Integrating computational pathology and proteomics to address tumor heterogeneity

Dent, Anglin; Diamandis, Phedias

doi:10.1002/path.5905

Cited by 10 publications

(22 citation statements)

References 64 publications

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“…Intra-tumoral heterogeneity has emerged as a key concept in current precision medicine efforts 42 . While there have been technological breakthroughs to map such spatial biodiversity at the genomic, transcriptomic and proteomic level, the relative discord between the upper limits of tissue profiling throughput and tumor sizes creates an important bottleneck for comprehensive analysis of most tumor specimens 2,4,7 . Here, we highlight how a neural network-based tool (HAVOC) can detect and quantify spatial distributions of cancer biodiversity across not only individual sections, but also on larger (entire) tumor specimens in a hypothesis-agnostic manner.…”

Section: Discussionmentioning

confidence: 99%

HAVOC: Small-scale histomic mapping of biodiversity across entire tumor specimens using deep neural networks

Dent

Faust

Lam

et al. 2023

Preprint

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Intra-tumoral heterogeneity can wreak havoc on current precision medicine strategies due to challenges in sufficient sampling of geographically separated areas of biodiversity distributed across centimeter-scale tumor distances. In particular, modern tissue profiling approaches are still largely designed to only interrogate small tumor fragments; which may constitute a minute and non-representative fraction of the overall neoplasm. To address this gap, we developed a pipeline that leverages deep learning to define topographic histomorphologic fingerprints of tissue and create Histomic Atlases of Variation Of Cancers (HAVOC). Importantly, using a number of spatially-resolved readouts, including mass-spectrometry-based proteomics and immunohistochemisy, we demonstrate that these personalized atlases of histomic variation can define regional cancer boundaries with distinct biological programs. Using larger tumor specimens, we show that HAVOC can map spatial organization of cancer biodiversity spanning tissue coordinates separated by multiple centimeters. By applying this tool to guide profiling of 19 distinct geographic partitions from 6 high-grade gliomas, HAVOC revealed that distinct states of differentiation can often co-exist and be regionally distributed across individual tumors. Finally, to highlight generalizability, we further benchmark HAVOC on additional tumor types and experimental models of heterogeneity. Together, we establish HAVOC as a versatile and accessible tool to generate small-scale maps of tissue heterogeneity and guide regional deployment of molecular resources to relevant and biodiverse tumor niches.

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Section: Discussionmentioning

confidence: 99%

HAVOC: Small-scale histomic mapping of biodiversity across entire tumor specimens using deep neural networks

Dent

Faust

Lam

et al. 2023

Preprint

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“…While this has significantly furthered our biological models of the brain and various neuropathologies, including neurodegeneration [39,40] and malignancies [41] recent proteogenomic comparisons have shown that downstream phenotypic programs cannot always be reliably inferred from transcriptional readouts (R = ∼0.35) [42][43][44]. Therefore, there is a need to complement these genomic atlases with spatially preserved proteomic profiles in tissue and cell-types specific profiling of complex experimental modes [45].…”

Section: Challenges With Proteomic Characterization Of Human Neural T...mentioning

confidence: 99%

“…Some emerging protocols even allow for single-cell analysis of hundreds of proteins per cell [46][47][48]. While there are, of course, important advantages and limitation to each of these strategies, the spatial organization of many important aspects of tissue and disease heterogeneity offer an opportunity to profile relatively biologically enriched tissue compartments to achieve higher and potentially more meaningful profiling depths [45]. Over the years, our group has significantly invested in this neuroanatomical strategy to decipher the dynamic proteome of the brain and disease, including different stages and regions of the developing brain, common tumor malignancies, and cell-type-specific enrichment and profiling of cerebral organoid tissue.…”

Section: The Brain Protein Atlas: a Proteomic Catalog Of Spatially An...mentioning

confidence: 99%

“…This has proven to be very effective for the initial efforts, given the well-characterized and heavily studied nature of these anatomical niches. Future datasets could explore more subtle regional differences by integrating LCM and LC-MS/MS with immunohistochemically-, spatial proteomic-(e.g., MALDI [59][60][61]) and even computationally-defined subregions [45,62]. Finally, as high-resolution multi-electrode array (HD-MEA) systems emerge, efforts can also expand to measure relevant phenotypes such as electrical connectivity.…”

Section: Brain Protein Atlas: Limitations Future Directions and Alter...mentioning

confidence: 99%

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The Brain Protein Atlas: A conglomerate of proteomics datasets of human neural tissue

et al. 2022

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The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcriptional architecture, more downstream phenotypic readouts are relatively scarce due to limitations with tissue heterogeneity and accessibility, as well as an inability to amplify protein species prior to global -OMICS analysis. To address some of these barriers, our group has recently focused on using mass-spectrometry workflows compatible with small amounts of formalin-fixed paraffin-embedded tissue samples. This has enabled exploration into spatiotemporal proteomic signatures of the brain and disease across otherwise inaccessible neurodevelopmental timepoints and anatomical niches.Given the similar theme and approaches, we introduce an integrated online portal, "The Brain Protein Atlas (BPA)" (www.brainproteinatlas.org), representing a public resource that allows users to access and explore these amalgamated datasets. Specifically, this portal contains a growing set of peer-reviewed mass-spectrometry-based proteomic datasets, including spatiotemporal profiles of human cerebral development, diffuse gliomas, clinically aggressive meningiomas, and a detailed anatomic atlas of glioblastoma. One barrier to entry in mass spectrometry-based proteomics data analysis is the steep learning curve required to extract biologically relevant data. BPA, therefore, includes several built-in analytical tools to generate relevant plots (e.g., volcano plots, heatmaps, boxplots, and scatter plots) and evaluate the spatiotemporal patterns of proteins of interest. Future iterations aim to expand available datasets, including those generated by the community at large, and analytical tools for exploration. Ultimately, BPA aims to improve knowledge dissemination of proteomic information across the neuroscience community in hopes of accelerating the biological understanding of the brain and various maladies.

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“…The final review in this section is from Dent and Diamandis (Toronto, Canada) and deals with the use of computational pathology to address the issue of intra‐tumoural heterogeneity, in this case applied to guide objective proteomic analysis and support the design of personalised combination therapeutic combinations [6]. This notion of combining ‘omics technologies with spatial information from image analysis is an exciting area for the future.…”

Section: Digital Pathologymentioning

confidence: 99%

Recent Advances in Pathology: the 2022 Annual Review Issue of The Journal of Pathology

Herrington

Poulsom

Pillay

et al. 2022

The Journal of Pathology

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The 2022 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 15 invited reviews on research areas of growing importance in pathology. This year, the articles include those that focus on digital pathology, employing modern imaging techniques and software to enable improved diagnostic and research applications to study human diseases. This subject area includes the ability to identify specific genetic alterations through the morphological changes they induce, as well as integrating digital and computational pathology with 'omics technologies. Other reviews in this issue include an updated evaluation of mutational patterns (mutation signatures) in cancer, the applications of lineage tracing in human tissues, and single cell sequencing technologies to uncover tumour evolution and tumour heterogeneity. The tissue microenvironment is covered in reviews specifically dealing with proteolytic control of epidermal differentiation, cancer-associated fibroblasts, field cancerisation, and host factors that determine tumour immunity. All of the reviews contained in this issue are the work of invited experts selected to discuss the considerable recent progress in their respective fields and are freely available online (https://onlinelibrary.wiley.com/journal/10969896).

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Integrating computational pathology and proteomics to address tumor heterogeneity

Cited by 10 publications

References 64 publications

HAVOC: Small-scale histomic mapping of biodiversity across entire tumor specimens using deep neural networks

HAVOC: Small-scale histomic mapping of biodiversity across entire tumor specimens using deep neural networks

The Brain Protein Atlas: A conglomerate of proteomics datasets of human neural tissue

Recent Advances in Pathology: the 2022 Annual Review Issue of The Journal of Pathology

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