Generation and multi-dimensional profiling of a childhood cancer cell line atlas defines new therapeutic opportunities

Sun, Claire; Daniel, Paul; Bradshaw, Gabrielle; Shi, Hui; Loi, Melissa; Chew, Nicole; Parackal, Sarah; Tsui, Vanessa; Liu, Yuqing; Koptyra, Mateusz; Adjumain, Shazia; Sun, Christie; Chong, Wai Chin; Fernando, Dasun; Drinkwater, Caroline; Tourchi, Motahhareh; Habarakada, Dilru; Sooraj, Dhanya; Carvalho, Diana; Storm, Phillip B.; Baubet, Valérie; Sayles, Leanne C.; Fernandez, Elisabet; Nguyen, Thy; Pörksen, Mia; Doan, Anh; Crombie, Duncan E.; Panday, Monty; Zhukova, Nataliya; Dun, Matthew D.; Ludlow, Louise E.; Day, Bryan W.; Stringer, Brett W.; Neeman, Naama; Rubens, Jeffrey; Raabe, Eric H.; Vinci, Maria; Tyrrell, Vanessa; Fletcher, Jamie I.; Ekert, Paul G.; Dumevska, Biljana; Ziegler, David S.; Tsoli, Maria; Sulaiman, Nur Farhana Syed; Loh, Amos Hong Pheng; Low, Sharon Yin Yee; Sweet-Cordero, E. Alejandro; Monje, Michelle; Resnick, Adam; Jones, Chris; Downie, Peter; Williams, Bryan; Rosenbluh, Joseph; Gough, Daniel J.; Cain, Jason; Firestein, Ron

doi:10.1016/j.ccell.2023.03.007

Cited by 21 publications

(12 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in brief, AI may be explored to investigate the complex landscape of somatic DNA variants detectable in tumors (i.e., through NGS-based cancer genome profiling). Applying powerful AI methods in this manner promises to uncover relationships between cancer risk or diagnosis and multi-omic data derived from DNA sequencing, RNA sequencing, epigenetic signatures of methylation, histology, radiology, and clinical observations (Espín-Pérez et al, 2022;Silvestri et al, 2023;Sun et al, 2023). It also promises to help in monitoring molecular residual disease at different points in an individual's cancer treatment journey (Chen, Zhang, et al, 2023) and in identifying novel biomarkers detectable by liquid biopsy.…”

Section: Discussionmentioning

confidence: 99%

Applications of artificial intelligence in clinical laboratory genomics

Aradhya

Facio

Metz

et al. 2023

American J of Med Genetics Pt C

View full text Add to dashboard Cite

The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of “big data” in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre‐test and post‐test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

show abstract

Section: Discussionmentioning

confidence: 99%

Applications of artificial intelligence in clinical laboratory genomics

Aradhya

Facio

Metz

et al. 2023

American J of Med Genetics Pt C

View full text Add to dashboard Cite

show abstract

“…Foundation models could also be explored for the application of models trained in adult cancers to paediatric cancers, though biomarkers may differ greatly between adult and childhood cancers. 82 An additional consideration is the requirement for validation of any machine learning model in an independent cohort. A typical proteomic validation cohort should be completely independent of the training datasets, ideally with MS data generated at a different time point, on a different instrument or at a different site.…”

Section: Artificial Intelligence and Advanced Computational Approachesmentioning

confidence: 99%

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities

Padhye,

Nawaz,

Hains

et al. 2024

Pediatric Blood & Cancer

View full text Add to dashboard Cite

Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk‐directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome. Proteomics holds significant promise for cancer research, as proteins are ultimately responsible for cellular phenotype and are the target of most anticancer drugs. Here, we review the discoveries, opportunities and challenges of proteomic analyses in paediatric cancer, with a focus on mass spectrometry (MS)‐based approaches. Accelerating incorporation of proteomics into paediatric precision medicine has the potential to improve survival and quality of life for children with cancer.

show abstract

“…The genetic dependency data were derived from the Victorian Paediatric Cancer Consortium's Childhood Cancer Model Atlas (CCMA) Data Portal that contains dependency data on 352 genes across 38 DMG cell line models (25).…”

Section: Dipg Genetic Dependency Datamentioning

confidence: 99%

“…To determine the importance of the expression of PI3K/Akt/mTOR genes in the transmission of oncogenic signals that promote the growth and proliferation of DIPG, we analyzed a CRISPR-Cas9 loss-of-function screen dataset performed on 38 DMG cell lines, representing all DMG H3-altered subtypes (25). Of the 13 genes mapping to the PI3K/Akt/mTOR signaling axis, strong genetic dependency is shown for PIK3CA and MTOR (Figure 1A).…”

Section: Mtor To Be Genetic Dependencies In Dmgmentioning

confidence: 99%

PI3K/mTOR is a therapeutically targetable genetic dependency in diffuse intrinsic pontine glioma

Duchatel

Jackson

Parackal

et al. 2023

Preprint

View full text Add to dashboard Cite

Diffuse midline glioma (DMG), including tumors diagnosed in the brainstem (diffuse intrinsic pontine glioma DIPG), are uniformly fatal brain tumors that lack effective pharmacological treatment. Analysis of pooled CRISPR-Cas9 loss-of-function gene deletion screen datasets, identified PIK3CA and MTOR as targetable molecular dependencies across DIPG patient derived models, highlighting the therapeutic potential of the blood-brain barrier penetrant PI3K/Akt/mTOR inhibitor paxalisib. At the human equivalent maximum tolerated dose, mice treated with paxalisib experienced systemic feedback resulting in increased blood glucose and insulin levels, commensurate with DIPG patients in Phase 1b clinical trials who experienced hyperglycemia/hyperinsulinemia. To exploit genetic dependences, but maintain compliance and benefit, we optimized a paxalisib treatment regimen that employed reduced dosing more frequently, in combination with the anti-hyperglycemic drug, metformin. Combining optimized dosing with metformin restored glucose homeostasis and decreased phosphorylation of the insulin receptor in vivo, a common mechanism of PI3K-inhibitor resistance, extending the survival of DIPG xenograft models. RNA sequencing and phosphoproteomic profiling of DIPG models treated with paxalisib identified increased calcium-activated PKC signaling. Using the brain penetrant PKC inhibitor, enzastaurin in combination with paxalisib, we synergistically extended the survival of orthotopic xenograft models, benefits further promoted by metformin; thus, identifying a clinically relevant DIPG combinatorial approach.

show abstract

Generation and multi-dimensional profiling of a childhood cancer cell line atlas defines new therapeutic opportunities

Cited by 21 publications

References 55 publications

Applications of artificial intelligence in clinical laboratory genomics

Applications of artificial intelligence in clinical laboratory genomics

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities

PI3K/mTOR is a therapeutically targetable genetic dependency in diffuse intrinsic pontine glioma

Contact Info

Product

Resources

About