Genome-Wide CRISPR Screen for Essential Cell Growth Mediators in Mutant KRAS Colorectal Cancers

Yau, Edwin; Kummetha, Indrasena Reddy; Lichinchi, Gianluigi; Tang, Rachel; Zhang, Yunlin; Rana, Tariq M.

doi:10.1158/0008-5472.can-17-2043

Cited by 98 publications

(77 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, sequence-based models can also produce signed predictions of DNase I hypersensitivity 14,15,19 , histone modifications 15,19 , splicing 16,158 , and transcription initiation 159 . Additionally, allele-specific molecular assays, massively parallel assays, and CRISPR screens are increasingly yielding high-resolution experimental information about the effects of genetic variation on gene expression 29,45,[160][161][162][163] as well as cellular processes such as growth [164][165][166] and inflammation 167 . Finally, perturbational differential expression experiments can yield signed predictions for the relationships of genes to a variety of biological processes such as drug response 168 , immune stimuli 169 , and many others 170 .…”

Section: Discussionmentioning

confidence: 99%

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Reshef

Finucane

Kelley

et al. 2017

Preprint

View full text Add to dashboard Cite

Biological interpretation of GWAS data frequently involves analyzing unsigned genomic annotations comprising SNPs involved in a biological process and assessing enrichment for disease signal. However, it is often possible to generate signed annotations quantifying whether each SNP allele promotes or hinders a biological process, e.g., binding of a transcription factor (TF). Directional effects of such annotations on disease risk enable stronger statements about causal mechanisms of disease than enrichments of corresponding unsigned annotations. Here we introduce a new method, signed LD profile regression, for detecting such directional effects using GWAS summary statistics, and we apply the method using 382 signed annotations reflecting predicted TF binding. We show via theory and simulations that our method is well-powered and is well-calibrated even when TF binding sites co-localize with other enriched regulatory elements, which can confound unsigned enrichment methods. We apply our method to 12 molecular traits and recover many known relationships including positive associations between gene expression and genome-wide binding of RNA polymerase II, NF-κB, and several ETS family members, as well as between known chromatin modifiers and their respective chromatin marks. Finally, we apply our method to 46 diseases and complex traits (average N = 289, 617) and identify 77 significant associations at per-trait FDR < 5%, representing 12 independent signals. Our results include a positive association between educational attainment and genome-wide binding of BCL11A, consistent with recent work linking BCL11A hemizygosity to intellectual disability; a negative association between lupus risk and genome-wide binding of CTCF, which has been shown to suppress myeloid differentiation; and a positive association between Crohn's disease (CD) risk and genome-wide binding of IRF1, an immune regulator that lies inside a CD GWAS locus and has eQTLs that increase CD risk. Our method provides a new way to leverage functional data to draw inferences about causal mechanisms of disease.

show abstract

Section: Discussionmentioning

confidence: 99%

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Reshef

Finucane

Kelley

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…The observation that INO80D mutations are statistically significantly associated with time to breast cancer development suggests a possible tumor suppressor role. While no information is available regarding the D subunit, INO80C has been identified as a novel tumor suppressor in KRAS mutant colorectal and pancreatic tumor xenografts 60 .…”

Section: Somatic Mutations and Time To Breast Cancer Developmentmentioning

confidence: 99%

Somatic Genetic Aberrations in Benign Breast Disease and the Risk of Subsequent Breast Cancer

Zeng¹,

et al. 2019

Preprint

View full text Add to dashboard Cite

One Sentence Summary: Genetic aberrations in benign breast lesions distinguish breast cancer cases from controls and predict cancer risk.Abstract: It is largely unknown how the risk of development of breast cancer is transduced by somatic genetic alterations. To address this lacuna of knowledge and acknowledging that benign breast disease (BBD) is an established risk factor for breast cancer, we established a case-control study: The Benign Breast & Cancer Risk (BBCAR) Study. Cases are women with BBD who developed subsequent invasive breast cancer (IBC) at least 3 years after the biopsy and controls are women with BBD who did not develop IBC (median follow-up 16.6 years). We selected 135 cases and individually matched controls (1:2) to cases based on age and type of benign disease: non-proliferative or proliferation without atypia. Whole exome sequencing was performed on DNA from the benign lesions and from subsets with available germline DNA or tumor DNA. Although the number of cases and controls with copy number variation data is limited, several amplifications and deletions are exclusive to the cases. In addition to two known mutational signatures, a novel signature was identified that is significantly (p=0.007) associated with triple negative breast cancer. The somatic mutation rate in benign lesions is similar to that of invasive breast cancer and does not differ between cases and controls. Two mutated genes are significantly associated with time to the diagnosis of breast cancer, and mutations shared between the benign biopsy tissue and the breast malignancy for the ten cases for which we had matched pairs were identified. BBD tissue is a rich source of clues to breast oncogenesis.

show abstract

“…We contrast ACE estimation of essentiality with results derived from the average fold changes as used in studies such as (17,28) in Fig. 3.…”

Section: Identification Of Essentiality Within a Samplementioning

confidence: 99%

ACE: A Probabilistic Model for Characterizing Gene-Level Essentiality in CRISPR Screens

Hutton

Siepel

2019

Preprint

View full text Add to dashboard Cite

High-throughput knockout screens based on CRISPR-Cas9 are widely used to evaluate the essentiality of genes across a range of cell types. Here we introduce a probabilistic modeling framework, Analysis of CRISPR-based Essentiality (ACE), that enables new statistical tests for essentiality based on the raw sequence read counts from such screens. ACE estimates the essentiality of each gene using a flexible likelihood framework that accounts for multiple sources of variation in the CRISPR-Cas9 experimental process. In addition, the method can identify genes that differ in their degree of essentiality across samples using a likelihood ratio test. We show using simulations that ACE is competitive with the best available methods in predicting essentiality, and is especially useful for the identification of differential essentiality. Furthermore, by applying ACE to publicly available CRISPR-screen data, we are able to identify both known and previously overlooked candidates for genotype-specific essentiality, including RNA m6-A methyltransferases that exhibit enhanced essentiality in the presence of inactivating TP53 mutations. In summary, ACE provides improved quantification of essentiality specific to cancer subtypes, and a robust probabilistic framework for identifying genes responsive to therapeutic targeting.

show abstract

Genome-Wide CRISPR Screen for Essential Cell Growth Mediators in Mutant KRAS Colorectal Cancers

Cited by 98 publications

References 42 publications

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Somatic Genetic Aberrations in Benign Breast Disease and the Risk of Subsequent Breast Cancer

ACE: A Probabilistic Model for Characterizing Gene-Level Essentiality in CRISPR Screens

Contact Info

Product

Resources

About