Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

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We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

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Bioengineering; Systems Biology; Cancer; In Silico Biology

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Most cited references 141

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Hallmarks of Cancer: The Next Generation

Douglas Hanahan, Robert A. Weinberg (2011)

The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.

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New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

E A Eisenhauer, P Therasse, J Bogaerts … (2009)

Assessment of the change in tumour burden is an important feature of the clinical evaluation of cancer therapeutics: both tumour shrinkage (objective response) and disease progression are useful endpoints in clinical trials. Since RECIST was published in 2000, many investigators, cooperative groups, industry and government authorities have adopted these criteria in the assessment of treatment outcomes. However, a number of questions and issues have arisen which have led to the development of a revised RECIST guideline (version 1.1). Evidence for changes, summarised in separate papers in this special issue, has come from assessment of a large data warehouse (>6500 patients), simulation studies and literature reviews. HIGHLIGHTS OF REVISED RECIST 1.1: Major changes include: Number of lesions to be assessed: based on evidence from numerous trial databases merged into a data warehouse for analysis purposes, the number of lesions required to assess tumour burden for response determination has been reduced from a maximum of 10 to a maximum of five total (and from five to two per organ, maximum). Assessment of pathological lymph nodes is now incorporated: nodes with a short axis of 15 mm are considered measurable and assessable as target lesions. The short axis measurement should be included in the sum of lesions in calculation of tumour response. Nodes that shrink to <10mm short axis are considered normal. Confirmation of response is required for trials with response primary endpoint but is no longer required in randomised studies since the control arm serves as appropriate means of interpretation of data. Disease progression is clarified in several aspects: in addition to the previous definition of progression in target disease of 20% increase in sum, a 5mm absolute increase is now required as well to guard against over calling PD when the total sum is very small. Furthermore, there is guidance offered on what constitutes 'unequivocal progression' of non-measurable/non-target disease, a source of confusion in the original RECIST guideline. Finally, a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included. Imaging guidance: the revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions. A key question considered by the RECIST Working Group in developing RECIST 1.1 was whether it was appropriate to move from anatomic unidimensional assessment of tumour burden to either volumetric anatomical assessment or to functional assessment with PET or MRI. It was concluded that, at present, there is not sufficient standardisation or evidence to abandon anatomical assessment of tumour burden. The only exception to this is in the use of FDG-PET imaging as an adjunct to determination of progression. As is detailed in the final paper in this special issue, the use of these promising newer approaches requires appropriate clinical validation studies.

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Dimensionality reduction for visualizing single-cell data using UMAP

Etienne Becht, Leland McInnes, John Healy … (2018)

Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.

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Author and article information

Contributors

Thomas E. Yankeelov

Journal

Journal ID (nlm-ta): iScience

Journal ID (iso-abbrev): iScience

Title: iScience

Publisher: Elsevier

ISSN (Electronic): 2589-0042

Publication date PMC-release: 13 November 2020

Publication date Collection: 18 December 2020

Publication date (Electronic): 13 November 2020

Volume: 23

Issue: 12

Electronic Location Identifier: 101807

Affiliations

[1 ]Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA

[2 ]Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA

[3 ]Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA

[4 ]Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA

[5 ]Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA

[6 ]Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78712, USA

[7 ]Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

Author notes

[∗ ]Corresponding author thomas.yankeelov@ 123456utexas.edu

[8]

These authors contributed equally

[9]

Present address: Department of Radiology, University of Washington, Seattle, WA 98195

Article

Publisher Item ID: S2589-0042(20)31004-X Publisher ID: 101807

DOI: 10.1016/j.isci.2020.101807

PMC ID: 7704401

PubMed ID: 33299976

SO-VID: fd2cb4ec-7605-4ad4-b812-6ca27644fef3

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

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Online interactive cancer simulations and demos

Most cited references 141

Hallmarks of Cancer: The Next Generation

New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

Dimensionality reduction for visualizing single-cell data using UMAP

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