Nutlin-3a efficacy in sarcoma predicted by transcriptomic and epigenetic profiling

Nutlin-3a is a small-molecule antagonist of p53/MDM2 that is being explored as a treatment for sarcoma. In this study, we examined the molecular mechanisms underlying the sensitivity of sarcomas to Nutlin-3a. In an ex vivo tissue explant system, we found that TP53 pathway alterations (TP53 status, MDM2/MDM4 genomic amplification/mRNA overexpression, MDM2 SNP309, and TP53 SNP72) did not confer apoptotic or cytostatic responses in sarcoma tissue biopsies (n = 24). Unexpectedly, MDM2 status did not predict Nutlin-3a sensitivity. RNA sequencing revealed that the global transcriptomic profiles of these sarcomas provided a more robust prediction of apoptotic responses to Nutlin-3a. Expression profiling revealed a subset of TP53 target genes that were transactivated specifically in sarcomas that were highly sensitive to Nutlin-3a. Of these target genes, the GADD45A promoter region was shown to be hypermethylated in 82% of wild-type TP53 sarcomas that did not respond to Nutlin-3a, thereby providing mechanistic insight into the innate ability of sarcomas to resist apoptotic death following Nutlin-3a treatment. Collectively, our findings argue that the existing benchmark biomarker for MDM2 antagonist efficacy (MDM2 amplification) should not be used to predict outcome but rather global gene expression profiles and epigenetic status of sarcomas dictate their sensitivity to p53/MDM2 antagonists.Kathleen I. Pishas, Susan J. Neuhaus, Mark T. Clayer, Andreas W. Schreiber, David M. Lawrence, Michelle Perugini, Robert J. Whitfield, Gelareh Farshid, Jim Manavis, Steve Chryssidis, Bronwen J. Mayo, Rebecca C. Haycox, Kristen Ho, Michael P. Brown, Richard J. D'Andrea, Andreas Evdokiou, David M. Thomas, Jayesh Desai, David F. Callen and Paul M. Neilse

Dissecting signalling contributions of the alpha and beta subunits of the GM-CSF receptor

Normal tissue homeostasis and appropriate responses to injury and infection are dependent on cellular communication mediated by cell surface receptors that respond to extrinsic stimuli. The GM-CSF receptor was the major focus of this project. This receptor shares a common signalling subunit, β [subscript c], with the IL-3 and IL-5 receptors. The unique GM-CSF receptor α-subunit ( GMRα ) confers ligand binding specificity to the complex and is essential for GM-CSF receptor signalling, although the full complement of signalling events mediated by GMRα remains elusive. Through cloning of candidate interacting proteins, expression and co-immunoprecipitation studies, we have confirmed interactions for two proteins previously reported to interact with the GMRα, p85 and IKKβ. Additionally, we identified the Src family kinase, Lyn, as a novel direct interacting partner of GMRα and provide insights into possible roles of this kinase in initiating signalling from the GM-CSF receptor. In addition to GMRα associated events we aimed to further characterise the role of the common β [subscript c] subunit in GM-CSF mediated signalling. We utilised two classes of consitutively active β [subscript c] mutants ( extracellular or transmembrane ) which transform the bi-potential myeloid FDB1 cell line to either factor-independent growth and survival, or granulocyte-macrophage differentiation, respectively. Here we report a comprehensive biochemical analysis of signalling by these two classes of mutants in this cell line. The two activated GMR mutants displayed distinct and non-overlapping signalling capacity. In particular, expression of a mutant with a substitution in the transmembrane domain ( V449E ) selectively activated JAK / STAT5 and MAPK pathways resulting in a high level of sensitivity to JAK and MEK inhibitors. In contrast, expression of a mutant with a 37 amino acid duplication in its extracellular domain ( FI Δ ) selectively activates the PI3K / AKT and IKKβ / NFkB pathways. Cells responding to this mutant display a relative high level of sensitivity to two independent PI3K inhibitors and relative resistance to inhibition of MEK and JAK2. The non-overlapping nature of signalling by these two activated mutants suggests that there are alternative modes of receptor activation that differentially dependent on JAK2 and that act synergistically in the mature liganded cytokine receptor complex. Further detailed analysis of these mutants will facilitate the dissection of the signalling pathways involved in the GM-CSF response that mediate proliferation, survival and differentiation.Thesis (Ph.D.)--University of Adelaide, School of Medicine, 2007

#91 : Development of a Combined Artificial Intelligence Score for Evaluating Both Embryo Ploidy and Viability to Aid in Embryo Selection During IVF

Background and Aims: Artificial intelligence (AI) is being increasingly used for non-invasive evaluation of embryo quality during IVF. Previous studies described development of AI for selecting embryos likely to be euploid (genetics AI), or likely to lead to clinical pregnancy (viability AI), based on analysis of images of blastocysts on day 5 of development. The aim of this study was to determine if a combination of these AI scores could be used to effectively evaluate both outcomes. Method: 936 embryo images with pre-implantation genetic testing for aneuploidies (PGT-A) outcomes, and 479 embryo images with clinical pregnancy outcomes, were retrospectively obtained from 12 IVF clinics in 5 countries. Performance was evaluated for each AI score alone, and the average score of the two AIs. The ability to select euploid or viable embryos was evaluated using ROC-AUC analyses, and a simulated cohort ranking method reported in the literature. Results: The average score of the two AIs was generally as effective at selecting euploid embryos as the genetics AI, and just as effective at selecting viable embryos as the viability AI. Results for both analyses are presented below. Conclusion: An AI score that can evaluate both embryo ploidy and viability simultaneously is useful for selecting preferred embryos for analysis or transfer. These results suggest that it is feasible to generate a single score for evaluating overall embryo quality using a non-invasive approach

#92 : An Artificial Intelligence Algorithm Outperforms Highly Variable Embryologist Grading for Predicting the Likelihood of Pregnancy Outcome from Embryo Images

Background and Aims: Embryologist evaluation of embryos is critical for ensuring successful pregnancy outcomes. Standard, manual evaluation is variable, subjective, and time-consuming. The aim of this study was to evaluate whether an artificial intelligence (AI) algorithm can standardize and improve embryo evaluation during IVF. Method: 20 images of blastocyst-stage embryos on day 5 of in vitro development were selected to represent a range of morphological qualities. All embryos had been transferred and the clinical pregnancy outcome was known for each embryo based on detection of fetal heartbeat at first ultrasound scan (∼7-9 weeks gestation). 50% of embryos in the dataset resulted in pregnancy. 158 embryologists made a total of 236 attempts at providing their evaluation of the morphological quality of the 20 embryo images using the Gardner system. The embryologist-assigned grades were then used to generate their prediction of whether that embryo would lead to pregnancy or not (≥ 3BB indicated a pregnancy prediction, and <3BB indicated a non-pregnancy prediction). The same 20 embryo images were also assessed by a previously developed viability AI algorithm for evaluating the likelihood of clinical pregnancy based on embryo images. An AI score of ≥5.0/10 indicated a pregnancy prediction, and <5.0/10 indicated a non-pregnancy prediction. The AI algorithm provided the same score for each embryo image regardless of how many times the analysis was performed. Results: The AI algorithm correctly predicted pregnancy outcome for 14/20 embryo images (70%). Embryologists also correctly predicted 14/20 images in 14/236 attempts (6%), and in 1 attempt correctly predicted 15/20 images. In the remaining 221 attempts (94%) embryologists correctly predicted between 6-13 images, representing a range of accuracies from 30-75%. Conclusion: This study demonstrates the inherent variability and lack of objectivity associated with an embryologist’s evaluation of embryos. It highlights the benefits of accurate AI algorithms for standardizing embryo assessmen

#316 : Improved Time to Pregnancy When Combining an Artificial Intelligence Score and Morphology Grading for Embryo Selection During IVF

Background and Aims: Embryo selection is critical in determining IVF success yet continues to be challenging due to the subjectivity of morphology grading methods, especially when grading fair/average quality embryos. Improving embryo selection could optimise implantation rates and minimise financial/emotional burden on patients. Artificial Intelligence (AI) algorithms represent promising, non-invasive methods of standardising embryo grading and potentially increasing IVF success rates. This study assessed whether an AI algorithm (Life Whisperer Viability) for evaluating the likelihood of clinical pregnancy improves time to pregnancy (TTP) when compared to or combined with standard morphology grading. Method: 305 de-identified 2D images of day 5 blastocysts (121 fresh/184 frozen) with matched clinical pregnancy outcomes (fetal heartbeat at first scan) from women who underwent IVF treatment from 2020-2023 were retrospectively assessed. All images were taken prior to transfer/freezing. TTP was assessed using a simulated cohort ranking method, with TTP being defined as the average number of transfers needed to obtain a clinical pregnancy. Results: A positive linear correlation of LWV scores with pregnancy outcomes was observed (p<0.001). ROC-AUC results indicate that LWV is selecting embryos leading to pregnancy at least as well, if not better, than Gardner morphology grading (0.641 vs 0.624), with further improvement observed when LWV and Gardner grading were combined. The TTP analysis showed a 7.3% reduction in TTP when using LWV over Gardner grading. Combined use of LWV+Gardner grading reduced TTP by up to 10.8%, with the largest improvement (5.3%) seen in the frozen group, where there was a higher distribution of average quality embryos. Conclusion: LWV showed improved embryo rankingand reduction in the estimated average number oftransfers needed to achieveclinical pregnancy. Furthermore, evaluation of TTP supports the combined use of LWV+Gardner grading, showing that they work synergistically to further improve ranking performance when selecting average quality embryos

#ESHREjc report: on the road to preconception and personalized counselling with machine learning models

Extract Introduction Artificial intelligence (AI) is a tool thought to revolutionize the field of reproductive medicine in the years to come. Specifically, machine learning (ML), which is a subset of AI methods used to detect patterns and make predictions based on large datasets, has been used in ART to predict implantation outcome, embryo transfer strategies or adverse outcomes for IVF treatments (for a review see, Wang et al., 2019). However, ML methods have not yet been widely adopted in medically assisted reproduction (MAR). This slow uptake could be due to a lack of in-depth interdisciplinary communication between ML experts and clinicians/embryologists, as well as some uncertainty on how ML models could be generalized for different populations. The February edition of the ESHRE Journal Club discussed a paper from Yland et al. (2022) where the authors compared different ML algorithms to predict the chance of pregnancy in couples actively trying to conceive (without undergoing MAR). By using data from the Pregnancy Study Online (PRESTO), a web-based prospective cohort study of 4133 couples in North America (Wise et al., 2015), the authors analysed 163 potential variables and used supervised ML classification algorithms to identify the strength of each variable in predicting pregnancy outcomes of couples across the fertility spectrum (infertile: <12 menstrual cycles; subfertile: within 6 menstrual cycles; and fecundability: the average probability of pregnancy per menstrual cycle). The authors were able to predict pregnancy with a discrimination as high as 71.2% and identify the variables that most consistently predicted conception. These variables included lifestyle and reproductive characteristics such as age, BMI, history of infertility, daily use of vitamins or folic acid, intercourse timing, diet quality and reduced stress. ESHRE Journal Club discussion focused on promoting interdisciplinarity among ML and reproductive medicine specialists. It hosted 50 participants on Twitter together with experts Michelle Perugini, Vajira Thambawita and authors Jennifer Yland, Yannis Paschalidis and Lauren Wise. The discussion resulted in 902 tweets and around 1 million impressions over a 24-h perio

Molecular basis of cytokine receptor activation

Cytokines are secreted soluble peptides that precisely regulate multiple cellular functions. Amongst these the GM-CSF/IL-3/IL-5 family of cytokines controls whether hematopoietic cells will survive or apoptose, proliferate, differentiate, migrate, or perform effector functions such as phagocytosis or reactive oxygen species release. Their potent and pleiotropic activities are mediated through binding to high affinity membrane receptors at surprisingly low numbers per cell. Receptor binding triggers a cascade of intracellular signaling events, including reversible phosphorylation of receptor subunits and associated signaling molecules, leading to multiple biological responses, with the prevention of apoptosis or “cell survival” being a key cellular function that underpins all others. Many chronic inflammatory diseases and a number of haematological malignancies are driven by deregulated GM-CSF, IL-3, or IL-5 cytokine receptor signaling, highlighting their importance in disease. A major step in understanding how these cytokine receptors function is to elucidate their three dimensional structure and to relate this to the many signaling pathways emanating from their receptors. We have recently solved the structure of the human GM-CSF receptor complexed to GM-CSF which revealed distinct forms of receptor assembly: a hexamer that comprises two molecules each of GM-CSF, GM-CSF receptor alpha chain and GM-CSF receptor beta chain; and an unexpected dodecamer in which two hexameric complexes associate through a novel site 4. This latter form is necessary to bring JAK2 molecules sufficiently close together to enable full receptor activation. In this review we focus on the most recent insights in cytokine receptor signaling, and in receptor assembly. The stage is now set to link distinct forms of cytokine receptor assembled structures to specific forms of cytokine receptor signaling and function. Armed with this knowledge it may be possible to map distinct cytokine receptor signaling pathways from the cell surface to the cell nucleus which may themselves become new therapeutic targets.