@article {761, title = {Immunotherapy in nonsmall-cell lung cancer: current status and future prospects for liquid biopsy.}, journal = {Cancer Immunol Immunother}, volume = {70}, year = {2021}, month = {2021 May}, pages = {1177-1188}, abstract = {

Immunotherapy has been one of the great advances in the recent years for the treatment of advanced tumors, with nonsmall-cell lung cancer (NSCLC) being one of the cancers that has benefited most from this approach. Currently, the only validated companion diagnostic test for first-line immunotherapy in metastatic NSCLC patients is testing for programmed death ligand 1 (PD-L1) expression in tumor tissues. However, not all patients experience an effective response with the established selection criteria and immune checkpoint inhibitors (ICIs). Liquid biopsy offers a noninvasive opportunity to monitor disease in patients with cancer and identify those who would benefit the most from immunotherapy. This review focuses on the use of liquid biopsy in immunotherapy treatment of NSCLC patients. Circulating tumor cells (CTCs), cell-free DNA (cfDNA) and exosomes are promising tools for developing new biomarkers. We discuss the current application and future implementation of these parameters to improve therapeutic decision-making and identify the patients who will benefit most from immunotherapy.

}, keywords = {Animals, Biomarkers, Tumor, Carcinoma, Non-Small-Cell Lung, Cell-Free Nucleic Acids, Exosomes, Humans, Immunotherapy, Liquid Biopsy, Lung Neoplasms}, issn = {1432-0851}, doi = {10.1007/s00262-020-02752-z}, author = {Brozos-V{\'a}zquez, Elena Mar{\'\i}a and D{\'\i}az-Pe{\~n}a, Roberto and Garc{\'\i}a-Gonz{\'a}lez, Jorge and Le{\'o}n-Mateos, Luis and Mondelo-Mac{\'\i}a, Patricia and Pe{\~n}a-Chilet, Maria and L{\'o}pez-L{\'o}pez, Rafael} } @article {692, title = {Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.}, journal = {IEEE J Biomed Health Inform}, volume = {24}, year = {2020}, month = {2020 07}, pages = {2119-2130}, abstract = {

Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94\% and 80\%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.

}, keywords = {Biomarkers, Tumor, Computational Biology, Diagnosis, Computer-Assisted, Gene Expression Profiling, Humans, Machine Learning, RNA-seq, Skin Neoplasms}, issn = {2168-2208}, doi = {10.1109/JBHI.2019.2953978}, author = {Galvez, Juan M and Castillo-Secilla, Daniel and Herrera, Luis J and Valenzuela, Olga and Caba, Octavio and Prados, Jose C and Ortuno, Francisco M and Rojas, Ignacio} } @article {612, title = {Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 06 17}, pages = {2674}, abstract = {

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca{\textquoteright}s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60\% of combinations. However, 20\% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

}, keywords = {ADAM17 Protein, Antineoplastic Combined Chemotherapy Protocols, Benchmarking, Biomarkers, Tumor, Cell Line, Tumor, Computational Biology, Datasets as Topic, Drug Antagonism, Drug Resistance, Neoplasm, Drug Synergism, Genomics, Humans, Molecular Targeted Therapy, mutation, Neoplasms, pharmacogenetics, Phosphatidylinositol 3-Kinases, Phosphoinositide-3 Kinase Inhibitors, Treatment Outcome}, issn = {2041-1723}, doi = {10.1038/s41467-019-09799-2}, author = {Menden, Michael P and Wang, Dennis and Mason, Mike J and Szalai, Bence and Bulusu, Krishna C and Guan, Yuanfang and Yu, Thomas and Kang, Jaewoo and Jeon, Minji and Wolfinger, Russ and Nguyen, Tin and Zaslavskiy, Mikhail and Jang, In Sock and Ghazoui, Zara and Ahsen, Mehmet Eren and Vogel, Robert and Neto, Elias Chaibub and Norman, Thea and Tang, Eric K Y and Garnett, Mathew J and Veroli, Giovanni Y Di and Fawell, Stephen and Stolovitzky, Gustavo and Guinney, Justin and Dry, Jonathan R and Saez-Rodriguez, Julio} } @article {445, title = {Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer.}, journal = {Oncotarget}, volume = {7}, year = {2016}, month = {2016 Mar 15}, pages = {12904-16}, abstract = {

Lung cancer (LC) is responsible for most cancer deaths. One of the main factors contributing to the lethality of this disease is the fact that a large proportion of patients are diagnosed at advanced stages when a clinical intervention is unlikely to succeed. In this study, we evaluated the potential of metabolomics by 1H-NMR to facilitate the identification of accurate and reliable biomarkers to support the early diagnosis and prognosis of non-small cell lung cancer (NSCLC).We found that the metabolic profile of NSCLC patients, compared with healthy individuals, is characterized by statistically significant changes in the concentration of 18 metabolites representing different amino acids, organic acids and alcohols, as well as different lipids and molecules involved in lipid metabolism. Furthermore, the analysis of the differences between the metabolic profiles of NSCLC patients at different stages of the disease revealed the existence of 17 metabolites involved in metabolic changes associated with disease progression.Our results underscore the potential of metabolomics profiling to uncover pathophysiological mechanisms that could be useful to objectively discriminate NSCLC patients from healthy individuals, as well as between different stages of the disease.

}, keywords = {Adult, Aged, Biomarkers, Tumor, Carcinoma, Non-Small-Cell Lung, Disease Progression, Female, Humans, Lung Neoplasms, Male, metabolomics, Middle Aged, Proton Magnetic Resonance Spectroscopy}, issn = {1949-2553}, doi = {10.18632/oncotarget.7354}, author = {Puchades-Carrasco, Leonor and Jantus-Lewintre, Eloisa and P{\'e}rez-Rambla, Clara and Garcia-Garcia, Francisco and Lucas, Rut and Calabuig, Silvia and Blasco, Ana and Dopazo, Joaquin and Camps, Carlos and Pineda-Lucena, Antonio} } @article {460, title = {A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces.}, journal = {PLoS Comput Biol}, volume = {11}, year = {2015}, month = {2015 Oct}, pages = {e1004518}, abstract = {

Despite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated.

}, keywords = {Animals, Base Sequence, Biomarkers, Tumor, Catalogs as Topic, Chromosome Mapping, Computer Simulation, DNA Mutational Analysis, Genetic Predisposition to Disease, Humans, Models, Genetic, Molecular Sequence Data, mutation, Neoplasm Proteins, Neoplasms, Polymorphism, Single Nucleotide, Protein Interaction Mapping, Signal Transduction}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1004518}, author = {Porta-Pardo, Eduard and Garc{\'\i}a-Alonso, Luz and Hrabe, Thomas and Dopazo, Joaquin and Godzik, Adam} } @article {597, title = {Molecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information.}, journal = {Oncogene}, volume = {27}, year = {2008}, month = {2008 Mar 06}, pages = {1554-61}, abstract = {

Undifferentiated and poorly differentiated thyroid tumors are responsible for more than half of thyroid cancer patient deaths in spite of their low incidence. Conventional treatments do not obtain substantial benefits, and the lack of alternative approaches limits patient survival. Additionally, the absence of prognostic markers for well-differentiated tumors complicates patient-specific treatments and favors the progression of recurrent forms. In order to recognize the molecular basis involved in tumor dedifferentiation and identify potential markers for thyroid cancer prognosis prediction, we analysed the expression profile of 44 thyroid primary tumors with different degrees of dedifferentiation and aggressiveness using cDNA microarrays. Transcriptome comparison of dedifferentiated and well-differentiated thyroid tumors identified 1031 genes with >2-fold difference in absolute values and false discovery rate of <0.15. According to known molecular interaction and reaction networks, the products of these genes were mainly clustered in the MAPkinase signaling pathway, the TGF-beta signaling pathway, focal adhesion and cell motility, activation of actin polymerization and cell cycle. An exhaustive search in several databases allowed us to identify various members of the matrix metalloproteinase, melanoma antigen A and collagen gene families within the upregulated gene set. We also identified a prognosis classifier comprising just 30 transcripts with an overall accuracy of 95\%. These findings may clarify the molecular mechanisms involved in thyroid tumor dedifferentiation and provide a potential prognosis predictor as well as targets for new therapies.

}, keywords = {Adenoma, Adolescent, Adult, Aged, Biomarkers, Tumor, Carcinoma, Carcinoma, Papillary, Cell Differentiation, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Male, Middle Aged, Oligonucleotide Array Sequence Analysis, Prognosis, Reverse Transcriptase Polymerase Chain Reaction, RNA, Neoplasm, Signal Transduction, Thyroid Neoplasms}, issn = {1476-5594}, doi = {10.1038/sj.onc.1210792}, author = {Montero-Conde, C and Mart{\'\i}n-Campos, J M and Lerma, E and Gimenez, G and Mart{\'\i}nez-Guitarte, J L and Combal{\'\i}a, N and Montaner, D and Mat{\'\i}as-Guiu, X and Dopazo, J and de Leiva, A and Robledo, M and Mauricio, D} }