Exploring new biomarkers and emerging techniques for breast cancer diagnosis
Bibliographic record
Abstract
Breast cancer is the most common cancer in Canadian women. The choice of treatment is based on the expression of markers such as the estrogen receptor (ER), the membrane receptor HER2 and the proliferation marker KI-67, detected by immunohistochemistry (IHC). Although some breast tumors may be heavily infiltrated by immune cells, the expression of immune checkpoint inhibitors (ICIs) by both immune and cancer cells promotes tumor growth. In particular, tumor expression of B7H4 has been associated with a high rate of metastasis and an inverse correlation with CD8+ T cells. In order to better characterize B7H4 expression patterns, levels of this protein as well as markers of immune infiltrate, immune checkpoint and EMT were characterized by IHC using six tissue microarrays constructed from 123 FFPE samples from the Centre Hospitalier de l'Université de Montréal (CHUM). B7H4 expression (% positive cells) was quantified by QuPath software (version 0.2.0 m9), while EMT was assessed semi-quantitatively by Khadri et al. (2020). Our results show that B7H4 is highly expressed exclusively by tumor cells in all subtypes (mean 22.5% to 43.3%), with lower levels in luminal B (LB) and HER2-positive (HER2+) tumors. Multivariate analysis of the Metabric cohort of breast tumor transcriptome profiles (1980 tumors) further showed that B7H4 RNA levels are inversely associated with those of Ecad (p=0.0001), the same trend was observed in our cohort, and are strongest in lobular tumors (Ecad-). Furthermore, analysis of a TCGA cohort (754 tumors) revealed a positive correlation of B7H4 transcripts with YAP, TAZ, VGLL1 and TRIM29 (Pearson correlation coefficient >0.4), inducers of TEAD transcription factors repressed by the Hippo signaling pathway. In particular, YAP has a similar expression pattern to B7H4, higher in basal and luminal subtypes. These results suggest that the role of B7H4 in the progression of these tumors may be linked to the Hippo pathway, which controls the growth, proliferation and migration of mammary tumor cells. Capturing the intertumoral and intratumoral tumor heterogeneity necessitates new detection methods enabling higher multiplexing than IHC or immunofluorescence (IF). Nanoparticles (NP) such as carbon-nanotubes (CNT) or gold nanoparticles (AuNPs), which can be functionalized with antibodies and detected without fluorescent labels, are promising to become powerful tools in bioimaging. We have explored the capacity of PEGylated CNT linked to anti-KRT19 and anti-Ecad antibodies to detect the corresponding proteins in breast cancer T47D (ER+/Ecad+/KRT19+) and MDA-MB-231 (ER-/Ecad-/KRT19-) cell lines. NP-Ecad and NP-KRT19 with internalized -sexithiophene and β-carotene dyes, respectively, present specific Raman signature upon excitation with 532 nm laser. Dynamic light scattering detected a shift in the size of functionalized NP and immunogold labeling confirmed the antibodies are in the right orientation. Although NPs remained in cluster, both bound the surface of T47D cells while only background was observed in MDA-MB-231 cells, as expected. In an alternative approach, PEGylated gold-NP were conjugated to anti-HER2 and anti-CD44 antibodies and detected by scattered light in dark-field microscope. NP-HER2 were incubated with HER2+ SKBR3 and MDA-MB-453 cells in increasing concentration and time. Results show an average of 48 NP per cell in SKBR3 and 8 NP-HER2/cell in MDAMB453, consistent with IF detection. NP-CD44 are highly sensitive, as we observed up to 1000 NP-CD44/cell in CD44+ cells versus 10 NP-CD44/cell in CD44- cells, with an average of 250 NP per cell in SKBR3, stratified as CD44+. These promising approaches both bear the potential of multiplexed biomarker detection in a rapid and quantitate manner.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".