Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent malignancies and a major cause of cancer related death worldwide, especially in China. Cell lines are widely used disease models for basic medical research, however, well characterized ESCC cell models from China were seldom reported. Misidentifying and cross-contaminations of cell lines also hamper the way of producing solid and reproductive data.CSEC216 was originated from a 45-year-old male ESCC patient from Chaoshan littoral, China. Specimens were minced into fragments and seeded in T-25 flask for primary culture. Immunoflourescence staining was performed for identifying the origination and proliferation activity. In vitro migration and invasion abilities was tested by transwell assay. DNA Short Tandem Repeats profiling was implemented for cell authorization. Karyotype was investigated by spectrum karyotyping. Whole genome sequencing was utilized to investigate genomic alterations. Background information and genomic mutation data of published ESCC cell lines were obtained from online databases.CSEC216 was an uncontaminated cell line, exhibited epithelial cell features with polygonal morphology and adherent growth as monolayer. Immuno staining demonstrated its epithelial origination and high proliferation rate. The Population Doubling time was 29.7 h. The karyotype demonstrated tumor cell patterns with aneuploidy and complex chromosomal aberrations. Mutation signatures, genes with SNA or CNA of CSEC216 and published ESCC cell lines were similar with the mutation spectrum of original ESCC tumors.ESCC cell line CSEC216 from high incidence region in China was established with no cross-contamination. Biological features were studied. Genomic mutation features of CSEC216 and 28 ESCC cell lines were characterized which provided thorough cytogenetic background that facilitated future usage. Esophageal cancer (EC) is the eleventh most commonly diagnosed malignancy, and the sixth leading cause of cancer mortality worldwide, an estimated number of 483,000 new cases and 439,000 deaths occurred in 2015 worldwide [Sustainable and uncontaminated cancer cell lines provide available resources for researchers to utilize. Human cell lines are now widely used in laboratories worldwide. The first continuous cell line Hela, was established in 1952 (7). In 1981, Nelson-Rees et al. reported that many cell lines had been switched or cross-contaminated with Hela cells. To date, a significant proportion of cell based research is misleading and misrepresentative, because the cell lines are of different origin to that claimed or the cross-contamination between cell lines . TherefoThere are abundant esophageal squamous cell carcinoma (ESCC) cell lines around the world. However, few of them were derived from China. Here we report a newly established and authorized ESCC cell line designated CSEC216, derived from a 45-year old male patient from the Chaoshan littoral. This cell line would serve as potent in vitro model for the exploration of the pathogenesis of ESCC. The current study also reported the genomic similarity of 28 ESCC cell lines and original ESCC tumors.CSEC216 was established from an endoscopic biopsy sample derived from a 45-year-old male patient who underwent esophageal endoscopy in the Cancer Hospital of Shantou University Medical College. The tumor located at middle thoracic esophagus, 23–30 cm from the incisor with constrictive lumen and elevated mucosa observed in the esophageal tract . The specimen was then washed 3 times in PBS and minced with a sterile scalpel into small fragments of 1 mm1 were used for growth curve plotting.The CSEC216 cell line had been subcultured for 50 passages, during which the total culturing time and population doubling time(PDT)http://www.doubling-time.com/compute.php 18/10/2015).For population doubling time calculation, 10,000 cells at passage 28 in 300 μl culture medium were inoculated into each well of a 24-well plate and culture medium was replaced every 3 days. Three wells of cells were harvested by trypsinization each time with 24 h intervals, the cell number of each well was determined using a Neubauer hemacytometer, then the cell numbers were averaged. Cell growth during log phase was used to calculate doubling time . To obtain cells at metaphase, cells at 70% confluence at passage 20 were treated with 0.03 μg/ml colcemid for 7 h then harvested by trypsinization. After neutralization by mixing the cell suspension with equal amount of culture medium and centrifugation at 1000 rpm, the supernatant was discarded. Cells were resuspended in 2 ml 0.8% sodium citrate and kept at 37 °C for 15 min. 250 μl fresh made fix solution (methanol:acetic acid = 3:1) was added to the cell suspension at 37 °C for 5 min to prefix the cells. Cells were collected by centrifugation and washed 3 times with fix solution. After the final wash, supernatant was discarded. Cells were resuspended in 500 μl fix solution and 15 μl cell suspension was dropped onto an adhesive slide, air dried and the hybridization area was marked with a diamond pen. The slide was stored at room temperature and kept from light for 7 days. SKY was performed using a SKY probe kit SkyPaint™ mixture , according to the manufacturer’s instruction). The karyotype was analysed by SkyView™ Imaging System  equipped with a Zeiss Axioplan-2 fluorescence microscope.A total amount of 0.5 μg DNA per sample was used for the DNA library preparations. Sequencing library was generated using Truseq Nano DNA HT Sample Prep Kit (Illumina USA) following manufacturer’s recommendations and index codes were added to each sample. Briefly, genomic DNA sample was fragmented by sonication to a size of 350 bp. Then DNA fragments were end-polished, A-tailed, and ligated with the full-length adapter for Illumina sequencing, followed by further PCR amplification. After PCR products were purified (AMPure XP system), libraries were analyzed for size distribution by Agilent 2100 Bioanalyzer and quantified by real-time PCR (3 nM).The clustering of the index-coded samples was performed on a cBot Cluster Generation System using HiSeq X PE Cluster Kit V2.5 (Illumina) according to the manufacturer’s instructions. After cluster generation, the DNA libraries were sequenced on Illumina HiSeq platform and 150 bp paired-end reads were generated.Raw data (short reads) in FASTQ format (.fq) were processed though in–house perl scripts. In this step, cleans data (clean reads) were obtained by removing reads containing adapter, reads containing poly-N and low quality reads from raw data. At the same time, Q20, Q30, error rate and GC content of clean data were calculated. All the further analysis was based on the high quality clean data.Mapping: Burrows-Wheeler Aligner (BWA2) software is utilized to map the paired-end clean reads to the reference genome (UCSC hg19). The original mapping result in BAM format can be obtained. Picard, GATK and Samtools are then used to do duplicate removal, local realignment, and base quality recalibration etc.http://annovar.openbioinformatics.org/en/latest/user-guide/download/) were regarded as germline mutations. Somatic SNA data were utilized for mutational signature analysis by R package MutationalPatterns to decypher the probable mechanism underlying the mutational process. COSMIC cancer mutation signatures were used as reference signatures [Single Nucleotide Alterations (SNA) GATK HaplotypeCalleris used to do variant calling and identify SNA, insertions and deletions (INDELS). In this step, raw callsets with false positive variant calls are obtained. Then variant Filtration in GATK is performed to make the raw callsets suitable for meaningful analysis. High quality mutations were retained for germline mutations filtration. Mutations identified in gnomad database  Control-FREEC was utilized to do somatic CNA detection. GISTIC algorithm was used to infer recurrently amplified or deleted genomic regions. G-scores were calculated for genomic and gene-coding regions on the basis of the frequency and amplitude of amplification or deletion affecting each gene. A significant CNA region was defined as having amplification or deletion with a G-score > 0.1, corresponding to a p-value threshold of 0.05 from the permutation-derived null distribution.IF staining against Cytokeratin , E-Cadherin ,Vimentin , and Ki-67  was performed to confirm the origination and proliferation activity of CSEC216 cells. Cells were harvested when reached 80% confluence. Then 10,000 cells were inoculated onto a coverslip in a 24-well plate and incubated for 24 h. Cells were fixed with 4% paraformaldehyde at 4 °C for 15 min, permeabilized with 0.5% Triton X-100 at room temperature for 30 min and washed with PBS after each treatment. Primary antibody was applied to the slide and incubated in humidified box at 4 °C overnight. Secondary antibody  was applied after PBS washing and incubated in a humidified chamber at 37 °C for 30 min. The slide was washed with PBS after incubation and mounted by anti-fade DAPI . Photos were taken by Zeiss Axio CSan Z1.https://www.atcc.org/STR_Database.aspx 20/11/2015) and DSMZ (https://www.dsmz.de/fp/cgi-bin/str.html 20/11/2015). Matching criteria for human cell line authentication was set as 0.8 recommended by the website.Genomic DNA of cells at passage 5 was extracted using DNA Mini Kit (QIAGEN) under manufacture’s protocol. DNA samples were sent to Genetica DNA Laboratories USA for Short Tandem Repeats(STR) profiling test. Fifteen STR loci and AMEL were tested. STR electropherogram and signed STR profiling report were shown in supplementary data. Eight STR loci and AMEL were used for online matching in the STR databases of ATCC .https://web.expasy.org/cellosaurus/) [ESCC cell lines were searched on Cellosaurus (saurus/) . KeywordCSEC216 cells showed polygonal epithelial cell morphology and adherent growth as monolayer. None of these cell lines was cultured during the CSEC216 establishment. Both results showed that CSEC216 was a unique cell line that was not misidentified and contaminated with other registered cell lines. Full report and electropherogram were shown in Additional file SKY analysis yielded representative karyotype photograph of CSEC216 cells at passage 20, which was demonstrated in Fig. Frequently observed chromosomal numerical abnormalities were presented by chromosome loose of Y,8,11,19 and chromosome gain of 1,3,5,17,20. Structural aberrations were complicated as deletion, translocation, isochromosome and derivation chromosome were frequently observed. Derivation chromosome of 2, 3, 13 composed by the most sophisticated translocations was observed, i.e. der (2) t  ttt、der(3)tttt、der (13) tttt. Chromosomal break points at p1 or q1 was common in chromosome 1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 16, 17, 18, 21, X.The genomic DNA of CSEC216 were sequenced with 30X average depth. WGS yielded 3,290,751 SNAs, 572,345 INDELs and 1860 CNA regions. After removal of germline mutations by comparing with gnomad database file (hg19_gnomad211_genome),75527 SNAs and 61866 INDELs were obtained. The genome-wide mutation rate of SNA and INDEL were 2.52 and 2.06 mutations per megabase respectively. The density of SNAs and INDELs per megabase along the chromosomes were plotted in Fig. https://cancer.sanger.ac.uk/cosmic/signatures_v2), which featured by transcriptional strand bias for T > C substitution at ApTpX context, and has been identified in all kinds of cancer types with yet unknown aetiology , of which most genes were chosen from our previous work [The etiological implication of the mutation signature extracted from all somatic SNAs of CSEC216 was vague. Since 98.8% of somatic SNAs of CSEC216 were located in intron and intergenic region, the mutational context of those SNAs could over rule the mutations that literally contributed to relative etiology. To investigate the impact of genomic mutations to those genes which were implicated to play a role in the carcinogenesis of ESCC, mutations at exon, up/down stream were extracted from SNA, INDEL and CNA data. Resulted in 910 SNAs and 1310 INDELs which affected 999 genes and 14072 genes in 1166 CNA regions. A list of 44 genes related with ESCC were selected from literatures and COSMIC database . CNA and SNA data of cell line were shown in Additional file After removing problematic cell lines, a total of 28 SNA and 24 CNA mutation data were downloaded from CCLE database  in 32.1% (9 of 28)/21.4% 6 of 28)/17.9% (5 of 28) cell lines respectively. Extreme cases were found in OE21 and TE15 where Signature C take 93.7% and 91.3% proportion among the 3 signatures , CSMD3 (39.3%), NOTCH1 35.7%), ZFHX4 (32.143%), NFE2L2 (28.6%) (Fig. %, ZFHX4 Esophageal carcinoma is one of the most common and lethal malignancies in China and worldwide , 21. ProShort tandem repeat profiling is the standard method for authenticating cell lines by using at least eight STR loci and the application of match criteria , 24. By Aneuploidy and chromosome aberrations in human solid tumors are hallmarks of gene deregulation and genome instability , 26. TheMutation process during carcinogenesis leave traces on the genome, which could be discriminated by different features, i.e. mutation type, transcriptional strand bias and sequence context. Mutation signatures are combined features of cataloged mutation types which are footprint from variety mutagenesis processes . The de CNA is one of major mutation in human cells. Normal human cells are diploid, but in tumor cells, there are copy number amplified or deleted regions in the genome . DeletioWe also investigated mutation status of ESCC related genes in ESCC cell lines. Genes that participate in cell cycle regulation, cell differentiation, oxidative damage, etc. were found to have SNA or CNA mutations in ESCC cell line. Large scale genomic data analysis had been pulled out by the TCGA . Their rIn summary, a novel and well characterized human ESCC cell line CSEC216 has been established. CSEC216 was derived from a 45-year old male ESCC patient from Chaoshan littoral, China. CSEC216 cells exhibit epithelial cell features and possess high proliferative activity, relatively low in vitro migration and invasion ability. CSEC216 exhibited hypotriploidy karyotype with complicated chromosomal aberrations. One somatic stop gain mutation and two somatic missense mutations were found in TP53 and NFE2L2 at exonic region respectively. MYC, CCND1, SOX2, PIK3CA, TP53, et al. were found gene copy number gained, while LRP1B was found copy number lost among selected ESCC related genes. Genomic background study of CSEC216 together with 28 ESCC cell lines demonstrated that those cellular disease models, to some extent, at least genetically, might be representable of ESCC. The current study could provide useful ESCC cell line information which would facilitate researchers for choosing appropriate cell model for their study.Additional file 1: Table S1. ESCC cell line information from Cellosaurus and literatures.Additional file 2: Table S2. Background information of CCLE ESCC cell lines.Additional file 3: Figure S1. Report and electropherogram of STR profiling. A, STR profiling report of CSEC216 at P5. For what’s worth mentioning, CSEC216 was originally designated as ECST109, to distinguish from widely used ESCC cell line EC109, the name was changed as CSEC216. B, Electropherogram.Additional file 4: Table S3. SNA and INDEL data of CSEC216.Additional file 5: Table S4. CNA data of CSEC216.Additional file 6: Table S5. List of 44 genes related with ESCC were selected from literatures and COSMIC database.Additional file 7: Table S6. CNA data of 24 ESCC cell lines.Additional file 8: Table S7. SNA data of 28 ESCC cell lines.Additional file 9: Figure S2. SNA spectrum of ESCC cell lines. The spectrum of 96 mutation context of 28 ESCC cell lines.


Klotho was initially introduced as an antiaging molecule. Klotho deficiency significantly reduces lifespan, and its overexpression extends it and protects against various pathological phenotypes, especially renal disease. It was shown to regulate phosphate and calcium metabolism, protect against oxidative stress, downregulate apoptosis, and have antiinflammatory and antifibrotic properties. The course of diabetes mellitus and diabetic nephropathy resembles premature cellular senescence and causes the activation of various proinflammatory and profibrotic processes. Klotho was shown to exert many beneficial effects in these disorders. The expression of Klotho protein is downregulated in early stages of inflammation and diabetic nephropathy by proinflammatory factors. Therefore, its therapeutic effects are diminished in this disorder. Significantly lower urine levels of Klotho may serve as an early biomarker of renal involvement in diabetes mellitus. Recombinant Klotho administration and Klotho overexpression may have immunotherapeutic potential for the treatment of both diabetes and diabetic nephropathy. Therefore, the current manuscript aims to characterize immunopathologies occurring in diabetes and diabetic nephropathy, and tries to match them with antiinflammatory actions of Klotho. It also gives reasons for Klotho to be used in diagnostics and immunotherapy of these disorders. Diabetes mellitus (DM) is a serious, chronic disease that is characterized by high blood glucose levels, resulting in aberrant insulin production and lower sensitivity of acceptor cells of this hormone. The prevalence of DM is increasing worldwide, and its complications are one of the leading causes of mortality from noncommunicable diseases ,2. Due tProper kidney function is essential for maintaining body homeostasis through selective plasma filtration. Blood filtration occurs in the cortex of the kidney, in the glomerulus of the nephron, which apart from a glomerulus also consists of the proximal tubule, Henle’s loop, and distal tubule. Blood flows to glomeruli and is filtered by a three-layer structure : pores between endothelial cells of blood capillaries, the glomerular basement membrane , and spaces between neighboring podocytes . A podocyte cell consists of a cell body, major processes, and foot processes that branch out from major processes and is tightly laid on blood capillaries to form slit diaphragms. The structure of the glomerulus is also supported by mesangial cells.During the course of diabetes and diabetic kidney disease, many metabolic and hemodynamic stresses occur, among others hypoxia and hyperfiltration ,4. The dα-Klotho was discovered in 1997 and simply named Klotho because no other Klotho proteins were identified at that time. It was shown to be an antiaging molecule. Mice that exhibited Klotho deficiency presented a premature aging phenotype, whereas Klotho overexpression extended their lifespan by up to 30% and protected them against many pathological phenotypes, especially renal disease ,14.Klotho is expressed mostly in the kidneys and choroid plexus in the brain. Its expression was also detected in the pituitary, parathyroid glands, the heart, and reproductive organs, with low levels in other tissues . In the KL gene. The extracellular domain of the Klotho protein can be cleaved and separated from the cell membrane by the metalloproteinases ADAM-10 and ADAM-17  and β-secretase 1 (BACE1). This proteolytic cleavage by ADAM-10 and ADAM-17 can be stimulated inter alia by insulin. The extracellular domain of Klotho contains two homologous repeat sequences (KL1 and KL2), which are separated from each other by unknown proteases. Both the whole released extracellular domain and separated KL1 and KL2 are referred to as soluble Klotho particles [KL gene that possesses a STOP codon was thought to result in the production of a shorter, secretory form of the protein [Two or three forms of α-Klotho protein exist. Membrane-bound Klotho is a 130 kDa single-pass transmembrane protein that is encoded by a  protein . However protein .Both transmembrane Klotho and soluble Klotho serve as coreceptor proteins for fibroblast growth factor (FGF) 23 (binding to FGF receptors 1–4), which promotes phosphaturia to control phosphate metabolism . Klotho The disruption of phosphate and calcium metabolism, oxidative stress, inflammation, the fibrotic process, and an increase in the ratio of β-cell and podocyte loss through apoptosis can result in pathologies that resemble premature aging. Klotho protects cells against accelerated aging and damage during the course of DM and diabetic nephropathy ,18. More+ and CD4+ T cells and CD20+ B cells), and the expression of proinflammatory cytokines, especially interleukin 1β (IL-1β) and TNF-α. High levels of proinflammatory and profibrotic IL-6 are produced in initial stages of T1DM. Moreover, IL-6-inducible autoimmunity-related gene (HIP/PAP) has been shown to be expressed in the pancreas in patients with T1DM, providing further evidence that IL-6 participates in the autoimmune process in type 1 DM [Type 1 DM is triggered by unknown environmental factors and develops in individuals with a polygenetic predisposition through the destruction of insulin-producing pancreatic β-cells. The damage to these cells is caused by impairments in the immune response and local inflammation , includiype 1 DM .Type 2 DM develops during the course of a prolonged high-glucose diet and consists of impairments in pancreatic insulin secretion and a lower cellular response to insulin stimulation. These metabolic changes are accompanied by a low-grade inflammatory process in adipose tissue, the liver, and pancreatic islets, in which IL-1β plays a major role . The proThis multiorgan inflammatory reaction, accompanied by higher levels of its marker C-reactive protein (CRP), causes pathological changes in the microvascular circulation in the kidneys, macrophage infiltration of this organ, and the local production of a wide range of proinflammatory cytokines and mediators that are detectable beginning in early stages of diabetic nephropathy in serum and peripheral blood cells in affected individuals.Inflammatory macrophage accumulation in the interstitium of the kidney correlates with glomerulosclerosis, tubular atrophy, interstitial fibrosis, albuminuria, and the loss of function of this organ. Albuminuria leads to an increase in the uptake of proteins in proximal tubules of the nephron, thereby increasing the tubular protein load, which acts as a trigger of the even greater activation of signaling cascades that cause inflammation and fibrosis in the tubule-interstitial region . M1 macrOther receptors also contribute to an accelerated immune response, albuminuria, and podocyte damage in diabetic nephropathy. These are intracellular nucleotide-binding oligomerization domain (NOD)-like receptors  that initiate NLRP3 inflammasome formation and the expression of proinflammatory molecules by immune cells , also called chemokine-CC motif ligand (CCL2)) and intracellular adhesion molecule 1 (ICAM-1) ,29. The Further immune response facilitation in diabetic nephropathy is caused by the activation of cytokine, chemokine, and angiotensin receptors that influence signaling through Janus kinase-signal transducer and activator of transcription (JAK-STAT) proteins, the mRNA levels of which correlate with the progression of diabetic nephropathy. Urine levels of MCP-1 increase in DM patients with micro- and macroalbuminuria. Moreover, MCP-1 levels correlate with the urine albumin/creatinine ratio and a decrease in renal function. In diabetic patients with macroalbuminuria, MCP-1 levels are a predictor of the progression of nephropathy. The course of the disease may also be monitored by measuring the level of other inflammation-related molecules, such as soluble TNF receptor-2 (sTNFR-2), which is associated with a decrease in the GFR. A ligand of sTNFR-2, TNF-α, and some of the other aforementioned molecules  are responsible for interstitial kidney tissue destruction through the generation of fibrotic lesions . Additio+/−), a spontaneous and persistent increase in blood pressure was observed, which was accelerated by a high-salt diet. High salt intake in Klotho+/− mice significantly increased the expression of MCP-1 in the kidneys and caused the infiltration of this organ with macrophages and T lymphocytes [+ lymphocytes (T-helper cells) in healthy elderly individuals and individuals with rheumatoid arthritis, a chronic autoimmune disorder that causes joint inflammation. In T-helper cells in these patients, Klotho was downregulated at the mRNA, protein, and enzyme activity (β-glucuronidase) levels. The role of Klotho in these cells would be to contribute to an antiinflammatory process, present in young and healthy subjects but lowered together with senescence and in inflammatory disorders, such as rheumatoid arthritis, diabetes, and diabetic nephropathy. This hypothesis has been supported by the fact that a reduction of Klotho expression and activity in T-helper cells in elderly individuals and rheumatoid arthritis patients occurred concomitantly with the downregulation of costimulatory CD28 molecules in these cells, which depend on higher levels of proinflammatory TNF-α [The downregulation of renal Klotho mRNA and protein expression leads to an increase in inflammation in the kidneys in diabetic mice . Additiophocytes . Prolongphocytes . A decrephocytes ,22, was ry TNF-α . As mentry TNF-α ,40.Klotho also stimulates the production of antiinflammatory factors. In human monocytes that were stimulated with lipopolysaccharide (LPS) to induce an immunosenescent-like phenotype, Klotho production, induced by calcitriol and transfection of cells with Klotho gene-possessing plasmid, upregulated the secretion of IL-10 . IL-10 i536, which is a NF-κB p65 subunit. The phosphorylation of RelA at Ser536, which was induced by TNF-α, caused NF-κB activation and the expression of several proinflammatory cytokines that are relevant to diabetes development, including TNF-α, which were found to be higher in the renal cortex in db/db diabetic mice and correlated with the duration of diabetes [536 and its further recruitment on IL-6, IL-8, MCP-1, and RANTES (chemokine (C-C motif) ligand 5 (CCL5)) promoters, thereby inhibiting the production of these proinflammatory molecules and protecting kidney tissue against inflammation-associated damage , demonstrated the usefulness of immunotherapy for DM and diabetic nephropathy by lowering the degree of renal inflammation. These drugs reduced development of the inflammatory reaction in the kidneys and protected against podocyte loss, glomerulosclerosis, and albuminuria. Another strategy to diminish renal inflammation and injury in an animal model of diabetic nephropathy was to inhibit NLRP3-inflammasome activation. A superoxide dismutase mimetic (MitoTempo) was used in this study to reduce oxidative stress and shown to downregulate NLRP3-inflammasome activation and renal injury. NLRP3-inflammasome activation upregulates expression of the proinflammatory cytokines IL-1β and TNF-α . Based oAbelmoschus manihot (L.) medic), traditionally used against CKD, was proved to act in part via the Klotho/TGF-β1/p38MAPK signaling pathway and to significantly lower the weight of the rats with DN, reduce their blood glucose and urine protein level, and the degrees of renal tubule damage, apoptosis, glomerulopathy, and renal fibrosis [Targeting other signaling pathways that are inhibited by Klotho has shown promising results with regard to improving health in patients with DM and diabetic nephropathy. Strategies of interfering with MyD88 signaling complex formation were tested in a mouse model of type 1 DM. Small molecules, such as L6H21, were shown to suppress the development of T1DM and kidney injury in these rodents by affecting MyD88 signaling and the expression of many proinflammatory cytokines  in kidney tubular cells. Furthermore, the abrogation of p38 MAPK signaling activation by GS-4997 in the kidneys in diabetic mice halted the progression of diabetic nephropathy and reduced the macrophage infiltration of these organs and the expression of genes that encode MCP-1 and TNF-α. Therefore, GS-4997 has been proposed as a therapeutic agent for the treatment of diabetic nephropathy in patients with T2DM. Similarly, the lower expression of MCP-1 and ICAM-1, which are inhibited by Klotho, reduced the accumulation of macrophages in the kidneys in animals with diabetic nephropathy and decreased tubulointerstitial injury after PKC-β inhibition by ruboxistaurin [fibrosis . A presefibrosis .Furthermore, a few studies have evaluated the therapeutic effects of recombinant Klotho and the stimulation of Klotho overexpression (e.g., by calcitriol, peroxisome proliferator-activated receptor γ , substanTherefore, preclinical data on Klotho have shown its potential usefulness as an early biomarker for DN development and its progression, and a possible, safe and effective agent in ameliorating diabetic kidney function ,60,62,63


However, KGF molecular and experimental information can sometimes be sparse and partially overlapping, suggesting the need for a specific annotation database of KGFs, conveniently condensing all the molecular details that can support targeted drug development pipelines and diagnostic approaches. Here, we describe KuNG FU (KiNase Gene FUsion), a manually curated database collecting detailed annotations on KGFs that were identified and experimentally validated in human cancer cell lines from multiple sources, exclusively focusing on in-frame KGF events retaining an intact kinase domain, representing potentially active driver kinase targets. To our knowledge, KuNG FU represents to date the largest freely accessible homogeneous and curated database of kinase gene fusions in cell line models.Inhibition of kinase gene fusions (KGFs) has proven successful in cancer treatment and continues to represent an attractive research area, due to kinase druggability and clinical validation. Indeed, literature and public databases report a remarkable number of KGFs as potential drug targets, often identified by  The occurrence of complex chromosomal rearrangements, such as inversions or translocations, can result in novel chimeric fusion genes, potentially representing driver events in cancer development3. Fusion genes were the focus of recent systematic analyses of Next-Generation Sequencing (NGS) transcriptomic datasets: Picco and colleagues reported the prediction of 7,430 unique gene fusions in more than 1,000 human cancer cell lines, across 42 different tissue types4; Gao and colleagues identified over 25,000 fusion transcripts, revealing 1,275 KGFs involving an intact catalytic domain in almost 10,000 clinical tumor samples, representing 33 cancer types in The Cancer Genome Atlas (TCGA)6. In this analysis, an overall low gene expression level was described for tumor suppressor gene fusions. At the same time, high gene expression was observed for fusions involving kinases and oncogenes, with the latter representing appealing druggable targets by approved or newly designed anticancer compounds6.Genomic instability is one of the hallmarks of cancer7, the majority of which are Tyrosine Kinase inhibitors. The first example of a successful KGF targeted therapy is imatinib , specifically active on a BCR-ABL1 kinase fusion in chronic myeloid leukemia patients8. Later on, more targeted drugs have been developed against well-recognized driver kinases activated in cancer due to KGFs, such as those involving ALK, RET, ROS1, NTRK or FGFR39. Remarkably, two drugs targeting KGF-activated NTRK kinases, namely entrectinib , also approved for the treatment of ROS1-positive patients with metastatic non-small cell lung cancer (NSCLC)11, and larotrectinib , got accelerated FDA approval as the first tissue-agnostic drugs for the treatment of tumors testing positive for NTRK kinase fusions, regardless of the cancer type, shedding further light on the importance of KGFs as cancer targets.As of today, there are about 50 kinase inhibitor drugs approved by FDA in Oncology14 and cancer cell lines can represent widely accessible tools for the investigation and the functional characterization of therapeutically actionable KGF targets, paralleling the genetic alterations present in primary tumor samples19. Extensive KGF molecular annotation would therefore greatly support the drug development pipeline and aid the parallel advancement of companion diagnostic tools for the assessment of patient treatment eligibility, such as multiplex amplicon approaches20 or Anchored Multiplex PCR (AMP)21.Precision medicine strongly relies on research models22; among these, the major sources of predicted KGFs in human cancer cell lines are the work by Klijn et al.13 and the LiGeA DB22. Klijn and colleagues have collected over 2,200 gene fusions resulting from the bioinformatics analysis of RNA-seq data through multiple fusion calling algorithms; however, experimental assessment of the oncogenic KGF functional role is not provided and the specific accession IDs of the transcripts involved in the fusion events are not specified. Gene fusions are reported as plain text lists, preventing interactive queries. The LiGeA database is a searchable and interactive resource of gene fusions in human cancer cell lines, containing ~1700 in silico predicted fusions identified by reanalyzing RNA-sequencing experiments performed by the Cancer Cell Line Encyclopedia (CCLE23), using four different gene fusion detection algorithms22. Though more practical, this database still suffers from the lack of experimental validation information and from incomplete sequence annotation, such as the exact breakpoints or the transcript IDs of the genes involved in the fusion events; moreover, no coverage is provided for KGFs identified in cell lines not included in the CCLE dataset. Indeed, a considerable number of human cancer cell lines with characterized and functionally validated KGFs are described in sparse literature, but are not included in any collective repository available for this kind of information.Public databases collecting newly NGS-identified gene fusions are increasingly available, as listed by Gioiosa and colleagueshttp://www.kungfudb.org/).To overcome these limitations, we have implemented the KuNG FU (KiNase Gene FUsion) database, a manually curated repository of KGFs in cancer cell lines, collecting comprehensive and detailed annotations of in-frame KGF events retaining an intact kinase domain sequence and for which experimental evidence has been reported. The above selection criteria represent fundamental KGF prerequisites for potential druggability investigations, making KuNG FU the largest homogeneous and curated database of KGFs in cell line models available to date , for the extraction of KGF information obtained from the data mining of over a million scientific abstracts, dated starting from 2013 and integrated with public datasets and previous literature, as summarized in the schema reported in Fig. 25.KuNG FU data access is equipped with a search engine that supports free-text searches, as well as filtering based on keywords, through an intuitive web interface, allowing researchers to query among a broad panel of human cancer cell lines and providing graphical summaries of the database content Fig. , Output.The KuNG FU query output  group is observed, in particular for Receptor Tyrosine Kinases (RTKs). The remaining gene fusion kinases reported in KuNG FU belong to Tyrosine Kinase-Like (TKL) and Atypical groups Fig. . Moreove35. Almost all the fusion transcripts reported in KuNG FU are specific for a single cancer type, however three fusions  were found across a variety of different cancer types.ABL1 is the most frequently rearranged kinase found in cancer cell lines, fused with different gene partners in 46 distinct cell lines Fig. , with th36. Among the 29 identified fusion partners, BCR is the most frequently observed in cancer cell lines  and RT-PCR methods43, or for multiplex amplicon and Anchored Multiplex PCR (AMP) NGS approaches44. These recently developed diagnostic strategies have some advantages, requiring low input, potentially increasing sensitivity associated to extensive amplification, short turnaround time and reducing the complexity of data analysis31.There are currently thousands of clinical trials testing the efficacy of novel kinase inhibitor drugs, and new molecules are approved every yearImportantly, KuNG FU is freely accessible to the scientific community and is open to regular updates by literature checking for new validated KGFs on a yearly basis; additionally, it offers the unique feature for users to submit their validation experiments on KGFs, thus contributing to extending and maintaining the database up to date and making the tool a comprehensive useful resource for cancer investigators and drug research.Abstracts published in PubMed between January 2013 and December 2019 were initially filtered using Python scripts for the presence of the keyword “cell line” appearing either in the Title or Abstract. The selected abstracts were then queried for the presence of the following keywords: “fusion”, “inversion”, “translocation”, “rearrangement” and “kinase” appearing either in the Title, Abstract or among the MeSH terms. A negative dictionary of terms was also created containing false positive terms to be excluded, such as “nuclear translocation”, “bacterial translocation”, “cell-cell fusion”, “membrane fusion”, etc.In-depth revision of the processed literature was applied for extensive manual curation of the identified KGFs. Reviewed information was integrated into the KuNG FU database schema.Yu et al.45, Cellosaurus database46, HUGO Gene Nomenclature Committee (HGNC) database47, Tumor Fusion Gene Data Portal27, WiKinome48, National Center for Biotechnology Information (NCBI)49 and UniProt50 websites through Python scripts. Three major fields describe the cell lines reported in KuNG FU: (1) ‘cell line name’, a unique name identifying the cell line; (2) ‘synonym/s’, cell line name synonyms (3) ‘primary tissue’, indicating the tissue from which the cancer cell was derived. Cell line names and synonyms were disambiguated by creating a list of unique identifiers extracted from Yu et al.45 and integrated with cell line synonyms from Cellosaurus46. Additional cell line description fields were included, reporting gender, ethnicity, patient age at diagnosis. Cross-contaminated cell lines, as per the register of misidentified cell lines curated by the International Cell Line Authentication Committee (ICLAC)51, were excluded from KuNG FU. In the ‘Fusion’ sub-section, HGNC47 approved gene symbols and RefSeq.49 transcript identifiers were used, with HGNC47 chromosomal coordinates of kinases and gene partners; 5′ and 3′ Gene Junction fields indicate the specific exon/intron involved in each breakpoint, and the ‘Chromosomal rearrangement’ field indicates the event generating the aberrant transcript . ‘Validation methods’ is a manually curated field indicating the technical methodologies used for experimental KGF evidence found in the supporting literature along with the PubMed unique Identifier (PMID) for the respective reference(s). Additional wet-lab techniques used for experimental validation of the KGF sequence features were retrieved from 62 scientific papers published before January 2013 and integrated in KuNG FU, referenced with the corresponding PMID. TCGA5 identifiers of the samples harboring the same KGF found in KuNG FU cancer cell lines were also extracted and provided with a link to the TCGA Data Fusion Portal27. In the ‘Kinase’ section, for each kinase, NCBI49 Entrez Gene ID and UniProt50 protein accessions were provided, together with the ‘Kinase Group name’ and the ‘KD boundaries’ fields (KD: kinase domain).All the fields listed in Table 49 reported in HGNC47 that refers to a canonical UniProt50 sequence, chosen by criteria described in UniProt/Swiss-Prot website available at https://www.uniprot.org/help/canonical_and_isoforms. The conservation of the entire kinase domain within the KGFs and the observation of the reading frame in the resulting chimeric protein were calculated by means of the AGFusion tool26.The nucleotide sequence at the breakpoint, the amino acid breakpoint position in the kinase and the length of the resulting chimeric protein were also automatically calculated and reported in KuNG FU. When the transcript ID of kinase and fusion partner could not be retrieved from literature, fusion gene breakpoints were predicted based on the transcript RefSeq ID26 (Fig. Collected data were stored in three MySQL relational tables: “cell_line_table”, storing meta information relative to cell lines; “abstract_table”, collecting the information extracted from the abstracts; “fusion_table”, reporting fields related to the KGF. MySQL database has been provided with a user-friendly graphical interface developed using AngularJS, a Javascript framework, and powered by JQuery and Bootstrap. Statistics are visualized through interactive graphs, implemented in PHP and Javascript. The fusion data stored in the database can be exported as txt and sql files, and a graphical representation of the gene fusion construct with respective protein domains is also provided through the AGFusion plot tool26 Fig. .An application form allows users to submit experimentally validated kinase fusions not previously included in the database. Upon revision, data will be added to KuNG FU with the appropriate reference, if available, together with submitters’ acknowledgments.


Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines . Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line  and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704 Cancer is a major cause of death in developed countries and increasingly also in developing countries. Based on the GLOBOCAN 2018 estimates of cancer incidence and mortality by the International Agency for Research on Cancer, an estimated 18.1 million cancer cases (17.0 million excluding nonmelanoma skin cancer) were newly diagnosed in 2018, and 9.6 million individuals (9.5 million excluding nonmelanoma skin cancer) died from cancer in 2018. The cancer incidence increases with age. Hence, the number of cancer cases is anticipated to rise further as a consequence of a rising lifespan. Thus, research is needed to improve cancer therapies.2. However, many cell lines are misidentified, i.e. they have been swapped or contaminated with other cell lines, and as a result, researchers may work with cells that are not what they think they are. This has been a problem since the work with cancer cell lines started and continues to be a problem today. Short Tandem Repeat (STR) analysis has been available as a reliable method to identify the genetic origin of a cancer cell line for a while3. Short Tandem repeats are accordion-like stretches of DNA containing core repeat units of between two and seven nucleotides in length that are tandemly repeated from approximately a half dozen to several dozen times4. By comparing this DNA, scientists have successfully managed to determine samples coming from the same genetic origin, and have been widely applied in forensic studies. Although STR test have been available for a while, 15–20% of the currently used cell lines have been estimated to be misidentified5. For example, in a study based on the analysis of 482 different human tumor cell lines, up to 96 cell lines were misidentified6. Moreover, another study6 found that STR profiling alone is insufficient to exclude inter-species cross-contamination of human cell lines, and the study argued for the need of additional testing and authentication methods. Hence, additional authentication methods that can be easily applied in the laboratory are highly desirable.Cancer cell lines are cancer cells that have been isolated from human or animal cancers and that can be grown continuously as cell cultures in a laboratory. They are important and probably the most commonly used model system for both studying cancer biology and the discovery of novel anti-cancer drugs11. We have established the Resistant Cancer Cell Line (RCCL) collection, the worldwide largest collection of drug-adapted cancer cell lines, currently consisting of > 1,500 models of acquired resistance12. Automated image recognition approaches may be an additional way to authenticate cells that may complement STR analysis and enable the differentiation between isogenic cell lines13.Additionally, methods are needed that reliably discriminate between isogenic cell lines, such as clonal sublines and drug-adapted cancer cell lines, since this is not achievable using STR analysis. Drug-adapted cancer cell lines are used as models of acquired drug resistance and have been used to identify many relevant drug resistance mechanisms14 used decision tree algorithms to perform classification of cancer cells into benign and malignant with a precision of 97.7%. In another study, Smita et al.15, found that SVM performed best out of a range of conventional machine learning models at classifying cancer images. The use of conventional machine learning to perform classification can be complex, relying on manual feature extraction (feature engineering) from images, which can be challenging13. The performance of most conventional machine learning methods are highly dependent on feature engineering16. Therefore, some researchers have turned their attention to deep learning approaches, which can extract and organize the discriminative features from data without the need of manual feature engineering. Deep learning approaches have achieved remarkable performance in tasks such as speech recognition17, natural language processing18, and image classification19. Deep learning has begun to be used for image recognition in cancer, for example, Angel et al.20 and Jinhua et al.21 both used convolutional Neural Networks (CNN) for detecting the presence of invasive tumour cells in breast cancer tissue samples.Conventional machine learning models such as Support Vector Machines (SVM), Artificial Neural networks (ANN), Naïve Bayes and Decision Trees, have been used to perform cancer cell classification based on microscopy images. These models have been able to detect similarities/differences between cancer cell images, which can be missed by the human eye. Nusaibah et al.22. When learning on a limited amount of data, data augmentation and transfer learning are two methods that can be used to improve the performance of deep learning models. Data augmentation is a technique which is used to artificially increase the diversity and size of the training sample. This is done by performing operations like shift, zoom, flip and rotation on the available training data. Zeshan at.al23 reported how data augmentation contributes to the performance of deep learning models in classification of medical images. Authors further analysed which data augmentation techniques retain the properties of the original image as it contributes to the model performance. Another study has surveyed the significance of different data augmentation techniques in fields which is difficult to get enough training data sets24.However, the performance of deep learning largely depends on the amount of training data available. Training a deep learning model is computationally expensive and time-consuming25, VGG26, MobileNet27, and InceptionResNet V228. Most of the existing pre-trained models for image classification are trained on the Large-Scale Visual Recognition Challenge (ILSVRC) ImageNet dataset29, a general object recognition dataset which includes 1000 classes30. Transfer learning technique was used to classify lung cancer types in a study by Nicolas et al.31 with a high level of accuracy (0.97 AUC- Area under the Receiver Operating Characteristic Curve). Using Inception V3, a pre-trained model on ImageNet, it was also possible to predict the ten most mutated genes in lung adenocarcinoma (LUAD)32, using the same model31. Sung-Jin et al.33 used VGG19, another pre-trained model on ImageNet, to perform classification in molecular diagnostics, a collection of techniques used to analyse biological markers in the genome and proteome. Mark et al.22 explored several deep learning architectures to detect glaucomatous optic neuropathy in fundus photography with Transfer learning, outperforming all other methods. J. Huang et al.34 proposed InceptionResNet V2 and MobileNet as a feature extractor together with Faster Regions Convolutional Neural network (R-CNN) as a meta-architecture for the image identification and detection. InceptionResNet V2 has provided a good accuracy at the cost of more computational time, whereas MobileNet’s accuracy is comparable to the VGG model but has 1/30 of the computational cost. Google’s Inception V3, a deep learning model pre-trained on ImageNet, was used to perform the classification of histopathological images of breast cancer in a study by Jongwon et al.35.Transfer learning involves the use of pre-trained models designed to perform a particular task in one context, their top layers are removed and new layers attached, to be trained to perform a new task, in a different context. Since only the last few layers of the model will be retrained, computation costs and training time are reduced and only a smaller amount of data is needed. There are several pre-trained models available, such as GoogLeNet36 trained a CNN to classify between normal and breast cancer cells using actin-labelled fluorescence microscopy images based on the knowledge that the actin cytoskeletons differ between normal tissue and tumour tissue, while others have used inverted phase microscopy images to classify breast cancer cell lines from normal breast cell lines37. Most recently, Meng et al.38 used a CNN trained on cell images of single cells from ultra-high-throughput microscopy  to classify four different cell types with > 99% accuracy. However, they used two epithelial breast cancer cell lines , one acute myeloid leukaemia cell line (THP-1), and peripheral blood mononuclear cells (PBMCs).In the laboratory environment, microscopic images are the most digitised form of images which can be produced from different samples under investigation. Deep learning and its techniques have been playing an important role in computerised analysis of these microscopic images. For example, Oei et.al28. Inception incorporates each convolution in parallel and concatenates them at the end as proposed by C. Szegedy et al.27. The key benefit of the architecture is that, it increases the number of units at each stage without increasing the computational complexity much28. Each inception unit has several non-linear convolution modules with various resolution which makes it more applicable to tasks that need to process data with multiple resolutions like medical images33. As Inception networks tends to be very deep, and residual connections adopted from ResNet perform good in training deep networks, the filter concatenation stage of the Inception networks can be replaced by residual connections28 and give rise to InceptionResNet which takes advantage of both Inception and Residual architectures. Another model, MobileNet, deploys Depthwise Separable Convolution which applies a single filter to each input channel instead of standard convolution29. In MobileNet, a standard convolution is factorised into Depthwise convolution and a 1 × 1 convolution called a pointwise convolution. Depthwise convolution applies a single filter to each input channel. The pointwise convolution then applies a 1 × 1 convolution to combine the outputs of the Depthwise convolution. The MobileNet reduces the computational costs by avoiding standard convolution which filters and combines inputs into a new set of outputs in one step29.In this study, we investigate the use of two deep learning models, MobileNet and InceptionResNetV2, in the authentication of cancer cell lines through a classification process by using the microscopic images of the cancer cell lines. Both models are based on convolutional neural networks (CNN), which have the capabilities of finding their own related features from the training data without the need of feature engineering. InceptionResNet combines the two major concepts of Inception and ResNet models as used by C. Szegedy et al.38, whose method depends on expensive machinery that is only available in very few specialised laboratories, our approach is based on phase-contrast images from well plates or cell culture flasks that can be obtained with every standard inverted cell culture microscope and will, therefore, be available to every standard cell culture lab. Our authentication method for drug-adapted sublines based on deep learning will complement STR and help to ensure that researchers know what cell lines they are working with.The cell line data we used contain a set of four parental cancer cell lines and their sublines adapted to anti-cancer drugs. This dataset enables us to consider the ability to distinguish between cancer cell lines of different genetic origins and also those of the same genetic origin. We further perform separate classifications of the four parental cell lines and their respective drug-adapted sublines in a separate classification task as part of the authentication process; this is a task which cannot be performed by STR authentication. In contrast to Meng et al.34. For model training, random initialisation of the model weights (training from scratch) and transfer learning methods were used and compared. In our analysis, for transfer learning, we used weights of the models pre-trained on ImageNet, a large-scale dataset used for Large-Scale Visual Recognition Challenge (ILSVRC), which contains 1.2 million images of general objects29. Several strategies were tested for data pre-processing and training in order to find the optimum strategy and the optimum model configuration for our problem  collection7. The cisplatin-resistant ovarian cancer sublines had been adapted to 1 µg/mL (COLO-704rCDDP1000) or 2 µg/ml (EFO-27rCDDP2000 and EFO-21rCDDP2000) cisplatin. The oxaliplatin-resistant UKF-NB-3 subline (UKF-NB-3rOXALI4000) was adapted to 4 µg/mL oxaliplatin39. Table The dataset consisted of microscopy images of parental cancer cell lines and their sublines, which had been adapted to grow in the presence of anti-cancer drugs. The set of cancer cell lines consisted of three ovarian cancer cell lines  and their sublines adapted to the anti-cancer drug cisplatin, as well as the neuroblastoma cell line UKF-NB-3 and its subline adapted to the anti-cancer drug oxaliplatin. The ovarian cancer cell lines were obtained from DSMZ . The neuroblastoma cell line was established from a bone metastasis of a stage IV neuroblastoma patientThis dataset was collected at the School of Bioscience, University of Kent, Canterbury Campus, UK. All the images are in RGB, JPEG (Joint Photographic Experts Group) format with a resolution of 2560 × 1922 pixels.As most of the available pre-trained models for transfer learning are trained on non-medical images, we proposed a multi-stage transfer learning through an inter-mediate training step using breast cancer cell images, to make sure our selected model had the knowledge of medical images before fine-tuning it for our cancer cell lines cell line authentication task. A multi-stage transfer learning is a technique of transfer learning in which a model can undergo several transfer learning before fine-tuned to its target task. Mostly, the intermediate transfer learning stage involves training a model with a data set from the domain similar to domain of the target task. For this multi-stage transfer learning, we used publicly available breast cancer cells data set. The data had been produced by the Laboratory of Vision, Robotics and Imaging (VRI) at the Federal University of Parana, and collected from 82 patients and comprised of 7909 microscopy images of breast tumour tissue. It is divided into two categories with 5429 malignant samples and 2480 benign samples, both with 700 × 460 pixels resolution and 3 channel RGB.40 and different data augmentation methods were performed41. Data normalisation is a technique of restricting features of the data to within a specific range42. In machine learning, the technique ensures each parameter (pixels in our case) has similar data distribution. Data normalisation was performed to remove variance in brightness and to enhance the contrast of the images. This was done by subtracting each image from its mean pixel-intensity value to make convergence faster while training the network. In the case of RGB images, the mean was calculated channel wise. All the processing conducted in this paper was done only on the cancer cell line dataset which is our target task. Our models were trained with both grayscale and RGB formats for comparison; the conversion to grayscale, data normalisation and augmentation were done online during the training process. For image splitting, the dataset was separated into two main categories, parental cancer cell lines and their drug-adapted sublines. The four parental cancer cell lines, their four sublines of drug-adapted and the combination of the two which form a group of eight cell lines were treated as three separate authentication problems. tenfold cross validation was performed on all three tasks. Each fold had 10% of the data set selected randomly and without repetition. As we had a relatively small data set, data augmentation approaches were used to artificially increase the sample size. As different data augmentation techniques have been proved to contribute different performance on the model24, two data augmentation approaches were employed and compared, which are nearest width shift and Constant width shift. Examples of augmented images are shown in the supplementary information, in Fig. Prior to model training, pre-processing steps like image splitting, cross validation, conversion to grayscale, data normalisation43. The data preparation procedures, training and model selection procedures are illustrated in insets A and B of Fig. A pilot classification was conducted to determine the optimal model and training strategy for the cell lines authentication task. In this process, three authentication tasks were performed by using the two selected models with different method combinations. The three tasks were (a) the authentication of parental cancer cell lines, (b) the authentication of drug treated cancer cell lines and (c) the authentication of the combined dataset of parental and drug treated cancer cell lines . In the experiments performed in the pilot classification, the images were resized by squashing the image to 299 × 299 pixels as a predefined input shape for InceptionResNetV2 and 224 × 224 pixels for MobileNet. As the predefined input shape of our best performing model was 299 × 299 pixels, several tests to transform the input image into the required input shape from our sample images were performed and compared. Govindaiah et al.Centre crop the image without resizing.Resize the image to 1280 × 961 pixels which is half of the resolution of the original image and then centre crop the image.Resize the image to 640 × 480 pixels which is a quarter of the resolution of the original image and then centre crop the image.Resize the image to 426 × 320 pixels which is 1/6 of the resolution of the original image and then centre crop the image.To maintain the aspect ratio, the following methods were applied and then compared with image squashing and reported.45. Hence, determining the number of layers to be added on a pretrained model for transfer learning becomes an important aspect. In fact, the added layers play the crucial role in the learning of specific features for the target problem by the model. When using transfer learning, it is essential to control the depth at which the amount of ‘knowledge’ transfer between the source and target problem is optimal for the target problem. In deep neural networks, most of the parameters are in the fully connected layers46. Furthermore, different popular models which has won ImageNet competition has used different number of fully connected layers, AlexNet has 3 fully connected layers, GoogLeNet and ResNet which both contributed to the development of our best performing model as they use Inception and ResNet techniques, respectively, both have one fully connected layer47. We performed an experiment to find the optimal number of fully connected layers to be added and the number of neurons in those layers for our problem.In deep learning models, layers closer to the input learn general features of the input data while deeper layers closer to the output learn more specific features for the target problem48. The idea is contradicted in49 where it is stated that models trained with large batch size have high chance to generalize more poorly than those trained with small batch sizes. Studies51 suggested a batch size of no more than 64 samples. In addition, a large batch size has high computational cost when training a deep learning model; previous studies53, recommended large batch size when training with large datasets, but this choice comes with optimization difficulties. Since there is no clear recommendation of the batch size to use in general, different batch sizes were tested to find the optimum batch size value for our problem.The batch size, which is the number of samples processed before the model is updated during training, has an influence on the model performance in terms of both accuracy and training time. It is reported that greater the batch size the higher the accuracy of the model54. The used models in our case were trained with ImageNet, a non-medical image dataset. To make sense of transfer learning, a multi stage transfer learning was considered by training the model through medical images, before fine tuning it on our targeted task. Some investigations into medical images have adopted multi-stage transfer learning by fine-tuning their models through other publicly available medical data sets, before fine tuning the model to their target problem. Ravi et al.45 observed an increment of 4 percent in F1-score when performing multi-stage transfer learning by using publicly available breast cancer data set to fine tune a deep learning model as an intermediate transfer learning stage, before fine tuning the model for their target task of classification of breast cancer. As there are common high-level features in cancer cells such as shape and size55, we used the breast cancer cells data set as an intermediate transfer learning stage, since there is no public available data set to our specific problem (cell lines authentication), to the best of our knowledge.A common practice in deep learning is that training and testing data should come from the same domain. In some scenarios, such as training a deep learning model with medical images, that practice becomes more challenging as it is hard to obtain sufficient medical images. Transfer learning could provide a solution by training the models with easily obtained data from a different problem and fine tune the model with the smaller dataset for the target problem. Transfer learning makes sense when low-level features from the problem you are transferring from could be helpful and are relevant to the problem you are transferring toUnlike in the pilot classification task and in the fine-tuning of the optimal model were the training, validation and test folds were fixed, at this stage, nine folds were used for training and validation and one for testing for all the authentications conducted by using the optimal fine-tuned model using the optimal training strategy. The process was repeated 10 times for each fold to be used for training, validation and testing, the mean results with their standard deviation of all 10 folds are reported in this paper. In addition, we investigated the effect of sample size on cancer cell line authentication and the results are reported in this paper. The predictions of any trained deep learning model are subjected to a degree of uncertainty; a trained model can perform well in identifying some classes and perform poorly on other classes. It is necessary to understand how a trained model performs in identifying individual classes trained on instead of just an overall performance, as some classes are hard to authenticate compared to others. We studied by investigating the confidence of our optimal model in authentication of each cancer cell line separately and the findings reported. All the experiments conducted using our optimal model are as explained bellow;To complement the current authentication methods used in biology research (i.e. STR), which is not able to differentiate between parental cancer cell lines and their drug-adapted sublines, eight-class authentication of the mixed four (4) parental and the four (4) drug treated cancer cell lines was conducted. To apply STR profiling for authentication, standardized protocol and a data-driven, quality-controlled and publicly searchable database will be required which is a complex and time-consuming process. To complement STR with a cheaper and quicker computerised method of authenticating cancer cell lines, we further performed authentication of the four (4) parental cancer cell lines and the authentication of the four (4) drug-resistant sublines separately using our optimal fine-tuned model. Furthermore, we perform a two-class task to authenticate the parental cell lines and their drug-adapted sublines in one approach.57. To study the effect of sample size, we studied our selected optimal model performance at different training sample sizes. The number of images in the test fold were kept constant while the number of images in the training folds were reduced in steps of 20%.Deep learning can easily overfit when trained on a small sample size, hence it is important to study the effect of sample size when training deep learning models to avoid overfitting58. This might be due to different reasons like the quality of data of that particular class or the confusion of the model due to similarities existing between the classes with low confidence. This rises the importance of studying the confidence of a trained model in prediction. I Cortés-Ciriano et.al59, studied about model confidence in assessing prediction uncertainties of their trained deep learning model for drug discovery application. To study the model’s confidence, some studies advise not to use prediction probabilities as the confidence of a deep learning model60. Researchers in61 and62 instead, used the probabilities of classes generated by the trained CNN models as the confidence score to reject noises and keep the predictions with high class probabilities in a face detection task. Also, in an object detection network by Redmon et.al58, probabilities of the bounding boxes containing objects were used as the confidence score of the model. We therefore studied the prediction probabilities of our model in predicting a small set of randomly selected samples from our test set by extracting their probabilities of the assigned class. The predicted probabilities were extracted from the softmax layer of the trained model. Post processing of the softmax layer has proved to be useful in delivering calibrated class probabilities in classification problems64. Both probabilities in correct and wrong predictions were studied and reported in this paper.Despite the high accuracy attained by a trained CNN model, it can have difficulties in predicting some of the classesk-fold cross validation (k = 10 in our case) because when comparing model’s performance using simple train/test split, different results may be obtained each time when different data samples are selected in the train/test sets49. It is more computationally expensive to train a model with RGB than with grayscale images. This is because RGB images carries more information which can be essential for a model to learn. Hence the two ways training with grayscale and with RGB images were tested and results compared. Both models were trained with normalised images. For data augmentation several hyper-parameters were selected. The hyper-parameters selected for augmentation reflected that the cells can be anywhere in an image independently of how the cell culture vessel is placed under the microscope. The selected method and the optimal training strategy were determined during the pilot classification task, which were later fine-tuned. InceptionResNet V2 was found to be the optimal model for our problem over MobileNet through the pilot classification task, using RGB images, nearest width shift data augmentation with transfer learning techniques (Method combination V). In fine-tuning of the optimal model, we also found that, distorting the aspect ratio by resizing the input image direct to the required input shape does not have an effect on the model performance in our problem. Multi-stage transfer learning had little impact on our problem, this might be due the kind of data we used to fine tune our model in a multi-stage process, breast cancer cells dataset, being different from our target task (cancer cell lines) data set as the breast cancer cells images were produced from the stained biopsies which are highly associated with colour changes while our cancer cell lines data set were produced from cell culture grown in the lab. Our optimal model was trained to perform different authentication tasks to automate the standard existing authentication method, the STR. Further authentication tasks were conducted using the optimal model which cannot be done by the standard authentication method, the STR, and achieved remarkable results. The following subsections presents our results.We adopted a Pilot classification was conducted on three main authentication tasks, which are the authentication of the four classes of parental cancer cell lines, four classes of their drug-adapted sublines and an eight-class combination of the previous two. This was done by fixing the training, validation and test folds through all the three tasks to determine the optimal model with the optimal method combination.40. As the RGB images contain more information, this makes them computational expensive to train compared to grayscale images. Both data augmentation techniques, Nearest width shift and Constant width shift, had a positive impact by significantly increasing the F1-score in both models, with nearest width shift performing better than constant width shift. With reference to training strategies selections in Table Figures The performance of the models (F1-scores), with the optimal strategy (method combination V) observed in our pilot classification on the authentication of an eight-class authentication task, is shown in Table Based on results from the three authentication tasks, InceptionResNet V2 was selected as the optimum performing model compared to MobileNet. Also, a combination of RGB images, data augmentation and transfer learning (training strategy V) was observed to be the optimal configuration.As described in the “45 in which the intermediate stage dataset was of the same domain as the target task. Our target task, in fact, includes images from ovarian and neuroblastoma cancer cells, although they are all digital images of cancer cells. The small increment of F1-score in our case means there might be some little common features among cancer cells from different types of cancer.A modest increment of 1 percent was observed on the F1-score when a multi-stage transfer learning was conducted compared to the single stage transfer learning. This may mean that, in multi-stage transfer learning, our optimal model was able to learn extra cancer related features from the breast cancer cells data set. This increment of F1-score produced by multi stage transfer learning might have been small because we used images of breast cancer in the intermediate stage which is little different from our target task, Unlike a study by Ravi et.alThis section presents results of the optimal model, following hyper-parameter tuning, which are based on tenfold cross validation. With our fine-tuned optimal model and training strategy of 0.96 F1-score on authentication of four classes of parental cancer cell lines and an average of 0.91 F1-score on the four classes of drug-adapted sublines. An average of 0.91 F1-score was obtained on the eight-class authentication task. We also obtained an average of 0.95 F1-score in two-classes for authentication between the parental and its drug treated subline of the same cell line. These results are presented and further explained in the following subsections.Using the fine-tuned optimal model and training strategies, our model performed better on authentication of parental cancer cell lines than on their drug-adapted sublines Table  as two sOn the combined data set classification problem (eight-class task), which is a more complex authentication task as it combines both four classes of parental and drug-treated cancer cell lines, an average of 0.91 F1-score is obtained with a standard deviation of 0.03, as shown in Table 65, which might be a reason why our model was able to differentiate between the parental and the drug-treated cancer cells.Two class authentications were also performed to see how the model performs in classifying between the parental cell lines and their drug-adapted sublines Table . AlthougThe promising results of our method means that it may be used for authentication of cancer cell lines in conjunction with STR or for tasks where STR cannot be utilised.Table rCDDP2000 (mean F1-score 0.76) and EFO-27rCDDP2000 (mean F1-score 0.71), with the highest standard deviations out of all other cell lines. Further analysis on this case is presented later in the paper to find out the reason of this case.When training a machine learning model, the model can learn to classify some classes well and fail on other classes of the same problem. This might be due to the quality of the data in a particular class, imbalances between the datasets or a lack of diversity among the training samples in a particular class that prevents the model from capturing the patterns. It is important, therefore, to see how the trained models perform in classifying each class separately. The model performance in authenticating each class was studied and reported in Table It is important to understand how confident a trained model is in predicting the unseen dataset. Low confidence when making a correct prediction or high confidence when making an incorrect prediction may indicate the low performance of a model while high confidence in correct predictions and low confidence in wrong confidence may indicate the high performance of a model.rCDDP2000 and EFO-21rCDDP2000, respectively. We conducted a further investigation on the EFO-21 and EFO-27, both parental and drug treated cancer cell lines to find out the reasons of the low model performance on the two cell lines, as reported in “The probabilistic confidence of the model when making correct predictions was compared to when incorrect predictions were made Table . This waDue to the low performance in authentication of the drug-adapted sublines of EFO-21 and EFO-27 in the per-class performance of the optimal model Table  and alsoThe results show that our optimal model performed well in the authentication of the parental cancer cell lines, but was less reliable in the authentication of the cisplatin-resistant sublines Table . These r7. By using deep learning, we have demonstrated that, it is possible to authenticate cancer cell lines, including parental cancer cell lines and their drug-adapted sublines, based on image recognition. Our data also suggest that resistance formation to a certain drug may be associated with specific morphological changes. An improved understanding of such processes may enable the further development of image-based strategies to gain mechanistic insights. Furthermore, our results suggest that, apart from the drug-adapted cancer cell lines developing the same resistance mechanism, cancer cell lines treated with the same drug develop much similar resistance mechanisms compared to those treated with different drugs, as it was demonstrated in the studying of the trained model confidence. Hence, our results are not only promising with regard to the development of novel cell line authentication approaches but also provide initial evidence that image-based methodologies can be developed as tools for the performance of functional and mechanistic studies. This is a proof of concept that, image-based methods with deep learning can be used to assist the current existing state of the art authentication methods like the STR.The common issue of misidentification of cancer cell lines requires new ways for performing cell line authentication in a laboratory environment. Current authentication methods are expensive, time-consuming and cannot differentiate between cell lines of the same genetic origin, like parental cancer cell lines and their drug-adapted sublines. Resistance formation is associated with morphological changes that make drug-adapted cell lines distinguishable from the parental cell line. Hence, approaches using computer-aided digital image analysis can be used to develop effective authentication approaches that can be easily included into the daily laboratory routine and which may complement, to assist other authentication methods. Such methods may also enable the discrimination between cell lines of the same genetic origin, a task for which established methods are currently lacking. Such approaches have the potential to improve the reliability of research results due to the reduction of the use of misidentified cell lines45 because they used a dataset from their domain in the intermediate stage, compared to a 1% increase in F1-score in our case, this is because we were limited with the unavailability of publicly available cancer cell lines to be used in the intermediate stage. This means researchers should look for datasets from the domain of their target task when in need of applying multistage transfer learning. Finally, studying of a model confidence might be used as a way for detecting the prediction uncertainties of a trained model as it has been demonstrated in this paper.To this end, this thesis has successfully demonstrated the application of CNN in the authentication of cancer cell lines. Based on the pilot classification task result, techniques like transfer learning and data augmentation has significantly improved the model’s performance. Furthermore, a multi-stage transfer learning has shown a significant increase of 4% in F1-score in another study by Ravi et al.Supplementary Information.