Curated Papers > Highly Cited Authoritative Literature on "Single-Cell Sequencing"

Tumour heterogeneity and resistance to cancer therapies.

Literature Information

DOI	10.1038/nrclinonc.2017.166
PMID	29115304
Journal	Nature reviews. Clinical oncology
Impact Factor	82.2
JCR Quartile	Q1
Publication Year	2018
Times Cited	1696
Keywords	Tumour heterogeneity, Cancer therapy, Resistance, Molecular signatures, Personalized therapy
Literature Type	Journal Article, Research Support, N.I.H., Extramural, Review
ISSN	1759-4774
Pages	81-94
Issue	15(2)
Authors	Ibiayi Dagogo-Jack, Alice T Shaw

TL;DR

This review highlights the increasing heterogeneity of cancer throughout its progression, which leads to diverse tumor cell populations with varying treatment sensitivities and contributes to therapeutic resistance. It discusses emerging technologies for assessing intratumoral heterogeneity and their potential to inform the development of more effective personalized therapies.

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Tumour heterogeneity · Cancer therapy · Resistance · Molecular signatures · Personalized therapy

Abstract

Cancer is a dynamic disease. During the course of disease, cancers generally become more heterogeneous. As a result of this heterogeneity, the bulk tumour might include a diverse collection of cells harbouring distinct molecular signatures with differential levels of sensitivity to treatment. This heterogeneity might result in a non-uniform distribution of genetically distinct tumour-cell subpopulations across and within disease sites (spatial heterogeneity) or temporal variations in the molecular makeup of cancer cells (temporal heterogeneity). Heterogeneity provides the fuel for resistance; therefore, an accurate assessment of tumour heterogeneity is essential for the development of effective therapies. Multiregion sequencing, single-cell sequencing, analysis of autopsy samples, and longitudinal analysis of liquid biopsy samples are all emerging technologies with considerable potential to dissect the complex clonal architecture of cancers. In this Review, we discuss the driving forces behind intratumoural heterogeneity and the current approaches used to combat this heterogeneity and its consequences. We also explore how clinical assessments of tumour heterogeneity might facilitate the development of more-effective personalized therapies.

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Primary Questions Addressed

1. How does intratumoural heterogeneity influence the effectiveness of specific cancer therapies?
2. What are the latest advancements in technologies for assessing tumour heterogeneity in clinical settings?
3. In what ways can understanding temporal heterogeneity impact treatment strategies for cancer patients?
4. How might the identification of distinct molecular signatures within tumours lead to more personalized therapeutic approaches?
5. What role do spatial variations in tumour-cell subpopulations play in the development of resistance to cancer treatments?

Key Findings

1. Research Background and Objectives: Cancer is recognized as a dynamic and evolving disease characterized by significant intratumoural heterogeneity, which refers to the presence of diverse cell populations within a single tumor. This heterogeneity complicates treatment responses, as different subpopulations can exhibit varying sensitivities to therapeutic interventions. The objective of this review is to explore the mechanisms driving tumor heterogeneity, assess its implications for treatment resistance, and discuss emerging methodologies for accurately characterizing this complexity to inform the development of more effective, personalized cancer therapies.

2. Main Methods and Findings: The review highlights several cutting-edge technologies that have emerged to analyze tumor heterogeneity comprehensively. These include:

• Multiregion sequencing: This method allows for the genetic profiling of different regions within a tumor, revealing spatial heterogeneity.
• Single-cell sequencing: This technique enables researchers to evaluate the genetic makeup of individual cells, providing insights into the clonal architecture of tumors.
• Autopsy sample analysis: Investigating tumors post-mortem can reveal the full spectrum of heterogeneity that may not be captured during initial biopsies.
• Longitudinal liquid biopsy analysis: This non-invasive approach monitors cancer evolution over time, capturing temporal changes in tumor composition.

The findings indicate that tumor heterogeneity is a significant driver of resistance to therapies, necessitating more granular assessments to devise effective treatment strategies.

3. Core Conclusions: The review concludes that an accurate understanding of tumor heterogeneity is crucial for developing effective cancer treatments. By employing advanced analytical techniques, clinicians can gain deeper insights into the complex architecture of tumors, which can inform the design of personalized therapies tailored to the specific genetic profiles and treatment responses of individual patient tumors. This approach aims to combat resistance and improve overall outcomes in cancer treatment.

4. Research Significance and Impact: This research is significant as it addresses a critical challenge in oncology: the heterogeneity of tumors and its role in therapy resistance. By emphasizing the importance of sophisticated diagnostic tools to characterize tumor diversity, the review encourages a shift towards personalized medicine in oncology. The insights gained from this research could lead to the development of targeted therapies that are more effective and reduce the likelihood of resistance, ultimately improving patient outcomes and survival rates. The exploration of tumor heterogeneity not only enhances our understanding of cancer biology but also positions personalized therapies as a vital component in the future of cancer treatment.

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Literatures Citing This Work

1. Intratumoral heterogeneity of Notch1 expression in small cell lung cancer. - Takaaki Ito - Journal of thoracic disease (2018)
2. Targeted Tumor Therapy Remixed-An Update on the Use of Small-Molecule Drugs in Combination Therapies. - Martina V Gatzka - Cancers (2018)
3. PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data. - Elena Piñeiro-Yáñez;Miguel Reboiro-Jato;Gonzalo Gómez-López;Javier Perales-Patón;Kevin Troulé;José Manuel Rodríguez;Héctor Tejero;Takeshi Shimamura;Pedro Pablo López-Casas;Julián Carretero;Alfonso Valencia;Manuel Hidalgo;Daniel Glez-Peña;Fátima Al-Shahrour - Genome medicine (2018)
4. Molecular imaging and molecular diagnostics: two sides of the same coin? - Marcus Hacker;Gregor Hoermann;Lukas Kenner - European journal of nuclear medicine and molecular imaging (2018)
5. Applications of CRISPR-Cas Enzymes in Cancer Therapeutics and Detection. - Chun-Hao Huang;Ko-Chuan Lee;Jennifer A Doudna - Trends in cancer (2018)
6. Current understanding of reovirus oncolysis mechanisms. - Matthew B Phillips;Johnasha D Stuart;Roxana M Rodríguez Stewart;Jameson Tl Berry;Bernardo A Mainou;Karl W Boehme - Oncolytic virotherapy (2018)
7. Epithelial-to-mesenchymal transition in cancer: complexity and opportunities. - Yun Zhang;Robert A Weinberg - Frontiers of medicine (2018)
8. Overview of precision oncology trials: challenges and opportunities. - Elena Fountzilas;Apostolia M Tsimberidou - Expert review of clinical pharmacology (2018)
9. Global Stabilization of Boolean Networks to Control the Heterogeneity of Cellular Responses. - Jung-Min Yang;Chun-Kyung Lee;Kwang-Hyun Cho - Frontiers in physiology (2018)
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... (1686 more literatures)

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