dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, these novel findings might have an impact on the development of new therapeutic strategies for liver metastatic CRC patients. Currently, new anti-CD44v6 treatment strategies, such as half antibodies conjugated nanoparticles  [72] , peptides (NCT03009214) and CD44v6-specific CAR gene-engineered T cells (NCT04427449,  [73] ) are under investigation and might also become a treatment option for CRC patients with CD44v6-positive liver metastases. Combination of two biomarkers might help to stratify patients more precisely for targeted therapy compared to single biomarker expression. For example, Shek et al. , 2021, reported that only a subgroup of PD-L1-positive mCRCs responded to checkpoint inhibitor therapy  [74]. In addition, dual expression of druggable biomarkers will further promote the promising concept of multiple target inhibition, aiming to improve treatment outcome and reduce the risk of drug resistance.
Patients Patients and selection were clarified in paper from Boulagnon-Rombi et al. [29]. The study was conducted on adult patients who underwent surgery for sporadic colon cancer in the Digestive Surgery Department of the University Hospital of Reims between September 2006 and December 2012. Patients with rectal cancer were excluded. Clinical data including age at the time of surgery, sex, performance status, surgical circumstances (tumor perforation, occlusion), tumor location, synchronous or metachronous metastases, tumor recurrence, treatment, death and pathological and molecular data including adenocarcinoma type, grade, and pTNM stage were collected. Patients were classified as having a right colonic cancer if the primary tumor was located in the caecum, ascending colon, hepatic flexure or transverse colon, and left colonic cancer if the tumor site was within the splenic flexure, descending colon, sigmoid colon, or rectosigmoid junction.
9102, Figure  3C ). In our cohort DDR1 mRNA expression levels successfully evaluated in 66 patients were not correlated with OS (p = 0. 86) nor EFS (p = 0. 46), whatever the CCR stage (data not shown). To corroborate our previous results, we next performed survival analyses in Sieber-Smith (n = 286), Martineau (n = 124)  [34]  and TCGA cohorts (n = 174) obtained from R2 database  [35, 36]. In these cohorts, DDR1 mRNA expression was not correlated with overall nor relapse free survival (Figure  4 ).
86) nor EFS (p = 0. 46), whatever the CCR stage (data not shown). To corroborate our previous results, we next performed survival analyses in Sieber-Smith (n = 286), Martineau (n = 124)  [34]  and TCGA cohorts (n = 174) obtained from R2 database  [35, 36]. In these cohorts, DDR1 mRNA expression was not correlated with overall nor relapse free survival (Figure  4 ).
Study Design This study includes a total of 18 PB samples collected between May 2016 to March 2017 from 10 patients with mCRC. Patients were found as part of the GS-US-296-0101 phase I clinical trial (#NCT01803282) evaluating the safety and tolerability of a novel therapeutic in combination with standard-of-care chemotherapy in two different mCRC indications. Apart from their diagnosis of CRC and survival data, no other clinical or demographic information was available for this study cohort per IRB protocol at the time of enrollment. Patients 1, 3, 5, 6 and 9 were first-line inoperable mCRC patients receiving the test compound in combination with mFOLFOX6 and bevacizumab. Patients 2, 4, 7, 8 and 10 were secondline inoperable patients receiving the test compound in combination with FOLFIRI and bevacizumab.
An agglomerative clustering algorithm was used, imported from the scikit-learn library version 0. 23. 2  [54]  in Python. We used a Euclidian metric to compute the distance and the ward linkage criterion. In addition, 111 cells and the oncosome population were removed after manual inspection from the clustering due to highly aberrant nuclear and membrane masking. The cohort's rare-cell population with various cluster assortment options are included as part of an interactive webpage that can be used for additional analyses and discovery (https://pivot. usc. edu/pivot/CRC_MultiAssay. html). The optimal number of clusters from 2 to 16 was selected based on quantitative cluster-separation metrics and the separation of noteworthy, rare cell types. Based on the silhouette average method, 8 clusters provided the optimal separation between the cell groups.
Statistical Analysis The distinction between the rare-event enumerations of the mCRC and ND samples was determined by a Wilcoxon rank sum test  [59]. Statistical correlations between rareevent enumerations were performed using the Spearman's rank correlation coefficient  [55]. A correlation was significant if the two-tailed p-value ≤ 0. 05. The statistical analysis was done using scipy  [57]  in Python.
PKH67 Labelling of Extracellular Vesicles and Their Uptake into TDM Cells Extracellular vesicles were labelled using PKH67 Fluorescent Cell Linker kit (Sigma-Aldrich) according to the manufacturer's instructions, with minor modifications  [51]. After 4 h of PKH67-labelled EV incorporation into TDM, cells were fixed, treated with 0. 1% Triton X-100 and incubated in sequence with Texas Red ® -X phalloidin (F-actin marker) and Hoechst 33,342 (cell-permeant nuclear dye). The uptake was visualized using a confocal microscope (Nikon D-Eclipse C1) and analysed with EZ-C1 software.
Cell Motility Measurements Measurements of cell motility were performed between the 7th and 25th hour after beginning of incubation of TDM with EVs using HoloMonitor M4 (Phase Holographic Imaging PHI AB, Lund, Sweden-Courtesy of the representative of company). Cell speed was calculated using the App Suite software of the same company. A plot of cell speed versus time was generated and the area under curve (AUC) was computed using GraphPad Prism 7. 05 software. Cancers 2021, 13, 172.
Measurements of Cytokine Release Macrophages were incubated with EVs in FBS-free RPMI-1640 culture medium for 24 h, the medium collected and centrifuged for 20 min at 18,000× g. IL-8 levels in the cells supernatants were analysed by flow cytometry according to the manufacturer's procedure using the BD Cytometric Bead Array (CBA) Human Inflammatory Cytokines Kit. The BD Cytometric Bead Array (CBA) Mouse Inflammation Kit was used to measure MCP-1/CCL2 in the plasma of mice. Data were analysed using FCAP Array TM Software Version 3. 0 (BD Life Sciences-Biosciences, San Jose, CA, USA).
Statistical Analyses Data are presented as mean ± SEM. All experiments were performed at least in triplicate. The Shapiro-Wilk test was used to confirm the Gaussian distributions of raw data. Analysis of variance (ANOVA) was used for multiple comparisons. The Kruskal-Wallis analysis was performed to test the differences between groups of data with nonnormal distributions. For analyses of two groups, the appropriate Student's t test (or the Welch's test for unequal variances) was performed to test the differences between groups for normally distributed data. p value less than 0. 05 was considered statistically significant. All statistical analysis was performed using GraphPad Prism 7. 05 software.
Genotyping and Imputation Illumina Global Screening Array was used to genotype the DNA samples of the individuals analyzed in this work. For this, Illumina iScan high-throughput screening system was used in the Institute of Clinical Molecular Biology (Kiel, Germany). Raw intensities were transformed to alleles using the GenCall algorithm available in Illumina GenomeStudio software. Then, the called genotypes and samples were filtered using the following criteria: samples with ≥5% missing rates; markers with non-called alleles; markers with missing call rates > 0. 05; related samples (PI-HAT > 0. 1875); samples whose genotyped sex could not be determined; samples with high heterozygosity rate (more than 3 times SD from the mean) were excluded. In addition, only autosomal SNPs were kept; markers with Hardy-Weinberg equilibrium p < 1 × 10 -5 ; markers whose P of difference in missingness between cases and control was <1 × 10 -5 ; samples that were outliers, identified using principal component analysis (deviation of more than 6 times interquartile range), using FlashPCA (v2.
Mendelian Randomization Analyses For carrying out Mendelian Randomization (MR) analyses TwoSampleMR (v0. 5. 6)  [27]  and gsmr (v1. 0. 9)  [28]  packages from R language (v4. 0. 5) were used  [29] , as we have used previously to study the effect of modifiable risk factors in CRC risk  [13]. First, we selected the modifiable risk factors based on a previous work  [12]  which analyzed modifiable risk factors using Mendelian Randomization that affects CRC (BMI, cholesterol, triglycerides, selenium, iron, vitamin B12, metabolism, body fat percentage, waist circumference, IL6 receptor and height). Then, we retrieved the instruments avail-Cancers 2022,  14, 4193  able in MRC-IEU (https://gwas. mrcieu. ac. uk, accessed: 14 February 2022) of those traits through TwoSampleMR  [27]. In addition, to analyze the effect of the microbiota in CRC cancer, we retrieved instruments of bacterial phyla which are available from MiBioGen consortium data  [30].
Then, the analysis was carried out if 10 or more instruments were available, and HEIDI outlier analysis was used to discard heterogenous instruments. The strength of the instruments was measured by the F-statistic: F = R 2 (N -K -1)/K(1 -R 2 ), where R 2 is the variance explained by genetic variance, N is the sample size, and K is the number of instruments  [31]. In addition, I 2 was calculated using TwoSampleMR R Package. The MR analyses were carried out using Inverse Variance Weighted, Weighted Median and MR Egger methods. In addition, the heterogeneity Q test and pleiotropy test available in TwoSampleMR R Package were used as sensitivity tests. The analysis was applied to all CRC cases, as well as, right colon cancer, left colon cancer and rectal cancer analyses.
Polygenic Risk Scores Polygenic risk scores (PRS) were retrieved from PGS Catalog  [32]. 29 scores available in the "Colorectal cancer" term (EFO_0005842) derived using cohorts with >90% samples of European ancestry and whose assembly version was known were used for the PRS analysis  [33] [34] [35] [36] [37] [38] [39] [40] [41]. From those 29 panels, our cohort had available SNPs to apply in 25 of them. In addition, the PRS used previously in the Basque population was tested  [16]. The weights of the SNPs present in our data were applied in our cohort using Plink  [24]. The performance of the PRS was measured by comparing the PRS score distribution of CRC cases and non-CRC controls using a T-test using R language  [29] ; the effect size of the T-test was calculated using Cohen's d through the package rstatix (https://CRAN.
MMR Gene Promoter Methylation Analysis The analysis of MMR gene promoters methylation was performed by methylationspecific multiplex ligation-dependent probe amplification (MS_MLPA)  [33] , using the MS-MLPA kits ME011 MMR (MRC-Holland, Amsterdam, the Netherlands). MS-MLPA reactions were performed as described by the manufacturer. MS-MLPA fragments were analyzed on the ABI Prism 3130TM Genetic Analyzer (Applied Biosystems) and normalized using the Coffalyser. NET software (MRC-Holland, Amsterdam, the Netherlands). A baseline for positive methylation was calculated for each gene as described previously  [34]. A ratio of 0. 15 or higher, corresponding to 15% of methylated DNA, was indicative of MLH1 promoter methylation.
Statistical Analysis For statistical analysis, SPSS Statistics 26 (IBM) was used. Demographic and clinical characteristics were presented as frequencies. Continuous variables were expressed as median and standard deviation or as median and interquartile range, according to data distribution, and were compared using t-Student or Wilcoxon tests, respectively. Qualitative variables were compared using chi-square or Fisher Exact tests. Multiple variables were analyzed using logistic regression models. A p value lower than 0. 05 was considered statistically significant.
Ongoing Clinical Trials and Novel Anti-ERBB2 Therapies Several ongoing clinical trials are exploring anti-ERBB2 therapies that evaluate the efficacy of small molecule inhibitors, ADCs, and their combination with established therapies  [84]. The MATCH trial is a clinical trial of targeted therapy diagnosed by genetic testing in solid tumors or lymphomas after progression of at least one line of treatment. Two cohorts of patients with ERBB2-amplified tumors are treated with trastuzumab plus pertuzumab (cohort J) or T-DM1 (cohort Q)  [85]. The MOUNTAINEER trial has been expanded to include a cohort of tucatinib monotherapy (NCT03943313)  [86]. NSABP FC-11 is a three-cohort, phase II clinical trial in patients with RAS/BRAF/PIK3CA WT ERBB2-positive mCRC. This study compares the efficacy of neratinib and trastuzumab (Arm-1: patients who have ERBB2 amplification and prior anti-EGFR treatment or ERBB2 mutation with or without prior anti-EGFR treatment) vs.
neratinib plus cetuximab (Arm-2: patients who are ERBB2 non-amplificated or ERBB2 amplification without prior anti-EGFR treatment) (NCT03457896)  [87]. The first results of NSABP FC-11 were presented at the ASCO 2022 meeting  [88]. Arm-1 closed due to Cancers 2022,  14, 3718  poor accrual, and those patients have been excluded from further analysis. Arm 2 enrolled 21 patients with 15 evaluable for response by imaging. Of the 15 evaluable patients, there were 6 PR, 5 of 13 ERBB2 non-amplification, 1 of 2 ERBB2 amplification, and 5 SD. The ORR in all patients who were treated with at least one dose was 33%. Common grade 3-4 were diarrhea (24%), rash (8%), and abdominal pain/distension (8%). Following the results of DESTINY-CRC01, DESTINY-CRC02 is a phase II clinical trial that is going to determine the efficacy and safety of TD in patients ERBB2-positive at 5.
4 mg/kg and 6. 4 mg/kg doses  [89]. The dose of 5. 4 mg/kg has not been tested in ERBB2-positive mCRC patients, but this dose has shown efficacy in other tumors  [79] [80] [81]. Several trials explore the role of anti-ERBB2 therapies in earlier lines of treatment compared to QT. The MODUL trial is a randomized, open-label, parallel-group study that evaluates the efficacy and safety of biomarker-driven maintenance treatment in the first line of treatment in mCRC, including an ERBB2-positive cohort (capecitabine, trastuzumab, and pertuzumab) (NCT02291289). SWOG study (S1613) is a multicenter, randomized, phase II clinical trial that tries to compare the efficacy of trastuzumab plus pertuzumab vs. cetuximab plus irinotecan in patients with RAS/RAF WT ERBB2-positive mCRC (NCT03365882). Patients have to have been treated with at least one prior line of therapy for mCRC that did not include anti-EGFR or anti-ERBB2 agents.
Zanidatamab (ZW25) is a bispecific antibody that binds to two different regions on the ERBB2 receptor, increasing antibody binding density and improving receptor internalization and downregulation. It is used in phase I and II clinical trials in patients with ERBB2-positive gastrointestinal cancers, including mCRC (NCT02892123, NCT03929666)  [90]. A166 uses an antibody with the same amino acid sequence as trastuzumab and it is linked to duostatin-5. Safety profile of A166 has been observed in a phase I clinical trial. ZW49 has an auristatin payload conjugated to the antibody ZW25, which binds to the same ERBB2 domains as trastuzumab and pertuzumab. ZW49 is being evaluated in a phase I clinical trial (NCT03602079). A phase I clinical trial will investigate the efficacy and safety of two chimeric (trastuzumab-like and pertuzumab-like) ERBB2 vaccines in patients with various metastatic solid tumors, including mCRC (NCT01376505).
Another phase I trial uses an allogenicdonor-derived natural killer (NK) cell cancer immunotherapy (FATE-NK100) as monotherapy or in combination with trastuzumab or cetuximab in multiple ERBB2-positive tumors (NCT03319459). An anti-ERBB2 chimeric antigen receptor (CAR)-modified T cell therapy is evaluated in several ERBB2-positive solid tumors, including mCRC (NCT02713984)  [91]. Moreover, HER2-AdVST (ERBB2 chimeric antigen receptor-modified adenovirus-specific cytotoxic T lymphocytes) joined to an intra-tumor injection of CAdVEC (an oncolytic adenovirus that helps the immune system) is being evaluated in an ongoing clinical trial (NCT03740256). Other clinical trials are trying to show the effectiveness of peptide vaccines (NCT01376505). Patients receive an ERBB2/neu peptide vaccine comprising measles virus epitope MVF-ERBB2-2 (266-296) and MVF-ERBB2 (597-626) emulsified with nor-MDP in ISA 720 intramuscularly.
The combination of NOX-A12 and PD-1 checkpoint inhibitor acts synergistically to facilitate T cell infiltration into spheroids. These observations were validated in vivo in a mouse model of syngeneic CRC in which treatment with NOX-A12 improved the response to anti-PD-1 therapy to reduce tumor size  [259]. A Phase I/II clinical trial is underway to study the effects of the NOX-A12 and anti-PD-1 combination in patients with advanced CRC or pancreatic carcinoma (NCT03168139). NOX-A12 nd 5-200 nM CXCL12  [257]  nd: not determined; IC50: 50% inhibitory concentration.
It showed a good prediction power for OS after the resection of CRLM  [35]. However, more recently, Tsilimigras et al. [36]  reported a poor prediction power of the "Tumor Burden Score" in KRAS mutated tumors. The Tumor Burden score reflected the morphologic characteristics of metastases based on the maximum tumor size and number of lesions  [37]. The authors reviewed, in an international multi-institutional database, the results of 1361 patients who underwent hepatic resection for CRLM and analyzed the prognostic impact of the Tumor Burden Score depending on the KRAS status. This score was associated with worse overall survival for the KRAS wild type but not for KRAS mutated tumors  [36]. Although there is a large consensus on the negative prognostic impact of RAS mutations after liver surgery for CRLM, several recent data suggested an overestimation of its value, in particular, the possibility of different biological patterns between RAS mutants with there, subsequently, being a difference in their effect on the risk of recurrence and survival after treatment of CLRM  [38, 39].
Acknowledgments: We thank  Frederic Saltel  ( INSERM U1053 ,  Bordeaux ) for the gift of the lentivirus encoding DDR1-GFP. We also thank  Damien Rioult  from the technical platform MOBICYTE of the  University of Reims Champagne-Ardenne  for their excellent technical assistance. Conflicts of Interest: S. D. serves as Chair of the  Scientific and Clinical Advisory Board  and has equity interest in Apmonia Therapeutics (Reims, France), a biotechnology company developing anticancer strategy. The other authors declare that they have no further financial or other conflicts of interest in relation to this research and its publication.
Acknowledgments: The Authors gratefully acknowledge  Łucja Balcerzak  and  Sylwia Michlewska  from  Laboratory of Microscopic Imaging and Specialized Biological Techniques, Faculty of Biology and Environmental Protection, University of Lodz, Poland , for transmission electron microscopy (TEM) imaging of the EVs samples. Acknowledgments: Samples and data used in the present work were provided by the  Basque Biobank  (http://www. biobancovasco. org,accessed:  1 August 2022 ). We want to thank  Miguel Ángel Vesga  from the  Basque Centre of Transfusion  and  Human Tissues  for providing the access to control samples.
Acknowledgments: The authors wish to thank the patients who participated and are currently participating in the studies mentioned and their families. Acknowledgments: Work in the author's laboratory is supported by the  Inserm  ( AD10 Grand Est ) and the  Ligue Contre le Cancer  ( Comité du Grand Est ). Aïssata Aimée GOÏTA is a predoctoral fellow from the  French Ministère de la Recherche et de l'Enseignement Supérieur. Dominique Guenot  is a research director at the CNRS.
Data Availability Statement: The data presented in this study are available on request from the corresponding author. Data Availability Statement: All data discussed in this manuscript are included in the main manuscript text or Supplementary Materials. Some of the data can be accessed through our website http://pivot. usc. edu/. The imaging data is available through the BloodPAC Data Commons Accession ID "BPDC000124" (https://data. bloodpac. org/discovery/BPDC000124).
1% formic acid in milliQ-grade H 2 O to 40% of solvent B containing 80% acetonitrile, 0. 085% formic acid in mQH2O. The mass spectrometer acquired data throughout the elution process and operated in a data-dependent scheme with full MS scans acquired, followed by up to 10 successive MS/ MS HCD-fragmentations on the most abundant ions detected. Settings for Q-Exactive were: full MS AGC target 1. 10 6 with 60 ms maximum ion injection time (MIIT) and resolution of 70 000. The MS scans spanned from 350 to 1500 Th. Precursor selection window was set at 2 Th. HCD Normalized Collision Energy (NCE) was set at 27% and MS/MS scan resolution was set at 17,500 with AGC target 1. 10 5 within 60 ms MIIT. Dynamic exclusion time was set to 30 s and spectra were recorded in profile mode.
Protein quantification and comparison The mass spectrometry data were analyzed using Maxquant version 1. 6. 17 [21]. The database used was a concatenation of Homo sapiens sequences from the Swissprot databases (release 2020-10) and an in-house list of frequently found contaminant protein sequences. The precursor and fragment mass tolerance were set to 20 ppm. Carbamidomethylation of cysteins was set as permanent modification and acetylation of protein N-terminus and oxidation of methionines were set as variable modification. Second peptide search was allowed and minimal length of peptides was set at seven amino acids. False discovery rate (FDR) was kept below 1% on both peptides and proteins. Label-free protein quantification (LFQ) was done using both unique and razor peptides. At least 2 such peptides were required for LFQ.
The "match between runs" (MBR) option was allowed with a match time 0. 7 min window and an alignment time window of 20 min. For differential analysis, LFQ results from MaxQuant were quality-checked using PTXQC  [22] , imported into the Perseus software (version 1. 6. 14) [23]  Reverse and contaminant proteins were excluded from analysis. A protein was considered as significantly differentially expressed between two conditions when it was quantified in only one condition (ON and OFF proteins), or when: (1) the fold change is superior or inferior to 1. 2, (2) ANOVA p-value is inferior to 0. 01, and (3) Student t-test p-value is inferior to 0. 01. Venn diagram were designed using FunRich (http:// www. funri ch. org/), and graphs were made with Graph-Pad Prism.
Enrichments were further explored using Gene Set Enrichment Analysis (GSEA) 4. 1. 0 software (https:// www. gsea-msigdb. org/ gsea/)  [24, 25]. GSEA determined whether gene sets from the Molecular Signatures Database (MSigDB) Wikipathways collection (v7. 4) were randomly distributed throughout our ranked protein lists or if they were located at the top or bottom of them. For these pathway analyses, selection criteria were enlarged (with p-values until 0. 05) to provide an overview of the pathways and functions affected by SS.
Image acquisition Immunostaining was analyzed by confocal imaging. The spinning disk system Yokogawa CSU-W1 integrated in Metamorph software was mounted to the camera port Nikon Ti-E inverted microscope (20 × Plan Fluor NA 0. 45 and 60 × Plan Apo NA 1. 27 water objective lenses). Images were acquired by a 16-bit cooled sCMOS camera (Prime 95B, Photometrics). For each condition, a Z-dimension series of images were acquired from four random locations. All images were taken with the same acquisition parameters and processed using ImageJ software (NIH). No detectable signal was observed when primary antibodies were omitted.