Cancer Biology

Targeting the Hippo pathway in cancers via ubiquitination dependent TEAD degradation

1. Trang H Pham
2. Kanika Bajaj Pahuja
3. Thijs J Hagenbeek
4. Jason Zbieg
5. Cameron L Noland
6. Victoria C Pham
7. Xiaosai Yao
8. Christopher M Rose
9. Kristen C Browder
10. Ho-June Lee
11. Mamie Yu
12. May Liang-Chu
13. Scott Martin
14. Erik Verschueren
15. Jason Li
16. Marta H Kubala
17. Rina Fong
18. Maria Lorenzo
19. Paul Beroza
20. Peter Hsu
21. Sayantanee Paul
22. Elisia Villemure
23. Wendy Lee
24. Tommy K Cheung
25. Saundra Clausen
26. Jennifer Lacap
27. Yuxin Liang
28. Jason Cheng
29. Steve Schmidt
30. Zora Modrusan
31. Michael Cohen
32. James Crawford
33. Heinrich Jasper
34. Alan Ashworth
35. Jennie R Lill
36. Shiva Malek
37. Joachim Rudolph
38. Ingrid E Wertz
39. Matthew T Chang
40. Xin Ye
41. Anwesha Dey

• Curated by eLife

  eLife Assessment

  This important study describes two findings: first, that TEAD is subject to turnover by the ubiquitin-proteasome system involving RNF146 and Parylation, and second, the development of a pan-TEAD heterobifunctional degrader that is used to inhibit growth of a YAP-dependent cancer cell line and to characterize TEAD binding sites in the genome. Convincing evidence supports the development and specificity of the degrader. This article will be of relevance to cancer biologists and scientists interested in proteostasis, cellular signaling, and post-translation modification of proteins.

Reviewed by eLife

Establishing the foundations for a data-centric AI approach for virtual drug screening through a systematic assessment of the properties of chemical data

1. Allen Chong
2. Ser-Xian Phua
3. Yunzhi Xiao
4. Woon Yee Ng
5. Hoi Yeung Li
6. Wilson Wen Bin Goh

• Curated by eLife

  eLife Assessment

  This study reports valuable findings that highlight the importance of data quality and data representation for ligand-based virtual screening experiments. The authors' claims are supported by solid evidence, although the conclusions have been inferred from only two datasets. The work would gain much impact if additional datasets were used. The main findings will be of interest to cheminformaticians and medicinal chemists working in QSAR modeling, and possibly in other areas related to machine learning.

Reviewed by eLife

Finding novel vulnerabilities of hypomorphic BRCA1 alleles

1. Anne Schreuder
2. Klaas de Lint
3. Mariana M. Góis
4. Rosalie A. Kampen
5. Marta San Martin Alonso
6. Ilse Nootenboom
7. Veronica Garzero
8. Rob M. F. Wolthuis
9. Sylvie M. Noordermeer

Reviewed by Review Commons

Stochastic variation in the FOXM1 transcription program mediates replication stress tolerance

1. Hendrika A. Segeren
2. Kathryn A. Wierenga
3. Frank M. Riemers
4. Elsbeth A. van Liere
5. Bart Westendorp

Reviewed by Review Commons

Atypical contribution of caspase-3 to melanoma cancer cell motility by regulation of coronin 1B activity

1. Kevin Berthenet
2. Serena Diazzi
3. Catherine Jamard
4. Kinga B. Stopa
5. Clara Gil
6. Stefan Dragan
7. Deborah Fanfone
8. Trang TM. Nguyen
9. Nathalie Al
10. Francois Virard
11. Olivier Meurette
12. Nikolay Popgeorgiev
13. Hector Hernandez-Vargas
14. Julien Ablain
15. Gabriel Ichim

Reviewed by Review Commons

StereoSiTE: a framework to spatially and quantitatively profile the cellular neighborhood organized iTME

1. Xing Liu
2. Chi Qu
3. Chuandong Liu
4. Na Zhu
5. Huaqiang Huang
6. Fei Teng
7. Caili Huang
8. Bingying Luo
9. Xuanzhu Liu
10. Min Xie
11. Feng Xi
12. Mei Li
13. Liang Wu
14. Yuxiang Li
15. Ao Chen
16. Xun Xu
17. Sha Liao
18. Jiajun Zhang

Reviewed by GigaScience