The impressive clinical efficacy of tyrosine kinase inhibitors for oncogene-addicted subgroups of non-small-cell lung cancer (NSCLC) has oriented in the last two decades towards an oncogene-centric molecular classification paradigm of this tumors, where the identification of single or largely non-overlapping oncogenic driver events leads the therapy choice and response.
As our understanding of the genomic landscape of NSCLC deepens, there is a growing evidence that this model fails to adequately capture the clinical complexity of NSCLC and warrants revision. Multiple non-random patterns of co-occurring mutations downstream the tumor-initiating oncogenes are emerging as core determinants of the molecular and clinical heterogeneity of oncogene-driven NSCLC subgroups through their effects on cancer hallmarks.
The determination of the clonal or subclonal nature, as well as the arising time of co-alterations, may also provide important information regarding their contributions to different stages of carcinogenesis, the shape of the NSCLC microenvironment, and its immune context. It is important for the future to catalog, functionalize and evaluate the therapeutic utility of co-occurring pathogenic alterations in NSCLC and to translate this knowledge into more precise therapeutic strategies that yield improved clinical outcomes for patients.
In this Research Topic, we will capture the role of concomitant pathogenic mutations in current and novel stratification networks of oncogene-driven NSCLC subgroups and their impact on the pathogenesis, biology, microenvironmental interactions, and therapeutic vulnerabilities of NSCLC. We will also highlight challenges and opportunities that the detection of co-mutations in tissue and liquid biopsy offers for personalized anticancer therapy, as well as the expanding field of precision immunotherapy.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
The impressive clinical efficacy of tyrosine kinase inhibitors for oncogene-addicted subgroups of non-small-cell lung cancer (NSCLC) has oriented in the last two decades towards an oncogene-centric molecular classification paradigm of this tumors, where the identification of single or largely non-overlapping oncogenic driver events leads the therapy choice and response.
As our understanding of the genomic landscape of NSCLC deepens, there is a growing evidence that this model fails to adequately capture the clinical complexity of NSCLC and warrants revision. Multiple non-random patterns of co-occurring mutations downstream the tumor-initiating oncogenes are emerging as core determinants of the molecular and clinical heterogeneity of oncogene-driven NSCLC subgroups through their effects on cancer hallmarks.
The determination of the clonal or subclonal nature, as well as the arising time of co-alterations, may also provide important information regarding their contributions to different stages of carcinogenesis, the shape of the NSCLC microenvironment, and its immune context. It is important for the future to catalog, functionalize and evaluate the therapeutic utility of co-occurring pathogenic alterations in NSCLC and to translate this knowledge into more precise therapeutic strategies that yield improved clinical outcomes for patients.
In this Research Topic, we will capture the role of concomitant pathogenic mutations in current and novel stratification networks of oncogene-driven NSCLC subgroups and their impact on the pathogenesis, biology, microenvironmental interactions, and therapeutic vulnerabilities of NSCLC. We will also highlight challenges and opportunities that the detection of co-mutations in tissue and liquid biopsy offers for personalized anticancer therapy, as well as the expanding field of precision immunotherapy.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.