dataesr/scientific-paragraphs-categorization huggingface dataset:
Applying CanSig to real-world datasets To assess the performance of CanSig on real-world scRNA sequencing data and to verify that CanSig can successfully rediscover known signatures, we ran the pipeline and performed a stability analysis on a set of three cancer types with known signatures. We filtered cells presenting less than 1,500 total counts (potentially empty or broken cells) and more than 50,000 counts (to exclude doublets). We also filtered cells whose transcripts mapped to less than 700 genes. The mitochondrial content threshold was dependent on the dataset: for breast and ESCC the cells with a mitochondrial content over 20% had been prefiltered. For CRC, the threshold was set at 30%. We then applied scVI to all three datasets, using between 4 and 10 dimensions in the latent space.
Additionally to the batch and count correction, we used the percentage of mitochondrial counts as a confounding factor. We ran the integration module both by conditioning on the cell cycle (referred to as "all cells") or by removing the cells in G2 or S phase according to the function score_genes_cell_cycle by scanpy, and we performed Leiden clustering using between 6 and 15 clusters, followed by differential gene expression analysis with a t-test or with a Wilcoxon rank-sum test and computed GSEA on the set of cancer hallmarks which we retrieved from MSigDB (http://www. gsea-msigdb. org/gsea/msigdb/genesets. jsp?collection=H).
Use case: Finding new signatures in Esophageal Squamous Cell Carcinoma using WikiPathways To show how one can use CanSig to uncover novel associations with known signatures, we applied CanSig to the ESCC dataset 7 using WikiPathways  37  as an exploratory database in the GSEA step. We investigated the significantly enriched pathways with Leiden clustering using clusters to partition the latent space obtained with scVI with latent space dimensionality  = 10. = 6.
Use case: Investigating Copy Number Variations associated with the basal and luminal subtypes in breast cancer To show how one can use CanSig to understand the underlying biology of a signature, we investigated the link between the CNV profile and the known histopathological signatures in the breast cancer dataset  36. We applied differential CNV analysis with Leiden clustering using clusters to partition the latent space obtained with scVI with latent space dimensionality  = 10. = 6 Use case: Discovering a de novo signature predictive of survival in breast cancer To show how one can use CanSig to find de novo signatures, we applied CanSig to the breast cancer dataset  36  and investigated the signatures associated with the different clusters using Leiden with clusters to partition the latent space obtained with scVI with latent space  = 10 dimensionality.
A signature was defined as the genes with the 100 highest z-scores  = 6 associated with a cluster. To link the signatures with survival, we used as validation cohort The Cancer Genome Atlas (TCGA) breast cancer dataset (BRCA), using Fragments per Million Kilobases Upper Quartile (FPKM-UQ) gene expression and clinical information retrieved from the GDC (https://portal. gdc. cancer. gov/), as well as Consensus Purity Estimates  38  for purity values for each tumor, a metric that aggregates results from several purity estimation methods. The samples of TCGA were scored according to the signature by using the mean of the standardized FPKM-UQ values of the genes in the signature. We then computed a Cox regression using the min-max normalization of the signature score, the standardized age, the CPE purity value and the stage as covariates.
Results CanSig allows for cancer-specific de novo discovery of transcriptional signatures CanSig is a method that allows for de novo discovery of shared transcriptional signatures in single-cell RNA sequencing (scRNA-seq) data. This method takes patient-level raw counts as input and outputs significantly enriched pathways associated with a subset of cells. CanSig consists of three main modules: pre-processing, integration, and postprocessing (Fig. 1 ). The first module, pre-processing, takes as input patient-level raw counts with which we first perform standard quality control (QC)  24. We then infer the copy number variation (CNV) profile of the cells and use it jointly with provided cell type annotation to partition the data into malignant, non-malignant and cells for which we are uncertain of the origin (termed undetermined).
The malignant cells are used for the rest of the analysis; these cells can be scored according to user-provided known signatures. The second module, integration, finds a meaningful latent space representation of the data that retains biological signals while removing sources of unwanted variation such as batch or cell cycle for instance. The third module, postprocessing, partitions the latent space using a user-defined clustering algorithm, performs differential gene expression analysis and subsequent gene set enrichment analysis to uncover pathways significantly enriched in parts of the latent space. Some optional steps can be used to investigate the latent space, such as differential CNV analysis or performing a stability analysis to evaluate the consistency of the uncovered pathways. CanSig is implemented as a documented Python package at https://github.
com/BoevaLab/CanSig. program expression (programs 0, 1, 2). (C) Heatmap representing 100 runs of CanSig on the simulated dataset A with different hyperparameter configurations, using scVI in the integration module and Leiden clustering in the postprocessing. Each square corresponds to a single run. Each meta-row represents the expression of a program. Programs 0, 1, 2 are expressed in the dataset; programs random 0, 1, 2 are not expressed in the dataset and used as random controls. Each meta-column represents a different configuration for Leiden clustering (2 to 6 clusters). Each sub-row represents a different configuration for scVI (dimensionality of the latent space from 2 to 8). Each sub-column represents a different random seed to compute the scVI latent space. The squares are colored according to the maximum Normalized Enrichment Score (NES) associated with this pathway computed with GSEA across all clusters.
CanSig successfully rediscovers known pathways in simulated data To investigate if CanSig could rediscover known pathways while removing unwanted sources of variation in a controlled setting, we applied CanSig to two simulated datasets (Appendix A) with three expressed programs and eight samples. The latent spaces computed with scVI successfully removed the strong batch effect present in the data (Fig 2A ) while retaining an meaningful organization according to the three expressed programs (Fig  2B ). The three expressed programs were consistently discovered using CanSig over a range of hyperparameters while the random controls were never uncovered (Fig  2C ). Noteworthily, the only setting in which the three programs were not recovered is when the number of clusters used in CanSig was smaller than the number of programs (k=2).
CanSig rediscovers previously investigated signatures in three experimental datasets We applied CanSig to three experimental scRNA-seq datasets: esophageal squamous cell carcinoma  7  (ESCC), colorectal cancer 5 (CRC), and breast cancer  36  (Fig  3A ). After pre-processing (see Methods), between 19 and 62 individual patients were used per study, comprising around 15,000 to 41,000 malignant cells and around 14,000 to 30,000 genes. The number of samples, of malignant cells and of genes retained after pre-processing for the three analyzed datasets. (B) UMAP representation of the latent space of Esophageal Squamous Cell Carcinoma (ESCC) obtained using scVI (d=6), colored according to the sample ID, the number of counts in the cell, the percentage of counts mapping to the mitochondrial genome, and six signatures described in the original paper 7 : Epi1, Epi2, Mesenchymal (Mes), Mucosal, Oxidative Stress (Oxd) and Antigen Processing (AP).
For signatures, the color scheme is centered around the associated score median. (C) UMAP representation of the same latent space, cluster 6 colored in orange; GSEA plots showing the three most enriched pathways of the Hallmarks of Cancer discovered in cluster 6. (D) Heatmap representing 125 runs of CanSig on all three scRNA-seq datasets with different hyperparameter configurations, using scVI in the integration module and Leiden clustering in the postprocessing. Each square corresponds to a single run. Each meta-row represents the expression of a hallmark of cancer. Each meta-column represents a different configuration for Leiden clustering (6 to 15 clusters). Each sub-row represents a different configuration for scVI (dimensionality of the latent space from 4 to 10). "All cells" corresponds to a run without filtering out cells in the G2M or S phase.
Each sub-column represents a different random seed to compute the scVI latent space. The squares are colored according to the maximum Normalized Enrichment Score (NES) associated with this pathway computed with GSEA in any cluster. The latent spaces computed with scVI successively captured previously known signatures as described in the original papers describing the studied datasets (Fig 3B, Supplementary Fig. 2 3 ). For instance, we see for ESCC that the latent space captured a gradient for the Mucosal signature and the Antigen Processing (AP) signature. The Mucosal signature is associated with the innate immunity while the AP signature is associated with the adaptive immunity. Thus, the gradient of Mucosal towards AP as well as the overlap of both signatures was consistent with the transition from an innate immune response to an adaptive immune response.
78), followed by GM7 (r=0. 44), and IFN-response most γ strongly correlated with GM3 (r=0. 70), followed by GM6 (r=0. 44) as described in the original paper  36. Another example is the strong correlation between EMT and the Mesenchymal signature (r=0. 85) and between TNF-signaling and the Stress signature (r=0. 90) in ESCC as α previously described  7. CanSig uncovers the expression of orexin and corticotropin releasing hormone receptors that can inform potential treatment resistance and therapies To showcase how CanSig can be used to investigate uncovered signatures to inform treatment resistance or therapy, we applied CanSig using the WikiPathways 37 dataset to ESCC. We uncovered a number of significantly enriched pathways in at least one cluster that showed a strong Normalized Enrichment Score (NES) (Fig 4A, Supplementary Fig.
To showcase how CanSig can be used to uncover potential new links between copy number variation and known signatures, we applied differential CNV analysis (see Methods) to the breast cancer dataset. We observed significant gains in cluster 9 associated with the Basal phenotype (Fig. 5C-F , Supplementary Table  1 ). Notably, we observed gains in the 15q region. Interestingly, a number of oncogenes and tumor suppressors were present in these CNVs. Some of the strongest gains in 15q included TMED3, IMP3, PSMA4 and RCN1. In the context of breast cancer, TMED3 and RCN1 overexpression promote proliferation  48, 49  , IMP3 is associated with EMT  50  and PSMA4 correlated with poorer survival  51. The basal phenotype in breast cancer is an aggressive type associated with poor prognosis, that typically expresses genes associated with proliferation  52  and EMT  53.
Discussion We propose CanSig, a modular method that enables de novo discovery of transcriptional signatures in cancer and their subsequent analysis. CanSig contains a pre-processing module that takes patient-level raw counts as input and performs standard quality control and additional cleaning using CNVs. CanSig then integrates the data from several patients while removing sources of unwanted variation through its integration module and uses the transformed space latent representation to obtain pathways enriched in subspaces through sequential clustering, differential gene expression analysis and gene set enrichment analysis in the postprocessing module. CanSig outputs consistently enriched pathways through a stability analysis and additionally gives the opportunity to study potential driving CNVs through optional differential CNV analysis.
Finally, the linear setting of scETM makes for less expressive power, notably to correct for batch effects. CanSig shows some limitations. First, it is not necessarily adapted to datasets with very strong inter-patient heterogeneity. Indeed, if the inter-patient heterogeneity is too strong, the latent space might be driven by inter-rather than intra-patient heterogeneity, thus effectively masking the shared signal. Second, to discover rare subtypes, one needs either a large number of patients presenting said subtype or a strong identity of the subtype that will enable the latent space to capture it. Third, no single-cell specific differential gene expression methods are currently implemented. This was motivated by the ease of use of the implemented methods paired with reported good performance on single cell data  64.
Fourth, the de novo uncovered signatures are somewhat dependent on the cluster; however, as seen in the stability analysis, uncovered known pathways are consistent, which argues in favor of the stability of the biological meaning of the differentially expressed genes. Finally, CanSig uncovers signatures in a sequential manner as opposed to an integrated manner, which might be argued to yield false positives in the differential gene expression step  24. However, as we do not use the significance associated with differentially expressed genes but rather the ranking of z-scores, we argue that this should not strongly impact the enriched pathways obtained. The choice of a sequential discovery scheme was motivated by the desire to have a very modular pipeline that could subsequently integrate novel methods and give the user flexibility to adapt each step to the needs of their dataset.
For instance, the pre-processing module gives the opportunity to further classify the data into malignant and nonmalignant cells by jointly considering cell type annotation and the CNV profile. This step might not be adapted to a cancer type presenting very little CNVs. In this case, the user can choose to skip the cleaning step altogether. CanSig can be used in a number of manners. As illustrated by the discovery of a receptor-based signature potentially beneficial in stratification for patient treatment in ESCC, researchers can use CanSig to discover signatures that can then be further investigated for applicability in the clinics. As showcased by the discovery of significant gains and losses linked to the basal and luminal subtypes in breast cancer, researchers can use CanSig to investigate potential links between CNVs and discovered signatures.
Finally, as shown by the discovery of a de novo signature predictive of survival in breast cancer, researchers can use CanSig as an exploratory tool to find new signatures to then further validate experimentally. CanSig can also be harnessed for other investigations, e. g. , linking the Tumor Microenvironment (TME) composition with a specific signature. Some developments of the method include the addition of other integration models or the conditioning on CNV information as a proxy for patient identity in the integration module to allow for better patient-level integration without over-correction (in cases where the CNV profiles do not drive the program expression). To conclude, CanSig is a user-friendly tool that enables known and de novo discovery of transcriptional programs in cancer. CanSig was capable of rediscovering known pathways in two simulated datasets and three real-world datasets.
The module automatically formats the provided input so that it fits the constraints of the chosen model. The latent space can be visualized using the Uniform Manifold Approximation and Projection (UMAP). (C) The full dataset raw count matrix and the latent space coordinates computed with the integration module are provided as input. CanSig's postprocessing module first performs clustering in the latent space using an algorithm of choice. Then it performs differential gene expression analysis for each cluster versus the rest of the cells. Then the genes are ranked according to their z-score and the ranked list is used as input for the Gene Set Enrichment Analysis (GSEA) algorithm on a database of choice. For each cluster, the list of significantly associated pathways are ranked according to their Normalized Enrichment Score (NES).
(D) Heatmap representing 125 runs of CanSig on all three scRNA-seq datasets with different hyperparameter configurations, using scVI in the integration module and Leiden clustering in the postprocessing. Each square corresponds to a single run. Each meta-row represents the expression of a hallmark of cancer. Each meta-column represents a different configuration for Leiden clustering (6 to 15 clusters). Each sub-row represents a different configuration for scVI (dimensionality of the latent space from 4 to 10). "All cells" corresponds to a run without filtering out cells in the G2M or S phase. Each sub-column represents.
Eight hours after transfection, the antibiotic-free medium was replaced with a complete medium. RNA extraction and relative expression by qRT-PCR. Total RNA was extracted from cells at 75% confluence using TRIZOL (Supplementary Data 3) according to the manufacturer's guidelines. cDNA was synthesized from 1 μg of total RNA using SuperScript™ VILO™ cDNA Synthesis Kit. All reverse transcriptase reactions, including no-template controls, were run on an Applied Biosystem 7900HT thermocycler. The expression for all the genes was assessed using SYBR and all qPCR experiments were conducted at 50 °C for 2 min, 95 °C for 10 min, and then 40 cycles of 95 °C for 15 s and 60 °C for 1 min on a QuantStudio 3 Real-Time PCR System (Applied Biosystems). The specificity of the reactions was verified by melting curve analysis.
Measurements were normalized using GAPDH level as reference. The fold change in gene expression was calculated using the standard ΔΔCt method  70. All samples were analyzed in triplicate. A list of primers is available in Supplementary Data 3. Immunoblot. Total proteins were extracted by directly lysing the cells in Co-IP lysis buffer (100 mmol/L NaCl, 50 mmol/L Tris pH 7. 5, 1 mmol/L EDTA, 0. 1% Triton X-100) supplemented with 1× protease inhibitors and 1× phosphatase inhibitors. Cell lysates were then treated with 1× reducing agent, 1× loading buffer, boiled, and loaded onto neutral pH, pre-cast, discontinuous SDS-PAGE mini-gel system. After electrophoresis, proteins were transferred to nitrocellulose membranes using the Trans-Blot Turbo Transfer System (Bio-Rad). The transblotted membranes were blocked for 1 h in TBST 5% milk and then probed with appropriate primary antibodies (from 1:200 to 1:1000) overnight at 4 °C.
A list of antibodies and working concentrations are available in Supplementary Data 3. Next, the membranes were incubated for 1 h at room temperature with fluorescent secondary goat anti-mouse (IRDye 680) or anti-rabbit (IRDye 800) antibodies (both from LI-COR Biosciences). Blots were scanned using the Odyssey Infrared Imaging System (LI-COR Biosciences) and band intensity was quantified using ImageJ software. The ratio of proteins of interest/loading control in idasanutlintreated samples was normalized to their DMSO-treated control counterparts. All experiments were performed and analyzed in triplicate. Drug treatment. In all, 10 × 10 3 exponentially growing cells were plated in a 96well plate. After 24 h, cells were treated with serial dilution of RG7388-idasanutlin, RAIN-32, MI-733 (Supplementary Data 3), or dimethyl sulfoxide (DMSO).
DMSO served as the drug vehicle, and its final concentration was no more than 0. 1%. Cell viability was measured after 72 h using CellTiter-Glo Luminescent Cell Viability Assay reagent. Results were normalized to the vehicle (DMSO). For the treatment experiments of the PDOs, PDOs were plated as single cells in a 96-well plate at a density of 1 × 10 4 cells in 10 μl Matrigel droplets. Prior to treatment, cells were allowed to recover and form organoids for 2 days. At day 3, idasanutlin, RAIN-32, or MI-733 at different dilutions was added to the medium, and cell viability was assessed after 5 days using CellTiter-Glo 3D reagent (Supplementary Data 3). Luminescence was measured on Varioskan Microplate Reader (ThermoFisher Scientific). Results were normalized to DMSO control. All experiments were performed in triplicate.
Results are shown as mean ± SD. Curve fitting was performed using Prism (GraphPad) software and the nonlinear regression equation. Proliferation assay. For cell lines, cell proliferation was assayed using the xCELLigence system (RTCA, ACEA Biosciences) as previously described  71. Background impedance of the xCELLigence system was measured for 12 s using 50 μl of room temperature cell culture media in each well of E-plate 16. Cells were grown and expanded in tissue culture flasks as previously described  71. After reaching 75% confluence, cells were washed with PBS and detached from the flasks using a short treatment with trypsin/EDTA. 5000 cells were dispensed into each well of an E-plate 16. Cell growth and proliferation were monitored every 15 min up to 120 h via the incorporated sensor electrode arrays of the xCELLigence system, using the RTCA-integrated software according to the manufacturer's parameters.
In the case of transient siRNA transfection, cells were detached and plated on xCELLigence 24 h post-transfection. For all the experiments with idasanutlin (RG7388), the drug or DMSO was added to the cells 24 h post-seeding on the xCELLigence system, as indicated in the figures. All experiments were performed in triplicate. Results are shown as mean ± SD. For the PDOs, cell proliferation was assayed using the Incucyte S3 Live-Cell analysis system (Sartorius). Briefly, PDOs were plated as single cells in a 96-well plate at a density of 1 × 10 4 cells in 10 μl Matrigel droplets and allowed to recover and form organoids for 2 days. At day 3 after seeding, the 96-well plate was placed in the Incucyte incubator where a camera automatically acquired images of each well every 8 h (up to 120 h). Kinetic curves were obtained using the Incucyte analysis software and the Spheroid analysis module.
As soon as possible cells were analyzed by flow cytometry, measuring the fluorescence emission at 530 nm and >575 nm. Cell states were defined as previously described  72  as follow: live cells, defined as PI (-) and Annexin V (-) cells; late apoptotic cells, defined as PI (+) and Annexin V (+) cells; early apoptotic (pre-apoptotic cells), defined as PI (-) and Annexin V (+) cells; necrotic cells, defined as PI (+) and Annexin V (-) cells. Both late and early apoptotic cells were counted as "apoptotic. " Unstained cells and cells stained with PI or Annexin V alone were used in each individual experiment to compensate for the fluorescence emission of each fluorochrome in the other channel and define the gating strategy more precisely. Data were analyzed by FlowJo software version 10. 5. 3.
40 mice were enrolled in the study. The PDX-inoculated mice were selected and randomly categorized into vehicle, fulvestrant, RAIN-32 50 mg/kg or RAIN-32 100 mg/kg groups (eight mice per group) when the mean tumor size reached approximately 144 mm3. The treatments started when the mean tumor size reached approximately 150 mm 3 and lasted for 29 days. Randomization was performed based on the "Matched distribution" method using the StudyDirector TM software, version 3. 1. 399. 19 randomized block design. Tumor volumes were measured two times per week in two dimensions using a caliper, and the volume was expressed in mm 3 using the formula: V = 0. 5 a × b 2 where a and b are the long and short diameters of the tumor, respectively. The entire procedures of dosing as well as tumor and body weight measurement were conducted in a Laminar Flow Cabinet.