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The prognostic and therapeutic value of the tumor microenvironment (TME) in various cancer types is of major interest. Characterization of the TME often relies on a small representative tissue sample. However, the adequacy of such a sample for assessing components of the TME is not yet known. Here, we used immunohistochemical (IHC) staining and 7-color multiplex staining to evaluate CD8 (cluster of differentiation 8), CD68, PD-L1 (programmed death-ligand 1), CD34, FAP (fibroblast activation protein), and cytokeratin in 220 tissue cores from 26 high-grade serous ovarian cancer samples. Comparisons were drawn between a larger tumor specimen and smaller core biopsies based on number and location (central tumor vs. peripheral tumor) of biopsies. Our analysis found that the correlation between marker-specific cell subsets in larger tumorversussmaller core was stronger with two core biopsies and was not further strengthened with additional biopsies. Moreover, this correlation was consistently strong regardless of whether the biopsy was taken at the center or at the periphery of the original tumor sample. These findings could have a substantial impact on longitudinal assessment for detection of biomarkers in clinical trials.

 

While plasma concentration kinetics has traditionally been the predictor of drug pharmacological effects, it can occasionally fail to represent kinetics at the site of action, particularly for solid tumors. This is especially true in the case of delivery of therapeutic macromolecules (drug‐loaded nanomaterials or monoclonal antibodies), which can experience challenges to effective delivery due to particle size‐dependent diffusion barriers at the target site. As a result, disparity between therapeutic plasma kinetics and kinetics at the site of action may exist, highlighting the importance of target site concentration kinetics in determining the pharmacodynamic effects of macromolecular therapeutic agents. Assessment of concentration kinetics at the target site has been facilitated by non‐invasive in vivo imaging modalities. This allows for visualization and quantification of the whole‐body disposition behavior of therapeutics that is essential for a comprehensive understanding of their pharmacokinetics and pharmacodynamics. Quantitative non‐invasive imaging can also help guide the development and parameterization of mathematical models for descriptive and predictive purposes. Here, we present a review of the application of state‐of‐the‐art imaging modalities for quantitative pharmacological evaluation of therapeutic nanoparticles and monoclonal antibodies, with a focus on their integration with mathematical models, and identify challenges and opportunities.

This article is categorized under:

Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease

Diagnostic Tools > in vivo Nanodiagnostics and Imaging

Nanotechnology Approaches to Biology > Nanoscale Systems in Biology

 

It is challenging to design effective drug delivery systems (DDS) that target metastatic breast cancers (MBC) because of lack of competent imaging and image analysis protocols that suitably capture the interactions between DDS and metastatic lesions. Here, we integrate high temporal resolution of in vivo whole-body PET-CT, ex vivo whole-organ optical imaging, high spatial resolution of confocal microscopy, and mathematical modeling, to systematically deconstruct the trafficking of injectable nanoparticle generators encapsulated with polymeric doxorubicin (iNPG-pDox) in pulmonary MBC. iNPG-pDox accumulated substantially in metastatic lungs, compared to healthy lungs. Intratumoral distribution and retention of iNPG-pDox varied with lesion size, possibly induced by locally remodeled microenvironment. We further used multiscale imaging and mathematical simulations to provide improved drug delivery strategies for MBC. Our work presents a multidisciplinary translational toolbox to evaluate transport and interactions of DDS within metastases. This knowledge can be recursively applied to rationally design advanced therapies for metastatic cancers. 

We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti–CTLA-4 or anti–PD-1/PD-L1 antibodies. We found that model parameters distinctly identified patients with common ( n = 18) and rare ( n = 10) malignancy types who benefited and did not benefit from these monotherapies with accuracy as high as 88% at first restaging (median 53 days). Further, the parameters successfully differentiated pseudo-progression from true progression, providing previously unidentified insights into the unique biophysical characteristics of pseudo-progression. Our mathematical model offers a clinically relevant tool for personalized oncology and for engineering immunotherapy regimens. 

Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer typesfor its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents,thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinaryapproach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis,and treatment. To this end, the application of quantitative tools can help improve the understanding ofdisease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategiesto improve therapeutic outcomes. However, such approaches have only been minimally applied towardsthe investigation of PDAC, and we review the current status of mathematical modeling works inthis field.