Infrared spectroscopy has long been used to characterize chemical compounds, but the applicability of this technique to the analysis of biological materials containing highly complex chemical components is arguable. However, recent advances in the development of infrared spectroscopy have significantly enhanced the capacity of this technique in analyzing various types of biological specimens. Consequently, there is an increased number of studies investigating the application of infrared spectroscopy in screening and diagnosis of various diseases. The lack of highly sensitive and specific methods for early detection of cancer has warranted the search for novel approaches. Being more simple, rapid, accurate, inexpensive, non-destructive and suitable for automation compared to existing screening, diagnosis, management and monitoring methods, Fourier transform infrared spectroscopy can potentially improve clinical decision-making and patient outcomes by detecting biochemical changes in cancer patients at the molecular level. Besides the commonly analyzed blood and tissue samples, extracellular vesicle-based method has been gaining popularity as a non-invasive approach. Therefore, infrared spectroscopic analysis of extracellular vesicles could be a useful technique in the future for biomedical applications. In this review, we discuss the potential clinical applications of Fourier transform infrared spectroscopic analysis using various types of biological materials for cancer. Additionally, the rationale and advantages of using extracellular vesicles in the spectroscopic analysis for cancer diagnostics are discussed. Furthermore, we highlight the challenges and future directions of clinical translation of the technique for cancer.
Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.
Here, we tested the hypothesis that specific salivary vibrational modes can be used to discriminate patients with breast cancer from benign patients and matched healthy controls, which may prove that salivary spectral biomarkers are suitable in diagnosing breast cancer. In this manner, the aim of the present study was to establish specific salivary vibrational modes, analyzed by ATR-FTIR spectroscopy, to detect breast cancer fingerprints that are suitable for diagnosis.
Our models to detect breast cancer achieve an average overall performance of 0.79 in terms of area under the curve (AUC) of the receiver operating characteristic (ROC). In addition, we uncover a relationship between the effect size of the measured infrared fingerprints and the tumor progression.
This pilot study provides the foundation for further extending and evaluating blood-based infrared probing approach as a possible cross-molecular fingerprinting modality to tackle breast cancer detection and thus possibly contribute to the future of cancer screening.
Liquid biopsies have attracted interest over the past decade as a non-invasive approach for disease detection, screening and cancer monitoring . Molecular analyses of human blood derivatives, such as plasma or serum, provide systemic molecular information, and enable novel routes of diagnostics [8, 10]. So far, most liquid biopsies predominantly rely on the analysis of a few pre-selected analytes and biomarkers. Although the emergence of highly sensitive and molecule-specific methods in the fields of proteomics [11,12,13], metabolomics [14, 15], and genomics [16,17,18] has led to the discovery of thousands of different biomarker candidates, only a few of them have been validated and transferred to the clinic so far . Moreover, given the complexity of the disease as well as its etiology, increasing the number of analytical methods for cancer detection, such as in multi-omics, could potentially lead to higher detection rates at early stage. However, practically, this will lead to unfeasibly high costs for broad clinical use. It is thus evident that methods that have the capacity to capture information across the entire molecular landscape would be advantageous.
Presented results are based on a prospective, single center, observational clinical study. The aim of the study was to assess whether the combination of infrared spec- troscopy of liquid biopsies (blood plasma) with machine learning infrared spectral analyses has any capacity to detect breast cancer (BC). For this purpose, a cohort of female patients diagnosed with BC at the Oncology Centre, King Saud Univer- sity Medical City (KSUMC), Riyadh, Saudi Arabia, was compared with a cohort of women without BC, reference individuals. Inclusion criteria for participation in the study were as follows: Asymptomatic reference individuals were adult females participating in organized or voluntary BC screening, assessed with mammography and (if necessary) breast ultrasound and/or magnetic resonance imaging (MRI). Patients with BC were included after confirmation of pathological diagnosis of invasive breast cancer and prior to any therapeutic intervention for breast cancer. Subjects included in the trial were identified by a trial-specific code, guaranteeing their anonymity.
To evaluate whether IMF probing of liquid plasma has any capacity to detect BC, we performed binary classification for distinction between the BC patients and the matched asymptomatic reference individuals (Table 1 and Fig. 1a). The detection efficiencies achieved on the test sets correspond to an AUC value of 0.79 for normalized FTIR spectra. A higher AUC value of 0.81 could be achieved using non-normalized spectra (Fig. 1b). Despite the higher AUC obtained for non- normalized spectra, we consider the analysis of normalized data to be more reliable. Vector normalization reduces measurement uncertainty which can be a major factor of bias, especially in cases of small sample sizes. Overall, these results deliver the first evidence that the molecular differences between reference individuals and matched therapy-naive BC patient females can be detected with infrared fingerprinting of fluid blood plasma.
In order to understand infrared spectral information responsible for BC identification, we have evaluated the infrared spectral signatures that are relevant for distinguishing breast cancer cases from the reference, control individuals. For this purpose, we evaluated the differential fingerprints that we defined as the difference between the mean IMF of the case cohort and that of the reference cohort (Fig. 2a). This quantity, when compared to the standard deviation of the reference group (shaded area in Fig. 2a), reveals the locations along the spectrum for which the difference between the means of the two groups is larger than the sample standard deviation. These differences become even more apparent in Fig. 2b, which depicts the effect size, defined as the differential fingerprint divided by the standard deviation of the reference group. We reveal that at specific spectral locations, the effect size exceeds the barrier of one standard deviation, indicating potentially significant differences between the sample means of the two distributions.
Cancer detection is challenged by the enormous biological and clinical complexity of cancer, and detection is further complicated by the significant intra-tumor heterogeneity as well as by the impact of the tumour micro-environment . To evaluate whether the blood-based IMFs are sufficiently sensitive to detect tumors at different stages of progression, we first investigated whether the IMF characteristics depend on the stage of the tumor, characterized in terms of clinical TNM (tumor node metastasis) staging . For this purpose, we split the BC cases into two groups and compared them separately with the non-symptomatic, reference individuals. The first group corresponds to the non-metastatic (M0) patients (stages I, II, III) and the second group to metastatic (M1) patients at tumor stage IV. The characteristics of the two groups are shown in Table 2.
This study provides the first indication that the molecular differences of blood plasma between reference individuals and matched therapy-naive breast cancer females have the potential to be detected with infrared fingerprinting of crude, native liquid plasma. Although previous studies on BC detection have yielded fairly high classification efficiencies , they have used dried sera samples, which is known for