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<p>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.</p>

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Spatially dissecting drivers of inter- and intra-tumoral heterogeneity in Kenyan breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Mustapha Abubakar,
  • Shahin Sayed,
  • Hela Koka,
  • Scott Lawrence,
  • Karun Mutreja,
  • Xing Hua,
  • Petra Lenz,
  • Samuel Anyaso-Samuel,
  • Rosemary Wangari Kamau,
  • Beryl Akinyi Ooro,
  • Stefan Ambs,
  • Francis Makokha,
  • Li Feng,
  • Jonine Figueroa,
  • Difei Wang,
  • Chad Highfill,
  • Maria Brown,
  • Aaron M. Rozeboom,
  • Kristine Jones,
  • Belynda Hicks,
  • Paul S. Albert,
  • Jianxin Shi,
  • Xiaohong Rose Yang

摘要

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.