Periodic Reporting for period 2 - CONTRA (Computational ONcology TRaining Alliance)
Período documentado: 2020-01-01 hasta 2022-06-30
Cancer is a major cause of death and suffering, rendering it a huge concern to the general public. Consequently, it has been targeted by application of the molecular techniques developed during the last 20-25 years, thereby improving diagnosis and treatment. Nevertheless, although current biomarker and treatment concepts often are successful initially, they subsequently frequently fail to achieve durable drug response and long-term survival for cancer patients. We study somatic evolution in cancer, which is a very promising approach to rectify this situation. Fortunately, single-cell genomic sequencing has recently begun to provide an opportunity to unprecedented detailed insights into tumour evolution, and new techniques are emerging for assaying the spatial distribution of tumour heterogeneity. Analysing and developing methods for these emerging data sets have been under-researched areas that lie at the intersection of medical, evolutionary biology, and computational research. In CONTRA, European researchers with complementary expertise have joined forces in order to collectively facilitate training of future European computational cancer researchers.
We have applied modern recruitment and selection methods in combination and also offered excellent training and employment conditions, including social security, which have facilitated recruitment of 15 excellent ESRs, across genders and geographical areas. We have trained these 15 ESRs so that they now possess the mathematical, computational, and applied skills required to tackle the complex analysis problems posed by somatic evolution in cancer. We also teached them entrepreneurship and the requirements of pharmaceutical and biotech industry. Our substantial academic cancer expertise has been complemented with industrial expertise in software development, especially for biotech and pharmaceutical industry, which in particular has allowed us to organise a successful training event on software development. The result of the successful ESRs’ doctoral training was clearly visible at the CONTRA International Conference; this final training event was largely organized and managed by the ESRs, and they also gave scientific presentations and showed their knowledge during discussions and by asking interesting questions.
We have applied modern recruitment and selection methods in combination and also offered excellent training and employment conditions, including social security, which have facilitated recruitment of 15 excellent ESRs, across genders and geographical areas. We have trained these 15 ESRs so that they now possess the mathematical, computational, and applied skills required to tackle the complex analysis problems posed by somatic evolution in cancer. We also teached them entrepreneurship and the requirements of pharmaceutical and biotech industry. Our substantial academic cancer expertise has been complemented with industrial expertise in software development, especially for biotech and pharmaceutical industry, which in particular has allowed us to organise a successful training event on software development. The result of the successful ESRs’ doctoral training was clearly visible at the CONTRA International Conference; this final training event was largely organized and managed by the ESRs, and they also gave scientific presentations and showed their knowledge during discussions and by asking interesting questions.
CONTRA’s specific research and innovation objectives have been:
To develop novel, powerful single cell models and tools for somatic evolution in cancer, including spatial aspects, building on methods for classic evolution as well as probabilistic machine learning in general.
Among the partners and in collaboration with other groups apply new tools in cutting edge cancer studies - with a special focus on clinical and pharmaceutical relevance.
Construct a novel translational training triangle between academia, software industry, and pharmaceutical and biotech industry.
Develop novel methods & tools with potential for improving clinical cancer care.
Regarding the first objective. We have developed and published a number of computaional methods building on machine learning methodology, such as probabilistic graphical model.
For instance, the CACTUS method for analysing the evolutionary history of tumours and the phenotypes of their clones from integrated whole exome sequencing (WES), single cell RNA sequencing (scRNA-seq) and additional type of data that induces a clustering of the single cells [cite]. CACTUS leverages the additional prior information on the clustering to better map the single cells to the clones identified from WES, based on the alternative and total read counts found in scRNA-seq. Another example, is the SIEVE method, an approach combining statistical phylogenetic model of tumor evolution with a probabilistic model of the raw single cell DNA sequencing data (scDRNA-seq) [cite]. The latter method allows us to simultaneously reconstruct the partial order of occurrence of gene mutations, and calls somatic single nucleotide variants.
Both CACTUS and SIEVE are single cell models and tools for somatic evolution in cancer, provide major innovative solutions and can be used to advance knowledge on cancer progression.
Regarding the second and fourth objective. For example, we have developed and applied and approach that takes advantage of biopsies from HGSOC patients to studying the biological association between different radiomic habitats and pathology. The development method extracts radiomic habitats from standard of care CT scans in High Grade Serous Ovarian Cancer (HGSOC). HGSOC patients most often have multiple metastatic lesions at diagnosis, which makes it hard to quantify the intra and inter lesion heterogeneity. However, clustering patch-wise texture features into radiomic habitats, render quantitative studies possible. The results of this studies have been published in European Radiology.
Regarding the third objective. Our innovative training network included partners not only from academia but also from software and biotech industry, such as Ardigen and Gradient, as well as pharma (Merck Healthcare). The training events exposed the ESRs to not only research and academic tutors, but also included interactions and tutoring from these partners. Specifically, a highly programming intensive 2nd workshop: “Software development” was organised by Ardigen, 3rd summer school: “Promoting your career” was co-organized by Gradient, while our partner PI from Merck, Eiike Staub, gave lectures on two training events, (1st workshop: “kick-off and background” and 1st summer school: “Handling single cell data”).
To develop novel, powerful single cell models and tools for somatic evolution in cancer, including spatial aspects, building on methods for classic evolution as well as probabilistic machine learning in general.
Among the partners and in collaboration with other groups apply new tools in cutting edge cancer studies - with a special focus on clinical and pharmaceutical relevance.
Construct a novel translational training triangle between academia, software industry, and pharmaceutical and biotech industry.
Develop novel methods & tools with potential for improving clinical cancer care.
Regarding the first objective. We have developed and published a number of computaional methods building on machine learning methodology, such as probabilistic graphical model.
For instance, the CACTUS method for analysing the evolutionary history of tumours and the phenotypes of their clones from integrated whole exome sequencing (WES), single cell RNA sequencing (scRNA-seq) and additional type of data that induces a clustering of the single cells [cite]. CACTUS leverages the additional prior information on the clustering to better map the single cells to the clones identified from WES, based on the alternative and total read counts found in scRNA-seq. Another example, is the SIEVE method, an approach combining statistical phylogenetic model of tumor evolution with a probabilistic model of the raw single cell DNA sequencing data (scDRNA-seq) [cite]. The latter method allows us to simultaneously reconstruct the partial order of occurrence of gene mutations, and calls somatic single nucleotide variants.
Both CACTUS and SIEVE are single cell models and tools for somatic evolution in cancer, provide major innovative solutions and can be used to advance knowledge on cancer progression.
Regarding the second and fourth objective. For example, we have developed and applied and approach that takes advantage of biopsies from HGSOC patients to studying the biological association between different radiomic habitats and pathology. The development method extracts radiomic habitats from standard of care CT scans in High Grade Serous Ovarian Cancer (HGSOC). HGSOC patients most often have multiple metastatic lesions at diagnosis, which makes it hard to quantify the intra and inter lesion heterogeneity. However, clustering patch-wise texture features into radiomic habitats, render quantitative studies possible. The results of this studies have been published in European Radiology.
Regarding the third objective. Our innovative training network included partners not only from academia but also from software and biotech industry, such as Ardigen and Gradient, as well as pharma (Merck Healthcare). The training events exposed the ESRs to not only research and academic tutors, but also included interactions and tutoring from these partners. Specifically, a highly programming intensive 2nd workshop: “Software development” was organised by Ardigen, 3rd summer school: “Promoting your career” was co-organized by Gradient, while our partner PI from Merck, Eiike Staub, gave lectures on two training events, (1st workshop: “kick-off and background” and 1st summer school: “Handling single cell data”).
The methods developed by CONTRA ESR are building on state of the art methodologies and constitute methods that progress beyond the state of art in the application area of cancer genomics. In addition, these 15 well trained and ESRs with well-developed contact networks will leave our training program and participate in translating the academic approach into accessible software products and will also make sure that these approaches have an impact on clinical research or even clinical decision making.