Linking disease phenotypes with diagnosis and translational predictions.
It is clear from a historical background that in the future biomedical sciences will be driven by the ability to adopt novel technologies, which will in turn generate huge amounts of data outputs from clinical samples. One major consequence will be to utilize the new technology deliveries as the basis to understand the disease complexity and to develop new treatments. COPD is a major concern in the world because of the high value of smoking prevalence rate. Clinically these patients experience breathlessness, productive cough, shortage in many life functions especially those associated with constitutional capability. The diagnostic tools currently address structural (CT lung density scan, CT airway wall thickness) and functional abnormalities (spirometry for expiratory flow, residual volume (RV), total lung capacity (TLC), forced residual capacity (FRV), and airway specific resistance and conductance). These diagnostic approaches are valid in the more advanced COPD leaving, however, out the early onset of lung functions decline in chronic smokers who destined to but have not yet developed irreversible changes in lung structure and function. The development of new protein biomarker assays that could diagnose early ongoing disease greatly assist here and add context to and complement the existing diagnostic tools. The CT scans that are typically used in lung density measurements in smokers will provide information on the distribution of emphysema, but will not differentiate between older inactive emphysema lesions and active areas of current parenchyma destruction. Protein measurements capable of providing quantitative information on current ongoing lung tissue matrix destruction would be invaluable assets in monitoring progression of disease in the context of the CT scans measuring emphysema and the functional tests.
Over recent time, it has been demonstrated that early detection, prescription of personalized medicine based on disease type, and evaluation of response to treatment has significantly impacted on advancing outcomes, and reducing cost to healthcare systems. These diseases are known to be highly complex and multifactorial. It is not possible at this stage, to assign a single molecule related to one disease or clinical complaint. On the contrary there are hundreds (multiple signals), that relate to a change of a normal state to a diseased. Consequently, there is a need of selecting the key regulators from multiple signals. This is a highly demanding task, as this is hampered by the lack of tools and data for defining the biological mechanisms involved in the early onset of establishing of disease development. This is probably the biggest challenge within the field of translational medicine.
In addition, the modeling of disease progression and the evaluation of treatment response are also active areas within the science community. Lung cancer and COPD are both known to cluster in families and are more common in elderly population. Aggregation has been observed in families which would suggest a genetic or an environmental connection.
Clinically and pathologically it has been observed that smokers with COPD often develop lung cancer. The transition between normal epithelial cell biology and transformation into a malignant cell status can be defined histologically (Figure 2), as morphological changes developing hyperplasia, followed by dysplasia and a final diagnosis of carcinoma. Diagnostically, these changes can be observed microscopically at pathology examination but also macroscopically in situ at the sites of disease by changes in fluorescent patterns seen by endoscopy [19, 20].
Currently these disease areas are facing significant challenges where major research resources are directed, such as:
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Stratifying large and increasing number of lung cancer phenotypes and their link to COPD
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Diagnosis, at an early stage, where limited markers are available, with a need for potential targeted medical opportunities
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Lack of monitoring of any given treatment’s effectiveness
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Correlation between in-situ molecular mechanism of the disease and peripheral molecular changes
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Methods to measure at a molecular level what changes have occurred at sites of targeted drug intervention.
The optimization of treatment, based on individual medical need is currently a fundamental cornerstone to the rebuilding of the entire medical and clinical system. In this respect, the concept of personalized medicine has been established as a standard working proposition worldwide. On the technology side, a major and unremitting effort is underway to achieve these developments such as the requirement to establish the standard ranges of expression in quantitative assays. These assays need to be able to separate healthy from diseased individuals in a variety of basic sciences such as genomics, proteomics, and metabolomics, as well as clinical sciences. Clinically, surveillance, diagnosis and treatment are in focus, which are in turn applied to match scenarios of individual disease with best practice treatment efficacy at the level of each individual patient. Lately, a major focus of the introduction of targeted personalized medicine is marker associations with drug efficacy and safety. In this respect, the interface that relates the localization of drugs and metabolites that can be related to pharmacokinetic-, and pharmaco-dynamic data will form the basis for optimal conditions whereby drug dosing and delivery could be made. In particular, targeted drug treatments, that is directed towards a specific patient groups will benefit from a predictive guidance utilizing matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). As mass spectrometry imaging does not require any chemical labeling, the “cold compound” has the great advantage to provide data that can be directly linked to the pharmacological effects of the drug.
Drug localization by MALDI-MS imaging
For many drug therapies, there is little knowledge about the ultimate distribution patterns of the compounds within tissue compartments following treatment. It is possible to label drugs with a tracer and follow their uptake using technologies such as positron-emission tomography (PET) and autoradiography. For both of these methods the physical manipulation of the compound by the labeling could change the properties of the compound. Over the last decade a method to identify unlabeled drugs in tissue has been under development using MALDI-MSI. With MALDI-MSI continual incremental sampling can be performed upon tissue taken directly from the body to identify cellular locations that contain the specific drug ion signatures of the compound in question [21].
We recently performed a proof-of-principle in order to test the power of applying MALDI-MSI to demonstrate the qualitative drug distribution within pulmonary microenvironments. The concept and mass spectrometry imaging is illustrated in Figure 3. This is the first reported study in man, at a resolving power of 30 μm, of drug localization within organ compartments using normal clinical dosing schemes and standardized laboratory measurement. We mapped the occurrence of an inhaled bronchodilator, the muscarinic receptor antagonist ipratropium, within human bronchial biopsies obtained by fiber optic bronchoscopy shortly after dosing exposure [22]. Samples coated with a matrix compound were analyzed by a MALDI LTQ Orbitrap XL mass spectrometer at a resolving power of 30 μm, spatial resolution. Ipratropium parent ion (m/z 332.332) and daughter ions (m/z 166.2 and 290.2) were coincidently partitioned within the sub mucosal spaces containing targeted airway smooth muscle in 4/5 subjects. We could conclude from our investigations that ipratropium is rapidly absorbed into the airway wall. Of interest here was that the airway biopsy not showing ipratropium uptake was histologically found to represent a tumor forming within the airway wall (see Figure 4). The limited drug uptake in the tumor could be due to a variety of reasons including defects in drug transport and changes in muscarinic receptor density within the effected tissue microenvironment. The ability to discriminate between positive and negative examples of drug uptake provides a very powerful tool in understanding drug efficacy.
We could also conclude from our study that the specific region of tissue isolation (from the patient presented in Figure 4, where the tumor growth was identified), that the tumor could be visualized in real-time upon endobronchial biopsy isolation.
With endobronchitis imaging, using the white light source in comparison to autofluorescence, tumor differentiation can be made, by identifying the tumor regions within microenvironments of the bronchi. Figure 5 provides a nice example and illustration of a healthy endobronchial image using white light (Figure 5A) in relation to the autofluorescence bronchoscopy (Figure 5B). The COPD patient, with tumor growth, is illustrated in Figure 5C in relation to Figure 5D, that provides evidence of the tumor region. This can be seen as a white region on the upper regions of image capture Figure 5C, in comparison to the autofluorescence image (Figure 5D), where this white region comes up as a black area. This is the ultimate differential read-out that is used for tumor diagnosis on a routine basis by clinicians.
The theoretical target of ipratropium was the muscarinic receptors M1-M3 that are expressed differentially on smooth muscle cells and inflammatory cells. Our results showed that the ipratropium signal (typical mass spectra of ipratropium in lung tissue presented in Figure 6) could be observed in areas of the biopsy that coincidently contained either or both, smooth muscle cells and/or inflammatory cells. Our current work is characterizing the expression of the specific muscarinic receptor in this material using immunohistochemistry. This is a powerful advantage of the MALDI-MSI technique in that the same tissue section that has been used to quantify drugs can be used in additional assays to characterize the microenvironment.
