Intra-tumour heterogeneity: a looking glass for cancer?

By A. Marusyk, V. Almendro, K. Polyak. Nature Reviews. Cancer, 2012

1,979 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/44e142a7d469aed86e07335165d8d9fb94bf01d1

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Citations in the corpus

Precision Oncology: The Road Ahead (2017, background): “ITH manifests as differences in genetic, epigenetic and signaling networks of individual tumor cells coupled with heterogeneity within the stromal compartment [25, 27].”
Intratumor heterogeneity: the Rosetta stone of therapy resistance (2020, background): “Epigenetically defined phenotypic traits range from essentially hard-wired ones, which can be conceptualized as epimutations, such as silencing of key tumor suppressor genes, mediated by DNA hypermethylation on one end of the spectrum, to noise-driven cell-to-cell differences that dissipate within a”
New horizons in tumor microenvironment biology: challenges and opportunities (2015, background): “Further, given the accumulating evidence of overwhelming heterogeneity at each aspect of tumor evolution which is significantly subject to functional influence of cancer cell extrinsic compartments, targeting the TME turns out to be quite urgent and should be given enough priority [97-99].”
Cancer Cell Metabolism: One Hallmark, Many Faces (2012, background): “Instead, a non-genetic component in the form of the tumor microenvironment must additionally be considered as a component in the equation that influences metabolic changes in cancer cells (7).”
Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis (2018, background): “Nonetheless, given that fact that a high degree of phenotypic and genetic intratumor heterogeneity exists in breast tumors, the molecular, genetic and epigenetic mechanisms underlying intratumor heterogeneity remain to be fully understood.(23,306,309) The tumor microenvironment may also contribute s”
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis (2019, methodology): “For feature fusion, the Kronecker product of the respective unimodal feature representations for each modality was computed, creating feature maps of size: [33 × 33] , [33 × 33] , [33 × 17 × 17] for our CNN ⊗ SNN, GCN ⊗ SNN, and CNN ⊗ GCN ⊗ SNN.”
EMERGING BIOLOGICAL PRINCIPLES OF METASTASIS (2017, positive): “And while numerous studies have examined the genetic and phenotypic diversity of the neoplastic cells that comprise primary tumors (Marusyk et al., 2012), the level of genetic and epigenetic heterogeneity and phenotypic plasticity that operates in metastatic growths is still in question.”
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning (2022, background): “Moreover, WSIs also exhibit a hierarchical structure of visual tokens at varying image resolutions at 20× magnification: the 16 × 16 images encompass the bounding box of cells and other fine-grained features (stroma, tumor cells, lymphocytes) [22, 36], 256 × 256 images capture local clusters of cell”
Computational pan-genomics: status, promises and challenges (2016, background): “Heterogeneity also manifests ‘intra-patient’, with different populations of cells presenting different complements of mutations in the same tumor [64, 65].”