Single-Cell Spatial Analysis of the Tumor Microenvironment in Colorectal Cancer Recurrence
Amruta Velamuri & Ellie Chan
Lay Summary:
Colorectal cancer comes back in up to 30% of patients after surgery, but doctors currently have no reliable way to predict who is at risk. Using advanced technology that maps individual cells within tumors, we discovered that patients whose cancer stayed away had a specific type of immune cell (called IgG plasma cells) organized into tight clusters, suggesting that how immune cells are arranged in a tumor -not just how many there are - could help predict and prevent cancer recurrence.
Abstract:
Colorectal cancer recurs in up to 30% of patients after resection, yet current staging poorly predicts which tumors will recur. We analyzed G4X single-cell spatial transcriptomics from 127 tissue sections of the ColoCare colorectal cancer cohort with matched recurrence outcomes to test whether the spatial organization of immune cells predicts recurrence beyond cell counts alone. Cells were annotated by marker-gene scoring and assigned to spatial niches based on neighborhood composition, and plasma cells were further resolved by immunoglobulin isotype. We found that a plasma-cell-rich spatial niche was significantly protective against recurrence, with recurrent tumors containing less than half the plasma-rich niche of non-recurrent tumors (p=0.006, surviving correction for multiple testing). This protection was isotype-specific- IgG plasma cell abundance was strongly protective (p=0.002) while IgA was not, and recurrent tumors were shifted toward IgA. Notably, IgG plasma cells were significantly more spatially clustered in non-recurrent patients (p=0.006), indicating that their organization –not just their abundance– distinguished outcomes, and this effect was independent of tumor content and overall immune infiltration. Together, these findings identify spatially clustered IgG plasma cells as protective against colorectal cancer recurrence and highlight plasma cell spatial organization as a candidate recurrence biomarker at single-cell resolution.
Q&A:
Bios: Amruta Velamuri,Ellie Chan
Program Track: Advanced Research
GitHub Username:
amrutaVelamuri -Amruta Velamuri
sleepyllie -Ellie Chan
What was your favorite seminar? Why?
My favorite seminar was Dr. Thomas Canour’s talk on BRIDGE, which predicts breast cancer treatment response by measuring the mixture of molecular subtypes within a tumor rather than assigning a single label. I loved the ‘smoothie’ analogy he used to explain deconvolution, and how the work connected computational methods directly to real clinical decisions. -Amruta Velamuri
My favorite seminar was probably Ming Yu’s seminar on potential anti-aging agents as preventative care for cancer. I found the way she explained the mechanism involving senescent cells very interesting, and it was something new I had learned. She also answered questions raised very clearly and it made the seminar all the more engaging. -Ellie Chan
If you were to summarize your summer internship experience in one sentence, what would it be?
This internship taught me what real research feels like - the setbacks, the problem-solving, and the satisfaction of working through a hard problem to a genuine result -Amruta Velamuri
I think there could have been more room for improvement in communication and working on the project synchronously, but the experience was mostly enlightening, and it was fascinating to see the different ways artificial intelligence techniques can be applied to a vast domain of medical subfields. -Ellie Chan