Recent genomic analysis has shown that many of the most commonly used ovarian cancer cell lines have been mischaracterised, leading to erroneous conclusions and a gap in the translation of laboratory research into novel treatments for patients. Here, we use 10 epithelial ovarian cancer cell lines to investigate 2D migration, cell cycle parameters and 3D invasion behaviour into different substrates and find significant differences between the behaviours of cell lines from different origins. Cell lines derived from non-serous carcinomas migrated more quickly and were more likely to invade into Matrigel and collagen I substrates than cell lines derived from high-grade serous carcinomas. However not all cell lines derived from non-serous carcinomas exhibited similar invasive behaviour. These findings may reflect differences in the behaviour of the primary tumour types from which the cell lines were derived, given that high-grade serous carcinomas typically expand and spread over peritoneal surfaces. These results provide the basis of an  Despite the improvement in prognosis for most solid tumours, epithelial ovarian cancer prognosis remains relatively unchanged2, with only one major new treatment introduced in the last 30 years3. The term ovarian cancer describes several histotypes which show distinct cellular origin, molecular aberrations and disease progression in patients4; however, this heterogeneity is not reflected in the treatments currently available. Furthermore, the term ovarian cancer may be misleading as many ovarian cancers arise from non-ovarian tissue. The single consolidating feature of ovarian cancers is the localised dissemination of tumour cells to the ovary and pelvic organs2. Ovarian cancer is difficult to identify clinically since patients often present with non-specific symptoms such as bloating and fatigue5. Indeed, 80% of women are diagnosed at an advanced stage where 5-year survival is only 5% compared to 92% if the cancer is detected early5. For women diagnosed at a late stage, the treatment options remain limited and, even if the cancer is detected early, the chance of relapse is 70% within 18 months5.Ovarian cancer is the most lethal gynaecological disease in women and, in 2018, there will be approximately 300,000 new diagnoses of ovarian cancer worldwide6; it encompasses all cancers that arise from epithelium and involve the ovary7. The most common histological types are high-grade serous, low-grade serous, endometrioid, clear cell and mucinous carcinomas. High-grade serous ovarian carcinomas (HGS) commonly arise in the epithelium of the fallopian tube fimbria and subsequently present as apparently ovarian tumours after implantation in the ovary  and have strong links to endometriosis, a disease that affects 10% of the female population9. Ovarian endometriosis is thought to arise from retrograde menstruation and results in endometrial tissue growing outside the uterine body, most commonly on the ovaries . We show clear behavioural differences between high-grade serous and non-serous ovarian cancer cell lines and propose the use of these cell lines as an in vitro model to investigate distinct ovarian cancer tumour types.In 2013 Domcke et al.11 and Beaufort et al.12. On this basis, OVCAR3, COV318, PE01, PE04 and FUOV1 were considered high -grade serous (HGS) and A2780, OVCAR8, OAW42, SKOV3 and TOV21G non-serous (NS). The keratinocyte-derived HACAT cell line was included as a non-ovarian control.The panel of cell lines was categorised based on the genomic profiles outlined by Domcke 2D migration behaviour was assessed by relative wound density using the IncuCyte ZOOM® wound healing assay , the 2 least migratory cell lines (FUOV1 and PEO1) and the 2 cell lines that behaved differently from the other cell lines in the HGS and NS groups in the migration assay (A2780 and COV318). In the collagen invasion assay, only 2 cell lines invaded into the collagen matrix, both NS cell lines (TOV21G and A2780). None of the HGS cell lines proliferated on top of or invaded into the collagen matrix Fig. . SKOV3 s17 commonly associated with epithelial-mesenchymal transition (EMT)18. A2780 cells formed a thick proliferative cell layer on top of the collagen and invaded in small clumps of cells. This cluster invasion phenotype is characteristic of epithelial tumour cells16.The collagen invasion assay is more qualitatively informative than the Matrigel invasion assay as it is possible to visualise the morphology of the invading cells by taking a cross-section of the collagen matrix. TOV21G cells formed a thin layer of cells on top of the collagen, degraded the collagen or proliferated in a cavity and invaded as single cells with mesenchymal morphology. The single cells invaded outwards from the proliferative cell layer. This spindle-like morphology is characteristic of sarcomatous tumour cell invasionPrevious studies investigating ovarian cancer cell line behaviour commonly use cell lines such as OVCAR3 and SKOV3 and focus on investigating how genetic manipulation or drug treatments can increase or decrease invasive and migratory behaviour. However, to our knowledge the endogenous behaviour of these cell lines stratified by histotype has not been carried out previously. We show that high-grade serous carcinoma-derived (HGS) cell lines behave differently from cell lines derived from non-serous carcinomas (NS) in four different behavioural assays. Our findings highlight the need for careful cell line selection in order that the data derived from these cell lines can be interpreted appropriately.in vitro is very difficult and many of the genetic mutations required to immortalise cell lines are found only in late-stage tumours, consistent with the observation that most cell lines have the potential to grow as metastases19. In keeping with this argument, TOV21G was derived from stage 3 clear cell carcinoma20; and SKOV3 and OAW42 were both derived from ascitic fluid (from post-chemotherapy patients)22, which is associated with higher stage disease. Given that NS tumours are normally diagnosed and treated prior to metastasis, the behaviour differences of these cell lines cannot therefore be used to replicate and understand the behaviour of primary NS tumours; however, they are useful for understanding the rare but more aggressive advanced stage NS disease.In general, the NS cell lines migrated more rapidly than HGS cell lines, which seems counter-intuitive as HGS tumours are typically more clinically aggressive, spread throughout the peritoneum and have a worse patient prognosis compared to NS tumours. Moreover, NS ovarian tumours commonly present at an early stage before metastasis has occurred. However, establishing cell lines from primary tumours in vitro do not necessarily segregate the same way. Most importantly the cell lines do not all behave as expected based on their putative identity.It is important to highlight that although the HGS and NS cell lines showed different migration behaviour when analysed as distinct groups, the behaviours of individual cell lines did not segregate as clearly. A2780 is an NS cell line but behaved similarly to PEO1, PEO4 and FUOV1 which are HGS cell lines. Similarly, COV318 is classified genomically as a HGS cell line but behaves similarly to TOV21G and OVCAR8 which are NS cell lines. These findings highlight that, although these cell lines can be divided into different categories by their mutation profiles, their behaviours 0/G1 is well documented24. Once a cell enters S phase it is committed to DNA replication, which is a very energy intensive process, making it unlikely that these cells can be involved in other cellular processes, such as invasion. The cell cycle findings provide evidence that the migration behaviour differences are not a result of proliferation. Furthermore, we found no systematic relationship between doubling time, estimated by cell count, and migration behaviour for the cell lines used in this study. For example, A2780, HACAT and TOV21G cells all had similar doubling times  but their migration behaviour was markedly different; A2780 migrated much more slowly than TOV21G and HACAT, failing to close the wound area after 60 hr. In contrast HACAT cells migrated quickly, closing the wound area in 33 hr but TOV21G cells took 60 hr despite a similar proliferation rate. These results are also in keeping with data derived from live imaging of invasion showing that cells in G1/G0 are more likely to invade and, if the same invasive cells cycle into S phase, they cease invasion until the cell cycle is complete24.The link between cancer cell invasion and cell cycle arrest in G8. Even very large tumours only invade the superficial layers of the bowel and omentum and not the deeper layers8. It is rare to find HGS metastases to the bone or brain, which are common with other epithelial tumours such as breast and colon cancers which undergo several stages of intra-/extravasation before they seed metastases in other organs8. The behaviour of the NS cell lines is similarly counter-intuitive given that the disease is primarily non-metastatic. However, TOV21G was derived from the tumour of a patient diagnosed with stage 3 clear cell carcinoma20 and, anecdotally, patients with advanced stage NS tumours have a poor prognosis, similar to or even worse than those with HGS tumours, in keeping with the hypothesis that the behaviour of TOV21G replicates the rare advanced stage of the disease. Information on the origins of SKOV3 and OVCAR8 is limited25 but SKOV3 was derived from ascitic fluid rather than primary tumour27. Given that the formation of ascites is associated with advanced disease the invasive behaviour exhibited by SKOV3 also represents the behaviour of advanced stage disease28. HGS cells are characterised by a TP53 mutation but OVCAR8, which is categorised as non-serous and behaves similarly to other NS cell lines, also has a TP53 mutation . It is therefore unlikely that, in these cell lines, the differences in the migration and invasion behaviours of HGS compared to NS cells are driven by TP53 mutation as previously suggested by Ahn et al.29. Indeed, the most motile cell lines (including OVCAR8) have mutations in signalling pathways such as MAPK, PI3K and WNT11, which generally lead to overactivity in those pathways, and could explain the NS phenotype we observe. HGS cell lines characterised by only a TP53 mutation would not have this phenotype and would be susceptible to genomic instability. In parallel, clinical evidence supports the idea that HGS tumour cells do not have to migrate quickly to seed metastasis as the disease develops undetected over several years.Similarly to migration, the finding that HGS cell lines are not invasive may seem unexpected. However, HGS tumours do not have to physically invade through tissue to seed metastatic tumours throughout the peritoneal cavityin vivo tumourigenicity experiments. Hernandez et al.30 compared the ability of a different but overlapping panel of ovarian cancer cell lines to form tumours in nude mice over 6 weeks. They found that OVCAR8, SKOV3 and A2780 had high tumourigenicity when injected intraperitoneally, whereas OVCAR3, PEO1 and PEO4 had low tumourigenicity. Although the cell lines were not stratified by histotype in the in vivo study, the tumourigenic cell lines OVCAR8, SKOV3 and A2780 are derived from NS tumours and the non-tumourigenic cell lines OVCAR3, PEO1 and PEO4 are derived from HGS tumours.The differences in invasive behaviour between HGS and NS cell lines used in this study are supported by in vitro is challenging. The transwell invasion assay uses Matrigel, an extract of Engelbreth-Holm-Swarm (EHS) tumour which contains basement membrane components. Matrigel is commonly used in invasion assays but its suitability as an in vitro basement membrane substitute has been challenged31. Furthermore, the epithelial ovarian cancer microenvironment is composed of various different proteins and cell types. Collagen I is the most abundant structural protein within the ovarian extracellular matrix (ECM)32; therefore we used the collagen/fibroblast organotypic invasion assay to determine whether ovarian cancer cell lines behave differently when they encounter an ECM component more replicative of the in vivo environment.Replicating the tumour microenvironment 35. However, this heterogeneity cannot be explained by differences in tumour origin or molecular profile as both are associated with endometriosis and they have a similar genomic landscape. The differences in behaviours of NS cell lines in Matrigel and collagen highlights the importance of the substrate and may be related to the ECM context that the original cell line was derived from. Although the details in the original paper are sparse, A2780 was derived from untreated primary tumour material34. TOV21G was also derived from tumour material whereas SKOV3 was derived from ascites21. It is therefore interesting that TOV21G invades aggressively into both Matrigel and collagen, whereas SKOV3 shows invasion into Matrigel but not collagen; conversely A2780 invades aggressively into collagen but not Matrigel. Moreover, all the HGS cell lines apart from FUOV1 are derived from ascites39. Thus, overall, 2 of the 3 cell lines derived from primary tumours (TOV21G and A2780) attached and invaded into collagen but none of the cell lines derived from ascites exhibited this behaviour. This suggests that cell lines derived from the primary tumour have the ability to attach and invade into collagen whereas cell lines derived from ascites may not. This is consistent with the fact that tumour cells floating in ascitic fluid are able to survive and grow in suspension, without attachment to, or invasion into, a solid matrix.The heterogeneity of the NS cell lines compared to the HGS cell lines may in part be explained by the fact that the NS category encompasses cell lines which were derived from clear cell and endometrioid carcinomasin vitro experiments using these cell lines.Cell lines are vital as a first step to understanding molecular and behavioural changes that result in tumour development and progression. However, the current approach of using cell lines to investigate ovarian cancer, without stratifying by histotypes, is hindering the advancement of the field. Our data provide evidence that ovarian cancer cell line behaviour correlates to some extent with tumour histotype and hence origin. However, the variability between cell lines indicates that behavioural characterisation should be taken into account, in addition to tumour histotype, origin and molecular profile, when interpreting the results of in vitro. Representative in vitro models will help overcome the current gap in the translation of laboratory research to novel therapies for ovarian cancer patients. Future research should focus on using appropriately selected cell lines for the development of in vitro models to understand the molecular and behavioural differences between ovarian tumour types.Taken together these findings provide a basis for the selection of relevant ovarian cancer cell lines to investigate the differences between HGS and NS tumours All reagents were purchased from Thermo Fisher Scientific  unless otherwise stated.2. OVCAR3, SKOV3, OVCAR8, PEO1, PEO4, COV318, FUOV1, A2780, TOV21G and OAW42 were cultured in RPMI Medium 1640 (1 × ) supplemented with 10% fetal calf serum (FCS), 1X non-essential amino acids , 100 µL/mL (v/v) penicillin-streptomycin, 1 mM sodium pyruvate (Sigma Aldrich) and 1 µL/mL bovine insulin (Sigma Aldrich). All ovarian cancer cell lines were cultured in the same medium to control for biases due to varied levels of growth factors or different serum levels, in keeping with the approach of Beaufort et al.12. All of the lines had been acclimatised to this medium for at least 3 months prior to experimentation. HACAT was cultured in DMEM (1×) + GlutaMAX supplemented with 10% FCS, 100 µL/mL (v/v) penicillin-streptomycin. Fibroblasts were cultured in DMEM (1×) +GlutaMAX supplemented with 10% FCS. Cell lines were routinely tested for mycoplasma contamination and all cell experiments were carried out in sterile conditions. Cell lines were passaged using 0.05% trypsin-EDTA (1×). All experiments were carried out with cell lines no more than 10 passages apart to ensure biological repeats and limit the mutational effect of passaging. Cell lines were not synchronised prior to experimentation.Human epithelial ovarian adenocarcinoma cell line SKOV3 was acquired from ATCC. Human epithelial ovarian adenocarcinoma cell lines NIH: OVCAR-3 (OVCAR3), OVCAR8, PEO1, PEO4, COV318 and FUOV1 were kindly supplied by David Melton from the University of Edinburgh. Human epithelial ovarian adenocarcinoma cell lines A2780, TOV21G and OAW42 were kindly supplied by Charlie Gourley from the University of Edinburgh. Human keratinocyte cell line HACAT was kindly supplied by Lana Woolford from the University of Edinburgh. All cell lines were authenticated using STR profiling . Red fluorescent protein telomerase reverse transcriptase (TERT) immortalised fibroblasts (RTIF) generated from skin punch biopsies were a kind gift of Andrew Campbell . All cell lines were maintained in a humidified incubator at 37 °C with 5% CO6 cells/mL cell suspension (5 × 105 cells) were seeded into every well of an ImageLock™ 96 well plate . The plate was then incubated at 37 °C, 5% CO2 for 4 hours (hr). After the incubation period, the 96 well WoundMaker™ (Essen Bioscience) was used to simultaneously create wounds in all wells. The plate was then washed twice with culture media  to remove any cell debris. 100 µL of normal cell culture media was then added to each well and the plate was placed inside the IncuCyte ZOOM® incubator system (Essen Bioscience). Plates were imaged every 3 hr for 60 hr. The percentage area covered by cells inside the wound area relative to the percentage area covered by cells outside of the wound area (relative wound density) at every time point was analysed. 3 technical repeats were carried out for each cell line and this procedure was repeated 3 times to ensure 3 biological repeats.For each cell line, 100 µL of a 5 × 105 cells were transferred to a fluorescence-activated cell sorting (FACS) tube and the content centrifuged for 5 minutes (min) at 400xg. To fix the cells, the supernatant was discarded and the pellet resuspended in 300 μL PBS supplemented with 50% FCS. 900 μL of ice-cold 70% ethanol was then slowly added whilst gently tube vortexing. The samples were stored at 4 °C. On the day of analysis, 3 mL PBS was added to the samples and the content centrifuged for 5 min at 400xg. The supernatant was discarded and the pellet re-suspended in 300 μL PBS supplemented with 0.1 μL/mL DAPI, 0.1% Triton x100. The samples were then stored for 1 hr in the dark and analysed using flow cytometry (BD LSRFortessa X-20). This procedure was carried out with 3 biological repeats per cell line.For each cell line, 5 × 10et al.40. Matrigel  was diluted in a 1:1 ratio with 1% serum culture media. Then 100 μL of the dilute Matrigel was added into the centre of each Transwell (Corning) and the plate was incubated for approximately 1 hr at 37 °C, 5% CO2. During this time, a cell suspension of 5 × 105 cells/mL was prepared in normal cell culture media for each cell line. Once the Matrigel had set, the transwells were inverted and placed onto the lid of the 24-well plate. Next 100 μL of the cell suspension (5 × 104 cells) was pipetted onto the underside of the filter. The transwells were then carefully covered with the base of the plate and placed (inverted) in an incubator at 37 °C, 5% CO2 for 3 hr to allow the cells to attach to the transwell. After the incubation period, the plate was turned the right way up and each transwell dipped into 1 mL 1% serum media to wash; this was repeated 3 times. The transwell was left in the final wash media and 100 μL normal cell culture media was added to the top of the transwell (on top of the Matrigel). The plate was then incubated at 37 °C, 5% CO2 for 5 days. On the 5th day SYTO™9 green fluorescent nucleic acid stain was diluted 1/2000 into 1% serum media in a falcon tube and vortexed. Next 0.5 mL of this solution was added to the wells of a 24-well plate. The plate was removed from the incubator and a transwell was added to each of the wells with SYTO™9 media. Then 0.5 mL of the SYTO™9/media solution was pipetted onto the top of each transwell to submerge the gel in staining solution. The plate was then incubated for at least 1 hour at 37oC with 5% CO2 and imaged immediately afterwards.This method is a modified version of Hennigan 13. Three separate assays were carried out per cell line giving 3 biological repeats with four Z-series taken for each sample per assay.Transwells were placed on a thin coverslip and images were acquired on a Nikon Confocal A1R confocal microscope using a 20X NA 0.75 objective. The microscope is equipped with a 405 nm diode, Argon laser and 561 and 648 nm laser lines and four photomultiplier tubes. Data were acquired using NIS Elements AR software . Images were scanned at 1x zoom, 0.25 speed and 1x line average. Optical sections were scanned at 15 μm intervals (Z-steps) moving up from the underside of the filter into the Matrigel. The percentage area covered by cells in each Z-step image was quantified in Image J using the thresholding feature. For quantification purposes, only cells in the Z-step 45μm or above were considered invasive as described previouslyet al.41. A confluent T75 flask of fibroblasts was passaged and a cell suspension of 1 × 106 cells prepared. The fibroblasts were then centrifuged at 400xg for 5 min. During this time, the collagen mix was prepared and all components kept on ice to ensure the collagen did not set; 3 mL MEM (10 × ) was added to 25 mL rat tail collagen I (approximately 2.6 mg/mL). Then 0.25 M NaOH (in-house) was added dropwise whilst gently mixing until the collagen mix turned orange but not pink (approximately 2.5 mL NaOH). The fibroblasts were resuspended in 3 mL FCS, added to the collagen solution and gently mixed. 2.5 mL of the collagen/fibroblast mixture was then quickly added to each well of two 6-well plates avoiding any bubbles. The plates were incubated at 37 °C, 5% CO2 for 10 min to allow the mixture to set. The collagen/fibroblast matrix was then detached from the sides of the wells and 1 mL of fibroblast culture media carefully added on top of the matrix. The plates were incubated at 37 °C, 5% CO2 for approximately 8 days to allow the collagen/fibroblast matrix to contract to 1.5 cm diameter.Collagen I was prepared from rat tails as described by Timpson 4/mL. 1 mL of the cell suspension was then seeded on top of the matrix. The 24-well plate was then incubated at 37 °C with 5% CO2 for 5 days to allow cells to grow confluent.Once the collagen/fibroblast matrices had contracted, blunt forceps were used to gently move them to a 24-well plate. All cell lines were then split and resuspended in normal growth media at approximately 8 × 102 for 10 days to allow the cells to invade. After 10 days the matrix was removed from the grid and cut in half using a scalpel. The collagen matrix was then added to a falcon tube with 5 mL 4% (w/v) paraformaldehyde to fix overnight.Tripods were made out of stainless steel grids and autoclaved prior to use. The sterile grids were placed into a 6 cm dish and normal cell culture media added to a level just above the grid. Using blunt forceps the collagen/fibroblast matrix was transferred to the grid and the media gently aspirated so that the bottom of the matrix is in contact with media but not submerged. This creates an air/liquid interface to facilitate invasion. Dishes were incubated at 37 °C with 5% COThe fixed collagen/fibroblast matrix was embedded in wax, sectioned and stained with haematoxylin and eosin (H&E) by the CRUK Edinburgh Centre Pathology & Phenomics Lab. Stained sections were imaged on the Nanozoomer XR model, data captured using NDP scan v 3.1. 40x magnification.All statistical analysis was carried out in SPSS and graphs created in GraphPad Prism unless otherwise stated. The Kolmogorov-Smirnov (KS) test was used to determine the normality of the data. For normally distributed data (KS: p > 0.05), parametric tests such as the independent samples t-test were used. For non-normally distributed data (KS: p < 0.05), the non-parametric Mann-Whitney U test was used. Significance was set at p < 0.05 for all statistical analyses. Wound healing data were analysed using two-way repeated measures ANOVA (GraphPad Prism); the normality of the data was assumed and limitations of this are acknowledged.


Neopterin has been extensively used as a clinical marker of immune activation during inflammation in a wide range of conditions and stresses. However, the analysis of neopterin alone neglects the cellular reactions that generate it in response to interferon-γ. Neopterin is the oxidation product of 7,8-dihydroneopterin, which is a potent antioxidant generated by interferon-γ-activated macrophages. 7,8-Dihydroneopterin can protect macrophage cells from a range of oxidants through a scavenging reaction that generates either neopterin or dihydroxanthopterin, depending on the oxidant. Therefore, plasma and urinary neopterin levels are dependent on both macrophage activation to generate 7,8-dihydroneopterin and subsequent oxidation to neopterin. This relationship is clearly shown in studies of exercise and impact-induced injury during intense contact sport. Here, we argue that neopterin and total neopterin, which is the combined value of 7,8-dihydroneopterin and neopterin, could provide a more comprehensive analysis of clinical inflammation than neopterin alone. The level of clinical inflammation occurring as a result of physical trauma, cardiovascular disease, cancer, bacterial, parasitic infections, and viral infections is often assessed by measuring the concentration of plasma and urinary neopterin ,2,3,4,5.The convenience of measuring neopterin ignores the basic biology that macrophages do not enzymatically synthesize neopterin. Neopterin is an oxidized form of 7,8-dihydroneopterin, a product of γ-interferon-mediated upregulation of GTP cyclohydrolase I (GTPCH1) . TherefoInterferons α and γ, lipopolysaccharide, and possibly phorbol ester all upregulate the activity of monocyte-derived macrophage GTPCH1 . GTPCH1 In turn, 7,8-dihydroneopterin and neopterin are readily transported in either direction across the cell membrane. In cell culture, the addition of 7,8-dihydroneopterin to an incubation media results in the relatively rapid accumulation of 7,8-dihydroneopterin within the cells .7,8-Dihydroneopterin is a potent radical scavenging and chain-breaking antioxidant, which can out compete α-tocopherol for the lipid peroxyl radical during low-density lipoprotein (LDL) oxidation, even though it is water soluble . Peroxyl7,8-Dihydroneopterin, but not neopterin or xanthopterin, downregulates the level of CD36, which is the primary scavenger receptor responsible for the uptake of oxidized LDL and foam cell formation in atherosclerosis ,39. OxLDNeopterin has been reported to activate inducible nitric oxide synthase (iNOS) in rat smooth muscle cells , but supThe evidence suggests that measuring either neopterin or 7,8-dihydroneopterin alone cannot provide a complete picture of oxidative stress or immune activation. Rather, the combination of both biomarkers elucidates a more robust mechanism of the inflammatory process, with an analysis of neopterin being a measure of the oxidative status within the cells, and 7,8-dihydroneopterin being a measure of interferon-mediated cellular activation. Moreover, measuring the ratio between 7,8-dihydroneopterin and neopterin may elucidate differences in overall oxidative stress versus immune activation in different disease models and physical trauma.Neopterin was first isolated from human urine in 1967  and usedAlthough ELISA offers high throughput analysis within hospitals, small-scale clinical and research laboratory analysis have used HPLC. Since neopterin is highly fluorescent, nanomolar concentrations can be reliably detected after separation from other compounds. C18 has been popular, but we have found isocratic analysis on strong cation exchange (SCX) columns at pH 2.5 that were excellent for urine analysis ,35. HoweAlthough an abundance of detection methodologies is promising, sample preparation still remains an important aspect. Since urine contains very low levels of proteins, and HPLC columns have become relatively inexpensive compared to sample clean-up costs, an HPLC analysis of urine usually involves the direct injection of diluted urine into the HPLC . In compIn spite of 7,8-dihydroneopterin’s biosynthetic heritage providing potential for an excellent marker of immune activation, it has been widely neglected in clinical or experimental research. 7,8-Dihydroneopterin’s low fluorescence and nM concentration in plasma make it relatively difficult to detect. 7,8-Dihydroneopterin can be easily detected at μM concentration by its absorbance at 254 nm, but in clinical samples, it is usually at the nM level. There is also no ELISA kit that is commercially available for 7,8-dihydroneopterin, kits are only available for neopterin. 7,8-Dihydroneopterin is also both heat and UV light-sensitive in comparison to neopterin .The usual approach to measure 7,8-dihydroneopterin is oxidation to neopterin using an acidic iodine solution ,13,58 orAnother reason that neopterin has been favored over total neopterin analysis is because 7,8-dihydroneopterin can be labile. In air-saturated solution at 25 °C, 7% of the 7,8-dihydroneopterin was lost over 4 h . This raThe two-step process to measure 7,8-dihydroneopterin can be avoided using HPLC coupled with mass spectrometry (LC-MS or LC-tandem mass spectrometry (LC-MS/MS)) ,59. The While neopterin has been measured repeatedly as an assessment of macrophage activation and infiltration for several decades ,61,62,63Recently, neopterin and 7,8-dihydroneopterin have been used as indicators of immune system activation in sport and exercise medicine, and are gaining momentum over other traditional inflammatory markers . Their bNeopterin has been routinely measured in several high-intensity exercise studies. Neopterin has been shown to rise in response to high-intensity cycling , ultra-eTypically, exercise studies have only measured neopterin ,7. WhilsRecent studies examining impact-induced trauma during exercise have begun to elucidate the importance of measuring neopterin in addition to 7,8-dihydroneopterin. The combined analysis provides a comprehensive overview of exercise-induced changes in oxidative status and immune system activation ,71,72. FTo complicate the exercise-related research on neopterin, recent evidence ,71,72 haCollectively, sport and exercise research that have measured neopterin and 7,8-dihydroneopterin have provided evidence that the change in one does not necessarily result in a change in the other. To critically and correctly evaluate a clinical outcome, treatment efficacy, or the oxidative status/immune system activation of an individual, neopterin and 7,8-dihydroneopterin should be simultaneously measured. There is a clear need to bring the analysis of neopterin and 7,8-dihydroneopterin from sport and exercise medicine back into the classical clinical environment. The measurement of neopterin and total neopterin would provide a clearer and more sensitive measure of patient oxidative stress and inflammation during clinical events.


Vitamin A is an essential micronutrient that participates in a wide rangeof biological processes. Retinoic acid (RA) is an active metabolite of vitamin Athat functions as an immune regulator. Graft-versus-host disease (GVHD) is amajor complication of allogeneic hematopoietic stem cell transplantation (HSCT).It is characterized by extensive inflammation arising from an alloimmuneresponse involving various host and donor immune cells. Since vitamin A affectsdifferent immune cell lineages and regulates an array of immune responses,vitamin A, and more specifically retinoic acid, is likely to influence theincidence and/or severity of GVHD. Indeed, recent preclinical and clinical datasupport this concept. In this review, we briefly summarize recent advances inour understanding of the potential role of vitamin A in modulating GVHD riskafter allogeneic HSCT. Vitamin A is a fat-soluble vitamin that plays an essential role in a widerange of biological processes. Retinoic acid (RA), the major metabolite ofvitamin A, is vital for embryonic development, cell differentiation,proliferation, and apoptosis. RA is also important for visual function andimmune homeostasis. Vitamin A is obtained from a diet containing carotenoids(vitamin A precursors found in plants) or retinyl esters  . Retinyl9-cis-RA binds RXRspreferentially, whereas both all-trans-RA and9-cis-RA are ligands of RARs [Vitamin A has long been recognized as an important factor for maintainingimmune homeostasis. It has been well documented that vitamin A deficiencyimpairs anti-pathogen immunity and is associated with increased susceptibilityto infections . Most im of RARs ,7. Thee+ T cell polarization. RA inhibits the generation of Th17cells in vitro. In the presence of TGF-β, RA facilitates the conversionof CD4+Foxp3- T cells intoCD4+Foxp3+ induced regulatory T cells (iTregs) [RA can act on many innate and adaptive immune cell lineages to regulatevarious immune responses –10. One (iTregs) . RA also(iTregs) . In addi(iTregs) . Therefo(iTregs) ,16. On t(iTregs) .Both st(iTregs) . Thus, RAll-trans RA is used clinically fortreating patients with acute promyelocytic leukemia due to its ability to inducethe terminal differentiation of leukemic cells. Retinoids have been reported toregulate the survival and maturation of human DCs, the most potent antigenpresenting cells of the immune system [in vitro andin vivo [RA is also well known for its effects on myeloid cell differentiation,survival, and function. e system . Recent e system ,22. RA cin vivo ,24. Furtin vivo ,26.+T cells as well as CD8+ cytotoxic T cells cause target tissue damagecharacteristic of GVHD. Among these three phases, T cell activation,differentiation, and migration take the central stage of GVHD pathogenesis.Therefore, any factors that can influence these T cell properties have thepotential to affect GVHD risk after allogeneic HSCT.Allogeneic hematopoietic stem cell transplantation (HSCT) is an effectivetreatment for many hematological malignancies and nonmalignant disorders. Eachyear, many patients diagnosed with leukemia, lymphoma, and multiple myeloma willreceive an allogeneic stem cell transplant in an effort to cure theirhematologic malignancy. Although the conditioning regimen consisting ofradiation and chemotherapy may directly kill tumor cells, the curative potentialof allogeneic HSCT relies on a donor T cell mediated graft-versus-leukemiaeffect . UnfortuEmerging evidence suggests that vitamin A can affect GVHD development andseverity. Indeed, recent pre-clinical and clinical data support the concept thatvitamin A and/or retinoic acid can modulate GVHD risk after allogeneic HSCT.+CD4+/Foxp3+CD4+inVAD recipients compared to control mice. These studies demonstrated that dietaryvitamin A levels can modulate GVHD mortality by influencing donor T cellmigration to GVHD target organs after experimental allogeneic HSCT.The GI tract is one of major target organs of acute GVHD . To causSubsequently, two independent groups reported that genetic inhibition ofRAR-α signaling in donor T cells significantly reduced theiralloreactivity and ability to cause lethal GVHD ,37. TheyThus, we further examined the effects of donor vitamin A deficiency andpharmacological inhibition of donor T cell retinoic acid pathway on GVHDseverity . We founEmerging clinical studies have also supported the concept that vitamin Ais a potential factor that modulates GVHD risk, although some of the resultsobtained so far are inconsistent with animal studies.+ effector T memory cells in patients with vitamin A levelsbelow the median.In a recent study , LounderIn another interesting clinical study, Tong and colleagues investigatedthe association between serum vitamin A levels and the development of ocularGVHD . They meall-trans RA isnot the only active metabolite of vitamin A, whether vitamin A can act throughan RA-independent manner to modulate GVHD warrants further study.Furthermore, Tong et al. found that treatment with vitamin A ointmentimproved ocular GVHD in vitamin A deficient patients, providing further evidencethat vitamin A metabolism may be involved in the pathogenesis of ocular GVHD. Itwill be interesting to see if vitamin A supplementation can reduce intestinalGVHD in pediatric patients after allogeneic BMT . ContinuIt is interesting that a common micronutrient such as vitamin A can modulatethe severity of a systemic disease such as GVHD. Preclinical studies demonstratedthat exogenous RA intensifies GVHD and vitamin A-deficient recipients show reducedintestinal GVHD after allogeneic HSCT –38. Clin


Cell lines are widely-used models to study metastatic cancer although the extent to which they recapitulate the disease in patients remains unknown. The recent accumulation of genomic data provides an unprecedented opportunity to evaluate the utility of them for metastatic cancer research. Here, we reveal substantial genomic differences between breast cancer cell lines and metastatic breast cancer patient samples. We also identify cell lines that more closely resemble the different subtypes of metastatic breast cancer seen in the clinic and show that surprisingly, MDA-MB-231 cells bear little genomic similarities to basal-like metastatic breast cancer patient samples. Further comparison suggests that organoids more closely resemble the transcriptome of metastatic breast cancer samples compared to cell lines. Our work provides a guide for cell line selection in the context of breast cancer metastasis and highlights the potential of organoids in these studies. While cell lines and organoids are extensively used to study cancer, how closely they resemble the disease in patients remains unclear. Here, Liu et al. shed light on this issue by comparing the genomic and transcriptomic profiles of different breast cancer cell lines and organoids to data from patient-derived breast cancer metastases. However, the fact that many drugs with promising preclinical evidence fail in the clinic urges the reinvestigation of cell lines as tumor models2. The differences between cell lines and tumors have raised the critical question to what extent cell lines recapitulate the biology of tumor samples4.Cancer cell lines were initially derived from tumors and cultured in a two-dimensional environment. Due to the merit of cell culture, they have been widely used as models to study cancer biology and test drug candidates5. The Cancer Cell Line Encyclopedia (CCLE) characterized both genomic and transcriptomic profiles for more than 1000 cell lines6. Domcke et al.7 performed a comprehensive comparison of molecular profiles between 47 ovarian cancer cell lines and ovarian tumor samples and showed that several rarely used cell lines more closely resembled high-grade serous ovarian tumor samples than popular cell lines. We examined the transcriptome similarity between hepatocellular carcinoma (HCC) cell lines and HCC tumor samples and demonstrated that nearly half of the HCC cell lines did not resemble HCC tumor samples8. Jiang et al.9 conducted a comprehensive comparison of molecular portraits between breast cancer cell lines and primary breast cancer samples, and uncovered both similar and dissimilar molecular features.The emergence of large-scale genomic data provides an unprecedented opportunity to quantify the biological differences between cancer cell lines and human tumors. The Cancer Genome Atlas (TCGA) project characterized both genomic and transcriptomic profiles for more than 10,000 human tumor samples across over 32 tumor types11. Previous cell line evaluation analysis was mainly performed in reference to primary tumors. It remains unknown whether cell lines closely resemble metastatic cancer and thus are appropriately used in translational research. Robinson et al.12 performed whole-exome and transcriptome sequencing on 500 adult patients with metastatic solid tumors and recently released their dataset (MET500). This large-scale genomic profiling combined with existing genomic data allows the evaluation of the utility of cell lines as models for metastatic cancer. Using breast cancer as a case study, we comprehensively compare multiple types of molecular features between breast cancer cell lines and metastatic breast cancer samples , PIK3CA (0.35), TTN (0.29), OBSCN (0.19), and ESR1 (0.14). We identified 19 differentially mutated genes between MET500 and TCGA samples (FDR < 0.001) and the five most significant genes were ESR1, TNK2, OBSCN, CAMKK2, and CLK1  were mutated in at least 50% of cell lines, suggesting that majority of these gene mutations could be recapitulated by only a few cell lines. Surprisingly, 9 of the 75 genes  were not mutated in any cell line. Notably, ESR1 has been identified as a driver gene of cancer metastasis and associated mutations can cause endocrine resistance of metastatic breast cancer cells14, yet none of the cell lines could be used to appropriately model it.Consistent with previous research, we identified a long-tailed mutation spectrum of the 1630 genes in MET500 breast cancer samples and 69 of them were highly mutated  in MET500 breast cancer samples. Further analysis of somatic mutation profiles of the 25 genes in TCGA breast cancer samples confirmed their hypermutations were specific to breast cancer cell lines  profiles between MET500 breast cancer samples and CCLE breast cancer cell lines. We observed a high correlation of median CNV values across the 1630 commonly genotyped genes . The top 20 cell lines were all breast cancer cell lines, suggesting metastatic breast cancer cells retain the transcriptomic signature from the tissue they originated in and cell lines have the potential to resemble the transcriptome of them  is proven to be an effective approach to evaluate cell lines for research purposehem Fig. . MDA-MB-We next assessed whether cell lines resembling the transcriptome of samples from different metastatic sites were identical. We were only able to consider liver and lymph node (the two sites which have at least nine samples) due to the lack of enough samples from other sites in the MET500 dataset. For each of them, we performed metastatic-site-specific TC analysis  and found the results were highly correlated Fig.  with MDAGiven the genomic heterogeneity of breast cancer, we further asked whether cell lines resembling the transcriptome of metastatic breast cancer of different subtypes were identical. To address this question, we first determined the PAM50 subtype of MET500 breast cancer samples with R package genefu and then applied t-SNE to visualize them Fig. . We founTo confirm the robustness of our TC analysis results derived from the comparison between CCLE and MET500 RNA-Seq data, we downloaded CCLE gene expression data profiled by microarray and then searched the GEO database and assembled a microarray dataset containing the expression values of another 103 independent metastatic breast cancer samples. We repeated the TC analysis with microarray data and found results obtained from the two platforms were highly consistent with each other. First, there was a large overlap of the top-ranked cell lines. Out of the 10 cell lines having highest transcriptome similarity with the 103 metastatic breast cancer samples, 6 of them were within the 10 cell lines having highest transcriptome similarity with MET500 breast cancer samples. Second, both metastatic-site-specific and subtype-specific TC analysis results showed high correlations , BT483 , EFM192A , and HCC70 , respectively. Using criteria of FDR ≤ 0.01, we identified 20, 28, and 24 significant cell lines for LuminalA, LuminalB, and Her2-enriched subtypes, respectively. Notably, most of these significant cell lines were derived from metastatic sites and 18 were shared by the three subtypes. Surprisingly, no cell line passed the criterion for Basal-like subtype. We further examined whether this was due to the limited number of Basal-like MET500 breast cancer samples, but found that the number of LuminalA samples was even less than that of Basal-like samples. After we used a more loosened FDR cutoff of 0.05, we obtained 22 significant cell lines for Basal-like subtype. All statistical testing results are listed in Supplementary Data We attempted to identify cell lines which closely resemble the transcriptome of different subtypes of metastatic breast cancer based on the results of subtype-specific TC analysis. Given a subtype, we noticed that for a random CCLE cell line, its transcriptome similarity with MET500 breast cancer samples of that subtype approximately followed a normal distribution ; however, such significant KRT14 differential expression was not detected between MDA-MB-231 and MET500 breast cancer samples of non-Basal-like subtypes ; in addition, we also re-processed the RNA-Seq data of another seven MDA-MB-231 cell line samples collected from SRA database. We re-performed TC analysis between the breast cancer cell lines (with re-processed data) and Basal-like MET500 breast cancer samples and drew similar conclusion as our previous analysis Fig. . Recentlpes Fig. . Our ana21. However, their utility to model metastatic cancer has not been comprehensively evaluated with large-scale genomic data. To fill this gap, we performed additional TC analysis on 26 patient-derived breast cancer organoids using RNA-Seq data. The aforementioned subtype-specific TC analysis showed that the Basal-like subtype had relatively lower correlation with other subtypes and we also observed similar trend in organoids  culture technology, more and more tumor patient-derived organoids have been established and widely used in translational researchids Fig. . We nextids Fig. .Fig. 5Co22 to infer the values of hidden factors (k = 1) and re-performed DE analysis mentioned above and identified 749 subtype-and-model-independent DE genes, all of which were among the previously identified 1017 subtype-and-model-independent DE genes.Our TC analysis has shown that in vitro models such as cell lines and organoids could resemble the transcriptome of metastatic breast cancer to some extent. However, they are still different in many aspects. To characterize such differences, we performed differential gene expression analysis among MET500 breast cancer samples, CCLE breast cancer cell lines, and organoids .Category 2: Only significant in MET500-vs-CCLE comparison .Category 3: Significant in both MET500-vs-organoids and MET500-vs-CCLE comparisons .Category 4: Not significant in either comparison .We further performed gene set differential activity (DA) analysis on the 50 MSigDB hallmark gene sets to characterize differences regarding to specific biological processes Fig. . For nonInterestingly, 27 gene sets could be consensually classified into one specific category, regardless of the subtype. Figure In cancer research, cell lines have been traditionally used to test drug candidates and study disease mechanism. The genomic profile comparison showed that breast cancer cell lines poorly recaptured somatic mutation patterns of metastatic breast cancer samples, while their CNV profiles were more consistent. Moreover, it is worth noting that cell lines carried many specific genomic alternations, possibly due to culture effects. Examples included the 25 genes presenting cell-line-specific hypermutation. Such large genomic differences and variations revealed by the comparison indicates the importance of selecting cell lines to represent heterogeneous metastatic cancer samples. This study investigated two important factors  that need to be considered during cell line selection.Metastatic sites have their distinct microenvironment that has a large impact in shaping the genomic profiles of metastatic cancer cells. However, the metastatic-site-specific TC analysis did not identify cell lines with metastatic-site-specific utility, which seems not to reflect the impact of microenvironment. Further differential gene expression analysis revealed higher expression of immune-related genes in metastatic cancer samples (compared to cell lines), suggesting that the media used to culture cancer cell lines did not model tumor microenvironment appropriately. Therefore, we conclude cell lines evaluated in this study do not carry indicative genomic signatures that are shaped by the microenvironment of individual metastatic sites and that might be the reason why we did not find metastatic-site-specific cell lines.Breast cancer is quite heterogeneous and we showed that PAM50 subtypes were maintained in metastatic breast cancer cells. Considering the large genomic difference between Basal-like and other subtypes, it is not surprising that in subtype-specific TC analysis Basal-like subtype showed lower correlation with others. Our analysis reveals the importance and necessity of subtype-specific cell line selection. In the future as data continue to accumulate, more factors can be considered for appropriate cell line selection and we can start building an ad-hoc mapping algorithm: inputs would be the characteristics of metastatic cancer samples  as well as the specific scientific question of interest and the output would be a list of appropriate cell lines.19 showed that breast epithelial cell populations corresponded to breast cancer subtypes. Previous researchers have associated MDA-MB-231 with Claudin-low subtype and our analysis suggested that the cell type of origin of MDA-MB-231 cell line may not be Basal cells23. In the future, as more single-cell RNA-Seq data become available, it would be valuable to accurately determine the cell type of origin of MDA-MB-231 and optimize its usage in cancer metastasis-related studies.Surprisingly, we found MDA-MB-231, the widely used triple-negative cell line in metastatic breast cancer research, was dramatically different from Basal-like metastatic breast cancer samples. According to our analysis, HCC70 seems to be a better model, but this does not mean it can be directly employed to study cancer metastasis as many other criteria are needed for the assessment. Triple-negative breast cancer is itself highly heterogeneous. In a recent study, Nguyen et al.24. Together with our comparative studies, we conclude that although the value of organoids in translational research has not been fully recognized, while their high genomic similarity with patient samples warrants further investigation.Organoids are recently established using 3D culture methods. Our analysis suggested that compared to cell lines, they resemble the transcriptome of patient samples more closely, which is a critical characteristic in drug testing. It is also important to note that cell lines evaluated in our study were established much earlier than organoids. During the culturing process, they could have accumulated additional genomic alternations, which may partially explain why organoids are more tightly correlated with patient samples. Recent studies have shown that organoids preserve the histological architecture, gene expression, and genomic landscape of the original tumor9. It has been shown that gene expression is one of the most informative features to predict drug response and weighting cell lines based on their transcriptome similarity with patient samples increases predictive power in gene expression-based drug discovery27. Therefore, in this paper we ranked cell lines according to TC analysis results. Note that different expression features could be employed to measure transcriptome similarity depending on the question. For example, for researchers who specifically focused on the molecular mechanisms of cancer metastasis, it may not be necessary to require the cell line model (such as MDA-MB-231) to resemble the whole transcriptome of patient samples as long as it could mimic the key biological processes in cancer metastasis. In such scenario, ranking cell lines according to their invasiveness might be more appropriate.Prior to this study, researchers have proposed different computational methods to measure the similarity between cell lines and patient samplesIn summary, by leveraging publicly available genomic data, we comprehensively evaluated the utility of breast cancer cell lines as models for metastatic breast cancer. Our study introduces a simple framework for cell line selection which can be easily extended to other cancer types. Although there are concerns about data quality and discrepancies between different studies/platforms, our large-scale analysis and cross-platform validation hopefully addresses these concerns and demonstrates the power of leveraging open data to gain biological insights of cancer metastasis. We hope that the recommendations in this study may facilitate improved precision in selecting relevant cell lines for modeling in metastatic breast cancer research, which may accelerate the translational research.29. FPKM values were used as gene expression measure. To keep consistent with other RNA-Seq datasets, only the RNA-Seq samples profiled with PolyA protocol were considered. The somatic mutation and CNV data of MET500 samples were downloaded from MET500 web portal (https://met500.path.med.umich.edu/downloadMet500DataSets).The raw RNA-Seq data of MET500 breast cancer samples  were downloaded from dbGap (under accession number phs000673.v2.p1) and further processed using RSEMhttps://portals.broadinstitute.org/ccle). The raw RNA-Seq data of 55 CCLE breast cancer cell lines were downloaded from GDC Legacy Archive  and further processed by RSEM. For each CCLE breast cancer cell line, we computed spearman rank correlation between gene expression values quantified by CCLE pipeline and RSEM. Supplementary Fig. 30. Therefore, in our analysis we used the filtered version of CCLE somatic mutation MAF file  in which common polymorphism variants have been excluded.All CCLE pre-processed data  were downloaded from the CCLE data portal (32 on 12 April 2018 (using R package cgdsr) and RSEM-processed gene expression data were downloaded from UCSC Xena data portal (https://xena.ucsc.edu/)33.For TCGA Breast Invasive Ductal Carcinoma samples, somatic mutation calling results were downloaded from cBioPortal34.The RNA-Seq data of patient-derived organoids were from BC Organoids Biobank38. The GEO accession numbers used were GSE11078, GSE14017, GSE14018, and GSE54323.We searched GEO and manually assembled a microarray dataset containing gene expression values of 103 metastatic breast cancer samplesThe gene expression data of lung-metastasis-derived MDA-MB-231 were downloaded from GEO under accession number GSE2603. The RNA-Seq data of non-CCLE MDA-MB-231 were downloaded from SRA and the accession numbers are ERR1982279, ERR1982280, ERR2022825, SRR2532366, SRR4822549, SRR6451704, and SRR6451705.Detailed statistics of the above datasets are listed in Supplementary Data https://data.broadinstitute.org/ccle_legacy_data/hybrid_capture_sequencing/CCLE_hybrid_capture1650_HGNC_info_2012.02.20.txt). Then, we applied the following steps to get the final 1630 highly confident genes.Ten gene symbols which do not have associated HGNC records were removed.Nine gene symbols which do not have associated RefSeq records  were removed.One Y-chromosome located gene PRKY was removed.We downloaded the list of 1650 genes covered by CCLE hybrid capture sequencing from CCLE data portal  to test whether a gene has significantly lower mutation frequency in metastatic breast cancer samples. To control FDR, we applied the Benjamini–Hochberg procedure on left-tailed and right-tailed p-values, respectively39. We noticed that somatic mutations of MET500 cohorts were called by Varscan2 (ref. 40) while TCGA somatic mutation data hosted on cBioPortal were called by MuTect30. To exclude the possibility that the differences inferred between MET500 and TCGA primary tumors were due to pipeline batch effects, we downloaded Varscan2 processed TCGA somatic mutation data from the GDC portal  and found gene mutation frequency was highly consistent between the two databases  were also highly correlated . Given RNA-Seq profiles of a cell line (or an organoid) and several patient samples, we compute spearman rank correlation (across the 1000 marker genes) between the cell line (organoid) and each sample and the median value of computed spearman rank correlation values was defined as the transcriptome similarity of the cell line (organoid) with the patient samples. For microarray data, a similar procedure was applied and the 1000 most-varied probe sets were used to compute correlation values.We also extended the above method to compute CNV similarity. Instead of selecting “marker genes”, all of the 1630 commonly genotyped genes were used.42. To visualize tumor samples with t-SNE, we first computed the pair-wise distance between every two samples as 1 minus the spearman rank correlation across PAM50 genes and then applied the function Rtsne to perform 2D reduction43.The genefu package was used to determine breast cancer subtypehttps://www.ncbi.nlm.nih.gov/pubmed/). For each cell line, we searched with a keyword”[cell line name] metastasis”. We repeated this step for the terms “metastatic”, “breast cancer”, and “metastatic breast”. These searches returned highly correlated results, so we used the search terms which returned the most results: “[cell line name] metastasis”.The number of PubMed abstracts or full texts mentioning a CCLE breast cancer cell line was determined using the PubMed Search feature on 10 May 2018  > 1) and DAVID bioinformatics sever was used to perform Gene Ontology enrichment analysishttp://software.broadinstitute.org/gsea/msigdb/)49. Then, for each gene set the two-sided Wilcoxon rank-sum test was used to assign the p-value in the comparison of ssGSEA scores between MET500 samples and cell lines (or organoids).In DA analysis, we first used the R package GSVA to compute ssGSEA scores for the 50 MSigDB hallmark gene sets (51. Tumor purity was estimated using ESTIMATE16. CNTools was used to map the segmented CNV data to genes52. If not specified, the two-sided Wilcoxon rank-sum test was used to compute the p-value in hypothesis testing.All of the analysis was conducted in R. The ggplot2 and ComplexHeatmap packages were used for data visualizationFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting SummarySource Data


We analyzed 4467 breast cancers and identified GABRP as top expressed gene in TNBC with low expression in most normal tissues. GABRP protein was localized to cell membrane with broad range of receptors/cell  and expressed by nearly half of breast cancers tissues. GABRP gene knockdown inhibited TNBC cell growth and colony formation in vitro and growth of MDA-MB-468 xenografts in nude mice. Commercially available anti-GABRP antibody (5–100 μg/ml) or de novo generated Fabs (20 μg/ml) inhibited TNBC cell growth in vitro. The same antibody conjugated to mertansine (DM1) also showed significant anticancer activity at nanomolar concentrations. Our results indicate that GABRP is a potential novel therapeutic target for ADC development.The goal of this study was to identify a novel target for antibody-drug conjugate (ADC) development in triple negative breast cancer (TNBC), which has limited treatment options, using gene expression datasets and  Antibody drug conjugates (ADCs) allow targeted delivery of highly toxic agents to cancer cells that express a specific antigen without extensive adverse effects on normal tissues4. These drugs are emerging as a potential new strategy to treat TNBC. Preliminary results from clinical trials with IMMU-132 , CDX-011 , and SGN-LIV1A (that targets a zinc transporter SLC39A6) show around 30% objective tumor response rates in patients with metastatic TNBC that progressed on multiple prior lines of chemotherapies . Our goal was to identify new targets for ADC development in TNBC. We compared mRNA expression profiles of TNBC with other breast cancer subtypes to identify genes that are overexpressed on TNBC surface, and identified gamma-amino-butyric-acid receptor π subunit (GABRP) as the most promising candidate gene for further functional experiments.Triple negative breast cancers (TNBC) lack amplification of HER2 and expression of estrogen and progesterone receptors, and represent 15% of all breast cancers. Currently the only approved treatment options for newly diagnosed TNBC patients are chemotherapy agents7. GABAA receptors in the central nervous system are most frequently made up of two α, two β and γ subunits7 and function as inhibitors of synaptic transmission. The function of the π subunit is not clear; it is not detectable in neuronal tissues and it is predominantly expressed at low levels in reproductive organs such as the uterus, ovary, prostate and breast9. GABRP levels change during the reproductive cycle and gestation and are upregulated during the window of implantation in the human endometrium, suggesting a role in these processes12. We have previously identified GABRP as a highly expressed gene in TNBC13. In this paper, we investigate the function of GABRP in TNBC growth in vitro and in vivo and assess its potential as a candidate for ADC development.The GABRP gene encodes the π subunit of the gamma-aminobutyric acid (GABA) A receptor. The GABAA receptor is a heteropentameric, ligand-gated chloride channel composed of multiple subunits including α1–6, β1–3, γ1–3, δ, ε, θ, π and ρ1–3. Each subunit consists of a conserved extracellular domain, 4 transmembrane domains (TM1–4) and intracellular loops). GABRP was also overexpressed in the basal subtype of TCGA breast cancer cohort  < 0.0001 observed in two independent datasets. GABRP displayed the highest fold-change (8.18×) in the MDACC cohort Fig.  and in t1Cort Fig. . All expTM) and gpNMB   and non-TNBC (SKBR3 and BT-474) cells express comparable GABRP protein levels Fig. . This ob21. However, clinical response to ADC is determined by multiple factors including receptor turnover rate, inflammatory and immune response, and by-stander effects of the toxins released, in addition to surface receptor density.We quantified GABRP density on cell surface using QuantiBRITE PE flowcytometry assay Fig. . ReceptoWe next examined GABRP protein with IF Fig. , and chrin vitro results, we observed significantly reduced tumor growth and tumor formation in the knockdown cells compared to shRNA control or the parent MDA-MB-468 cells in female athymic Nude-Foxn1 mice  were 3.80 and 4.10 respectively.We commissioned ADCs from Levena Biopharma and requested conjugation of DM1 using a stable linker to (i) commercially available ECD bindingpolyclonal GABRP-antibody (GABRP-Ab) and (ii) polyclonal isotype control IgG. Excess DM1 was removed after conjugation and free DM1 was limited to <5% in each ADC batch by centrifugal ultrafiltration using 50 kDa molecular weight cut-off (MWCO) Amicon columns (7x rounds) and size exclusion chromatography (SEC). Unconjugated antibody was 5% for each ADC, as determined by hydrophobic interaction chromatography (HIC)-HPLC. Drug to antibody ratios (DAR) calculated using ultraviolet-visible spectrophotometry 22. We therefore, tested the naked (unconjugated) commercially available ECD-binding GABRP-Ab that suppressed the growth of breast cancer cell lines, particularly MDA-MB-468, in a dose-dependent manner starting at 20 μg/ml. The ICD-binding GABRP-Ab had no effect in concentrations up to 100 μg/ml  in the same cell lines. These effects are specific as CRISPR KO cell lines are more refractory to anti-GABRP Fab induced growth inhibition . These effects of anti-GABRP-DM1 were significant but modest indicating that the antibody and the conjugation technique will require further optimization23.The anti-GABRP antibody-DM1 conjugate (GABRP Ab-DM1) showed significantly higher growth inhibitory effect at nanomolar concentrations (10–500) than control (IgG)-DM1 Fig. . InteresFig. . These eOur results suggest that GABRP is a potential novel therapeutic target in TNBC. Its membrane localization, high expression in breast cancers, low expression in most normal tissues and important role to sustain cell growth makes it a candidate for ADC development. We also provide proof-of-concept experiments demonstrating anticancer activity of a commercial naked anti-GABRP antibody, anti-GABRP Fab, and a DM1 conjugated ADC version of the same commercial antibody.19; 1000–10,000 for CD33, the target of gemtuzumab ozogamicin21; 1000–9000 for CD54, the target of the therapeutic antibody BI-505, on the surface of myeloma cells19.The distribution of GABRP mRNA expression across many cancer types suggests that an anti-GABRP-ADC may have therapeutic activity in subsets of several currently difficult-to-treat cancers including gastric, colorectal and lung. The generally low expression in normal tissues is reassuring, but we noted relatively high mRNA expression in the upper airways and lungs, although expression levels were still lower than seen in breast cancer  and synthetically generated Fabs induced greater growth inhibition than the commercially available polyclonal antibodies.Antitumor activity of naked antibody is not a prerequisite for successful ADC development, as naked antibodies against targets such as CD30, CD138, SLC39A6, gpNMB alone do not show significant antitumor activity, despite substantial clinical activity of the conjugated antibodies. However, several successful ADCs’ targets are important for cell survival and therefore the naked antibody also has clinical activity (e.g. trastuzumab). We demonstrated in siRNA and CRISPR knockout experiments that GABRP expression is important for TNBC cell growth, both in vitro. We observed enhanced inhibitory activity compared to the isotype-matched control in GABRP expressing cells and not in GABRP KO isogenic background. The relatively low activity of our ADCs may be due to less than optimal internalization of the IgG and non-uniformity of DM1 conjugation. Using our method of conjugation, DM1 can bind to any of ~30 available IgG lysine sites and therefore the resulting ADCs are a mixture composed of variable number of toxic cargo on each antibody. Higher than 3/4 DM1 molecules conjugated to an antibody can reduce the antigen-binding potency, solubility and stability of an ADC26. The observed cytotoxicity of the isotype control-DM1 conjugates at higher concentrations is likely due to non-specific antibody internalization or free DM1 in the medium. These results support the target function of GABRP but also highlight the need for further antibody and conjugation optimization23.To further credential GABRP as a potential therapeutic target, we used a commercially available anti-GABRP ECD-antibody for ADC generation and tested its effect on cell growth Our systematic evaluation for target identification and functional validation followed by generation of specific GABRP Fabs and ADCs helped building a robust preclinical package for drug development. These steps provide a framework that can be extended to the discovery of other novel ADC targets, and in other cancer types.28 were used as the discovery cohort to define candidate TNBC-genes. A previously described gene expression dataset (Affymetrix U133A or U133Plus2.0 arrays) of 4467 breast cancer patients compiled from 40 publicly available data sets30 was used for validation. Additional details are in the Supplementary Methods.Affymetrix U133A gene expression profiles from 294 fine-needle aspiration (FNA) biopsies of stage I-III breast cancers at MD Anderson Cancer Center (MDACC)TNBC  and non-TNBC HER2-amplified (SKBR-3 and BT-474) cell lines were purchased from the American Type Culture Collection  where all cell lines were authenticated by short tandem repeat profiling, karyotyping, morphology and cytochrome C oxidase I testing. Cell lines were obtained between 2011 and 2016, and used at passages 3–9, and cultured less than 3 months after resuscitation. Additional details are in the Supplementary Methods.6cells/100 mm plate and grown to subconfluency, followed by extraction by RIPA buffer containing proteinase and phosphatase inhibitors for whole cell lysates. Membrane and cytoplasmic protein fractions were extracted using Subcellular Protein Fractionation Kit (Thermo Scientific). Lysate samples were electrophoresed in 4–12% NuPAGE SDS-polyacrylamide midigels (Life Technologies Corporation) and transblotted onto PVDF membrane as described previously31. For details, see Supplementary Methods.Cells were plated at 1 × 10Number of GABRP receptors on cell surface was quantified by BD QuantiBRITE PE (BD BioSciences) flow-cytometric analysis. GABRP ECD-binding specific monoclonal antibody-fragments (Fabs) were used in GABRP detection on cell surface in Fig. Cells grown to subconfluency in 24-well culture plates were fixed with 4% formaldehyde, permeabilized with 0.5% triton-X, blocked by 1% BSA and incubated overnight with primary antibody against GABRP (ICD binding), followed by staining with rabbit secondary antibody-tagged with AlexaFlor488 fluorochrome (green). Cell nuclei were counter-stained with Hoechst (blue) dye. Fluorescence was visualized under confocal microscope and 20X images were captured using imaging software (Perkin Elmer).GABRP IHC was performed on 5-μm whole tissue-sections of formalin-fixed paraffin embedded (FFPE) breast cancer tissue microarray  This TMA containing 100 invasive breast cancer tissues with annotated clinical stage, pathology grade, IHC markers  status, was stained using GABRP antibody (Santa Cruz Biotech). For details, see Supplementary Methods.Supplementary Methods. Knockdown of GABRP was also achieved by ~60% efficiency of GABRP CRISPR knockout (KO) in cell lines (polyclones); GABRP knockout (KO-1 and KO-2) MDA-MB-468 and HCC1143 cell lines were generated according to protocol described previously32. Surveyor T7 endonuclease assays were conducted as described previously33. Primer sequences and protocol details are in Supplementary Methods.Human GABRP siRNA oligonucleotides were purchased from Ambion . For stable knockdown, we produced GABRP shRNA encoding pRETRO-SUPER-GABRP plasmid. For details, see Soft agar plates were photographed and colonies were enumerated using ImageJ software version 1.46r.Female athymic Nude-Foxn1 nude mice (Nu/Nu) (Harlan) were housed under specific pathogen-free conditions, at The University of Texas MDACC approved by the American Association for Accreditation of Laboratory Animal Care in accordance with the current regulations and standards of the U.S. Department of Agriculture, Department of Health and Human Services, and National Institutes of Health. Experimental details are in the Supplementary Methods.Cell lines were plated at 2000 cells/well (3 wells/group) in clear bottom opaque-walled 96-well culture plates (Thermo Scientific) in media containing 5% FBS, and exposed to respective experimental treatments and counted in a hemocytometer after staining by 0.4% trypan blue or luminescence measured using the ATP-based CellTiter-Glo® luminescent cell-viability assay (Promega).To generate a novel ADC against GABRP, commercially available ECD binding GABRP antibody (Abcam) was conjugated to mertansine (DM1), a potent thiol-containing maytansinoid toxin, by ‘Immunogen Conjugation’ method to achieve drug to antibody ratio (DAR) of 3–4 . Further details are in the Supplementary Methods.Supplementary InformationSupplementary Table 1Supplementary Table 3