dataesr/scientific-paragraphs-categorization huggingface dataset:
Full annihilation of PI3Ka prevents pancreatic cancer cell migration We then investigated whether pancreatic cancer cells depended on the activity of a specific PI3K isoform. We used a panel of pancreatic tumour cell lines generated by KRAS mutation combined with other genetic alterations including PIK3CA mutation and PTEN deletion (Dataset EV4A). We compared two different pharmacological strategies of PI3K inhibition using compounds with either isoform-selective or pan-PI3K pharmacological profiles as shown by inhibitory concentration 50 (IC50) on recombinant protein in vitro  (Fig 2A, Dataset EV4B). We analysed protein expression levels on four cell lines and we observed detectable effects on pS473Akt levels (used as read-out of PI3K activity) for all inhibitors at 1 and 10 µM, thus allowing comparison of the differential downstream actions of PI3K isoforms.
D Scoring of the PI3Ka activation transcriptomic signature was used to cluster patients with high, medium (both groups were combined, high+medium: red) and low (black) scoring levels in the primary tumours of confirmed PDAC patients from PAAD (TCGA) or PACA-AU (BH corrected P-values). The survival curves of each cluster were then plotted, and the statistical significance was calculated using the log rank test. E PI3Ka activation scores from localised PDAC in the TCGA database according to their UICC staging were retrieved, clustered into two groups (low and med) (BH corrected P-values) and their overall survival was plotted, with log rank testing. As a positive control, we confirmed the demonstrated isoform dependency that was published by others in other solid and liquid cancer cell lines  (Park et al, 2008; Torbett et al, 2008; Lynch et al, 2018).
In the KPC model, aggressive pancreatic tumours spontaneously develop under KRAS and p53 oncogenic mutations  (Hingorani et al, 2005) ; however, detectable levels of cfDNA have not been described. Tumours were diagnosed through high-resolution US imaging as well as quantification of cfDNA (for the setting of threshold limit, see Materials and Methods below). We quantified the cfDNA in blood plasma samples by longitudinally measuring the relative concentration of two expressed genes, TP53 and GAPDH, and then correlated those findings with the anatomo-pathological results from the pancreas and metastatic site organs (Dataset EV5). The longitudinal average levels of cfDNA correlated with disease progression and mouse lethality  (Fig 5C). We analysed two independent cohorts of KPC mice and were able to discriminate between localised primary tumour and metastasis, by cfDNA levels at the time of sacrifice  (Fig 5C and D, Dataset EV5).
Tail vein injection of murine pancreatic cancer cells, engineered to express secreted luciferase (R211-Luc cells) in Nude mice, was carried out in order to confirm the action of BYL-719 treatment on the evolution of micro-metastatic foci. Administration of BYL-719 treatment for 21 days significantly prevented tumour cell growth in lungs  (Fig 6K and L, Dataset EV5). Interestingly, vehicle-treated mice presented an increased area of pancreatic epithelial marker CK19 (Fig  6M ). Similar results were observed in C57/B6 mice when comparing the area of metastatic foci after tail vein injection of syngeneic Kras mutant pancreatic cells (KC;p110a +/+ , A338) compared to syngeneic Kras mutant pancreatic cells partly lacking PI3Ka activity through a genetic inactivation of one allele of Pik3ca (KC;p110a +/lox , A260, see also Appendix Fig S17, Appendix Supplementary Methods)  (Fig 6N).
Tumour-intrinsic PI3Ka alters tumour cell chemokine secretion and promotes the acquisition of tumour-associated inflammatory macrophage characteristics in the peritumoural tissue Transition from micro-to macro-metastasis is now well described as promoted by tumour-extrinsic factors including immune cells (Celi a-Terrassa & Kang, 2016). Hence, we first analysed systemic alterations on circulating blood cells during PDAC progression in our KPC model. We performed a full blood count to assess immune cell populations (Dataset EV5), as macrophages promote PDAC progression  (Padoan et al, 2019). We noticed that metastatic KPC mice presented significantly increased counts of white blood cells ( To test whether tumour-intrinsic PI3Ka could be involved in this tumour/stroma interaction, we used derived cell lines from  ).
Transcriptomics and bioinformatics analysis We selected transcriptional profiling data sets of normal pancreas, chronic pancreatitis and pancreatic primary tumoural tissues from localised (PDACloc) or metastatic patients (PDACmet). Published data on human samples were retrieved from public databases E_EMBL_6  (Abdollahi et al, 2007)  from compatible platforms, normalised using the RMA method (R 3. 2. 3, bioconductor version 3. 2), collapsed (collapse microarray), filtered (SD > 0. 25) and statistically tested using an ANOVA test corrected using the Benjamini and Hochberg method (BH). For each sample, individual scoring for hallmarks or Reactome (actualised list of genes downloaded from MSigDB version [software. broadinstitute. org/gsea/msigdb] and Reactome [www. reactome. org]) was performed using Autocom-pare_SES software (available at https://sites.
google. com/site/fred softwares/products/autocompare_ses) using the "greater" (indicating an enriched gene set) Wilcoxon tests with frequency-corrected null hypotheses  (Tosolini et al, 2016) , followed by values in each group of patients compared using an ANOVA test. Hierarchical patient clustering was performed using the PI3Ka activation signature. The PI3Ka gene signature was designed as the intersection of genes up-regulated and down-regulated in 20 breast tumours after BYL-719 treatment  (Bosch et al, 2015)  and LINCS shRNA CMAP sig gene list  (Zhang et al, 2017)   (Appendix Fig S1). This list was narrowed down to 20 genes, which expression was found compatible with the quality criteria (filter) detailed above. Unsupervised hierarchical clustering of the E_EMBL_6 data set was performed focusing on the expression of these 20 genes regulated by PI3Ka in cancer.
Confirmed PDAC samples from public databases were selected for further bioinformatics analysis. In detail, mRNA expression data and clinical data from confirmed PDAC patients of PAAD (TCGA) (175 patients) and PACA-AU (267 patients) cohorts were retrieved. Amongst the 175 well-annotated TGCA patients, 21 patients were considered localised according to their UICC staging (T = 0, 1 or 2, N = 0, M = 0). For each patient, a PI3Ka activation signature score or immune cell infiltration score (to quantify LTcd, NK, LT CD8, Monocyte-Macrophage-DC, B cells, granulocytes, LT CD4) was given using SES auto compare software, and patients were hierarchically clustered in three groups corresponding to high, medium or low scoring. Scores in each group were statistically tested using an ANOVA test corrected according to the Benjamini and Hochberg method (BH).
For PI3Ka activation signature, high and medium groups were then pooled. The overall survival of patients in each cluster was plotted and statistical differences were calculated using the log rank test. The prognosticator value of PI3Ka activation signature and IUCC staging were tested independently and compared to the value of clinical T,N,M staging using the multivariate Cox test and the PAAD database. We verified that within each patient cluster, there was no enrichment in terms of genetic changes associated with the PI3K/Akt pathway and, in particular, that oncogenic mutations of PI3Ka and PTEN were infrequent and equally distributed in each group of patients (mutational pattern available in PAAD cohort only: Dataset EV3, Appendix Fig  S2 ). SES scores for PI3Ka activation were also calculated in PDAC RNA subtypes using classification method from  Puleo et al (2018).
IHC is detailed in Appendix, antibody used in Dataset EV4. Blood was collected from patients diagnosed with pancreatic adenocarcinoma and analysed by digital droplet PCR (ddPCR) for KRAS mutation. Briefly, 10 ml of blood was centrifuged twice in PAXgene blood ccfDNA tubes (Qiagen) at 1,200 g for 10 min at 4°C and 16,000 g for 10 min at 4°C. Cell-free plasma was collected and total DNA was extracted from 3ml of plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen) according to the manufacturer's recommendation. Circulating cell-free DNA (cfDNA) ranging from 110-210 base pairs was qualified and quantified using the DNF-474 high sensitivity ngs fragment analysis kit (Agilent). For ddPCR, 5 ng of cfDNA were analysed using the ddPCR TM KRAS G12/ G13 Screening Kit #1863506 (Bio-Rad) and the QX200 Droplet Digital PCR (ddPCR) System (Bio-Rad), according to the manufacturer's recommendation.
Inhibitors and ligands For in vitro use, all PI3K inhibitors  (Knight et al, 2004; Jackson et al, 2005; Pomel et al, 2006; Ali et al, 2008; Folkes et al, 2008; Raynaud et al, 2009; Burger et al, 2011; Jamieson et al, 2011; Fritsch et al, 2014; Barlaam et al, 2015)  (Dataset EV4) and the FGFR inhibitor AZD4547 were purchased (CliniSciences) and dissolved in dimethyl sulfoxide (DMSO) to obtain a stock concentration of 10 mM, subsequently diluted as indicated and compared to the diluted DMSO vehicle (vehicle). In vivo, BYL-719 (ApexBio) was dissolved in 0. 5% methyl cellulose with 0. 2% Tween-80 and administered by oral gavage at 50 mg/kg daily. TNF-alpha was purchased (Sigma-Aldrich) and dissolve in PBS to obtain a stock concentration of 1 mg/ml.
Cell lines All the cell lines described in Dataset EV4 were obtained from the American Type Culture Collection (ATCC, Manassas, VA) or from genetically engineered mouse models or from CRB, Toulouse, IUCT-O. Mutant KRAS, with or without partially deficient PI3Ka activity pancreatic cancer cell lines were derived from the pancreas and lung of KC or KC;p110a +/lox animals (aged 10-13 months). Peritoneal metastatic cells pASC1 and pASC3 are primary cells isolated from metastatic patients with PDAC in the IUCT-O. R211-Luc cells are R211 cells modified to express the luciferase. General methods were used and are detailed in the Appendix and Dataset EV4. IC21 cells were treated for 1 day with the conditioned medium, pre-incubated with control Antibody (0. 5 µg/ml, MAb rat IgG1 isotype control) or anti-IL3 antibody (0.
For the standard curve, we did serial dilutions up to 1/1,000 of the two DNA extracts and a qPCR of two different genes, p53 and GAPDH. We designed and selected the most specific and efficient primers (Sigma-Aldrich â ) using Primer-Blast (NCBI). The obtained standard curves allowed subsequent quantification of cfDNA in mouse plasma samples. The following primers were used: p53 gene (Forward: 5 0 -CC AGCTCAGCCTTTGTAGTGAA-3 0 ; reverse: 5 0 -GTGCAGCCCTAAGCA TCTAGC-3 0 ; chromosome 11), GAPDH gene (Forward: 5 0 -AGCCC CAGGCTATCTGATGT-3 0 ; 5 0 -ATAGCTGATGGCTGCAGGT-3 0 ; chromosome 6). cfDNA thresholds were calculated by compiling all cfDNA measurements from mice with normal pancreas, high-grade PanINs, localised and metastatic PDAC (Dataset EV5). For the survival curve (Kaplan-Meier), mice were categorised as having a normal, low or high level of cfDNA according to their mean cfDNA values.
As for thresholds, the low range corresponds to mice presenting 0 -0. 003 AU of cfDNA and the high range to mice presenting cfDNA above the mentioned level. In order to establish these ranges, we analysed the histology of each mouse at end point. cfDNA was quantified using blood collected at sacrifice/death. All raw data and threshold calculations are shown in Dataset EV5. The Fragment Analyzer TM was used to determine the size of DNA fragments in blood plasma. The DNF-474 High Sensitivity NGS Fragment Kit was used to characterise cfDNA. Three different cfDNA fragmentation profiles were obtained: for normal and healthy mice, the electropherogram did not present any fragment; for mice with high-grade PanINs and localised PDAC, a 160-210 bp fragment was always found; for mice with metastatic PDAC, the electropherogram presented the aforementioned 160-210 bp fragment, in addition to larger fragments but at lower concentrations.
Statistical analysis Experimental data provided at least three experimental replicates and three to five measurement replicates, except for WB experiments. Statistical analyses were performed with GraphPad Prism using student t-test for in vitro data; non-parametric Mann-Whitney for all in vivo data; Log rank test was used for all survival curves: *P < 0. 05, **P < 0. 01, ***P < 0. 001. Correlation analysis was performed using Pearson r test. Statistical relevance of the murine KPC cohort size was determined using a Power Calculation test (www. lasec. cuhk. edu. hk/. /power_calculator_14_may_2014. xls), those data are shown in Dataset EV5. For patient data analysis, ANOVA test corrected using the Benjamini and Hochberg method (BH) on SES (sample enrichment score).
tumoural lesions induced either by Kras mutant or Kras mutant in pancreatic cells partly lacking PI3Ka activity, presenting differential metastatic potential (Fig 6N). We found that the genetic inactivation of PI3Ka in pancreatic cancer cells led to an altered cytokine secretion pattern in vitro (a panel of 16 cytokines were tested), with decreased levels of IL-3 in two mutant Kras pancreatic cancer cell lines partly lacking PI3Ka activity (genetic inactivation) compared to three mutant Kras pancreatic cancer cell lines, including R211 cells (Fig 7E, Appendix Fig S17). Pharmacological inactivation of PI3Ka in three different mutant Kras pancreatic cancer cell lines significantly and reproducibly decreased IL3 levels (Fig 7F). PDAC patients with a high PI3Ka activity signature presented a significant increase on the gene signature for the selective immune population of cd T lymphocytes (LTcd) and not on generic signatures of other immune cell populations (Fig 7G, Dataset EV2C.
laboratory for this topic was/is funded by Europe EU-ERG FP7 (270696 PaCa/ PI3K), ARC (PJA20171206596; salary for RB), Toucan ANR Laboratory of Excellence, MSCA-ITN/ETN PhD-PI3K (Project ID: 675392, salary for FR-D and SA), Fondation de France (salary for BT), GSO, Ligue Nationale Contre le Cancer (salary for CC and CC), Fondation Toulouse Cancer Sant e (revision experiments). Both FR-D and SA disseminated their research to high school students, as part of their commitment in MSCA-ITN funding. DS laboratory for this research was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project ID 329628492-SFB 1321 (DS) and the European Research Council (ERC CoG No. 648521 (D. S. ).
Acknowledgements We are grateful to SigDYN members, past and present, for their technical support, sample banks, common tools, scientific and protocol discussions,  CRB  and  BACAP consortium  for patient samples,  UMS006/CREFRE ,  Anexplo Platform , Toulouse (mouse breeding and experimental zone; ENI core platform), the CRCT core technology platform in particular  Laetitia Ligat ,  Carine Valle  and  Emeline Sarot ,  ImagIN platform  ( FX Fresnois ),  Dr. Barbara Garmy-Susini  for access to her laboratory while moving our own facilities, the staff of Histology platform ( I2MC, Inserm 1048, Toulouse, France ),  MetaToul-Lipidomique Core Facility  ( I2MC, Inserm 1048, Toulouse, France ),  MetaboHUB - ANR-11-INBS-0010  for lipidomic analysis, advices and technical assistance, and  Genentech  for GDC0326.
With these conditions, we might consider that the log transformed data should follow a normal distribution. Because using the log transformation reduces the variability, the distributions are nearly proportional pointing out the need to applied with cautious the data transformations to avoid violation of normal assumption for ANOVA test. HD204 was well tolerated without serious AEs related to the study drug. The proportion of subjects who experienced AEs was also comparable between the 3 groups. Immunogenicity was assessed after study drug infusion, and no subject displayed a positive result for ADA. The overall low incidence of ADA is consistent with other study in healthy subjects assessing an unique infusion. Overall, HD204 demonstrated an equivalent PK profile with both US-sourced bevacizumab and EU-sourced bevacizumab in healthy subjects.
The next step requested by regulatory agencies in the development of this bevacizumab biosimilar candidate will be to establish an equivalence activity in a sensitive population  [4, 5]. Currently, metastatic non small cell lung cancer (NSCLC) was selected, and the randomized clinical trial SAMSON-II comparing paclitaxelcarboplatin with HD204 versus paclitaxel-carboplatin EU-sourced bevacizumab is ongoing (NCT03390686). Because several factors might theoretically impact the PK results, including administered dosages, regimen scheme (multiple versus single), chemotherapy partners, cancer setting and extension, a limited PK assessment in a subset of patients receiving iterative administration of bevacizumab or its biosimilar candidate in this large randomized study has been planned.
Step 2: Generate N realizations of the random fields within MATLAB 2015 code using the Karhunen-Loève expansion method, as presented in Section 2  [22]. It should be noted that a smaller value of error estimation for the truncated series expansion obtained by Equation (  3 ) can lead to more precise results, but the series expansion S increases at the same time. The critical error estimation is considered to be 10% in this study, which seems a good compromise between result accuracy and computational burden  [22]. Step 3: Map the random fields into the deterministic models. The largest element size of the deterministic mesh in horizontal or vertical directions does not exceed 0. 5 times of the corresponding autocorrelation lengths  [22]. Perform stability analysis N times to obtain the safety factors and the corresponding critical slip surfaces according to Section 3.
1. Step 4: Construct a metamodel based on the N input-output sets and the principle of Step 1: Determine the input parameters related to the random field generation, which include mean, standard deviation, cross-correlation coefficient, autocorrelation length, and rotation angle of the fabric orientation. Moreover, build the deterministic (DKA/FELA) models. Step 2: Generate N realizations of the random fields within MATLAB 2015 code using the Karhunen-Loève expansion method, as presented in Section 2  [22]. It should be noted that a smaller value of error estimation for the truncated series expansion obtained by Equation (  3 ) can lead to more precise results, but the series expansion S increases at the same time. The critical error estimation is considered to be 10% in this study, which seems a good compromise between result accuracy and computational burden  [22].
The crack propagation issue In order to validate the SCC model on lab specimens (or to evaluate it on realistic industrial components) with a global 3D view and an improved added value of data analyses, a digital tool named Code_Coriolis has been developed  [13]. It consists in a nite element modeling post analysis module. The inputs are the geometry of the piece (modelled with a CAD software), the mesh of the piece, SCC models, and the boundary conditions (surfaces in contact with water and parameters of water). From these information, the tool computes the most probable path of the crack, and can display several outputs: a 3D map of crack initiation probabilities, a 3D view of the crack path and the kinetics of the crack propagation (crack depth versus time), etc. Figure  1  summarizes the principle used in Code_Coriolis to compute the propagation of a crack: at instant t, the crack tip is at a given node, and its neighbouring nodes are questioned (Figure  1b ).
Through mechanical calculations (i. e. the maximum crack propagation speed between two nodes), the code determines the most critical node to which the crack can move (Figure  1c ), then the crack advances to this node (Figure  1d ). The process is repeated at instant t + 1 and so on, thus the crack propagation path can be determined. In the current version of Code_Coriolis, only macroscopic parameters, such as FEM outputs (stress and strain elds) are used. Thus, it uses coarse meshes which do not account for the 'local' model that relies on microscopic behaviour of the grain boundaries. This work aims at linking the local models with the microscopic behaviours, and proposing a new propagation criterion that gives the possibility to estimate the time to rupture of the material while taking into account the microscopic properties.
Training dataset The data used in this work are KAM (Kernel Average Misorientation) maps, and are obtained through EBSD (Electron Back Scatter Di raction). KAM is a value representing the local misorientation between a pixel and its neighbours. The KAM map commonly exhibits a greater misorientation at grain boundaries than within the grains  [8]. Here, the right map exhibits a higher mean stress, but the left one shows a critical path of high stresses that goes through the map (dots). This is due to the di erence in the crystallographic orientations of neighbour grains and their deformations under loading e ects. The data type studied in this work are KAM maps, where each coordinate is the KAM value at the given pixel on a 2D Cartesian grid. Figure  5  presents the training map obtained at the macroscopic strain level = 12%, displayed as an image for a better understanding of the physical interpretation of the parameters.
One can see that the high values of KAM are concentrated at the grain boundaries, as expected. The training dataset consists of 13 KAM maps of size 500 × 500, each realized at a di erent macroscopic strain ∈ {0%, 1%,. , 9%, 10%, 12%, 15%} during a in situ tensile test. Each map was obtained by scanning the same zone in the material, one at each increment of deformation.
The dataset used in this section is the same batch than the one used in the section 3: 100 realizations of the training 500 × 500 training image (Figure  11a ) for each T and OL such as:  (20, 8) ,  (30, 10) ,  (40, 14)   for each vertex V in graph do (T, OL) ∈ { 3: dist[V ] = ∞ 4: previousl[V ] = 0 5: dist[source] = 0 6: Q = set of all the nodes in graph 7: while Q = ∅ do 8: u = node in Q with min(dist) 9: remove u from Q 10: for each neighbour V of U do 11: alt = dist[u] + dist(u,v) 12: if alt ≤ dist[V ] then 13: dist[V ] = alt 14: previous[V ] = u return previous The purpose here is the study of the in uence of both T and OL on the times to rupture, hence Dijkstra's algorithm was applied to the 1 300 realizations of the KAM maps at both = 2% and = 12%.
Programming tools The main programming tool used for the simulation of the microstructure is MATLAB 2017b, with the toolbox Image Processing useful for its function xcorr2. m. It is signi cantly faster than its Python equivalent correlate2d(x,y) from the library scipy. signal. As mentioned above, the EMD is computed with the function scipy. stats. wasserstein_distance. Note that other packages dedicated to optimal transport can be found (e. g POT), but optimization of computation times is not the focus of this work. The main tool used to deal with the simulation of the crack paths is Python 3. 6: the de nition of the adjacency matrix was realized with the function dok_matrix from the package scipy. sparse. dok. It builds a sparse matrix that is to be parametrized by the user. Dijkstra's algorithm is also available on scipy: scipy.
Acknowledgement The authors are grateful to the  EDF R&D microscopy department , the  Materials Ageing Institute (MAI) , for having supplied with the KAM data. They also thank two anonymous referees for their remarks, as well as  Julien Vidal  for having provided his remarks on a preliminary version of this paper. Finally, the authors wish to acknowledge Thierry Couvant and  Jerome Ferrari  for having provided helpful opinion during this work, and  Géraud Blatman  for his preliminary studies on the subject.