Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models

Parisi, Fulvio; González, Antonio; Nadler, Yasmine; Camp, Robert L.; Rimm, David L.; Kluger, Harriet M.; Kluger, Yuval

doi:10.1186/bcr2633

Cited by 14 publications

(9 citation statements)

References 31 publications

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“…Since the beginning of the century, much of the research has been focused on issues related to personalized or stratified medicine with the assessment of genetic markers and analyses of high dimensional data as the challenge for researchers in many disciplines. A substantial part of such studies investigates issues for patients with cancer, breast cancer thereby being the disease considered most often [ 5 – 11 ]. Unfortunately, most of the results from the very large number of individual studies have not been validated and the number of clinically useful markers is pitifully small [ 12 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

et al. 2016

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BackgroundPrognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches.Methods and FindingsReanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived.ConclusionsThe prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases.

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Section: Introductionmentioning

confidence: 99%

“…For some years it has been discussed to improve prediction rules through the integration of clinical and gene expression data [ 5 , 16 – 20 ]. However, applying combined prediction rules at a broader level would cause difficulties in many (smaller) centers and increase costs.…”

Section: Introductionmentioning

confidence: 99%

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

et al. 2016

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“…If the variables in Net-score are highly correlated with each other, it will cause multicollinearity problem. For example, Nottingham prognostic index (NPI), which includes tumor size, lymph node status and histological grade information, and tumor stage are well-known prognostic factor for breast cancer, however, they were not selected in the current study [ 23 , 24 ]. NPI or tumor stage has high correlation coefficients with tumor size ( Supplementary Figure 2 , NPI-tumor size: 0.55, tumor stage-tumor size: 0.48 calculated by spearman correlation or Point-Biserial correlation respectively).…”

Section: Discussionmentioning

confidence: 99%

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients

et al. 2017

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Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

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“…They concluded that measuring bcl-2 expression in tumor biopsies was an independent predictor of breast cancer outcomes and could be useful as a prognostic adjunct to NPI, particularly in the first 5 years after diagnosis. Parisi et al [ 28 ] outlined the benefits of the inclusion of biomarkers with clinico-pathological covariate in breast prognostic models. They examined the expression of 14 biomarkers out of the 21 present in the Oncotype Dx test (Genomic Health) in addition to tumor characteristics found in NPI.…”

Section: Discussionmentioning

confidence: 99%

Determination of GP88 (progranulin) expression in breast tumor biopsies improves the risk predictive value of the Nottingham Prognostic Index

Serrero¹,

Hawkins

Bejarano

et al. 2016

Diagn Pathol

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BackgroundThe Nottingham Prognostic Index (NPI), which combines numerical values for nodal status, tumor size and histological grade, is used in the standard of care to provide predictive value information on post-surgery survival for patients with primary breast cancer. Attempts to improve the performance of the NPI algorithm have been carried out by testing the inclusion of other biomarker expression and morphological features such as vascular invasion. In the present study, we investigated whether expression of the autocrine growth and survival factor GP88 (progranulin), known to be overexpressed in breast cancer, would improve NPI’s predictive value.MethodsWe examined by immunohistochemistry (IHC) the GP88 expression in 508 cases of estrogen receptor positive invasive ductal carcinoma with known clinical outcomes and for which NPI had been determined. GP88 IHC expression was scored by two board certified pathologists and classified into two score groups of GP88 <3+ (0, 1+, 2+) and GP88 = 3+. The correlation between GP88 scoring, NPI and disease-free (DFS) or overall survival (OS) outcomes was then examined by Kaplan-Meier analysis, Cox proportional Hazard (CPH) ratio and Pearson’s X2 test.ResultsKaplan-Meier survival graphs of cases categorized by their NPI scores (<3.4, 3.4–5.4, >5.4) and GP88 expression showed that for patients within the same NPI subgroup, patients having tumors with a high GP88 expression (GP88 IHC score of 3+) had a worse DFS than patients with tumors that had a low GP88 expression (GP88 IHC score <3+). When adjusted for NPI, high GP88 score was significantly associated with recurrence with a hazard ratio of 3.30 (95 % CI 2.12 to 5.14).ConclusionsThe data suggest that the determination of GP88 tumor expression at time of diagnosis for early stage breast cancer patients can provide additional survival information to that provided by NPI alone and thus may be useful for risk management of patients diagnosed with breast cancer.

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Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models

Cited by 14 publications

References 31 publications

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients

Determination of GP88 (progranulin) expression in breast tumor biopsies improves the risk predictive value of the Nottingham Prognostic Index

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