The role of PD-L1 as a prognostic and predictive biomarker is an area of great interest. However, there is a lack of consensus on how to deliver PD-L1 as a clinical biomarker. At the heart of this conundrum is the subjective scoring of PD-L1 IHC in most studies to date. Current standard scoring systems involve separation of epithelial and inflammatory cells and find clinical significance in different percentages of expression, e.g., above or below 1%. Clearly, an objective, reproducible and accurate approach to PD-L1 scoring would bring a degree of necessary consistency to this landscape. Using a systematic comparison of technologies and the application of QuPath, a digital pathology platform, we show that high PD-L1 expression is associated with improved clinical outcome in Triple Negative breast cancer in the context of standard of care (SoC) chemotherapy, consistent with previous findings. In addition, we demonstrate for the first time that high PD-L1 expression is also associated with better outcome in ER- disease as a whole including HER2+ breast cancer. We demonstrate the influence of antibody choice on quantification and clinical impact with the Ventana antibody (SP142) providing the most robust assay in our hands. Through sampling different regions of the tumour, we show that tumour rich regions display the greatest range of PD-L1 expression and this has the most clinical significance compared to stroma and lymphoid rich areas. Furthermore, we observe that both inflammatory and epithelial PD-L1 expression are associated with improved survival in the context of chemotherapy. Moreover, as seen with PD-L1 inhibitor studies, a low threshold of PD-L1 expression stratifies patient outcome. This emphasises the importance of using digital pathology and precise biomarker quantitation to achieve accurate and reproducible scores that can discriminate low PD-L1 expression. DNA-based high-throughput mutational analysis , 2 and hIt has long been thought that the ultimate cure for cancer would arise from harnessing the host immune system that, through its adaptive nature, could eradicate cancer cells even as they evolve. Indeed, in breast cancer, the presence of Tumour Infiltrating Lymphocytes (TILs) is a positive prognostic factor , 5 and aMost predictors of drug response also have a pure prognostic value. Indeed, PD-L1 expression has been studied in multiple cancer types and correlated with various clinicopathological parameters –19. In gAt the heart of this conundrum is the subjective scoring of PD-L1 IHC. Current standard scoring systems for PD-L1 as a companion diagnostic involve separation of epithelial and inflammatory cells and find clinical significance in percentages of expression above or below 1% , 35. CleWe conducted a retrospective case-control study involving a cohort of TNBC patients; ethical approval was obtained and tissue was acquired through the Northern Ireland Biobank (NIB ref: 15-0168). The dataset was compiled for a cohort of 109 patients and included a range of clinical and pathological parameters Supp . All of Original haematoxylin and eosin (H&E)-stained sections of all of the blocks were reviewed for tumour block selection. Following block selection, a new H&E section was cut and the slide was annotated for TMA construction. Where possible, from each selected donor block, nine representative areas were annotated for targeted coring – 3 tumour epithelial rich cores (T), 3 tumour cores from lymphoid rich areas (TL), and 3 stromal rich tumour cores (TS) in the 90-patient cohort. The TMA was constructed with 1-mm-diameter cores using a manual tissue arrayer  as described previously .The PD-L1 IHC Reference Standard consists of a genetically defined 11-spot custom cell line array (CLMA) with a range of negative, low, medium, and strong protein expressing cell lines developed using Horizon Discovery's gene editing technology. Parental cells were purchased from a commercial source and were characterised short tandem repeat (STR) analysis. Cell lines were engineered under the control of promoters of different strengths that resulted in a range of controlled PD-L1 protein expressing cell lines by IHC. Individual cell lines in the 11 spot CLMA were extensively characterized and verified using molecular assays, IHC, and quantitative digital pathology by Horizon Discovery.All IHC was performed in an integrated laboratory (Northern Ireland Molecular Pathology Laboratory) that has UK Clinical Pathology Accreditation . SectionTILs were assessed by three independent experienced immunohistochemists on H&E sections as recommended by the International TILs working group 2014 . TILs weDigital pathological analysis of the IHC stained TMA slides was performed using QuPath , 41, an Quantitation of PD-L1 was conducted as previously described . BrieflyManual chromogenic RNAScope for PD-L1  was performed on TMA sections as previously described . RNAscopLin's concordance analysis was used to examine the relationship between the PD-L1 scores using different antibodies with two-sided correlation coefficients reported. Results were interpreted using the cutoffs proposed by McBride . Fisher'In order to assess the reliability of PD-L1 analysis using the digital pathology platform, QuPath, we first utilised a genetically defined 11-spot custom cell line array PD-L1 IHC Reference Standard from Horizon Discovery . We compIn addition to IHC, PD-L1 may also be quantified using in situ hybridisation using RNAScope. In situ PD-L1 mRNA expression was not detectable in the null cell line standards (A1-2). As seen with the IHC experiment, more varied expression was observed for the low to medium expressing cell line standards . Increased expression was seen in the high cell line standards (C3-5) although there appears to be discordance between protein expression (measured by IHC) and mRNA (as measured by RNAScope) in 2 of the 3 high cell lines (C4-5) . This coBased on these results, we took forward the SP142 IHC on the Benchmark platform as well as the RNAScope assay for assessment in patient samples.In order to fully investigate the role of PD-L1 as a biomarker we initially focussed our assessment on TNBC as it demonstrates the greatest range of PD-L1 expression and most significant correlation with patient outcome. Therefore, we constructed a bespoke TMA that would allow us to compare not only the different technology platforms  but also different tumour areas. For each patient a total of nine cores were selected; three tumour rich cores (T), three tumour cores with high stroma (TS), and three tumour cores tumour from a lymphoid rich region (TL) .Microscopic IHC analysis with SP142 PD-L1 antibody on individual TMA cores demonstrated various expression patterns of PD-L1 in the different tissue compartments  PD-L1 expression across all 9 cores per patient. A threshold of 1% was used to stratify IHC-based PD-L1 expression as high (>1%) or low (<1%) in keeping with the clinically relevant thresholds reported in the literature with 44% of cases called PD-L1 positive using the SP142 antibody. Median expression was used to dichotomise the mRNA expression. While similar trends were observed with high PD-L1 expression associated with increased relapse free survival (RFS), only assessment of PD-L1 expression by IHC using the SP142 antibody was significant correlation (HR0.536 (95%CI 0.316-0.94) p=0.0294) . SimilarNext we wanted to compare the correlation with survival of PD-L1 expression in the different regions of the tumour . Galon and colleagues were the first to highlight the importance of the immune contexture showing that type, density, and location of immune cells within colon cancer predict clinical outcome , 50. TheFollowing on from this we wanted to further determine the importance of the cell context of PD-L1 expression. Therefore PD-L1 was categorised based on expression within the stroma (lymphoid cells) vs the tumour nest . Analysis was restricted to the tumour rich (T) cores given the significant correlation with clinical outcome. The range of expression was very similar and there was a highly significant correlation between stromal and tumour derived expression  . Further2 = 0.7456). Furthermore, as previously reported in the context of anthracycline based chemotherapy [2 = 0.151) and when analysed in the context of low (<1%) and high (>1%) PD-L1, the percentage of TILs observed was significantly higher in the high PD-L1 cases (p = <0.0001)  using the methodology recommended by the International TILs Working Group 2014  to reporotherapy , high TIotherapy . A weak <0.0001) . It is iIn order to further characterise the composition of the immune infiltrate in the context of PD-L1 expression, we utilised IHC and gene expression analysis that had been previously carried out on a subset 67/109) of the TNBC cohort  of the T and a siWe then wanted to extend the analysis of PD-L1 expression into other breast subtypes and this was achieved using an additional cohort accessed through the Northern Ireland Biobank (NIB). This was made up of 300 breast cancer patients, all of which were treated with FEC, creating a relatively uniform clinical cohort . Given tAs expected, high PD-L1 expression was associated with improved RFS and OS in TNBC patients (HR 0.355 (95%CI 0.148-0.84) p= 0.019 and HR 0.255 (95% CI 0.087-0.861) p=0.0269, respectively) . HoweverThis is an important finding for this poor outcome group of patients which represents 20-30% of all breast cancer cases and is associated with a disproportionately high number of deaths especially in the first five years following diagnosis.The role of PD-L1 as a prognostic and predictive biomarker is an area of great interest within oncology due to its association with the novel immune checkpoint inhibitors. It is also showing great promise in the treatment of a range of cancer types. However, inconsistent results have been obtained in the analysis of the PD-L1 biomarker. We propose that lack of an objective, reproducible and accurate approach to PD-L1 quantification and scoring may, at least in part, underlie this. Therefore, the aim of this study was to perform a comprehensive study of PD-L1 as a biomarker in breast cancer using automated tumour recognition and quantitative IHC image analysis together with objective scoring systems on uniform patient tissue cohorts with extensive clinical annotation. We show that the use of different antibodies during IHC can have a significant impact on the detected PD-L1 expression and its association with clinical outcome. This was compared to mRNA expression as determined by RNAScope. We show here that high PD-L1 expression, as measured by IHC using the Ventana SP142 clone on a Ventana automated platform and quantified using the QuPath analysis platform, is significantly associated with better clinical outcome in TNBC treated with current standard of care chemotherapy. This observation was confirmed using a second TMA in which a large proportion of the TNBC cases were replicated. When immune topography was taken into account, tumour rich cores showed the greatest predictive power with analysis of PD-L1 expression regardless of cell type  demonstrating the most clinical relevance. Looking at breast cancer as a whole, there was also an association between high PD-L1 expression and improved clinical outcome. Moreover, we show for the first time that PD-L1 expression significantly predicts clinical outcome in all ER negative patients, including HER2 enriched disease.This study highlights the potential risk of obtaining varied results when investigating the role of PD-L1 as a biomarker and may explain some of the inconsistencies within the field. As previously mentioned, there is a range of commercially available antibodies for PD-L1 IHC studies. Mahoney et al. have shown that the region of PD-L1 against which the antibody is raised can have a significant impact on the staining patterns observed . They coCurrent standard scoring systems for PD-L1 as a companion diagnostic to immunotherapy find clinical significance in percentages of expression above or below 1% . We alsoThis sensitivity and specificity has allowed us to elucidate a role for PD-L1 in predicting response in patients within the ER negative HER2 enriched subgroup. To the best of our knowledge, this is the first time the utility of PD-L1 as a biomarker has been demonstrated to predict improved outcome in this subgroup. Along with TNBC, this is a subgroup of unmet clinical need. While use of HER2 targeted agents has had undeniable clinical benefit, response rates are low with best responses rates (~50%) observed in the neo-adjuvant setting with a combination of targeted agents , 60. FurOf interest, high levels of TILs are associated with better response to Trastuzumab in both the neo-adjuvant and adjuvant setting potentially associated with antibody-dependent cell-mediated cytotoxicity (ADCC) mechanism of action of the drug . FurtherMuenst et al.  previousIn addition to highlighting the novel role of PD-L1 in predicting outcome in ER-negative breast cancer, this study has also shown that high PD-L1 expression is associated with improved outcome in breast cancer as a whole. This is in contrast to the overall findings from two recent meta-analyses showing high PD-L1 expression associated with poor outcome , 66. HowThe finding that PD-L1 expression, regardless of the cell type , is prognostic is keeping with the guidelines for the companion assay for Atezolizumab using SP142 . FurtherThis study highlights the need for high quality information at multiple levels  in order to accurately interpret the utility of a biomarker. We have shown the impact of high-resolution image analysis on PD-L1 interpretation from antibody choice to IHC versus mRNA-based quantification. This comprehensive comparison would not have been possible without the objective, reproducible and accurate PD-L1 quantification provided by our digital pathology platform QuPath. We believe this study provides vital information impacting on the clinical delivery and interpretation of PD-L1 as a biomarker and provides clarity to the role of PD-L1 in the prediction of response to standard of care in breast cancer.


Non-toxic liquid-metals offer a viable solution for flexible electrodes due to deformability and low bulk resistivity. However, fabrication processes utilizing liquid-metals suffer from high complexity, low throughput, and significant production cost. Our team utilized an inexpensive spray-on stencil technique to deposit liquid-metal Galinstan electrodes in top-gated graphene field-effect transistors (GFETs). The electrode stencils were patterned using an automated vinyl cutter and positioned directly onto chemical vapor deposition (CVD) graphene transferred to polyethylene terephthalate (PET) substrates. Our spray-on method exhibited a throughput of 28 transistors in under five minutes on the same graphene sample, with a 96% yield for all devices down to a channel length of 50 μm. The fabricated transistors possess hole and electron mobilities of 663.5 cm Since the 1960’s, integrated circuit electrodes have been fabricated from traditional copper, nickel, silver, and gold metals . As the 6 S/m, a desirable vapor pressure (<1 × 10−6 Pa at 500 °C) compared with mercury (0.1713 Pa at 20 °C), and a stable liquid state across a broad temperature range (−19 °C to 1300 °C) [Liquid-Metal (LM) Galinstan is a commercially available eutectic alloy comprising of 68% gallium, 22% indium, and 10% tin that exhibits a conductivity of 2.30 × 101300 °C) ,5.To deposit liquid-metal on electronic devices, mask deposition and microcontact printing have been adopted to improve manufacturing yield to over 95%, while simultaneously decreasing feature size down to 1 µm ,11,12. SIn this article, we demonstrate the use of a low-cost novel spray-on deposition technique for LM using off-the-shelf equipment that improves upon contemporary deposition techniques. We demonstrate the utility of the spray-on deposition of liquid-metal with a proven flexible material combination, consisting of Galinstan source and drain electrodes, an electrolytic gate comprised of honey, and a graphene channel that forms a flexible graphene field-effect transistor . GalinstIt is important to note the quality of monolayer graphene produced commercially has been an issue when fabricating graphene devices, and verification of quality is mandatory before experimentation . The RamTo complete the three-terminal graphene field-effect transistors (GFET) device, honey was loaded into a syringe and drop-casted between each electrode pair to act as an electrolytic gate dielectric, as seen in gV) sweep from ±5 V with a drain voltage (dV) of 10 mV and the charge transport characteristics were plotted in To demonstrate the utility of our spray-on LM technique, graphene charge transport characteristics for several GFET devices with varying channel lengths  were extracted from Current-Voltage (I-V) measurements taken via an Agilent 4155C Semiconductor Parameter Analyzer  and probe station. Each GFET device was subjected to a gate voltage , that can be actuated by altering the gate voltage [The ambipolar nature of all graphene field-effect transistors is made clear with the V-shape in dulation . Notice c oxygen . Therefoc oxygen . To our  voltage ,24,25.The high gate-leakage current density at the smaller channel lengths may be due to the reduced electrode separation distance between the graphene-dielectric interface. It is noted that in the aforementioned process, the PET stencil did not form an ideal, airtight contact with the graphene in all devices during fabrication. Therefore, there were stray microscale LM-spray residues deposited under the masked areas, and in some cases shorted the source and drain electrodes. In addition, for ease of fabrication, the honey dielectric was drop-casted by hand and applied unnecessary pressure to the LM electrodes causing the LM to move, hence the failure of our methods to produce repeatable working devices at the 50 μm channel length size. Due to these circumstances, there was additional stray conductance between the source/drain electrodes and gate electrode, and the additional gate-leakage current density at smaller channel lengths is to be expected. However, the readers are encouraged to optimize this spray-on process with more ideal stencil-mask materials that provide stronger adhesion to graphene, and produce minimal LM residue. Additionally, other liquid dielectrics, such as lower-conductivity ionic gels, can be utilized in place of honey. Lastly, traditional back-gated graphene field effect transistor topologies can be utilized to eliminate stray conductance produced from overlap of the top-gate dielectric.mg) of a 1 mm GFET, The transconductance (Transconductance:Electron and Hole Mobility:The extracted hole and electron mobilities measured for the GFET devices are shown in It has been demonstrated that our spray-on LM method is a viable technique to fabricate nanomaterial devices. We have shown that top-gated graphene field-effect transistor devices with feature sizes down to 100 μm can be fabricated and exhibit standard charge transport characteristics with minimal effort. All devices were produced in a time frame of 30 s per transistor with a reliability of 96%. Our methods can be improved with fully-automated spray and drop-casting techniques, or even with back-gated devices, to improve the speed and reliability of this method. Furthermore, a strong-adhesion stencil mask and higher-resolution stencil-cutter can be used to significantly improve the minimum channel length achieved in this first attempt. This technique, with further modification, can be used inexpensively to mass-produce nanomaterial devices.


The cytoplasmic accumulation of the nuclear TAR DNA-binding protein 43 (TDP-43) is a pathologic hallmark in amyotrophic lateral sclerosis, frontotemporal lobar degeneration, and other neurological disorders. However, most transgenic TDP-43 rodent models show predominant nuclear distribution of TDP-43 in the brain. By expressing mutant TDP-43 (M337V) in the brains of rhesus monkeys and mice, we verified that mutant TDP-43 is distributed in the cytoplasm of the monkey brain and that the majority of mutant TDP-43 remains in the nuclei of the mouse brain. The primate-specific caspase-4, but not mouse homologue caspase-11, could remove the NLS-containing N-terminal domain and generate fragmented TDP-43 that accumulates in the cytoplasm. Moreover, increased expression of caspase-4 in the monkey brain promotes the cytoplasmic accumulation of endogenous TDP-43, and suppressing caspase-4 reduces the cytoplasmic distribution of endogenous TDP-43 in cultured human neural cells. Our findings suggest that primate-specific caspase-4-mediated cleavage of TDP-43 accounts for its cytoplasmic mislocalization in the primate brains and may serve as a potential therapeutic target.The online version of this article (10.1007/s00401-019-01979-0) contains supplementary material, which is available to authorized users. Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease, characterized by muscle weakness due to the degeneration of large motor neurons. TAR DNA-binding protein of 43 kDa (TDP-43) accumulates and forms aggregates in the brains of patients with ALS and frontotemporal lobar degeneration (FTLD) , 7, 35. The cytoplasmic distribution of mutant TDP-43 appears to be species dependent. This is because most transgenic mouse models of ALS show the predominant nuclear localization of transgenic TDP-43 , 59 and 13 vg/ml for AAV-hTDP-43, 2.3 × 1013 vg/ml for AAV-GFP, and 1.8 × 1013 vg/ml for AAV-caspase-4. The following antibodies were used: mouse anti-human TDP-43  for the N-terminal TDP-43 (1-261 amino acids), rabbit anti-TDP-43  for a synthetic peptide corresponding to residues surrounding Gly400 of human TDP-43, rabbit anti-phospho-TDP-43 (Ser409/Ser410) , rabbit anti-LSD1 , rabbit anti-COX-IV , rabbit anti-PDI , rabbit anti-HSP90 , mouse anti-GFP , rabbit anti-NeuN , rabbit anti-GFAP , rabbit anti-caspase-4  and mouse anti-GST . All secondary antibodies were purchased from Jackson Immuno-Research Laboratories. Inhibitors of caspase family members included ZVAD-fmk (Selleckchem), DEVD-fmk (Tocris Bioscience), LEHD-fmk (Tocris Bioscience), and LEVD-fmk (BioVision).The human mutant TDP-43(M337V) cDNA , 59 was Monkeys were housed in accordance with Chinese National standards, which are consistent with the standard set forth in the eighth edition of the NRC Guide for the Care and Use of Laboratory at the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences, Peking Union Medical College, which is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC), International. The animal use and experiments followed the protocol that was approved by the Institutional Animal Care and Use Committee (IACUC) of the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences. This study was conducted in strict compliance with the “Guide for the Care and Use of Laboratory Animals of the Institute of Laboratory Animal Science (est. 2006)” and “The use of non-human primates in research of the Institute of Laboratory Animal Science (est. 2006)” to ensure the personnel safety and animal welfare.All mice (C57BL/6) were bred and maintained in the animal facility at Emory University under specific pathogen-free conditions in accordance with institutional guidelines of the Animal Care and Use Committee at Emory University. The studies followed the protocol approved by the Animal Care and Use Committee at Emory University.n = 12 each group, six males and six females per group) were anesthetized by i.p. injection of 2.5% Avertin, and their heads were placed in a Kopf stereotaxic frame (Model 1900) equipped with a digital manipulator, a UMP3-1 Ultra pump, a 10 μl Hamilton microsyringe. A 33G needle was inserted through a 1 mm drill hole on the scalp. Injections occurred at the following stereotaxic coordinates: 3.1 mm posterior to bregma, 1.0 mm lateral to the midline, 4.7 mm ventral to the dura, with bregma set at zero. The microinjections were carried out at a rate of 0.2 μl/min. The microsyringe was left in place for an additional 10 min before and after each injection. 0.5 μl AAV-TDP43, AAV-caspase-4 or AAV-GFP virus was stereotaxically injected into the right substantia nigra of mice. Viral injection of monkey brains was performed using the facilities at Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences, Peking Union Medical College.Adult wildtype C57BL/6 mice at 8 months of age (n = 6 each group). Each monkey was anesthetized by intraperitoneal injection of 0.3–0.5 mg of atropine, followed by 10–12 mg of ketamine, and 15–20 mg of pelltobarbitalumnatricum per kg body weight. The monkeys were stabilized on a stereotaxic instrument (David Kopf). The precise position of substantia nigra for stereotaxic injection was located by MRI before injection. Ten μl viruses were injected at five different locations in the right substantia nigra  in which the injection site was determined and the depth of needle insertion was calculated from the pre-operatively taken MRI. After the injection was given for 12 weeks, the monkeys with substantia nigra injections were subjected to behavioral analysis, and at the end of behavioral analysis, their brain tissues were isolated for immunohistochemical analysis. We also injected AAV-caspase-4 viruses (10 μl) into the prefrontal cortex of three monkeys. After 12 weeks, we isolated the injected brain tissues for studying the effect of caspase-4 on the cleavage of TDP-43.For AAV viral injection into the monkey brain, we injected AAV-GFP or AAV-TDP-43 into the substantia nigra of male monkeys at the age of 8–12 years . The balance beam apparatus consists of 1 m beams with a flat surface of 12 mm or 6 mm width resting 50 cm above the table top on two poles. A black box at the end of the beam is the finish point. Nesting material from home cages in the black box serves to attract the mouse to the finish point. A lamp serves as an aversive stimulus, shining light above the start point. The time required for a mouse to cross to the center (80 cm) is measured by two motion detectors: one at 0 cm that starts a timer and one at 80 cm that stops the timer. The video camera records the performance.All animal tests were performed in accordance with NIH guidelines for procedures and approved by the Institutional Animal Care and Use Committee of Emory University. Mouse behavior was assessed using a rotarod . Mice (C57BL/6) were trained for 10 min on three consecutive days with the rotarod speed at 5 rpm, and testing commenced after 3 days. The speed of the rod was set to 5 rpm and increased by 0.1 rpm/s. Each mouse went through three trials, and the average data of each group charted. The moribund mice were scored as “dead” and euthanized, and tissues were collected  who died at 67–74 years of age and were confirmed to have TDP-43 aggregates via postmortem histologic analysis. Non-ALS control tissues were obtained from neurologically unaffected patients  who died at 53–92 years of age.g for 3 min, and then placed for initial growth in a 50% glial-conditioned medium . The cells were cultured in neurobasal/B27 medium.Mouse neural crest-derived N2A cell line and human neuroblastoma SH-SY5Y cell line were purchased from ATCC and cultured in DMEM/F12 medium . Medium was changed every 2 days. For culturing mouse primary neuronal cells, cortical neurons were isolated from the cortex of postnatal day 1 mice. Dissected tissue was treated with 0.0625 mg/ml trypsin and 0.0625 mg/ml DNasein 1 × HBSS buffer without calcium and magnesium for 10 min at 37 °C. Cells were washed once with the tissue culture medium, centrifuged at 1500 × For analyzing TDP-43 expression and distribution in the monkey brain, we used three AAV-TDP-43 or AAV-GFP monkeys for Western blotting and another three AAV-TDP-43 or AAV-GFP monkeys for immunocytochemical studies. Animals were anesthetized and perfused with 10 ml 0.9% NaCl, and then with 20 ml of 4% paraformaldehyde in 0.1 M PBS through the left cardiac ventricle. Brains were removed and fixed overnight in the same solution and cryopreserved with 15% and 30% sucrose before sectioning into 10 µM sections with a cryostat (Leica CM1850) at − 20 °C. Sections from monkey or mouse brains or cultured cells were fixed in 4% paraformaldehyde in PBS for 10 min, permeabilized with 0.2% Triton X-100 in PBS for 30 min, blocked with 3% normal donkey serum in 3% BSA for 1 h, and incubated with primary antibodies in 3% BSA overnight at 4 °C. After several washes with PBS, the brain sections or fixed cells were incubated with secondary antibodies conjugated with either Alexa-488 or Alexa-594 (Invitrogen). 0.01 μg/ml DAPI was used to label the nuclei. Fluorescent images were taken with a Zeiss Axiovert 200 MOT microscope of the 40 ×/0.6 lens or 63 ×/0.75 lens, equipped with a digital camera  and Openlab software (Improvision). The immunostaining analysis of TDP-43 subcellular distribution in the injected monkey or mouse brains was performed completely blinded on standardized 40 mm sections. The monkey brain sections were prepared using a brain slicer including the injected regions . Each brain region was used to take at least six images (40 × magnification) that can clearly reveal the subcellular distribution of TDP-43. For the quantitative analysis of differential subcellular location of TDP-43 in the monkey and mouse brain, the numbers of cells showing the nuclear or cytoplasmic TDP-43 per image were presented as the mean ± SEM, and the quantitative data were obtained from three monkeys or six mice per group. Densitometry analyses of fluorescent intensities of aggregates were quantified by ImageJ software .3, 2 mM ATP, pH 8.0) containing protease inhibitor (Roche) and 100 μM PMSF. Ten percent lysates were stored as the total lysate sample. Nuclei and cellular debris were pelleted (P1) at 800 × g for 5 min. The supernatant (S1) was transferred to a new tube and centrifuged at 20,000 × g for 30 min at 4 °C to obtain the mitochondria-enriched pellet (P2). The supernatant (S2) was then used for the soluble cytoplasmic fraction. The S2 was centrifuged at 100,000 × g for 30 min at 4 °C to obtain the endoplasmic reticulum-enriched pellet (P3). Crude nuclear pellets were washed four times with ice-cold homogenization buffer to remove cytoplasmic contaminants. For nuclear purification, the pellets were re-suspended in 374 μl of buffer  with 26 μl of 4.6 M NaCl to generate the final concentration at 300 mM NaCl, homogenized with 20 full strokes in Teflon homogenizer on ice, and sonicated for 10 s. The homogenized samples were kept on ice for 20 min and then centrifuged at 24,000 × g for 20 min at 4 °C.Monkey or mouse brain tissues were homogenized for 25 strokes with a dounce homogenizer ice-cold buffer . Equal amounts (10 μg) of the extracts were incubated with corresponding substrates in 100 μl caspase-4 activity assay buffer  for 30–60 min at 37 °C. Cold water (0.8 ml) was used to stop the reactions, and the reaction mixtures were iced for at least 10 min. The free AFC fluorescence was quantified using the CytoFluor multi-well plate reader  with excitation and emission wavelengths at 400 nm and 500 nm, respectively. All readings were standardized using the fluorescence intensity of an equal volume of free 7-amino-4-trifluoromethylcoumarin (AFC) solution (40 mM), normalized by the protein concentrations and expressed as nmol/min/mg protein.2, 100 µg/ml purified rabbit creatine kinase, 50 mM phosphocreatine, 1 mM ATP, pH 7.6). Monkey or mouse tissues form the brain striatum and cortex were homogenized at 1 g/ml in cold assay buffer using 20 strokes of a glass dounce hand homogenizer and were centrifuged at 500 × g for 5 min at 4 °C to pellet unbroken tissues and membranes. The supernatant was collected and stored on ice, while protein concentrations were determined using a BCA Protein Assay Kit (Thermo Scientific). The lysates (200 μl) at 0.5 mg protein/ml were incubated with GST-TDP-43 beads (200 μl) at 37 °C with 300 rpm shaking for 24 h. The beads were centrifuged and combined with the protein loading dye (0.2% SDS) for SDS-PAGE and Western blotting analysis to detect expression of the cleaved GST-TDP-43 using an anti-GST antibody. An anti-C-terminal TDP-43 antibody was used to detect the presence of C-terminal TDP-43 in the supernatant. Different caspase inhibitors at the concentrations (50–100 μM) were included in the lysates to inhibit caspase activity during the incubation process with GST-TDP-43 beads.Purified GST-mut-TDP-43 (M337V) in Sepharose beads were diluted in cold assay buffer  according to the manufacturer’s protocol. At 48 h following transfection, cells were harvested for immunofluorescent staining and Western blotting. For caspase-4 knockdown experiment, the cultured human neural SH-SY5Y cells were transiently transfected with caspase-4 siRNA (Gene Pham Co. sequence GUGUAGAUGUAGAAGAGAAtt or AAGUGGCCUCUUCACAGUCAUtt) or control siRNA (scrambled sequence) using RNAi Max transfection reagent (Invitrogen) according to the manufacturer’s protocol. At 48 h following transfection, cells were harvested for immunofluorescent staining and Western blotting.4 cells/100 µl/well) was dispensed in a 96-well plate. Each well of the plate was added with 10 µl CCK-8 solution, and incubated for 2 h at 37 °C. The absorbance values at the wavelength of 450 nm were spectrophotometrically measured using an CytoFluor multi-well plate reader . Each sample was tested in three replicates. SH-SY5Y cells without transfection and tunicamycin treatment were used as negative and blank controls. The values (mean ± SEM) of treated SH-SY5Y cells were calculated as  % of control cells.The cell viability assay was done using Cell Counting Kit-8 (CCK-8) , which determines the number of viable cells in proliferation and cytotoxicity. It utilizes tetrazolium-8-[2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5--2H-tetrazolium] monosodium salt that produces a water-soluble formazan dye in living cells. The amount of this dye is directly proportional to the number of living cells, offering a sensitive assay to detect viability of many cell lines including SH-SY5Y cells , 56. Brit test for comparing two groups. When analyzing multiple groups, we used one-way ANOVA to determine statistical significance. For mice or monkey that was repeatedly subjected to behavioral tests, we analyzed the data using two-way ANOVA. Data are mean ± SEM. Calculations were performed with GraphPad Prism software.Statistical significance was assessed using the two-tailed Student’s Our previous studies demonstrated that stereotaxic injection of mutant TDP-43 in the striatum of mice led to abundant nuclear TDP-43 in the injected brain . Using aThree months after the injection, the AAV-GFP monkeys did not show any motor symptoms, but all the TDP-43-injected monkeys developed the obvious weakness of the left upper limb, whose movement is controlled by the contralateral right substantia nigra that was injected with AAV-TDP-43 Fig. b. These The neurological phenotypes of the TDP-43-injected mice and monkeys confirmed the neurotoxicity of overexpressed mutant TDP-43 and also allowed us to compare TDP-43’s expression and localization in these two different species. The brains of mice and monkeys were dissected after euthanization and prepared for Western blotting and immunocytochemistry. We used an antibody against a C-terminal peptide corresponding to residues surrounding Gly400 of human TDP-43 to detect the overexpressed exogenous TDP-43. Immunocytochemistry revealed that injection of the control AAV-GFP yielded diffuse green fluorescence throughout the entire cell . However, the injected mutant TDP-43 was diffusely distributed in the cytoplasm or adjacent to the nuclei in the monkey brain Fig. a. In conThe above findings have shown that different subcellular localization of TDP-43 is species-dependent even when mutant TDP-43 is overexpressed. To investigate whether there is any difference in TDP-43 cleavage, we performed Western blotting using anti-C-terminal TDP-43 antibody. Interestingly, truncated TDP-43 fragments 35- and 25-kD) were abundant in the monkey brain, whereas full-length TDP-43 is expressed at a comparable level as endogenous TDP-43 in the AAV-GFP-injected monkey. However, in the mouse brain, full-length TDP-43 was much more abundant than C-terminal truncated TDP-43, which is consistent with the previous findings from transgenic TDP-43 mice , 52, 55.5-kD wereThe above striking difference led us to examine the differential processing of TDP-43 in the monkey and mouse brains. TDP-43 consists of 414 amino acids with NLS in the N-terminal region and NES in the region close to the C-terminus  that was produced in bacteria and then purified Fig. b, c and In vitro experiments revealed that multiple caspases  cleave TDP-43 in cultured cells , 29, 42.Caspase-4 is an enzyme that cleaves proteins proteolytically at an aspartic acid residue and contributes to inflammatory activation in mono-myelocytic cells , 50. IntCaspase-4 is bound to the endoplasmic reticulum (ER) and can be activated under ER stress . PreviouA previous study reported the increased amounts of caspase-4 in the spinal cords of ALS patients . WesternIf caspase-4 is absent in the mouse brain, but is responsible for the cytoplasmic accumulation of TDP-43, transgenic expression of caspase-4 in the mouse brain should also lead to the cytoplasmic accumulation of TDP-43. We generated AAV vector-expressing human caspase-4 under the control of the ubiquitin promoter Fig. a and conTo provide direct evidence for the causative role of casapse-4 in the cleavage of TDP-43 in cells, we used siRNAs to knockdown the expression of endogenous caspase-4 in cultured human neural SH-SY5Y cells. The siRNA (siCAS4-1) had higher efficiency on silencing endogenous caspase-4 than siCAS4-2 . The siCAS4-1 was then transfected into SH-SY5Y cells for 48 h, and the transfected cells were treated with tunicamycin (1 μg/ml) for 12 h, which is a known ER stress activator for caspase-4 induction , 58. As If TDP-43 is cleaved by activated caspase-4, increased activity or expression of caspase-4 should promote the cytoplasmic distribution of endogenous TDP-43 in the monkey brain. To test this idea, we injected the same titer of AAV-caspase-4 or control AAV-GFP into the prefrontal cortex in 11-year-old wildtype monkeys. After 3 weeks, the injected brain prefrontal cortex was isolated for immunofluorescent staining with antibodies to caspase-4 and C-terminal TDP-43. Double immunofluorescent staining showed that endogenous TDP-43 in the normal monkey brain is predominantly distributed in the nucleus Fig. a. HoweveMislocalization of TDP-43 in the cytoplasm and loss of its nuclear distribution are the major pathological hallmarks in ALS and FTLD , 15, 35 TDP-43 is a major component of cytoplasmic aggregates in the brains and spinal cords of nearly all patients ~ 97%) with ALS and in ~ 45% of FTLD cases % with AL, 35, 40.Recent studies suggest that impaired nuclear–cytoplasmic transport contributes to ALS. The mutations in C9orf72 mRNA, which can also cause ALS by impairing the nuclear–cytoplasmic transport , 62, canIn the current studies using non-human primates, we identified four lines of evidence supporting caspase-4 as a critical contributor to the cytoplasmic accumulation of TDP-43 in the primate brains. First, caspase-4 is found in non-human primates and humans, but not in mice. Second, caspase-4 cleaves TDP-43, but its mouse homologue caspase-11 does not. Third, co-expression of caspase-4 with mutant TDP-43 in the mouse brain leads to the cytoplasmic redistribution of TDP-43. Lastly, overexpression of caspase-4 can increase the cytoplasmic distribution of endogenous TDP-43 in the monkey brain, whereas suppressing caspase-4 expression can reduce the distribution of endogenous TDP-43 in the cytoplasm in cultured human neural cells.In vitro studies demonstrated that TDP-43 was initially cleaved after Asp174 by caspase-4 , which cIt should be pointed out that overexpression of TDP-43 can only partially mimic ALS pathology that is caused by endogenous mutant TDP-43. Similarly, neurodegeneration in TDP-43 transgenic mice may not depend on TDP-43 cleavage and is mediated by different mechanisms. The critical role of nuclear TDP-43 in gene transcription and RNA processing is well documented , 39, 47.Our studies indicate that caspase-4 plays a critical role in the cytoplasmic accumulation of TDP-43. The relevance of this finding to other pathological conditions is also supported by the regulation of caspase-4 activity and expression. Caspase-4 is an endoplasmic reticulum (ER) membrane-bound enzyme and is activated under ER stress , which cSupplementary material 1 (PDF 1272 kb)Supplementary material 2 (MP4 489 kb)Below is the link to the electronic supplementary material.


PD-L1 mRNA expression levels and furthermore, efficacy of predicting response to ICIs as compared to routinely used, standardized IHC procedures.PD-L1 immunohistochemistry (IHC) has been traditionally used for predicting clinical responses to immune checkpoint inhibitors (ICIs). However, there are at least 4 different assays and antibodies used for PD-L1 IHC, each developed with a different ICI. We set to test if next generation RNA sequencing (RNA-seq) is a robust method to determine A total of 209 cancer patients treated on-label by FDA-approved ICIs, with evaluable responses were assessed for PD-L1 expression by RNA-seq and IHC, based on tumor proportion score (TPS) and immune cell staining (ICS). A subset of serially diluted cases was evaluated for RNA-seq assay performance across a broad range of PD-L1 expression levels.PD-L1 mRNA levels by RNA-seq demonstrated robust linearity across high and low expression ranges. PD-L1 mRNA levels assessed by RNA-seq and IHC (TPS and ICS) were highly correlated (p < 2e-16). Sub-analyses showed sustained correlation when IHC results were classified as high or low by clinically accepted cut-offs (p < 0.01), and results did not differ by tumor type or anti-PD-L1 antibody used. Overall, a combined positive PD-L1 result (≥1% IHC TPS and high PD-L1 expression by RNA-Seq) was associated with a 2-to-5-fold higher overall response rate (ORR) compared to a double negative result. Standard assessments of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) showed that a PD-L1 positive assessment for melanoma samples by RNA-seq had the lowest sensitivity (25%) but the highest PPV (72.7%). Among the three tumor types analyzed in this study, the only non-overlapping confidence interval for predicting response was for “RNA-seq low vs high” in melanoma.Assessment of PD-L1 mRNA expression by RNA-seq is comparable to PD-L1 expression by IHC both analytically and clinically in predicting ICI response. RNA-seq has the added advantages of being amenable to standardization and avoidance of interpretation bias. PD-L1 by RNA-seq needs to be validated in future prospective ICI clinical studies across multiple histologies.Measurement of The online version of this article (10.1186/s40425-018-0489-5) contains supplementary material, which is available to authorized users. Five trial-evaluated immunohistochemistry (IHC) assays for the assessment of CD274  expression in formalin-fixed paraffin-embedded (FFPE) samples have been developed as companion and complementary diagnostics alongside immune checkpoint inhibitors (ICIs) targeting PD-L1 and its main receptor  . While tRNA-based assays on FFPE tissues are currently used in the clinic to classify or predict recurrence risk in patients affected by various tumor types, These assays include DecisionDX-Melanoma (Castle Biosciences), Prosigna® (Nanostring Technologies), MammaPrint® (Agendia), Afirma® Thyroid FNA Analysis (Veracyte), and OncoType DX®  –19. MostPD-L1 RNA-seq as a standalone assay, we tested several tumor samples across multiple dilutions. We then used objective response criteria (RECISTv1.1) to compare measurements of PD-L1 by IHC versus RNA-seq to assess clinical utility.The objective of this study was to compare RNA-seq to IHC for the assessment of PD-L1, at both analytical and clinical levels, with the intent to validate RNA-seq as a predictor of response in 209 patients with multiple tumor types treated with ICIs. To demonstrate the linearity and sensitivity of n = 45; 7 responders, 38 non-responders), metastatic cutaneous melanoma , and NSCLC . Our primary clinical endpoint for analysis was objective response rate (ORR), defined as patients with complete responses (CR) or partial responses (PR), and patients with progressive disease (PD) or stable disease (SD) classified as non-responders  to submit existing de-identified specimens and associated clinical data for use in this study. Patients were identified for inclusion of electronic pharmacy records indicated they received at least one dose of checkpoint inhibition therapy in the course of standard care, had adequate pre-treatment FFPE tissue  collected within 2 years of first dose, were evaluable for response by RECIST v.1.1, and had known overall survival from first dose of checkpoint blockade. A total of 209 patients were included, encompassing renal cell carcinoma  and the 28–8 pharmDx antibody , which is the FDA-approved complementary diagnostic for nivolumab. For RCC and NSCLC samples, the 22c3 pharmDx antibody  was employed on Autostainer Link 48 , which is the FDA-approved companion diagnostic for pembrolizumab. Established cutoffs for the diagnostics in each histologic type were used to score PD-L1 IHC tumor proportion score (TPS) and immune cell staining (ICS) as follows: melanoma TPS, 1%,  NSCLC TPPD-L1, nRPM values were subsequently ranked (gene expression rank) from 0 to 100 based on expression of these genes in a reference population representing a wide range of gene expressions in various tumor types, as previously described [PD-L1 expression levels were serially diluted to demonstrate sensitivity and linearity of detection.RNA was extracted from each sample following microscopic tissue review by an anatomical pathologist and selection of specimen representing tumor cells and associated microenvironment. Gene expression was evaluated by targeted RNA-seq of 384 immune transcripts using an analytically validated assay . Absolutescribed . A subsePD-L1 mRNA detection, coefficient of determination (R2) was calculated for the absolute reads generated across various library dilutions. To investigate the relationship between PD-L1 expression by targeted RNA-seq and IHC, IHC TPS and ICS results were categorized as either high or low using the previously described FDA-approved complementary and companion diagnostic scoring guidelines and one-way ANOVA and Tukey honest significant difference (HSD) was performed for all PD-L1 values across all samples. To compare IHC versus RNA-seq for prediction of response, values of TPS ≥1% for melanoma, TPS ≥1% and ≥ 50% for NSCLC, and TPS and ICS ≥1% for RCC were compared to RNA-seq expression interpretations of high (rank ≥75) and not-high (rank < 75), relative to a reference population. To compute sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, a positive result was considered as IHC TPS of ≥1% for melanoma, TPS of ≥1% and ≥ 50% for NSCLC, and TPS and ICS ≥1% for RCC, and high value for RNA-seq expression (rank ≥75). A negative result was considered as IHC TPS of < 1% for melanoma, TPS of < 1 and < 50% for NSCLC, and TPS and ICS < 1% for RCC, and a moderate or low value for RNA-seq expression. Logistic regression was then performed to evaluate the prediction of response based on tumor type, IHC result, and RNA-seq result.To demonstrate the linearity of PD-L1 assessment by RNA-seq was determined by comparing the absolute reads relative to an input of 1.5625, 3.125, 6.25, 12.5, 25, and 50 pM RNA library for tumor samples representing diverse levels of expression , while samples #3 and #4 represent moderate expressors (PD-L1 = 25–75 rank). For samples #1 and #2, PD-L1 transcript detection values ranged from 0 to > 2400 absolute reads, demonstrating a robust positive linear correlation (R2 > 0.98) for clinical specimens expressing high PD-L1 levels. For samples #3 and #4, PD-L1 transcript detection values ranged from 0 to < 450 absolute reads, demonstrating a positive linear correlation (R2 > 0.98) for clinical specimens expressing low-to-moderate PD-L1 levels. Overall, these results demonstrate that detection of PD-L1 mRNA levels in FFPE samples by RNA-seq is consistent across a dynamic range of expression, and that PD-L1 transcripts can be reliably quantified by a continuous variable of absolute transcript reads down to values approaching background.Linearity of p < 2e-16) across the three tumor types. The null hypothesis, which specified that the means of PD-L1 gene expression ranks in each IHC group would not differ, was rejected. Therefore, we performed Tukey HSD for multiple pairwise-comparisons between the means of the IHC TPS/ICS high and not-high groups. Ad-hoc Tukey’s HSD comparisons of NSCLC mean TPS at < 1%  between the various groups that were consistent with RNA-seq ranks.For the 209 samples evaluated, the highest rate of a positive result, defined as IHC TPS of ≥1% for melanoma, TPS of ≥1% and ≥ 50% TPS for NSCLC, and TPS or ICS ≥1% for RCC, or RNA-seq rank ≥75, was observed with NSCLC samples for both IHC and RNA-seq (Table n = 45), melanoma (n = 76), and NSCLC (n = 88) patients receiving an FDA-approved ICI  and NSCLC (n = 2) had limited number of patients treated with anti-CTLA4 agents, but RNA-seq high was associated with the only RCC response to ipilimumab + nivolumab, as well as non-response for all RNA-seq low patients  were associated with the lowest observed ORR across all three tumor types. Conversely, concordant positive results (IHC/RNA-seq +/+) were not consistently associated with the highest ORR. Although only four cases, discordant result between IHC and RNA-seq (IHC/RNA-seq −/+) were associated with 75% ORR in melanoma, (3 responders and 1 non-responder), the highest ORR of any tumor. A high ORR was also documented amongst NSCLC patients with discordant results (IHC/RNA-seq +/−) at a ≥ 50% TPS cutoff (ORR = 66.7%), but not at a ≥ 1% value (ORR = 20%)  have a higher response rate than patients with RCC and NSCLC, and that the expression rank interpretation (“RNA-seq”) has a significant linear (“RNA-seq.L”) relationship to response . The RNA-seq.L estimate, 0.96, is the increase in the log odds of response moving from low to moderate, or from moderate to high . There is no indication of a further quadratic effect (“RNA-seq.Q”), but the sample size is small for detecting such a departure from equal spacing. A model examining whether the relationship between RNA-seq and ORR varies by tumor type showed no significant interaction (p = 0.80). A model examining whether the PD-L1 RNA-seq and IHC results interact in predicting response also showed no significant interaction (p = 0.45). Among the three tumor types analyzed in this study, the only non-overlapping confidence interval for predicting response was for “RNA-seq low vs high” in melanoma.To evaluate RNA-seq as the gold standard and determine whether IHC adds predictive value, a logistic regression model was employed to evaluate the prediction of response to treatment based on tumor type, PD-L1 by IHC, and eq Table . As expen = 38) that are typically easier to evaluate. While ‘no’ and ‘very high’ PD-L1 expression were mostly concordant, the Blueprint study demonstrated that the ‘low’ to ‘intermediate’ expression levels seen in most NSCLC patients can result in discrepancy. In these instances, a continuous measure, rather than using a specific TPS cutoff, may better predict ICI treatment efficacy. The more recent phase 2 study [PD-L1 appears as one of the most controversial biomarkers to be introduced into clinical practice. Despite prior evidence demonstrating that both technologic and histologic variability limit clinical utility , 32, fou 2 study  confirmsPD-L1 by RNA-seq. Unlike IHC, RNA-seq quantitates the number of expressed mRNA transcripts in the entire tumor microenvironment without subjective scoring methods and cell type discrimination. When performed in a CLIA laboratory setting with a validated protocol [PD-L1 across a continuum of expression levels.In contrast to the Blueprint study, our study explores measuring PD-L1 by a single alternative methodology, RNA-seq by direct comparison between FDA-approved PD-L1 IHC assays and a laboratory developed RNA-seq test. Although previous studies demonstrate PD-L1 as measured by IHC is a predictive biomarker of response to ICIs , it was protocol , our datn = 209) cohort of samples from multiple institutions treated with one or more FDA-approved ICIs. The specimens were of variable tumor mass, including a large fraction of needle core biopsies and FNA cell blocks. Furthermore, multiple tumor types were evaluated and the pathologist reading IHC slides was not a renowned expert in any one particular disease. In total, the study reflects a real-world clinical scenario in which archival specimens representing several commonly tested histologies are evaluated for PD-L1 expression.Our study employed a considerably large  samples were associated with an overall higher ORR than the combined negative (IHC/RNA-seq −/−) samples, the highest ORR of any tumor type were associated with discordant melanoma IHC and RNA-seq results (IHC/RNA-seq −/+). Given the small number of samples (n = 4) in this grouping, a larger cohort with this phenotype is required to understand if the predictive power is tumor type specific or a direct result of PD-L1 quantitation in melanoma by RNA-seq. NSCLC samples, which had the largest number of PD-L1 positive RNA-seq and IHC cases, did not however demonstrate predictive power for either test. Most interesting is the ORR for combined positive (IHC/RNA-seq +/+) and negative (IHC/RNA-seq −/−) results. While a combined positive did not always demonstrate the highest ORR across tumor types, the combined negative result did have the lowest ORR, except for RCC IHC/RNA-seq +/− which was represented by only one case.Data obtained from this study revealed that PD-L1 by RNA-seq quantification was an even better predictor of response to the same monotherapies. This is a provocative finding given that the PD-1/PD-L1 axis is currently thought to be entirely distinct and not interacting with the CTLA-4 axis [Unexpectedly, we found that melanomas with PD-L1 IHC TPS > 1% had a significantly improved response to all ICI monotherapies, including anti-PD-1 and anti-CLTA-4 monotherapy. We were also surprised to find that elevated A-4 axis . HoweverPD-L1 assessment by RNA-seq, this technology is also convenient as it enables highly multiplexed testing of several patients in a single run, with per sample costs approaching those of IHC when performed in batch sizes greater than twenty. By measuring multiple transcripts simultaneously, RNA-seq is well suited to characterize the functional state of immune cells in the tumor microenvironment for biomarkers of antigen presentation, IFN-γ signaling, T-cell active cytokines and other biological features that are responsive to PD-1 checkpoint blockade. It is beyond the scope of this study to report data for the focused set of nearly 400 other genes included in the transcriptome panel, however evaluating RNA for immune gene expression in addition to PD-L1 has been shown to be predictive of efficacy to anti-PD-1 therapy across multiple tumor types with more accuracy than PD-L1 IHC [In addition to the inherent advantages of standardized methods for D-L1 IHC –37. For D-L1 IHC , and an D-L1 IHC  than a sD-L1 IHC  across mD-L1 IHC . Co-overD-L1 IHC , 43. ThoD-L1 IHC .PD-L1 RNA-seq by cell specific markers. Normalization against one or several markers, such as an immunohistochemical stain which the surgical pathologist determines to be highly specific for neoplastic cells in a given case, CD45 for hematopoietic cells, CD3 for T-cells, CD8 for cytotoxic T-cells, or CD68 for macrophages could result in an objective qualitative PD-L1 result that can be automated to reproducibly report PD-L1 expression relative to specific cell types in the tumor microenvironment. With proper tissue review and selection, RNA-seq analysis of the tumor and associated microenvironment is designed to provide a PD-L1 score minimally influenced by sample selection bias and tumor heterogeneity, and to minimize the current PD-LI testing variability which may impact clinical decisions and the uptake of precision immune oncology treatments [For analytical purposes, the expression of other genes has value relative to PD-L1 for use as an endogenous control for PD-L1 normalization. It is feasible that the various subjective IHC interpretation requirements for estimating percentages of either tumor, immune or combined cell staining could be replaced by normalization of eatments .PD-L1 expression, is a lack of external standards. In our study, RNA-seq measurements have a proprietary method of normalization that influence the results for all genes in the panel. Our control samples and genes with a similar impact are also proprietary. Additionally, the interpretation of these measurements by rank is derived via comparison to a unique non-public reference database. Although these are significant issues, the standardization of PD-L1 measurement by RNA-seq is much more attainable than in the case of IHC. External, publicly available standards could be developed and shared similarly to how minimal residual disease for BCR-ABL has become standardized [A major limitation of our study linked to development of RNA-seq as a standardized measurement of dardized . AnotherThe confluence of the Blueprint study and our work supports that alternative measurements of PD-L1 expression beyond IHC, such as RNA-seq, should be considered for clinical use to improve response prediction in patients being considered to receive ICI treatments.PD-L1 mRNA expression by RNA-seq is comparable to PD-L1 expression by IHC assays, both analytically and clinically, with evidence that for melanoma samples RNA-seq may be superior to IHC. At minimum, mRNA expression by RNA-seq provides another layer of PD-L1 detection which can exploited to predict tumor response to ICI. The predictive performance of RNA-seq to measure PD-L1 expression (in comparison to IHC) is affected to some extent by histologic factors, but the overall results suggest that moving forward with this technology is a viable approach for this dynamic biomarker.In summary, our study shows that measurement of Additional file 1:Table S1. Samples (n=209) sorted within tumor type by PD-L1 rank (RNA-seq). Table S2. PD-L1 (CD274) RNA-seq dilution series. Table S3. Responders and non-responders across tumor types and biomarker result. Table S4. ORR across tumor type and combined biomarker results for all ICI therapies. Table S5. Responders and non-responders across tumor types and individual biomarker result. (XLSX 39 kb)


Retrotransposons are one of the oldest evolutionary forces shaping mammalian genomes, with the ability to mobilize from one genomic location to another. This mobilization is also a significant factor in human disease. The only autonomous human retroelement, L1, has propagated to make up 17% of the human genome, accumulating over 500,000 copies. The majority of these loci are truncated or defective with only a few reported to remain capable of retrotransposition. We have previously published a strand-specific RNA-Seq bioinformatics approach to stringently identify at the locus-specific level the few expressed full-length L1s using cytoplasmic RNA. With growing repositories of RNA-Seq data, there is potential to mine these datasets to identify and study expressed L1s at single-locus resolution, although many datasets are not strand-specific or not generated from cytoplasmic RNA.We developed whole-cell, cytoplasmic and nuclear RNA-Seq datasets from 22Rv1 prostate cancer cells to test the influence of different preparations on the quality and effort needed to measure L1 expression. We found that there was minimal data loss in the identification of full-length expressed L1 s using whole cell, strand-specific RNA-Seq data compared to cytoplasmic, strand-specific RNA-Seq data. However, this was only possible with an increased amount of manual curation of the bioinformatics output to eliminate increased background. About half of the data was lost when the sequenced datasets were non-strand specific.The results of these studies demonstrate that with rigorous manual curation the utilization of stranded RNA-Seq datasets allow identification of expressed L1 loci from either cytoplasmic or whole-cell RNA-Seq datasets. Mobile elements are repetitive sequences that make up half to two thirds of the human genome . Long inThere have been many approaches used to study L1 RNA expression (as reviewed in ). Most oUsing RNA Next Generation sequencing (RNA-Seq), we have developed several bioinformatics approaches for locus-specific L1 mRNA expression as previously described , 20. OneWith growing repositories of RNA-Seq data, there is potential to pool and mine these data sets to identify and study expressed L1 s at a single-locus resolution in a variety of models and pathologies –23. HoweOur findings demonstrate that whole-cell RNA analysis can provide similar results to cytoplasmic L1 RNA analysis. However a close agreement between the two approaches is only possible with rigorous manual curation of the results of whole cell RNA-Seq bioinformatics analysis in order to eliminate high levels of transcripts incorporated as portions of other RNAs (co-transcription). We refer to these co-transcripts as ‘background’ in this manuscript because our focus is on sense transcripts from the L1 promoter and the high levels of L1-chimeric co-transcription interfere with these studies. We also determined that analysis of L1 expression using non-stranded RNA-Seq can identify authentic expression of some L1 loci. However, the number of identified L1 loci is reduced by half as a significant portion of authentic loci cannot be distinguished from the background and a much greater effort in manual curation is required compared to the analysis of stranded cytoplasmic or whole cell RNA-Seq data sets. Our results clearly demonstrate that existing whole cell and/or non-stranded RNA-Seq data sets should not be used for L1 mRNA expression analysis without eliminating every and all sources of background L1 sequences as such analyses produce false positive results.22Rv1 cells  were kinCells were collected by scraping from two, 75–100% confluent T-75 flasks. The flasks were first washed two times in 5 mL cold PBS (Invitrogen). In the last wash, cells were scraped and transferred to a 15 mL conical tube and centrifuged for 2 min at 1000 rpm at 4 °C and the supernatant was discarded. For whole cell RNA preparations, the cell pellet was added to pre-chilled 7.5 mL Trizol (Invitrogen) and 1.5 mL chloroform (Fisher). For cytoplasmic RNA preparations, the cell pellet was incubated in 500 uL of lysis buffer (150 mM NaCl (Invitrogen), 50 mM HEPES pH 7.4 (Affymetrix), 25 μg/mL digitonin  with 1000 U/mL RNase inhibitor (Invitrogen) added just before use, placed on ice for 5 min and then centrifuged for 2 min at 1000 rpm at 4 °C. The supernatant was added to pre-chilled 7.5 mL Trizol and 1.5 mL chloroform. For nuclear RNA preparations, the pellet remaining after RNA cytoplasmic extraction was added to pre-chilled 7.5 mL Trizol and 1.5 mL chloroform. All Trizol-based solutions were then centrifuged for 35 min at 4000 rpm at 4 °C. The aqueous portion was transferred to 4.5 mL of chilled chloroform and centrifuged for 10 min at 4000 rpm at 4 °C. The resulting aqueous portion was precipitated with 4.5 mL of isopropanol (Fisher) overnight in -80 °C overnight, centrifuged for 45 min at 4 °C at 4000 rpms, washed with 10 mL 100% ethanol (Fisher) and re-suspended in RNAse-free water (Fisher). A further detailed explanation of the RNA preparation is previously described in .RNA samples were analyzed for quality on an Agilent 2100 Bioanalyzer System according to the Agilent RNA 6000 Nano kit guide. Cytoplasmic, or whole-cell samples were submitted for sequencing with RIN > 8, and the nuclear RNA sample was submitted without this quality control.Whole cell, cytoplasmic, and nuclear RNA samples were submitted to BGI genomics for selection of polyadenylated RNAs, and sequencing by the Illumina TruSeq strand-specific, and paired-end library preparation with barcodes. Samples were pooled in groups of 2 and applied to a single lane of an Illumina HiSeq 2500/4000 instrument. Data were sorted based on barcodes attached to each individual sample providing between 150 and 250 million paired-end reads per sample. This represents a higher depth of sequencing than normal in order to provide higher quality data. For detection of L1 locus-specific expression we typically recommend sequencing with around 50 million paired-end reads per sample.The annotations for full-length L1s have been previously described , 20 and The alignment strategy for RNA-Seq data to the human genome for endogenous L1 expression studies has been previously described , 20. BriHomo sapiens whole genome sequence file used in these studies was ERR492384. A further detailed explanation of how mappability is assessed is previously described in [Our bioinformatic strategy is to only consider reads that mapped uniquely to one locus. In order to better understand and assess how ‘mappable’ regions are in the genome, we downloaded species-specific whole genome Illumina paired-end sequence files from NCBI. We used the same bowtie1 alignment approach as for RNA-Seq to assign whole genome reads that mapped uniquely to the genome . The accribed in .Following the bioinformatic analyses, a table per sample was generated displaying the annotated L1 loci that had 10 or more reads mapping. These full-length L1 loci with mapped RNA-Seq reads were then visually inspected to validate that reads were expressed using the L1 promoter. To manually curate authentically expressed L1s, the gene annotation of the reference genome of interest, the L1 annotation, the RNA-Seq and whole genome alignments were uploaded in IGV, a genomics visualization tool . Any expIn order to compare expression at the specific locus level among multiple sequenced samples, the raw transcript reads mapping to each manually curated L1 locus were then normalized by calculating individual L1 loci FPKM values. As the full-length L1s in the human reference genome are all approximately 6 kb in length, the FPKM value was calculated by dividing the number of uniquely mapped transcript reads to an individual L1 locus and the product of the million mapped reads specific to the sequence sample of interest and 6. The described formula is demonstrated here:Using the aligned sequence files for each sample as developed in the Bioinformatics Analysis Methods section, the ratio of reads that mapped to the exonic regions over the intronic regions of the following housekeeping genes: B2M, GAPDH, GUSB, HPRT, PGK1, and TK1 were calculated. The average of these ratios for each gene were assessed to give a final exonic:intronic ratio in order to assess the quality of the cytoplasmic/nuclear fractionations. A low ratio for example would indicate more nuclear, pre-processed RNA content in sample.n  =  2 groups. Statistical analysis was performed using GraphPad Prism.Data are presented as mean with standard error bars. Data were analyzed by Student’s t-test for ). Overall the majority of loci identified to be authentically expressed were found in both replicates of the cytoplasmic- and whole cell- 22Rv1 RNA samples  indicating that the two RNA preparations yield similar results when used to identify expressed L1s. Specifically, there were a total of 191 distinct loci identified to be authentically expressed in the cytoplasmic and whole cell RNA sequenced data in which 169 loci were found in both preparations, 3 uniquely found in cytoplasmic preparations, and 19 uniquely found in the whole cell preparations . When the expressed L1 s were subdivided by subfamily compared to all the full length L1s in the genome, there was a nearly two-fold enrichment for L1PA2 and L1PA3 L1s and a greater than 2 fold decrease for L1PA4 and L1PA5 L1s . Three loci with a total of 40 reads were curated as authentically expressed in the non-stranded data when in fact the mapped reads were antisense to the L1. These loci became false positive calls when the non-stranded format was used . There were 46 loci or 480 reads that were curated to have expression un-related to the L1 promoter because of non-stranded upstream reads which were labeled as false negative calls . These L1 loci were authentic L1s according to the analysis of the matched strand-specific data, but had antisense promoter related upstream reads. In the 1st replicate of whole cell prepared 22Rv1 RNA analyzed in a non-stranded manner, 451 loci or 14,137 reads had to be manually curated. Sixty-three loci and 712 reads were found to be authentically expressed and 330 loci and 11,863 reads found to be falsely expressed . Two loci with a total of 20 reads were curated to be expressed in the non-strand-specific data when the mapped reads were antisense to the L1 so were marked as false positive calls . There were 56 loci or 986 reads that were curated to have expression un-related to the L1 because of non-stranded upstream reads, but were authentic L1s according to the matched strand-specific data with antisense promoter-related upstream reads. These were consequently labeled as false negative calls . The second biological replicate of whole cell and cytoplasmic, non-strand-specific RNA-Seq followed the same pattern and distribution of true loci, false loci, false positive loci, and false negative loci when compared to their matched stranded data sets , the amount of manual curation could be cut approximately in half Fig. . Ultimatin trans expression of sense L1 transcripts generated by other L1 loci [Although our goal has been primarily an understanding of expression of the L1 elements themselves, it is worth noting that including antisense reads in our analysis allowed us to find transcripts from the antisense L1 promoter as well. While it is known that the sense L1 promoter can make transcripts without the formation of stable antisense transcripts , our fin L1 loci . AnotherAlthough we still recommend cytoplasmic mRNA for studying L1 expression, this study provides strong support that with rigorous curation high quality data can be obtained from whole cell RNA preparations. Strand-specific RNA-Seq seems to be the most important criteria in obtaining high quality mapping data for L1 loci. With growing repositories of RNA-Seq samples available to study, it is critical that we are able to maximize the impact of these data on our understanding of mobile element biology.Additional file 1. Manually curated set of L1 s with uniquely mapped sense reads in stramd-specific 22Rv1 A) whole cell RNA-Seq data from replicate 1, B) cytoplasmic RNA-Seq data from replicate 1, C) whole cell RNA-Seq data from replicate 2, D) cytoplasmic RNA-Seq data from replicate 2, and E) nuclear RNA-Seq data from replicate 2. L1 s curated to be authentically expressed were labeled with a green color and L1 s curated to be rejected as authentically expressed were labeled with a red color and its reason for rejection or acceptance was noted in the most right column.Additional file 2. FPKM values for manually curated true L1 expression.Additional file 3. Exon:Intron ratio calculations: A) whole-cell RNA-Seq data from replicate 1, B) cytoplasmic RNA-Seq data from replicate 1, C) whole-cell RNA-Seq data from replicate 2, D) cytoplasmic RNA-Seq data from replicate 2, and E) nuclear RNA-Seq data from replicate 2. The number of mapped reads from the RNA-Seq sample of interest to the exons and introns of different housekeeping genes like B2M, GAPDH, GUSB, HPRT, PGK1, TK1 are in column J. The sum of exon reads and intron reads for each gene are in column N. The ratio of exon:intron calculations are in column O. The average of these ratios per Seq sample are found in column O, row 98.Additional file 4. The number of uniquely mapped upstream reads up to 1000 and 5000 bps upstream aligned with the manually curated, strand-specific, whole-cell, RNA-Seq data from replicate 1. In the first column are the L1 locus ID numbers, in the second column are the number of sense reads that map uniquely to the specific L1, in the third column is the reason for acceptance or rejection as authentically expressed L1 s, in the fourth column are the number of sense reads uniquely mapping up to 1000 bps upstream the specific L1, and in the fifth column are the number of sense reads uniquely mapping up to 5000 bps upstream the specific L1. In green are the L1 s curated to be expressed off their own promoters. In red are the L1 s curated to be passively transcribed off a promoter unrelated to the L1.Additional file 5. Manually curated set of L1 s with uniquely mapped non-strand-specific reads in 22Rv1 stranded in whole cell RNA-seq data from replicate 1. L1 s curated to be authentically expressed were labeled with a green color and L1 s curated to be rejected as passively expressed were labeled with a red color and its reason for rejection or acceptance was noted in the most right column following the guidelines for manual curation. In purple are examples of L1 s with antisense promoter activity. As the orientation of reads can not be distinguished in non-stranded data these L1 loci were curated to be not expressed off their own promoter and represent false negative calls. In blue are L1 loci that were curated to be authentically expressed in non-stranded data, but in fact had antisense reads mapped to it. These were considered false positive calls.Additional file 6: Figure S1. Examples of curated L1 loci in 22RvI. Loaded into IGV are the human reference genome, the human full-length L1 annotation, whole cell 22RvI bam file from replicate 1, and lastly the genomic HeLa bam file to assess mappability, which are all available upon author request. Arrows have been added to aid in the visualization of direction of the annotated L1. Arrows and reads in red are oriented in sequence from right to left. Arrows and reads in blue are oriented in sequence from left to right. A) In IGV, this L1 locus appears to be expressed off its own promoter as there are no reads upstream the L1 in the sense orientation for over 5 kb. This L1 has low mappability and is within a gene of opposite direction. B) In IGV, this L1 locus was rejected as an expressed L1 as there are upstream reads in the same orientation within 5 kb. This L1 is within a gene of the same direction so the transcript reads are most likely originating from the promoter of the expressed gene. C) In IGV, this L1 locus was rejected as an expressed L1 as there are upstream reads in the same orientation within 5 kb. This L1 is downstream of a highly expressed gene in the same direction so the transcript reads are most likely originating from the promoter of that expressed gene and extending beyond the normal gene terminator. D) In IGV, this L1 locus was rejected as an expressed L1 as there are upstream reads in the same orientation within 5 kb. This L1 is not within or near an annotated gene in the reference gene so the origin of these transcripts within and upstream of the L1 element suggest an un-annotated promoter.Additional file 7: Figure S2. A) Subfamily distribution of full length L1 s in the human genome. B) Subfamily distribution of full length L1 s expressed in the whole cell preparation of 22Rv1 with n = 2. C) Subfamily distribution of full length L1 s expressed in the cytoplasmic preparation of 22Rv1 with n = 2. Colors are designated according to the legend by subfamilies L1HS, L1PA2, L1PA3, L1PA4, L1PA5, L1PA6, L1PA7, L1PA8, and Other. The other category includes L1MA4A, L1MA7, L1P1, L1P2, L1PA16, L1PA8A, L1 PB1, and L1BP4. Percentages of the L1 subfamilies are noted around the pie charts.Additional file 8: Figure S3. A) Estimated ratio of exonic reads to intronic reads in replicate 22Rv1 RNA-seq samples. The black bars represent the ratio of exonic to intronic reads in the cytoplasmic RNA samples, the purple bars represent the ratio of exonic to intronic reads in the whole cell RNA samples, and the green bar represents the ratio of exonic to intronic reads in the nuclear RNA samples.Additional file 9: Figure S4. Example of L1 with antisense promoter activity de-coupled from sense promoter activity visualized in IGV. Loaded into IGV are the human reference genome, the human full-length L1 annotation, WC 22RvI bam file from replicate 1, and lastly the genomic HeLa bam file to assess mappability, which are all available upon author request. Arrows have been added to aid in the visualization of direction of the annotated L1. Arrows and reads in red are oriented in sequence from right to left. Arrows and reads in blue are oriented in sequence from left to right.


Background: Soluble PD-L1 (sPD-L1) in the circulation has been documented to activate global immunosuppression and is considered a predictor of negative clinical outcomes in several malignances. However, the clinical significance of sPD-L1 in the peripheral blood and cerebrospinal fluid (CSF) of patients with glioma remains unclear.Objective: The aim of this study was to detect the correlations of sPD-L1 with clinical features in brain tumors and assess the diagnostic value of this protein in gliomas.Methods: Serum samples were obtained from 73 patients with glioma, 20 patients with meningioma, and 49 healthy controls (HCs) in this study. In total, 31 CSF samples were collected from the matched glioma patients, and seven samples were collected from the matched meningioma patients. The expression of serum sPD-L1 in the glioma cohort was followed for 20 days after surgery to examine the kinetics in the circulation. Inflammatory markers were evaluated based on preoperative blood parameters. The sPD-L1 levels in the serum and CSF were determined by enzyme-linked immunosorbent assay (ELISA). The logistic regression model was used to assess the independent associations of sPD-L1 with gliomas, including high-grade gliomas.Results: Serum and CSF levels of sPD-L1 were significantly elevated in patients with gliomas compared to those with meningiomas and HCs. Additionally, increased levels of sPD-L1 were observed in relatively advanced tumors. sPD-L1 overexpression in the CSF appears to be more representative of aggressive tumor features than overexpression in the serum. For glioma diagnosis, both serum and CSF sPD-L1 showed significant value in the diagnosis and stratification of glioma, and the best diagnostic performance was obtained with serum sPD-L1 rather than blood-based inflammatory markers. In addition, a descending trend in the level of serum sPD-L1 was observed in postoperative patients.Conclusion: In gliomas, elevated circulating and CSF sPD-L1 levels are associated with aggressive biological activities. The results of the current study suggest that sPD-L1 is a promising biomarker for gliomas that can be used in clinical practice. Gliomas, which account for the majority of central nervous system (CNS) tumors, are correlated with high rates of recurrence and mortality . HoweverFor liquid-based biomarkers, several blood-based inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), and platelet-to-lymphocyte ratio (PLR), have been highlighted for their pivotal roles in the stratification and prognosis of gliomas , 7. LikeRecently, the discovery of immune checkpoint molecules, such as programmed death-1 (PD-1) protein and its ligand, namely, programmed death-ligand 1 (PD-L1), has provided novel therapeutic targets. Considering the lack of effective treatments for gliomas, immunotherapy, especially anti-PD-1/PD-L1 antibodies, has brought hope for brain malignances , 12. In Similar to membrane-binding PD-L1, soluble PD-L1 (sPD-L1) is currently thought to contribute to systemic immunosuppression , 19. RecIt is worth mentioning that our previous study showed that sPD-L1 in the peripheral blood was a potential predictor for the diagnosis and prognosis of preoperative patients with glioma. Nevertheless, blood is one of the easiest biofluids to obtain, but it is not the optimal fluid for collecting precise biomarkers derived from brain tumors due to the isolation established by the blood-brain barrier (BBB) . InsteadAs such, in this study, we aimed to elucidate these issues through the following clinical evaluations: (a) exploring the relationships of serum sPD-L1 with blood-based inflammatory markers; (b) detecting the concentration of sPD-L1 in the CSF and illustrating the relationship between sPD-L1 measurements in the serum and CSF; (c) determining the correlations of sPD-L1 levels with glioma classification, histological characteristics and molecular features; and (d) evaluating the predictive significance of sPD-L1 in gliomas. This report may provide new clues for developing and validating reliable and minimally invasive biomarkers for CNS tumors in future clinical practices.The study protocol was approved by the ethics committee of Beijing Tiantan Hospital.Patients with brain tumors who underwent magnetic resonance imaging (MRI) between January and April 2019 were consecutively followed. In total, 73 patients with histologically confirmed glioma were included in this study. Furthermore, 20 patients with meningioma were sex-matched with the glioma patients and enrolled in this study. All patients underwent maximal safe surgical resection and were diagnosed histologically based on the recent WHO classification guidelines. Molecular markers of glioma, such as the IDH1 genotype (IDH1 mutant or wild type), Ki-67 expression status and 1p/19q status (1p/19q codeleted or maintained), were recorded when possible. Immunohistochemistry (IHC) and direct gene sequencing were conducted by pathologists according to routine methods at Beijing Tiantan Hospital , 29. TheForty-three healthy volunteers, who were matched with the selected glioma patients in terms of sex and age, were included in the healthy control (HC) group.Peripheral blood samples were obtained upon admission to the hospital before surgery, and clinical parameters  were part of the standard workup at the Laboratory Diagnosis Center of Beijing Tiantan Hospital. In addition, the preoperative NLR (ratio of the neutrophil count to the lymphocyte count), dNLR [ratio of (the WBC count—the neutrophil count) to the lymphocyte count], PLR (ratio of the platelet count to the lymphocyte count), AGR , and PNI [the albumin level (g/L) + the total lymphocyte count × 5] were calculated.To assess the temporal dynamics of serum sPD-L1 in glioma patients, we performed monitoring every 3 days postoperatively. Collectively, we obtained postoperative samples as follows: 44 samples at 48 h, 34 samples on day 5, 27 samples on day 8, 9 samples on day 11, 5 samples on day 14 and 4 samples on day 20.Serum was obtained from each subject during routine venipuncture. To remove blood cells, the serum tubes were centrifuged at 2,500×g for 10 min at room temperature. The separated serum samples were immediately stored at −80°C until analysis. Repeated cycles of freezing and thawing of the samples were avoided.Thirty-one matched CSF samples from the glioma cohort and seven from the meningioma cohort were collected via lumbar puncture on days 3–6 after resection. The initial 2 mL of CSF was used for protein level measurements and cytology examination. CSF supernatants were transferred to cryotubes and stored at −80°C.2SO4. The OD was measured at 450 nm. All steps were run at 37°C. Each sample was tested in duplicate. The detection limit of the ELISA kit was 0.056 ng/mL.Both serum and CSF sPD-L1 were examined using a specific enzyme-linked immunosorbent assay  according to the manufacturer's instructions. In brief, samples to be tested were added to the wells of microtiter plates and incubated for 1 h. Then, the biotinylated antibody was incubated for 1 h, followed by incubation with the avidin-peroxidase conjugate for 30 min. Finally, the substrate TMB was incubated for 15 min, after which the reaction was terminated using Hpost-hoc Bonferroni test was used for multiple comparisons). The differences between the two groups were calculated using the t-test or Mann-Whitney U-test according to the normality of the data. Categorical variables were analyzed and compared between two groups using the chi-squared test or Fisher's exact test. The association of the sPD-L1 with glioma and glioma grade was analyzed using multivariate logistic regression. For Pearson's correlation analysis, sPD-L1 data were log 10 transformed to obtain a more symmetric data distribution. For rank correlation analysis, Spearman's correlation coefficient (rho) was used. A receiver operating characteristic (ROC) curve was generated for each marker. The areas under the curves (AUCs) were assessed to evaluate the performance of each marker for predicting gliomas and distinguishing high- and low-grade gliomas. The dynamics of sPD-L1 in the serum were analyzed by the mixed-model approach. Statistical analysis was performed using SPSS  or GraphPad Prism 8 software .Continuous variables are presented as the means ± standard deviation (SD) or medians and the minimum-maximum range. Comparisons among different cohorts were performed using analysis of variance (ANOVA) with the Kruskal-Wallis test for nonnormally distributed variables  group. The clinical and demographic features and hematological parameters of the study subjects are listed in p < 0.001) and HC cohorts   compared with that in the meningioma  .p = 0.001) and HC cohorts (p < 0.001). However, when the correlation of serum PD-L1 levels with age was analyzed according to the different cohorts, we did not find any significant differences   .p < 0.001). Nevertheless, the other variables  were not significantly associated with glioma . Low-grade tumors were more frequently observed in the meningioma cohort vs. the glioma cohort (p < 0.001). However, the tumor size was not significantly different , and Spearman's rank correlation was markedly positive between the WHO grade and sPD-L1 level . In contrast, neither the Ki-67 index nor the WHO grade was significantly associated with serum sPD-L1 levels in meningioma . Moreover, the age at the time of diagnosis was younger in LGG patients  and NLR  were independent factors for the prediction of HGGs, while the other parameters  were not independent predictors  compared with that of meningioma patients   .r = 0.577, p = 0.001), but this relationship was not significant in the meningioma CSF samples .As shown in p = 0.029)  . Neverth= 0.029) .The glioma cohort consisted of patients with different histopathological types of disease, including pilocytic astrocytoma (PA), low-grade astrocytoma (LAG), oligodendroglioma (ODG), anaplastic glioma (AG), and glioblastoma (GBM). The sPD-L1 levels in the serum and CSF for each disease type are shown in p = 0.036). As indicated in p = 0.043). Similar to the PD-L1 expression pattern in the serum, the CSF PD-L1 level differed according to histological type (p = 0.032), but no significant differences were found between any two types  or 1p/19q codeletion (p = 0.017) were found to have relatively low levels of CSF sPD-L1.As shown in p = 0.002).Thirty-one glioma patients had both their serum and CSF sPD-L1 levels measured. As indicated in r = 0.428, p = 0.016, The level in the serum appeared to positively correlate with the level in the CSF of the glioma patients  and meningioma patients  , while the concentrations were notably lower in HCs  . Likewis< 0.001) .It has been documented that inflammation is involved in the pathogenesis and promotion of cancer . Therefop = 0.013). Collectively, this observation has highlighted the potential role of sPD-L1 in glioma diagnosis and stratification.The multivariate analysis revealed that sPD-L1 is independently associated with glioma after adjusting for age and the mentioned hematological markers . For gliNext, we attempted to uncover the perioperative dynamics of circulating sPD-L1 levels in glioma patients. The expression of sPD-L1 in the serum was prospectively evaluated after craniotomy for up to 20 days. In this context, the sPD-L1 levels in the circulation were observed to consistently decrease after tumor removal, which persisted regardless of the use of steroids . The serHerein, an elevated serum sPD-L1 level was present in patients with relatively advanced brain tumors, as expected . Combinep = 0.002]. Subsequently, a positive correlation between serum and CSF PD-L1 levels was observed, although the degree of significance was mild (r = 0.428).There are two ways for a brain protein to access the peripheral blood, that is, by CSF flow into the venous blood or by penetrating though the BBB . Given tAnother important purpose of this study was to investigate the roles of sPD-L1 in different biofluids for the diagnosis and prediction of gliomas. For this purpose, we evaluated the differences in the sPD-L1 levels in the serum and CSF according to varied clinicopathological features of gliomas. Similar to the expression in the serum, sPD-L1 expression in the CSF was higher in more advanced gliomas than in less advanced gliomas . HoweverNumerous published reports have declared that an IDH mutation or 1p/19q codeletion can affect the pathological behaviors of gliomas , 53. TheIt is much more preferable to adopt CSF evaluation than peripheral blood evaluation for diagnostic analysis since the CSF directly contacts the CNS. Conversely, the AUC for serum sPD-L1 was markedly higher than that of CSF sPD-L1 for predicting gliomas  in this context. Moreover, the blood-based biomarkers such as NLR (AUC: 0.752) and dNLR (0.733) showed a better performance than CSF sPD-L1 (0.731) for the differentiation of gliomas. The lack of predictive power of CSF sPD-L1 might be due to the small number of CSF samples; therefore, further studies involving a larger cohort of patients might be needed to investigate the role of sPD-L1 in the CSF.This study has several limitations. Although we detected upregulated levels of serum sPD-L1 among preoperative patients with glioma and observed a descending trend during the early postsurgical phase, it is not known whether sPD-L1 levels were affected by the concomitant therapy and whether sPD-L1 levels in serum fluctuate during tumor progression or remission. In addition, further investigation is required to determine whether sPD-L1 is robustly correlated with tumor activity, which would validate this soluble protein as a screening tool in future studies.Our findings show the significant value of sPD-L1 in the serum and CSF for the diagnosis and discrimination of gliomas, providing the rationale to further study the role of sPD-L1 as a surrogated biomarker for the future clinical management of gliomas. Thus, the detection of sPD-L1 by intravenous or lumbar puncture rather than tumor tissue sampling could impact treatment decisions.All datasets generated for this study are included in the article/The studies involving human participants were reviewed and approved by the ethics committee of Beijing Tiantan Hospital. The patients/participants provided their written informed consent to participate in this study.SL and YZ: study concept and design. SL, GZ, and CZ: methodology. SL, CZ, and XM: experiments. SL and BS: analysis and interpretation of data. SL: writing-original draft preparation. YF and XK: writing-review and editing. XK: supervision.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.