A comprehensive review of predictive and prognostic composite factors implicated in the heterogeneity of treatment response and outcome across disease areas

Predicting Survival of Metastatic Clear Cell Renal Cell Cancer Treated with VEGFR-TKI-Based Sequential Therapy

(1) Objective: To develop a clinically useful nomogram that may provide a more individualized and accurate estimation of cancer-specific survival (CSS) for patients with clear-cell (CC) metastatic renal cell carcinoma (mRCC) treated with nephrectomy and vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI)-based sequential therapy. (2) Methods: A prospectively maintained database of 145 patients with mRCC treated between 2008 and 2018 was analyzed to predict the CSS of patients receiving sunitinib and second- and third-line therapies according to current standards of practice. A nomogram based on four independent clinical predictors (Eastern Cooperative Oncology Group status, International Metastatic RCC Database Consortium score, the Morphology, Attenuation, Size and Structure criteria and Response Evaluation Criteria in Solid Tumors response criteria) was calculated. The corresponding 1- to 10-year CSS probabilities were then determined from the nomogram. (3) Results: The median age was 60 years (95% CI 57.9-61.4). The disease was metastatic at diagnosis in 59 (40.7%), and 86 (59.3%) developed metastasis during follow-up. Patients were followed for a median 48 (IQR 72; 95% CI 56-75.7) months after first-line VEGFR-TKI initiation. The concordance probability estimator value for the nomogram is 0.778 ± 0.02 (mean ± SE). (4) Conclusions: A nomogram to predict CSS in patients with CC mRCC that incorporates patient status, clinical risk classification and response criteria to first-line VEGFR-TKI at 3 months is presented. This new tool may be useful to clinicians assessing the risk and prognosis of patients with mRCC.

Allele frequency deviation (AFD) as a new prognostic model to predict overall survival in lung adenocarcinoma (LUAD)

Background

Lung adenocarcinoma (LUAD) remains one of the world’s most known aggressive malignancies with a high mortality rate. Molecular biological analysis and bioinformatics are of great importance as they have recently occupied a large area in the studies related to the identification of various biomarkers to predict survival for LUAD patients. In our study, we attempted to identify a new prognostic model by developing a new algorithm to calculate the allele frequency deviation (AFD), which in turn may assist in the early diagnosis and prediction of clinical outcomes in LUAD.

Method

First, a new algorithm was developed to calculate AFD using the whole-exome sequencing (WES) dataset. Then, AFD was measured for 102 patients, and the predictive power of AFD was assessed using Kaplan–Meier analysis, receiver operating characteristic (ROC) curves, and area under the curve (AUC). Finally, multivariable cox regression analyses were conducted to evaluate the independence of AFD as an independent prognostic tool.

Result

The Kaplan–Meier analysis showed that AFD effectively segregated patients with LUAD into high-AFD-value and low-AFD-value risk groups (hazard ratio HR = 1.125, 95% confidence interval CI 1.001–1.26, p = 0.04) in the training group. Moreover, the overall survival (OS) of patients who belong to the high-AFD-value group was significantly shorter than that of patients who belong to the low-AFD-value group with 42.8% higher risk and 10% lower risk of death for both groups respectively (HR for death = 1.10; 95% CI 1.01–1.2, p = 0.03) in the training group. Similar results were obtained in the validation group (HR = 4.62, 95% CI 1.22–17.4, p = 0.02) with 41.6%, and 5.5% risk of death for patients who belong to the high and low-AFD-value groups respectively. Univariate and multivariable cox regression analyses demonstrated that AFD is an independent prognostic model for patients with LUAD. The AUC for 5-year survival were 0.712 and 0.86 in the training and validation groups, respectively.

Conclusion

AFD was identified as a new independent prognostic model that could provide a prognostic tool for physicians and contribute to treatment decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02127-z.

Extranodal site of diffuse large B-cell lymphoma and the risk of R-CHOP chemotherapy resistance and early relapse

BACKGROUND

About 20%-30% of diffuse large B-cell lymphoma (DLBCL) patients experience early disease progression despite R-CHOP chemotherapy treatment. Revised international prognostic index (R-IPI) score could risk stratify DLBCL patients but does not identify exactly which patient will be resistant to R-CHOP therapy or experience early relapse.

AIMS OF THE STUDY

To analyse pre-treatment clinical features of DLBCL patients that are predictive of R-CHOP therapy resistance and early disease relapse after R-CHOP therapy treatment.

METHODS USED TO CONDUCT THE STUDY

A total of 698 lymphoma patients were screened and 134 R-CHOP-treated DLBCL patients were included. The Lugano 2014 criteria was applied for assessment of treatment response. DLBCL patients were divided into R-CHOP resistance/early relapse group and R-CHOP sensitive/late relapse group.

RESULTS OF THE STUDY

81 of 134 (60%) were R-CHOP sensitive/late relapse, while 53 (40%) were R-CHOP resistance/early relapse. The median follow-up period was 59 months ± standard error 3.6. Five-year overall survival rate of R-CHOP resistance/early relapse group was 2.1%, while it was 89% for RCHOP sensitive/late relapse group. Having more than one extranodal site of DLBCL disease is an independent risk factor for R-CHOP resistance/early relapse [odds ratio = 5.268 (1.888-14.702), P = .002]. The commonest extranodal sites were head and neck, gastrointestinal tract, respiratory system, vertebra and bones. Advanced age (>60 years), advanced disease stage (lll-lV), raised pre-treatment lactate dehydrogenase level, bone marrow involvement of DLBCL disease high Eastern Cooperative Oncology Group status (2-4) and high R-IPI score (3-5) showed no significant association with R-CHOP therapy resistance/early disease relapse (multivariate analysis: P > .05).

CONCLUSION AND CLINICAL IMPLICATIONS

DLBCL patients with more than one extranodal site are 5.268 times more likely to be R-CHOP therapy resistance or experience early disease relapse after R-CHOP therapy. Therefore, correlative studies are warranted in DLBCL patients with more than one extranodal site of disease to explore possible underlying mechanisms of chemoresistance.

Health technology assessment with risk aversion in health

Standard cost-effectiveness models compare incremental cost increases to incremental average gains in health, commonly expressed in Quality-Adjusted Life Years (QALYs). Our research generalizes earlier models in several ways. We introduce risk aversion in Quality of Life (QoL), which leads to "willingness-to-pay" thresholds that rise with illness severity, potentially by an order of magnitude. Unlike traditional CEA analyses, which discriminate against persons with disabilities, our analysis implies that the marginal value of improving QoL rises for disabled individuals. Our model can also value the uncertain benefits of medical interventions by employing well-established analytic methods from finance. Finally, we show that traditional QALYs no longer serve as a single index of health, when consumers are risk-averse. To address this problem, we derive a generalized single-index of health outcomes-the Generalized Risk-Adjusted QALY (GRA-QALY). Earlier models of CEA that abstract from risk-aversion nest as special cases of our more general model.

Reporting of heterogeneity of treatment effect in cohort studies: a review of the literature

Background

This article corresponds to a literature review and analyze how heterogeneity of treatment (HTE) is reported and addressed in cohort studies and to evaluate the use of the different measures to HTE analysis.

Methods

prospective cohort studies, in English language, measuring the effect of a treatment (pharmacological, interventional, or other) published among 119 core clinical journals (defined by the National Library of Medicine) in the last 16 years were selected in the following data source: Medline. One reviewer randomly sampled journal articles with 1: 1 stratification by journal type: high impact journals (the New England Journal of Medicine, JAMA, LANCET, Annals of Internal Medicine, BMJ and Plos Medicine) and low impact journal (the remaining journals) to identify 150 eligible studies. Two reviewers independently and in duplicate used standardized piloted forms to screen study reports for eligibility and to extract data. They also used explicit criteria to determine whether a cohort study reported HTE analysis. Logistic regression was used to examine the association of prespecified study characteristics with reporting versus not reporting of heterogeneity of treatment effect.

Results

One hundred fifty cohort studies were included of which 88 (58%) reported HTE analysis. High impact journals (Odds Ratio: 3.5, 95% CI: 1.78–7.5; P < 0.001), pharmacological studies (Odds Ratio: 0.26, 95% CI: 0.13–0.51; P < 0.001) and studies published after 2014 (Odds Ratio: 0.5, 95% CI: 0.25–0.97; P = 0.004) were associated with more frequent reporting of HTE. 27 (31%) studies which reported HTE used an interaction test.

Conclusion

More than half cohort studies report some measure of heterogeneity of treatment effect. Prospective cohort studies published in high impact journals, with large sample size, or studying a pharmacological treatment are associated with more frequent HTE reporting. The source of funding was not associated with HTE reporting. There is a need for guidelines on how to perform HTE analyses in cohort studies.

Electronic supplementary material

The online version of this article (10.1186/s12874-017-0466-6) contains supplementary material, which is available to authorized users.

Field testing the ENSEMBLE Minimum Dataset: performance of an instrument to address heterogeneity of treatment effects

AIM

To assess the ability of ENterprising SElective Multi-instrument BLend for hEterogeneity analysis (ENSEMBLE) Minimum Dataset instrument dimensions to discriminate among subgroups of patients expected to have differential outcomes.

MATERIALS & METHODS

Patients with Type 2 diabetes, knee osteoarthritis, ischemic heart disease or heart failure completed a survey designed to represent three dimensions (health, personality and behavior). Health-related outcomes and utilization were investigated using claims data. Discriminant validity and associations between the dimensions and outcomes were assessed.

RESULTS

A total of 2625 patients completed the survey. The dimensions discriminated 50-100% of the outcome levels across disease cohorts; behavior dimension scores did not differ significantly among the healthcare utilization level subgroups in any disease cohort.

CONCLUSION

ENSEMBLE Minimum Dataset dimensions discriminated health-related outcome levels among patients with varied diseases.

A comprehensive review of nongenetic prognostic and predictive factors influencing the heterogeneity of outcomes in advanced non-small-cell lung cancer

While there have been advances in treatment options for those with advanced non-small-cell lung cancer, unmet medical needs remain, partly due to the heterogeneity of treatment effect observed among patients. The goals of this literature review were to provide updated information to complement past reviews and to identify a comprehensive set of nongenetic prognostic and predictive baseline factors that may account for heterogeneity of outcomes in advanced non-small-cell lung cancer. A review of the literature between 2000 and 2010 was performed using PubMed, Embase, and Cochrane Library. All relevant studies that met the inclusion criteria were selected and data elements were abstracted. A classification system was developed to evaluate the level of evidence for each study. A total of 54 studies were selected for inclusion. Patient-related factors (eg, performance status, sex, and age) were the most extensively researched nongenetic prognostic factors, followed by disease stage and histology. Moderately researched prognostic factors were weight-related variables and number or site of metastases, and the least studied were comorbidities, previous therapy, smoking status, hemoglobin level, and health-related quality of life/symptom severity. The prognostic factors with the most consistently demonstrated associations with outcomes were performance status, number or site of metastases, previous therapy, smoking status, and health-related quality of life. Of the small number of studies that assessed predictive factors, those that were found to be significantly predictive of outcomes were performance status, age, disease stage, previous therapy, race, smoking status, sex, and histology. These results provide a comprehensive overview of nongenetic prognostic and predictive factors assessed in advanced non-small-cell lung cancer over the last decade. This information can be used to inform the design of future clinical trials by suggesting additional subgroups based on nongenetic factors that may be analyzed to further investigate potential prognostic and predictive factors.

Factors affecting response to biologic treatment in psoriasis

Psoriasis is a chronic, immune-mediated inflammatory skin disease, affecting approximately 2-4% of the population in western countries. Patients with a more severe form of the disease are typically considered for systemic therapy, including biologics. In spite of the overall superiority of biologic agents, the treatment response may differ substantially among individual patients. As with other medical conditions, a range of factors contribute to response heterogeneity observed in psoriasis. Proper identification of these factors can significantly improve the therapeutic decisions. This review focuses on potential genetic and nongenetic factors that may affect the treatment response and outcomes in patients with psoriasis.

The emerging relevance of heterogeneity of treatment effect in clinical care: a study using stage IV prostate cancer as a model

AIM

Heterogeneity of treatment effect (HTE) occurs when patient factors modify a treatment's effect on health outcomes due to interactions between these factors and the treatment. This article reviews evidence regarding HTE in stage IV prostate cancer (S4PC).

METHOD

A systematic literature review was conducted in the MEDLINE and PubMed databases. Inclusion criteria required that articles examine the treatment-related impact of HTE factors on survival, adverse events or health-related quality of life in S4PC patients. The quality of evidence was graded good, fair or poor based on Agency for Healthcare Research and Quality guidelines.

RESULTS

The search identified 2659 articles, of which 92 met the inclusion/exclusion criteria. HTE in S4PC was studied for biologic factors including age, race, clinical signs/symptoms, measures of S4PC disease severity, genetic factors, laboratory data, prior treatment, concurrent medications and comorbidities. Nonbiologic factors that were studied included social, geographic and dietary factors. Age and race seldom showed any correlation with S4PC outcomes.

CONCLUSION

Diverse biologic and nonbiologic factors contribute to HTE in S4PC. This review in S4PC also provides an approach for examining HTE for other medical conditions. Ultimately, such knowledge can help oncologists prescribe more personalized medicine, help patients make more informed treatment choices, and inform policy-making and treatment coverage decisions.

From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer

Implicit in the growing interest in patient-centered outcomes research is a growing need for better evidence regarding how responses to a given intervention or treatment may vary across patients, referred to as heterogeneity of treatment effect (HTE). A variety of methods are available for exploring HTE, each associated with unique strengths and limitations. This paper reviews a selected set of methodological approaches to understanding HTE, focusing largely but not exclusively on their uses with randomized trial data. It is oriented for the “intermediate” outcomes researcher, who may already be familiar with some methods, but would value a systematic overview of both more and less familiar methods with attention to when and why they may be used. Drawing from the biomedical, statistical, epidemiological and econometrics literature, we describe the steps involved in choosing an HTE approach, focusing on whether the intent of the analysis is for exploratory, initial testing, or confirmatory testing purposes. We also map HTE methodological approaches to data considerations as well as the strengths and limitations of each approach. Methods reviewed include formal subgroup analysis, meta-analysis and meta-regression, various types of predictive risk modeling including classification and regression tree analysis, series of n-of-1 trials, latent growth and growth mixture models, quantile regression, and selected non-parametric methods. In addition to an overview of each HTE method, examples and references are provided for further reading.

By guiding the selection of the methods and analysis, this review is meant to better enable outcomes researchers to understand and explore aspects of HTE in the context of patient-centered outcomes research.