Memory loss is the most common clinical sign in Alzheimer's disease (AD); thus, searching for peripheral biomarkers to predict cognitive decline is promising for early diagnosis of AD. As platelets share similarities to neuron biology, it may serve as a peripheral matrix for biomarkers of neurological disorders. Here, we conducted a comprehensive and in‐depth platelet proteomic analysis using TMT‐LC‐MS/MS in the populations with mild cognitive impairment , severe cognitive impairments , and the age‐/sex‐matched normal cognition controls (MMSE = 29–30). A total of 360 differential proteins were detected in MCI and AD patients compared with the controls. These differential proteins were involved in multiple KEGG pathways, including AD, AMP‐activated protein kinase (AMPK) pathway, telomerase RNA localization, platelet activation, and complement activation. By correlation analysis with MMSE score, three positively correlated pathways and two negatively correlated pathways were identified to be closely related to cognitive decline in MCI and AD patients. Partial least squares discriminant analysis (PLS‐DA) showed that changes of nine proteins, including PHB, UQCRH, CD63, GP1BA, FINC, RAP1A, ITPR1/2, and ADAM10 could effectively distinguish the cognitively impaired patients from the controls. Further machine learning analysis revealed that a combination of four decreased platelet proteins, that is, PHB, UQCRH, GP1BA, and FINC, was most promising for predicting cognitive decline in MCI and AD patients. Taken together, our data provide a set of platelet biomarkers for predicting cognitive decline which may be applied for the early screening of AD. Deep profiling of platelet proteome in healthy aging subjects and MCI/AD cases. Some individual candidate biomarkers in platelets closely reflect the pathophysiological process of AD. The identified novel platelet combination biomarkers can distinguish healthy aging subjects and cognitively impaired subjects. The majp < 0.05)  Figure , while 1) Figure . Moreove) Figure , and all) Figure .n = 160), including CD63, PHB, UQCRH, ANXA5, and EGF, showed a decreasing tendency from Ctrl to MCI to AD  displayed significant decrease in MCI and increase in AD compared with the normal controls  showed an increasing tendency from Ctrl to MCI to AD  network analyses in MCI, AD, and the cognitively normal control populations. Proteins in cluster 1 , including 44 negatively correlated (NC) proteins and 129 positively correlated (PC) proteins  score has been wildly used as a subjective measure of cognitive performance. To explore the periphery molecular markers that can objectively predict cognitive impairment, we performed correlation analysis of MMSE score to the entire omics data received from normal Ctrl, MCI, and AD. A total of 173 proteins were identified to be strongly correlated to MMSE score ‐related protein reticulon‐4 (RTN4), in AD pathways ; complement activation pathway proteins C1S, SERPINA1, SERPINF2 (| r | =0.63–0.75)   Figure . In addi) Figure , suggest2.3Further machine learning was applied to select the best combination of the biomarkers. Considering the effectiveness of the biomarkers, seven proteins involved in the complement pathway were excluded, and 19 candidate proteins were selected from 26 Figure  for subsTo receive the best combination of the platelet biomarkers for predicting cognitive decline, we further analyzed 9 core candidate proteins using leave‐one‐out (LOO) method. This method leaves out one sample at a time as validation set and uses the rest samples as the training set, so that all samples were trained n times and validated n times. By LOO analysis, various specificity and accuracy were observed using different combinations of the 9 biomarkers, and the combination of PHB, UQCRH, GP1BA, and FINC showed the highest specificity with a maximum receiver operating characteristic (ROC) (AUCROC=0.965) and the highest accuracy (89.3%) Figure . Using tBy using Western blotting to verify the above‐mentioned nine target proteins , we observed a decreasing trend of the levels of PHB, CD63, GP1BA, and FINC in MCI or/and AD, which was consistent with the proteomic results, but no significant decrease for the other five molecules Figure . The disTogether, the machine leaning further selects out the combination of PHB, UQCRH, GP1BA, and FINC as the best platelet biomarkers for evaluating the cognitive decline in MCI and AD patients.3AD is most common neurodegenerative disorder affecting an increasing number of the populations with old age. As there is no efficient cure for this devastating disease, finding objective periphery biomarkers is extremely important for early diagnosis and drug development of AD.Recently, proteomics has been widely applied for biomarker studies. With breakthroughs of the technology, we can comprehensively and in‐depth explore the expression changes of the peripheral platelet protein profile for the screening of the periphery biomarkers. Previous studies using platelet samples from AD patients were mostly limited to two‐dimensional gel electrophoresis methods, and only a few to dozens of differential proteins were identified , non‐amyloid protein production related proteins ADAM10, endoplasmic reticulum protein RTN4. PHB (inhibin) plays a key role in the regulation of mitochondrial protein homeostasis through the proteolytic machinery m‐AAA protease in the inner mitochondrial membrane   were detected in MCI and AD patients. Recently, the complement pathway has attracted great attention, which is involved in the regulation of microglial synaptic pruning in the early stage of AD  and platelet activation .In summary, such in‐depth and comprehensive analysis of peripheral platelet protein expression profiles of MCI and AD patients has given us new understanding of the role of platelets in AD. Bioinformatics analysis revealed that the linkage effect between peripheral and AD reflected by platelet omics involved platelet activation, complement pathway activation, mitochondrial dysfunction, calcium ion imbalance, and APP metabolic abnormality. Machine learning identified distinctive cognitive impairment‐platelet combination biomarkers . Altogether, the exploration of platelet proteomics is novel and a great supplement to understanding the peripheral changes of AD, and platelet combination biomarkers have great application potential in precision medicine for AD.44.1According to mini‐mental state examination (MMSE) score  and centrifuged at 120 g for 4 to obtain purified platelet samples and stored at −80°C.Platelet samples were added with lysis buffer  and completely lysed by ultrasound . After lysis of the ice for 30 minutes, the samples were centrifuged at 12,000 g for 10 minutes to obtain protein solution.4.3n = 9–10), and each sample corresponds to a TMT label (ThermoFisher 90406). Each sample was digested with mass spectrometric trypsin  into peptides and then labeled with TMT. Each labeled peptides was mixed into a group of 10 different labeled samples, which were divided into 15 components by high‐performance liquid chromatography (HPLC) for subsequent experiments.We performed a proteomic analysis of a large sample size (4.4The dried components were dissolved in 0.1% formic acid (FA), and captured with a silica gel capillary column filled with C18 resin  for subsequent Q Exactive  mass spectrometer analysis. Full scan in Orbitrap mass analyzer in data‐dependent acquisition (DDA) mode, the specific parameters are set as follows: 400–1, 800 m/z, 70000 resolution; MS/MS scans . Using Proteome Discoverer 2.1 software (Thermo Scientific) to retrieve MS/MS data according to Uniport‐human database (2020–05). The searching parameters were modified on the previous research settings   and heatmap gplots package were used for cluster analysis and heatmap drawing. Volcano plot and heatmap were performed using GraphPad Prism 8.00. Pathway and biological function enrichment of statistical clusters was performed using Metascape (http://metascape.org), WEB‐based GEne SeT AnaLysis Toolkit  and DAVID version 6.7 (https://david‐d.ncifcrf.gov/). Cytoscape 3.6.1 and STRING  plug‐in were used for visual analysis of protein–protein interaction (PPI) network. We use 'Wu Kong' platform (https://www.omicsolution.com/wkomics/main/) for relative Pearson analysis.After we normalized and filled the data on the Perseus platform, we used the 4.6SIMCA (version 14.0) software was used for partial least squares discrimination analysis (PLS‐DA). The protein of predictive variable importance in projection larger than 1 (VIPpred >1) was considered to be meaningful for sample discrimination. Further leave‐one‐out (LOO) cross validation was applied to select candidate biomarkers. The samples were trained and evaluated in a leave‐one‐out manner using scikit‐learn python package. Logistic regression was chosen as the classifier with {\rm solver='liblinear’} and {\rm class weight='balanced’}. Specifically, twenty‐eight cross‐validations of the proteome samples  were performed, in which twenty‐seven were randomly attributed into the training set, and one was in the validating set for each analysis. By twenty‐eight cycles, all the samples had been validated. Based on the LOO algorithm, the under the receiver operating characteristic curve (AUCROC) and precision‐recall curve (AUCPR) for each best combination biomarkers. AUCROC was based on true positive rate and false positive rate: True positive rate = [true positive / ]; False positive rate = [false positive / ]. PRAUC was obtained based on precision and recall: Precision = [true positive / ]; Recall = [true positive / ]. In addition, F1 score=2 *  / .4.7The primary antibodies, anti‐ITPR1 , anti‐ITPR2 , anti‐FINC , anti‐GP1BA , anti‐ADAM10 , anti‐CD63 , anti‐PHB , anti‐RAP1A , anti‐UQCRH  were added and incubated on ice overnight. After washing with TBST, the membranes were incubated with anti‐rabbit or anti‐mouse IgG HRPs  for 50 min at room temperature. Then the membranes were treated with enhanced chemiluminescence (ECL) reagents from an ECL kit  for exposure.4.8p < 0.05 was considered to be significant, and the data were expressed as mean ± SEM.Statistical analysis was performed by SPSS 24.0 software . We used one‐way variance analysis (ANOVA) to evaluate the statistical differences for the population information and Western blotting results, and the student's t test to compare the proteomic results of two groups. The authors declare that they have no conflict of interest to disclose.Experimental design: HY, XY, and JW; recruitment of subjects and sample collection: HY, YL, BH, and TH; experimental methods: HY, YL, CC, XY, and JW; data analysis: HY, YL, CC, and JH; manuscript writing: HY, XY, and JW.Fig S1Click here for additional data file.


However, DCIS prognosis remains unclear. To address this gap, we developed an in vivo model, Mouse‐INtraDuctal (MIND), in which patient‐derived DCIS epithelial cells are injected intraductally and allowed to progress naturally in mice. Similar to human DCIS, the cancer cells formed in situ lesions inside the mouse mammary ducts and mimicked all histologic subtypes including micropapillary, papillary, cribriform, solid, and comedo. Among 37 patient samples injected into 202 xenografts, at median duration of 9 months, 20 samples (54%) injected into 95 xenografts showed in vivo invasive progression, while 17 (46%) samples injected into 107 xenografts remained non‐invasive. Among the 20 samples that showed invasive progression, nine samples injected into 54 xenografts exhibited a mixed pattern in which some xenografts showed invasive progression while others remained non‐invasive. Among the clinically relevant biomarkers, only elevated progesterone receptor expression in patient DCIS and the extent of in vivo growth in xenografts predicted an invasive outcome. The Tempus XT assay was used on 16 patient DCIS formalin‐fixed, paraffin‐embedded sections including eight DCISs that showed invasive progression, five DCISs that remained non‐invasive, and three DCISs that showed a mixed pattern in the xenografts. Analysis of the frequency of cancer‐related pathogenic mutations among the groups showed no significant differences (KW: p > 0.05). There were also no differences in the frequency of high, moderate, or low severity mutations . These results suggest that genetic changes in the DCIS are not the primary driver for the development of invasive disease. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.Due to widespread adoption of screening mammography, there has been a significant increase in new diagnoses of ductal carcinoma  Following incubation with rotation for 16 h (50 rpm at 37 °C), the specimens were removed, briefly shaken by hand, and centrifuged at 200 × Recipient mice were 8‐ to 10‐week‐old virgin female NOD‐SCID IL2Rgamma‐null (NSG) mice, which were purchased from The Jackson Laboratory . Animal experiments were conducted following protocols approved by the University of Kansas School of Medicine Animal Care and Use and Human Subjects Committee.For MIND surgeries, a Hamilton syringe with a 50‐μl capacity and a blunt‐ended ½'' 30‐gauge needle was used to deliver the cells as described previously . Two mic2. Total growth was determined by adding the positive areas from all imaged slides.To measure the extent of growth, mammary glands containing xenografted lesions were fixed overnight in 4% paraformaldehyde and processed into paraffin wax. Each entire gland was serially sectioned at 5 μm. Every tenth section was mounted, and human cells growing in the mouse gland were identified by immunofluorescence (IF) using a human specific anti‐CK19 antibody following epitope retrieval in a high‐pH Tris buffer. Growth areas on every tenth section were imaged at 20× magnification on a Zeiss Imager M‐2 microscope  fitted with an AxioCam MRm camera (Zeiss). The perimeter of all growth areas was drawn using the outline tool in Axiovision software (Zeiss), and the area of growth was measured in μm2O2 for 10 min, followed by incubation with primary antibody for 40 min followed by Mach 2 HRP‐Polymer  for 30 min, and 3,3'‐diaminobenzidine (DAB)+chromogen  for 5 min. Immunohistochemical staining was performed using an IntelliPATH FLX Automated Stainer  at room temperature. Light hematoxylin counterstaining was performed, after which the slides were dehydrated, cleared, and mounted using a permanent mounting medium.Antibodies used for IF and IHC are listed in supplementary material, Table IF was performed as previously described . AntibodTo allow re‐transplantation of MIND DCIS cells, mammary glands from MIND xenografts were excised  and digested overnight as described above. Single mammary epithelial cells were then magnetically labeled with mouse MHCI/II antibodies  containing 2% FBS. The antibodies used are listed in supplementary material, Table Formalin‐fixed, paraffin‐embedded (FFPE) specimens were cut at 10 μm, placed on slides, and stored overnight to air dry. The slides were then deparaffinized and stained using a Paradise staining kit (Arcturus™ Paradise™)  in preparation for microdissection. Microdissection was performed using an ArcturusXT™ LCM System (Thermo Fisher Scientific) following the system manual. A PicoPure™ DNA Extraction Kit  was used for DNA extraction, and DNA quantification was performed fluorimetrically .To compare molecular aberrations between patient DCIS and corresponding xenografts, a high‐depth targeted sequencing platform of 201 genes was utilized . Librariwww.broadinstitute.org/picard) and GATK (www.broadinstitute.org/gatk). The BAM files were then analyzed by MuTect2 (http://gatk.broadinstitute.org) and Pindel  [EGFR (158N) [ATM (P1054R) [STK11 (D194N) [PIK3CA (E545K) [KIT  [RUNX1 (L56S) [Molecular aberrations that were shared in at least one P/X pair and that were previously recognized to have deleterious effects in human cancers included S (SNP)] , 24, EGFR (158N) , ATM (P1(P1054R) , STK11 ( (D194N) , PIK3CA  (E545K) , KIT  , 30, and1 (L56S) . Notably1 (L56S) , 33, 34,ATM, P/X 33 in EGFR and STK11, P/X 20 in EGFR and PIK3CA, and P/X 16 in RUNX1 and KIT. The existence of P/X unique mutations is consistent with inter‐tumoral heterogeneity and the existence of individualized drivers of DCIS malignancy. Interestingly, molecular aberrations in CHEK2, EGFR, ATM, KIT, and RUNX1 were also found in adjacent normal cells. The detection of these aberrations in adjacent normal tissues supports a field cancerization phenomenon in which genetic alterations in adjacent normal tissues may predispose to future DCIS recurrences  19 carried a pathogenic mutation in In addition to finding some shared mutations, a number of private mutations were found in P/X pairs. Although this was unexpected, these findings demonstrated that our xenograft models may represent clonal subpopulations of the original patient DCIS. Another possibility is that the PDX DCIS lesions continue to evolve within the microenvironment of MIND mammary glands.p ≥ 0.05). There were also no differences in the frequency of high, moderate, or mild severity mutations .A major goal of this study was to compare mutation signatures in patient DCIS, i.e. comparing those that showed invasive progression with those that remained non‐invasive in the MIND models. The Tempus XT oncology assay, which combines a 648‐gene panel to detect clinically actionable variants from FFPE tissues, was utilized. This assay has demonstrated a high sensitivity (>95%) and specificity (>99%) for DNA‐derived variants and can identify actionable variants of both somatic and germline origins. We utilized FFPE sections of DCIS biopsy samples from 16 patients, 11 of which showed invasive progression in our MIND models, while five remained non‐invasive. Among the 11 samples, three showed a mixed pattern in which some xenografts showed invasive progression while some remained non‐invasive. A heatmap which includes patient‐specific gene variants is shown in Figure ATM (p.ARG1575His) in the progressed group, KMT2C (p.Ala976Thr) in the mixed group, and SLX4 (p.Gly1447Ser) in the non‐progressed group. The most common moderate severity variants that were also pathogenic included WNK2 (p.Arg543Trp) and PIK3CA  in the progressed group, AKT1 (p.Glu17Lys) and ATM (p.Arg1575His) in the mixed group, and KMT2D  and PIK3CA  in the non‐progressed group. The most common mild severity variants that were also pathogenic included KMT2C (p.Ala976Thr) and ATM (p.Arg1575His) in the progressed group, ATM (p.Arg1575His) in the mixed group, and ATM (p. Arg1575His) and KMT2C (p.Ala976Thr) in the non‐progressed group. These results indicate that high severity and pathogenic variants existed in the patient's DCIS regardless of whether they advanced to invasive lesions or remained non‐invasive in the MIND models.Among the groups, the most common high severity sequence variants that were also pathogenic included in vivo growth compared with non‐invasive models. To show the contribution of specific gene mutations in patient DCIS to extent of in vivo growth in the DCIS MIND models, specific pathogenic mutations were listed next to each line representing median extent of DCIS MIND in vivo growth and IQR. As shown in supplementary material, As shown in Figure et al reported the transplantation of small fragments of human ‘cystic hyperplasias’ into cleared mammary fat pads of nude mice [et al reported the transplantation of fragments from 25 cases of human DCIS into athymic Balb/c nu/nu mice [in situ, and invasive cancers [et al demonstrated the successful xenotransplantation of freshly procured DCIS organoids derived from patient DCIS biopsies or surgical specimens into cleared mammary fat pads of immunocompromised mice. Tumors formed in the mice at a rate of ~80%, and most progressed to invasive lesions [Efforts to develop animal models of preinvasive breast lesions date back to 1975, when Outzen ude mice . Later, /nu mice  that mai cancers , 38. A c cancers . Similar cancers . Recentl lesions . Despitein situ lesions inside the mammary ducts followed by their invasion as they bypass the natural barriers of the ductal myoepithelial cell layer and basement membrane. The take rate for the MIND method using primary human cells, including hyperplastic, in situ, and invasive epithelial cells, is 70–90%. DCIS‐like lesions injected using the MIND method mimic all histologic subtypes of human DCIS, including micropapillary, papillary, solid, comedo, and cribriform. This is remarkable because DCIS epithelial cells transplanted as single cells grow and re‐establish the various histologic subtypes. Most importantly, a fraction of xenografted DCISs undergo invasive progression, while the remainder persist as non‐invasive and non‐progressed DCIS‐like lesions. Among the 37 patient samples injected by the MIND method, 20 samples (54%) showed in vivo invasive progression, while 17 (45%) of the samples remained non‐invasive. The rate of invasive progression in our models is similar to the rate at which human untreated high‐grade DCIS exhibited progression to invasive breast cancer (54% MIND models versus 48% untreated human DCIS). Notably, the majority of the DCIS samples that we receive are high grade (70%), while the remaining are intermediate grade (27%) and low grade (3%). One important caveat is that DCIS progression was followed for a median of 9 months in our models, whereas human untreated DCISs were followed for a median of 3.75 years (range 1–12 years). Another important consideration is that our models are immunocompromised, and the contribution of the immune system to DCIS invasive progression is not taken into account.With the idea that human DCIS is initiated inside the ducts, we established the MIND method in which patient‐derived DCIS surgical and biopsy specimens are digested into single epithelial cells followed by their intraductal injection . This isA critical question in the field is whether any DCIS biomarkers can predict the risk of disease recurrence or evolution to invasive cancer. A number of previous studies have proposed biomarkers such as tumor size, nuclear grade, the presence of necrosis and expression of certain biomarkers such as HER2 overexpression, high proliferation (as detected using Ki67), p53 expression, and hormone receptor negativity in DCIS as risk factors for breast cancer recurrence , 43. Howin vivo growth compared with non‐invasive and mixed invasive and non‐invasive xenografts. Tumor size has been proposed as a biomarker of aggressiveness in DCIS and this finding also validated our model as a valuable tool for studying the underlying mechanisms of DCIS invasive progression. Notably, there was a significant level of inter‐ and intra‐tumoral heterogeneity with respect to the xenografts' extent of in vivo growth and invasiveness. Importantly, nine samples showed a mixed pattern in which some xenografted DCISs advanced to invasive lesions while others remained non‐invasive. These data indicate that both invasive and non‐invasive cells co‐existed within a single DCIS sample and again highlight DCIS cellular heterogeneity, similar to other reports [Another important finding of this study was that the invasive DCIS MIND models showed significantly higher median extent of  reports , 51.A side‐by‐side comparison of patient and xenografted DCIS biomarkers  showed lack of 100% concordance. While this was unexpected, due to cellular heterogeneity, a single section from a xenograft and patient DCIS used for biomarker studies may not represent the entire DCIS clonal cell subpopulations. Nonetheless, biomarker expression showed higher concordance in the xenografts that exhibited invasive progression compared with xenografts that remained non‐invasive.TP53, PDGFRA, KMT2C, CHECK2, and EGFR, there were also P/X pair unique pathogenic mutations, including ATM (P/X 19), STK11 (P/X 33), PIK3CA/KIT (P/X 20), NOTCH1 (P/X 23), and RUNX1/KIT (P/X 16). There were also a number of private mutations in patient DCISs and their corresponding xenografts. The existence of private mutations in xenografts supports DCIS multi‐clonality. Others previously reported the potential existence of multiple subclones within DCIS. For example, Allred et al reported that a large percentage of DCIS lesions (48%) exhibited intralesional heterogeneity [et al, using immunofluorescence in situ hybridization (IFISH) for common genomic aberrations in breast cancer (i.e. 8q24), also showed a high degree of genetic heterogeneity among cells within each patient's DCIS [To determine whether the mutation landscape in xenografts mimicked that of the corresponding patient DCIS, we performed high‐depth targeted sequencing of DNA isolated from five pairs of patient DCISs and their corresponding xenografts . While aogeneity . Intraleogeneity . Park ett's DCIS . Recentlt's DCIS , 55. Thein vivo growth. These data are in agreement with one previous study which showed no significant differences in the frequency of non‐synonymous mutations and CNAs when comparing pure DCIS with synchronous IDC to DCIS that did not progress to invasion for a median follow‐up of 72 months (range 22–85 months) [TP53 and PIK3CA in DCIS that progressed to invasion, the results did not reach statistically significant differences. In addition, the repertoires of somatic mutations were significantly different according to ER and HER2 status [We also analyzed the mutation signature of patient DCIS by using the Tempus XT oncology assay on 16 archival patient DCIS samples, 11 of which showed invasive progression in our MIND models, while five remained non‐invasive. Among the 11 patient samples, three showed a mixed pattern in which some xenografts showed invasive progression, while others remained non‐invasive. This analysis showed that there were no significant differences in the type, number, or severity of mutations among the groups. Furthermore, there was no significant correlation between the type, number or severity of mutations and the extent of xenografted DCIS  months) . While t2 status . One cavTP53 mutations. A recent study found higher FOXP3+ TILs to be associated with DCIS that showed a future recurrence in six cases [One potential limitation of our mouse models is the lack of human microenvironments, including the immune cells and stroma. Studies comparing immune microenvironments in DCIS, DCIS with associated IDC, and IDC have reported the existence of a more immunosuppressive environment associated with a transition from DCIS to IDC . DCIS alix cases . Therefoin vivo growth, and mutation landscape, the molecular drivers of DCIS malignancy may be specific to each DCIS lesion. Furthermore, since our studies showed that genetic changes were not the primary drivers of DCIS malignancy, future studies should focus on studying other factors including clonal heterogeneity, stroma, and epigenomic reprogramming. These future studies will help to ultimately achieve the goal of identifying more reliable indicators for dangerous lesions and more effective treatments.In conclusion, MIND is a valuable tool for studying the molecular and cellular mechanism underlying DCIS invasive progression. Since our studies showed a significant level of DCIS inter‐ and intra‐tumoral heterogeneity with respect to invasive progression, extent of YH, DL, HSE, HH, HH, HH, MR, CK and EW collected the data, performed analyses, and contributed to manuscript write‐up. MX and JZ contributed data and data analysis tools. LM, TC, MI, MR, JG, OW, AA, AH, CB, JLW, ALM, KEL, LR, OT, HR, ROM and RM contributed tissue, conceived and designed the analysis, and contributed to manuscript write‐up. JT and SGH contributed to data statistical analysis. AKG, AF, AT, ESH and FF conceived and designed the analysis, and contributed to manuscript write‐up. FB conceived and designed the analysis and wrote the manuscript. All the authors reviewed and approved the manuscript.Figure S1. Rate of engraftment in the MIND model following intraductal transplantation of human breast malignant and nonmalignant epithelial cellsFigure S2. Representative IF images of non‐progressed and progressed PDX DCIS MIND modelsFigure S3. T200 targeted sequencing‐DNA mutational analysis between primary patient DCISs lesions and corresponding xenograftsFigure S4. Contribution of pathogenic mutations to PDX DCIS MIND in vivo growth and progressionClick here for additional data file.Table S1. List of antibodies usedClick here for additional data file.Table S2. List of patients and their corresponding xenografts, progression status, extent of xenograft growth and IQR, patient and xenograft histology and biomarkers, and patient demographicsClick here for additional data file.Table S3. Comparison of patients' and their corresponding xenografts' biomarker expressionClick here for additional data file.Table S4. Molecular aberrations shared in patients and their corresponding xenograftsClick here for additional data file.Table S5. T200 targeted sequencing analyzed dataClick here for additional data file.Table S6. Tempus XT targeted sequencing analyzed dataClick here for additional data file.


Sarcopenia, which is characterized by a decrease in muscle quantity or quality, is commonly observed in patients with cancer. Recent research has reported contradictory results on the association between sarcopenia and the efficacy of immune checkpoint inhibitors (ICIs). We conducted a systematic review and meta‐analysis to investigate this discrepancy. We systematically searched three electronic databases to identify articles reporting on the association between sarcopenia and treatment outcomes in patients with solid cancers who received ICIs. The outcomes assessed were hazard ratios (HRs) for overall survival (OS) and progression‐free survival (PFS), and odds ratios (ORs) for objective response rate (ORR), disease control rate (DCR), and toxicity. Pooled estimates and their 95% confidence intervals (CIs) were calculated. A total of 2501 patients from 26 studies were analysed. Sarcopenia was observed in 44.7% (95% CI: 38.2–51.3) of the patients and was significantly associated with poor survival . The HRs (95% CIs) for OS according to the diagnostic measures used were 1.97 (0.88–4.41) for psoas muscle index (PMI), 1.41 (0.87–2.28) for skeletal muscle density (SMD), and 1.43 (1.23–1.67) for skeletal mass index (SMI). The HRs (95% CIs) for PFS were 1.86 (1.08–3.21) for PMI, 1.27 (0.94–1.71) for SMD, and 1.38 (1.11–1.71) for SMI. Poor radiological response to ICI therapy was observed in patients with sarcopenia . The ORs for ORR (95% CIs) were 0.56 (0.15–2.05) for PMI and 0.78 (0.56–1.09) for SMI. The oncologic outcomes associated with melanoma and non‐small cell lung cancer (NSCLC) were comparable with those observed overall . In contrast, the occurrence of severe toxicity was not associated with sarcopenia . Poor survival and poor response in patients with sarcopenia indicate a negative association between sarcopenia and efficacy of ICIs. Sarcopenia's predictive ability is consistent across various tumour types. For the selection of patients who may respond to ICIs pre‐therapeutically, the presence of sarcopenia should be assessed in clinical practice. However, recent rapid progress in immunotherapy has changed this paradigm.Sarcopenia is a skeletal muscle disorder characterized by reduced muscle strength and muscle quantity.Meta‐analyses have advantages in that they can generate a pooled effect size, as deduced from the results of previous studies and thus can yield more reliable conclusions using data from a larger number of patients. This study aimed to investigate, using a meta‐analysis, whether sarcopenia status is predictive of oncologic outcomes in patients treated with ICIs. Further, we also sought to determine the differences between various tools and tests for sarcopenia in the prediction of prognoses.22.1www.ncbi.nlm.nih.gov/pubmed, Scopus www.elsevier.com/online‐tools/scopus, and Ichushi‐Web https://search.jamas.or.jp, which contains bibliographic information and abstracts of articles in Japanese journals  from inception to 4 May 2021. The search terms were (i) ‘CTLA‐4’ or ‘CTLA4’ or ‘cytotoxic T‐lymphocyte‐associated protein 4’ or ‘CD152’ or ‘PD‐1’ or ‘PD1’ or ‘programmed cell death protein 1’ or ‘CD279’ or ‘PD‐L1’ or ‘PDL1’ or ‘programmed death‐ligand 1’ or ‘CD274’ or ‘atezolizumab’ or ‘avelumab’ or ‘cemiplimab’ or ‘durvalumab’ or ‘ipilimumab’ or ‘nivolumab’ or ‘pembrolizumab’ and (ii) ‘sarcopenia’ or ‘sarcopenic’ or ‘muscle index’ or ‘muscle mass’ or ‘muscle depletion’ or ‘muscular atrophy’ or ‘muscle strength’ or ‘muscle quality’ or ‘muscle quantity’. The references in the retrieved articles were manually searched for associated studies.This study was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines.2.2Articles in English or Japanese that met the following criteria were included in this study: (i) patients: patients with solid cancers treated with ICIs; (ii) exposure: sarcopenia was defined based on the diagnostic modalities recommended by consensus statements2.3et al.The following data were extracted: name of first author, year of publication, institution and country, number of patients, number of outcomes according to sarcopenia status, disease stage, ICI drug names, toxicity, diagnostic measures for sarcopenia and their cut‐off methods and cut‐off values, and HRs and ORs and their 95% confidence intervals (CIs). The HRs, ORs, and 95% CIs were extracted preferentially from multivariate or univariate analyses. When HRs were not provided in the manuscript, survival data were extracted from Kaplan–Meier curves and estimated using the method proposed by Tierney 2.4Q test and I2 statistics. All statistical tests were two‐sided, and significance was defined by a P‐value <0.05. The included studies differed in the tumour sites, prior treatment, ICIs used, institutions, and diagnostic measures for sarcopenia and their cut‐off values. Owing to the heterogeneity among the studies, a random effect model was preferred in this manuscript.Pooled HRs, ORs, and their 95% CIs were estimated with both a random effect model and a fixed effect model using Comprehensive Meta‐Analysis Version 2 . First, we investigated the predictive impact of sarcopenia on OS, PFS, objective response, disease control, and toxicity. The mean HR was used as the representative of the study in a meta‐analysis when more than one diagnostic procedure for sarcopenia was used.The protocol for this meta‐analysis is available in UMIN (registration code: UMIN000042621).33.1Figureet al. contain overlapping data.The electronic database search for articles from the inception of each database to 4 May 2021 led to the retrieval of 597 records .The HRs for OS according to the diagnostic measures used are shown in the Supporting Information, 3.4FigureTableEighteen studiesTableP = 0.207).The HRs for PFS according to the diagnostic measures employed are shown in the Supporting Information, 3.5FigureTableObjective response rate was investigated in 15 studies.TableP = 0.153). The ORs and 95% CIs for other diagnostic procedures are also shown in the Supporting Information, TablePMI, SMD, and SMI were employed for dichotomization in five,3.6FigureTableDisease control rate was investigated in 10 studies.TableP = 0.754).Psoas muscle index and SMI were employed for dichotomization in three studies each3.7TableSubgroup analyses using a random effect model were performed according to the primary tumour site Table. MelanomTableP = 0.670 for OS, P = 0.291 for PFS, P = 0.107 for ORR, and P = 0.876 for DCR).Next, we conducted a subgroup analysis for the ICI drugs Table. Data on3.817,19. The ORs for severe toxicity ranged from 0.26 to 5.34. The pooled OR (95% CI), irrespective of the diagnostic procedure, was 1.13 (0.51–2.52)  without survival benefits in others. To personalize treatment, facilitate the cost‐effective use of ICIs, and avoid unnecessary irAEs, predictive and prognostic biomarkers have been sought. Some predictive factors for ICI treatment include PDL‐1 expression, haematologic markers, tissue infiltration lymphocytes, metastatic site, inflammatory cytokines, T cell markers, and irAEs.Sarcopenia is a muscle disease defined by muscle quantity or quality.Previous meta‐analyses on cancer and sarcopenia incorporated only SMI or other muscle mass evaluations as a requirement for inclusion.It may be argued that sarcopenia is reflective of a person's advanced disease status and deteriorated physical condition, resulting in a worse survival. However, our ORR and DCR results suggest that sarcopenia is not a mere prognostic factor but also a predictive factor. Skeletal muscle is known to release myokines, which are muscle‐derived cytokines that exert their effects through the autocrine, paracrine, and endocrine routes.Tables S2 and S4). There are several causes for sarcopenia associated with cancer treatment, which include impaired food intake, reduced activity secondary to fatigue, and a direct effect of drugs on muscle.In vitro experiments demonstrated that pembrolizumab activated mTOR pathway.Table S9 summarises the results of these studies. Six out of seven studies assessed skeletal muscle change from 3 weeks to 3 months after baseline and showed reduced muscle mass or muscle attenuation.Skeletal muscle decrease after the initiation of ICIs treatment; that is, PMI and SMI decrease showed higher HRs than pretreatment sarcopenia did (Supporting Information, This study has several strengths. First, we investigated a large number of patients using a meta‐analysis. The studies included in the present meta‐analysis were small‐scale retrospective studies. By combining the results, we obtained more reliable estimates of the predictive impact of sarcopenia. Till this date, only one published meta‐analysis has focused on the effect of sarcopenia on ICI efficacy.FigureHowever, our study also has some limitations that must be considered. First, the studies included were of a retrospective nature. A majority of the enrolled studies retrospectively collected patient data. For the precise determination of the response rate and PFS, predefined protocols are mandatory. Second, the methods used for the calculation of the HRs and ORs differed across the studies. Although the use of data from multivariate analyses was desirable, we also included HRs from univariate analyses and estimated HRs from Kaplan–Meier curves. Moreover, the ORs for ORR were adjusted in only one study,5The number of patients who respond to ICIs is limited. Additionally, ICI treatment imposes a huge financial burden and is associated with irAEs. The identification of responders pre‐therapeutically or in the early phase of the treatment course is critically important. Unfortunately, current companion and complementary diagnostics are insufficient. In the present study, we demonstrated the predictive impact of sarcopenia in patients treated with ICIs. However, sarcopenia alone as a predictor would not be sufficiently useful. Indices comprising the combination of predictive factors are warranted. Further research is required to elaborate on the effective use of ICIs.Journal of Cachexia, Sarcopenia and Muscle: update 2019.The approval of the institutional review board was not required because this study was conducted using only previously published data. The authors certify that they comply with the ethical guidelines for publishing in the 6There are no conflicts of interest to declare.This work was supported by JSPS KAKENHI Grant Number 19K09868. This work was partially supported by a grant awarded to B Gagnon and M. L. Tremblay from the Terry Fox Research Institute, Canada.Y.T. conceived and designed the study and wrote the paper. Y.T. and R.O. collected and analysed the data. N.T., R.O., and H.I. reviewed and revised the manuscript.7Data S1. PRISMA ChecklistClick here for additional data file.Figure S1. Forest plot showing the prevalence of sarcopenia. The squares represent the hazard ratios for each study. The sizes of the squares and the horizontal lines crossing the squares represent the weight of the study in the random effect model and the 95% confidence intervals, respectively.Click here for additional data file.Table S1. Sensitivity analysis for overall survivalTable S2. Hazard ratios for overall survival according to diagnostic measures for sarcopeniaTable S3. Sensitivity analysis for progression‐free survivalTable S4. Hazard ratios for progression‐free survival according to diagnostic measures for sarcopeniaTable S5. Sensitivity analysis for objective response rateTable S6. Odds ratios for objective response rate according to diagnostic measures for sarcopeniaTable S7. Sensitivity analysis for disease control rateTable S8. Odds ratios for disease control rate according to diagnostic measures for sarcopeniaTable S9. Skeletal muscle change after treatment initiationClick here for additional data file.


In humans, exercise‐induced thermogenesis is a markedly variable component of total energy expenditure, which had been acutely affected worldwide by COVID‐19 pandemic‐related lockdowns. We hypothesized that dietary macronutrient composition may affect metabolic adaptation/fuel selection in response to an acute decrease in voluntary activity. Using mice fed short‐term high‐fat diet (HFD) compared to low‐fat diet (LFD)‐fed mice, we evaluated whole‐body fuel utilization by metabolic cages before and 3 days after omitting a voluntary running wheel in the cage. Short‐term (24–48 h) HFD was sufficient to increase energy intake, fat oxidation, and decrease carbohydrate oxidation. Running wheel omission did not change energy intake, but resulted in a significant 50% decrease in total activity and a ~20% in energy expenditure in the active phase (night‐time), compared to the period with wheel, irrespective of the dietary composition, resulting in significant weight gain. Yet, while in LFD wheel omission significantly decreased active phase fat oxidation, thereby trending to increase respiratory exchange ratio (RER), in HFD it diminished active phase carbohydrate oxidation. In conclusion, acute decrease in voluntary activity resulted in positive energy balance in mice on both diets, and decreased oxidation of the minor energy (macronutrient) fuel source, demonstrating that dietary macronutrient composition determines fuel utilization choices under conditions of acute changes in energetic demand. Acute decline in voluntary energy expenditure, as occurred in COVID19‐related lockdowns, results in mice in changes in oxidation fuel selection: Low‐fat fed mice decreased fat oxidation during the active phase of the day; Short‐term high‐fat fed mice decreased carbohydrate oxidation. Acute decrease in voluntary activity resulted in positive energy balance in mice on both low‐ and high‐fat diets . Yet, dietary macronutrient composition determined fuel selection adaptation: mice on LFD decreased fat oxidation during the active phase of the day, while those on HFD reduced carbohydrate oxidation. Thus, acute decrease in voluntary activity decreased oxidation of the minor calorie macronutrient source in either diet.1Regular physical activity has beneficial effects on multiple aspects of health, and is recommended, by various health organizations, for the general population Chung, . PhysicaHow voluntary energy expenditure (VEE) interacts with dietary energy sources through oxidation (mainly lipid and carbohydrate) is a field of active research , and was conducted according to the Israeli Animal Welfare Act following the guidelines of the Guide for Care and Use of Laboratory Animals . Five‐week‐old male C57BL/6J mice  were housed, 2 mice/cage in a room with constant temperature (23 ± 3°C) and relative humidity, water and food ad‐libitum, and with a 12/12 h light/dark cycle . Mice were fed low‐fat diet (LFD) containing 10% energy from fat  for a 2 week period, after which they were transferred to Promethion High‐Definition Behavioral Phenotyping System . Mice were divided randomly into two groups: a control group that continued to consume LFD, and a group fed with HFD containing 60% energy from fat . The two diets’ composition was identical, apart for corn starch (506 and 0 g/Kg) and Lard (20 and 245 g/Kg) for LFD and HFD, respectively. Twenty‐four hours acclimation period with a free access to food and water and standard 12/12 h light/dark cycle were followed by 72 h sampling duration, after which free‐running wheel device was omitted from the cages, and metabolic recording continued for an additional 72 h Figure . Data ac2.2CO2 (L/h) to VO2 (L/h) using stoichiometric equations, with the assumption that protein oxidation during exercise was negligible, as follows: Respiratory exchange rate (RER) = VCO2/VO2 . Body weight was measured before introducing the running wheel, when taking it out of the metabolic cages, and at the end of the study. Given the short duration of the intervention and small differences in body weight between the groups, measurements were not adjusted to body weight.RER (respiratory exchange rate), total carbohydrate, and fat oxidation (g/h) were computed from V2.3U test between mice's groups LFD versus HFD, and non‐parametric paired Wilcoxon test between groups with wheel versus without wheel; and active phase (night) versus inactive phase (day). A p ≤ 0.05 was considered to be a statistically significant difference. All data shown are mean ± SEM.Analyses were performed by using GraphPad Prism 9.1.0. Statistical significance was examined using: non‐parametric unpaired Mann–Whitney 33.1Seven‐week‐old C57BL/6J mice were placed individually in metabolic cages after a 2‐week adaptation to LFD and 4 days adaptation to metabolic cages, and then fed either LFD or HFD Figure . The iniMice on either diet exhibited a significant diurnal  difference in Energy Intake , increasing the wheel running distance by >fivefold compared to the day/inactive phase, reaching ~550 m/12 h Figure . Omittin3.3p = 0.819. Correspondingly, wheel omission significantly increased 3 d weight gain in both LFD and HFD mice compared to weight gain in the preceding, 3 d with wheel, period  voluntary activity induced by running wheel omission affected energy intake, expenditure, and fuel utilization in mice on either LFD or HFD. The trend for increased EI in HFD compared to LFD mice Figure  was stild Figure . Thus, aWe next evaluated how an acute decline in voluntary activity during the active phase of the day, as modeled by wheel omission, affected fuel utilization. Active phase RER trended to increase in mice on LFD, and exhibited a minor decline in mice on HFD Figure . Interes4In the current study we evaluated changes in fuel oxidation selection when omitting free‐running wheel in mice fed either LFD or HFD. When running wheel was freely used, mice on HFD for >24 h significantly increased both EI and EE in the active (night) and inactive (day) phases, and switched to energy utilization predominated by fat oxidation. Following running wheel omission, mice on either LFD or HFD similarly decreased total activity during the active phase by ~50%, but did not acutely decrease their active phase EI, resulting in positive energy balance and significantly increased weight gain. Change in fuel utilization depended on dietary composition: wheel omission in LFD mice significantly decreased active phase fat oxidation, while it diminished active phase carbohydrate oxidation in HFD. Thus, dietary composition determined fuel selection, with acute decrease in VEE reducing oxidation of the minor dietary energy source.Consistent with our results, previous studies demonstrated that short and longer‐term HFD increases both EI and EE, with augmented fat oxidation and consequently reduced RER  fuel source, demonstrating that dietary macronutrient composition determines fuel selection under conditions of acute changes in energetic demand.There is no conflict of interest to declare for any of the co‐authors.NSB: Study design, operation of metabolic cages, analysis and interpretation of data, critical review of the manuscript, and approval of the manuscript final version; RL: Operation of metabolic cages, analysis and interpretation of data, critical review of the manuscript, and approval of the manuscript final version; NM: Study design, analysis and interpretation of data, critical review of the manuscript, and approval of the manuscript final version. YH: Study design, operation of metabolic cages, analysis and interpretation of data, critical review of the manuscript, manuscript writing, and final approval. GWW: analysis and interpretation of data, critical review of the manuscript, and approval of the manuscript final version. AR: Study design, analysis and interpretation of data, critical review of the manuscript, manuscript writing and final approval, and correspondence. UY: Analysis and interpretation of data, critical review of the manuscript, manuscript writing and final approval, and correspondence.


Despite primary percutaneous coronary intervention (PPCI) is generally considered the best therapy in older frail adults with ST-segment elevation myocardial infarction (STEMI), the incidence of re-hospitalization for cardiovascular diseases remains significant in these patients.We hypothesized that thrombus aspiration (TA) before PPCI could be a useful treatment for reducing mortality and rehospitalizations in frail patients undergoing PPCI for STEMI.vs TA + PPCI in frail STEMI patients. We examined a cohort of consecutive frail patients aged ≥ 65 years with first STEMI treated with PPCI between February 2008 and July 2015 at the Department of Cardiology of the “Cardarelli” Hospital in Naples, Italy.We conducted a study comparing PPCI alone p 0.036), whereas death from cardiovascular causes was 6.0% in the PPCI group vs 3.0% in the TA + PPCI group (p 0.028). Equally important, the rate of re-hospitalization due to heart failure was 7.5% in the PPCI group vs 4.0% in TA + PPCI group (p 0.025) and the rate of re-hospitalization due to acute coronary syndrome was 10.0% in the PPCI group vs 4.5% in the TA + PPCI group (p 0.016).The study was completed by 389 patients . At 1-month follow-up, the rate of death from any cause was 7.0% in patients treated with PPCI alone vs 3.0% in patients treated with TA + PPCI (These results indicate the importance of TA in the treatment of STEMI in a group of high-risk patients such as elderly with frailty. Frailty has been shown to increase the risk of cardiovascular diseases –6. The CThrombus aspiration (TA) before PPCI has been proposed as a useful treatment to reduce oxidative stress and distal embolization, thereby improving microvascular perfusion and reducing no-reflow phenomena –18. HoweNonetheless, to our knowledge there are no studies investigating the effects of TA in addition to PPCI in frail patients with STEMI.Of note, advanced age is an important determinant of severe clinical outcomes in patients with STEMI and frailty is a strong predictor of cardiovascular events , 25. ThuGrade 0 (G0), no angiographic characteristics of thrombus;Grade 1 (G1), possible thrombus presence;Grade 2 (G2), definite thrombus with largest dimension ≤ 1/2 the vessel diameter;Grade 3 (G3), definite thrombus, with largest linear dimension > 1/2 but < twice vessel diameter;Grade 4 (G4), definite thrombus, with the largest dimension ≥ 2 vessel diameters;Grade 5 (G5), total occlusion, unable to assess thrombus burden due to total vessel occlusion.This is an observational study investigating the relationship between TA use and outcome in addition to PPCI in frail STEMI patients. We examined consecutive patients with first STEMI treated with PPCI between February 2008 and July 2015 at the Department of Cardiology and PCI center of the “Cardarelli” Hospital in Naples, Italy. All patients with onset of symptoms < 12 h and at least 1-mm ST-segment elevation in 2 or more contiguous limb leads or at least 2 mm in two or more contiguous precordial leads or left bundle branch block underwent PPCI. Coronary angiography was performed as we previously described and validated , 26–28. Luigi Vanvitelli”, Naples, Italy approved the protocol and written informed consent was given by each patient.Inclusion criteria were: age ≥ 65 years with a confirmed frail condition (see below); presentation to the hospital for PPCI in the setting of first STEMI and feasibility of performing TA, as judged by the cardiologists. Patients with age < 65 years or non-frails, left ventricular ejection fraction < 25%, with previous myocardial infarction, revascularization, fibrinolytic therapy, or terminal cancer were excluded from the study. The investigation was designed and conducted according to the principles outlined in the Declaration of Helsinki for use of human tissue or subjects. The Institutional Review Board of University of Campania “Weight loss .Weakness .Exhaustion .Slowness .Low physical activity level .Physical frailty assessment was performed right before the discharge, following the Fried Criteria , 35. A d. bolus); 30 days after the procedure, all patients returned to our ambulatory for follow-up. The primary outcome was all-cause and cardiovascular death. Other outcomes were re-hospitalization for ACS and HF.Routine blood analyses were obtained on admission before coronary angiography. TA procedure was performed as previously described . All patt test for continuous variables. Normality was assessed using the Shapiro–Wilk test. We calculated Kaplan–Meier product limits for cumulative probability of reaching an endpoint and used the log-rank test for evidence of a statistically significant difference between the groups; time was measured from the first admission for the procedure until outcome. We calculated via a priori power analysis (GPOWER software) the number of patients required to reject the null hypothesis with a one-tailed type II error rate of 0.05 and a two-tailed type I error of 0.05, yielding a value of 184 participants. All calculations have been performed using the software SPSS 24.Clinical characteristics of patients were compared using the Pearson Chi square test for categorical variables and Student’s A total of 956 frail patients with suspected STEMI were admitted to the PCI center. 298 patients were excluded because PPCI was not performed, 182 patients were excluded for delays in treatment greater than 24 h, 61 patients were unwilling to provide clinical information, and biochemical analysis was not available for 26 subjects. Hence, a total of 389 patients completed the study    Fig. . Similaroup p 0.08   (Fig. The rate of re-hospitalization due to HF was 7.5% in the PPCI group vs 4.0% in TA + PPCI group p 0.05 Fig. 2p 0.025 (The exact role of coronary TA in PPCI for STEMI is very debated , 36–40. Frail patients are known to present an intrinsic risk of cardiovascular diseases with higher mortality and higher risk of re-hospitalization , 7, 43. The most impactful result of our study, shown by Kaplan–Meier curves, was on the hospitalization rate for ACS at 30-day follow-up Fig. . SignifiNowadays, TA + PPCI is debated; some years ago, it was considered an important aspect of PPCI, particularly in patients with a high thrombus burden , 49–51. Several limitations of the present study warrant consideration. First, the follow-up period of 30 days is short; nevertheless, observing significant differences between PPCI and TA + PPCI in several outcomes already after 1 month is noteworthy, especially in a population of elderly patients. Second, the sample size of our two groups is relatively small; however, as mentioned in “Taken together, our data suggest that in the treatment of STEMI, adding TA to PPCI is associated with a significant improvement in 30-day mortality and hospitalizations due to HF and ACS in frail elderly patients, compared to conventional PPCI alone.


Lung adenocarcinoma (LUAD) is a heterogeneous disease. However the inner sub‐groups of LUAD have not been fully studied. Markers predicted the sub‐groups and prognosis of LUAD are badly needed.To identify biomarkers associated with the sub‐groups and prognosis of LUAD.Using nonnegative matrix factorization (NMF) clustering, LUAD patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and LUAD cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) dataset were divided into different sub‐consensuses based on the gene expression profiling. The overall survival of LUAD patients in each sub‐consensus was determined by Kaplan‐Meier survival analysis. The common genes which were differentially expressed in each sub‐consensus of LUAD patients and LUAD cell lines were identified using TBtools. The predictive accuracy of TPX2 and SELENBP1 for theinner sub‐consensuses of LUAD was determined by Receiver operator characteristic (ROC) analysis. The Kaplan‐Meier survival analysis was also used to test the prognostic significance of TPX2 and SELENBP1 in LUAD patients.GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets were divided into three sub‐consensuses. Sub‐consensus3 LUAD patients were with low overall survival and were with high TP53 mutations. Similarly, LUAD cell lines were also divided into three sub‐consensuses by NMF method, and sub‐consensus2 cell lines were resistant to EGFR inhibitors. Identification of the common genes which were differentially expressed in different sub‐consensuses of LUAD patients and LUAD cell lines revealed that TPX2 was highly expressed in sub‐consensus3 LUAD patients and sub‐consensus2 LUAD cell lines. On the contrary, SELENBP1 was highly expressed in sub‐consensus1 LUAD patients and sub‐consensus1 LUAD cell lines. The expression levels of TPX2 and SELENBP1 could distinguish sub‐consensus3 LUAD patients or sub‐consensus2 LUAD cell lines from other sub‐consensuses of LUAD patients or cell lines. Moreover, compared with normal lung tissues, TPX2 was highly expressed, while, SELENBP1 was lowly expressed in LUAD tissues. Furthermore, the higher expression levels of TPX2 were associated with the lower relapse‐free survival and the lower overall survival of LUAD patients. While, the higher expression levels of SELENBP1 were associated with the higher relapse‐free survival and higher overall survival. At last, we showed that TP53 mutant LUAD patients were with higher TPX2 and lower SELENBP1 expressions.Using nonnegative matrix factorization clustering, LUAD patients in The Cancer Genome Atlas (TCGA), Both iCluster and NMF method are proved to be robust LUAD classification systems. However, the LUAD patients in different iclusters had no significant clinical overall survival, while, sub‐consensus3 LUAD patients from NMF classification were with lower overall survival than other sub‐consensuses.By integrated analysis of 1765 LUAD patients and 64 LUAD cell lines, we showed that NMF was a robust inner sub‐consensuses classification method of LUAD. TPX2 and SELENBP1 were differentially expressed in different LUAD sub‐ consensuses, and predicted the inner sub‐consensuses of LUAD with high accuracy. TPX2 was an unfavorable prognostic biomarker of LUAD which was up‐regulated in LUAD tissues and associated with the low overall survival of LUAD. SELENBP1 was a favorable prognostic biomarker of LUAD which was down‐regulated in LUAD tissues and associated with the prolonged overall survival of LUAD. NMF is a robust method for the inner sub‐consensuses classification of LUAD. TPX2 and SELENBP1 are two predictors of the inner sub‐consensuses of LUAD. Our results provide deep understanding of the inner heterogeneity of LUAD. Our data also suggests the potential prognostic biomarkers and therapeutic targets of LUAD.22.1https://tcga.xenahubs.net).TCGA LUAD gene expression dataset, DNA mutation dataset, and LUAD clinical dataset were downloaded from the TCGA hub (www.ncbi.nlm.nih.gov/geo), including GSE30219,GSE42127,GSE50081,GSE68465,GSE72094GSE7670,GSE10072,GSE18842,GSE27262,GSE32863http://cbio.mskcc.org/Public/lung_array_data/.The gene expression matrix of LUAD patients along with the clinical survival information was downloaded from the Gene Expression Omnibus (GEO) website (https://www.cancerrxgene.org/).Gene expression matrix and drug sensitivity of LUAD cell lines were downloaded from Genomics of Drug Sensitivity in Cancer (GDSC) project . The number of sub‐consensuses was determined by the number of ranks.2.3P values were calculated by the log‐rank test.The overall survival of LUAD patients in each sub‐consensus was determined by the Kaplan–Meier survival analysis which was performed using “survival” package (version 3.1‐8) in R statistics software. The prognostic values of TPX2 and SELENBP1 on the relapse‐free survival or overall survival were also determined using “survival” package. 2.4The heatmaps were generated using “pheatmap” package (version 1.0.12) in R statistics software. The “average” method determined the clustering scale and “correlation” method determined the clustering distance.2.5The Venn diagram was generated using Wonderful Venn in TBtools software (version x32_1_064).2.6The ROC curves were plotted by “pROC” package (version 1.16.2) in R statistics software. The area under the ROC curve (AUC) was also calculated by “pROC” package.2.7https://david.ncifcrf.gov).p values < 0.05 were considered to be statistically significant.The enriched biological process was identified using The Database for Annotation, Visualization, and Integrated Discovery (DAVID) website . Statistical analysis was performed using the two‐tailed paired Student's 33.1We designed a working process to study the molecular sub‐consensuses of LUAD based on NMF classification Figure . First, One advantage of NMF classification is that the number of sub‐consensuses can be easily determined by the number of ranks.3.2GSE30219,GSE42127,GSE50081,GSE68465,GSE72094 datasetsGSE30219, GSE50081, GSE68465, and GSE72094 datasets .Using the expression datasets deposited in GEO website, we further validated the sub‐consensuses NMF classification of LUAD. Collectively, 85 LUAD patients from GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets were divided into three sub‐consensuses, as illustrated in the consensus maps  project.We then divided the LUAD cell lines into three or four sub‐consensuses. However, the number of cell lines of sub‐consensus 1 in the three sub‐consensuses classification and sub‐consensus 3 in the four sub‐consensuses classification was very low Figure A. Theref3.4GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets, the sub‐consensus 3 LUAD patients had unfavorable prognosis, while, the sub‐consensus 1 LUAD patients had favorable prognosis. Based on the absolute fold changes >2 and p values <0.001 criterion, the differentially expressed genes between sub‐consensus 1 and sub‐consensus 3 were determined. As demonstrated in the clustering heatmaps, 1903 genes were differentially expressed in TCGA dataset, 490 genes were differentially expressed in GSE30219 dataset, 1466 genes were differentially expressed in GSE42127 dataset, 528 genes were differentially expressed in GSE50081 dataset, 153 genes were differentially expressed in GSE68465 dataset, and 727 genes were differentially expressed in GSE72094 dataset, respectively . Moreover, high predictive specificity and sensitivity of SELENBP1 in TCGA, GSE10072, and GSE18842 datasets were also observed  clustering,GSE30219, GSE50081, GSE68465, and GSE72094 datasets were divided into two‐consensuses with different overall survival. However, in GSE42127 dataset, there was no significantly different overall survival between sub‐consensus 1 and sub‐consensus 2 LUAD patients. Integrating various independent datasets may increase the statistical robustness. So, in this study, we analyzed 1765 LUAD patients from TCGA and five independent GEO datasets, we found that three sub‐consensuses of NMF method was a robust classification method of LUAD. Moreover, TPX2 and SELENBP1 were differentially expressed in the different sub‐consensuses of LUAD patients in TCGA, GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets. Furthermore, TPX2 and SELENBP1 predicted the inner sub‐consensuses of LUAD with high accuracy.Different cohorts of patients and gene expression technologies pose another level of complexity and further influence the classification results.Except transcriptional profiling, the immune cells infiltration signature or tumor immune microenvironment is another important factor influencing the heterogeneity of LUADGSE7670, GSE10072, GSE18842, GSE27262, GSE32863, and GSE30219 datasets. And the overexpression of TPX2 was associated with low relapse‐free survival and low overall survival in TCGA, GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets. SELENBP1 was reported as a tumor suppressor gene in many types of tumors.Previous reports showed that, in NSCLC and in some smoking related LUAD patients, TPX2 was associated with poor prognosis.5By integrated analysis of 1765 LUAD patients and 64 LUAD cell lines, we showed that NMF was a robust inner sub‐consensuses classification method of LUAD. Sub‐consensus 3 LUAD patients were with low overall survival and were with high TP53 and KRAS mutations. And sub‐consensus 2 LUAD cell lines were resistant to EGFR inhibitors. TPX2 and SELENBP1 were differentially expressed in different LUAD sub‐consensuses, and predicted the inner sub‐consensuses of LUAD with high accuracy. TPX2 was an unfavorable prognostic biomarker of LUAD which was upregulated in LUAD tissues and associated with the low overall survival of LUAD. SELENBP1 was a favorable prognostic biomarker of LUAD which was downregulated in LUAD tissues and associated with the prolonged overall survival of LUAD.Not applicable.The authors declare that they have no conflict of interest.Not applicable.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.


ASCL1, LHX8, SST, WDR17, ZIC1 and ZNF582). Accuracy for detection of AIN3 and cancer (AIN3+) was determined by univariable and multivariable mixed‐effect ordinal logistic regression. Methylation levels of all markers increased with increasing severity of disease (P < 0.0001) and were comparable to results in HIV+ MSM. All markers showed high accuracy for AIN3+ detection [area under the curve (AUC): 0.83–0.86]. The optimal marker panel  detected 98% of cancers at 79% specificity. In conclusion, DNA methylation markers show a high diagnostic performance for AIN3+ detection in HIV+ and HIV‐negative risk groups, justifying broad application of methylation analysis for anal cancer prevention programmes.Anal cancer has increasing incidence and is preceded by high‐grade anal intraepithelial neoplasia . Previously, we identified and validated several methylation markers for accurate detection of anal cancer and HGAIN with cancer risk in HIV‐positive (HIV+) men who have sex with men (MSM). This study aimed to evaluate these markers in HIV‐negative risk groups. A cross‐sectional series of 176 tissue samples of anal cancer, AIN3, AIN2, AIN1 and control biopsies obtained in HIV‐negative women and men was tested for six methylation markers  and HIV‐positive (HIV+) individuals, including men who have sex with men (MSM). Methylation levels are increased in high‐grade anal intraepithelial neoplasia at risk of progression towards anal cancer, making methylation markers promising prognostic biomarkers.  ACTBβ‐actinAINanal intraepithelial neoplasiaAUCarea under the curveCIconfidence intervalCpGcytosine located 5′ of a guanineCqquantification cycleEIAenzyme immunoassayFFPEformalin‐fixed paraffin‐embeddedHGAINhigh‐grade anal intraepithelial neoplasiaHPVhuman papillomavirusHRAhigh‐resolution anoscopyhrHPVhigh‐risk human papillomavirusIBDinflammatory bowel diseaseIQRinterquartile rangelrHPVlow‐risk human papillomavirusJYouden's IndexLOOCVleave-one-out cross-validationlrHPVlow‐risk human papillomavirusMSMmen who have sex with mennon‐CVnon‐cross‐ validatednsnonsignificantPPpredicted probabilitiesqMSPquantitative methylation‐specific PCRROCreceiver operating characteristicsSOTRsolid organ transplantationSCCsquamous cell carcinomaWTSwhole tissue section1Anal cancer incidence is increasing worldwide. Although still relatively rare in the general population (age‐adjusted incidence rates of 1–2 per 100 000 person‐years), incidence rates are slightly higher (1.5–2‐fold) in women compared with men, increase with increasing age and are disproportionally high in several high‐risk groups . BesidesAlmost all anal cancers are squamous cell carcinoma (SCC) and are, similar to cervical cancer, mainly caused by a persistent high‐risk (hr)HPV infection and preceded by precursor lesions: anal intraepithelial neoplasia  . High‐grIn analogy with cervical cancer screening, screening and treatment of HGAIN to prevent anal cancer in high risk groups is under debate. High‐resolution anoscopy (HRA)‐guided biopsies, the gold standard for HGAIN diagnostics, is a burdensome, expensive and complicated procedure . AlternaASCL1, LHX8, SST, WDR17, ZIC1 and ZNF582) in a large independent series, confirmed the accuracy (AUC = 0.89) and showed that high methylation levels are associated with progression towards cancer. We therefore consider methylation analysis using these markers to be a promising tool to detect HGAIN and anal cancer in HIV+ MSM and for cancer risk stratification, identifying HGAIN in need of treatment, thereby reducing overtreatment , were retrieved from the Pathology Archive of the Amsterdam UMC. For 39 patients, multiple biopsies from different lesions were included. Normal control samples included biopsies taken from lesions suspected for AIN but histopathologically graded as normal or reactive anal epithelium. Median age of patients at time of biopsy per histological group and per gender is reported in Table This study involved a molecular analysis of a cross‐sectional series of formalin‐fixed paraffin‐embedded (FFPE) anal tissue samples across the full spectrum of anal (pre)cancer lesions in HIV‐negative women and men Table . These pn = 345; 30 anal SCC, 74 AIN3, 98 AIN2, 37 AIN1 and 106 normal) of HIV+ MSM tested with the same methylation markers and assays. This concerned a population in screening for anal (pre)cancer at the Amsterdam UMC. Further details have been reported previously .2.3INK4A immunohistochemistry was used to determine the final study diagnosis . To allow for sufficient power, we combined methylation results of HIV‐negative women and men for these analyses. All markers showed a significant distinction between cases and controls (P < 0.001). Following LOOCV AUCs ranged from 0.83 to 0.86 (Table SST (AUC = 0.86), followed by ZIC1 (AUC = 0.85), LHX8 and ZNF582 (AUC = 0.84). SST classified 98% (39/40) of cancers as methylation‐positive at the J‐threshold (≥ 0.332), corresponding to an AIN3+ sensitivity and specificity of 90% and 73%, respectively. Using this threshold, SST classified 79% (22/28) of AIN3, 67% (14/21) of AIN2, 11% (6/57) of AIN1 and 30% (9/30) of normal control samples as methylation‐positive : 0.80–0.91; Table J‐threshold (≥ 0.483), this panel provided a sensitivity of 85% and specificity of 78% and classified 98% (39/40) of the cancers, 68% (19/28) of AIN3, 62% (13/21) of AIN2, 12% (7/57) of AIN1 and 13% (4/30) of normal control samples as methylation‐positive . At this threshold, 98% (39/40) of the cancers were classified as methylation‐positive, 64% (18/28) of AIN3, 62% (13/21) of AIN2, 12% (7/57) of AIN1 and 10% (3/30) of normal control samples , supporting the combined analysis of HIV‐negative women and men. Marker 3.7ZNF582, ASCL1 and SST) previously designed for AIN3+ detection in HIV+ MSM at the non‐CV J‐threshold,[By applying the marker panel  for the detection of AIN3 and anal cancer with an optimal sensitivity and specificity of 90% and 73%, respectively, and classified 98% of the cancers as methylation‐positive. By evaluating combinations of markers, we composed an optimal marker panel for HIV‐negative women and men consisting of ASCL1 and ZIC1 with a cross‐validated AUC of 0.85. Although the marker panel had a slightly lower AUC than SST alone, it detected a similar proportion of SCC at a higher specificity (79%).The most important outcome of this study is the confirmation that akin HIV+ MSM, host cell DNA methylation also plays a role in anal carcinogenesis in HIV‐negative women and men. We observed comparable methylation level patterns in samples of HIV‐negative women and men as to our previous studies in HIV+ MSM including a significant increase in methylation levels with increasing severity of anal disease , 11. AllFor HIV+ MSM, we recently showed that high methylation levels are associated with progression towards cancer. Accordingly, methylation markers may be used to stratify HGAIN at risk of progression to cancer and therefore requiring treatment, while preventing overtreatment of HGAIN lesions with a low progression risk . Intereset al. [et al. [We observed a comparable HPV distribution in this series of HIV‐negative women and men to what was reported in a recent meta‐analysis by Lin et al. , includiet al. , 11. Com [et al. . The ove [et al. . PotentiTo the best of our knowledge, we are the first to have evaluated host cell DNA methylation markers in anal tissue samples from multiple target risk populations of sufficient size. Previous studies on host cell DNA methylation investigated samples of small mixed populations of predominantly HIV+ men, not allowing comparison of methylation levels between risk groups , 28.Our study has several limitations. First, for this study we included samples from a HIV‐negative population consisting of patients being referred for anal complaints and patients in screening for having an increased risk of developing anal cancer. Therefore, our findings cannot be extrapolated to a screening population, but are merely a proof of principle to show the potential of methylation markers to, upon discovery with HRA, identify HGAIN biopsies in need of treatment to prevent overtreatment in a HIV‐negative referral population. Notwithstanding, present findings also indicate that methylation analysis on less‐invasive anal swabs to triage for HRA, especially in risk groups other than HIV+ MSM, who have a lower cancer risk and are not routinely screened with HRA may warrant further investigation. Second, we compared current series with our previous study on a HIV+ MSM screening population with a different study population composition, possibly leading to selection bias. HGAIN lesions found in screening programmes might be detected earlier compared with lesions found in a referral population and could therefore represent lesions earlier during carcinogenesis. Methylation levels in the present study were comparable to those in our previous study on samples of a HIV+ MSM screening population. Nevertheless, the different study populations may mask a potential difference in cancer risk between HIV+ and HIV‐negative patients. This difference in populations, together with different multivariable models for marker panel development, hindered a direct comparison of the marker panels developed for HIV‐negative patients and HIV+ MSM. Third, although medical records were thoroughly reviewed, risk factors and HIV status were not always documented or available. HIV‐negativity could therefore not be confirmed for some of the women with anal cancer. Fourth, while maximising our series by including all available archival tissue samples, included samples were not balanced across the histological categories. Also, occasionally multiple samples per patient were included. As a result, the independent assumption between samples is violated since observations from the same patient tend to be more correlated. To account for this correlation structure within the same patients, we incorporated a random subject effect in our model to minimise sampling bias. Last, although this is the largest study evaluating host cell DNA methylation markers in HIV‐negative women and men to date, numbers were still generally too low for several subgroups analyses, for example comparing HIV‐negative women and men. Although we found no significant contribution of gender in the multivariable analysis and we could combine HIV‐negative women and men, we acknowledge that this was still a heterogeneous group with different risk factors. Moreover, by comparing results with our previous series on HIV+ MSM, which was much larger, comparisons could have been influenced by an imbalance in numbers.5We showed that similar to anal carcinogenesis in HIV+ MSM, host cell DNA methylation also plays a role in anal carcinogenesis in HIV‐negative women and men. The high resemblance in methylation levels and good diagnostic performance of the methylation markers in samples of both HIV‐negative and HIV+ patients open up a broad application of methylation markers in anal cancer prevention strategies for all risk groups of anal cancer.RDMS and DAMH are minority stockholders of Self‐screen BV, a spin‐off company of VUmc, which owns patents on methylation markers and HPV detection. DAMH has been on the speaker's bureau of Qiagen and serves occasionally on the scientific advisory boards of Pfizer and Bristol‐Myers Squibb. HJCdV received financial compensation or goods for research from MediGene, Gilead and MSD; financial compensation for presentations from Abbott and Janssen; and financial compensation for advice to MediGene and Novartis. All other authors report no potential conflicts.https://publons.com/publon/10.1002/1878‐0261.12926.The peer review history for this article is available at RDMS, JMP and HJCdV are principal investigators of the study. RPvdZ and RDMS designed the study. RPvdZ, CJMvN, DAMH, HJCdV, JMP and RDMS were involved in providing and collecting clinical material and data collection. RPvdZ and TJtB performed the laboratory work. RPvdZ and RDMS performed the data analysis. RPvdZ and IM performed the statistical analysis. RPvdZ managed the database. RPvdZ and RDMS drafted the manuscript. All authors critically reviewed the manuscript and approved the final version. All authors had full access to all of the data in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis and believe that the manuscript represents honest work. RDMS affirms that the manuscript is an honest, accurate and transparent account of the study being reported, that no important aspects of the study have been omitted and that any discrepancies from the study as planned have been explained; and has full responsibility for the decision to submit for publication.Fig. S1. Boxplots of DNA methylation levels relative to a reference gene ACTB  in relation to HPV status in the different histological categories of anal tissue samples of HIV‐negative women and men combined (x‐axis) for six markers.Click here for additional data file.Table S1. Number of included tissue samples of HIV‐negative men (A) en HIV‐negative women (B), including age at biopsy, anatomical location, HIV status and risk factors per histological category.Click here for additional data file.


Platelets are prone to activation from handling; they are therefore transported as gently as possible, most commonly by courier. Speedier methods like pneumatic tube system (PTS) transport could improve patient care but may subject platelets to mechanical stress. To test the impact of mechanical stress caused by transport, we compared a PTS with a conveyor box and courier transport on apheresis platelet function.Fourteen apheresis platelet concentrate triple donations were analyzed by light transmission aggregometry (LTA), rotational thrombelastometry (ROTEM), and flow cytometry before and after indoor transport over 800 m by PTS, conveyor, and courier, respectively, while recording shocks and vibrations with a high‐frequency acceleration data logger. Shock index scores were calculated as shock intensity (g‐force) times frequency.The shock index was 81 for courier, 6279 for conveyor, and 9075 for PTS. Flow cytometry revealed no significant difference in platelet surface expression of CD62p before (16%) and after transport via courier (15%), conveyor (14%), or PTS (16%). LTA with adenosine phosphate and thrombin receptor‐activating peptide‐6 resulted in comparable platelet aggregation for courier, conveyor, and PTS. ROTEM assays showed no relevant differences in coagulation time, clot formation time, and maximum clot firmness between transport modes.Though the mechanical challenge was smallest with courier transport, there were no significant differences in platelet activation or aggregation between the three transport modes. These data contradict restrictions on the use of PTSs for platelet concentrate transport. Due to their fragile nature, platelets are stored in blood donation bags after collection under gentle agitation to prevent sedimentation and activation.Therefore, this three‐arm study was designed to comparatively evaluate the impact of three different transport modes—PTS, conveyer system, and manual transport—on platelet quality and thus focused on functional tests of apheresis platelet concentrates by accelerometry, aggregometry, thrombelastography, and flow cytometry22.19/L in peripheral blood who donated triple platelet concentrates in autologous plasma via the Trima Accel apheresis system , yielding a total of 42 units (collected between February and April 2020). All donors gave a written, informed consent for a research study. For ethical reasons we limited the number of donations. The platelet concentrates transported by courier and conveyor were applied for clinical use, and concentrates transported by PTS were discarded after analysis. The apheresis products were stored at 20−24°C in the connected bags with a resting time of 60–90 min, and under agitation for 60 min.The study included 14 participants  with a minimum platelet count of 300 × 102.2The first bag of each triple donation was sent by conveyor, the second by PTS, and the third by courier over a distance equivalent to 800 m. Each transport container was equipped with a high‐frequency acceleration data logger . All transports and tests were performed on the same day. The conveyor  is a rail transport system with special transport boxes for light freight; it transported the platelet concentrates at walking speed (0.5 m/s) and covered the transport distance in 30 min.2.32.3.1X, Y, and Z axes, the acceleration vector sum (Asum) of a time series was calculated as the square root of the sum of acceleration along each axis squared (Ax² + yA² + zA²) to express the shock load. Shock index scores were calculated as the shock intensity (g‐force) times frequency of shocks greater than the predetermined threshold of 2 g (force). The cut‐off value of 2 g was chosen in such a way that, on the one hand, the noise of small vibrations was not taken into account, and on the other hand, it was chosen so low that as many shocks as possible could still be taken into account. This was assumed at 2 g but is based on assumptions.During transport, an acceleration data logger  measured shocks and vibrations over time (shock load) at a frequency of 800 Hz with a three‐axis acceleration sensor. Raw data were converted to.csv files and analyzed with R. G‐force measurements for each transport mode are shown in Figure 2.3.2LTA was performed for measurement of platelet aggregation 20 min after transport. Briefly, 300 µl samples from the transported platelet units were diluted with 300 µl physiological saline . After 3 min incubation, activation was started by the following agonists: adenosine diphosphate , arachidonic acid , and thrombin receptor‐activating peptide 6 . After 6 min, the maximum increase in light transmission (percent aggregation) was measured with a Multiplate Analyzer  as described previously.2.3.3Platelet staining was performed after a resting period of about 30 min. Briefly, 20,000 cells/tube were either added to phosphate‐buffered saline or stimulated with TRAP‐6 for 10 min . Phenotyping of platelets was performed with commercially available CD62P‐FITC, CD41‐PE (both Beckmann Coulter), and CD61‐PerCP antibodies (BD‐Biosciences). All antibodies were titrated to obtain an optimal concentration. Flow cytometry analyses were performed with the Navios Ex flow cytometer from Beckman Coulter (software version 2.1). Activation of platelets was measured by CD62p before and after TRAP6 stimulation and expressed as a fraction of this. The basal CD62p expression was calculated from the shift of mean fluorescence intensity (MFI) of unstimulated versus stimulated CD62p‐positive platelets [%]  or EXTEM reagent  and incubated for analysis of post‐transport platelet stability. Ellagic acid‐activated coagulation was determined by INTEM, and clotting time, clot formation time, and maximum clot firmness by EXTEM (results not shown).Thromboelastography of each triple (3‐unit) donation was performed before and about 2 h after transport using the rotational thrombelastometry (ROTEM) INTEM and EXTEM assays and software version 2.6.3  as described previously.2.3.5Cell concentrations were measured undiluted on an XN‐550 Automated Hematology Analyzer (Sysmex) as per the manufacturer's instructions.2.3.6The pH was measured undiluted on the blood gas analyzer ABL 90 Flex (Radiometer) as per the manufacturer's instructions.2.4t test for independent samples. p values below 0.05 were considered statistically significant.Microsoft Excel 2010, IBM SPSS Statistics (version 25), and R (version 3.6.3) with its library pracma were used to collect data, generate box plots, and to determine median, mean, and standard deviation values. Normal distribution was tested with Shapiro−Wilk. Data are presented as median and range. In addition, the 95% confidence interval (CI) of the mean value was given for all data. We performed the Levene test on the assumption of variance homogeneity and a two‐sided 311 platelets per apheresis (Table n = 14).Fourteen triple donations yielded a total of 42 units with a median volume of 249 ml and a maximum content of 8 × 10The shock load index was 81 for courier, 6279 for conveyor, and 9075 for PTS Figure . Thus, tn = 13, see Table p = 0.92, 95% CI: 14%−21% basal expression), conveyor  or manual transport  compared to before transport.Median platelet surface expression of CD62p was 16% before transport , 15% after manual transport, 14% after conveyor transport, and 16% after transport with PTS  and the PTS in INTEM measurement for clotting time . To guarantee accuracy, we ran four consecutive measurements per INTEM and EXTEM sample and transport mode. Accuracy was almost 100%, as was confirmed by a comparison of significance.ROTEM analysis yielded the following median clotting time, clot formation time, and maximum clot firmness values in the EXTEM test (activation of clotting by thromboplastin) for the three transport modes studied versus before transport , conveyer with p = 0.05 (95% CI: 100%−117%) and courier with p < 0.05 (95% CI: 101%−118%). Since courier transport also resulted in a significant difference, these results could be considered comparable.LTA with ADP, AA (ASPI test), and TRAP‐6 showed light transmission before versus after transport of 99%, 107%, and 103% for manual transport, 96%, 104%, and 98% for transport via conveyor, and 93%, 103%, and 106% via PTS Figure . There w4Platelet concentrates are indispensable for persons requiring platelet support, especially hematology, oncology, and surgery patients. Fast but gentle transport to the patient is essential for preserving platelet quality. The aim of our study was the evaluation of a safe transport mode. Therefore, we examined the impact of PTS, conveyor, and manual transport on platelets. The comparability of pooled and apheresis platelet concentrates is a much‐debated topic.PTS is a popular and widely used means of transporting platelets in hospitals. Compared to manual transport, both PTS and conveyor ensure a rapid supply of patients with blood preservation and additionally save personnel by decreasing time‐consuming manual transport. However, the PTS could be susceptible to mechanical failure, defects, overuse, and abuse.2, ATP, hypotonic shock response reactivity, CD62P, PAC‐1, platelet endothelial cell adhesion molecule 1 (PECAM1), or CD42b and caused no impairment of platelet quality. Lancé et al.Several studies considered platelet quality after pneumatic tube transport. Sandgren et al.Although further studies showed no relevant negative impact on platelet quality,In addition, the shock load also plays a decisive role and must be considered when evaluating the influence of transport on platelet quality. Thus, all transport containers used in our study were equipped with acceleration data loggers, because, for example, the higher the acceleration, the higher the influence on the ROTEM parameters.As transport conditions vary between locations, each hospital should individually assess the suitability of its local automated transport system. Based on the available evidence, we conclude that all three transport modes analyzed are suitable for the transport of apheresis platelet concentrates and that PTS exhibited no relevant quality loss.Lena Reichert: data curation; formal analysis; investigation; writing—original draft. Stefan Wallner: data curation; formal analysis. Ralph Burkhardt: resources; writing—review and editing. Robert Offner: writing—review and editing. Norbert Ahrens: conceptualization; formal analysis; project administration; supervision; writing—review and editing. Viola Hähnel: conceptualization; data curation; formal analysis; writing—review and editing. Open access funding enabled and organized by Projekt DEAL.The authors declare no conflicts of interest.The corresponding author Viola Hähnel affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.