preclinical-positron-emission-tomography-with-body-conforming-animal

Preclinical Positron Emission Tomography with Body Conforming Animal Molds for Cloud-Based Automated Image Analysis in Mice

Positron Emission Tomography (PET) is a molecular imaging modality that can be used to investigate a multitude of pharmacological questions, such as ...

Pfizer, Inc.

Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.

Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.

IL13Rα2 is a cell surface tumor antigen that is overexpressed in multiple tumor types. Here, we studied biodistribution and targeting potential of an anti-IL13Rα2 antibody (Ab) and anti-tumor activity of anti-IL13Rα2-antibody-drug conjugate (ADC). The anti-IL13Rα2 Ab was labeled with fluorophore AF680 or radioisotope Zr for  tracking using fluorescence molecular tomography (FMT) or positron emission tomography (PET) imaging, respectively. Both imaging modalities showed that the tumor was the major uptake site for anti-IL13Rα2-Ab, with peak uptake of 5-8% ID and 10% ID/g as quantified from FMT and PET, respectively. Pharmacological  competition with excess of unlabeled anti-IL13Rα2-Ab significantly reduced the tumor uptake, indicative of antigen-specific tumor accumulation. Further, FMT imaging demonstrated similar biodistribution and pharmacokinetic profiles of an auristatin-conjugated anti-IL13Rα2-ADC as compared to the parental Ab. Finally, the anti-IL13Rα2-ADC exhibited a dose-dependent anti-tumor effect on A375 xenografts, with 90% complete responders at a dose of 3 mg/kg. Taken together, both FMT and PET showed a favorable biodistribution profile for anti-IL13Rα2-Ab/ADC, along with antigen-specific tumor targeting and excellent therapeutic efficacy in the A375 xenograft model. This work shows the great potential of this anti-IL13Rα2-ADC as a targeted anti-cancer agent.

Targeting and pharmacology of an anti-IL13Rα2 antibody and antibody-drug conjugate in a melanoma xenograft model.

KRAS is one of the most commonly mutated oncogenes in lung, colorectal, and pancreatic cancers. Recent clinical trials directly targeting KRAS G12C presented encouraging results for a large population of non-small cell lung cancer (NSCLC), but resistance to treatment is a concern. Continued exploration of new inhibitors and preclinical models is needed to address resistance mechanisms and improve duration of patient responses. To further enable the development of KRAS G12C inhibitors, we present a preclinical framework involving translational, non-invasive imaging modalities (CT and PET) and histopathology in a conventional xenograft model and a novel KRAS G12C knock-in mouse model of NSCLC. We utilized an in-house developed KRAS G12C inhibitor (Compound A) as a tool to demonstrate the value of this framework in studying in vivo pharmacokinetic/pharmacodynamic (PK/PD) relationship and anti-tumor efficacy. We characterized the Kras G12C-driven genetically engineered mouse model (GEMM) and identify tumor growth and signaling differences compared to its Kras G12D-driven counterpart. We also find that Compound A has comparable efficacy to sotorasib in the Kras G12C-driven lung tumors arising in the GEMM, but like observations in the clinic, some tumors inevitably progress on treatment. These findings establish a foundation for evaluating future KRAS G12C inhibitors that is not limited to xenograft studies and can be applied in a translationally relevant mouse model that mirrors human disease progression and resistance.

Efficacy and Imaging-Enabled Pharmacodynamic Profiling of KRAS G12C Inhibitors in Xenograft and Genetically Engineered Mouse Models of Cancer.

Lung cancer remains a leading cause of cancer-related death, but scientists have made great strides in developing new treatments recently, partly owing to the use of genetically engineered mouse models (GEMMs). GEMM tumors represent a translational model that recapitulates human disease better than implanted models because tumors develop spontaneously in the lungs. However, detection of these tumors relies on in vivo imaging tools, specifically micro-Computed Tomography (micro-CT or µCT), and image analysis can be laborious with high inter-user variability. Here we present a deep learning model trained to perform fully automated segmentation of lung tumors without the interference of other soft tissues. Trained and tested on 100 3D µCT images (18,662 slices) that were manually segmented, the model demonstrated a high correlation to manual segmentations on the testing data (r=0.99, DSC=0.78) and on an independent dataset (n = 12 3D scans or 2328 2D slices, r=0.97, DSC=0.73). In a comparison against manual segmentation performed by multiple analysts, the model (r=0.98, DSC=0.78) performed within inter-reader variability (r=0.79, DSC=0.69) and close to intra-reader variability (r=0.99, DSC=0.82), all while completing 5+ hours of manual segmentations in 1 minute. Finally, when applied to a real-world longitudinal study (n = 55 mice), the model successfully detected tumor progression over time and the differences in tumor burden between groups induced with different virus titers, aligning well with more traditional analysis methods. In conclusion, we have developed a deep learning model which can perform fast, accurate, and fully automated segmentation of µCT scans of murine lung tumors.

Article	Year
Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography. PloS one\| PubMed ID: 34138905	2021
Targeting and pharmacology of an anti-IL13Rα2 antibody and antibody-drug conjugate in a melanoma xenograft model. mAbs\| PubMed ID: 34347577	2021
Efficacy and Imaging-Enabled Pharmacodynamic Profiling of KRAS G12C Inhibitors in Xenograft and Genetically Engineered Mouse Models of Cancer. Molecular cancer therapeutics\| PubMed ID: 37186518	2023
Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography. Translational oncology\| PubMed ID: 38128467	2024

Ziyue Karen Jiang

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