Periodic Reporting for period 1 - NIMICRY (Poor Prognosis Colorectal Cancers Display Self-sustained Growth by Niche-mimicry)
Reporting period: 2022-10-01 to 2025-03-31
Colorectal cancer (CRC) is a heterogeneous disease with widely variable clinical outcomes. I previously contributed to a unifying molecular classification of CRC, the consensus molecular subtypes (CMSs). The mesenchymal subtype (CMS4), representing ~25% of all CRC patients, is characterised by early metastatic dissemination and poor response to therapy. This is often attributed to activated and rich stroma and therefore much attention in the field goes to dissecting the interaction of the mesenchyme with the cancer cells in these tumours. However, in this research program I investigated a radically different hypothesis: Mesenchymal CRCs display self-sustained growth by niche-mimicry (nimicry). I defined nimicry as the adoption of niche features by cancer cells, thereby rendering the cancers independent of micro-environmental signals for their expansion. This hypothesis is directly based on preliminary experiments from my laboratory, which demonstrated that mesenchymal CRCs are not dependent on external growth factors for expansion in vitro and display autocrine and paracrine loops that drive self-sustained growth. The abundant stroma in mesenchymal CRCs is secondary to the growth factors and cytokines produced by the tumour cells.
To study this concept, I employed state-of-the-art in vivo models, patient-derived organoids, and single cell sequencing combined with lineage tracing using advanced mathematical and bioinformatic analysis techniques, to address three main aims:
AIM 1: Unravel the signals that allow for niche-independent growth in a subset of colorectal cancers;
AIM 2: Determine the mode of growth of colorectal cancers characterized by niche-independence;
AIM 3: Establish the impact of niche-independence on tumour evolution and metastasis formation.
To study this concept, I employed state-of-the-art in vivo models, patient-derived organoids, and single cell sequencing combined with lineage tracing using advanced mathematical and bioinformatic analysis techniques, to address three main aims:
AIM 1: Unravel the signals that allow for niche-independent growth in a subset of colorectal cancers;
AIM 2: Determine the mode of growth of colorectal cancers characterized by niche-independence;
AIM 3: Establish the impact of niche-independence on tumour evolution and metastasis formation.
We could demonstrate that mesenchymal CRC cells have the ability for niche-independent proliferation by a multitude of autocrine loops, which supports the core hypothesis of the NIMCRY program. We found this property has major implications for the growth dynamics of mesenchymal cancers and contributes to the poor disease outcome of this subtype, including peritoneal dissemination. These results validated the central hypothesis of the NIMICRY program. We are currently finalizing a manuscript describing these findings.
Within this project, we have developed multiple bioinformatics tools to interrogate various types of RNA sequencing data for self-interactions or autocrine signalling. In a collaborative effort together with the bioinformatics team of Dr. Jan Koster, Amsterdam UMC, we were able to develop tools to calculate the number of different autocrine interaction pairs per cell from single cell sequencing data, using the annotated interaction database of CellPhoneDB. Similarly, we established methods to estimate the number of different autocrine interactions from bulk RNA sequencing, and implemented these data in the R2 Genomics Analysis and Visualization Platform (http://r2.amc.nl(opens in new window)) where these metrics will be available for all researchers.
Within this project, we have developed multiple bioinformatics tools to interrogate various types of RNA sequencing data for self-interactions or autocrine signalling. In a collaborative effort together with the bioinformatics team of Dr. Jan Koster, Amsterdam UMC, we were able to develop tools to calculate the number of different autocrine interaction pairs per cell from single cell sequencing data, using the annotated interaction database of CellPhoneDB. Similarly, we established methods to estimate the number of different autocrine interactions from bulk RNA sequencing, and implemented these data in the R2 Genomics Analysis and Visualization Platform (http://r2.amc.nl(opens in new window)) where these metrics will be available for all researchers.
We could not execute the project to its full potential due to early termination of the project.