An Overview of Lung Cancer Genomics and Proteomics

Abstract Introduction Immune checkpoint inhibitors (ICIs) have become a frontier in the field of clinical technology for advanced non-small cell lung cancer (NSCLC). Currently, the predictive biomarker of ICIs mainly including the expression of PD-L1, TMB, TIICs, MMR and MSI-H. However, there are no official biomarkers to guide the treatment of ICIs and to determine the prognosis. Therefore, it is essential to explore a systematic nomogram to predict the prognosis of ICIs treatment in NSCLC Methods In this work, we obtained gene expression and clinical data of NSCLC patients from the TCGA database. Immune-related genes (IRGs) were downloaded from the ImmPort database. The detailed clinical annotation and response data of 240 advanced NSCLC patients who received ICIs treatment were obtained from the cBioPortal for Cancer Genomics. Kaplan–Meier survival analysis was used to perform survival analyses, and selected clinical variables to develop a novel nomogram. The prognostic significance of FGFR4 was validated by another cohort in cBioPortal for Cancer Genomics. Results 3% of the NSCLC patients harbored FGFR4 mutations. The mutation of FGFR4 were confirmed to be associated with PD-L1, and TMB. Patients harbored FGFR4 mutations were found to have a better prolonged progression-free survival (PFS) to ICIs treatment (FGFR4: P = 0.0209). Here, we built and verified a novel nomogram to predict the prognosis of ICIs treatment for NSCLC patients. Conclusion Our results showed that FGFR4 could serve as novel biomarkers to predict the prognosis of ICIs treatment of advanced NSCLC. Our systematic prognostic nomogram showed a great potential to predict the prognosis of ICIs for advanced NSCLC patients.

Download Full-text

Comprehensive cancer genomics-based screening of new therapeutic targets and biomarkers for lung cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e21031 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e21031-e21031

Author(s):

Yataro Daigo ◽

Atsushi Takano ◽

Yusuke Nakamura

Keyword(s):

Lung Cancer ◽

Cancer Cells ◽

Cancer Patients ◽

Therapeutic Target ◽

Cancer Genomics ◽

Cancer Biomarkers ◽

Lung Cancer Cells ◽

Lung Cancers ◽

Lung Cancer Patients ◽

Target Molecules

e21031 Background: Since the clinical outcome of advanced lung cancer patients is still poor after standard therapies, development of new anti-cancer drugs with minimum risk of adverse effects and cancer biomarkers for precision medicine is urgently required. Methods: We have been screening new therapeutic target molecules and molecular biomarkers for lung cancers as follows; i) To identify overexpressed genes in lung cancers by the gene expression profile analysis, ii) To verify the target genes for their scarce expression in normal tissues, iii) To validate the clinicopathologic importance of their protein expression by tissue microarray covering 263 lung cancers, and iv) To confirm their function for the growth and/or invasive ability of the lung cancer cells by siRNAs and gene transfection assays. Results: We identified dozens of candidate target molecules and selected a gene encoding protein with a GAP domain, LAPG1 (lung cancer-associated protein with Gap domain 1). Immunohistochemical analysis showed that LAPG1 expression was observed in 69.9% of lung cancers. Moreover positivity of LAPG1 expression was associated with poor prognosis of lung cancer patients. Knockdown of LAPG1 expression by siRNAs suppressed growth of lung cancer cells. Introduction of LAPG1 increased the invasive activity of mammalian cells, indicating that LAPG1 could be a prognostic biomarker and therapeutic target for lung cancers. Conclusions: Comprehensive cancer genomics-based screening could be useful for selection of new cancer biomarkers and molecular targets for developing small molecules, antibodies, nucleic acid drugs, and immunotherapies.

Download Full-text

Cancer genomics of lung cancer including malignant mesothelioma: A brief overview of current status and future prospects

Advances in Biological Regulation ◽

10.1016/j.jbior.2020.100723 ◽

2020 ◽

Vol 78 ◽

pp. 100723

Author(s):

Suresh C. Jhanwar ◽

Xiaoliang Leon Xu ◽

Abul H. Elahi ◽

David H. Abramson

Keyword(s):

Lung Cancer ◽

Malignant Mesothelioma ◽

Cancer Genomics ◽

Current Status ◽

Future Prospects

Download Full-text

Lessons Learned From Lung Cancer Genomics: The Emerging Concept of Individualized Diagnostics and Treatment

Journal of Clinical Oncology ◽

10.1200/jco.2012.45.9867 ◽

2013 ◽

Vol 31 (15) ◽

pp. 1858-1865 ◽

Cited By ~ 92

Author(s):

Reinhard Buettner ◽

Jürgen Wolf ◽

Roman K. Thomas

Keyword(s):

Lung Cancer ◽

Mechanism Of Action ◽

Trial Design ◽

Cancer Genomics ◽

Lung Tumor ◽

Genetic Alterations ◽

Lessons Learned ◽

Cancer Medicine ◽

Egfr Mutant ◽

Preclinical Work

The advent of novel therapeutics that specifically target signaling pathways activated by genetic alterations has revolutionized the way patients with lung cancer are treated. Although only few and largely ineffective chemotherapeutic regimens were available 10 years ago, a lung tumor diagnosed today requires extensive pathologic subtyping and diagnosis of genome alterations to afford more effective treatment (eg, in EGFR-mutant adenocarcinoma). This change of paradigm has several profound implications, ranging from preclinical work on the mechanism of action to a novel, more biologically oriented taxonomy and from genome diagnostics to trial design. Here, we have summarized these developments into six conceptual paradigms that illustrate the transition from empirical cancer medicine to mechanistically based individualized oncology.

Download Full-text

Cancer Genomics and Proteomics

10.1007/978-1-4939-0992-6 ◽

2014 ◽

Keyword(s):

Cancer Genomics ◽

Genomics And Proteomics

Download Full-text

Genomics and proteomics-based discovery of novel cancer biomarkers and molecular targets for cell-permeable peptide/small molecule-based drugs.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.e13552 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. e13552-e13552

Author(s):

Yataro Daigo ◽

Atsushi Takano ◽

Yusuke Nakamura

Keyword(s):

Lung Cancer ◽

Cancer Cells ◽

Tissue Microarray ◽

Small Molecule ◽

Phosphorylation Site ◽

Lung Cancer Cells ◽

Lung Cancers ◽

Genomics And Proteomics ◽

Cell Permeable Peptide ◽

Erk Kinase

e13552 Background: Identification of cancer-specific oncoproteins is an effective approach to develop new diagnostics and therapeutics. Methods: We have established a strategy as follows to identify new oncoproteins, which can be applied as biomarkers and drug development; i) To identify genes overexpressed in 120 clinical lung cancers using the cDNA microarray representing 27,648 genes, ii) To verify the genes for their low expression in 23 normal tissues by northern-blotting, iii) To validate the clinicopathological significance of their protein expression by tissue microarray covering 262 cases of non-small cell lung cancers (NSCLCs), iv) To verify whether they are essential for the growth of cancer cells by siRNAs, v) To do immunoprecipitation assays and mass-spectrometric analysis to identify their interacting proteins in cancer cells, and screening of cell-permeable peptides that could inhibit the protein-protein interaction that is essential for carcinogenesis. Results: We identified 35 oncoproteins to be upregulated in the majority of lung cancers, and further selected CDCA5 (cell division cycle associated 5) as a good candidate. Tumor tissue microarray analysis of 262 NSCLC patients revealed that CDCA5 positivity was an independent prognostic factor. Suppression of CDCA5 expression with siRNAs inhibited the growth of lung cancer cells; concordantly, induction of exogenous expression of CDCA5 conferred growth-promoting activity in mammalian cells. We also found that extracellular signal-regulated kinase (ERK) kinase interacted with and phosphorylated CDCA5 at Serine 209 in vivo. Functional inhibition of the interaction between CDCA5 and ERK kinase by a cell-permeable peptide corresponding to a 20-amino-acid sequence part of CDCA5, which included the Serine 209 phosphorylation site by ERK, significantly reduced phosphorylation of CDCA5 and resulted in growth suppression of lung cancer cells. Conclusions: CDCA5 positivity should be useful as a novel prognostic biomarker in the clinic. Selective suppression of the ERK-CDCA5 pathway by cell-permeable peptide or small molecule-based drugs could be a promising strategy for cancer therapy.

Download Full-text

Development and validation of a novel EHR-based tumor progression outcome to support biomarker discovery.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e19297 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e19297-e19297

Author(s):

Kenneth L. Kehl ◽

Michael J. Hassett ◽

Katherine A. Stafford ◽

Wenxin Xu ◽

Bruce E. Johnson ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Cancer Progression ◽

Language Processing ◽

Biomarker Discovery ◽

Cancer Genomics ◽

Kras Mutations ◽

Palliative Intent ◽

The Difference ◽

Real World Datasets

e19297 Background: Obtaining clinical outcomes for analysis has historically been a critical barrier to cancer genomics research. EHRs could constitute an important data source to bridge this gap, but EHRs rarely capture structured outcomes such as cancer progression. Novel, robust methods are needed to capture clinically relevant outcomes from EHRs. Methods: Among patients with lung adenocarcinoma whose tumors were sequenced via the Dana Farber Cancer Institute/Brigham and Women’s PROFILE study from 2013-2018, imaging reports following first palliative-intent systemic therapy were annotated using natural language processing (NLP) models trained to capture cancer progression according to the structured “PRISSMM” framework. NLP-based cancer progression and imaging report frequency were jointly modeled using inverse-intensity weighted generalized estimated equations, censored at six months, to explore associations between alterations in lung cancer biomarkers (ALK, EGFR, ROS1, BRAF, KRAS, SMARCA4) and progression. Among patients with KRAS mutations who received immunotherapy, we also analyzed the association between STK11 mutations and progression. The novel outcome generated by the model – imaging report-based progression (iPROG) – corresponded to the difference in the mean log odds of progression per inverse-intensity weighted report associated with a given biomarker; it was reported as adjusted mean probability and in exponentiated form as an odds ratio (OR). Results: Among 690 patients with lung adenocarcinoma, associations between tumor mutations and the iPROG outcome are listed in the Table. Conclusions: A deep NLP model applied to EHR data can capture a novel cancer progression outcome, which is associated with known prognostic markers in lung cancer. Application of this method to large “real world” datasets, with attention to interactions between treatment and genomics, could speed biomarker discovery. [Table: see text]

Download Full-text