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Special Issue "Biomarkers in Lung Cancer"
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Biomarkers".
Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 28106
Special Issue Editor
Interests: epidermal growth factor receptor (EGFR); anaplastic lymphoma kinase (ALK); non-small-cell lung carcinoma (NSCLC); PDL1
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Lung cancer is the most frequently diagnosed cancer, and the leading cause of cancer-related death worldwide. Non-small cell lung cancer (NSCLC) represents approximately 85% of all cases and includes lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Small-cell lung cancer (SCLC) accounts for most of the remaining lung cancer diagnoses. Lung cancer is a very complex and heterogeneous disease, and its clinical course is determined by patient characteristics and cancer-specific features ranging from the histologic subtype to peculiar genetic alterations at a somatic level. The treatment of locally advanced and metastatic lung cancer is often challenging and relies on systemic agents. Chemotherapy has represented the mainstay of lung cancer treatment for many decades. However, the recent innovation in biotechnologies has on one hand allowed for a more granular dissection of the disease at a biomolecular and genomic level, and on the other hand has permitted the development of a plethora of targeted therapeutic agents. This ongoing complex process is encompassing all cancers and is the basis of the precision medicine concept in oncology.
Lung cancer is the pioneer disease in this context, with the identification of EGFR mutations as a driver factor in advanced LUAD, conferring sensibility for treatment with specific tyrosine kinase inhibitors (TKIs), which have significantly improved clinical outcomes. Subsequently, additional actionable alterations were identified, particularly enriched in LUAD, such as ALK or ROS1 fusions. More recently, the paradigm has further evolved with the identification of a variety of agnostic genomic driver alterations in cancer, such as BRAF V600E, METex14, KRASG12C, and ERBB2 mutations and NTRK and RET fusions. Ongoing clinical trials are testing the clinical activity of specific target agents on lung cancer patients affected by tumors harboring such genomic alterations, with promising results. Innovative technologies allow for the detection of these somatic genomic alterations at the tumor tissue level, but also at the serum level, after a liquid biopsy. Nevertheless, the detection of a specific targetable mutation does not always translate into effectiveness of the relative target agent. In fact, a relevant subset of LUAD patients do not benefit from treatment with TKIs, even in the presence of actionable mutations. Therefore, a deeper understanding of the context in which these genomic alterations occur is necessary. Moreover, while targetable aberrations are fairly common in LUAD, they have not been identified in LUSC and SCLC. Thus, scientific investigation to specifically explore for the presence of driver and actionable alterations in these two additional subsets of lung cancer is a current unmet need.
Another major development that has taken place in recent years, involving all of the histologic subtypes of lung cancer to a different degree, is the efficient employment of anti-cancer immunotherapy. The inhibition of the PD-1/PD-L1 checkpoint has emerged as the leading path in this process. However, a valid universal biomarker predicting the efficacy of immune checkpoint inhibitors (ICIs) is lacking. The immunohistochemical quantification of PD-L1 expression has shown some predictive value for ICI activity, but a stronger standardization of the method is necessary. Another potential predictive biomarker of ICI efficacy that has been suggested is a high tumor mutational burden (TMB), but evidence in this regard is controversial and not mature enough to drive decisions in NSCLC. For instance, recent data allude to a reduced benefit from ICI in tumors harboring mutations in specific genes, such as KEAP1 and STK11, despite high TMB. Presumably, the current view on TMB needs to be better defined in the wider context of genomic instability.
Hence, the identification of new predictive biomarkers able to anticipate efficacy or resistance to a specific lung cancer treatment is a current priority for the scientific community. A predictive biomarker plays the important role of the matching factor between our ability to obtain a refined classification of lung cancer subtypes at a molecular level and the increasing availability of new targeted agents and immunotherapy options.
Investigation into new biomarkers can be performed at the tumor tissue level, or by measuring and qualifying specific factors in the serum. At a biological level, biomarkers can be represented by the expression of definite proteins, specific alterations in blood count, distinct transcriptional and/or genetic alterations, or even peculiar epigenetic events. Cancer-related biomarkers can be found at a tumor tissue level, or even on liquid biopsy, in which additional factors such as circulating tumor cells, exosomes, microRNAs, and cell-free DNA can be detected and further analyzed.
From a functional point of view, it is very common for a predictive biomarker to be strictly connected to the targetable and altered biological mechanism that conferred a growth advantage to the cancer cells. Nevertheless, as evidence has shown, the functionality aspect alone is not sufficient for the identification of a predictive biomarker. Moreover, there is a possibility that a multiparametric combination of biomarkers into compound classifiers could synergistically improve their final predictive potential. This aspect highlights the importance of biomarker "post-processing" and opens a new window on the possibility to employ proteomic, transcriptomic, and next-generation genomic sequencing data produced by innovative high-throughput technologies in order to predict treatment outcomes. The analysis of "big data" utilizing emerging artificial intelligence algorithms could make this feasible. Most importantly, such complex classifiers could also offer some potential predictive value for other lung cancer histologic subtypes besides LUAD, such as LUSC and SCLC.
Prof. Dr. Federico Cappuzzo
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- lung cancer
- Non-small cell lung cancer (NSCLC)
- lung adenocarcinoma (LUAD)
- lung squamous cell carcinoma (LUSC)
- tyrosine kinase inhibitors (TKIs)