Refining colorectal cancer classification and clinical stratification through a single-cell atlas View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-05-11

AUTHORS

Ateeq M. Khaliq, Cihat Erdogan, Zeyneb Kurt, Sultan Sevgi Turgut, Miles W. Grunvald, Tim Rand, Sonal Khare, Jeffrey A. Borgia, Dana M. Hayden, Sam G. Pappas, Henry R. Govekar, Audrey E. Kam, Jochen Reiser, Kiran Turaga, Milan Radovich, Yong Zang, Yingjie Qiu, Yunlong Liu, Melissa L. Fishel, Anita Turk, Vineet Gupta, Ram Al-Sabti, Janakiraman Subramanian, Timothy M. Kuzel, Anguraj Sadanandam, Levi Waldron, Arif Hussain, Mohammad Saleem, Bassel El-Rayes, Ameen A. Salahudeen, Ashiq Masood

ABSTRACT

BackgroundColorectal cancer (CRC) consensus molecular subtypes (CMS) have different immunological, stromal cell, and clinicopathological characteristics. Single-cell characterization of CMS subtype tumor microenvironments is required to elucidate mechanisms of tumor and stroma cell contributions to pathogenesis which may advance subtype-specific therapeutic development. We interrogate racially diverse human CRC samples and analyze multiple independent external cohorts for a total of 487,829 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells.ResultsTumor cells recapitulate individual CMS subgroups yet exhibit significant intratumoral CMS heterogeneity. Both CMS1 microsatellite instability (MSI-H) CRCs and microsatellite stable (MSS) CRC demonstrate similar pathway activations at the tumor epithelial level. However, CD8+ cytotoxic T cell phenotype infiltration in MSI-H CRCs may explain why these tumors respond to immune checkpoint inhibitors. Cellular transcriptomic profiles in CRC exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics. We note a dichotomy in tumor microenvironments across CMS subgroups exists by which patients with high cancer-associated fibroblasts (CAFs) and C1Q+TAM content exhibit poor outcomes, providing a higher level of personalization and precision than would distinct subtypes. Additionally, we discover CAF subtypes known to be associated with immunotherapy resistance.ConclusionsDistinct CAFs and C1Q+ TAMs are sufficient to explain CMS predictive ability and a simpler signature based on these cellular phenotypes could stratify CRC patient prognosis with greater precision. Therapeutically targeting specific CAF subtypes and C1Q + TAMs may promote immunotherapy responses in CRC patients. More... »

PAGES

113

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    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-022-02677-z

    DOI

    http://dx.doi.org/10.1186/s13059-022-02677-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1147776499

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35538548


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    55 schema:description BackgroundColorectal cancer (CRC) consensus molecular subtypes (CMS) have different immunological, stromal cell, and clinicopathological characteristics. Single-cell characterization of CMS subtype tumor microenvironments is required to elucidate mechanisms of tumor and stroma cell contributions to pathogenesis which may advance subtype-specific therapeutic development. We interrogate racially diverse human CRC samples and analyze multiple independent external cohorts for a total of 487,829 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells.ResultsTumor cells recapitulate individual CMS subgroups yet exhibit significant intratumoral CMS heterogeneity. Both CMS1 microsatellite instability (MSI-H) CRCs and microsatellite stable (MSS) CRC demonstrate similar pathway activations at the tumor epithelial level. However, CD8+ cytotoxic T cell phenotype infiltration in MSI-H CRCs may explain why these tumors respond to immune checkpoint inhibitors. Cellular transcriptomic profiles in CRC exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics. We note a dichotomy in tumor microenvironments across CMS subgroups exists by which patients with high cancer-associated fibroblasts (CAFs) and C1Q+TAM content exhibit poor outcomes, providing a higher level of personalization and precision than would distinct subtypes. Additionally, we discover CAF subtypes known to be associated with immunotherapy resistance.ConclusionsDistinct CAFs and C1Q+ TAMs are sufficient to explain CMS predictive ability and a simpler signature based on these cellular phenotypes could stratify CRC patient prognosis with greater precision. Therapeutically targeting specific CAF subtypes and C1Q + TAMs may promote immunotherapy responses in CRC patients.
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    63 CAF subtypes
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    66 CRC patient prognosis
    67 CRC patients
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    70 ResultsTumor cells
    71 TAM
    72 TAMs
    73 ability
    74 activation
    75 atlas
    76 bulk transcriptomics
    77 cancer classification
    78 cancer-associated fibroblasts
    79 cell contribution
    80 cells
    81 cellular diversity
    82 cellular phenotypes
    83 characteristics
    84 characterization
    85 checkpoint inhibitors
    86 classification
    87 clinical stratification
    88 clinicopathological characteristics
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    92 continuum
    93 contrast
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    95 depiction
    96 development
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    99 distinct subtypes
    100 diversity
    101 epithelial level
    102 external cohort
    103 fibroblasts
    104 greater precision
    105 heterogeneity
    106 high levels
    107 high-resolution depiction
    108 human CRC samples
    109 immune checkpoint inhibitors
    110 immunotherapy resistance
    111 immunotherapy response
    112 independent external cohort
    113 infiltration
    114 inhibitors
    115 levels
    116 mechanism
    117 mechanism of tumor
    118 microenvironment
    119 microenvironmental cells
    120 microsatellite stable CRC
    121 molecular subtypes
    122 outcomes
    123 pathogenesis
    124 pathway activation
    125 patient prognosis
    126 patients
    127 personalization
    128 phenotype
    129 poor outcome
    130 precision
    131 predictive ability
    132 profile
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    134 resistance
    135 response
    136 samples
    137 signatures
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    139 single cells
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    141 single-cell characterization
    142 stratification
    143 stromal cells
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