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Heterogeneity in Signaling Pathway Activity within Primary and between Primary and Metastatic Breast Cancer
Márcia A Inda1, Paul van Swinderen1, Anne van Brussel2
1Precision Diagnostics Department, Philips Research, 5656 AE Eindhoven, The Netherlands.
Abstract:
Targeted therapy aims to block tumor-driving signaling pathways and is generally based on analysis of one primary tumor (PT) biopsy. Tumor heterogeneity within PT and between PT and metastatic breast lesions may, however, impact the effect of a chosen therapy. Whereas studies are available that investigate genetic heterogeneity, we present results on phenotypic heterogeneity by analyzing the variation in the functional activity of signal transduction pathways, using an earlier developed platform to measure such activity from mRNA measurements of pathways' direct target genes. Statistical analysis comparing macro-scale variation in pathway activity on up to five spatially distributed PT tissue blocks (n = 35), to micro-scale variation in activity on four adjacent samples of a single PT tissue block (n = 17), showed that macro-scale variation was not larger than micro-scale variation, except possibly for the PI3K pathway. Simulations using a "checkerboard clone-size" model showed that multiple small clones could explain the higher micro-scale variation in activity found for the TGFβ and Hedgehog pathways, and that intermediate/large clones could explain the possibly higher macro-scale variation of the PI3K pathway. While within PT, pathway activities presented a highly positive correlation, correlations weakened between PT and lymph node metastases (n = 9), becoming even worse for PT and distant metastases (n = 9), including a negative correlation for the ER pathway. While analysis of multiple sub-samples of a single biopsy may be sufficient to predict PT response to targeted therapies, metastatic breast cancer treatment prediction requires analysis of metastatic biopsies. Our findings on phenotypic intra-tumor heterogeneity are compatible with emerging ideas on a Big Bang type of cancer evolution in which macro-scale heterogeneity appears not dominant.
Insights
Tumor heterogeneity in breast cancer impacts targeted therapy. Analyzing pathway activity reveals that while primary tumors show similar activity, metastatic sites require separate biopsies for accurate treatment prediction.
Area of Science:
- Oncology
- Molecular Biology
- Cancer Research
Background:
- Targeted therapy efficacy relies on blocking tumor-specific signaling pathways, typically assessed via a single primary tumor (PT) biopsy.
- Tumor heterogeneity, both within a PT and between PT and metastases, can significantly affect treatment outcomes.
- Existing research often focuses on genetic heterogeneity, leaving phenotypic heterogeneity under-explored.
Purpose of the Study:
- To investigate phenotypic heterogeneity in breast cancer by analyzing signal transduction pathway activity.
- To compare macro-scale (inter-sample) and micro-scale (intra-sample) variation in pathway activity within primary tumors.
- To assess the correlation of pathway activity between primary tumors and metastatic lesions.
Main Methods:
- Utilized a platform measuring signal transduction pathway activity via mRNA of direct target genes.
- Statistically compared pathway activity variation across spatially distributed PT tissue blocks (macro-scale) and adjacent samples from a single PT block (micro-scale).
- Employed a
- checkerboard clone-size
- model to simulate clonal contributions to observed pathway activity variations.
- Analyzed pathway activity correlations between PT and lymph node/distant metastases.
Main Results:
- Macro-scale variation in pathway activity was generally not greater than micro-scale variation within PTs, with a possible exception for the PI3K pathway.
- Simulations suggested small clones explain micro-scale variation (TGFβ, Hedgehog pathways) and intermediate/large clones explain macro-scale variation (PI3K pathway).
- Pathway activity correlations were high within PTs but decreased significantly between PT and lymph node metastases, and further between PT and distant metastases, with a negative correlation observed for the ER pathway.
Conclusions:
- Analysis of multiple sub-samples from a single primary tumor biopsy may suffice for predicting its response to targeted therapies.
- Accurate treatment prediction for metastatic breast cancer necessitates the analysis of biopsies from metastatic sites.
- Findings support the
- Big Bang
- model of cancer evolution, where macro-scale heterogeneity is not dominant.
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