bims-pimaco 2024-06-16 papers

Issue of 2024–06–16
three papers selected by
Lucas B. Zeiger

EMBO J. 2024 Jun 10.

K128 ubiquitination constrains RAS activity by expanding its binding interface with GAP proteins.

Wout Magits, Mikhail Steklov, Hyunbum Jang, Raj N Sewduth, Amir Florentin, Benoit Lechat, Aidana Sheryazdanova, Mingzhen Zhang, Michal Simicek, Gali Prag, Ruth Nussinov, Anna Sablina.

The RAS pathway is among the most frequently activated signaling nodes in cancer. However, the mechanisms that alter RAS activity in human pathologies are not entirely understood. The most prevalent post-translational modification within the GTPase core domain of NRAS and KRAS is ubiquitination at lysine 128 (K128), which is significantly decreased in cancer samples compared to normal tissue. Here, we found that K128 ubiquitination creates an additional binding interface for RAS GTPase-activating proteins (GAPs), NF1 and RASA1, thus increasing RAS binding to GAP proteins and promoting GAP-mediated GTP hydrolysis. Stimulation of cultured cancer cells with growth factors or cytokines transiently induces K128 ubiquitination and restricts the extent of wild-type RAS activation in a GAP-dependent manner. In KRAS mutant cells, K128 ubiquitination limits tumor growth by restricting RAL/ TBK1 signaling and negatively regulating the autocrine circuit induced by mutant KRAS. Reduction of K128 ubiquitination activates both wild-type and mutant RAS signaling and elicits a senescence-associated secretory phenotype, promoting RAS-driven pancreatic tumorigenesis.

Keywords: NF1; RAS Interactome; RAS Signaling; Senescence-Associated Secretory Phenotype; Ubiquitination

DOI: https://doi.org/10.1038/s44318-024-00146-w

Cytoskeleton (Hoboken). 2024 Jun 14.

MEK inhibitors and DA-Raf, a dominant-negative antagonist of the Ras-ERK pathway, prevent the migration and invasion of KRAS-mutant cancer cells.

Aoi Matsuda, Ryuichi Masuzawa, Kazuya Takahashi, Kazunori Takano, Takeshi Endo.

The Ras-induced ERK pathway (Raf-MEK-ERK signaling cascade) regulates a variety of cellular responses including cell proliferation, survival, and migration. Activating mutations in RAS genes, particularly in the KRAS gene, constitutively activate the ERK pathway, resulting in tumorigenesis, cancer cell invasion, and metastasis. DA-Raf1 (DA-Raf) is a splicing isoform of A-Raf and contains the Ras-binding domain but lacks the kinase domain. Consequently, DA-Raf antagonizes the Ras-ERK pathway in a dominant-negative manner and can serve as a tumor suppressor that targets mutant Ras protein-induced tumorigenesis. We show here that MEK inhibitors and DA-Raf interfere with the in vitro collective cell migration and invasion of human KRAS-mutant carcinoma cell lines, the lung adenocarcinoma A549, colorectal carcinoma HCT116, and pancreatic carcinoma MIA PaCa-2 cells. DA-Raf expression was silenced in these cancer cell lines. All these cell lines had high collective migration abilities and invasion properties in Matrigel, compared with nontumor cells. Their migration and invasion abilities were impaired by suppressing the ERK pathway with the MEK inhibitors U0126 and trametinib, an approved anticancer drug. Expression of DA-Raf in MIA PaCa-2 cells reduced the ERK activity and hindered the migration and invasion abilities. Therefore, DA-Raf may function as an invasion suppressor protein in the KRAS-mutant cancer cells by blocking the Ras-ERK pathway when DA-Raf expression is induced in invasive cancer cells.

Keywords: Ras–ERK pathway; cancer cell; invasion; metastasis; migration

DOI: https://doi.org/10.1002/cm.21881

Nat Commun. 2024 Jun 11. 15(1): 4596

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.

Adalberto Claudio Quiros, Nicolas Coudray, Anna Yeaton, Xinyu Yang, Bojing Liu, Hortense Le, Luis Chiriboga, Afreen Karimkhan, Navneet Narula, David A Moore, Christopher Y Park, Harvey Pass, Andre L Moreira, John Le Quesne, Aristotelis Tsirigos, Ke Yuan.

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.

DOI: https://doi.org/10.1038/s41467-024-48666-7