A new sensitivity-preferred strategy to build prediction rules for therapy response of cancer patients using gene expression data

Jung, Klaus; Grade, Marian; Gaedcke, Jochen; Jo, Peter; Opitz, Lennart; Becker, Heinz; Ghadimi, B. M.; Beiíbarth, Tim

doi:10.1016/j.cmpb.2010.03.016

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…The decrease of miR-18b and miR-20a expression during CRT can be used as a predictor for negative postoperative nodal stage. Different strategies to choose appropriate cut-offs for predictors are available [12], relating to a trade-off between sensitivity and specificity. For further analyses we chose a conservative cut-off at 4 % decrease, in order to strengthen specificity and minimize the false positive rate.…”

Section: The Reduction Of the Expression Of Mir-18b And Mir-20a Durin...mentioning

confidence: 99%

Preoperative Prediction of Lymph Node Status by Circulating Mir‐18b and Mir‐20a During Chemoradiotherapy in Patients with Rectal Cancer

Azizian

Krämer

et al. 2015

World j. surg.

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MicroRNA expression in patient plasma changes during preoperative CRT. The alteration is not continuous and the meaning requires additional analysis on a larger patient cohort. The co-occurrence of reduced miR-18b and miR-20a expression with lymph node negativity after preoperative CRT could help to stratify the surgical procedure with respect to total mesorectal excision and LR if validated prospectively.

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Section: The Reduction Of the Expression Of Mir-18b And Mir-20a Durin...mentioning

confidence: 99%

Preoperative Prediction of Lymph Node Status by Circulating Mir‐18b and Mir‐20a During Chemoradiotherapy in Patients with Rectal Cancer

Azizian

Krämer

et al. 2015

World j. surg.

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“…It identifies over 1/3 of the patients with pCR in all datasets examined and, most importantly, when considering a possible clinical application, never classifies any patient with an incomplete response as pCR. Unlike previous studies we aimed to predict pCR with the highest accuracy, at the cost of sensitivity [ 58 ]. Allowing mistakes when training a classifier would increase the rate of identifying patients with pCR; however, the clinical benefit of such a predictor would be limited.…”

Section: Discussionmentioning

confidence: 99%

Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy

Emons

Auslander

et al. 2022

Br J Cancer

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Purpose Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a “watch and wait” strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging. Experimental design We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial. Results A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76). Conclusion The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a “watch and wait” strategy. Translational relevance Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for “watch and wait”.

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Statistical Aspects in Proteomic Biomarker Discovery

Jung

2016

Methods in Molecular Biology

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In the pursuit of a personalized medicine, i.e., the individual treatment of a patient, many medical decision problems are desired to be supported by biomarkers that can help to make a diagnosis, prediction, or prognosis. Proteomic biomarkers are of special interest since they can not only be detected in tissue samples but can also often be easily detected in diverse body fluids. Statistical methods play an important role in the discovery and validation of proteomic biomarkers. They are necessary in the planning of experiments, in the processing of raw signals, and in the final data analysis. This review provides an overview on the most frequent experimental settings including sample size considerations, and focuses on exploratory data analysis and classifier development.

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A new sensitivity-preferred strategy to build prediction rules for therapy response of cancer patients using gene expression data

Cited by 7 publications

References 10 publications

Preoperative Prediction of Lymph Node Status by Circulating Mir‐18b and Mir‐20a During Chemoradiotherapy in Patients with Rectal Cancer

Preoperative Prediction of Lymph Node Status by Circulating Mir‐18b and Mir‐20a During Chemoradiotherapy in Patients with Rectal Cancer

Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy

Statistical Aspects in Proteomic Biomarker Discovery

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