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Emergence of oncofetal plasticity is ubiquitous in early colorectal cancers

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Why This Matters

This study highlights the widespread presence of oncofetal plasticity in early colorectal cancers, revealing insights into tumor progression and potential therapeutic targets. Understanding these cellular dynamics can improve early diagnosis and personalized treatment strategies, ultimately benefiting patient outcomes and advancing cancer research. The findings underscore the importance of molecular profiling in early-stage cancers to better predict metastasis and treatment response.

Key Takeaways

Patients

This study was approved by the University Medical Centre (UMC) Utrecht ethical committee, carried out in accordance with the ethical guidelines and regulations and all patients provided written informed consent. FFPE specimens for immunohistochemistry and spatial transcriptomics were requested from and provided by the UMC Utrecht pathology department. Patient inclusion for the organoid biobank was managed by the Utrecht Platform for Organoid Technology (https://uport.umcutrecht.nl/researcher/en/). The biobank participants were 16 patients suspected of having early-stage CRC who underwent surgery for removal of the primary tumour, instead of endoscopic removal, owing to inaccessibility of the tumour. Clinical data from patients featured in this study can be found in Supplementary Table 1.

GeoMx bulk spatial transcriptomics

Nanostring GeoMx experiments were conducted with the Utrecht Sequencing Facility (USEQ) and performed as previously described in ref. 65. In brief, 10 T1 CRCs (5× T1N0M0 and 5× T1N1M0) were analysed using the GeoMx CTA (Cancer Transcriptome Atlas) panel and 9 T1 CRCs (3× T1N0M0, 3× T1N1M0 and 3× T1N0M1) were analysed using the GeoMx WTA panel. The specimens analysed by CTA were selected such that risk factors, including lymphovascular invasion, tumour budding, location and morphology were similar between metastatic and non-metastatic primary tumours. Specimens were stained for PanCK (Novus Biologicals, NBP2-33200AF532, 2 µg ml−1) to visualize epithelium, CD45 (Novus Biologicals, NBP2-34528AF594, 5 µg ml−1) to visualize immune cells and SYTO13 (Invitrogen, S7575, 500 nM) to visualize nuclei. ROIs containing 100 to 1,000 nuclei were placed in 4 histopathological regions per tumour: normal tissue adjacent to the tumour, adenomatous tumour component, tumour core and invasive front. Invasive front ROIs were consistently placed, with epithelial tumour strands penetrating the supportive tissue for roughly three-quarters of the ROI edge perpendicular to the tumour border. After ROI placement, PanCK immunofluorescence was used to segment epithelial (PanCK+) and stromal (PanCK−) compartments for separate transcriptomic profiling. For the CTA cohort, CD45 negative and positive areas within the stromal compartment were profiled separately, but were summed during analysis for comparability with the WTA experiment. Standard quality control (unified quality control threshold) was applied to both experiments and can be viewed in Supplementary Reports 1 and 2. In total 426 (CTA) and 285 (WTA) ROIs were sampled across all specimens of which 373 and 281 ROIs were retained after quality control for the CTA and WTA experiments, respectively. At the gene level, 1,781 out of 1,812 and 18,441 out of 18,677 genes were retained after quality control for the CTA and WTA experiments, respectively. Probe counts were aggregated per gene target, Q3 normalized, batch corrected (with ‘slide name’ as the batch to be corrected for) and log 2 transformed. For all downstream analyses, sample pt17 (T1_NANO_013) was excluded, because it is classified as a T3 tumour. For variance partition analysis the VariancePartition66 (v.1.38.1) R package was used. To compare different tissue regions within a specimen and across different specimens, we used a linear mixed model approach to model the normalized expression separately for epithelial and stromal segments: log 2 (gene) ~ tissue region + (1 + tissue region | patient ID). For gene set enrichment analysis (GSEA), two methods were applied: preranked GSEA (fgsea67 v.1.24.0) and single-sample GSEA (ssGSEA68 implemented in GSVA v.1.46.0). Gene sets tested originated from MsigDB (https://www.gsea-msigdb.org/gsea/msigdb), from this study or from published literature (summarized in Supplementary Table 3).

GeoMx CMS classification

Regions from the WTA cohort were used for CMS19 and iCMS classification20. For CMS classification, raw transcript counts of adjacent PanCK+ and PanCK− segments were summed per area of interest and thereafter summed by patient ID and tissue region. Patient F was excluded from this analysis, because the PanCK− and PanCK+ segments were not located within the same areas of interest. These pseudo-bulk samples were used as input for CMScaller19 (v.2.0.1), which was run with ‘RNAseq = TRUE’ alongside default parameters. Finally, the fraction of stromal nuclei for each area of interest was calculated. For iCMS classification CMScaller was run with raw PanCK+ gene counts only and ‘RNAseq = TRUE’. CMS2 and iCMS3 Up gene sets20 were used as templates to classify the segments.

GeoMx CNA prediction

Copy number alteration (CNA) profiles of epithelial cells from the different histopathological regions were estimated using inferCNV (v.1.14.2; ‘cutoff = 0.1’; using normal tissue as a reference group and excluding chromosome XY and mitochondrial genes). Chromosome arm gains and losses were defined as an average residual expression of more than 1.1 or less than 0.9 across all genes on that arm, respectively. Short arms of acrocentric (13p, 14p, 15p, 21p, 22p) and both arms of sex chromosomes were excluded. To calculate pairwise cosine similarities among ROIs from the same tumour, the average residual expression per chromosome arm was rounded to the nearest decimal.

Immunohistochemistry of CRCs

Spatial transcriptomics findings were validated with immunohistochemistry labelling on consecutive slides of the selected T1 tumours. Here 5-µm thick FFPE-embedded tumour sections were mounted on glass slides and baked in at 60 °C for 1 h. Deparaffinization and rehydration was performed as follows: xylene (3 min, 1 change), 96% ethanol (3 min, 1 change), 70% ethanol (3 min, 1 change), rinse in deionized water and rinse in tap water. Heat-mediated antigen retrieval was performed for 20 min in 50 mM Tris/1 mM EDTA pH 9.4 buffer at 95 °C. The following primary antibodies were used: SFRP2 (PA5-29390, Invitrogen, 1:200), LAMC2 (AMAb91098, Atlas Antibodies, 1:500), PanCK (AlexaFluor 532 conjugated; NBP2-33200 Novus 1:500 and NBP3-08398 Novus 1:300) and DNA Syto 13 (S7575, Invitrogen, 1:10,000). The following secondary antibodies were used: Alexa 594 anti-rabbit (Invitrogen A11037; 2 µg ml−1) and Alexa 594 anti-mouse (Invitrogen A11032; 2 µg ml−1). Slides were scanned on the GeoMx Digital Spatial Profiler (Nanostring) with a ×20 0.45 numerical aperture objective and analysed using the QuPath (v.0.6.0) Instanseg extension69. In brief, we quantified all cells within the invasive front (1 mm deep, measured from tumour margin), irrespective of tumour width. Within invasive fronts, nuclei and epithelial cell bodies were segmented on the basis of Syto13 and PanCK pixel intensities, after which percentages of LAMC2+ cells (in epithelium) and the percentages of SFRP2+ and FAP+ cells (in stroma) were calculated. Quantifications were visualized with GraphPad Prism (v.10.4.1).

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