Potential use of absorbance scanning in differentiating tumor microenvironment compositions from endoscopic biopsy of colorectal cancer patients.

Introduction

The tumor microenvironment is an area surrounding a tumor consisting of various cells including stromal cells (Wang et al., 2017), immune cells (Winkler et al., 2020), endothelial cells, blood, and lymph vessels (Wei et al., 2020). The intra-tumoral composition of the tumor microenvironment can influence the tumor progression (Whiteside, 2008) and the clinical outcome of patients. To be able to determine the intra-tumoural composition, one common method is histopathological evaluation of the patient's biopsy (Schillaci et al., 2019). However, the biochemical composition of the biopsy such as metabolites and cytokines requires further tests such as immunohistochemistry of cell markers. (Oudijk et al., 2015) On the other hand, tumor progression may be influenced by chemical compositions within the tumor microenvironment such as cytokines and growth factors (Landskron et al., 2014; Yuan et al., 2016).

One of the simple methods that can be used to distinguish chemical compositions is spectrometry and potential differences of chemical compositions in malignancy such as changes in amino acid concentrations (Balan et al., 2019). Previously, Nogueira et al., (2021) showed that a tumor can be detected using diffuse reflectance spectroscopy in colorectal cancer diagnosis. Another study by Yang et al., (2016) showed the potentiality of visible-absorption spectroscopy as a biomarker to predict treatment and prognosis of esophageal cancer. In this study, we report absorbance scanning to distinguish three biopsies from three different patients. The absorbance scanning was carried out at a wavelength of 400–1000 nm using a spectrophotometer followed by Principal Component Analysis Orange Data Mining.

Methods

Results

In this study, we recruited three different patients namely CRC006, CRC009, and CRC011. In the samples collected from patients, CRC006 and CRC011 similar clinical characteristics such as histopathology results were observed. Welldifferentiated rectal adenocarcinoma with metastatic sites were found in both patients. The sample collected from patient CRC009 had different clinical characteristics compared to CRC006 and CRC011, and CRC009 was found to have undifferentiated adenocarcinoma without metastasis. Sample data are shown in Table 1. (Abdullah et al., 2021)

Figure 1 shows that each cell produces an absorption spectrum fingerprint in the wavelength range of 400–1000 nm. Just by observing the form of the absorption/absorption spectrum, it can be seen at first glance that fingerprints were only contributed to by the concentration of the molecule. To investigate the characteristic features of the spectrum, a principal component analysis (PCA) was carried out, which resulted in PC1 as the main feature (Abdullah et al., 2021). The results of the scatterplot with PC1 and PC2 showed that the sample CRC011 was well grouped in quadrant 1, CRC006 in quadrant 4, while CRC009 was spread between quadrants 2 and 3. From Figure 1 it can also be seen that each type of sample group appears to experience separation/classification when compared to other groups.

Figure 1. Absorbance spectrum of the lysate cells scanned using nanospectrophotometers from 400–1000 nm.

CRC: sample number.

Discussion

The light absorption spectrum is known as a “molecular fingerprint” which allows the analysis of molecular types and their amounts/concentrations. In Table 1, we show three representative colorectal cancer biopsies collected endoscopically from patients: CRC006 was a patient with welldifferentiated rectal adenocarcinoma with metastasis, CRC009 was from a patient with undifferentiated adenocarcinoma, CRC011 was from a patient with welldifferentiated adenocarcinoma with metastasis. Interestingly, based on the PCA analysis of the absorption spectrum of the lysate sample (shown in Figure 2), the CRC006 quadrant was closer to CRC011, which has similar clinical features of welldifferentiated adenocarcinoma and metastasis. On the other hand, CRC009 with undifferentiated and located far from CRC006 and CRC011 respectively, which indicated their closer chemical composition. Such existence of grouping indicates that there were differences in cellular composition between the three samples; although further studies with a larger number of samples are needed to further confirm the clustering that occurs.

Figure 2. Scatterplot of cell suspensions from samples taken from patients CRC006, CRC009, and CRC011 (n = 3) based on PC1 and PC2 values.

In biological tissue samples, several molecules contribute to the absorption spectrum in the ultraviolet (UV) to nearinfrared (NIR) regions. Protein and DNA absorb in the UV region, whereas tryptophan, NAD⁺ (nicotinamide adenine dinucleotide), collagen, elastin, NADH, and FAD (flavin adenine dinucleotide) are important endogenous chromophores in biological tissue (Yang et al., 2016). FAD, tryptophan, and NADPH (nicotinamide adenine dinucleotide phosphate) have been used as a measuring tool to see the mitochondrial metabolic response before and after therapy such as in prostate cancer (Alam et al., 2017). In cell lysate samples such as in this study, further molecular analysis is needed to determine the differences in the composition of compounds in the absorption/absorption spectrum between lysates from cancer patients.

Conclusions

The combinations of principal component analysis (PCA) and absorbance scanning were able to group patients with similar histopathology and metastasis based on their chemical compositions. Although further investigation is needed, molecular fingerprinting using absorbance scanning to predict intratumoral similarity and difference is exciting.

Data availability

Consent

Written informed consent for publication of the patients’ details was obtained from the patients.

Grant information

We thank HIBAH PUTI Q2 Universitas Indonesia (NKB4127/UN2.RST/HKP.05.00/2020) for funding our study.