Nanodelivery of MRI Contrast Agent Enhances Sensitivity of Detection of Lung Cancer Metastases

Rationale and Objectives

Early detection of lung cancer can be problematic. Although current imaging methods can identify lung cancers, they are limited in the size of detectable nodules. There is also lack of evidence that these methods can correctly classify nodules <7 mm as malignant because lung cancer can be mimicked in appearance by benign lesions that lower specificity. Therefore, there is a need for enhanced sensitivity/specificity of detection for small lung cancers.

Materials and Methods

We have developed a nanosized (∼100 nm) immunoliposome complex for delivery of molecular medicines to tumors. In this complex, an anti-transferrin receptor single-chain antibody fragment (TfRscFv) decorates the surface of a cationic liposome encapsulating the payload. We have previously shown that this systemically administered complex (scL) selectively targets, and efficiently delivers its payload into, tumor cells. We have also encapsulated the magnetic resonance imaging (MRI) contrast agent gadopentetate dimeglumine (“gad-d”) within this complex, resulting in increased resolution and image intensity in a mouse model of primary cancer. Here we examine the ability of the scL-gad-d complex to increase the sensitivity of detection of lung metastases.

Results

These MRI studies show that the scL-gad-d nanocomplex is able to improve detection, and increase enhancement of, small lung cancers (400 μm and as small as 100 μm) compared to that of uncomplexed gad-d.

Conclusions

Because of its tumor targeting specificity, deliver of an MRI contrast agent via this nanocomplex has potential for use as an agent that can identify small lung cancers, thus improving early detection and possibly increasing survival.

Lung cancer is the second leading cause of cancer and the leading cause of cancer deaths for both men and women, with the number of cases in women on the rise. The American Cancer Society estimates that in 2008 there will be more than 215,000 new cases of lung cancer in the United States with more than 161,000 related deaths . The ratio of deaths to new cases (0.75) is significantly higher than the 0.4 ratio for cancer in general, indicating the dire prognosis of individuals who develop lung cancer and the importance of new approaches for early detection and diagnosis.

Currently the principal methods used for early identification of primary lung carcinoma are chest radiography and chest computed tomography (CT). Although both methods are somewhat effective in identifying curable lung cancer , they possess a major drawback in that they commonly result in false positives (ie, nodular areas that could indicate lung cancer, but are, in fact, scars or focal inflammatory/infectious processes) . For small lung nodules, this is a frequent occurrence and a serious problem. Although there are various diagnostic methods to distinguish between true malignancies and false positives , for small lung nodules, the primary method is to obtain serial images over time to assess growth, a very inefficient and costly process. Moreover, a recent report has associated the higher radiation dose from CT with an increased risk of cancer . Consequently, the need for multiple CT scans to confirm diagnosis may have a negative impact. There is also a risk entailed by delay in diagnosis. Cases with delays of 1 year have been associated with a change in stage (although delays of less than 6 months have not). This change in stage implies a worse prognosis . Furthermore, some small non–small-cell lung cancers can metastasize early .

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Methods

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Cell Lines

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Complex Formation

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Animal Models

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Animal Imaging

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Statistics

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Results

Enhanced Contrast in a Nodule 16 Pixels in Diameter with scL-gad-d

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Enhanced Contrast in Nodules 4 Pixels in Diameter with scL-gad-d

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Visualization of Nodules 1–2 Pixels in Diameter by scL-gad-d

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Table 1

Pixel Intensities of the Three Nodules Shown in Figure 4 a

Mean SD Maximum Z-Score Expected Pixel Frequency at and above this Z-Score Nodule 1 Pre-contrast 599 332 1567 2.92 4 per 1000 Post–scL-gad-d 667 391 1946 3.27 5 per 10,000 Nodule 2 Pre-contrast 591 299 1589 3.34 4 per 10,000 Post–scL-gad-d 665 339 2274 4.75 1 per 100,000 Nodule 3 Pre-contrast 566 278 1588 3.68 1 per 10,000 Post–scL-gad-d 981 515 2542 3.03 1 per 1000

gad-d, gadopentetate dimeglumine; scL, immunoliposome complex; SD standard deviation.

The Z-scores show that the maximum pixel intensity in the location of the visualized nodules is always at least 2.9 SD above the mean, indicating that the presumed 1–4 pixel nodules are not likely to be due to image noise.

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Histology Studies

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Comparison between Targeted and Untargeted Complex

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Discussion

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Conclusions

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Acknowledgments

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Nanodelivery of MRI Contrast Agent Enhances Sensitivity of Detection of Lung Cancer Metastases

Rationale and Objectives

Materials and Methods

Results

Conclusions

Methods

Cell Lines

Complex Formation

Animal Models

Animal Imaging

Statistics

Results

Enhanced Contrast in a Nodule 16 Pixels in Diameter with scL-gad-d

Enhanced Contrast in Nodules 4 Pixels in Diameter with scL-gad-d

Visualization of Nodules 1–2 Pixels in Diameter by scL-gad-d

Histology Studies

Comparison between Targeted and Untargeted Complex

Discussion

Conclusions

Acknowledgments

References

Further Reading

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Abstracts of Funded National Institutes of Health Grants

Breast Cancer