Molecular histology of lung cancer: From targets to treatments

Wood, Steven L.; Pernemalm, Maria; Crosbie, Philip; Whetton, Anthony D.

doi:10.1016/j.ctrv.2015.02.008

Cited by 146 publications

(99 citation statements)

References 204 publications

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“…It has a high malignant degree and a high mortality rate, and its 5-year survival rate is just 10–15% (11–13). In recent years, its morbidity and mortality rate have markedly increased (14).…”

Section: Discussionmentioning

confidence: 99%

CHKA mediates the poor prognosis of lung adenocarcinoma and acts as a prognostic indicator

et al. 2016

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Choline kinase α (CHKA), the enzyme that converts choline to phosphocholine, has been studied in human carcinogenesis widely. However, the expression and underlying clinicopathological characteristics of CHKA in lung adenocarcinoma remains elusive. In the present study, a tissue microarray of 119 pairs of lung adenocarcinoma samples and corresponding adjacent normal mucosae was used to analysis CHKA expression by immunohistochemistry, and CHKA was observed to exhibit enhanced expression in lung adenocarcinoma tissues. Elevated CHKA expression in lung adenocarcinoma tissues at the gene and protein level was observed. The levels of CHKA expression were closely associated with the poor prognosis status of lung adenocarcinoma patients. Furthermore, certain clinicopathological characteristics such as tumor diameter and differentiation were observed to be significant in those lung adenocarcinoma patients who displayed enhanced CHKA expression. The analysis of CHKA expression could provide a more precise way to predict the prognosis of lung adenocarcinoma patients. Collectively, the present study revealed a novel biomarker in lung adenocarcinoma, and indicated that CHKA may be a promising prognostic marker and therapeutic target for lung adenocarcinoma.

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Section: Discussionmentioning

confidence: 99%

CHKA mediates the poor prognosis of lung adenocarcinoma and acts as a prognostic indicator

et al. 2016

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“…With an even larger repertoire of TKI entering the clinic, screening tumors for genomic aberrations is increasingly important (11). Detection of these driver mutations, such as EGFR mutations, in cfDNA or CTCs, has allowed to use targeted therapies avoiding invasive procedures and to identify resistance mechanisms for TKIs, while quantification of these mutations has helped to monitor tumor burden and dynamics (12,13).…”

Section: Liquid Biopsymentioning

confidence: 99%

Liquid biopsy based biomarkers in non-small cell lung cancer for diagnosis and treatment monitoring

Pérez-Callejo¹,

Romero²,

Provencio³

et al. 2016

Transl. Lung Cancer Res.

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“…Biological subtyping of patients could improve patient selection for clinical trials with targeted therapies, including immunomodulatory interventions, as has been shown in other pulmonary and non-pulmonary diseases 13 14. Phenotyping of patients with ARDS can be done using clinical characteristics, causes of lung injury,15 individual or sets of biomarkers,16 or a combination of clinical and biological variables 17.…”

Section: Introductionmentioning

confidence: 99%

Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis

Bos

Schouten

Vught

et al. 2017

Thorax

237

212

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Rationale We hypothesized that acute respiratory distress syndrome (ARDS) patients can be clustered based on concentrations of plasma biomarkers and that the thereby identified biological phenotypes are associated with mortality. Methods Consecutive patients with ARDS were included in this prospective observational cohort study. Cluster analysis of 20 biomarkers of inflammation, coagulation and endothelial activation provided the phenotypes in a training cohort, not taking any outcome data into account. Logistic regression with backward selection was used to select the most predictive biomarkers, and these predicted phenotypes were validated in a separate cohort. Multivariable logistic regression was used to quantify the independent association with mortality. Results Two phenotypes were identified in 454 patients, which we named ‘uninflamed’ (N=218) and ‘reactive’ (N=236). A selection of four biomarkers (IL-6, interferon gamma, angiopoetin 1/2 and PAI-1) could be used to accurately predict the phenotype in the training cohort (area under the receiver operating characteristics curve: 0.98 [95%-confidence interval: 0.97–0.99]). Mortality rates were 15.6% and 36.4% (P<0.001) in the training cohort and 13.6% and 37.5% (P<0.001) in the validation cohort (N=207). The ‘reactive phenotype’ was independent from confounders associated with ICU mortality (training cohort: OR 1.13 [95%-CI: 1.04–1.23]; validation cohort: OR 1.18 [95%-CI: 1.06–1.31]). Conclusions ARDS patients can be clustered into two biological phenotypes, with different mortality rates. Four biomarkers can be used to predict the phenotype with high accuracy. The phenotypes were very similar to those found in cohorts derived from randomized controlled trials, and these results may improve patient selection for future clinical trials targeting host response in patients with ARDS.

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Molecular histology of lung cancer: From targets to treatments

Cited by 146 publications

References 204 publications

CHKA mediates the poor prognosis of lung adenocarcinoma and acts as a prognostic indicator

CHKA mediates the poor prognosis of lung adenocarcinoma and acts as a prognostic indicator

Liquid biopsy based biomarkers in non-small cell lung cancer for diagnosis and treatment monitoring

Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis

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