dataesr/scientific-paragraphs-categorization huggingface dataset:
Predicting the risk of liver metastasis can have important prognostic and therapeutic implications, given the availability of liver-directed therapy. Uveal melanoma has a striking predisposition for liver metastasis despite the absence of anatomical proximity. Understanding its biology may uncover factors promoting liver metastasis in other malignancies. We quantified gene expression by RNAseq in 76 uveal melanomas and combined with public data in a meta-analysis of 196 patients. The meta-analysis of uveal melanoma gene expression identified 63 genes which remained prognostic after adjustment for chromosome 3 status. Two genes, PTP4A3 and JPH1, were selected by L1-penalized regression and combined in a prognostic score. The score predicted liver-specific relapse in a public pan-cancer dataset and in two public colorectal cancer datasets.
Surgical resection of liver metastases from colorectal cancer can be performed with curative intent and is recognized as standard of care under certain conditions 2,3. The optimal management of oligometastatic liver spread from other tumor types is not as clearly defined but curative treatment can be contemplated on a case-by-case basis. In addition to surgery, less invasive modalities such as radiofrequency ablation 4 , hepatic arterial infusion chemotherapy 5 or local immunotherapy (trials NCT02509507 and NCT03256344) are being explored for patients with metastases limited to the liver. In conclusion, the identification of patients at high risk of liver metastasis could justify more aggressive treatment up-front (for example, adjuvant chemotherapy) and more intensive liver-specific follow-up with the intention to detect and treat liver metastases as early as possible with an appropriate treatment modality.
Results We pooled three gene expression studies into a training dataset with 196 uveal melanoma patients and 13560 genes. For each gene, we fitted a univariable Cox survival model of relapse-free survival against expression separately in each dataset and computed a meta-analysis summary. This approach identified 656 genes negatively associated and 810 genes positively associated with relapse (FDR < 0. 05). The Q-test for heterogeneity and visual inspection of forest plots did not show discrepancies between datasets. The top genes, ordered by FDR, are shown in Table  1  (detailed results in Table  S1 ). A recent study of uveal melanoma  19  identified 12 genes that have prognostic value. Eleven of these genes were available in the training dataset and for nine of them we confirmed the expected statistically significant positive (EIF1B, ID2, LMCD1, MTUS1, ROBO1, SATB1) respectively negative (ECM1, HTR2B, RAB31) association with relapse-free survival (all FDR < 0.
We further reasoned that it would be necessary to adjust for chromosome 3 loss, which is known to be associated with BAP1 biallelic inactivation and metastatic risk in uveal melanoma. BAP1 inactivation (mutation or loss) is a rare event in most cancer types and would be unlikely to be a universal predictor of liver metastasis. Therefore, we performed an additional meta-analysis. Within each dataset and for each gene we fitted multivariable Cox model of relapse-free survival that included gene expression and chromosome 3 status. In this approach, 63 genes were significantly correlated with relapse (FDR < 0. 05). Again, the Q-test for heterogeneity and visual inspection of forest plots did not show discrepancies between datasets. The top 10 genes are shown in Table  2  (full results in Table  S2 ).
After adjustment for chromosome 3 status, the relation of genes residing on chromosome 3 with relapse was attenuated. Indeed, the expression of most chromosome 3 genes (777/782), including BAP1 (HR = 0. 779, 95% 0. 620-0. 977, FDR = 0. 374), was not significantly associated with relapse-free survival when chromosome 3 status was included in the model. Twenty-eight significant genes (44% of all significant genes) were located on chromosome 8 and seven thereof in 8q24. 3, the distal end of the chromosome 8. The pooled data of the training dataset were further used to build a prognostic scoring formula with the L1-penalized likelihood algorithm of the GLMnet package (supplemental methods) limited to the genes that were prognostic after adjustment for chromosome status 3 at FDR < 0. 1 (Table  S2 ).
The procedure converged on a linear score based on the expression of only the two most significant genes, namely JPH1 and PTP4A3 (coefficients 0. 147 and 0. 249 respectively). In the training data, the two-gene prognostic score was significantly associated with progression-free survival in a multivariable survival model that included tumor class and chromosome 3 status (P < 0. 0001). The prognostic score was also correlated with the expression of class genes (R 2 = 0. 66 against the mean of class 1 minus the mean of class 2 genes, P < 0. 0001). Validation. Uveal melanoma. The prognostic performance of the two-gene score was validated in independent public uveal melanoma data (GSE39717, N = 30 and the TCGA, N = 60). Meta-analysis of univariable Cox survival regression confirmed a statistically significant association with prognosis (Figs 2A and S3, log HR = 3.
63, 95% 1. 07-6. 19, P = 0. 0055). As expected, the score remained prognostic in a bivariable model including chromosome 3 status (Figs  2B  and S3 , log HR score = 3. 75, 95% 1. 00-6. 50, P = 0. 0075). Pan-cancer. Our main objective was to examine whether the predisposition of uveal melanoma to liver relapse can be used to estimate the risk of liver metastasis in other malignancies, especially of epithelial origin. To this end, we examined the two-gene score values in a pan-cancer dataset (GSE2109) including 1181 primary tumors (1036 non-metastatic, 57 with liver metastases, 89 with other metastases). Most samples were obtained from colorectal (N = 274), breast (N = 248), endometrial (N = 147), ovarian (N = 132) and lung (N = 98) cancer. The risk score was significantly higher in tumors that developed liver metastases (t-test P < 0.
70-1. 32, P = 0. 789, 38 events) or liver-specific relapse (HR = 0. 99, 0. 55-1. 78, P = 0. 981, 11 events). Colorectal cancer. Finally, we used colorectal cancer data from the PETACC-3 trial (N = 604, 63 liver relapses) and a public retrospective series (GSE14095, N = 189, 53 liver relapses) to validate the risk score in colorectal cancer. In both datasets the risk score was significantly higher in patients with liver relapse (Wilcoxon's test P = 0. 047 and P = 0. 011). In a meta-analysis of logistic regression models, a high-risk score was significantly associated with liver metastasis (Fig. 5A , logOR = 0. 54, 95% 0. 24-0. 85, P = 0. 0005). Interestingly, in the PETACC data, which also included non-liver relapses, the score was not associated with relapse in general (logOR = 0. 00, 95% -0.
57 to 0. 57, P = 0. 98), while relapse to non-liver sites was a negative trend (logOR = -0. 65, 95% -1. 39 to 0. 06, P = 0. 078). The distribution of the risk score was visibly bimodal in both colorectal cancer datasets (Fig. S5 ), suggesting the presence of distinct sub-populations. Indeed, when tumors were separated by the consensus molecular subtype (CMS) classifier, the average risk score was highest in CMS2 and 4, and lowest in CMS1 and 3 (Fig. S5 ). The association between liver metastasis and the risk score remained significant in a bivariable model that included the CMS subtype (Fig. 5B , logOR = 0. 40, 95% 0. 09-0. 71, P = 0. 011). In the prospective PETACC data, the distribution of the risk score agreed with the proportion of liver relapses, which was lowest in CMS1 (Fig. 5C , logOR = -1.
Several of the genes, including MMP1, MMP2, TIMP1 and HIF1A, are known cancer genes and facilitate stromal invasion. In a study of over 1000 breast tumors the most important risk factor for liver metastasis was the HER2-enriched subtype, but without more specific information at the gene level  26. A predictor of liver metastasis can be derived from any gene expression dataset with adequate follow-up. The advantage of using uveal melanoma is that its proclivity for liver metastasis is highly specific (~90%), meaning that it rarely metastasizes to other organs, and this proclivity cannot be explained by anatomical or vascular proximity. In that sense, tumors that behave biologically like uveal melanoma may be better candidates for liver-specific follow-up and interventions than tumors with a general tendency to colonize multiple organs, which would be at risk of extrahepatic recurrence.
Although JPH1 has been found to be differentially expressed in metastatic versus non-metastatic uveal melanoma  33  and thymoma  34  , its biological significance in cancer remains to be elucidated. Even in the absence of a direct causal relation, JPH1 upregulation may reflect other upstream events, such as driver mutations in other genes or copy number gains in 8q21 involving nearby regulatory regions. Our hypothesis was that the gene expression of uveal melanoma could identify biological predictors of liver-specific metastasis. In agreement with our initial hypothesis, the risk score developed in uveal melanoma was associated specifically with the risk of liver relapse. In the pan-cancer dataset (GSE2109) the score values were highest in tumors which developed liver metastasis, compared with tumors which metastasized to other sites.
Her2+ breast tumors have a higher risk of liver metastasis 26 , but did not have higher score values in our study and may depend on the contribution of alternative biological pathways. For example, previous studies have shown that the expression of Claudin-2 is associated with early liver relapse in triple negative and hormone receptor positive breast cancer  37. The score was consistently associated with liver metastasis in a meta-analysis of two different colorectal cancer datasets. Other genes, such as EREG, AREG and LCK have also been associated with liver metastasis in a previous gene expression study of 160 colorectal cancer samples  38  although it is unclear whether these genes are generally related with liver metastasis or are specific to colorectal cancer. CMS was also predictive of liver relapse in colorectal cancer, with CMS1 having a significantly lower proportion of liver relapses than CMS2-4.
Methods Origin of samples. Untreated human samples from uveal melanoma and breast cancer were collected by the unit of surgical oncology from patients who provided written informed consent. The samples were stored in the Biological Resource Center (BRC) of the Institut Curie, France, in accordance with French regulations. The study was approved by the ethics committee and the institutional review board of the Curie Institute with approval number A10-024. All research was performed in accordance with relevant guidelines and regulations. We searched for gene expression data from human primary samples of uveal melanoma in the Gene Expression Omnibus  39  and manually curated datasets with adequate clinical annotation. At the time of retrieval, 18 data sets were screened and three were retained for analysis: GSE44295 (N = 57), GSE22138 (N = 63) and GSE39717 (N = 39, 30 with follow-up).
The two largest datasets were used in conjunction with our own RNAseq data for model development (training dataset). We also downloaded publicly available RNAseq data from the TCGA (accessed on November 2017) for 80 patients, of which 60 had follow-up information and were retained for analysis. GSE39717 and TCGA data were used for validation (see Supplement Sections 1-3 and Fig. S6 ). Gene expression data for colorectal cancer were obtained from the PETACC-3 study of adjuvant chemotherapy in colon cancer  40  , available in ArrayExpress (E-MTAB-990) and from a series of colorectal patients 41 that were followed for the occurrence of liver metastases (GSE14095, N = 187). Finally, we also obtained a pan-cancer dataset which contained information on the site of relapse (GSE2109, N = 2158, 1181 of which with metastatic relapse).
We considered the use of the PanCancer TCGA data, based on the recent publication of curated clinical annotation  42  (see Supplementary Table  1  in that article, columns T and U). Unfortunately, despite the inclusion of over 10,000 patients, the occurrence of liver events was recorded for only 262 patients, raising concerns about the quality of the follow-up with respect to that specific outcome. For example, none were documented for patients with colorectal cancer (N = 618), a fact that limited the usefulness of this resource for our purposes. Inactivation of BAP1 is common in uveal melanoma but can also occur in kidney cancer  7  , mesothelioma 8 and cholangiocarcinoma  9. However, mesothelioma and cholangiocarcinoma are rare and we could not find gene expression datasets documenting liver metastasis, not just overall survival or relapse-free survival, which could be used our purposes.
In addition, cholangiocarcinoma is in immediate proximity to the liver and would not be a useful model of liver metastasis, more likely reflective of local invasion instead. Out of 33 patients with renal cancer in the pan-cancer GSE2109 dataset only 10 had distant metastases and none had liver metastases, making any kind of statistical analysis impossible. Total RNA extraction. Total RNA was extracted from fresh frozen uveal melanoma and breast cancer samples with the acid-phenol guanidium method. The quantity of RNA was assessed by using an ND-1000 NanoDrop Spectrophotometer with corresponding software (Thermo Fisher Scientific Inc. , Wilmington, DE). RNA quality was determined by agarose gel electrophoresis.
RNAseq. Library preparation was done using the Illumina TruSeq kit according to manufacturer's instructions. Libraries were sequenced to produce paired-end reads. We used STAR v2. 5  43  to align against the GRCh37 DNA primary assembly and the Ensembl annotation r75 (available from ftp://ftp. ensembl. org/pub/release-75/). Quality control was done with RNA-SeQC (v1. 1. 8)  44 and custom scripts. The raw counts were estimated with the Rsubread package (v1. 2) and then imported by edgeR (v3. 12)  45. Raw counts were filtered by only keeping genes with at least 1 count-per-million in at least 5 samples, normalized with the trimmed mean of M-values (TMM) and log-transformed, according to the edgeR recommendations. Real-time PCR. The RT-qPCR method, including cDNA synthesis and PCR conditions, has been previously described in detail  46.
We quantified transcripts of an endogenous RNA control gene involved in cellular metabolic pathway, namely TBP (Genbank NM_003194), which encodes the TATA box-binding protein (a component of the DNA-binding protein complex TFIID)  47. Each sample was normalized on the basis of its TBP content and against a median of 10 normal breast tissue samples, with the ΔCt method. Data analysis and statistics. Statistical analyses were done in R version 3. 5. All statistical tests were two-sided and considered significant if P < 0. 05. P-value adjustment for multiple comparisons was used to control the false discovery rate  48. Survival models were built with the survival R package  49  and the coxph() function. By default, this function fits a Cox proportional hazards regression model with the Efron approximation for tied event times.
We used the metafor package with the random effects model and the default restricted maximum-likelihood estimator of residual heterogeneity (τ 2 ) to perform meta-analyses  50. Specifically, for each dataset and each gene the log-transformed hazard ratio estimate (yi) and its associated standard error (sei) were extracted from a univariable and bivariable (with chromosome 3 status) model. The rma() function from the metafor package was then called with rma(yi, sei). The Q-test was used to test for significant heterogeneity. The nearest-neighbor chain algorithm was used for hierarchical clustering (reviewed in  51  ). Pearson's correlation was used as a similarity metric with average linkage. We used the nclust package (v2. 0. 1) for this purpose, available from https://bcf. isb-sib. ch/nclust/.
The coxnet method  52  in the GLMnet package was used to derive a linear prognostic score. The development of the prognostic score was in accordance with the REMARK recommendations. Gene expression estimate of chromosome 3 status. Over all uveal melanoma datasets, the chromosome 3 status had been estimated by dedicated methods (aCGH or FISH) for 119 patients. For the remaining patients, we inferred the chromosome 3 status from gene expression data. Using patients with known chromosome 3 status, we trained a binomial (loss/no loss) GLMnet model on 95 patients and validated it on the remaining 24 patients. Data from the 24 patients were not considered during the derivation of the model. Only the expression of genes residing on chromosome 3 was used, as these genes would be directly affected by a loss of chromosome 3.
Data availability The RNAseq data produced by the study can be found in the European Genome-phenome Archive (accession EGAS00001002932). Public data used in this work: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE44295 https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE22138 https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE39717 https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE2109 https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-990/ https://portal. gdc. cancer. gov/projects/TCGA-UVM.
PTGS-induced CHH and CHG DNA methylation is established de novo in each generation To further address the relationship between CHH/CHG methylation and S-PTGS in L1 plants, we analyzed CHH and CHG methylation during the onset of S-PTGS at each generation and in various mutant backgrounds. To avoid using WGBS on a large scale, we assessed if a quantitative assay based on methylation-sensitive enzymes reflects the data obtained by WGBS. For this purpose, the level of DNA methylation at HaeIII and PagI sites (CHH) and MspI and ScrFI (CHG) sites distributed all over the GUS sequence (Supplementary Figure  S2A ) was monitored in silenced L1 and non-silenced L1 rdr6 adult plants. Methylation was not detected in L1 rdr6, but was detected at all tested CHH and CHG sites in L1 at a level similar to that observed in the methylome analysis (Supplementary Figure  S2B ), indicating that this simple method is a robust and reliable tool to monitor S-PTGS-induced DNA methylation.
ACKNOWLEDGEMENTS We thank  Marjori Matzke ,  David Baulcombe  and  Steve Jacobsen  for providing mutant seeds, and  Maud Rivard  for assistance in genotyping. Authors' contributions: C. T. , B. J. C. and H. V. designed the research. C. T. , A. Y. , N. B. , T. E. and U. W. performed the research. C. T. , A. Y. , N. B. , T. E. and H. V. analyzed the data. C. T. , B. J. C. and H. V. wrote the paper.
FUNDING French  Agence Nationale pour la Recherche  [ ANR-10-LABX-40 ,  ANR-11-BSV6-007  to H. V. ];  Fondation Louis D. de l'Institut de France  [to H. V. ];  Australian Research Council  [ DP120103966 ,  DP150104048  to B. J. C. ];  INRA   Post-doctoral Fellowship  (to C. T. ). Funding for open access charge:  ANR.
A recent EU-LIFE funded project, LIFE ASAP (http://lifeasap. eu) is entirely dedicated to increasing the awareness and active participation of citizens on the problem of Invasive Alien Species (IAS). Importantly, citizens can; thus, become aware that the release of exotic species into ecosystems is a dangerous action with sometimes severe consequences. It often happens that people, instead of sacrificing crayfish from an aquarium, think that their release is the most compassionate action. Citizen science can help explain the consequences of such actions, even reaching the youngest generations, and contribute to increasing public awareness. Besides the scientific literature, there are a number of regularly updated databases which provide the most recent information about IAS, such as ISSG: Invasive Species Specialist Group (http://www.
issg. org/database/), CIESM: Atlas of Exotic Crustaceans in the Mediterranean (http://www. ciesm. org/atlas/index. html) and ELNAIS: Ellenic Network on Aquatic Invasive Species (https://elnais. hcmr. gr/). It is worth mentioning that the first records of 14 marine IAS in Greek waters (6% of the total) are attributed to citizen scientists  [85].
22  This antibody inhibits HGF binding and abolishes the ligand-induced phosphorylation of c-Met. However, no evidence of further clinical benefit was observed in comparisons of onartuzumab plus bevacizumab with bevacizumab plus placebo in unselected patients with recurrent GB. Nevertheless, exploratory biomarker analyses have suggested that patients with tumors displaying high levels of HGF or unmethylated O 6 -methylguanine-DNA methyltransferase expression may benefit from treatment with a combination of these two antibodies. Phase I/II trials with three other monoclonal antibodies (cetuximab [anti-epidermal growth factor receptor (EGFR), Erbitux], tanibirumab [TTAC-0001, anti-VEGR2], and TRC105 [carotuximab, anti-CD105]) either as single agents or in combination with bevacizumab have either recently been completed or are currently underway in patients with recurrent GB (https://ClinicalTrials.
Acknowledgments This work was supported by grants from the  "Fondation de France", "Ligue Contre le Cancer du Grand-Ouest ,  Comité Départemental du Maine-et-Loire", "Association en Avant la Vie ", and the  European Commission, Education, Audiovisual and Culture Executive Agency (EACEA) , through the  NanoFar Erasmus Mundus joint Doctoral program. We thank  Professor Franck Boury  (Team 17, GLIAD, CRCINA, INSERM 1232, Angers),  Doctor Snow Stolnik , and  Doctor Cynthia Bosquillon  ( School of Pharmacy, University of Nottingham, Nottingham, UK ) for providing facilities. We also thank  Alex Edelman  and associates for correcting the manuscript.
4%, an N 50 value of 1,410,057 bp, and an L 50 value of 1. Prokka version 1. 13 (  14 ) was used for annotation, indicating that the genome contained 2,755 coding DNA sequences (CDSs) and 49 tRNA, 1 transfer-messenger RNA (tmRNA), and 3 rRNA genes. The average nucleotide identity BLAST against L. innocua Clip11262 (GenBank accession number NC_003212) was of 98. 73%, confirming species identity  (15). To better understand the phylogenetic placement of isolate MEZLIS26, a maximum likelihood phylogeny was inferred from the core genome alignment of 42 L. innocua and 4 L. monocytogenes public genomes (5) using Parsnp, implemented in Harvest suite v. 1. 1. 2 (  16 ) and visualized with iTol v. 4. 2  (17). Isolate MEZLIS26 clustered within clonal complex CC537 (nonhemolytic L. innocua) together with isolates MOD1-LS888 and 2015L-6726 (SRA accession numbers SRR1481929 and SRR2915359, respectively), isolated from food in the United States (Fig.
ACKNOWLEDGMENTS The whole-genome sequencing work is supported by the  National Institutes of Health/Food and Drug Administration  under award number  1U18FD006780-01. We thank the  South African National Research Foundation  for supporting this research through the  Thuthuka Funding Instrument  (grant number  TTK170411226583 ) and the  Swedish Research Council (VR)  through grant number  2016-02606. We thank  Lyndy Harden  and  Siddhartha Thakur  from  Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University (Raleigh, NC ). We thank the  Genome Trakr Network  and the  Whole Genome Sequencing Program  for foodborne pathogen traceback and the  Center for Food Safety and Applied Nutrition (CFSAN) ,  U. S. Food and Drug Administration (FDA) , for support in the whole-genome sequencing (WGS) of the strain MEZLIS26 as part of the  US Food and Drug Administration 's WGS surveillance effort.
Data availability. This whole-genome sequencing project has been deposited at DDBJ/ENA/GenBank under the BioProject number PRJNA514279 (BioSample accession number SAMN11604718 and GenBank accession number AADHQU000000000). The version described in this paper is the first version, AADHQU010000000. The sequences have been submitted to the Sequence Read Archive (SRA) under the accession numbers SRX5806851 and SRR9029426. All isolates used in this study are also publicly available in https://bigsdb. pasteur. fr/listeria/.