Cancer Biomarkers and Big Data: A Planetary Science Approach

Crichton, Daniel J.; Altınok, Alphan; Amos, Christopher I.; Anton, Kristen; Cinquini, L.; Colbert, Maureen; Feng, Ziding; Goel, Ajay; Kelly, Seán; Kincaid, Heather; Liu, David; Lombeyda, Santiago; Mahabal, A.; Mishra, Asitang; Patriotis, Christos; Srivastava, Sudhir

doi:10.1016/j.ccell.2020.09.006

Cited by 16 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a massive increase of data collection in biomarker cancer research 46 . While clustering leads to the loss of single-cell resolution, it also facilitates interpretation of large datasets and thus, multiple techniques for automated clustering were included in FlowCT (i.e., FlowSOM, Phenograph, Seurat and PARC).…”

Section: Discussionmentioning

confidence: 99%

“…There has been a massive increase in data collection in biomarker cancer research. 45 Clustering leads to the loss of single-cell resolution, but it also facilitates interpretation of large data sets, so multiple techniques for automated clustering were included in FlowCT (ie, FlowSOM, Phenograph, Seurat, and PARC). Unlike other algorithms, FlowSOM and PARC do not require down-sampling (ie, reducing the number of events being analyzed); therefore, they can analyze larger numbers of cells within a reasonable computation time.…”

Section: Discussionmentioning

confidence: 99%

“…Effective visualization of large, diverse, and high-dimensionality data is mandatory. 45 Although principal component analysis is commonly used as a linear approach, it is typically less powerful than nonlinear methods such as t-distributed stochastic neighbor embedding (t-SNE) and UMAP. t-SNE is frequently used in mass cytometry or for single-cell RNA-seq data, but it struggles to analyze large numbers of cells because of its slow computational time.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology

et al. 2022

View full text Add to dashboard Cite

Large-scale immune monitoring is becoming routinely used in clinical trials to identify determinants of treatment responsiveness, particularly to immunotherapies. Flow cytometry remains one of the most versatile and high throughput approaches for single-cell analysis; however, manual interpretation of multidimensional data poses a challenge to capture full cellular diversity and provide reproducible results. We present FlowCT, a semi-automated workspace empowered to analyze large datasets that includes pre-processing, normalization, multiple dimensionality reduction techniques, automated clustering and predictive modeling tools. As a proof of concept, we used FlowCT to compare the T cell compartment in bone marrow (BM) vs peripheral blood (PB) of patients with smoldering multiple myeloma (MM); identify minimally-invasive immune biomarkers of progression from smoldering to active MM; define prognostic T cell subsets in the BM of patients with active MM after treatment intensification; and assess the longitudinal effect of maintenance therapy in BM T cells. A total of 354 samples were analyzed and immune signatures predictive of malignant transformation in 150 smoldering MM patients (hazard ratio [HR]: 1.7; P <.001), and of progression-free (HR: 4.09; P <.0001) and overall survival (HR: 3.12; P =.047) in 100 active MM patients, were identified. New data also emerged about stem cell memory T cells, the concordance between immune profiles in BM vs PB and the immunomodulatory effect of maintenance therapy. FlowCT is a new open-source computational approach that can be readily implemented by research laboratories to perform quality-control, analyze high-dimensional data, unveil cellular diversity and objectively identify biomarkers in large immune monitoring studies.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The proposed environment is similar to the successful virtual observatory paradigm in the planetary sciences 112,113 . Moreover, effective methodology transfer from planetary science to biomedical research has already occurred between NASA's Jet Propulsion Laboratory and the National Cancer Institute's Early Detection Research Network 114 .…”

Section: Review Articlementioning

confidence: 99%

Biological research and self-driving labs in deep space supported by artificial intelligence

et al. 2023

Self Cite

View full text Add to dashboard Cite

Space biology research aims to understand fundamental spaceflight effects on organisms, develop foundational knowledge to support deep space exploration and, ultimately, bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data and model organisms from both spaceborne and ground-analogue studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally automated, light, agile and intelligent to accelerate knowledge discovery. Here we present a summary of decadal recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning and modelling applications that offer solutions to these space biology challenges. The integration of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modelling and analytics, support maximally automated and reproducible experiments, and efficiently manage spaceborne data and metadata, ultimately to enable life to thrive in deep space.Space biology research focuses on answering fundamental mechanistic questions about how molecular, cellular, tissue and whole organismal life responds to the space environment. Biological stressors of spaceflight include ionizing radiation, altered gravitational fields, accelerated day-night cycles, confined isolation, hostile closed environments, distance-duration from Earth 1 , planetary dust regolith 2,3 , and extreme temperatures and atmospheres 4,5 . Moreover, spaceflight stressors are probably compounded and amplified with increasing

show abstract

“…The ongoing accumulation of records on a large number of patients is reinforcing the pressing need of cancer research and clinical care to embrace computational solutions to effectively utilize all this information. The effective utilization of cancer big data entails all the steps from data processing and storage to data mining, analysis, and final applications, such as the identification of patient-specific oncogenic processes [16] and biomarkers [17]. Moreover, the continuous improvement of data quality through standardization procedures that ensure responsible molecular and clinical data sharing, interoperability, and security is a key aspect for cancer research that is strongly catalyzed by initiatives such as the Global Alliance for Genomics and Health (GA4GH; https://www.ga4gh.org).…”

Section: Big Data In Cancer Researchmentioning

confidence: 99%

Artificial intelligence in cancer research: learning at different levels of data granularity

2021

View full text Add to dashboard Cite

From genome‐scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co‐existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

show abstract

Cancer Biomarkers and Big Data: A Planetary Science Approach

Cited by 16 publications

References 13 publications

FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology

FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology

Biological research and self-driving labs in deep space supported by artificial intelligence

Artificial intelligence in cancer research: learning at different levels of data granularity

Contact Info

Product

Resources

About