How will spatial omics help us fight cancer in the future?
For it to have a real impact on the way we tackle human disease, scientists, technology makers, and healthcare professionals will need to join forces and generate the curated, large scale datasets that are missing today, along with the right processes and computational tools.
Unlike traditional next-generation sequencing methods, spatial transcriptomics maps RNA transcripts to their location within a tissue, capturing the spatial information.
In five years, spatial methods went from prototypes in a handful of labs to fully fledged, commercialized technologies with astonishing resolution and throughput (1).
Oncology labs are diving headfirst into these methods. They have understood that the implications of deconvoluting the spatial complexity of the tumour microenvironment are huge. The number of publications is skyrocketing (2).
With its ability to characterize tumour-immune system interactions, spatial omics is especially attractive to immuno-oncology (3). Researchers will discover new ways to cure cancer by targeting pathways in tumour and immune cells. Drug designers will develop more precise ways to empower a patient’s immune system to fight cancer cells.
Cancer heterogeneity, which fuels resistance to therapy, is another area that will hugely benefit from it. Understanding how biology differs in patients with the same cancers, through spatial omics, will drive the development of more effective combinatorial/personalized therapies, and their prescription to the right patients (4).
And then there is AI
At Owkin we are successfully using AI to predict the transcriptome and visualize it spatially as heat maps of gene expression from existing H&E slides (5), without having to run the spatial transcriptomics experiment itself.
Applications like this will change the face of healthcare, injecting new life into precision medicine and creating new avenues for its implementation.
To get there, as David W. Craig and Brooke Hjelm (USC) write in a commentary on the topic, “we need to come together and work as a community to build up the training datasets and other resources that will be essential for giving AI the best chance at success (6)."
The spatial revolution is only just beginning
If you are reading this, whether that’s improving the technology, creating the right datasets, or developing AI methods to generate more hypotheses and discoveries, there is something you can do to contribute and benefit from the success of the spatial omics revolution.
At Owkin, we are working on an initiative to do just that. We believe that the convergence between AI and spatial omics will fuel the next revolution in cancer research. Do you want to know more? Watch this space or get in touch.
References
- Marx, V. Method of the Year: spatially resolved transcriptomics. Nat Methods 18, 9–14 (2021).
- Pubmed (spatial+transcriptomics+oncology)
- Bassiuni R, et al., Applicability of spatial transcriptional profiling to cancer research, Molecular Cell 81, 1631-1639 (2021).
- Liu SQ, et al., Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer. J Hematol Oncol. 15, 19 (2022).
- Schmauch B, et al. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat Commun 11, 3877 (2020).
- AI Tools Will Help Us Make the Most of Spatial Biology. Contributed Commentary on Bio-IT World, by David W. Craig, Ph.D. and Brooke Hjelm, Ph.D.
Authors
How will spatial omics help us fight cancer in the future?
For it to have a real impact on the way we tackle human disease, scientists, technology makers, and healthcare professionals will need to join forces and generate the curated, large scale datasets that are missing today, along with the right processes and computational tools.
Unlike traditional next-generation sequencing methods, spatial transcriptomics maps RNA transcripts to their location within a tissue, capturing the spatial information.
In five years, spatial methods went from prototypes in a handful of labs to fully fledged, commercialized technologies with astonishing resolution and throughput (1).
Oncology labs are diving headfirst into these methods. They have understood that the implications of deconvoluting the spatial complexity of the tumour microenvironment are huge. The number of publications is skyrocketing (2).
With its ability to characterize tumour-immune system interactions, spatial omics is especially attractive to immuno-oncology (3). Researchers will discover new ways to cure cancer by targeting pathways in tumour and immune cells. Drug designers will develop more precise ways to empower a patient’s immune system to fight cancer cells.
Cancer heterogeneity, which fuels resistance to therapy, is another area that will hugely benefit from it. Understanding how biology differs in patients with the same cancers, through spatial omics, will drive the development of more effective combinatorial/personalized therapies, and their prescription to the right patients (4).
And then there is AI
At Owkin we are successfully using AI to predict the transcriptome and visualize it spatially as heat maps of gene expression from existing H&E slides (5), without having to run the spatial transcriptomics experiment itself.
Applications like this will change the face of healthcare, injecting new life into precision medicine and creating new avenues for its implementation.
To get there, as David W. Craig and Brooke Hjelm (USC) write in a commentary on the topic, “we need to come together and work as a community to build up the training datasets and other resources that will be essential for giving AI the best chance at success (6)."
The spatial revolution is only just beginning
If you are reading this, whether that’s improving the technology, creating the right datasets, or developing AI methods to generate more hypotheses and discoveries, there is something you can do to contribute and benefit from the success of the spatial omics revolution.
At Owkin, we are working on an initiative to do just that. We believe that the convergence between AI and spatial omics will fuel the next revolution in cancer research. Do you want to know more? Watch this space or get in touch.
References
- Marx, V. Method of the Year: spatially resolved transcriptomics. Nat Methods 18, 9–14 (2021).
- Pubmed (spatial+transcriptomics+oncology)
- Bassiuni R, et al., Applicability of spatial transcriptional profiling to cancer research, Molecular Cell 81, 1631-1639 (2021).
- Liu SQ, et al., Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer. J Hematol Oncol. 15, 19 (2022).
- Schmauch B, et al. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat Commun 11, 3877 (2020).
- AI Tools Will Help Us Make the Most of Spatial Biology. Contributed Commentary on Bio-IT World, by David W. Craig, Ph.D. and Brooke Hjelm, Ph.D.
Authors
About Owkin
Owkin is an AI biotechnology company that uses AI to find the right treatment for every patient. We combine the best of human and artificial intelligence to answer the research questions shared by biopharma and academic researchers. By closing the translational gap between complex biology and new treatments, we bring new diagnostics and drugs to patients sooner.
We use AI to identify new treatments, de-risk and accelerate clinical trials and build diagnostic tools. Using federated learning, a pioneering collaborative AI framework, Owkin enables partners to unlock valuable insights from siloed datasets while protecting patient privacy and securing proprietary data.
Owkin was co-founded by Thomas Clozel MD, a former assistant professor in clinical onco-hematology, and Gilles Wainrib, a pioneer in the field of machine learning in biology, in 2016. Owkin has raised over $300 million and became a unicorn through investments from leading biopharma companies (Sanofi and BMS) and venture funds (Fidelity, GV and BPI, among others).
The spatial revolution is only just beginning
For it to have a real impact on the way we tackle human disease, scientists, technology makers, and healthcare professionals will need to join forces and generate the curated, large scale datasets that are missing today, along with the right processes and computational tools.
Unlike traditional next-generation sequencing methods, spatial transcriptomics maps RNA transcripts to their location within a tissue, capturing the spatial information.
In five years, spatial methods went from prototypes in a handful of labs to fully fledged, commercialized technologies with astonishing resolution and throughput (1).
Oncology labs are diving headfirst into these methods. They have understood that the implications of deconvoluting the spatial complexity of the tumour microenvironment are huge. The number of publications is skyrocketing (2).
With its ability to characterize tumour-immune system interactions, spatial omics is especially attractive to immuno-oncology (3). Researchers will discover new ways to cure cancer by targeting pathways in tumour and immune cells. Drug designers will develop more precise ways to empower a patient’s immune system to fight cancer cells.
Cancer heterogeneity, which fuels resistance to therapy, is another area that will hugely benefit from it. Understanding how biology differs in patients with the same cancers, through spatial omics, will drive the development of more effective combinatorial/personalized therapies, and their prescription to the right patients (4).
And then there is AI
At Owkin we are successfully using AI to predict the transcriptome and visualize it spatially as heat maps of gene expression from existing H&E slides (5), without having to run the spatial transcriptomics experiment itself.
Applications like this will change the face of healthcare, injecting new life into precision medicine and creating new avenues for its implementation.
To get there, as David W. Craig and Brooke Hjelm (USC) write in a commentary on the topic, “we need to come together and work as a community to build up the training datasets and other resources that will be essential for giving AI the best chance at success (6)."
The spatial revolution is only just beginning
If you are reading this, whether that’s improving the technology, creating the right datasets, or developing AI methods to generate more hypotheses and discoveries, there is something you can do to contribute and benefit from the success of the spatial omics revolution.
At Owkin, we are working on an initiative to do just that. We believe that the convergence between AI and spatial omics will fuel the next revolution in cancer research. Do you want to know more? Watch this space or get in touch.
References
- Marx, V. Method of the Year: spatially resolved transcriptomics. Nat Methods 18, 9–14 (2021).
- Pubmed (spatial+transcriptomics+oncology)
- Bassiuni R, et al., Applicability of spatial transcriptional profiling to cancer research, Molecular Cell 81, 1631-1639 (2021).
- Liu SQ, et al., Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer. J Hematol Oncol. 15, 19 (2022).
- Schmauch B, et al. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat Commun 11, 3877 (2020).
- AI Tools Will Help Us Make the Most of Spatial Biology. Contributed Commentary on Bio-IT World, by David W. Craig, Ph.D. and Brooke Hjelm, Ph.D.