Multi-omic spatial heterogeneity and evolution landscape in primary breast cancer
Bibliographic record
Abstract
AbstractMulti-omic spatial heterogeneity and evolution landscape in primary breast cancer Jingping Qiao Doctor of philosophy Department of Molecular Genetics University of Toronto 2025 Clonal heterogeneity has emerged as a major factor in cancer treatment failure and recurrence[1]. One of the most commonly mentioned reasons for this is the existence of small clonal populations of resistant cells that grow over the course of therapy [2–4]. In this study, we applied a multi-omic strategy to investigate patterns of heterogeneity and clonal evolution in primary invasive breast cancer. We integrate the evolution history with the spatial distribution and demonstrate that the evolutionary and migratory processes in primary invasive breast cancer exhibit distinct patterns that correspond to the major tumor subtypes. Our findings reveal that a diverse array of subtype characteristics can coexist within a single breast cancer patient at the transcriptomic and protein level. Contrary to the prevalent understanding of ductal carcinoma in situ, our findings indicate that the in situ lesion is not an obligate precursor to invasive; it can evolve rapidly in parallel with invasive lesions rather than as precursor or indolent lesions, and that they show a heterogeneous cellular profile with descendant private clones. In addition, we find that the topological characteristics of the evolutionary tree are significantly correlated with the expression of clinical protein markers, with triple negative patients typically showing a significantly more linearly structured tree and a shorter trunk than hormone receptor positive tumors. Although triple negative tumors show a significant correlation between the pairwise heterogeneity of separate regions of the tumor and the physical distance of those regions, other clinical subtypes do not do so. Meanwhile, triple negative tumors exhibit greater heterogeneity between regions and a significantly lower Tajima’s D score compared to other subtypes. Finally, we discovered that heterogeneity at the immunohistochemical level can be an independent prognostic factor, which can also be predicted from imaging data with moderate to high accuracy. Our results shed light on the spatial heterogeneity of primary breast cancer, reveal subtype-specific patterns of evolution and dissemination
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".