microenvironment (TME). CAFs have been implicated in tumor progression, extracellular matrix (ECM) remodeling, and treatment resistance. Drug resistance is the primary limiting factor in achieving cures for patients with cancer, particularly ovarian cancer. Therefore, inhibiting CAFs can be an effective strategies for cancer treatment. In this research, we studied whether CAFs have an influence on drug-sensitive ovarian cancer cells to become more resistant. We examined the influence of CAFs on genes and proteins expression changes in sensitive ovarian cancer cells. We prepared a 3D co-culture to investigate the role of CAFs on cancer cell morphology. METHODS: Here, we performed a detailed analysis of drug-sensitive ovarian cancer cell lines (A2780 and W1) and the influence of ovarian CAFs on the A2780 and W1 cells morphology, genes and proteins expression. The 2D and 3D cultures, genes expression analysis (TaqMan qPCR), and proteins expression (Western blot analysis) were assessed in this study. RESULTS: We observed upregulation of ABCC5, CYP2C8, CYP2C9, and DHFR mRNA in cell lines supplemented by CAFs medium. We showed fibronectin overexpression and COL3A1 downregulation after supplementation with CAFs. Co-culturing with CAFs prevented the formation of spheroids in 3D conditions. CONCLUSION: We demonstrated that the process of drug resistance in ovarian cancer cells is launched by CAFs. CAFs not only simulate cancer cells to produce drug transporters and specific enzymes production, but also remodel the TME to increase drug resistance. We believe that cancer progression and migration is due to the CAFs po-tumorigenic activity.

CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles.

Cancer cell lines (CCLs) as important model systems play critical roles in cancer research. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphism, whereas no method is available for CCL authentication using gene expression profiles. Here, we developed a novel method and homonymic web server (CCLA, Cancer Cell Line Authentication, http://bioinfo.life.hust.edu.cn/web/CCLA/) to authenticate 1291 human CCLs of 28 tissues using gene expression profiles. CCLA showed an excellent speed advantage and high accuracy for CCL authentication, a top 1 accuracy of 96.58 or 92.15% (top 3 accuracy of 100 or 95.11%) for microarray or RNA-Seq validation data (719 samples, 461 CCLs), respectively. To the best of our knowledge, CCLA is the first approach to authenticate CCLs using gene expression data. Users can freely and conveniently authenticate CCLs using gene expression profiles or NCBI GEO accession on CCLA website.

Computational estimation of quality and clinical relevance of cancer cell lines.

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies.

Characterization of a novel peptide mined from the Red Sea brine pools and modified to enhance its anticancer activity.

Drug resistance is a major cause of the inefficacy of conventional cancer therapies, and often accompanied by severe side effects. Thus, there is an urgent need to develop novel drugs with low cytotoxicity, high selectivity and minimal acquired chemical resistance. Peptide-based drugs (less than 0.5 kDa) have emerged as a potential approach to address these issues due to their high specificity and potent anticancer activity. In this study, we developed a support vector machine model (SVM) to detect the potential anticancer properties of novel peptides by scanning the American University in Cairo (AUC) Red Sea metagenomics library. We identified a novel 37-mer antimicrobial peptide through SVM pipeline analysis and characterized its anticancer potential through in silico cross-examination. The peptide sequence was further modified to enhance its anticancer activity, analyzed for gene ontology, and subsequently synthesized. To evaluate the anticancer properties of the modified 37-mer peptide, we assessed its effect on the viability and morphology of SNU449, HepG2, SKOV3, and HeLa cells, using an MTT assay. Additionally, we evaluated the migration capabilities of SNU449 and SKOV3 cells using a scratch-wound healing assay. The targeted selectivity of the modified peptide was examined by evaluating its hemolytic activity on human erythrocytes. Treatment with the peptide significantly reduced cell viability and had a critical impact on the morphology of hepatocellular carcinoma (SNU449 and HepG2), and ovarian cancer (SKOV3) cells, with a marginal effect on cervical cancer cell lines (HeLa). The viability of a human fibroblast cell line (1Br-hTERT) was also significantly reduced by peptide treatment, as were the proliferation and migration abilities of SNU449 and SKOV3 cells. The annexin V assay revealed programmed cell death (apoptosis) as one of the potential cellular death pathways in SNU449 cells upon peptide treatment. Finally, the peptide exhibited antimicrobial effects on both gram-positive and gram-negative bacterial strains. The findings presented here suggest the potential of our novel peptide as a potent anticancer and antimicrobial agent.