Identification of tumor biomarkers for sunitinib in advanced renal cell carcinoma (RCC).

470 Background: Sunitinib is a standard of care for advanced RCC. Despite efforts to identify predictive molecular markers for patient selection, none are available, likely due to multiple resistance mechanisms. Using the Total Cancer Care (TCC) database, which integrates patient clinical, molecular, and biospecimen data, we devised a tumor genomics and transcriptomics experiment to identify differences between RCC patients who derive prolonged clinical benefit from sunitinib versus those who are resistant. Methods: A discovery set of 34 RCC patients treated with sunitinib at the approved regimen were identified in the TCC database (n=16 treated for ≤6 months, having primarily discontinued for reasons other than tolerability; n=18 treated for ≥18 months). Tumor samples were analyzed by whole exome sequencing (WES) and by parallel 400-gene expression profiling. Following gene mutation identification and supervised gene expression analysis, molecular differences between the two groups were identified and tested for potential association with treatment duration. Results: Of the 34 cases identified, 24 remained for analysis following sample QC failure and clinical review (n=10 and 14 treated for ≤6 and ≥18 months, respectively). Gene expression analysis revealed a 37-gene signature associated with treatment duration: MAPK8 (JNK1) was a leading candidate biomarker (Pearson correlation with log [treatment duration]=–0.70; p=0.06 after Bonferroni multiplicity correction). Pathway-based WES analyses identified 25 potential variants of interest, none remaining statistically significant after correction. However, following genome-wide analysis, a single variant in an intronic region of ING3 was statistically associated with treatment duration (p=0.02). Conclusions: Activation of the PI3K/AKT pathway was a marker of resistance to sunitinib. In contrast, activation of the angiogenic, NOTCH, or JAK-STAT pathways was, to some degree, associated with sensitivity to therapy. However, neither VHL alteration nor lack of expression, nor alteration in chromatin-rearrangement genes, was associated with sunitinib treatment duration. These findings require further validation in a larger and independent cohort.

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Prognostic gene signatures for lung adenocarcinoma using digital multiplexed gene expression in formalin-fixed paraffin embedded tissue.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.e20050 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. e20050-e20050

Author(s):

Ping Wei ◽

Xiang Du

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Expression Analysis ◽

Gene Expression Analysis ◽

Early Stage ◽

Gene Signature ◽

Risk Scores ◽

Cancer Center ◽

Microarray Gene Expression ◽

Nsclc Patients

e20050 Background: The outcome after resection of non-small-cell lung cancer (NSCLC) patients are poor, even in the early stage, there still has 35-50% recurrence rates. Current staging methods are not inadequate for predicting the outcome of NSCLC patients. Methods: 396 lung adenocarcinoma specimens were obtained for this study, of whom 78 frozen specimens (corhort1) and 223 FFPE specimens (corhort2) were from Shanghai Cancer Center, Fudan University and 85 FFPE specimens (independent corhort) were from Shanghai Pulmonary Hospital. The RNA was extracted from corhort1 and used in the microarray gene expression analysis to derive prognostic associated genes. The digital multiplexed technology (Nanostring) was then used to determine the expression of these genes in FFPE-derived RNA from corhort2. For validation, we used the random patients from the independent corhort. Results: Through microarray assay, the top 18 survival and 19 metastasis associated gene were chosen to digital multiplexed gene expression analysis using FFPE-derived RNA from corhort2. Four genes that correlated with the survival were then identified by risk scores. Kaplan-Meier analysis showed that patients of high risk scores had longer OS and DFS compared with patients of low risk scores in the corhort2. The four-gene signature was an independent predictor of OS and DFS. We validated the four-gene model in the independent corhort. Conclusions: Our results suggest that the four gene signature is a new biomarker for the prognosis of patients with NSCLC, enabling more accurate prediction of prognosis.

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Gene Expression Analysis of Oral Tongue Carcinoma TCGA Data Reveals an Eighteen-Gene Signature Associated with Survival Following Radiation Treatment

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2019.06.1596 ◽

2019 ◽

Vol 105 (1) ◽

pp. E405

Author(s):

N. Mladkova ◽

D.L. Mitchell ◽

M.E. Gamez ◽

J.C. Grecula ◽

S.R. Jhawar ◽

...

Keyword(s):

Gene Expression ◽

Expression Analysis ◽

Gene Expression Analysis ◽

Radiation Treatment ◽

Gene Signature ◽

Tongue Carcinoma ◽

Oral Tongue

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Predicting Chemotherapy Treatment Outcomes in Ovarian Cancer Patients Using Gene Expression Analysis

Inquiry@Queen's Undergraduate Research Conference Proceedings ◽

10.24908/iqurcp.10557 ◽

2018 ◽

Author(s):

Anastasiya Tarnouskaya

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Expression Analysis ◽

Gene Expression Analysis ◽

Standard Of Care ◽

The Cancer Genome Atlas ◽

Abnormal Development ◽

Chemotherapy Response ◽

Current Standard ◽

Cancer Society

Ovarian cancer is the abnormal development of cells found in the ovaries. It is the fifth most fatal cancer amongst woman and has an overall five-year survival rate of 45% (American Cancer Society, 2016). For women with newly-diagnosed, advanced stage ovarian cancer, the current standard of care is surgery – to remove as much of the cancer as possible – followed by chemotherapy – to kill the remaining tumour cells (Cancer.Net Editorial Board, 2016). However, chemotherapy can have devastating side-effects such as infection, nausea, reduced cognitive function, and death (Sun, et al., 2005). Using patients’ genomic profiles to predict how well they will respond to the standard of care will be valuable for patients when deciding whether to pursue standard or alternative forms of treatment. This study uses ovarian cancer patient data compiled by The Cancer Genome Atlas (TCGA). Clinical data – such as patient age, gender, ethnicity, disease severity and treatment undergone – is used to define which patients responded well to chemotherapy. Patient gene expression data – which gives insight into which genes are up- or down-regulated – will be used to identify markers of chemotherapy response. This will be done using differential gene expression analysis – to identify individual genes that contribute to chemotherapy-response – and network analysis – to understand how the expression of these genes functions as a system. To make the results of the study clinically relevant, chemotherapy-response markers will be correlated to single nucleotide polymorphisms – a form of genetic variation that is much quicker to test for in a patient than gene expression.

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Multiple Herbicide–Resistant Junglerice (Echinochloa colona): Identification of Genes Potentially Involved in Resistance through Differential Gene Expression Analysis

Weed Science ◽

10.1017/wsc.2018.10 ◽

2018 ◽

Vol 66 (3) ◽

pp. 347-354 ◽

Cited By ~ 11

Author(s):

Alice A. Wright ◽

Marianela Rodriguez-Carres ◽

Rajkumar Sasidharan ◽

Liisa Koski ◽

Daniel G. Peterson ◽

...

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Expression Analysis ◽

Herbicide Resistance ◽

Gene Expression Analysis ◽

Point Mutations ◽

Resistance Mechanisms ◽

Polymorphism Analysis ◽

Differential Gene Expression Analysis ◽

Differential Gene

AbstractHerbicide resistance, and in particular multiple-herbicide resistance, poses an ever-increasing threat to food security. A biotype of junglerice [Echinochloa colona (L.) Link] with resistance to four herbicides, imazamox, fenoxaprop-P-ethyl, quinclorac, and propanil, each representing a different mechanism of action, was identified in Sunflower County, MS. Dose responses were performed on the resistant biotype and a biotype sensitive to all four herbicides to determine the level of resistance. Application of a cytochrome P450 inhibitor, malathion, with the herbicides imazamox and quinclorac resulted in increased susceptibility in the resistant biotype. Differential gene expression analysis of resistant and sensitive plants revealed that 170 transcripts were upregulated in resistant plants relative to sensitive plants and 160 transcripts were upregulated in sensitive plants. In addition, 507 transcripts were only expressed in resistant plants and 562 only in sensitive plants. A subset of these transcripts were investigated further using quantitative PCR (qPCR) to compare gene expression in resistant plants with expression in additional sensitive biotypes. The qPCR analysis identified two transcripts, a kinase and a glutathione S-transferase that were significantly upregulated in resistant plants compared with the sensitive plants. A third transcript, encoding an F-box protein, was downregulated in the resistant plants relative to the sensitive plants. As no cytochrome P450s were differentially expressed between the resistant and sensitive plants, a single-nucleotide polymorphism analysis was performed, revealing several nonsynonymous point mutations of interest. These candidate genes will require further study to elucidate the resistance mechanisms present in the resistant biotype.

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