Next-generation characterization of the Cancer Cell Line Encyclopedia

Ghandi, Mahmoud; Huang, Franklin W.; Jané‐Valbuena, Judit; Kryukov, Gregory V.; Lo, Christopher; McDonald, Ellice; Barretina, Jordi; Gelfand, Ellen; Bielski, Craig M.; Li, Haoxin; Hu, Kevin; Andreev-Drakhlin, Alexander Y.; Kim, Jaegil; Hess, Julian M.; Haas, Brian J.; Aguet, François; Weir, Barbara A.; Rothberg, Michael; Paolella, Brenton R.; Lawrence, Michael S.; Akbani, Rehan; Lu, Yiling; Tiv, Hong L.; Gokhale, Prafulla C.; DeWeck, Antoine; Mansour, Ali Amin; Oh, Coyin; Shih, Juliann; Hadi, Kevin; Rosen, Yanay; Bistline, Jonathan; Venkatesan, Kavitha; Reddy, Anupama; Sonkin, Dmitriy; Liu, Manway; Lehár, Joseph; Korn, Joshua M.; Porter, Dale; Jones, Michael D.; Golji, Javad; Caponigro, Giordano; Taylor, Jordan E.; Dunning, Caitlin M.; Creech, Amanda L.; Warren, Allison; McFarland, James M.; Zamanighomi, Mahdi; Kauffmann, Audrey; Stransky, Nicolas; Imieliński, Marcin; Maruvka, Yosef E.; Cherniack, Andrew D.; Tsherniak, Aviad; Vázquez, Francisca; Jaffe, Jacob D.; Lane, Andrew A.; Weinstock, David M.; Johannessen, Cory M.; Morrissey, Michael; Stegmeier, Frank; Schlegel, Robert; Hahn, William C.; Getz, Gad; Mills, Gordon B.; Boehm, Jesse S.; Golub, Todd R.; Garraway, Levi A.; Sellers, William R.

doi:10.1038/s41586-019-1186-3

Cited by 2,533 publications

(2,650 citation statements)

References 64 publications

Supporting

Mentioning

2,441

Contrasting

Unclassified

Order By: Relevance

“…The EMBO Journal 39: e102924 | 2020 ª 2019 The Authors -present in all DLD-1 cells was never observed to mis-segregate in untreated or even auxin-treated cells ( Fig EV5C). Interestingly, chromosomes 1, 6, and 7 that carry intra-and/or inter-chromosome translocations (Ghandi et al, 2019) show a slighter, although not significant, increase in the mis-segregation rate ( Fig 7B). We then tested if reduced variability in DNA-dependent centromeric features could also partially explain the lack of chromosome-specific aneuploidy.…”

Section: Of 21mentioning

confidence: 89%

Human chromosome‐specific aneuploidy is influenced by DNA ‐dependent centromeric features

et al. 2019

View full text Add to dashboard Cite

Intrinsic genomic features of individual chromosomes can contribute to chromosome-specific aneuploidy. Centromeres are key elements for the maintenance of chromosome segregation fidelity via a specialized chromatin marked by CENP-A wrapped by repetitive DNA. These long stretches of repetitive DNA vary in length among human chromosomes. Using CENP-A genetic inactivation in human cells, we directly interrogate if differences in the centromere length reflect the heterogeneity of centromeric DNA-dependent features and whether this, in turn, affects the genesis of chromosome-specific aneuploidy. Using three distinct approaches, we show that mis-segregation rates vary among different chromosomes under conditions that compromise centromere function. Whole-genome sequencing and centromere mapping combined with cytogenetic analysis, small molecule inhibitors, and genetic manipulation revealed that inter-chromosomal heterogeneity of centromeric features, but not centromere length, influences chromosome segregation fidelity. We conclude that faithful chromosome segregation for most of human chromosomes is biased in favor of centromeres with high abundance of DNA-dependent centromeric components. These inter-chromosomal differences in centromere features can translate into non-random aneuploidy, a hallmark of cancer and genetic diseases.

show abstract

Section: Of 21mentioning

confidence: 89%

Human chromosome‐specific aneuploidy is influenced by DNA ‐dependent centromeric features

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Despite high efficiency of indel generation, no complete knock-out clones were obtained ( Figure S4A), suggesting that DHX30 is essential in HCT116. Anecdotally, DHX30 is considered essential in 80/625 cell lines based on CRISPR screening in the DepMap portal of the Achilles project (Ghandi et al, 2019), including cancer cell lines of intestinal origin.…”

Section: Dhx30 and Pcbp2 Silencing And Crispr/cas9 Based Knock-outmentioning

confidence: 99%

p53-induced apoptosis is specified by a translation program regulated by PCBP2 and DHX30

Rizzotto

Zaccara

Rossi

et al. 2019

Preprint

View full text Add to dashboard Cite

Activation of p53 by the small molecule Nutlin can result in a combination of cell cycle arrest and apoptosis. The relative strength of these events is difficult to predict by classical gene expression analysis, leaving uncertainty as to the therapeutic benefits of Nutlin. Here, we report a new translational control mechanism shaping p53-dependent apoptosis. Using polysome profiling, we establish Nutlin-induced apoptosis to be associated with the enhanced translation of mRNAs carrying multiple copies of a newly identified 3'UTR CG-rich motif mediating p53-dependent death (CGPD-motif). We identified PCBP2 and DHX30 as CGPDmotif interactors. We found that in cells undergoing persistent cell cycle arrest in response to Nutlin, CGPD-motif mRNAs are repressed by the PCBP2-dependent binding of DHX30 to the motif. Thus, upon DHX30 depletion in these cells, the translation of CGPD-motif mRNAs is increased, and the response to Nutlin shifts towards apoptosis. Instead, DHX30 inducible overexpression in SJSA1 cells, that undergo Nutlin-induced apoptosis, leads to decreased translation of CGPD-motif mRNAs. Overall, this work establishes the role of PCBP2-DHX30 in controlling the translation of CGPD-motif mRNAs and thus provide a new mechanism to modulate the induction of p53-dependent apoptosis.

show abstract

“…To evaluate pan-cancer drug response predictions in pre-clinical tumor models, we utilized publicly available datasets of tumor cell lines from a prior publication (referred to as Klijn 2015) (11) and the Cancer Cell Line Encyclopedia database (CCLE) (12). Klijn 2015 and CCLE include 349 and 503 tumor cell lines from >10 cancer types (Figure 1A), respectively, that have drug response data and RNA and DNA characterization, with 154 cell lines in common between the two datasets ( Figure 1B).…”

Section: Mek Inhibitor Sensitivity Across Cancer Types In Two Publicmentioning

confidence: 99%

“…Pharmacogenomic databases of patient-derived cancer cell lines covering a broad range of cancer types represent reusable pre-clinical models for deep interrogation of the cell lines' innate characteristics that account for their drug response profiles (8)(9)(10)(11)(12). Using tumor cell line resources, DNA variants and gene expression levels have been used to develop pan-cancer drug-response prediction models via a wide variety of computational methods, including regularized regression (8,13), random forests (14,15), neural networks (16), network modeling (17,18), quantitative structure-activity relationship analysis (19), and deep learning (20).…”

Section: Introductionmentioning

confidence: 99%

“…Further, a systematic investigation into whether pan-cancer approaches are more accurate than drug response predictions tailored to specific cancer types or subtypes is currently unavailable. Here, we analyzed data from ten cancer types that were well-represented in two public cell line datasets (11,12) and examined the between-tissue and within-tissue performances of pan-cancer drug response predictions for MEK inhibition. In addition to investigating tissue-level effects, we evaluated cross-MEK inhibitor prediction models and provided in silico replication by applying prediction models across datasets.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

Lloyd

Soellner

Merajver

et al. 2019

Preprint

View full text Add to dashboard Cite

Pharmacogenomic databases of drug response and genomics data in tumor cell lines allow the development of pan-cancer (i.e. tissue-agnostic) predictions of drug response. However, it is unclear whether between-tissue differences in both drug response and molecular characteristics bias pan-cancer drug response predictions. Using two datasets containing 346 and 504 cell lines with MEK inhibitor (MEKi) response data and RNA, SNP, and CNV data, we show between-tissue differences produced confounding effects that increase apparent performance in pan-cancer MEKi response predictions. We estimate that 45-58% of the variance in pan-cancer prediction scores was driven by accurate betweentissue predictions, rather than inter-individual predictions within a tissue. Nevertheless, MEKi response is predicted as well or better by pan-cancer approaches compared to tissue-specific approaches. Our results demonstrate strong between-tissue effects in tissue-agnostic drug response prediction models, but also highlight how drug response may invoke shared regulatory mechanisms although different cancer types arise in tissue-specific routes.

show abstract

Next-generation characterization of the Cancer Cell Line Encyclopedia

Cited by 2,533 publications

References 64 publications

Human chromosome‐specific aneuploidy is influenced by DNA ‐dependent centromeric features

Human chromosome‐specific aneuploidy is influenced by DNA ‐dependent centromeric features

p53-induced apoptosis is specified by a translation program regulated by PCBP2 and DHX30

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

Contact Info

Product

Resources

About