Characterisation of colorectal cancer by means of imaging to allocate patients to best treatment options
Doctoral thesis
Date of Examination:2024-05-29
Date of issue:2025-01-23
Advisor:Prof. Dr. Frauke Alves
Referee:Prof. Dr. Holger Bastians
Referee:Prof. Dr. Stefan Rieken
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Abstract
English
Colorectal cancer (CRC) is currently the second deadliest cancer in the world. Apart from the known prognostic factors, the role of tumour microenvironment (TME), in particular the extracellular matrix (ECM) has gained traction in the past few years. This thesis focuses on characterisation of ECM features in CRC tissues by non-invasive and label-free imaging modalities – two-photon laser scanning microscopy (2PLSM) and matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI MSI). In manuscript I, by performing texture analysis on the second harmonic generation (SHG) signal emitted by collagen during 2PLSM, I identified differences in the morphological features of collagen in LSCC and RSCC (n =17). CRC tissues were found to contain more collagen fibres and a straighter morphology of collagen fibres in comparison to healthy colorectal tissues. The amount of collagen fibres was higher in LSCC compared to RSCC. However, these collagen fibres were found to be more chaotic in their organisation in RSCC compared to LSCC. There was no significant distinction in the waviness of the collagen fibres based on the anatomical origin of the tumour. Therefore, our findings suggest that while a single feature might not fully define collagen morphology, using a combination of features allows us to clearly and objectively outline the morphological characteristics. In manuscript II, I quantified peptide features in the abovementioned CRC patient cohort and combined them with imaging features to extract peptide signatures from distinct tumor regions. On performing receiver operator characteristics (ROC) analysis on 156 peptide signatures, I identified 46 peptides that were discriminative in tissues originating from LSCC compared to RSCC. I performed unsupervised, supervised and protein-protein interaction studies on these differential peaks. Using the unsupervised approach of principal component analysis, I depicted that the expression of principal component one is higher in LSCC than in RSCC. The supervised approach of k-means clustering led us to defining proteomic clusters in CRC tissues. Quantification of these clusters highlighted how the peptide clusters are differentially distributed in tumour tissues based on the anatomical origin of the primary tumour, i.e., LSCC or RSCC. viii The protein-protein interaction studies using STRING analysis determined that the differential proteins are functionally related. Major peptides upregulated in LSCC tissues were plectin (PCN), collagen (COL14A1, COL1A2, COL1A1, COL6A3), laminin (LAMA5, LAMB1), myosin (MYH11, MYH14), sorbin and SH3 domain-containing protein (SORBS1), endoplasmin (HSP90B1), transitional endoplasmic reticulum ATPase (TER ATPase), gelsolin (GSN), palladin (PALLD), and vinculin (VCL). These proteins collectively perform cellular functions such as focal adhesions, ECM assembly, cytoskeletal organization and cell migration. Furthermore, I performed correlative multimodal imaging to combine peptide features obtained from MALDI MSI with imaging features from 2PLSM and histology. I conducted 2PLSM experiments, followed by MALDI MSI and then histology on the same tissue section. I segmented nuclei from histology images and generated nuclei distribution heatmaps. I also performed texture analysis on SHG images to generate heatmaps of coherence in collagen fibre architecture. The heatmaps generated from histology and 2PLSM were used to define and annotate regions of low and high nuclei distribution, as well as low (chaotic) and high (organised) coherence in collagen fibres. On performing ROC analysis, I identified 55 discriminative peptides in the high nuclei distribution regions of LSCC in comparison to RSCC, such as x-ray cross-complementing protein- 5 (XRCC5) and Non-POU domain-containing octamer binding peptide (NONO). Both peptides have been previously correlated to poor prognosis in CRC. I further identified 45 peptides to be discriminative in the chaotic regions of tumours and 33 peptides which are differentially expressed in the organised regions of LSCC in comparison to RSCC. Notably, these peptides (HSP90B1, HSP90AB1, COL6A3, COL1A1, COL1A2, LAMA5, and LAMB1) are involved in the P13K-Akt signalling pathway, which plays a role in ECM mediated, epithelial-to mesenchymal transition (EMT) of malignant cells. In manuscript III, I identified imaging features from the collagen structure in tumours of a large patient cohort using convolutional neural network (CNN), a deep-learning method, to correlate these features to the recurrence of the tumour. I present a retrospective study on tissue sections obtained from a large cohort of 100 CRC patients. I optimized and performed 2PLSM on these tissues. Using the clinical information on the patients from the biobank database, I segregated the patients into recurrence and non-recurrence groups. I predicted tumourrecurrence in CRC patients ix with an accuracy of 84.2%, a precision of 87.0% and sensitivity/recall of 81.6%. I report that wavier and bundled streaks of collagen fibres in the CRC tissues are associated with tumour recurrence. Moreover, a pattern of collagen fibres surrounding the crypts is equally essential for identification of tumour recurrence. Alternatively, more straight, scattered and non-bundled collagen fibres are associated with non-recurrence. These findings about the differences in collagen morphology and the peptide signatures in the LSCC and RSCC tissues and the correlation of the collagen features with tumour recurrence in CRC tissues reveal novel insights on the ECM in CRC and shed light on the complexity and heterogeneity of the TME. In the future, these quantified 2PLSM metrics can be integrated into real-time imaging in endoscopes. The peptide signatures obtained in this study can help in identifying selective drug target targets for LSCC and RSCC. Furthermore, the correlative imaging method of applying 2PLSM with MALDI MSI and histology can be incorporated in other studies where the collagen structure and the peptide signatures are equally essential, such as connective tissue disorders. Lastly, the neural network that I trained to predict tumour recurrence on 2PLSM images can also be integrated into real-time prediction of tumour relapse via 2PLSM endoscopes. Thus, these findings contribute to a clearer understanding of CRC progression, metastasis and treatment strategies.
Keywords: Colorectal cancer; second harmonic generation; spatial proteomics; deep learning; image processing; imaging biomarkers
Schlagwörter: colorectal cancer; second harmonic generation; spatial proteomics; deep learning; image processing; imaging biomarkers