Biological and therapeutic impact of intratumor heterogeneity in cancer evolution.

By Nicholas McGranahan, Charles Swanton. Cancer Cell, 2015

1,041 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/09df93226adf983f0be3c55af291a2b1eb06c6f8

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Citations in the corpus

Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer (2019, background): “(ITH), contributes to therapeutic failure, drug resistance and ultimately lethal outcomes (McGranahan and Swanton, 2015; McGranahan and Swanton, 2017).”
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology (2018, methodology): “This method could provide future insights into tumor heterogeneity and its connection with cancer progression [3,9].”
Roles of the immune system in cancer: from tumor initiation to metastatic progression (2018, background): “Various mechanisms have been proposed to explain intratumor heterogeneity: Genomic instability (McGranahan and Swanton 2015), hierarchical organization arising from initiating cancer stem cells (Kreso and Dick 2014), and selective pressure imposed by the immune system likely impact antigen…”
Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis (2018, background): “potential diagnostic, prognostic, and therapeutic implications.(307,308,310,311) Breast cancer intratumor heterogeneity is likely caused by the phenotypic plasticity, clonal evolution of cancer stem cells and epithelialemesenchymal transition (EMT) driven by epigenetic, genetic, and microenvironment”
Clonal status of actionable driver events and the timing of mutational processes in cancer evolution (2015, background): “Despite our emerging understanding of the catalog of cancer aberrations and mutational processes involved in tumor progression, we are only beginning to decipher the rules that constrain the evolution of tumors and to identify the later events thatmight guide or precipitate subclonal expansions (41,”
The value of prior knowledge in machine learning of complex network systems (2016, positive): “However, even in these target cases, there is a wide spectrum of response to the drugs, which is due to the heterogeneity across patients and within tumors (Huang et al., 2014; McGranahan and Swanton, 2015).”
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].”