Microarray-Based Response Prediction in Esophageal Adenocarcinoma

Schauer, Matthias; Janssen, Klaus–Peter; Rimkus, C.; Raggi, Matthias C.; Feith, Marcus; Friess, Helmut; Theisen, Jörg

doi:10.1158/1078-0432.ccr-09-1673

Cited by 52 publications

(32 citation statements)

References 32 publications

(30 reference statements)

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“…Bei den Adenokarzinomen des gastroösophagealen Über-gangs zeigt die Gebski-Metaanalyse sowohl für die neoadjuvante RCT wie auch für die neoadjuvante Chemotherapie [160].…”

Section: Vorgehen Beim Lokalisierten öSophaguskarzinomunclassified

Gastrointestinale Tumore – interdisziplinäre Diskussion neuer Daten

et al. 2011

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“…Bei den Adenokarzinomen des gastroösophagealen Über-gangs zeigt die Gebski-Metaanalyse sowohl für die neoadjuvante RCT wie auch für die neoadjuvante Chemotherapie [160].…”

Section: Vorgehen Beim Lokalisierten öSophaguskarzinomunclassified

Gastrointestinale Tumore – interdisziplinäre Diskussion neuer Daten

et al. 2011

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“…Other studies have explored the use of molecular and genetic predictors of pCR. In the study of the latter, microarray technology has been used to investigate the predictive genetic signatures in esophageal cancer, thereby generating classifiers capable of high predictive accuracy [9-12]. However, different studies have generated different sets of markers which do not overlap, and which have not been validated in large-scale human studies [9-12].…”

Section: Introductionmentioning

confidence: 99%

Predictors of pathological complete response to neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma

et al. 2014

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BackgroundsIn this study, we evaluated the factors associated with a pathologic complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for esophageal squamous cell carcinoma (ESCC).MethodsPre-nCRT parameters in ESCC patients treated between 1999 and 2006 were analyzed to identify predictors of pCR. All patients received 5-fluorouracil/cisplatin-based chemotherapy and external beam radiation followed by scheduled esophagectomy. Variables were analyzed using univariate and multivariate analyses with pCR as the dependent variable. Estimated pCR rate was calculated with a regression model.ResultsFifty-nine (20.9%) of 282 patients achieved pCR. Univariate analysis identified four patient factors (age, smoking status, drinking history and hypertension), one pre-nCRT parameter (tumor length) as significant predictors of pCR (all P <0.05). On multivariate analysis, tumor length ≤3 cm (favorable, odds ratio (OR): 4.85, P = 0.001), patient age >55 years (favorable, OR: 1.95, P = 0.035), and being a non-smoker (favorable, OR: 3.6, P = 0.003) were independent predictors of pCR. The estimated pCR rates based on a logistic regression including those three predictors were 71%, 35 to approximately 58%, 19 to approximately 38%, and 12% for patients with 3, 2, 1 and 0 predictors, respectively.ConclusionAge, smoking habit and tumor length were important pCR predictors. These factors may be used to predict outcomes for ESCC patients receiving nCRT, to develop risk-adapted treatment strategies, and to select patients who could participate in trials on new therapies.

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“…Comparison of the gene lists identified in the 3 previously published reports (18)(19)(20) shows that while there are no shared genes, pathway analysis reveals that the TNF pathway is common to all 3. In fact, a similar analysis on 3 publications aiming to predict response to CRT in esophageal cancer (46)(47)(48) highlighted the NF-kB pathway as common to all predictive gene sets (49). The NF-kB transcription factor is a downstream target of TNF, suggesting that the TNF/NF-kB molecular pathway may play a significant role in determining sensitivity to CRT.…”

Section: Discussionmentioning

confidence: 93%

Pretreatment Transcriptional Profiling for Predicting Response to Neoadjuvant Chemoradiotherapy in Rectal Adenocarcinoma

Brettingham-Moore

Duong

Greenawalt

et al. 2011

Clinical Cancer Research

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Purpose: Patients presenting with locally advanced rectal cancer currently receive preoperative radiotherapy with or without chemotherapy. Although pathologic complete response is achieved for approximately 10% to 30% of patients, a proportion of patients derive no benefit from this therapy while being exposed to toxic side effects of treatment. Therefore, there is a strong need to identify patients who are unlikely to benefit from neoadjuvant therapy to help direct them toward alternate and ultimately more successful treatment options.Experimental Design: In this study, we obtained expression profiles from pretreatment biopsies for 51 rectal cancer patients. All patients underwent preoperative chemoradiotherapy, followed by resection of the tumor 6 to 8 weeks posttreatment. Gene expression and response to treatment were correlated, and a supervised learning algorithm was used to generate an original predictive classifier and validate previously published classifiers.Results: Novel predictive classifiers based on Mandard's tumor regression grade, metabolic response, TNM (tumor node metastasis) downstaging, and normal tissue expression profiles were generated. Because there were only 7 patients who had minimal treatment response (>80% residual tumor), expression profiles were used to predict good tumor response and outcome. These classifiers peaked at 82% sensitivity and 89% specificity; however, classifiers with the highest sensitivity had poor specificity, and vice versa. Validation of predictive classifiers from previously published reports was attempted using this cohort; however, sensitivity and specificity ranged from 21% to 70%.Conclusions: These results show that the clinical utility of microarrays in predictive medicine is not yet within reach for rectal cancer and alternatives to microarrays should be considered for predictive studies in rectal adenocarcinoma. Clin Cancer Res; 17(9); 3039-47. Ó2011 AACR.

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Microarray-Based Response Prediction in Esophageal Adenocarcinoma

Cited by 52 publications

References 32 publications

Gastrointestinale Tumore – interdisziplinäre Diskussion neuer Daten

Gastrointestinale Tumore – interdisziplinäre Diskussion neuer Daten

Predictors of pathological complete response to neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma

Pretreatment Transcriptional Profiling for Predicting Response to Neoadjuvant Chemoradiotherapy in Rectal Adenocarcinoma

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