Dana-Farber Cancer Institute (complete)

Mapping the Function of Rare Oncogenic Variants

Although some oncogenes and tumor suppressor genes are recurrently mutated at high frequency, the majority of somatic sequence alterations found in cancers occur at low frequency, and the functional consequences of the majority of these mutated alleles remain unknown. We are developing a scalable systematic approach to interrogate the function of cancer-associated gene variants.

Experimental Approaches

We have developed an approach to generate mutant forms of cDNA clones at high efficiency and are introducing these into cell models to determine their impact on gene expression and tumorigenesis.

Discovery of Resistance Mechanisms: Breast Cancer

Resistance to targeted therapy is emerging as a bottleneck to achieving durable drug responses in cancer. The goal of the CTD² Center at Dana Farber Cancer Institute is to identify mechanisms of resistance for both existing therapeutics as well as for emerging targets even prior to the identification of lead compounds. They aim to use this information to inform combinatorial treatments. In representative examples they have found that YAP1 leads to resistance after KRAS targeting and that PRKACA mediates resistance to HER2 therapy.

Kinase ORF Library Screening for resistance to HER2 targeted therapy: A systematic interrogation of mechanisms of resistance to suppression of HER2 was used to identify the major mechanisms of resistance to HER2-directed therapy. 

Experimental Approaches

Two kinome open reading frame (ORF) screens were conducted in parallel to identify genes that confer resistance to the lapatinib-like dual EGFR/HER2 inhibitor AEE788 and to suppression of HER2 with a short hairpin RNA (shRNA). Then a V5 epitope-tagged kinase ORF collection was used to identify genes that mediate resistance to these manipulations.

Discovery of Resistance Mechanisms: Colon Cancer

Resistance to targeted therapy is emerging as a bottleneck to achieving durable drug responses in cancer. The goal of the CTD² Center at Dana Farber Cancer Institute is to identify mechanisms of resistance for both existing therapeutics as well as for emerging targets even prior to the identification of lead compounds. They aim to use this information to inform combinatorial treatments. In representative examples they have found that YAP1 leads to resistance after KRAS targeting and that PRKACA mediates resistance to HER2 therapy.

Determining Functional Substitutes for KRAS in KRAS-Dependent Cancer Cell Lines: We performed a genome-scale genetic rescue screen to identify genes that support the survival of KRAS-dependent cancer cells upon suppression of KRAS.

Experimental Approaches

A genome-scale genetic rescue screen was performed to identify genes that support the survival of KRAS-dependent cancer cells upon suppression of KRAS. A cell line for screening was generated by stably introducing a doxycycline-inducible shRNA targeting the KRAS 3′ untranslated region (UTR) into the HCT116 KRAS mutant colon cancer cell line. Then 15,294 ORFs were introduced into these cells in an arrayed format in triplicate under optimized conditions. Suppression of KRAS was induced and then cell proliferation/survival was assessed.

Discovery of Novel Oncogenes: Ovarian Cancer

Widespread recurrent copy number alterations are observed across the majority of human cancers, yet the specific targets of such amplified or deleted regions remain undefined. Here, the CTD² Center at the Dana Farber Cancer Institute took a systematic approach using cDNA overexpression screening to identify and validate oncogenes residing in such amplified regions. In representative examples, these experiments have identified the adaptor proteins CRKL, GAB2, FRS2 and the TLOC and SKIL proteins as novel amplified oncogenes.

Amplicon-Based Pooled in Vivo Transformation Screen: Amplified genes were systematically tested for their ability to promote tumor formation using an in vivo multiplexed transformation assay. One candidate, GAB2, was identified as a recurrently amplified gene that potently transforms immortalized ovarian and fallopian tube secretory epithelial cells.

Experimental Approaches

Copy number data generated by TCGA was queried to identify 1,017 recurrently amplified genes resident in the 63 recurrently amplified regions in high-grade serous ovarian cancer (HGSOC). From this data an arrayed collection of 587 ORFs representing 455 amplified ovarian genes was created. 26 pools composed of ORF-expressing cell lines representing 16–24 ORFs was created and each group was implanted subcutaneously in six separate replicates in immunodeficient mice.

Reference

1. Cheung HW, et al. (2011). Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. PNAS: 108(30):12372-7. (PMID: 21746896)

Discovery of Novel Oncogenes: Across Cancer Types

Widespread recurrent copy number alterations are observed across the majority of human cancers, yet the specific targets of such amplified or deleted regions remain undefined. Here, the CTD² Center at the Dana Farber Cancer Institute took a systematic approach using cDNA overexpression screening to identify and validate oncogenes residing in such amplified regions. In representative examples, these experiments have identified the adaptor proteins CRKL, GAB2, FRS2 and the TLOC and SKIL proteins as novel amplified oncogenes.

Identifying Cancer Driver Genes in the 3q26 Region: The CTD² Center at the Dana Farber Cancer Institute functionally interrogated genes within the frequently amplified 3q26 region through gain- and loss-of-function studies. These studies identified both TLOC¹ and SKIL as driver genes at 3q26 and broadly suggest that cooperating genes may be co-amplified in other chromosomal regions.

Experimental Approaches

Both gain- and loss-of-function approaches were applied to interrogate the 20 genes resident in the minimal common amplified region of 3q26 for effects on proliferation, anchorage-independent growth, and invasion. 

Reference

¹ Cheung HW, et al. (2011). Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. PNAS: 108(30):12372-7. (PMID: 21746896)

Discovery of Novel Oncogenes: High-grade Serous Ovarian Cancer

Widespread recurrent copy number alterations are observed across the majority of human cancers, yet the specific targets of such amplified or deleted regions remain undefined. Here, the CTD² Center at the Dana Farber Cancer Institute took a systematic approach using cDNA overexpression screening to identify and validate oncogenes residing in such amplified regions. In representative examples, these experiments have identified the adaptor proteins CRKL, GAB2, FRS2 and the TLOC and SKIL proteins as novel amplified oncogenes.

Finding Both Amplified and Specifically Essential Ovarian Cancer Genes: 50 genes that are recurrently amplified in HGSOC and essential for cancer proliferation and survival in ovarian cancer cell lines were interrogated.  One candidate, FRS2, was identified as an oncogene in a subset of HGSOC that harbor FRS2 amplifications.

Experimental Approaches

By combining the output of ovarian cancer genome analysis with Project Achilles, 1,825 recurrently amplified genes in ovarian cancer were systematically interrogated to identify genes that are essential in ovarian cancer cell lines that harbor such amplifications. FRS2 was identified as an amplified and essential gene in HGSOC.

Reference

1. Cheung et al. (2011). Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. PNAS: 108(30):12372-7.(PMID: 21746896)

Identification of Therapeutic Targets: Adult and Pediatric Cancer Types

The Dana Farber Cancer Institute CTD² Center focuses on the use of high-throughput genetic and bioinformatic approaches to identify and credential oncogenes and co-dependencies in cancers. This Center aims to provide the cancer research community with information that will facilitate the prioritization of targets based on both genomic and functional evidence, inform the most appropriate genetic context for downstream mechanistic and validation studies, and enable the translation of this information into therapeutics and diagnostics.

The Dana-Farber CTD² center’s Project Achilles is a systematic effort that aims to identify cancer genetic dependencies and link them to molecular characteristics in order to prioritize targets for therapeutic development and identify the patient population that might benefit from such targets. The ongoing project aims at screening hundreds of cell lines of a variety of lineages, including cell lines derived from both solid and hematopoietic tumors. An expanding suite of computational and analytical tools is being developed to derive biomarker-dependency relationships.

Experimental Approaches

The Dana Farber Cancer Institute CTD² Center uses pooled genome-wide genetic perturbation reagents (shRNAs or Cas9/sgRNAs) to silence or knock-out individual genes and identify genes that affect cell survival. Massively parallel pooled shRNA or sgRNA screens were performed in cancer cell lines to identify genes that are required for cell proliferation and/or viability.

In the case of shRNAs, a genome-scale, lentivirally delivered shRNA library is used, interrogating ~11,000 genes by on average 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,020 shRNAs after 16 population doublings. The abundance of shRNA constructs relative to the initial DNA plasmid pool was measured by either Affymetrix custom barcode microarrays (102 cell lines, Cheung et al.) or using Next Generation Sequencing (216 cell lines, Cowley et al.). Similarly, for Cas9/sgRNA screening, a genome-scale lentivirally delivered sgRNA library is transduced into cancer cell lines expressing the Cas9 nuclease.  Cell line dependencies on ~19,000 genes were assessed by on average 6 sgRNAs per gene in this library (GeCKOv2).  The relative enrichment or depletion of each of the 123,411 sgRNAs after 21-28 days in culture was assessed to determine the proliferation effect of each. The abundance of sgRNA constructs relative to the initial DNA plasmid pool was measured by using Next Generation Sequencing (33 cell lines, Aguirre et al.). All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines.

Reference

1. Shao D, et al. (2013) ATARis: Computational quantification of gene suppression phenotypes from multisample RNAi screens. Genome Res. 23(4):665-678 (PMID: 23269662)

Identification of Therapeutic Targets Across Cancer Types: ATARiS

The Dana Farber Cancer Institute CTD² Center focuses on the use of high-throughput genetic and bioinformatic approaches to identify and credential oncogenes and co-dependencies in cancers. This Center aims to provide the cancer research community with information that will facilitate the prioritization of targets based on both genomic and functional evidence, inform the most appropriate genetic context for downstream mechanistic and validation studies, and enable the translation of this information into therapeutics and diagnostics.

The Dana-Farber CTD² center’s Project Achilles is a systematic effort that aims to identify cancer genetic dependencies and link them to molecular characteristics in order to prioritize targets for therapeutic development and identify the patient population that might benefit from such targets. The ongoing project aims at screening hundreds of cell lines of a variety of lineages, including cell lines derived from both solid and hematopoietic tumors. An expanding suite of computational and analytical tools is being developed to derive biomarker-dependency relationships.

Experimental Approaches

The Dana Farber Cancer Institute CTD² Center uses pooled genome-wide genetic perturbation reagents (shRNAs or Cas9/sgRNAs) to silence or knock-out individual genes and identify genes that affect cell survival. Massively parallel pooled shRNA or sgRNA screens were performed in cancer cell lines to identify genes that are required for cell proliferation and/or viability.

In the case of shRNAs, a genome-scale, lentivirally delivered shRNA library is used, interrogating ~11,000 genes by on average 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,020 shRNAs after 16 population doublings. The abundance of shRNA constructs relative to the initial DNA plasmid pool was measured by either Affymetrix custom barcode microarrays (102 cell lines, Cheung et al.) or using Next Generation Sequencing (216 cell lines, Cowley et al.). Similarly, for Cas9/sgRNA screening, a genome-scale lentivirally delivered sgRNA library is transduced into cancer cell lines expressing the Cas9 nuclease.  Cell line dependencies on ~19,000 genes were assessed by on average 6 sgRNAs per gene in this library (GeCKOv2).  The relative enrichment or depletion of each of the 123,411 sgRNAs after 21-28 days in culture was assessed to determine the proliferation effect of each. The abundance of sgRNA constructs relative to the initial DNA plasmid pool was measured by using Next Generation Sequencing (33 cell lines, Aguirre et al.). All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines.

Reference

1. Shao D, et al. (2013) ATARis: Computational quantification of gene suppression phenotypes from multisample RNAi screens. Genome Res. 23(4):665-678 (PMID: 23269662)

Identification of Therapeutic Targets Across Cancer Types: Project Achilles

The Dana Farber Cancer Institute CTD² Center focuses on the use of high-throughput genetic and bioinformatic approaches to identify and credential oncogenes and co-dependencies in cancers. This Center aims to provide the cancer research community with information that will facilitate the prioritization of targets based on both genomic and functional evidence, inform the most appropriate genetic context for downstream mechanistic and validation studies, and enable the translation of this information into therapeutics and diagnostics.

The Dana-Farber CTD² center’s Project Achilles is a systematic effort that aims to identify cancer genetic dependencies and link them to molecular characteristics in order to prioritize targets for therapeutic development and identify the patient population that might benefit from such targets. The ongoing project aims at screening hundreds of cell lines of a variety of lineages, including cell lines derived from both solid and hematopoietic tumors. An expanding suite of computational and analytical tools is being developed to derive biomarker-dependency relationships.

Experimental Approaches

The Dana Farber Cancer Institute CTD² Center uses pooled genome-wide genetic perturbation reagents (shRNAs or Cas9/sgRNAs) to silence or knock-out individual genes and identify genes that affect cell survival. Massively parallel pooled shRNA or sgRNA screens were performed in cancer cell lines to identify genes that are required for cell proliferation and/or viability.

In the case of shRNAs, a genome-scale, lentivirally delivered shRNA library is used, interrogating ~11,000 genes by on average 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,020 shRNAs after 16 population doublings. The abundance of shRNA constructs relative to the initial DNA plasmid pool was measured by either Affymetrix custom barcode microarrays (102 cell lines, Cheung et al.) or using Next Generation Sequencing (216 cell lines, Cowley et al.). Similarly, for Cas9/sgRNA screening, a genome-scale lentivirally delivered sgRNA library is transduced into cancer cell lines expressing the Cas9 nuclease.  Cell line dependencies on ~19,000 genes were assessed by on average 6 sgRNAs per gene in this library (GeCKOv2).  The relative enrichment or depletion of each of the 123,411 sgRNAs after 21-28 days in culture was assessed to determine the proliferation effect of each. The abundance of sgRNA constructs relative to the initial DNA plasmid pool was measured by using Next Generation Sequencing (33 cell lines, Aguirre et al.). All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines.

Reference

1. Shao D, et al. (2013) ATARis: Computational quantification of gene suppression phenotypes from multisample RNAi screens. Genome Res. 23(4):665-678 (PMID: 23269662)

Identification of Therapeutic Targets in KRAS Driven Lung Cancer

The CTD² Center at Dana Farber Cancer Institute focuses on the use of high-throughput genetic and bioinformatic approaches to identify and credential oncogenes and co-dependencies in cancers. This Center aims to provide the cancer research community with information that will facilitate the prioritization of targets based on both genomic and functional evidence, inform the most appropriate genetic context for downstream mechanistic and validation studies, and enable the translation of this information into therapeutics and diagnostics.

The goal of this project is to detect synthetic lethal partners of oncogenic KRAS, using systematic RNAi and CRISPR screening across large numbers of genomically annotated cancer cell lines. For a representative example, they found that the non-canonical IkappaB kinase TBK1 was selectively essential in cells that contain mutant KRAS.

Experimental Approaches

In the case of TBK1, essential genes in human malignant and non-transformed cell lines were identified by performing arrayed format RNA interference (RNAi) screens in 19 cell lines using a short hairpin RNA (shRNA) library targeting kinases, phosphatases and oncogenes.

Identification of Therapeutic Targets in Ovarian Cancer

The goal of this project is to systematically identify and validate therapeutic targets in ovarian cancer by integrating information from cancer genomics and cancer dependencies projects.

shRNA Screening in Ovarian Cancer with Candidate BRD4 Analysis: An shRNA-based negative selection screen was designed to search for genes necessary for survival/proliferation of an ovarian cancer cell line growing as tumor masses in immunocompromised mice.  One candidate, BRD4, was confirmed using both genetic and pharmacologic approaches.

Experimental Approaches

To detect active genes in the in vivo expansion of high-grade serous ovarian cancer (HGSOC), an in vivo shRNA screen was designed for use in human xenografts of a HGSOC cell line (OVCAR8). The screen was used to test the effects on viability of depleting a library encoding ∼8,000 shRNAs directed at all human protein kinases plus ∼300 putative oncoproteins.

Genome-wide shRNA Screens with DEMETER Inferred Gene Effects

In this study RNA interference (RNAi) screens were performed on 285 cell lines and combined with 216 lines previously screened, which were then analyzed together with DEMETER to discover genetic dependencies across the entire pool of cell lines.

Experimental Approaches

DFCI extended their previous study of 216 cell lines¹ by performing genome-wide pooled loss - of - function screening on additional 285 cancer cell lines across approximately 100k shRNAs (final files include 107,523 shRNA values in Achilles_v2.19.2 to produce 17,098 DEMETER gene solutions in Achilles_v2.20.2). Each cell line was infected with the shRNA pool by lentivirus, in quadruplicate and propagated for at least 16 population doublings or 40 days, whichever came first. To determine the viral volume needed to achieve the desired transduction rate of ~40%, each cell line was titrated with 6 volumes of virus (0-500 ul) in a 12 well plate at a concentration of 3E6 cells/well. Then cells were cultured in the presence or absence of puromycin in 6 well dishes before infection rates were determined. Cells were expanded for infection in quadruplicate with a target of 3.7E7 infected cells. Before infection, cells were filtered through a 40 um cell strainer to remove clumps, then resuspended in media containing 4 ug/ml polybrene, and the appropriate volume of 98K library lentivirus to achieve a cell concentration of 1.5E6 cells/ml. This cell suspension was seeded into 12 well plates at 2 ml/well and centrifuged for 2 hours at 930xg at 30 °C. After the spin infection, 2 ml of fresh media was added to each well. After 24 hours, the cells from each replicate infection were pooled into T225 flasks with 60ml medium containing puromycin. To provide an in-line assessment of transduction rate, 150k of infected and uninfected cells were cultured in 6 well dishes in the presence or absence of puromycin. After 96 hours, both the in-line assay wells and the screen replicates were trypsinized. The infection rate was determined by calculating the number of viable cells selected in puromycin divided by the number of viable cells without puromycin selection.

Screening was continued if the infection rates were within the range of 30–65% so that the selected cells were nearly all MOI = 1 and so that there was a sufficient number of cells to provide adequate representation of each shRNA. For each of the replicates, 6E7 cells were plated into new T225 flasks in 60ml of media with puromycin. For the remaining passages, only 3E7 cells per replicate were carried over, and the remaining cells were spun down and resuspended in PBS for genomic DNA isolation. Passaging for each cell line was continued for at least 16 population doublings or 28 days, whichever was longer. Puromycin selection was maintained until day 7. At the end of passaging, genomic DNA from the screen endpoints were used to measure the abundance of shRNAs in comparison to the initial DNA plasmid pool. Samples were sequenced using a custom sequencing primer using standard Illumina conditions. Deconvolution was performed similar to that described in Ashton et al.² and all steps are described more completely in Cowley et al.¹ with the following alterations. A total of 280 μg gDNA was used as template for PCR from each replicate. Thermal cycler PCR conditions consisted of heating samples to 95 °C for 5 min; 28 cycles of 94 °C for 30 s, 53 °C for 30 s, and 72 °C for 20 s; and 72 °C for 10 min. PCR reactions were then pooled per sample. After PCR and additional of sample barcodes, 20 replicates were multiplexed into a single Illumina sample, and run on multiple lanes to achieve a minimum of 27 reads per replicate.

References

1. Cowley GS, et al. (2014). Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci Data. 1:140035. (PMID: 25984343)
2. Ashton JM, et al. (2012). Gene sets identified with oncogene cooperativity analysis regulate in vivo growth and survival of leukemia stem cells. Cell Stem Cell. 11(3):359-72. (PMID: 22863534)
3. Tsherniak A, et al. (2017). Defining a Cancer Dependency Map. Cell. 170(3):564-576.e16. (PMID: 28753430)

Computational Correction of Copy-number Effect in CRISPR-Cas9 Screens of Cancer Cells

Genome-wide CRISPR-Cas9 screens were performed in 625 cell lines. The results were processed with the CERES algorithm to produce copy-number and guide-efficacy corrected gene-knockout effect estimates.

Experimental Approaches

Cancer cell lines were transduced with a lentiviral vector expressing the Cas9 nuclease under blasticidin selection (pXPR-311Cas9). Each Cas9-expressing cell line was subjected to a Cas9 activity assay¹ to characterize the efficacy of CRISPR/Cas9 in these cell lines. Cell lines with less than 45% measured Cas9 activity were considered ineligible for screening. Stable polyclonal Cas9+ cell lines were then infected at low multiplicity of infection (MOI < 1) with a library of 76,106 unique sgRNAs (Avana), which upon remapping was composed of 72,753 targeting 18,566 genes (~4 sgRNAs per gene) annotated in the Consensus CoDing Sequence (CCDS) database, 3,353 targeting either non-coding sequences or sequences previously annotated as coding, and 995 non-targeting control sgRNAs. Cells were split into at least two replicates and selected in puromycin and blasticidin for 7 days and then passaged without selection while maintaining a representation of 500 cells per sgRNA until 21 days after infection. Genomic DNA was purified from endpoint cell pellets, the sgRNA barcodes were PCR amplified with sufficient gDNA to maintain representation, and the PCR products were sequenced using standard Illumina machines and protocols.

Cell lines that failed the Single Nucleotide Polymorphisms (SNPs) fingerprinting described above were removed. Raw sgRNA barcode counts were deconvoluted from sequence data using PoolQ software (https://portals.broadinstitute.org/gpp/public/software/poolq) and summed across sequencing lanes. Samples were removed if they failed to reach 15 million reads. Normalized read counts for each sample were calculated according to the procedure described in Cowley et al.² Pairwise Pearson correlation coefficients between replicate samples from the same cell line were calculated to identify and remove poor quality replicates using a threshold of 0.7. All sample read counts were then divided by their representation in the starting plasmid DNA library (pDNA) to compute a Fold-Change (FC). Strictly Standardized Mean Difference (SSMD)³ statistics were computed for the replicates using FCs between non-targeting control sgRNAs and FCs from sgRNAs targeting the spliceosomal, ribosomal, or proteasomal genes in KEGG genesets (https://www.kegg.jp/kegg). Replicates with SSMDs that fail to reach -0.5 were removed. logFC data were then normalized within each cell line replicate by subtracting the median logFC value and dividing by the Median Average Deviation (MAD) before input to CERES. After QC, 18 cell lines had one replicate, 206 cell lines had two replicates, 93 cell lines had three replicates, and 24 cell lines had four replicates.

Copy Number (CN) data for all cancer cell lines were obtained from the Cancer Cell Line Encyclopedia (CCLE)⁴ data portal (https://portals.broadinstitute.org/ccle). The dataset CCLE_copynumber_2013-12-03.seg.txt was used for analysis. This set was derived from Affymetrix SNP6.0 arrays. Segmentation of normalized log2 ratios was performed using the Circular Binary Segmentation (CBS) algorithm. All copy-number data presented represent a relative copy number for each cell line where a value of two represents the average ploidy of the cell line.

Subsequently, logFC scores for each sgRNA in each cell line and segmented copy number for each cell line were supplied to CERES, together with a mapping of sgRNAs to the hg19 reference genome to infer gene knockout effect and sgRNA on-target efficacy. Additional details regarding the experimental protocols and CERES algorithm can be found in Meyers et al ⁵.  Additional details regarding data processing can be found in Dempster et al ⁶. 

References

1. Aguirre AJ, et al. (2016). Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting. Cancer Discov. 6(8):914-929. (PMID: 27260156)
2. Cowley GS, et al. (2014). Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci Data. 1:140035. (PMID: 25984343)
3. Zhang XD. (2007). A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays. Genomics. 89(4):552-561. (PMID: 17276655)
4. Barretina J, et al. (2012). The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 483(7391):603-607. (PMID: 22460905)
5. Meyers RM, et al. (2017). Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat Genet. 49(12):1779-1784. (PMID: 29083409)
6. Dempster JM, et al. (2019). Extracting Biological Insights from the Project Achilles Genome-Scale CRISPR Screens in Cancer Cell lines. BioRxiv. (720243)

SMAD4 Represses FOSL1 Expression and Pancreatic Cancer Metastatic Colonization

In order to identify drivers of metastatic colonization in pancreatic cancer, we utilized an in vivo over expression library of SMAD4 target genes.

These overexpression constructs were delivered into the HPAC cell line and injected through the tail vein. This experiment and subsequent validation and mechanistic experiments revealed FOSL1 as a critical mediator of metastatic colonization.