dataesr/scientific-paragraphs-categorization huggingface dataset:
Results We have found that liver metastases acquire features of the adjacent liver tissue while partially losing those of the primary tumors they derived from. We have identified a 22-gene signature differentially expressed among primary tumors and metastases and validated in public databases. A pharmacogenomic screening for FDA-approved compounds capable of interfering with this signature has been performed. We have validated some of the identified representative compounds in CRC-Patient Derived Organoid cultures (PDOs) and found that pentoxyfilline and, to a minor extent, dexketoprofen and desloratadine, can variably interfere with number, size and viability of the CRC -PDOs in a patient-specific way. We explored the pentoxifylline mechanism of action and found that pentoxifylline.
RNA-seq analysis Low-quality reads or reads containing adapter sequences were filtered using Trimgalore (v. 0. 5. 5) [20]. Trimmed sequences that aligned to human rRNAs or snoRNAs (May 2021)  [21]  using Bowtie (v. 1. 2. 2)  [22]  were discarded. Unmapped sequences were aligned to human genome (GRCh38 Primary Assembly) using STAR (v. STAR-2. 7. 1a)  [23]  with Ensembl gene annotation (release 99). The Sequence Alignment/Map format and SAMtools were used to mark duplicated sequences and Stringtie (v. 1. 3. 6)  to estimate gene abundances. The same pipeline was used to analyze raw FASTQ data from two additional datasets obtained from ENA at EMBL-EBI (https:// www. ebi. ac. uk) under the accession number PRJNA288518 and PRJNA603221  [24, 25]. The first dataset include quadruple-matched tissues (primary colorectal carcinomas, liver metastasis, normal adjacent liver and colon samples) from five patients while the second dataset include matched samples from primary colon cancer, liver metastasis and normal colon tissue from three patients.
EdgeR was used for differential expression analysis filtering genes with low expression (> 10 counts in at least 10% of the samples). Multidimensional scaling (MDS) analysis was performed in R using plot MDS limma function (top = 2000). Differentially expressed (DE) genes were identified setting Benjamini-Hochberg adjusted p-value threshold to 0. 01 and |logFC|> 4 using both glmFIT and glmQLfit functions. For dataset PRJNA288518, PRJNA603221 we consider as DE genes with |logFC|> 4 and p-value < 10 -3 and p-value < 0. 01 respectively. Enrichment and gene ontology analysis was performed using Panther (v. 16) and David (v. 6. 8). List of tissue and cell line-enriched genes for intestine and liver was obtained from Protein Atlas database (Proteinatlas. org v. 20. 1). Clustering and principal component analysis was performed at single pathway level using the R packages Fac-toMineR, Cluster, Factoextra and Pheatmap.
We considered a total of 151 pathways from Panther database (v. Feb. 2020-3. ) for which human gene association was reported. For each dataset K-mean clustering was performed on those pathways with more than five genes expressed. For comparison between metastatic samples, the optimal number of clusters was found using clusGap R function. Briefly, clusGap function calculates the goodness of clustering measuring, for each number of clusters K i = 1,. ,K max , the distance of the withincluster dispersion from the same quantity in a reference distribution. Maximizing the gap between these two gives the optimal (local) number of clusters K. For the optimal K, K-mean clustering procedure was repeated 500 times over each pathway for each dataset and cluster composition is considered stable when is conserved in 95% of the replica.
Flow cytometry PDOs or freshly disaggregated tissue was mechanically and enzymatically disaggregated and filtered through a 70um filter mesh before staining. The following antibodies were employed, in separate tubes, each antibody matched to its isotype specific-related control antibody, in PBS1X-0. 2% BSA, for 45 min at 4 °C, light protected. Aspecific staining from the isotype matched antibody was deemed as background and subtracted to the specific staining. For the Ki67 and CK20 staining, cell permebilization was performed before staining with the Cell Fixation & Cell Permeabilization Kit (ThermoFisher, Whaltam, MA, US). For viability assay, the disaggregated PDOs were stained with Sytox Blue Helix NP Blue (Biolegend, CA, US) for 5 min on ice before flow cytometry. Data were acquired with Cyto-FLEX Flow Cytometer (Beckman Coulter, IN, US) and analyzed with the provided companion software.
We performed the same analysis on two publicly available datasets  [24, 25]  including matched primary and metastatic CRC samples as well as adjacent tissues. In both cases, PCA analysis provided similar results (Fig. 1b , upper and lower panels). To deepen this observation, we performed differential expression analysis between the mentioned subgroups of samples. Figure  1c  reports the total number (See figure on next page. ) Fig. 1  The colon to liver metastases are more similar to the adjacent liver tissue than the primary tumors. a Principal component analysis of all the samples in the study. (CC = Colon Cancer, red; LM1/LM2/LM3 = Colon-to-liver metastasis first/second/third wave, blue; AL = adjacent liver, green). b. Principal component analysis of two publicly available datasets of RNAseq of primary colon cancer (CC, red), liver metastases (LM, blue), adjacent liver (AL, green) and normal colon (NC, brown) samples.
1d , bottom) and one gene did not result as tissue-specific. This signature was even stronger when considering the whole set of overexpressed genes in unmatched specimens, with essentially all of them (226/228) annotated as liver-enriched or expressed in both liver and colon according to Protein Atlas (Fig. 1d, bottom ). On the other hand, we did not find a core of DE genes downregulated in metastatic matched and unmatched samples and less than half of all the genes downregulated (35/85) were characteristic of the intestine, while none was annotated as liver-enriched (Fig. 1e , bottom). Pathway enrichment analysis confirmed that the core of the upregulated genes was involved in liver-related processes such as blood coagulation, plasminogen activation and nicotin degradation (Table  S2 ). We verified whether the identified DE genes could be detected in independent datasets retrieved from public databases.
In detail, the comparison between primary and metastatic samples from two analyzed independent datasets  [24, 25]  resulted in 65 genes upregulated in metastasis versus primary CRCs and common to both the public datasets and to our samples (Table  S3 ). 22 out of the 65 genes were included within the more stringent core of the 43 DE genes derived by the intersection of fully matched samples (Table  1  and Table  S3 ). Thus, despite including unmatched samples and thereby increasing the number of DE and the general background noise of the analysis, a significant closer proximity of the liver metastasis to the liver tissue was still recorded (as compared to the primary CRC). Altogether, our analysis suggests that metastases exhibited an hybrid phenotype acquiring some characteristics and processes common to the surrounding, unaffected liver tissue while losing, at the same time, some features of the originating colon tumor, in agreement with the earlier PCA distribution data (Fig.
Further characterization of liver metastases We performed k-mean clustering of the samples based on their gene expression profile at the pathway level. When considering the 50 samples in our dataset we found that this classification in clusters resulted stable in 15 different pathways (Figure  S1 ). We could identify three main blocks, roughly corresponding to primary tumor, liver metastasis, and adjacent liver samples (Figure  S1 ). Interestingly, a small group of four metastatic samples clustered preferably (> 10/14) with liver tissue and only one sample clustered with primary tumor. This provided further support to the similarity of metastatic tissue to the liver tissue already mentioned (Figure  S1 ).
Identifying drugs to interfere with the colon to liver metastasis signature Next, we conducted a pharmacogenomic screening for compounds capable of interfering with the identified 22-gene signature (Table  1 ) which, as before mentioned, was found to differentiate primary CRC from matched liver metastases and was validated in a large public database (Fig. 2a ). To do this, the Library of Integrated Network-based Cellular Signatures (LINCS; http:// www. lincs proje ct. org/) was interrogated using Connectivity Map (CMAP, version 1. 1. 1. 43; https:// clue. io/) to identify compounds whose administration to cancer cells resulted in similar or opposite expression profile of the mentioned gene signature  [33]. We applied CMAP to the distinct signatures of 21 annotate genes (SLC13A5 was left out because not annotated in CMAP) (Fig.
Finally, we assessed the mRNA levels of the 22-gene signature originally used to identify pentoxifylline. Pentoxifylline treatment was administered in conditions (20uM, 8 h) roughly similar to those used to generate the CMAP data  [52, 53]. This showed that pentoxifylline downregulated 16/22 genes and 18/22 genes, in PDO#1 and PDO#4, respectively (Fig. 10a, b ). This partially validated the original CMAP targeted strategy, that the exposure of PDOs to pentoxifylline partially reversed the expression pattern of the biological signature. Further, pentoxifylline was capable of reducing the levels of most of the 22 genes, known to be STAT3 targets (https:// maaya nlab. cloud/ Harmo nizome/ gene_ set/ STAT3/ ENCODE+ Trans cript ion+ Factor+ Targe ts) implicating the STAT3 pathway engagement by this compound (Fig.
Discussion We focused here on characterizing the molecular determinants of colon to liver metastasis and on identifying and validating druggable targets by means of a pharmacogenomic approach. Initially, we performed a detailed analysis of RNA sequencing data for a cohort of 50 patient-derived samples comprising primary CRC, liver metastasis and adjacent liver samples. From this analysis, two important observations were recorded. First of all, the number of differentially expressed (DE) genes between metastatic samples and unaffected adjacent liver tissue was lower when compared to DE genes between primary CRCs and adjacent liver tissue. Moreover, a large fraction of DE genes between primary CRCs and metastatic samples were characteristic of liver tissue, raising the intriguing possibility that metastatic cells may acquire features of the host tissue, while losing some of the characteristics of the primary lesion they originated from.
Particle morphology The powder samples were adhered to a holder with double-sided copper tape. Their surface was coated with carbon using a 208 HR carbon sputter coater (Cressington Scientific Instruments, Watford, UK). Then, their morphology was analyzed by a Hitachi S-4700 (Japan) scanning electron microscope (SEM). The images were taken at a magnification of 100 ×, 2 000 × and 20 000 x. ImageJ v. 1. 53a software (NIH, USA) was used to estimate the particle size.
The amorphization kinetics of bosentan in the presence of copovidone was analyzed as well. Fig. 2  shows a good fitting of the exponential relaxation law Eq. (  1 ) to the experimental data showing the evolution of the amorphous bosentan fraction as a function of the milling time. X am (t) = 1 -exp(t/τ) (1) The amorphous fraction (X am ) was calculated according to Eq. (  2 ), where ΔH m is the enthalpy of melting of non-milled anhydrous bosentan and ΔH m milled is the enthalpy of melting of crystalline bosentan in ball milled samples. X am = 1 - ( ΔH milled m /ΔH m ) (2) It was revealed that bosentan could be amorphized very rapidly upon milling alone. Its relaxation time was more than ten times shorter than that recorded for other active pharmaceutical ingredients considered good glass formers such as dexamethasone  [20] , chlorhexidine  [21]  or lactulose  [22] , amorphized using the same procedure.
Acknowledgements The research was carried out thanks to  Sonata Bis  grant no  DEC-2019/34/E/NZ7/00245  financed by the  National Science Centre in Poland and BGF-Séjour  scientifique ed. 2020 grant funded by the  French Embassy in Poland on behalf of the French Government. Research data for this article. Krupa, Anna; Strojewski, Dominik; Majda,  Dorota; Wegrzyn ,  Agnieszka  ( 2023 ), "Dataset related to amorphous solid dispersions composed of a poorly soluble drug -bosentan & copovidone",  Mendeley Data ,  V1 , doi:  10. 17632/t8z4nhv2j7. 1.
Particle size and zeta potential measurements The mean particle size, polydispersity index (PDI), and zeta potential measurements of unloaded and TQ-loaded LNCs were determined using a Malvern Zetasizer Nano ZS setup (Malvern Instruments S. A. , UK). Before measurement, unloaded LNCs or TQ-LNCs were diluted in deionized water at 1 : 400 (v/v). All measurements were performed at room temperature aer equilibration for 2 min. The data were obtained with the average of three measurements.
DPD phenotyping Pretreatment Plasma Uracil [U] and dihydrouracil [UH 2 ] were quanti ed using a Waters TQD UPLC®-MS/MS System (Waters Corp. , Milford, MA, USA) equipped with an electrospray ionisation interface according to the method described by Coudore et al. [16]. Data acquisition and processing were performed using MassLynx v. 4. 0 software. DPD activity was categorized as normal, partial or complete de ciency based on previous reports using the [UH 2 ]/[U] ratio  [17] [18] [19] [20] [21] [22] [23]. Indeed, although no consensual cut-off values for the [UH 2 ]/[U] ratio has been established yet, a [UH 2 ]/[U] ratio cut-off below 10 was chosen for DPD de ciency as it has been previously demonstrated as a good predictor of uoropyrimidine toxicity  [12, 24]. Therefore, partial DPD de ciency was de ned as [UH 2 ]/[U] < 10 whereas complete DPD de ciency was de ned as [UH 2 ]/[U] < 1.
Ampli cation of genomic DNA was performed in up to 10-plex PCR reaction wells, followed by addition of barcode indexes and sequencing adaptors by further PCR according to manufacturer's instructions. Pooled amplicons were harvested and diluted to prepare unidirectional libraries for 150 base-pair (bp) paired-end sequencing on Illumina MiSeq sequencing platform (Illumina, San Diego, CA, USA). Illumina NGS reads were trimmed for base Phred quality control (mean quality in a 30 bp sliding window > 20 and 3′ base quality ≥ 6) and aligned with Burrows-Wheeler Aligner (v0. 6. 1-r112-master) on hg19 human genome reference sequence. Variantcalling was achieved using using MiSeq Reporter v2. 6, GATK v3. 7 or GATK v4. 1. 4. 0 (Genome Analysis Toolkit)  [25]  without downsampling or removal of PCR duplicates; variants with quality/depth < 5 or depth < 30 were ltered.
Statistical analyses Data are described as the medians ± standard deviations, or n (%). Since [U] and [UH 2 ]/[U] values were not normally distributed, non parametric tests were performed. Allelic frequencies and genotype distribution were estimated by gene counting and tested for Hardy-Weinberg equilibrium. For the comparison of proportions and to evaluate the Hardy-Weinberg equilibrium, we used the chi-square test. As in most cases, a low number of individuals carries the alternate allele homozygote, the in uence of the genotypes on DPD activity was assessed by clustering genotypes into a dominant inheritance model. Then, genotypes were compared using non parametric Mann-Whitney and Kruskal-Wallis tests. The level of signi cance was set at p < 0. 05. All analyses were two-sided. Statistical analyses were performed using Prism® 5.
Dried extracts were reconstituted with 200 µL of reagent water. For caffeine analysis, these reconstituted samples were diluted (20,000×) with reagent water. For metabolite analysis, dissolved residues were directly applied. Before caffeine chromatographic analysis, solutions were filtered with polytetrafluoroethylene (PTFE) syringe filters (13 mm × 0. 45 µm, Millipore, Burlington, MA, USA). In terms of the statistical analysis of membrane fraction data, variances in the data were analyzed through a two-way ANOVA or one-way ANOVA with Bonferroni's multiple comparison test. The analysis was performed with a 95% confidence interval using GraphPad Prism 5. 01 software (La Jolla, CA, USA). The significance level considered in all the statistical tests was 0. 05.
Computational Simulations For the structural analysis of CRP mutants and CYP1A2, four methods were used, each applied according to the required outputs. Method A involved Alphafold2 (AF2A Advanced version) to generate a set of raw complex structures from the sequences of CYP1A2 and the FD of human reductase (excluding membrane anchor parts). Such an approach does not consider the potential induced fits involved occurring at the interface during complex formation. Five alternate complex structures were generated by AF2A. The one featuring the best interdomain affinity evaluated using the Prodigy web server was selected then relaxed using Rosetta Relax software. Method B extended method A by using the resulting A structure to seed RosettaDock-4. 0 and calculate 1000 variants of this initial complex.
The variant structures featuring the best I-SC scores combined with the best interdomain affinities were selected. In the case where the plot of I-SC scores as a function of the root mean square deviation (RMSD) of variants with the starting structure did not exhibit clear convergence, a second cycle of refinement was initiated involving Rosetta relaxation and Rosetta docking (up to three cycles). This iterative procedure allowed the progressive adjustment of the complex interface when induced fits were involved. Methods C and D are variants of method B involving seeding of the GalaxyRefineComplex software instead of RosettaDock during refinement cycles. The docking methods involved did not correctly manage the flavin and the heme cofactors, and cofactors were replaced on modeled complex structures at each refinement cycle via RMSD minimalization with individual reference crystallographic structures before the Rosetta relaxation steps.
Three independent representative poses of FD-CYP complex models were first submitted to aggressive relaxation and annealing using Rosetta software. Each relaxed structure was split into 3 derived structures: one for the isolated CYP, the second for the unchanged complex and the third for the isolated FD. Individual structures of the three sets of split models were each submitted to three independent new cycles of Rosetta relaxation and annealing giving a total of 27 (3 × 3 × 3) independently relaxed poses. Following the global alignment of all structures via RMSD minimization on their C-alpha, the RMSD values of side chains of individual residues in the complex and in separated domains were calculated. All possible combinations between members of the 27 structure sets were considered and similar combinations were averaged to improve the signal to random noise ratio.
In all cases, residue side chain-specific RMSD values were normalized via division by the side chain atom count. Averaged side chain RMSD values resulting from the intradomain comparisons between relaxed poses were subtracted from the previously calculated RMSD values for the inter-model (complexed vs. free) comparison. As for the structural analysis of CPR mutants, the study of three mutants (H183Y, T142A and V164M) was performed using the CPR FD only, obtained from the crystal structure of human CPRwt (PDB 5FA6) as the starting material. Mutations were constructed with the YASARA software (www. yasara. org). Fully hydrated molecular dynamics simulations were performed for 80 ns using a step size of 100 ps with the Yasara software suite using the AMBER14 force field at a constant pressure (water density at 0.
997 g/mL) and temperature (298 K). Parameters were set as follows: cuboid cell extending 10 nm on each side of the protein, 0. 9% NaCl and pH = 7. 4. Electrostatic potentials were calculated on the average structures with the integrated module of the Pymol software (www. pymol. org) using default parameters (non-linear Poisson-Boltzmann equation; protein dielectric, 2. 0; solvent dielectric, 78. 0; temperature, 310 K; ion charge, +1 of radius 2. 0 Å and -1 of radius 1. 8 both at 0. 15 M) and plotted on the solvent-excluded surfaces (Connolly surfaces). With regard to docking to CYP1A2, structural analysis was performed using the crystal structure of human CYP1A2 (PDB 2HI4) as the starting material. All work was performed using Yasara Structure as the software suite  [40]. Prior to all subsequent work, the structure was relaxed with the refined procedure of Yasara using the following parameters: 298 K, density of 0.
997, pH of 7. 4 and the force field of YASARA2  [41]. The subsequent relaxed structure was then used for docking. Caffeine docking was performed with the VINA procedure of YASARA  [42]  with an ensemble of CYP1A2-relaxed structures. The caffeine ligand was docked 15 times against each of the 20 receptors in the ensemble, yielding 300 complexes. After clustering the 300 runs, 138 distinct complex conformations were found. The three clusters corresponding to the putative orientations yielding the three major caffeine products with CYP1A2 were retained for molecular dynamics analysis. They are named the 1st, 2nd and 3rd position, compatible with the formation of theophylline, paraxanthine and theobromine, respectively. In terms of the molecular dynamics of CYP1A2 with various caffeine poses, using the four different CYP1A2 starting structures (caffeine-void or bound in the first, second or third position), fully hydrated molecular dynamics simulations were performed for 25 ns using a step size of 100 ps with the Yasara software suite and the AMBER14 force field  [43]  at a constant pressure (water density at 0.
997 g/mL) and temperature (298 K). Parameters were set as follows: cubic cell extending 5 nm on each side of the protein, 0. 9% NaCl and a pH of 7. 4. RMSD and root mean square fluctuation (RMSF) values were collected after each molecular dynamics run and analyzed with the R software suite. RMSF differences were calculated as follows. The average RMSF for each residue was calculated with either all atoms or backbone atoms only and averaged between the four simulations. These average values were then subtracted from the same values (backbone atoms or all atoms) from the average RMSF from caffeine-free simulations. Figures were prepared with the Pymol software and R ggplot function. All modeling data are available upon request.
Thus, the data of CPRvar T142A were interpreted with caution. Protein stability differences among CPRvars during incubation may constitute an interfering factor. Stability was verified on the functional level by assessing cyt c reduction activity. No statistically significant alteration in cyt c reduction rates was observed for all variants studied, after 6 hrs of incubation vs. 0 hrs. of incubation, except for variant S243P (see Table  1 ). Therefore, any difference detected in CYP1A2 activity can be ascribed to deviations caused by the presence of CPR variants, except for CPRvar T142A and S243P, the data on which should be interpreted with caution (vide supra). a Membrane fractions from batches used in our former studies  [19, 21, 23]. Wt (A) was used as reference for T142A and A229T, Wt (B) was used as a reference for Q153R, V164M, D211N, P228L and S243P, and Wt (C) was used as a reference for P117H, G144C, N151D, G175D, H183Y, I245P and R246A, in determining the relative activities, metabolite profiles, and relative cyt c reduction rates.