dataesr/scientific-paragraphs-categorization huggingface dataset:
Our investment data stem from a proprietary dataset collected by Center of Private Equity Research (CEPRES), which offers detailed information on individual investments (including cash flows over time), the participating funds, and the fund managers. We offer different results that support the Risk Hypothesis, while also leaving room for other hypotheses to hold simultaneously. We find that for experienced VC firms, both dimensions of diversification considered in our study (i. e. , industry and stage of development) lead to higher returns (measured by fund IRR), consistent with the notion that diversification offers a premium on average. We find no relationship between diversification and performance for less experienced VC firms. A plausible explanation is that the more experienced VC firms are more likely to have access to a larger pool of investments opportunities, which helps them achieve a better mix of risk and return.
Data We use a research dataset provided by CEPRES. A unique feature of the data is that they provide detailed information, including cash flow data at the deal and fund levels; other commonly used databases tend to provide data at either the fund level or the deal level only. Several studies have used the database, including  Cumming, Schmidt, and Walz (2010) ,  Cumming and Walz (2010 ), Franzoni, Nowak, and Phalippou (2012 ), and Krohmer, Lauterbach, and Calanog (2009). CEPRES obtains data from private equity (VC and buyout) firms that participate in a general partner network (called Private Equity Analyzer). Private equity firms that participate in this network report monthly cash flows and investment details (e. g. , industry, investment stage) for each deal and fund in which they are involved.
In exchange, they receive statistics, such as riskadjusted performance measures, for their own investments and for the aggregate private equity market. CEPRES effectively anonymizes all the information to meet the confidentiality requirements of the VC and buyout firms that provide data to CEPRES. This means that third parties are not able to identify individual portfolio companies, funds, or management firms. This eliminates the incentives for management firms to overstate the results they report to CEPRES. Lack of anonymity in other databases may result in overstating, partial reporting, or back-filling of information, amounting to positive self-reporting biases. In addition, CEPRES independently checks all reported data for accuracy and impartiality. For example, they independently check whether the reported fund-level cash flows are consistent with the reported deal-level cash flows.
Additionally, CEPRES compares the information provided by the private equity firms on the funds and portfolio companies with information available from other commercial databases such as PREQIN. The total database as of March 2011 includes information for 1,314 funds that have invested in more than 30,000 portfolio companies worldwide. It is important to note that these figures correspond to the full universe of private equity investing covered by CEPRES which includes the VC, buyout, and mezzanine segments. Out of these 1,314 funds, a total number of 442 funds have a self-designated focus on VC investments while the remaining funds focus either on buyout investments (567 funds), mezzanine investments (225 funds), or invest in all segments of the private equity universe (80 funds). For our study, we focus on the subset of the 442 VC funds.
We apply several criteria to obtain our final sample. First, we only include funds in our sample which are fully realized or which are at least seven years old. This decreases the sample from 442 funds to 309 funds. Second, we exclude funds with vintage years prior to 1980 since the coverage of the CEPRES data is relatively poor in the 1970s. Excluding one fund with vintage year 1972 gives our final sample of 308 VC funds which have invested in 10,131 portfolio companies. Since CEPRES only includes funds in the database which provide complete data for all portfolio company investments of the fund, we did not have to exclude funds from our sample because of missing information. In terms of data representativeness, a comparison of the number of sample funds with that contained in other commercial and proprietary databases commonly used in private equity research is worthwhile.
The proprietary database  Robinson and Sensoy (2016)  use contains 292 VC funds, which is slightly less than our sample size of 308 VC funds. The commercially available private equity databases most often used in recent academic research on fund performance are BURGISS and PREQIN. For these databases,  Harris et al. (2014)  report sample sizes of 555 and 830 VC funds, respectively. Compared with BURGISS or PREQIN, our sample contains a lower but still sufficient coverage of VC funds. Importantly, the strength of the CEPRES dataset is its provision of detailed information on both the fund and deal level, whereas the mentioned data sources only provide fund-level information. This feature is crucial for our subsequent analysis because it enables us to calculate precise measures of fund diversification and risk.
To assess potential bias, we can compare the IRR statistics of our sample with those of other databases used in the literature. PREQIN reports an annual average IRR of 24. 9% for VC funds with the same vintage years as in our sample. Harris et al. (2014)  report an average annual performance for VC funds of 12. 8% for funds with vintage years in the 1980s, of 35. 2% for funds with vintage years in the 1990s, and of 1. 0% for funds with vintage years in the 2000s. Given the distribution of our sample funds by vintage years, this yields an average comparable annual IRR of their data of 22. 5%. These figures are substantially lower than the average annual IRR of 35. 5% of our sample VC funds. However, it is important to note that CEPRES reports performance before all fund-level fees while the PREQIN database and the data  Harris et al.
In contrast with most other studies, we measured performance by IRRs instead of indirect measures, such as initial public offering ratios. Similarly, our data allowed us to obtain good measures of fund risk. Our different results provide support for this risk channel, which highlights strategic considerations of VC fund managers in their investment decisions. These findings provide several practical implications for entrepreneurs and VC fund managers. First is the importance of good fit with the expertise of the fund manager, as deviations from accumulated expertise lead to lower performance. While this implication is intuitive at first glance, it is not immediately obvious in light of the documented premium for diversified funds. A more consistent view is that diversified VC funds are run by more knowledgeable managers, who are less likely to deviate because, with their accumulated expertise, they are better able to match investments in different areas.
One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. The so-called "drug repositioning process" propose to find new therapeutic indications to already approved drugs. For this, new analytic methods are required to optimize the information present in large-scale pharmacogenomics datasets. We analyzed data from the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia studies. We focused on common cell lines (n = 471), considering the molecular information, and the drug sensitivity for common drugs screened (n = 15). We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our robust molecular classification displays greater homogeneity of drug sensitivity than cancer cell line grouped based on tissue of origin.
We then identified significant associations between cell line cluster and drug response robustly found between both datasets. We further demonstrate the relevance of our method using two additional external datasets and distinct sensitivity metrics. Some associations were still found robust, despite cell lines and drug responses' variations. This study defines a robust molecular classification of cancer cell lines that could be used to find new therapeutic indications to known compounds. One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. Less than 5% of the drugs entering phase I trials eventually obtain marketing authorization 1. Clinical trials are the only real way to assess drug efficacy and toxicity, but this approach is inadequate for testing the hundreds of drugs currently being developed 2.
Scientists need to test hundreds of drugs on numerous tumor models therefore frequently make use of tumor-derived cell lines  [3] [4] [5]. Such studies aim to identify genomic biomarkers for predicting the responses of individual patients to the drug and, ultimately, for identifying the best drug for each patient. In 2012, the first large-scale pharmacogenomics studies provided an unprecedented wealth to the scientific community. The Broad Institute-Cancer Cell Line Encyclopedia (CCLE) provided a collection of 1,036 human cancer cell lines from 36 tumor types, tested for 24 anticancer drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) assessed the sensitivity of 727 cell lines, from 29 tissue types, to 138 drugs. Both datasets contain genome-wide gene expression and sequencing data for a subset of genes.
Results A biologically driven approach identifies four robust gene modules. Gene expression profiles were recovered for 471 cell lines, from 24 different tissues, tested in both CCLE and GDSC. Data were curated and annotated with the pipeline of Haibe-Kains et al. 12. We developed a three-step biological network-driven process based on transcriptomic data for identifying robust clusters of genes. This process was applied in parallel for each dataset. We first selected the most variant genes from the set of 12,153 genes common to GDSC and CCLE, by the inflexion point method. We then performed hierarchical consensus clustering  13  to identify robust gene modules. Finally, we used String© database software  14  to analyze our gene selection. The goal was to decrease the heterogeneity of each gene cluster.
We retained the genes from our initial selection that had (1) high String© database gene connection indices (greater than 0. 7), and (2) similar patterns of expression to other genes within the same biological network (correlation coefficient of at least 0. 5) (Fig. 1 step A ). This selection process identified four stable clusters in GDSC (n = 183 genes) and five in CCLE (n = 210 genes), including a subset of 170 genes common to the two datasets. Distinct functional gene ontologies were associated to each gene modules based on a gene ontology analysis: (Supplementary Fig. 1 ) Gene Cluster -Extracellular Matrix (GC-ECM; n_ccle = 48, n_GDSC = 36), Gene Cluster -Migration (GC-Migration; n_ccle = 56, n_GDSC = 75), Gene Cluster -Immunity-Interferon (GC-Immunity; n_ccle = 22, n_GDSC = 14) and Gene Cluster -Epithelial Phenotype (GC-Epithelial; n_ccle = 63, n_GDSC = 58).
A set of 21 genes enriched in development processes (GC-Development) was found exclusively in the CCLE dataset. Biologically driven gene selection identifies eleven reproducible cell line clusters. We performed a consensus clustering with the previously selected genes, for each dataset separately, to identify global differences in gene expression between cancer cell lines (Fig. 1 step B ). We obtained eleven stable clusters of cell lines in CCLE and GDSC (Fig. 2a  and Supplementary Fig. 2a ). Previous studies reported strong correlations between the expression profiles of identical cell lines  12. We therefore investigated the closeness of the cell line clusters obtained. We defined the similarity between any two cell lines as the number of datasets in which they clustered together (0 = none, 1 = CCLE or GDSC, 2 = CCLE and GDSC).
We assessed the consistency between the clustering patterns obtained with CCLE and GDSC data, using a heatmap clustering of the similarity matrix as a visualization tool. The heatmap shows the number of times that two samples are clustered together across datasets (Fig. 3a ). Groups of cell lines that frequently cluster with each other are shown in darker shades of blue. The heatmap revealed a well defined 11-block, corresponding to the 11 clusters previously identified. A high degree of consistency between the 11 clusters was observed, with 90% accuracy. As the cell line clusters were highly similar, we use the term "cluster" to denote the same group of cell lines from CCLE and GDSC, unless the dataset is specified.
About 70% of the cell lines of the GLSR cluster were derived from tumors of the central nervous system, bone, autonomic ganglia and soft tissue. Finally, the EDOT cell line cluster grouped together cell lines derived from tumors of different tissues (e. g. lung, pancreas, urinary tract) arising from the same germ layer (endoderm). The relevance of the EDOT cluster is supported by studies suggesting that oncogenesis may be initiated by the activation of a common pathway in an endodermal progenitor  16. The ADG, GI and EDOT clusters all displayed strong expression of the genes of the epithelial phenotype module and weak expression of the ECM gene module. According to GSEA, the migration gene module was less strongly expressed in GI cells. For the EDOT cluster, inconsistencies between the CCLE and GDSC datasets were observed concerning the activation or inhibition of migration gene expression at the transcriptomic level only.
The Mixed 2 cell line cluster appeared to contain mostly mesenchymal cell lines in GDSC but almost half the cell lines assigned to this cluster in CCLE were epithelial. The HAL cell lines were not concerned by this stratification. Finally, the epithelial/mesenchymal classification was consistent with that obtained with the epithelial phenotype gene module. Cell line clusters are enriched in somatic mutations. We investigated a common set of 64 genes for the presence of mutations in CCLE and GDSC datasets. However, many inconsistencies between both datasets led us to focus on a set of eight genes (TP53, KRAS, NRAS, APC, PIK3CA, BRAF, PTEN and RB1) for which at least 5% of identical cell lines display mutations in both datasets (Supplementary Information). The mutational profile of cell line clusters was then described based on these genes.
Molecular data have been systematically collected for each cell line, but far less information is available for drug screening (Supplementary Information). Moreover, in many cases (25% in CCLE and 45% in GDSC) it was not possible to extract the IC 50 from the dose-response curve. In order to overcome these issues, both study also report the AUC (area under the dose response curve) that can always be calculated. We evaluated whether our clustering was more discriminant than the tissue of origin of the cell lines, in terms of drug response. We calculated a pseudo F-statistic separately for IC 50 and AUC values for each of the 15 drugs common to CCLE and GDSC. This measurement should capture consistency between the clustering and screening data. It is calculated as the ratio of between-group variance in drug response to the corresponding within-group variance  19.
High pseudo F values indicate well-separated, compact clusters. We then compared the pseudo F values calculated with our clustering method with those obtained for 'tissue partitioning' for a given drug (i. e. each tissue being to correspond to a cluster of cell lines). Twelve of the fifteen drugs had a higher ratio in CCLE and GDSC for our clustering than for clustering based on tissue of origin with the IC50 (Fig. 4 ) and ten out of fifteen with the AUC (Supplementary Fig. 4 ). This trend was confirmed by a t-test comparing the pseudo F values for our clustering with those for 'tissue partitioning' (IC50: CCLE t. test p-value = 0. 041, GDSC t. test p-value = 0. 032, AUC: CCLE t. test p-value = 0. 011, GDSC t. test p-value = 0. 043). PLX4720 (Raf kinase B inhibitor) and PD0325901 (MEK1 and MEK2 inhibitors) were drugs with the largest pseudo F values in both dataset.
Robust identification of drug response across datasets. Subgroups of patients or cell lines defined on the basis of transcriptomic data have been shown to be associated with differences in drug sensitivity  8, 9. We sought to identify associations between clusters of cell lines and "sensitive" or "resistant" drug phenotypes, for the 15 drugs tested in both CCLE and GDSC. For each dataset and each drug separately, we investigated whether the mean IC 50 of a given cell line cluster differed significantly from those for the other cell line clusters (see Fig. 1  step C and Materials and Methods). Six molecules were found to be significantly associated with six different clusters in both CCLE and GDSC (Table  1  and Supplementary Table  3 , Supplementary Fig. 5 ). The SKCM and GI cell line clusters were both significantly more sensitive than the other cell lines to PD0325901 (MEK 1 and MEK 2 inhibitors) (Fig.
We then performed similar analysis using AUC. More than half of the associations between cell lines clusters and drug sensitivity were found still significant with AUC (Table  1  and Supplementary Information). We further evaluated the relevance of our clustering regarding the drug sensitivity using two external public datasets. We first study the 118 cell lines tested in common between the CCLE and the GlaxoSmithKline cell line collection (GSK)  21  on lapatinib and paclitaxel (GDSC was excluded due to small sample size, see Supplementary Information). We found that lapatinib was significantly inactive to kill cells from clusters SKCM and Mixed 1 in both CCLE and GSK (Table  1 ). Since the set of common cell lines and drugs was small between CCLE, GDSC and GSK (Supplementary Table  4 ), we consider the Genentech Cell Line Screening Initiative (gCSI)  22.
A panel of 244 unique cell lines and 5 drugs overlap between CCLE, GDSC and gCSI. Instead of AUC, the gCSI reported the mean viability statistic to measure drug efficacy in addition to the IC 50. Eight associations between cell lines clusters and drug sensitivity were found significant using the IC 50 and nine with the mean viability statistic. Among them, the sensitivity of ADG to erlotinib and lapatinib as well as the efficacy of PD0325901 to kill cells from SKCM cluster were common to CCLE, GDSC and gCSI (Table  1  and Supplementary Information). Our results suggest that our cell line clustering is able to find significant associations with drugs efficacy robustly in four different dataset, despite the large variations across pharmacological data and drug response measures.
Distinct drug profiles were associated with the various cell line clusters. We applied the same procedure to all the drugs tested in the CCLE (24 molecules) and GDSC (129 molecules) studies. For each dataset and each of the 153 drugs separately, we determined whether the mean IC 50 of a given cell line cluster was significantly different those of the other cell line clusters (Supplementary Tables  5  and 6 ). Overall, the most striking result was the very small number of drugs associated with a sensitive profile (88 associations, including 71 unique drugs) compared to drugs associated with a resistant profile (163 associations, including 92 unique drugs) (Supplementary Information). It was particularly interesting to observed that Mixed 2 and Mixed 3 clusters were each sensitive to only one drug: respectively midostaurin and vorinostat.
The authors put forward several hypotheses to explain these discrepancies, including differences in experimental protocols, the viability assay and procedures for summarizing dose response and non-observed IC 50 (the half maximal inhibitory concentration). Despite discrepancies between the drug sensitivity data retrieved from different databases, we were able to find some robust combinations. Well-known drug associations were found, such as the sensitivity of SKCM lines to vemurafenib. We also found that cancers with BRAF mutations, such as melanoma  31  and cancers with KRAS/BRAF mutations, such as colorectal cancer  32  , were more sensitive to MEK inhibitors. Furthermore, CDK4/6 inhibition-induced cell death has been noted in cell lines and xenografts derived from patients with T-cell leukemia  33.
These drugs are expected to be broadly active among the cell line panel since they are not specific molecules. On the contrary, targeted agents are expected to be active only in a subset of cell lines, at least, those carrying the given target. Furthermore, the recent study published by Rees et al. 42  demonstrated that target's expression and drug sensitivity were correlated in only 31% of the cases. Grouping cell lines on the basis of their transcriptomic profiles makes it possible to identify subsets of cells with common off-target features. It is then more relevant to compare the drug sensitivity between cell lines of these groups rather than examined the correlation of response of each cell line to a particular drug reported by one dataset with the response of the same cell line to the same drug reported by another dataset.
Materials and Methods Pharmacogenomics data. We collected data from the Broad and Sanger Institutes. The CCLE profiled 24 anticancer drugs on 1,036 cell lines. The GDSC screened 138 drugs on 727 cell lines. Both datasets contain genome-wide gene expression and massive parallel sequencing data. All data were recovered, curated and annotated with the pipeline developed by Haibe-Kains et al. 12  (the GDSC was referred to the Cancer Genome Project [CGP] in Haibe-Kains et al. ). We used this pipeline as described in the original article, but with a different method for the normalization of gene expression. Haibe-Kains et al. normalized gene expression data by frozen robust multiarray analysis, fRMA  43. This method was designed to combine several datasets and overcome multiple batch issues. This strategy is relevant when trying to ensure assay reproducibility.
Even though this approach would be unlikely to have a major effect on gene expression values, we chose to normalize the gene expression data separately with RMA  44  , to ensure that the two datasets were perfectly independent. Our analysis focused on 471 cell lines and 15 drugs for which we have transcriptomic and drug sensitivity data available in both the CCLE and GDSC studies. We also collected two large datasets to validate our classification. Data from the GlaxoSmithKline cell line collection were retrieved from Haibe-Kains et al. 12. The Genentech Cell Line Screening Initiative data were available from compareDrugScreens R package published by Haverty et al. 22.
(3) We identified known biological networks, for each gene cluster separately, using String© database software version 9. 1 (http://string-db. org/). We then applied a two-step selection process: (1) we selected strong biological networks by retaining only genes for which connection scores of at least 0. 7 were obtained with String© database software, (2) within each biological network, we selected groups of genes for which expression levels were correlated, with a correlation coefficient of at least 0. 5. We used the R package clusterProfiler  46  for comparing and visualizing gene ontologies profiles among gene modules. (B) We applied a consensus-clustering with hierarchical clustering to the cell line gene expression profiles, using the selected genes to visualize the optimal number of stable cell line clusters.