Resistance Development: A Major Piece in the Jigsaw Puzzle of Tumor Size Modeling

Terranova, Nadia; Girard, Pascal; Klinkhardt, Ute; Munafo, Alain

doi:10.1002/psp4.45

Cited by 11 publications

(17 citation statements)

References 10 publications

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“…Considering the total TS does not allow to capture tumor heterogeneity, then looking for differences across lesions dynamics is a crucial aspect to contemplate when developing new, convincing models of tumor dynamics and, in turn, new treatment paradigms 17. Even though the adoption of more mechanistic models might be discouraged by their complex formulation or limited availability of experimental data, we have shown that the CICIL methodology can be used efficiently to analyze and understand large‐scale datasets prior to modeling, and then guide the modeler in determining the most appropriate approach for a particular case study and for the questions to be addressed.…”

Section: Discussionmentioning

confidence: 99%

“…This omitted information could be relevant to tumor differentiation or resistance-related mechanisms. 17 In particular, differences among tumor lesions in their response to treatment might provide new quantitative insights on tumor heterogeneity (e.g., genetic/epigenetic alterations, nature of microenvironmental composition, and cell activation states) within a given tissue or among different tissues, 18 and/ or predict differences in disease progression. 19,20 Thus, a tool for comparing lesion dynamics prior to modeling is important to assess whether the total TS can reasonably capture the tumor lesion response.…”

Section: What Is the Current Knowledge On The Topic?mentioning

confidence: 99%

“…Indeed, the total TS is a highly approximated measure that does not capture differences in tumor dynamics of individual target lesions (iTLs), their number, and their locations. This omitted information could be relevant to tumor differentiation or resistance‐related mechanisms 17. In particular, differences among tumor lesions in their response to treatment might provide new quantitative insights on tumor heterogeneity (e.g., genetic/epigenetic alterations, nature of microenvironmental composition, and cell activation states) within a given tissue or among different tissues,18 and/or predict differences in disease progression 19, 20.…”

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confidence: 99%

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Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Terranova

Girard

Ιωάννου

et al. 2018

CPT Pharmacom & Syst Pharma

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Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non‐parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user‐friendly and efficient Java‐based framework.

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Section: Discussionmentioning

confidence: 99%

Section: What Is the Current Knowledge On The Topic?mentioning

confidence: 99%

mentioning

confidence: 99%

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Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Terranova

Girard

Ιωάννου

et al. 2018

CPT Pharmacom & Syst Pharma

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“…Terranova et al. proposed an ODE model incorporating different resistant cell subpopulations allowing the description of both primary and acquired resistance. Here again, the model was not calibrated using real data.…”

Section: Discussionmentioning

confidence: 99%

Analysis of temozolomide resistance in low‐grade gliomas using a mechanistic mathematical model

Ollier

Mazzocco

Ricard

et al. 2017

Fundamemntal Clinical Pharma

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Understanding how tumors develop resistance to chemotherapy is a major issue in oncology. When treated with temozolomide (TMZ), an oral alkylating chemotherapy drug, most low-grade gliomas (LGG) show an initial volume decrease but this effect is rarely long lasting. In addition, it has been suggested that TMZ may drive tumor progression in a subset of patients as a result of acquired resistance. Using longitudinal tumor size measurements from 121 patients, the aim of this study was to develop a semi-mechanistic mathematical model to determine whether resistance of LGG to TMZ was more likely to result from primary and/or from chemotherapy-induced acquired resistance that may contribute to tumor progression. We applied the model to a series of patients treated upfront with TMZ (n = 109) or PCV (procarbazine, CCNU, vincristine) chemotherapy (n = 12) and used a population mixture approach to classify patients according to the mechanism of resistance most likely to explain individual tumor growth dynamics. Our modeling results predicted acquired resistance in 51% of LGG treated with TMZ. In agreement with the different biological effects of nitrosoureas, none of the patients treated with PCV were classified in the acquired resistance group. Consistent with the mutational analysis of recurrent LGG, analysis of growth dynamics using mathematical modeling suggested that in a subset of patients, TMZ might paradoxically contribute to tumor progression as a result of chemotherapy-induced resistance. Identification of patients at risk of developing acquired resistance is warranted to better define the role of TMZ in LGG.

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“…For example, MRI, PET, CT, and other imaging systems for cancer diagnosis are ineffective to detect small metastatic tumors because contrast agents used for these diagnostic techniques fail to diffuse into such tumors and stay within them for a sufficient time (59). Fluorescent dyes are also widely used to visualize cancer cells and identify small tumors during intraoperative imaging, but common issues include poor diagnostic properties such as low signal-to-noise ratios and short half-lives in vivo (60).…”

Section: Discussionmentioning

confidence: 99%

Development of Halofluorochromic Polymer Nanoassemblies for the Potential Detection of Liver Metastatic Colorectal Cancer Tumors Using Experimental and Computational Approaches

et al. 2017

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Purpose To develop polymer nanoassemblies (PNAs) modified with halofluorochromic dyes to allow for the detection of liver metastatic colorectal cancer (CRC) to improve therapeutic outcomes. Methods We combine experimental and computational approaches to evaluate macroscopic and microscopic PNA distributions in patient-derived xenograft primary and orthotropic liver metastatic CRC tumors. Halofluorochromic and non-halofluorochromic PNAs (hfPNAs and n-hfPNAs) were prepared from poly(ethylene glycol), fluorescent dyes (Nile blue, Alexa546, and IR820), and hydrophobic groups (palmitate), all of which were covalently tethered to a cationic polymer scaffold [poly(ethylene imine) or poly(lysine)] forming particles with an average diameter < 30 nm. Results Dye-conjugated PNAs showed no aggregation under opsonizing conditions for 24 h and displayed low tissue diffusion and cellular uptake. Both hfPNAs and n-hfPNAs accumulated in primary and liver metastatic CRC tumors within 12 h post intravenous injection. In comparison to n-hfPNAs, hfPNAs fluoresced strongly only in the acidic tumor microenvironment (pH<7.0) and distinguished small metastatic CRC tumors from healthy liver stroma. Computational simulations revealed that PNAs would steadily accumulate mainly in acidic (hypoxic) interstitium of metastatic tumors, independently of the vascularization degree of the tissue surrounding the lesions. Conclusion The combined experimental and computational data confirms that hfPNAs detecting acidic tumor tissue can be used to identify small liver metastatic CRC tumors with improved accuracy.

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Resistance Development: A Major Piece in the Jigsaw Puzzle of Tumor Size Modeling

Cited by 11 publications

References 10 publications

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Analysis of temozolomide resistance in low‐grade gliomas using a mechanistic mathematical model

Development of Halofluorochromic Polymer Nanoassemblies for the Potential Detection of Liver Metastatic Colorectal Cancer Tumors Using Experimental and Computational Approaches

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