dataesr/scientific-paragraphs-categorization huggingface dataset:
The MS/MS spectra were searched against a decoy database to estimate the FDR for peptide identification. The decoy database consisted of reversed protein sequences from the same custom protein database as mentioned earlier. Different modification states of the same peptide sequences were considered distinct by the software. Peptides with confidence scores ≥ 95% were considered identified, and proteins with at least two unique identified peptides were quantified. Proteins were identified as upregulated when they had an iTRAQ ratio above 1. 5 (p-value < 0. 05) and downregulated when they had the ratio below 0. 67 (p-value < 0. 05). Proteins with a ratio from 0. 67 to 1. 5 were considered not regulated. Functional annotations were assigned using Blast2go software using the Aedes database from VectorBase (AaegL3.
7916)] with some sites allowed to be evolutionarily invariable ([+ I], 1. 98% sites). There were a total of 1035 positions in the final dataset. Evolutionary analyses were conducted in MEGA X  43, 44. Salivary gland gene silencing using double stranded RNA. Aedes aegypti salivary gland cDNA was used to amplify dsRNA targets with T7-tagged primers (Table  S1 ). The PCR products were in vitro transcribed using T7 Scribe kit (Cellscript, USA). dsRNA was annealed by heating to 95 °C and slow cooling in a thermocycler. Three to five-day-old adult female mosquitoes were cold-anesthetized and intra-thoracically injected with 2 µg of dsRNA using Nanoject II (Drummond Scientific Company, USA). The same quantity of dsRNA against the bacterial gene LacZ was injected as a control (dsCtrl). Four days post dsRNA injection, gene depletion was validated in SG by RT-qPCR.
Results High-resolution proteomics of salivary glands. A total of 218 proteins with at least two unique peptides (95% confidence per peptide) were identified in uninfected A. aegypti SG (Table  S3 ). To identify proteins, we used a custom protein database which included annotated Aedes proteins from VectorBase and UniProt, as well as from a de novo assembled transcriptome (unpublished) generated from our previous work. This A. aegypti SG transcriptome is specific for the same time point and mosquito colony  15  , unique to our study conditions for the A. aegypti Singapore strain. We identified five newly annotated proteins which included one putative-C-type lectin, one 18. 6 kDa secreted protein, one uncharacterized (no homolog in Drosophila melanogaster) protein, one aggrecan core-like protein and one 34 kDa salivary protein.
Seven proteins were commonly regulated by all infections (Fig. 2a ,b; Table  S4 ). Among them, the immunityrelated protein GILT-like (AAEL004873), the BF-related protein ADA (AAEL026165), two proteins without conserved functional domains named SGS1 (AAEL09993) and SGBAP (AAegL5. 3 AAEL019996/ NCBI GenBank Accession No. EAT45119. 1 48 , ABF18177. 1 49 ) were upregulated by all three virus infections (Fig. 2a, b ; Table  1 ). The BF-related protein D7 (AAEL006424) and the immunity-related protein CTL25 (AAEL000556) were upregulated by ZIKV infection (+ 1. 7 times and + 1. 5 times, respectively) and downregulated by DENV2 (-3. 3 times and -2. 5 times, respectively) and CHIKV (-2. 5 times for both proteins) infections. Finally, the immunity-related protein SRPN23 (AAEL002704) was increased 2. 5 times by CHIKV infection and decreased 2.
They are involved in perception of odor and chemosensory signals, which regulate host-seeking behavior  71. OBP22 was previously found to be a ligand for fatty acids  72  and required for efficient biting  16. Our data suggests that SG infection can modulate transmission by altering saliva composition. Moreover, we reported regulation of proteins related to digestion, metabolism and redox that are discussed in supplemental (S1 Text). A large proportion of SG proteins that were regulated by the infections remain uncharacterized. In this study, we determined the impact of four uncharacterized infection-responsive proteins in SG infection, i. e. , ADA, SGS1, GILT-like and SGBAP (Table  1 ). Tissue expression analysis based on available A. aegypti transcriptome literature  50, [73] [74] [75] [76]  showed that SGBAP and SGS1 are specifically expressed in SG (Fig.
We revealed the antiviral effect of SGBAP against two flaviviruses and one alphavirus in SG of A. aegypti. SGBAP does not contain conserved functional domains as determined from searches of NCBI conserved domain, InterPro-EMBL-EBI and PROSITE-Expasy databases, and has no homolog in D. melanogaster, making it difficult to speculate on its structure and mechanism of action. SGBAP is a small protein of 130 amino acid residues (mature form) (we suggest a re-annotation of the gene in S1 text and Fig. S5 ) and is secreted in A. aegypti saliva  27. An earlier transcriptomic study suggested it originated from a truncation of a gene from the 34 kDa protein family  49. To identify SGBAP-related genes in other arbovirus mosquito vectors, we built a phylogenetic tree based on cDNA sequences (Fig. S6 ).
Acknowledgements We thank  Professor Eng Eong Ooi  from  Duke-NUS Medical School , and  Professor Lisa Ng  from  Singapore Immunology Network (SIgN, A-STAR, Singapore ) for providing the DENV2, ZIKV and CHIKV stocks, respectively. We thank  Teck Kwang Lim  from  Protein and Proteomics Centre in NUS  for his support in the mosquito SG mass spectrometric analysis. We thank all staff in the core insectary facility at Duke-NUS Medical School where the mosquito experiments were conducted. Finally, we thank the  Ministry of Education, Singapore  ( MOE 2015-T3-1-003 ) and the  French Agence National pour la Recherche  ( ANR-20-CE15-006 ) for funding this work.
Results Biological characterization of the A3250 human IBC cell line. The A3250 breast carcinoma cell line was established from a primary tumor of a patient with triple negative IBC, as previously described for other A series lines  15. Short Tandem Repeat (STR) analysis confirmed that this line was unique based on its comparison to all human cell lines in ATCC STR and DSMZ STR databases, with less than 55% similarity to any of the existing STR profiles (Supplementary Table  1 ). In tissue culture, confluent A3250 cells exhibited epithelial morphology (Fig. 1a ). At lower cell densities, we observed two distinct phenotypes: cell colonies consisting of epithelial-like polygonal cells in close contact, and colonies comprised of scattered cells with more elongated appearance (Fig. 1b ). A3250 cells exhibited higher colonyforming ability than MDA-MB-231 cells, a commonly studied triple negative breast cancer (TNBC) line and retained the ability to form colonies even at low serum concentration (1% FBS) and at very low seeding densities (Supplementary Fig.
Among pathways inactivated in A3250 compared to MDA-MB-231, several were related to neuronal signaling, such as semaphorin, netrin and CREB signaling in neurons (Supplementary Data 2). In comparison to IBC3, A3250 showed activation of pathways related to fibrosis, EMT and cytoskeleton among others (Fig. 3c , Supplementary Data 3). Activation of PI3/AKT signaling and tumor microenvironment, and inactivation of semaphorin signaling pathway were observed in A3250 in comparison to both, MDA-MB-231 and IBC3. A3250 showed only five pathways enriched in comparison to SUM149, of which LXR/RXR canonical pathway, related to lipid metabolism and inflammation was most significantly upregulated (Fig. 3c , Supplementary Data 4). We next performed Gene Set Enrichment Analysis (GSEA) by using the hallmark collection of molecular signature databases (MSigDB), which capture and refine information from multiple gene sets and show high degrees of association with the corresponding protein profiles  17.
GSEA showed that from 50 hallmark datasets, 34 gene sets were significantly enriched in A3250 compared to MDA-MB-231, 31 gene sets compared to IBC3, and 22 gene sets compared to SUM149. Among the pathways implicated in inflammation, hallmark "TNFA signaling via NFKB" was significantly enriched in A3250 compared to MBA-MB-231, IBC3 and SUM149 (Fig. 3d ). Hallmark "IL6 JAK STAT3 signaling" was significantly enriched in A3250 compared to MDA-MB-231 and IBC3 (Fig. 3d ) and only marginally compared to SUM149 (p = 0. 098, FDR = 0. 12), indicating that JAK/STAT3 pathway is activated in A3250 and in agreement with previous studies implicating the JAK/STAT3 pathway in IBC  5. Consistent with our evidence indicating activation of multiple inflammatory pathways in A3250 by IPA and GSEA, analysis of activated upstream regulators showed a striking association with inflammation in comparison to MDA-MB-231 (Fig.
To investigate how the chemokine expression profile of A3250 cells related to that of human breast cancers, we analyzed expression levels of the top three most highly expressed chemokines, CCL2, CXCL5, and CCL20, in the TCGA-BRCA database of human invasive breast cancer samples of which none were annotated as IBC (n = 1222). All three chemokines were expressed at much higher levels in A3250 IBC compared to their median expression levels in TCGA breast cancer samples (Fig. 7e ). To interrogate expression of CCL2 in IBC patient samples, we analyzed a Van Laere microarray data set consisting of 41 human IBCs and 55 non-IBCs  24, 25  , and an Iwamoto data set consisting of 25 IBCs and 57 non-IBCs  26. These data sets were chosen because they had most IBC samples and because mRNA-Seq data for human IBCs have not yet been reported.
CCL2 median expression levels were ranked significantly higher in IBCs than in non-IBCs in both datasets (Fig. 7f, g ). Taken together, these data indicate that human IBCs, like A3250, commonly exhibit high CCL2 expression levels. Chemokine receptor CCR2 is highly expressed by myeloid cells in the A3250 IBC tumor model. CCL2 exerts its effects through CCR2, which is the main chemokine receptor mediating monocyte and macrophage recruitment to sites of inflammation  27, 28. We investigated the expression of CCR2 in A3250 tumors, lungs and on leukocytes in the blood. In the blood, CCR2 was expressed by ~30% of total CD11b + cells, and mainly by Ly6C hi inflammatory monocytes (76% CCR2 + of Ly6C hi on average), suggesting increased mobilization of these cells from the bone marrow. A small fraction of Ly6C lo monocytes (7% CCR2 + of Ly6C lo on average) also expressed CCR2, whereas CD11b + cells that were Ly6C -were also negative for CCR2 (Fig.
8c ; Supplementary Table  6 ). Downregulation of CCL2 in A3250 cells did not result in an altered expression of other human CCR2 ligands, which remained undetectable (Supplementary Table  6 ). CCL2 KD led to a striking inhibition of tumor growth, as determined by bioluminescent imaging (Fig. 8d ). A3250 CCL2 KD tumors also exhibited a striking reduction in clinical signs of skin inflammation characteristic of IBC (Supplementary Fig. 6a-c ). Erythema in particular was drastically diminished upon CCL2 KD, and this effect was independent of tumor size. By comparison, SUM149 IBC tumors did not show pronounced erythema (Supplementary Fig. 6a, b ). Furthermore, CCL2 KD   24  (f) and GSE22597  26  (g), measured as the percentile rank of signal intensity for the maximally expressed probe for CCL2.
Methods Cells and culture conditions. The A3250 breast carcinoma line was isolated from a primary tumor of a 50-year-old anonymized patient with IBC, provided to the Aaronson lab from a tissue biorepository directed by Dr. Dirk Iglehart, Duke University. We have with all relevant ethical regulations. Duke IRB Pro00012025 "Breast Cancer Blood/Tissue Repository and Database" is a continuation of Dr. Iglehart's IRB protocol permitting them to provide anonymized human tumor tissues as byproducts of standard clinical procedures to other investigators for cancer research. The Mount Sinai IRB considers research performed with anonymized human samples as non-human subjects research. The A3250 IBC cell line was established in culture by the Aaronson lab as performed for other A series tumor lines  15, 45, 46.
1 scramble or pLKO. 1 vector with CCL2 targeting sequence GATGTGAAACATTATGCCTTA from Sigma MISSION shRNA (Drug Discovery Institute, Mount Sinai). Cells were maintained in the presence of puromycin and neomycin. All transductions were performed in presence of 8 µg/ml polybrene. Cell line authentication. Short tandem repeat (STR) data were generated on the SeqStudio Genetic Analyzer, at the Dept. of Oncological Sciences Sequencing core. We confirmed that the STR signatures of A3250 cells did not match STRs of known cell lines in the ATCC or DSMZ STR database. SUM149, MDA-MB-157, MDA-MB-231, HCC3153, SKBR3, and MCF7 cell lines were also authenticated using STR analysis. Cells were prepared per the manufacturer's recommendations (FTA Sample Collection Kit, cat. no. 135-XV) and processed as described  48.
Subsequent to in vivo imaging, mice were sacrificed and organs were quickly harvested. D-luciferin was added again to excised organs and metastases in organs were imaged ex vivo. Organs from a mouse injected with the parental tumor cell line not labeled with Fluc were imaged ex vivo and used to determine background values for each tissue. Bioluminescent imaging was performed on the IVIS® Spectrum (PerkinElmer) and analyzed using LivingImage software, version 4. 5 (PerkinElmer). Visualization of metastases by GFP in whole organs and in tissues sections. To evaluate macro-metastases, formalin-fixed whole organs from mice bearing eGFP-Fluc expressing tumors were imaged with the Leica MZ16 FA stereomicroscope and DFC300 FX camera before embedding in OCT. Lymph node and lung metastases were evaluated at single-cell resolution in frozen sections (6 μm and 10 μm, respectively) of formalin-fixed tissues.
Nuclei were counterstained with DAPI (10 μg/ml). Slides were coverslipped using fluorescence mounting medium (Dako cat. no. S3023). Images were captured using the Nikon Eclipse E600 microscope and DS-Qi1 monochrome camera. Whole-slide scans were acquired with the NanoZoomer S60 scanner C13210-01 (Hamamatsu). Angiogenesis was quantified by measuring the area of CD34+/LYVE-1-vessels in tumor sections using NIS Elements 3. 22 (Nikon). Three 20X images per tumor were quantified (total area of 2. 5 × 10 6 μm 2 per tumor, n = 3 tumors per experimental condition) and data expressed as vessel area/total tumor area analyzed (% vessel area). TANs and TAMs were quantified by measuring Ly6G+ and F4/80+ cell area, respectively, in tumor sections using NIS Elements 3. 22 (Nikon). One entire scanned section of each tumor was quantified (total area ranged from 1-3.
Data were expressed as number of EdU+ cells/mm 2 GFP+ tumor area. To calculate tumor-stroma ratio, GFP images were overlaid onto the corresponding DAPI images to determine tumor boundaries and exclude necrotic areas. GFP+ viable tumor area was quantified as above and data expressed as GFP+ tumor area/ total tumor area analyzed (% GFP + area). Macrophage proliferation in tissues. EdU was visualized in formalin-fixed OCTembedded frozen tumor sections using the Click-iT™ EdU Imaging Kit with Alexa Fluor 488 green label (Invitrogen cat. no. A-11070, 1:300). Following EdU staining, sections were incubated in 0. 5% TritonX/PBS at room temperature for 20 min and immunostained with F4/80 antibody (Bio-Rad MCA497; 1:100) as described above. Optical clearing and 3D reconstruction of tumors. A3250-eGFP tumors were fixed in formalin for 1 hr, embedded in OCT and frozen.
Samples were stained with antibodies on ice for 30 minutes in FBS Stain Buffer (BD Pharmigen 554656), and run on the BD LSRFortessa X-20 cell analyzer. The antibodies used were all from Biolegend: anti-mouse CD45-BV785 (Biolegend 103149), CD11b-APC (Biolegend 101212), Ly6G-PE (Biolegend 127608), Ly6C-BV421 (128032), F4/80-FITC (123108), and CCR2-BV605 (Biolegend 150615) (all at 1 μg/ml). Dead cells were excluded from the analysis based on DAPI signal (50 ng/ml). Analysis was performed using BD FACSDiva™ software (v. 8. 0. 1) (BD Biosciences). Contour plot and histogram images were generated using FCS Express version 6. 0 (De Novo Software). Absolute cell numbers were calculated using AccuCheck Counting Beads (Invitrogen PCB100). Cells were identified on FSC-A/SSC-A plot and doublets were excluded using FSC-A/FSC-H plot.
Western blot source data is provided as a Source Data file. Olink multiplex immunoassay. Supernatants of A3250, SUM149 and MDA-MB-231 cultures were collected at 24 h and 48 h after seeding 1 × 10 6 cells per 10 cm plate in 8 ml of standard media for each cell line. The conditioned media was harvested and analyzed for chemokines within the Olink inflammation panel (Olink Bioscience, Sweden) by the Human Immune Monitoring Center, Mount Sinai. The assay uses proximity extension assay (PEA) technology wherein oligonucleotide labeled antibody pairs allow pair wise binding to target proteins. The antibody bound target antigens which correspond to oligonucleotides from an amplicon are then quantified by high throughput qPCR and provided as relative quantification of protein concentration denoted as Olink proteomics arbitrary unit NPX (normalized protein expression) on a log2 scale.
Acknowledgements This research was supported by grants to S. A. A. ( P01CA80058 ,  R01CA170702  and  UO1 OHO11328 ) and from the  Breast Cancer Research Foundation , and to M. S. from the  Susan G. Komen  and  Milburn Foundations  ( IBC17512321 ),  Eli Lilly , and the  Kathryn Crosby Cancer Research Initiative. The authors also wish to acknowledge support from the  Tisch Cancer Institute Shared Resource Facilities (NCI Cancer Center Support  Grant  P30 CA196521-01 ) including:  Jordy Ochando , Flow Cytometry Core;  Jose Javier Bravo-Cordero , Microscopy Core;  Jiayi Ji ,  Biostatistics Facility ;  Sungyee Kim-Schultz , Human Immune Monitoring Center; and  Saboor Hekmaty, Dept. of Oncological Sciences NextSeq Sequencing Facility. We thank  Drs. Bronek Pytowski  and  Dolores Hambardzumyan  for critical reading of the manuscript.
Data availability The mRNA-Seq datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE158974 and GSE180788. Publicly available mRNA-Seq data used in this study are available in the Gene Expression Omnibus database under accession code GSE27003 for MDA-MB-468, BT20, HCC3153 and BT474 cells and GSM3145605 for IBC3. Microarray datasets for the human IBC transcriptomic analysis conducted in this study are available in the EBI ArrayExpress database under accession code E-MTAB-1006 as well as the GEO database under accession code GSE22597. Remaining data are available within the Article, Supplementary Information, or the Source Data provided with this paper.
Motivation: The Cox proportional hazard models are widely used in the study of cancer survival. However, these models often meet challenges such as the large number of features and small sample sizes of cancer data sets. While this issue can be partially solved by applying regularization techniques such as lasso, the models still suffer from unsatisfactory predictive power and low stability. Methods: Here, we investigated two methods to improve survival models. Firstly, we leveraged the biological knowledge that groups of genes act together in pathways and regularized both at the group and gene level using latent group lasso penalty term. Secondly, we designed and applied a multi-task learning penalty that allowed us leveraging the relationship between survival models for different cancers.
Latent group lasso allows for genes that fall into multiple groups to have independent coefficients, while not biasing the model towards their inclusion  (Obozinski et al. (2011) ). Since their introduction for cancer, group lasso approaches have been used a number of times in survival analysis  (Kim et al. (2012) ;  Wang et al. (2018) ). For instance, group lasso was used to integrate multi-omics data at the gene level  (Xie et al. (2019) ). However, to the best of our knowledge, the application of pathway level latent group lasso to gene expression data for cancer survival has not been investigated for large cohorts of patients such as the Tumor Genome Atlas (TCGA). Of note, in addition to group lasso, there exist other pathway based approaches; they however failed to demonstrate major improvements compared with standard lasso.
Zheng et al. , using Gene Set Variation Analysis (GSVA) to reduce gene expression to pathway expression, showed no significant improvement over standard lasso  (Zheng et al. (2020) ). Our own preliminary work using pathway based dimension reduction via PCA and autoencoders also resulted in worse results compared with standard lasso and the latent group lasso method (results not shown). One further challenge associated with cancer survival modelling is that while across all cancers the number of samples is quite large (over 10,000 in the TCGA data set), the number of samples for any single cancer type can be as low as 36. Unfortunately, the naïve solution to this, merely training multiple cancers all together, does not perform well for a few reasons. Firstly, while there are many similarities across cancers, there are also many differences and thus building a single model to describe survival across all cancers is not feasible.
Data In this study, we used clinical and gene expression data generated by the TCGA Research Network: https://www. cancer. gov/tcga  (Tomczak et al. (2015) ). For this work, we selected 30 cancer types. From these, the 16 cancers with over 300 samples were used for the comparison of latent group lasso with naïve lasso and all 30 were used in the multi-tasking study. For each cancer, RNA-Seq data, time since inclusion in study, and survival status were used. The TCGA RNA-Seq data set was generated following the Firehose pipeline: MapSplice followed by RSEM  (Li and Dewey (2011) ), then normalized using upper quartile fragments per kilobase per million reads (FPKM-UQ). The following cancer types were selected: Adrenocortical Carcinoma (ACC), Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC), Cholangiocarcinoma (CHOL), Colorectal Adenocarcinoma (COADREAD), Diffuse Large B-Cell Lymphoma (DLBC), Esophageal Carcinoma (ESCA), Glioblastoma Multiforme (GBM), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Chromophobe (KICH), Kidney Renal Clear Cell Carcinoma (KIRC), Kidney Renal Papillary Cell Carcinoma (KIRP), Acute Myeloid Leukemia (LAML), Brain Lower Grade Glioma (LGG), Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Mesothelioma (MESO), Ovarian Serous Cystadenocarcinoma (OV), Pancreatic Adenocarcinoma (PAAD), Prostate Adenocarcinoma (PRAD), Sarcoma (SARC), Skin Cutaneous Melanoma (SKCM), Stomach Adenocarcinoma (STAD), Thyroid Carcinoma (THCA), Thymoma (THYM), Uterine Corpus Endometrial Carcinoma (UCEC), Uterine Carcinosarcoma (UCS), and Uveal Melanoma (UVM).
To group genes into pathways, we combined several databases of genes activated or repressed as a result of an activation of signaling pathway (pathway downstream genes): SPEED, PROGENy, Duke University and Curie Institutecurated data sets  (Martignetti et al. (2016) ;  Gatza et al. (2010) ;  Parikh et al. (2010) ;  Rydenfelt et al. (2020) ;  Schubert et al. (2018) ). Merging these databases resulted in a total of 69 unique sets of pathway downstream genes, which were further used in our study. Of note, we made a choice to use in this study only genes representing downstream targets of signaling pathways instead of other available gene sets representing pathway players, e. g. , Reactome or KEGG  (Fabregat et al. (2018) ;  Kanehisa and Goto (2000) ), or biological processes from Gene Ontology  (Ashburner et al.