The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Chromatin profiles classify castration-resistant prostate cancers suggesting therapeutic targets
Open accessAbstract
In castration-resistant prostate cancer (CRPC), the loss of androgen receptor (AR) dependence leads to clinically aggressive tumors with few therapeutic options. We used ATAC-seq (assay for transposase-accessible chromatin sequencing), RNA-seq, and DNA sequencing to investigate 22 organoids, six patient-derived xenografts, and 12 cell lines. We identified the well-characterized AR-dependent and neuroendocrine subtypes, as well as two AR-negative/low groups: a Wnt-dependent subtype, and a stem cell–like (SCL) subtype driven by activator protein–1 (AP-1) transcription factors. We used transcriptomic signatures to classify 366 patients, which showed that SCL is the second most common subtype of CRPC after AR-dependent. Our data suggest that AP-1 interacts with the YAP/TAZ and TEAD proteins to maintain subtype-specific chromatin accessibility and transcriptomic landscapes in this group. Together, this molecular classification reveals drug targets and can potentially guide therapeutic decisions.
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
Cited by
Xiang, D., He, A., Zhou, R., Wang, Y., Xiao, X., Gong, T., Kang, W., Lin, X., Wang, X., Consortium11, P.-B. D., et al. (2024) · Theranostics · doi:10.7150/thno.96027 · Cited by 108
Self-citationHypoxia‐inducible factor 1A inhibition overcomes castration resistance of prostate tumors
Terzic, J., Abu el Maaty, M. A., Lutzing, R., Vincent, A., El Bizri, R., Jung, M., Keime, C., & Metzger, D. (2023) · EMBO Molecular Medicine · doi:10.15252/emmm.202217209 · Cited by 21
Metadata
- Authors
- Tang, F., Xu, D., Wang, S., Wong, C. K., Martinez-Fundichely, A., Lee, C. J., Cohen, S., Park, J., Hill, C. E., Eng, K., Bareja, R., Han, T., Liu, E. M., Palladino, A., Di, W., Gao, D., Abida, W., Beg, S., Puca, L., Meneses, M., de Stanchina, E., Berger, M. F., Gopalan, A., Dow, L. E., Mosquera, J. M., Beltran, H., Sternberg, C. N., Chi, P., Scher, H. I., Sboner, A., Chen, Y., & Khurana, E.
- Venue
- Science
- Published
- 2022-05-27
- Volume
- 376
- Issue
- 6596
- Pages
- eabe1505-eabe1505
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4281564034
- Cited by
- 268
- References
- 80
- FWCI
- 30.30
- Type
- journal article
- Language
- en
- Open access
- green
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- Tang, F., Xu, D., Wang, S., Wong, C. K., Martinez-Fundichely, A., Lee, C. J., Cohen, S., Park, J., Hill, C. E., Eng, K., Bareja, R., Han, T., Liu, E. M., Palladino, A., Di, W., Gao, D., Abida, W., Beg, S., Puca, L., . . . Khurana, E. (2022). Chromatin profiles classify castration-resistant prostate cancers suggesting therapeutic targets. Science. https://doi.org/10.1126/science.abe1505
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