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Federica Piccioni

Bio: Federica Piccioni is an academic researcher from Broad Institute. The author has contributed to research in topics: Biology & MAPK/ERK pathway. The author has an hindex of 23, co-authored 62 publications receiving 4137 citations. Previous affiliations of Federica Piccioni include Massachusetts Institute of Technology.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
30 Nov 2017-Cell
TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.

1,943 citations

Posted ContentDOI
10 May 2017-bioRxiv
TL;DR: A new, low-cost, high throughput reduced representation expression profiling method, L1000, is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
Abstract: We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

636 citations

Journal ArticleDOI
03 Jul 2014-Cell
TL;DR: It is found that acquired resistance to Kras suppression in a Kras-driven murine lung cancer model also involved increased YAP1 signaling, which implicate transcriptional regulation of EMT by Yap1 as a significant component of oncogenic RAS signaling.

611 citations

Journal ArticleDOI
TL;DR: The recently-described CRISPRko library (Brunello) is more effective than previously published libraries at distinguishing essential and non-essential genes, providing approximately the same perturbation-level performance improvement over GeC KO libraries as GeCKO provided over RNAi.
Abstract: The creation of genome-wide libraries for CRISPR knockout (CRISPRko), interference (CRISPRi), and activation (CRISPRa) has enabled the systematic interrogation of gene function. Here, we show that our recently-described CRISPRko library (Brunello) is more effective than previously published libraries at distinguishing essential and non-essential genes, providing approximately the same perturbation-level performance improvement over GeCKO libraries as GeCKO provided over RNAi. Additionally, we present genome-wide libraries for CRISPRi (Dolcetto) and CRISPRa (Calabrese), and show in negative selection screens that Dolcetto, with fewer sgRNAs per gene, outperforms existing CRISPRi libraries and achieves comparable performance to CRISPRko in detecting essential genes. We also perform positive selection CRISPRa screens and demonstrate that Calabrese outperforms the SAM approach at identifying vemurafenib resistance genes. We further compare CRISPRa to genome-scale libraries of open reading frames (ORFs). Together, these libraries represent a suite of genome-wide tools to efficiently interrogate gene function with multiple modalities.

470 citations

Journal ArticleDOI
05 Dec 2013-Nature
TL;DR: Systematic gain-of-function resistance studies suggest that oncogenic dysregulation of a melanocyte lineage dependency can cause resistance to RAF–MEK–ERK inhibition, which may be overcome by combining signalling- and chromatin-directed therapeutics.
Abstract: Malignant melanomas harbouring point mutations (Val600Glu) in the serine/threonine-protein kinase BRAF (BRAF(V600E)) depend on RAF-MEK-ERK signalling for tumour cell growth. RAF and MEK inhibitors show remarkable clinical efficacy in BRAF(V600E) melanoma; however, resistance to these agents remains a formidable challenge. Global characterization of resistance mechanisms may inform the development of more effective therapeutic combinations. Here we carried out systematic gain-of-function resistance studies by expressing more than 15,500 genes individually in a BRAF(V600E) melanoma cell line treated with RAF, MEK, ERK or combined RAF-MEK inhibitors. These studies revealed a cyclic-AMP-dependent melanocytic signalling network not previously associated with drug resistance, including G-protein-coupled receptors, adenyl cyclase, protein kinase A and cAMP response element binding protein (CREB). Preliminary analysis of biopsies from BRAF(V600E) melanoma patients revealed that phosphorylated (active) CREB was suppressed by RAF-MEK inhibition but restored in relapsing tumours. Expression of transcription factors activated downstream of MAP kinase and cAMP pathways also conferred resistance, including c-FOS, NR4A1, NR4A2 and MITF. Combined treatment with MAPK-pathway and histone-deacetylase inhibitors suppressed MITF expression and cAMP-mediated resistance. Collectively, these data suggest that oncogenic dysregulation of a melanocyte lineage dependency can cause resistance to RAF-MEK-ERK inhibition, which may be overcome by combining signalling- and chromatin-directed therapeutics.

411 citations


Cited by
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Journal ArticleDOI
David E. Gordon, Gwendolyn M. Jang, Mehdi Bouhaddou, Jiewei Xu, Kirsten Obernier, Kris M. White1, Matthew J. O’Meara2, Veronica V. Rezelj3, Jeffrey Z. Guo, Danielle L. Swaney, Tia A. Tummino4, Ruth Hüttenhain, Robyn M. Kaake, Alicia L. Richards, Beril Tutuncuoglu, Helene Foussard, Jyoti Batra, Kelsey M. Haas, Maya Modak, Minkyu Kim, Paige Haas, Benjamin J. Polacco, Hannes Braberg, Jacqueline M. Fabius, Manon Eckhardt, Margaret Soucheray, Melanie J. Bennett, Merve Cakir, Michael McGregor, Qiongyu Li, Bjoern Meyer3, Ferdinand Roesch3, Thomas Vallet3, Alice Mac Kain3, Lisa Miorin1, Elena Moreno1, Zun Zar Chi Naing, Yuan Zhou, Shiming Peng4, Ying Shi, Ziyang Zhang, Wenqi Shen, Ilsa T Kirby, James E. Melnyk, John S. Chorba, Kevin Lou, Shizhong Dai, Inigo Barrio-Hernandez5, Danish Memon5, Claudia Hernandez-Armenta5, Jiankun Lyu4, Christopher J.P. Mathy, Tina Perica4, Kala Bharath Pilla4, Sai J. Ganesan4, Daniel J. Saltzberg4, Rakesh Ramachandran4, Xi Liu4, Sara Brin Rosenthal6, Lorenzo Calviello4, Srivats Venkataramanan4, Jose Liboy-Lugo4, Yizhu Lin4, Xi Ping Huang7, Yongfeng Liu7, Stephanie A. Wankowicz, Markus Bohn4, Maliheh Safari4, Fatima S. Ugur, Cassandra Koh3, Nastaran Sadat Savar3, Quang Dinh Tran3, Djoshkun Shengjuler3, Sabrina J. Fletcher3, Michael C. O’Neal, Yiming Cai, Jason C.J. Chang, David J. Broadhurst, Saker Klippsten, Phillip P. Sharp4, Nicole A. Wenzell4, Duygu Kuzuoğlu-Öztürk4, Hao-Yuan Wang4, Raphael Trenker4, Janet M. Young8, Devin A. Cavero4, Devin A. Cavero9, Joseph Hiatt9, Joseph Hiatt4, Theodore L. Roth, Ujjwal Rathore9, Ujjwal Rathore4, Advait Subramanian4, Julia Noack4, Mathieu Hubert3, Robert M. Stroud4, Alan D. Frankel4, Oren S. Rosenberg, Kliment A. Verba4, David A. Agard4, Melanie Ott, Michael Emerman8, Natalia Jura, Mark von Zastrow, Eric Verdin4, Eric Verdin10, Alan Ashworth4, Olivier Schwartz3, Christophe d'Enfert3, Shaeri Mukherjee4, Matthew P. Jacobson4, Harmit S. Malik8, Danica Galonić Fujimori, Trey Ideker6, Charles S. Craik, Stephen N. Floor4, James S. Fraser4, John D. Gross4, Andrej Sali, Bryan L. Roth7, Davide Ruggero, Jack Taunton4, Tanja Kortemme, Pedro Beltrao5, Marco Vignuzzi3, Adolfo García-Sastre, Kevan M. Shokat, Brian K. Shoichet4, Nevan J. Krogan 
30 Apr 2020-Nature
TL;DR: A human–SARS-CoV-2 protein interaction map highlights cellular processes that are hijacked by the virus and that can be targeted by existing drugs, including inhibitors of mRNA translation and predicted regulators of the sigma receptors.
Abstract: A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein–protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19. A human–SARS-CoV-2 protein interaction map highlights cellular processes that are hijacked by the virus and that can be targeted by existing drugs, including inhibitors of mRNA translation and predicted regulators of the sigma receptors.

3,319 citations

Journal ArticleDOI
08 Apr 2016-Science
TL;DR: The cellular ecosystem of tumors is begin to unravel and how single-cell genomics offers insights with implications for both targeted and immune therapies is unraveled.
Abstract: To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

3,061 citations

01 Jan 2011
TL;DR: The sheer volume and scope of data posed by this flood of data pose a significant challenge to the development of efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data.
Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

2,187 citations

Journal ArticleDOI
29 Jan 2015-Nature
TL;DR: Structural-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci is described and the potential of Cas9-based activators as a powerful genetic perturbation technology is demonstrated.
Abstract: Systematic interrogation of gene function requires the ability to perturb gene expression in a robust and generalizable manner. Here we describe structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci. We used these engineered Cas9 activation complexes to investigate single-guide RNA (sgRNA) targeting rules for effective transcriptional activation, to demonstrate multiplexed activation of ten genes simultaneously, and to upregulate long intergenic non-coding RNA (lincRNA) transcripts. We also synthesized a library consisting of 70,290 guides targeting all human RefSeq coding isoforms to screen for genes that, upon activation, confer resistance to a BRAF inhibitor. The top hits included genes previously shown to be able to confer resistance, and novel candidates were validated using individual sgRNA and complementary DNA overexpression. A gene expression signature based on the top screening hits correlated with markers of BRAF inhibitor resistance in cell lines and patient-derived samples. These results collectively demonstrate the potential of Cas9-based activators as a powerful genetic perturbation technology.

2,186 citations

Journal ArticleDOI
30 Nov 2017-Cell
TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.

1,943 citations