The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an ...interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint.
To favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weighting approaches (Single Wald (SW) and likelihood ratio test (LRT)) for the adaptive lasso method. The selection performance of these two approaches is compared to alternative lasso extensions (adaptive lasso with ridge-based weights, composite Minimax Concave Penalty, group exponential lasso and Sparse Group Lasso) through a simulation study. A RCT (NSABP B-31) randomizing 1574 patients (431 events) with early breast cancer aiming to evaluate the effect of adjuvant trastuzumab on distant-recurrence free survival with expression data from 462 genes measured in the tumour will serve for illustration. The simulation study illustrates that the adaptive lasso LRT and SW, and the group exponential lasso favored the hierarchical interaction constraint. Overall, in the alternative scenarios, they had the best balance of false discovery and false negative rates for the main effects of the selected interactions. For NSABP B-31, 12 gene-treatment interactions were identified more than 20% by the different methods. Among them, the adaptive lasso (SW) approach offered the best trade-off between a high number of selected gene-treatment interactions and a high proportion of selection of both the gene-treatment interaction and its main effect.
Adaptive lasso with Single Wald and likelihood ratio test weighting and the group exponential lasso approaches outperformed their competitors in favoring the hierarchical constraint of the biomarker-treatment interaction. However, the performance of the methods tends to decrease in the presence of prognostic biomarkers.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient ...characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.
We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000-2020 in the electronic databases PubMed, Scopus and Embase.
Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual's contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.
The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This single-institutional feasibility study prospectively characterized genomic alterations in recurrent or refractory solid tumors of pediatric patients to select a targeted therapy.
Following ...treatment failure, patients with signed consent and ages above 6 months, underwent tumor biopsy or surgical resection of primary or metastatic tumor site. These newly acquired samples were analyzed by comparative genomic hybridization array, next-generation sequencing for 75 target genes, whole-exome and RNA sequencing. Biological significance of the alterations and suggestion of most relevant targeted therapies available were discussed in a multidisciplinary tumor board.
From December 2012 to January 2016, 75 patients were included, 73 patients underwent 79 interventions, 56 of which were research biopsies with a low complication rate. All patients were pretreated, 37.0% had a brain tumor, and 63.0% had an extra-cranial solid tumor. Median tumor cell content was 70% (range, 0%-100%). Successful molecular analysis in 69 patients detected in 60.9% of patients an actionable alteration in various oncogenic pathways (42.4% with copy-number change, 33.3% with mutation, 2.1% with fusion), and change in diagnosis in three patients. Fourteen patients received 17 targeted therapies; two had received a matched treatment before inclusion.
Research biopsies are feasible in advanced pediatric malignancies that exhibit a considerable amount of potentially actionable alterations. Genetic events affecting different cancer hallmarks and limited access to targeted agents within pediatric clinical trials remain the main obstacles that are addressed in our two subsequent precision medicine studies MAPPYACTS and AcSé-ESMART.
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Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) ...estimation. However, fewer works have been conducted for IPD with time-to-event contrary to binary outcomes. We aimed to develop a general frequentist one-step model for evaluating TE in the presence of interaction in a three-node NMA for time-to-event data.
One-step, frequentist, IPD-based Cox and Poisson generalized linear mixed models were proposed. We simulated a three-node network with or without a closed loop with (1) no interaction, (2) covariate-treatment interaction, and (3) covariate distribution heterogeneity and covariate-treatment interaction. These models were applied to the NMA (Meta-analyses of Chemotherapy in Head and Neck Cancer MACH-NC and Radiotherapy in Carcinomas of Head and Neck MARCH), which compared the addition of chemotherapy or modified radiotherapy (mRT) to loco-regional treatment with two direct comparisons. AD-based (contrast and meta-regression) models were used as reference.
In the simulated study, no IPD models failed to converge. IPD-based models performed well in all scenarios and configurations with small bias. There were few variations across different scenarios. In contrast, AD-based models performed well when there were no interactions, but demonstrated some bias when interaction existed and a larger one when the modifier was not distributed evenly. While meta-regression performed better than contrast-based only, it demonstrated a large variability in estimated TE. In the real data example, Cox and Poisson IPD-based models gave similar estimations of the model parameters. Interaction decomposition permitted by IPD explained the ecological bias observed in the meta-regression.
The proposed general one-step frequentist Cox and Poisson models had small bias in the evaluation of a three-node network with interactions. They performed as well or better than AD-based models and should also be undertaken whenever possible.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In investigations of the effectiveness of surgery and adjuvant chemotherapy for gastric cancers, overall survival (OS) is considered the gold standard endpoint. However, the disadvantage of using OS ...as the endpoint is that it requires an extended follow-up period. We sought to investigate whether disease-free survival (DFS) is a valid surrogate for OS in trials of adjuvant chemotherapy for gastric cancer.
The GASTRIC group initiated a meta-analysis of individual patient data collected in randomized clinical trials comparing adjuvant chemotherapy vs surgery alone for patients with curatively resected gastric cancer. Surrogacy of DFS was assessed through the correlation between the endpoints as well as through the correlation between the treatment effects on the endpoints. External validation of the prediction based on DFS was also evaluated.
Individual patient data from 14 randomized clinical trials that included a total of 3288 patients were analyzed. The rank correlation coefficient between DFS and OS was 0.974 (95% confidence interval CI = 0.971 to 0.976). The coefficient of determination between the treatment effects on DFS and on OS was as high as 0.964 (95% CI = 0.926 to 1.000), and the surrogate threshold effect based on adjusted regression analysis was 0.92. In external validation, the six hazard ratios for OS predicted according to DFS were in very good agreement with those actually observed for OS.
DFS is an acceptable surrogate for OS in trials of cytotoxic agents for gastric cancer in the adjuvant setting.
Purpose: We used high-resolution oligonucleotide comparative genomic hybridization (CGH) arrays and matching gene expression array
data to identify dysregulated genes and to classify breast cancers ...according to gene copy number anomalies.
Experimental Design: DNA was extracted from 106 pretreatment fine needle aspirations of stage II-III breast cancers that received preoperative
chemotherapy. CGH was done using Agilent Human 4 × 44K arrays. Gene expression data generated with Affymetrix U133A gene chips
was also available on 103 patients. All P values were adjusted for multiple comparisons.
Results: The average number of copy number abnormalities in individual tumors was 76 (range 1-318). Eleven and 37 distinct minimal
common regions were gained or lost in >20% of samples, respectively. Several potential therapeutic targets were identified,
including FGFR1 that showed high-level amplification in 10% of cases. Close correlation between DNA copy number and mRNA expression levels
was detected. Nonnegative matrix factorization (NMF) clustering of DNA copy number aberrations revealed three distinct molecular
classes in this data set. NMF class I was characterized by a high rate of triple-negative cancers (64%) and gains of 6p21.
VEGFA, E2F3 , and NOTCH4 were also gained in 29% to 34% of triple-negative tumors. A gain of ERBB2 gene was observed in 52% of NMF class II and class III was characterized by a high rate of estrogen receptor–positive tumors
(73%) and a low rate of pathologic complete response to preoperative chemotherapy (3%).
Conclusion: The present study identified dysregulated genes that could classify breast cancer and may represent novel therapeutic targets
for molecular subsets of cancers.
The traditional end point for assessing efficacy of first-line chemotherapies for advanced cancer is overall survival (OS), but this end point requires prolonged follow-up and is potentially ...confounded by the effects of second-line therapies. We investigated whether progression-free survival (PFS) could be considered a valid surrogate for OS in advanced colorectal cancer.
Individual patient data were available from 10 historical trials comparing fluouracil (FU) + leucovorin with either FU alone (1,744 patients) or with raltitrexed (1,345 patients) and from three validation trials comparing FU + leucovorin with or without irinotecan or oxaliplatin (1,263 patients). Correlation coefficients were estimated in historical trials between the end points of PFS and OS, and between the treatment effects on these end points. Treatment effects on OS were predicted in validation trials, and compared with the observed effects.
In historical trials, 1,760 patients (57%) had progressed or died at 6 months, and 1,622 (52%) had died at 12 months. The rank correlation coefficient between PFS and OS was equal to 0.82 (95% CI, 0.82 to 0.83). The correlation coefficient between treatment effects on PFS and on OS ranged from 0.99 (95% CI, 0.94 to 1.04) when all trials were considered to 0.74 (95% CI, 0.44 to 1.04) after exclusion of one highly influential trial. In the validation trials, the observed OS hazard ratios were within the 95% prediction intervals. A hazard ratio of 0.77 or lower in terms of PFS would predict a benefit in terms of OS.
PFS is an acceptable surrogate for OS in advanced colorectal cancer.
Summary Background The gold standard endpoint in clinical trials of chemotherapy and radiotherapy for lung cancer is overall survival. Although reliable and simple to measure, this endpoint takes ...years to observe. Surrogate endpoints that would enable earlier assessments of treatment effects would be useful. We assessed the correlations between potential surrogate endpoints and overall survival at individual and trial levels. Methods We analysed individual patients' data from 15 071 patients involved in 60 randomised clinical trials that were assessed in six meta-analyses. Two meta-analyses were of adjuvant chemotherapy in non-small-cell lung cancer, three were of sequential or concurrent chemotherapy, and one was of modified radiotherapy in locally advanced lung cancer. We investigated disease-free survival (DFS) or progression-free survival (PFS), defined as the time from randomisation to local or distant relapse or death, and locoregional control, defined as the time to the first local event, as potential surrogate endpoints. At the individual level we calculated the squared correlations between distributions of these three endpoints and overall survival, and at the trial level we calculated the squared correlation between treatment effects for endpoints. Findings In trials of adjuvant chemotherapy, correlations between DFS and overall survival were very good at the individual level (ρ2 =0·83, 95% CI 0·83–0·83 in trials without radiotherapy, and 0·87, 0·87–0·87 in trials with radiotherapy) and excellent at trial level ( R2 =0·92, 95% CI 0·88–0·95 in trials without radiotherapy and 0·99, 0·98–1·00 in trials with radiotherapy). In studies of locally advanced disease, correlations between PFS and overall survival were very good at the individual level (ρ2 range 0·77–0·85, dependent on the regimen being assessed) and trial level ( R2 range 0·89–0·97). In studies with data on locoregional control, individual-level correlations were good (ρ2 =0·71, 95% CI 0·71–0·71 for concurrent chemotherapy and ρ2 =0·61, 0·61–0·61 for modified vs standard radiotherapy) and trial-level correlations very good ( R2 =0·85, 95% CI 0·77–0·92 for concurrent chemotherapy and R2 =0·95, 0·91–0·98 for modified vs standard radiotherapy). Interpretation We found a high level of evidence that DFS is a valid surrogate endpoint for overall survival in studies of adjuvant chemotherapy involving patients with non-small-cell lung cancers, and PFS in those of chemotherapy and radiotherapy for patients with locally advanced lung cancers. Extrapolation to targeted agents, however, is not automatically warranted. Funding Programme Hospitalier de Recherche Clinique, Ligue Nationale Contre le Cancer, British Medical Research Council, Sanofi-Aventis.
Summary The neoadjuvant setting provides a unique opportunity to study the effect of systemic treatments on breast cancer biology and to identify clinically useful prognostic and predictive ...biomarkers. Discrepancies and inconsistencies in the use of definitions and endpoint assessments in this setting confound the analysis and interpretation of results across clinical trials and hinder research progress. This Review represents a joint effort of the Breast International Group and the National Cancer Institute-sponsored North American Breast Cancer Group to provide clinicians and researchers with a series of standardised definitions and endpoints that could be implemented in future neoadjuvant clinical trials. Definitions of the setting of interest and of survival endpoints are recommended, together with proposals for standard assessment of the response to treatment, use of functional and molecular imaging endpoints, and characterisation and selection of the population to treat. We expect that implementation of these recommendations will improve the conduct, reporting, and effectiveness of clinical trials and fully exploit the clinical and scientific potential of the neoadjuvant setting in breast cancer.
Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized ...clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions.
Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso), we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation); estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap); and visualize them graphically (pointwise or smoothed with spline). We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured.
In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4 clinical covariates, the main effects of 98 biomarkers and 24 biomarker-by-treatment interactions, but there was high variability of the expected survival probabilities, with very large confidence intervals.
Based on our simulations, we propose a unified framework for: developing a prediction model with biomarker-by-treatment interactions in a high-dimensional setting and validating it in absence of external data; accurately estimating the expected survival probability of future patients with associated confidence intervals; and graphically visualizing the developed prediction model. All the methods are implemented in the R package biospear, publicly available on the CRAN.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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