Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Metabolic traits ruling the specificity of the immune response in different cancer types.

Cancer immunotherapy aims to augment the response of the patient's own immune system against cancer cells. Despite effective for some patients and some cancer types, the therapeutic efficacy of this treatment is limited by the composition of the tumor microenvironment (TME), which is not well-suited for the fitness of anti-tumoral immune cells. However, the TME differs between cancer types and tissues, thus complicating the possibility of the development of therapies that would be effective in a large range of patients. A possible scenario is that each type of cancer cell, granted by its own mutations and reminiscent of the functions of the tissue of origin, has a specific metabolism that will impinge on the metabolic composition of the TME, which in turn specifically affects T cell fitness. Therefore, targeting cancer or T cell metabolism could increase the efficacy and specificity of existing immunotherapies, improving disease outcome and minimizing adverse reactions.status: Published onlin

Impact of Immunometabolism on Cancer Metastasis: A Focus on T Cells and Macrophages

Despite improved treatment options, cancer remains the leading cause of morbidity and mortality worldwide, with 90% of this mortality correlated to the development of metastasis. Since metastasis has such an impact on treatment success, disease outcome, and global health, it is important to understand the different steps and factors playing key roles in this process, how these factors relate to immune cell function and how we can target metabolic processes at different steps of metastasis in order to improve cancer treatment and patient prognosis. Recent insights in immunometabolism direct to promising therapeutic targets for cancer treatment, however, the specific contribution of metabolism on antitumor immunity in different metastatic niches warrant further investigation. Here, we provide an overview of what is so far known in the field of immunometabolism at different steps of the metastatic cascade, and what may represent the next steps forward. Focusing on metabolic checkpoints in order to translate these findings from in vitro and mouse studies to the clinic has the potential to revolutionize cancer immunotherapy and greatly improve patient prognosis.status: publishe

Impact of Immunometabolism on Cancer Metastasis: A Focus on T Cells and Macrophages

Despite improved treatment options, cancer remains the leading cause of morbidity and mortality worldwide, with 90% of this mortality correlated to the development of metastasis. Since metastasis has such an impact on treatment success, disease outcome, and global health, it is important to understand the different steps and factors playing key roles in this process, how these factors relate to immune cell function and how we can target metabolic processes at different steps of metastasis in order to improve cancer treatment and patient prognosis. Recent insights in immunometabolism direct to promising therapeutic targets for cancer treatment, however, the specific contribution of metabolism on antitumor immunity in different metastatic niches warrant further investigation. Here, we provide an overview of what is so far known in the field of immunometabolism at different steps of the metastatic cascade, and what may represent the next steps forward. Focusing on metabolic checkpoints in order to translate these findings from in vitro and mouse studies to the clinic has the potential to revolutionize cancer immunotherapy and greatly improve patient prognosis

Impact of Immunometabolism on Cancer Metastasis: A Focus on T Cells and Macrophages

Despite improved treatment options, cancer remains the leading cause of morbidity and mortality worldwide, with 90% of this mortality correlated to the development of metastasis. Since metastasis has such an impact on treatment success, disease outcome, and global health, it is important to understand the different steps and factors playing key roles in this process, how these factors relate to immune cell function and how we can target metabolic processes at different steps of metastasis in order to improve cancer treatment and patient prognosis. Recent insights in immunometabolism direct to promising therapeutic targets for cancer treatment, however, the specific contribution of metabolism on antitumor immunity in different metastatic niches warrant further investigation. Here, we provide an overview of what is so far known in the field of immunometabolism at different steps of the metastatic cascade, and what may represent the next steps forward. Focusing on metabolic checkpoints in order to translate these findings from in vitro and mouse studies to the clinic has the potential to revolutionize cancer immunotherapy and greatly improve patient prognosis

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Systematic benchmarking of single-cell ATAC-sequencing protocols

Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols

Systematic benchmarking of single-cell ATAC-sequencing protocols.

Acknowledgements: H.H. received support for the project PID2020-115439GB-I00 funded by MCIN/AEI/10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement numbers 810287 and 874710). This work was supported by a European Research Council Consolidator grant to S.A. (724226_cis-CONTROL), KU Leuven (C14/22/125 to S.A.), Foundation Against Cancer (F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A. and a PhD fellowship to F.D.) and Aligning Science Across Parkinson’s (grant number ASAP-000430 to S.A.). K.B.M. and S.A.T. are supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194). Computing was performed at the Vlaams Supercomputer Center and high-throughput sequencing at the Genomics Core Leuven. M.R.C. is supported by the National Institutes on Aging K99/R00AG059918. This work was supported by funding from the Rita Allen Foundation (W.J.G.) and the Human Frontiers Science Program (RGY006S; W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative and National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01-HG00990901 and U19-AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective. B.D. received financial support by Swiss National Science Foundation 310030_197082 and the EPFL. L.S.L. receives support from an Emmy Noether fellowship by the German Research Foundation (LU 2336/2-1), a National Institutes of Health grant UM1HG012076, a Longevity Impetus grant and a Hector Research Career Development Award by the Hector Fellow Academy. A.C.A. is supported by National Institutes of Health grants RF1-MH128842, R35-GM124704 and R01-DA047237 as well as a Silver Family Foundation Innovator Award.Funder: H.H. received support for the project PID2020-115439GB-I00- funded by MCIN/AEI/ 10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 810287 and 874710).Funder: M.R.C. is supported by the National Institutes on Aging K99/R00AG059918.Funder: K.B.M. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).Funder: S.A.T. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).Funder: This work was supported by funding from the Rita Allen Foundation (W.J.G.), the Human Frontiers Science (RGY006S) (W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative, and the National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01- HG00990901, and U19- AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective.Funder: This work was supported by an ERC Consolidator Grant to S.A. (no. 724226_cis- CONTROL), KU Leuven (grant no. C14/22/125 to S.A.), Foundation Against Cancer (grant no, F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A.), Aligning Science Across Parkinson’s (ASAP, grant no. ASAP-000430 to S.A.)Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols