Modulation of the sensitivity to ruxolitinib-mediated JAK2 inhibition by mutationally activated SHP2 exhibits cell context dependency in pre-clinical models of myeloproliferative neoplasms
Classic Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs) are hematopoietic stem cell cancers that result in aberrant trilineage myeloid cell proliferation, bone marrow fibrosis, and increased risk of acute myeloid leukemia. MPNs are driven by deregulated activity of the JAK2 kinase, induced by mutations in the JAK2, CALR, and MPL genes, but approved JAK2 inhibitors primarily offer palliative effects, not remission. Cell models that demonstrate MPN oncogene driven JAK2 activity requisite for cell proliferation are important research tools for the development of anti-JAK2 and anti-JAK2 signaling therapeutics for MPN. SET2 and UKE1 cells are two such cell lines, as they express JAK2-V617F, one of the major driving mutations of MPN, and require signaling by JAK2 for their growth and viability. These cell lines are AML cell lines that were derived from patients with a previous diagnosis of MPN before they developed AML. Our previous studies demonstrated that the SHP2 phosphatase may be a therapeutic target for MPNs, and here we report our identification and characterization of an activating point mutation of SHP2 (encoded by the PTPN11 gene), SHP2-F71L, in UKE1 cells. Given SHP2 functions downstream of JAK2 and mediates JAK2 activation of RAS, we set out to determine the effect of mutational activation of SHP2 on the sensitivity of MPN model cells to JAK2 inhibition. We used CRISPR-Cas9 to edit this mutation in UKE1 cells back to wildtype such that these cells only express wildtype SHP2. These cells exhibited enhanced sensitivity to SHP2 inhibition and, notably, enhanced sensitivity to the JAK2 inhibitor ruxolitinib. This altered sensitivity was reverted by exogenous expression of SHP2-F71L but not SHP2-WT, indicating expression of an activated SHP2 may alter sensitivity to JAK2 inhibition in MPN model cells. We further explored this by genetically editing SET2 cells to express SHP2-F71L but observed no change in SHP2 inhibitor or JAK2 inhibitor sensitivity in cells with a SHP2-F71L encoding allele of PTPN11. Using the cytokine dependent BaF3 cell line where deregulation of JAK2 signaling by expression of JAK2-V617F induces cytokine independent transformation that remains dependent on this JAK2 signaling, we observed no effect of the expression of an activated SHP2 mutant on the sensitivity of the growth and viability of these cells to ruxolitinib. Recent studies have demonstrated activation of RAS signaling can antagonize JAK2 inhibition in pre-clinical MPN models, and the presence of RAS pathway mutations associates with patients whose disease advances on ruxolitinib therapy. Such mutations include activating mutations in PTPN11, as SHP2 is an upstream activator of RAS signaling. Our results suggest that activating PTPN11 mutations have the potential to desensitize the effects of JAK2 inhibition therapy in patients undergoing therapy and may be dependent on unknown cell and molecular profile contexts.
It is argued that formation of a tumour-intrinsic niche is a prerequisite for BRAF-mutant CRC seeding to distant organs and that interference with niche formation may help avoid metastatic relapse.
J. Bugter, L. El Bouazzaoui, E. Küçükköse et al.· bioRxiv· 2 citations
It is concluded that bridging the gap between foundational CRISPR research and its real-world applications is imperative and future efforts should focus on democratizing tools via open-source platforms, advancing delivery systems, and fostering sustainable innovation through synthetic biology integration to fully realize the transformative potential of genome editing in organisms beyond model organisms.
S. Sarsaiya, Archana Jain, Jishuang Chen et al.· Biotechnology Advances· 2 citations
A virus-like particle (VLP)-based toolkit that delivers diverse CRISPR editing modalities to human monocytes, macrophages and dendritic cells with high efficiency while preserving viability and innate immune responsiveness is presented.
Hyuncheol Jung, Pascal Devant, Carter Ching et al.· Nature Biotechnology· 0 citations
Findings provide direct functional evidence that szl regulates median caudal patterning in goldfish and suggest that szl-dependent modulation of the Chordin/BMP network can generate twin-tail-like caudal morphology.
Huijuan Li, Xiaoying Zhang, Xiaowen Wang et al.· International Journal of Mol...· 0 citations
Pooled genome-wide CRISPR-Cas9 knockout (CRISPR-KO) screening is a powerful approach for discovering new biology and identifying genetic vulnerabilities in cancers. This approach uses the Cas9 nuclease in combination with sgRNA libraries, typically consisting of 4-8 sgRNAs to induce mutations in each target gene. A critical assumption is that the effect of each sgRNA is solely due to Cas9 editing of the target gene. However, libraries can contain sgRNAs that direct Cas9 to multiple locations, thus potentially introducing bias into gene hit lists and leading to flawed biological hypotheses. Here we have developed GuideRefine, a pipeline to detect multi-targeting and off-targeting sgRNAs. GuideRefine outputs a virtual refined sub-library containing only on-target sgRNAs. Using GuideRefine with T2T-CHM13 as the reference genome, we surveyed the Brunello, TKOv3, Yusa, Avana, and Jacquere libraries, finding that ∼7.5% to ∼16% of sgRNAs are potentially problematic. We confirmed that multi-targeting sgRNAs disproportionately impair cell fitness and that sgRNAs aligning to more than one location with a single mismatch can also reduce fitness, although to a lesser extent. After flagging problematic sgRNAs and creating virtual “on-target only” sub-libraries, ∼10% to ∼16% of genes lose critical representation (< 3 sgRNAs per gene). Intriguingly, a set of 467 genes, characterised by short CDS length and lower PAM site density, have fewer than three sgRNAs in all sub-libraries, suggesting they cannot be well-targeted using current libraries. We anticipate that GuideRefine, together with caution in assessing the effects of problematic sgRNAs, will help prioritise biologically relevant hits.
S. Bernard, M. Rainey, Corrado Santocanale et al.· bioRxiv· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
MIT News · Artificial Intelligence· news.mit.eduAug 17, 2026