Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumors

Aim: The RAISE project aimed to find a surrogate end point to predict treatment response early in patients with enteropancreatic neuroendocrine tumors (NET). Response heterogeneity, defined as the coexistence of responding and non-responding lesions, has been proposed as a predictive marker for progression-free survival (PFS) in patients with NETs. Patients & methods: Computerized tomography scans were analyzed from patients with multiple lesions in CLARINET (NCT00353496; n = 148/204). Cox regression analyses evaluated association between response heterogeneity, estimated using the standard deviation of the longest diameter ratio of target lesions, and NET progression. Results: Greater response heterogeneity at a given visit was associated with earlier progression thereafter: week 12 hazard ratio (HR; 95% confidence interval): 1.48 (1.20-1.82); p < 0.001; n = 148; week 36: 1.72 (1.32-2.24); p < 0.001; n = 108. HRs controlled for sum of longest diameter ratio: week 12: 1.28 (1.04-1.59); p = 0.020 and week 36: 1.81 (1.20-2.72); p = 0.005. Conclusion: Response heterogeneity independently predicts PFS in patients with enteropancreatic NETs. Further validation is required.

The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors

Aim: The RAISE project assessed whether deep learning could improve early progression-free survival (PFS) prediction in patients with neuroendocrine tumors. Patients & methods: Deep learning models extracted features from CT scans from patients in CLARINET (NCT00353496) (n = 138/204). A Cox model assessed PFS prediction when combining deep learning with the sum of longest diameter ratio (SLDr) and logarithmically transformed CgA concentration (logCgA), versus SLDr and logCgA alone. Results: Deep learning models extracted features other than lesion shape to predict PFS at week 72. No increase in performance was achieved with deep learning versus SLDr and logCgA models alone. Conclusion: Deep learning models extracted relevant features to predict PFS, but did not improve early prediction based on SLDr and logCgA.

Adjuvant endocrine therapy uptake, toxicity, quality of life, and prediction of early discontinuation

BACKGROUND

Many patients receiving adjuvant endocrine therapy (ET) for breast cancer experience side effects and reduced quality of life (QoL) and discontinue ET. We sought to describe these issues and develop a prediction model of early discontinuation of ET.

METHODS

Among patients with hormone receptor-positive and HER2-negative stage I-III breast cancer of the Cancer Toxicities cohort (NCT01993498) who were prescribed adjuvant ET between 2012 and 2017, upon stratification by menopausal status, we evaluated adjuvant ET patterns including treatment change and patient-reported discontinuation and ET-associated toxicities and impact on QoL. Independent variables included clinical and demographic features, toxicities, and patient-reported outcomes. A machine-learning model to predict time to early discontinuation was trained and evaluated on a held-out validation set.

RESULTS

Patient-reported discontinuation rate of the first prescribed ET at 4 years was 30% and 35% in 4122 postmenopausal and 2087 premenopausal patients, respectively. Switching to a new ET was associated with higher symptom burden, poorer QoL, and higher discontinuation rate. Early discontinuation rate of adjuvant ET before treatment completion was 13% in postmenopausal and 15% in premenopausal patients. The early discontinuation model obtained a C index of 0.62 in the held-out validation set. Many aspects of QoL, most importantly fatigue and insomnia (European Organization for Research and Treatment of Cancer QoL questionnaire 30), were associated with early discontinuation.

CONCLUSION

Tolerability and adherence to ET remains a challenge for patients who switch to a second ET. An early discontinuation model using patient-reported outcomes identifies patients likely to discontinue their adjuvant ET. Improved management of toxicities and novel more tolerable adjuvant ETs are needed for maintaining patients on treatment.

More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study

Adjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing the increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sample size requirements in time-to-event trials. We consider both parametric simulations and simulations derived from the Cancer Genome Atlas (TCGA) cohort of hepatocellular carcinoma (HCC) patients to assess how sample size requirements are reduced with covariate adjustment. Simulations demonstrate that the benefit of covariate adjustment increases with the prognostic performance of the adjustment covariate (C-index) and with the cumulative incidence of the event in the trial. For a covariate that has an intermediate prognostic performance (C-index=0.65), the reduction of sample size varies from 3.1% when cumulative incidence is of 10% to 29.1% when the cumulative incidence is of 90%. Broadening eligibility criteria usually reduces statistical power while our simulations show that it can be maintained with adequate covariate adjustment. In a simulation of adjuvant trials in HCC, we find that the number of patients screened for eligibility can be divided by 2.4 when broadening eligibility criteria. Last, we find that the Cox-Snell [Formula: see text] is a conservative estimation of the reduction in sample size requirements provided by covariate adjustment. Overall, more systematic adjustment for prognostic covariates leads to more efficient and inclusive clinical trials especially when cumulative incidence is large as in metastatic and advanced cancers. Code and results are available at https://github.com/owkin/CovadjustSim .

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.

P6420Can machine learning help us improve risk stratification of diabetic patients with acute coronary syndromes? The answer will blow your mind

Background

Risk stratification following an acute coronary syndrome (ACS) is of utmost importance, in order to identify patients at higher risk of subsequent cardiovascular events. Diabetic patients have a significantly worse prognosis, so new risk prediction tools are important to better identify and risk stratify high risk patients within this important ACS subpopulation.

Aim

The aim of this study was to identify the best predictors of a new ACS, in a single-center database of ACS, resorting to machine learning and artificial intelligence, and to compare the Global Registry of Acute Coronary Events (GRACE) risk score's relevance for risk discrimination in a general ACS population versus a subpopulation of diabetic patients.

Methods

In a single center, 5977 patients admitted due to ACS between 2004 and 2017 and alive at discharge were studied. In the subpopulation of diabetic patients (n=3429), each covariate present in the database was analyzed separately with a Cox proportional hazard model with three terms – subpopulation belonging indicator, covariate, interaction term. The p-value of the interaction term was used to rank variables. The more significant the interaction term, the stronger the change in relationship between patients in the subpopulation and the risk of a new ACS, compared to the one in the general population.

Results

During long term follow-up, 13% of patients (n=771) experienced a second event. Kaplan-Meier curve represents how ACS free-survival depends on the GRACE risk score and group of interest. In the general population and in the subpopulation of diabetic patients, the GRACE score was used to further divide patients into 3 terciles, of which only the lower and upper tercile are shown (GRACE ≤113 and GRACE >144, respectively). The solid lines represent Kaplan-Meier curves for diabetic patients, and the dotted lines in the general population. Pink or grey colour of the curves represent the stratification level of the covariate.

Conclusions

In our model, the GRACE risk score was found to be a better discriminator of risk of futher ACS in diabetic patients than in the general ACS population. Strikingly, a higher GRACE score predicts a lower rate of readmission, probably because many patients will die in the index hospitalization or out of hospital. This finding reinforces the usefulness of the GRACE score in high risk patients and may improve risk stratisfication in diabetic post-ACS patients, making sure that they are closely followed and submitted to optimal risk factor management, in order to improve their post-ACS prognosis.

Association of environmental markers with childhood type 1 diabetes mellitus revealed by a long questionnaire on early life exposures and lifestyle in a case-control study

Background

The incidence of childhood type 1 diabetes (T1D) incidence is rising in many countries, supposedly because of changing environmental factors, which are yet largely unknown. The purpose of the study was to unravel environmental markers associated with T1D.

Methods

Cases were children with T1D from the French Isis-Diab cohort. Controls were schoolmates or friends of the patients. Parents were asked to fill a 845-item questionnaire investigating the child’s environment before diagnosis. The analysis took into account the matching between cases and controls. A second analysis used propensity score methods.

Results

We found a negative association of several lifestyle variables, gastroenteritis episodes, dental hygiene, hazelnut cocoa spread consumption, wasp and bee stings with T1D, consumption of vegetables from a farm and death of a pet by old age.

Conclusions

The found statistical association of new environmental markers with T1D calls for replication in other cohorts and investigation of new environmental areas.

Trial registration

Clinical-Trial.gov NCT02212522. Registered August 6, 2014. 

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3690-9) contains supplementary material, which is available to authorized users.