MMS19 as a potential predictive marker of adjuvant chemotherapy benefit in resected non-small cell lung cancer

Adam, Julien; Sourisseau, Tony; Olaussen, Ken A.; Robin, Angélique; Zhu, Chang-Qi; Templier, Alexandre; Civet, Alexandre; Girard, Philippe; Lazar, Vladimir; Validire, Pierre; Tsao, Ming‐Sound; Soria, Jean‐Charles; Besse, Benjamin

doi:10.3233/cbm-160644

Cited by 5 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature survey shows that many of these genes are associated with HCC or other types of cancer. Among the 3548 positive a GRP DEGs, for example, MMS19 ( a GRP = 0.69) is a DNA repair gene playing important role in Nucleotide Excision Repair (NER) pathway, whose single nucleotide polymorphism, rs3740526 has been reported to significantly distinguish adenocarcinoma with squamous cell carcinoma and whose expression levels are clinically related with ACT benefit of resected non-small cell lung cancer patients [ 43 , 44 ]. TRIB1 ( a GRP = 0.66) has been previously evidenced to be associated with tumorigeneses of various types of cancer, e.g., leukemia and colorectal cancer [ 45 , 46 ].…”

Section: Resultsmentioning

confidence: 99%

Adaptively capturing the heterogeneity of expression for cancer biomarker identification

Xie

Liu

et al. 2018

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundIdentifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells.ResultsIn this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer.ConclusionsExpression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use.Software availabilityThe source code of aGRP can be downloaded from https://github.com/hqwang126/aGRP.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2437-2) contains supplementary material, which is available to authorized users.

show abstract

Section: Resultsmentioning

confidence: 99%

Adaptively capturing the heterogeneity of expression for cancer biomarker identification

Xie

Liu

et al. 2018

BMC Bioinformatics

View full text Add to dashboard Cite

show abstract

“…MMS 19 expression was associated with metastasis and therapy response in esophageal squamous cell carcinoma (ESCC) ( 65 ). MMS19 was also identified as a predictive marker of adjuvant therapy response in NSCLC ( 66 ). Members of the cytosolic Fe/S assembly pathway- MMS19 and CIAO2B were found to be essential for replication stress tolerance of cancer cells towards Chk1 and ATR inhibition ( 67 ).…”

Section: Discussionmentioning

confidence: 99%

Serum autoantibody profiling of oral squamous cell carcinoma patients reveals NUBP2 as a potential diagnostic marker

Abdulla,

Devasia Puthenpurackal,

Pinto

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

IntroductionOral Squamous Cell Carcinoma (OSCC), a common malignancy of the head and neck region, is frequently diagnosed at advanced stages, necessitating the development of efficient diagnostic methods. Profiling autoantibodies generated against tumor-associated antigens have lately demonstrated a promising role in diagnosis, predicting disease course, and response to therapeutics and relapse.MethodsIn the current study, we, for the first time, aimed to identify and evaluate the diagnostic value of autoantibodies in serum samples of patients with OSCC using autoantibody profiling by an immunome protein array. The utility of anti-NUBP2 antibody and tissue positivity in OSCC was further evaluated.Results and discussionWe identified a total of 53 autoantibodies with significant differential levels between OSCC and control groups, including 25 that were increased in OSCC and 28 that were decreased. These included autoantibodies against Thymidine kinase 1 (TK1), nucleotide-binding protein 2 (NUBP2), and protein pyrroline-5-carboxylate reductase 1 (PYCR1), among others. Immunohistochemical validation indicated positive staining of NUBP2 in a large majority of cases (72%). Further, analysis of OSCC data available in TCGA revealed higher NUBP2 expression correlated with better disease-free patient survival. In conclusion, the differential serum autoantibodies identified in the current study, including those for NUBP2, could be used as potential biomarkers for early diagnosis or as screening biomarkers for OSCC pending investigation in a larger cohort.

show abstract

“…For further details, an in-depth discussion of Q-Finder is also proposed in Section 14 of supplementary materials. This approach has been applied in several therapeutic areas, with published examples available (Nabholtz, 2012;Eveno, 2014;Amrane et al, 2015;Adam et al, 2016;Dumontet et al, 2016;Gaston-Mathe, 2017;Dumontet et al, 2018;Rollot, 2019;Zhou et al, 2018;Ibald-Mulli, 2019;Zhou et al, 2019;Alves et al, 2020;Mornet, 2020).…”

Section: Q-finder's Pipeline To Increase Credible Findings Generationmentioning

confidence: 99%

“…Q-Finder was applied in the field of diabetes to many other research questions, such as the detection of patient profiles that benefit the most of SGLT2i compared to DDP4i in terms of renal function preservation, using Electronic Health Record data (Zhou al., 2018;Zhou et al, 2019); the identification of profiles of patients who better control their blood sugar, using data from pooled observational studies (Rollot, 2019, "Reali project"); and the discovery of new predictors of diabetic ketoacidosis (DKA), a serious complication of type 1 diabetes, using data from a national diabetes registry (Ibald-Mulli et al, 2019). Q-Finder was also successfully applied in the context of several other pathologies such as hypophosphatasia, using SNPs data (Mornet et al, 2020), dry eye disease using prospective clinical trials data (Amrane et al, 2015), and cancer using clinical data from RCTs (Nabholtz, 2012;Dumontet et al, 2016;Dumontet et al, 2018;Alves et al, 2020) or transcriptomic data from a research cohort (Adam et al, 2016). The Q-Finder approach is indeed generic by design and can be applied to any pathology and research questions, as can many SD algorithms, provided that the data can be represented in tabular form.…”

Section: Generalization To Other Pathologies or Research Questionsmentioning

confidence: 99%

Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study

Esnault¹,

Gadonna²,

Queyrel

et al. 2020

Front. Artif. Intell.

Self Cite

View full text Add to dashboard Cite

Addressing the heterogeneity of both the outcome of a disease and the treatment response to an intervention is a mandatory pathway for regulatory approval of medicines. In randomized clinical trials (RCTs), confirmatory subgroup analyses focus on the assessment of drugs in predefined subgroups, while exploratory ones allow a posteriori the identification of subsets of patients who respond differently. Within the latter area, subgroup discovery (SD) data mining approach is widely used—particularly in precision medicine—to evaluate treatment effect across different groups of patients from various data sources (be it from clinical trials or real-world data). However, both the limited consideration by standard SD algorithms of recommended criteria to define credible subgroups and the lack of statistical power of the findings after correcting for multiple testing hinder the generation of hypothesis and their acceptance by healthcare authorities and practitioners. In this paper, we present the Q-Finder algorithm that aims to generate statistically credible subgroups to answer clinical questions, such as finding drivers of natural disease progression or treatment response. It combines an exhaustive search with a cascade of filters based on metrics assessing key credibility criteria, including relative risk reduction assessment, adjustment on confounding factors, individual feature’s contribution to the subgroup’s effect, interaction tests for assessing between-subgroup treatment effect interactions and tests adjustment (multiple testing). This allows Q-Finder to directly target and assess subgroups on recommended credibility criteria. The top-k credible subgroups are then selected, while accounting for subgroups’ diversity and, possibly, clinical relevance. Those subgroups are tested on independent data to assess their consistency across databases, while preserving statistical power by limiting the number of tests. To illustrate this algorithm, we applied it on the database of the International Diabetes Management Practice Study (IDMPS) to better understand the drivers of improved glycemic control and rate of episodes of hypoglycemia in type 2 diabetics patients. We compared Q-Finder with state-of-the-art approaches from both Subgroup Identification and Knowledge Discovery in Databases literature. The results demonstrate its ability to identify and support a short list of highly credible and diverse data-driven subgroups for both prognostic and predictive tasks.

show abstract

MMS19 as a potential predictive marker of adjuvant chemotherapy benefit in resected non-small cell lung cancer

Abstract: We therefore propose the expression level of MMS19 as a candidate predictive marker of ACT benefit in resected NSCLC patients.

Cited by 5 publications

References 20 publications

Adaptively capturing the heterogeneity of expression for cancer biomarker identification

Adaptively capturing the heterogeneity of expression for cancer biomarker identification

Serum autoantibody profiling of oral squamous cell carcinoma patients reveals NUBP2 as a potential diagnostic marker

Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study

Contact Info

Product

Resources

About