We are analyzing https://link.springer.com/article/10.1007/s12105-015-0617-1.

Title:
Mutant Allele Tumor Heterogeneity (MATH) and Head and Neck Squamous Cell Carcinoma | Head and Neck Pathology
Description:
Intra-tumor heterogeneity, variation between individual tumor cells within a patient’s tumor, is increasingly seen as a critical mechanism underlying treatment resistance and therapeutic failure. Despite this growing awareness, few methods to assess intra-tumor heterogeneity exist outside the research laboratory, especially in the absence of a known marker. Mutant allele tumor heterogeneity (MATH) is a novel, non-biased, quantitative method to assess genetic heterogeneity based on tumor next generation exome sequencing. The quantitative aspect of MATH has allowed it to be verified as an actionable biomarker in a retrospective HNSCC data set with available exome sequencing and clinical data. In addition, it was also capable of stratifying patient outcome after controlling for other high-risk features such as p53 mutation, HPV status, and advanced tumor stage. Future work will explore the predictive power of MATH in larger data sets such as The Cancer Genome Atlas and examine the underlying cellular mechanisms responsible for intra-tumor heterogeneity.
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Keywords {🔍}

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Topics {✒️}

month download article/chapter mutant-allele tumor heterogeneity understanding intra-tumour heterogeneity neck pathology access single nucleotide resolution single-cell sequencing oropharyngeal cancer biology full article pdf neck pathology aims predictive power intra-tumor heterogeneity privacy choices/manage cookies related subjects individual tumor cells generation exome sequencing clinical oncology/college solove research institute tumour evolution inferred cancer genome atlas tumor heterogeneity confounds intratumor genetic heterogeneity james cancer hospital darwinian tumor evolution clin cancer res polyclonal drug resistance north american society combinatorial drug therapy european economic area branched evolution revealed post-genomic era advanced tumor stage larger data sets article rocco article log conditions privacy policy ed mroz high-risk features poor-outcome classes otolaryngol clin north mutational evolution spectrum breast cancer accepting optional cookies exome sequencing article head p53 mutation check access mutation calling instant access article cite tumour heterogeneity

Questions {❓}

Metronomics: towards personalized chemotherapy?
Understanding intra-tumour heterogeneity—the next holy grail of cancer therapeutics?

Schema {🗺️}

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         description: Intra-tumor heterogeneity, variation between individual tumor cells within a patient’s tumor, is increasingly seen as a critical mechanism underlying treatment resistance and therapeutic failure. Despite this growing awareness, few methods to assess intra-tumor heterogeneity exist outside the research laboratory, especially in the absence of a known marker. Mutant allele tumor heterogeneity (MATH) is a novel, non-biased, quantitative method to assess genetic heterogeneity based on tumor next generation exome sequencing. The quantitative aspect of MATH has allowed it to be verified as an actionable biomarker in a retrospective HNSCC data set with available exome sequencing and clinical data. In addition, it was also capable of stratifying patient outcome after controlling for other high-risk features such as p53 mutation, HPV status, and advanced tumor stage. Future work will explore the predictive power of MATH in larger data sets such as The Cancer Genome Atlas and examine the underlying cellular mechanisms responsible for intra-tumor heterogeneity.
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      description: Intra-tumor heterogeneity, variation between individual tumor cells within a patient’s tumor, is increasingly seen as a critical mechanism underlying treatment resistance and therapeutic failure. Despite this growing awareness, few methods to assess intra-tumor heterogeneity exist outside the research laboratory, especially in the absence of a known marker. Mutant allele tumor heterogeneity (MATH) is a novel, non-biased, quantitative method to assess genetic heterogeneity based on tumor next generation exome sequencing. The quantitative aspect of MATH has allowed it to be verified as an actionable biomarker in a retrospective HNSCC data set with available exome sequencing and clinical data. In addition, it was also capable of stratifying patient outcome after controlling for other high-risk features such as p53 mutation, HPV status, and advanced tumor stage. Future work will explore the predictive power of MATH in larger data sets such as The Cancer Genome Atlas and examine the underlying cellular mechanisms responsible for intra-tumor heterogeneity.
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