Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model
Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/10/2750 |
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| author | Mehdi Nikfar Haoyang Mi Chang Gong Holly Kimko Aleksander S. Popel |
| author_facet | Mehdi Nikfar Haoyang Mi Chang Gong Holly Kimko Aleksander S. Popel |
| author_sort | Mehdi Nikfar |
| collection | DOAJ |
| description | Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon’s entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as “cold”, “compartmentalized” and “mixed”, which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes. |
| first_indexed | 2024-03-11T03:53:11Z |
| format | Article |
| id | doaj.art-cfb5ea66e7834e5f98cc4dfbe463c79c |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T03:53:11Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-cfb5ea66e7834e5f98cc4dfbe463c79c2023-11-18T00:48:23ZengMDPI AGCancers2072-66942023-05-011510275010.3390/cancers15102750Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology ModelMehdi Nikfar0Haoyang Mi1Chang Gong2Holly Kimko3Aleksander S. Popel4Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USADepartment of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USAClinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Waltham, MA 02451, USAClinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD 20878, USADepartment of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USASpatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon’s entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as “cold”, “compartmentalized” and “mixed”, which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.https://www.mdpi.com/2072-6694/15/10/2750intratumoral heterogeneitycomputational digital pathologyquantitative systems pharmacology (QSP)agent-based Model (ABM)immunoarchitectureimmune checkpoint inhibitor |
| spellingShingle | Mehdi Nikfar Haoyang Mi Chang Gong Holly Kimko Aleksander S. Popel Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model Cancers intratumoral heterogeneity computational digital pathology quantitative systems pharmacology (QSP) agent-based Model (ABM) immunoarchitecture immune checkpoint inhibitor |
| title | Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model |
| title_full | Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model |
| title_fullStr | Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model |
| title_full_unstemmed | Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model |
| title_short | Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model |
| title_sort | quantifying intratumoral heterogeneity and immunoarchitecture generated in silico by a spatial quantitative systems pharmacology model |
| topic | intratumoral heterogeneity computational digital pathology quantitative systems pharmacology (QSP) agent-based Model (ABM) immunoarchitecture immune checkpoint inhibitor |
| url | https://www.mdpi.com/2072-6694/15/10/2750 |
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