Spatially dissecting drivers of inter- and intra-tumoral heterogeneity in Kenyan breast cancer.
7 affiliations
- 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Bethesda, Rockville, MD, 20850, USA.
- 2Aga Khan University, East Africa, Nairobi, P.O Box 30270-00100, Kenya.
- 3Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc, Frederick, MD, 21702, USA.
- 4AIC Kijabe Hospital, Kijabe, Kenya.
- 5Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20850, USA.
- 6Mount Kenya University, Thika, Kenya.
- 7Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Bethesda, Rockville, MD, 20850, USA. [email protected].
Abstract
Despite growing evidence on the impact of intra-tumoral heterogeneity (ITH) in breast cancer (BC), its biological drivers and clinically relevant mitigation strategies remain poorly characterized, particularly in low-resource settings where spatial profiling is rarely applied. To address this gap, we conducted a comprehensive, multiplatform spatial profiling study of BC in a Kenyan population. Using the NanoString GeoMx™ Digital Spatial Profiler, we quantified 44 protein markers across 707 spatially defined, PanCK-segmented Areas of Illumination (AOIs) from 31 tumors. All tumors exhibited measurable ITH, with one-third showing marked heterogeneity. Sixteen patients exhibited two or more intrinsic subtypes within their tumors, three of whom had three subtypes. Immune-related ITH was particularly pronounced for CD8 and CD68. AOI phenotype (epithelium- vs. stroma-rich) emerged as the primary driver of marker variability. Linear mixed-effects (LME) modeling further revealed that intrinsic subtype influenced not only classical subtyping markers (ER, PR, HER2) but also immune and signaling markers including IDO1, S100B, PTEN, and BCL-2. Tissue morphology and spatial neighborhood contributed additional variance, particularly in stroma-rich AOIs, while incorporating AOI spatial coordinates improved model fit and revealed further spatially structured heterogeneity. Nonetheless, over 25% residual variability remained for most markers, suggesting unmeasured biological or microenvironmental influences. Comparisons with InSituPlex and bulk RNA data demonstrated that bulk profiling could obscure or misrepresent tumor complexity, underscoring the need for integrative spatial approaches. Our findings highlight whole-slide, multi-region digital profiling as a practical and scalable strategy to capture clinically relevant heterogeneity in low-resource settings.
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BibTeX
@article{abubakar2026spatially,
title = {{Spatially dissecting drivers of inter- and intra-tumoral heterogeneity in Kenyan breast cancer.}},
author = {Mustapha Abubakar and Shahin Sayed and Hela Koka and Scott Lawrence and Karun Mutreja and Xing Hua and Petra Lenz and Samuel Anyaso-Samuel and Rosemary Wangari Kamau and Beryl Akinyi Ooro and Stefan Ambs and Francis Makokha and Li Feng and Jonine Figueroa and Difei Wang and Chad Highfill and Maria Brown and Aaron M Rozeboom and Kristine Jones and Belynda Hicks and Paul S Albert and Jianxin Shi and Xiaohong Rose Yang},
journal = {Breast cancer research : BCR},
year = {2026},
month = jul,
doi = {10.1186/s13058-026-02347-z},
url = {https://europepmc.org/article/MED/42928275},
note = {CC BY},
}
RIS
TY - JOUR
TI - Spatially dissecting drivers of inter- and intra-tumoral heterogeneity in Kenyan breast cancer.
AU - Mustapha Abubakar
AU - Shahin Sayed
AU - Hela Koka
AU - Scott Lawrence
AU - Karun Mutreja
AU - Xing Hua
AU - Petra Lenz
AU - Samuel Anyaso-Samuel
AU - Rosemary Wangari Kamau
AU - Beryl Akinyi Ooro
AU - Stefan Ambs
AU - Francis Makokha
AU - Li Feng
AU - Jonine Figueroa
AU - Difei Wang
AU - Chad Highfill
AU - Maria Brown
AU - Aaron M Rozeboom
AU - Kristine Jones
AU - Belynda Hicks
AU - Paul S Albert
AU - Jianxin Shi
AU - Xiaohong Rose Yang
PY - 2026
DA - 2026/07/01
JO - Breast cancer research : BCR
DO - 10.1186/s13058-026-02347-z
UR - https://europepmc.org/article/MED/42928275
AB - Despite growing evidence on the impact of intra-tumoral heterogeneity (ITH) in breast cancer (BC), its biological drivers and clinically relevant mitigation strategies remain poorly characterized, particularly in low-resource settings where spatial profiling is rarely applied. To address this gap, we conducted a comprehensive, multiplatform spatial profiling study of BC in a Kenyan population. Using the NanoString GeoMx™ Digital Spatial Profiler, we quantified 44 protein markers across 707 spatially defined, PanCK-segmented Areas of Illumination (AOIs) from 31 tumors. All tumors exhibited measurable ITH, with one-third showing marked heterogeneity. Sixteen patients exhibited two or more intrinsic subtypes within their tumors, three of whom had three subtypes. Immune-related ITH was particularly pronounced for CD8 and CD68. AOI phenotype (epithelium- vs. stroma-rich) emerged as the primary driver of marker variability. Linear mixed-effects (LME) modeling further revealed that intrinsic subtype influenced not only classical subtyping markers (ER, PR, HER2) but also immune and signaling markers including IDO1, S100B, PTEN, and BCL-2. Tissue morphology and spatial neighborhood contributed additional variance, particularly in stroma-rich AOIs, while incorporating AOI spatial coordinates improved model fit and revealed further spatially structured heterogeneity. Nonetheless, over 25% residual variability remained for most markers, suggesting unmeasured biological or microenvironmental influences. Comparisons with InSituPlex and bulk RNA data demonstrated that bulk profiling could obscure or misrepresent tumor complexity, underscoring the need for integrative spatial approaches. Our findings highlight whole-slide, multi-region digital profiling as a practical and scalable strategy to capture clinically relevant heterogeneity in low-resource settings.
SN - 1465-5411
SN - 1465-542X
ER -