Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial

There is critical need for a clinically useful tool to predict antidepressant treatment outcome in major depressive disorder (MDD) to reduce suffering and mortality. This analysis sought to build upon previously reported antidepressant treatment efficacy prediction from 2-[¹⁸F]-fluorodeoxyglucose - Positron Emission Tomography (FDG-PET) using metabolic rate of glucose uptake (MRGlu) from dynamic FDG-PET imaging with the goal of translation to clinical utility. This investigation is a randomized, double-blind placebo-controlled trial. All participants were diagnosed with MDD and received an FDG-PET scan before randomization and after treatment. Hamilton Depression Rating Scale (HDRS-17) was completed in participants diagnosed with MDD before and after 8 weeks of escitalopram, or placebo. MRGlu (mg/(min*100 ml)) was estimated within the raphe nuclei, right insula, and left ventral Prefrontal Cortex in 63 individuals. Linear regression was used to examine the association between pretreatment MRGlu and percent decrease in HDRS-17. Additionally, the association between percent decrease in HDRS-17 and percent change in MRGlu between pretreatment scan and post-treatment scan was examined. Covariates were treatment type (SSRI/placebo), handedness, sex, and age. Depression severity decrease (n = 63) was not significantly associated with pretreatment MRGlu in the raphe nuclei (β = -2.61e^-03 [-0.26, 0.25], p = 0.98), right insula (β = 0.05 [-0.23, 0.32], p = 0.72), or ventral prefrontal cortex (β = 0.06 [-0.23, 0.34], p = 0.68) where β is the standardized estimated coefficient, with a 95% confidence interval, or in whole brain voxelwise analysis (family-wise error correction, alpha = 0.05). MRGlu percent change was not significantly associated with depression severity decrease (n = 58) before multiple comparison correction in the RN (β = 0.20 [-0.07, 0.47], p = 0.15), right insula (β = 0.24 [-0.03, 0.51], p = 0.08), or vPFC (β = 0.22 [-0.06, 0.50], p = 0.12). We propose that FDG-PET imaging does not indicate a clinically relevant biomarker of escitalopram or placebo treatment response in heterogeneous major depressive disorder cohorts. Future directions include focusing on potential biologically-based subtypes of major depressive disorder by implementing biomarker stratified designs.

Discovery of a putative blood-based protein signature associated with response to ALK tyrosine kinase inhibition

Background

ALK tyrosine kinase inhibition has become a mainstay in the clinical management of ALK fusion positive NSCLC patients. Although ALK mutations can reliably predict the likelihood of response to ALK tyrosine kinase inhibitors (TKIs) such as crizotinib, they cannot reliably predict response duration or intrinsic/extrinsic therapeutic resistance. To further refine the application of personalized medicine in this indication, this study aimed to identify prognostic proteomic biomarkers in ALK fusion positive NSCLC patients to crizotinib.

Methods

Twenty-four patients with advanced NSCLC harboring ALK fusion were administered crizotinib in a phase IV trial which included blood sampling prior to treatment. Targeted proteomics of 327 proteins using MRM-MS was used to measure plasma levels at baseline (including pre-treatment and early treatment blood samples) and assess potential clinical association.

Results

Patients were categorized by duration of response: long-term responders [PFS ≥ 24 months (n = 7)], normal responders [3 < PFS < 24 months (n = 10)] and poor responders [PFS ≤ 3 months (n = 5)]. Several proteins were identified as differentially expressed between long-term responders and poor responders, including DPP4, KIT and LUM. Next, using machine learning algorithms, we evaluated the classification potential of 40 proteins. Finally, by integrating the different analytic methods, we selected 22 proteins as potential candidates for a blood-based prognostic signature of response to crizotinib in NSCLC patients harboring ALK fusion.

Conclusion

In conjunction with ALK mutation, the expression of this proteomic signature may represent a liquid biopsy-based marker of long-term response to crizotinib in NSCLC. Expanding the utility of prognostic biomarkers of response duration could influence choice of therapy, therapeutic sequencing, and potentially the need for alternative or combination therapy.Trial registration ClinicalTrials.gov, NCT02041468. Registered 22 January 2014, https://clinicaltrials.gov/ct2/show/NCT02041468?term=NCT02041468&rank=1.

18F-fluorothymidine (FLT)-PET and diffusion-weighted MRI for early response evaluation in patients with small cell lung cancer: a pilot study

Background

Small cell lung cancer (SCLC) is an aggressive cancer often presenting in an advanced stage and prognosis is poor. Early response evaluation may have impact on the treatment strategy.

Aim

We evaluated ¹⁸F-fluorothymidine-(FLT)-PET/diffusion-weighted-(DW)-MRI early after treatment start to describe biological changes during therapy, the potential of early response evaluation, and the added value of FLT-PET/DW-MRI.

Methods

Patients with SCLC referred for standard chemotherapy were eligible. FLT-PET/DW-MRI of the chest and brain was acquired within 14 days after treatment start. FLT-PET/DW-MRI was compared with pretreatment FDG-PET/CT. Standardized uptake value (SUV), apparent diffusion coefficient (ADC), and functional tumor volumes were measured. FDG-SUV_peak, FLT-SUV_peak, and ADC_median; spatial distribution of aggressive areas; and voxel-by-voxel analyses were evaluated to compare the biological information derived from the three functional imaging modalities. FDG-SUV_peak, FLT-SUV_peak, and ADC_median were also analyzed for ability to predict final treatment response.

Results

Twelve patients with SCLC completed FLT-PET/MRI 1–9 days after treatment start. In nine patients, pretreatment FDG-PET/CT was available for comparison. A total of 16 T-sites and 12 N-sites were identified. No brain metastases were detected. FDG-SUV_peak was 2.0–22.7 in T-sites and 5.5–17.3 in N-sites. FLT-SUV_peak was 0.6–11.5 in T-sites and 1.2–2.4 in N-sites. ADC_median was 0.76–1.74 × 10^− 3 mm²/s in T-sites and 0.88–2.09 × 10⁻³ mm²/s in N-sites. FLT-SUV_peak correlated with FDG-SUV_peak, and voxel-by-voxel correlation was positive, though the hottest regions were dissimilarly distributed in FLT-PET compared to FDG-PET. FLT-SUV_peak was not correlated with ADC_median, and voxel-by-voxel analyses and spatial distribution of aggressive areas varied with no systematic relation. LT-SUV_peak was significantly lower in responding lesions than non-responding lesions (mean FLT-SUV_peak in T-sites: 1.5 vs. 5.7; p = 0.007, mean FLT-SUV_peak in N-sites: 1.6 vs. 2.2; p = 0.013).

Conclusions

FLT-PET and DW-MRI performed early after treatment start may add biological information in patients with SCLC. Proliferation early after treatment start measured by FLT-PET is a promising predictor for final treatment response that warrants further investigation.

Trial registration

Clinicaltrials.gov, NCT02995902. Registered 11 December 2014 - Retrospectively registered.

BRAFV600E-mutant cancers display a variety of networks by SWIM analysis: prediction of vemurafenib clinical response

PURPOSE

Several studies have shown that different tumour types sharing a driver gene mutation do not respond uniformly to the same targeted agent. Our aim was to use an unbiased network-based approach to investigate this fundamental issue using BRAF^V600E mutant tumours and the BRAF inhibitor vemurafenib.

METHODS

We applied SWIM, a software able to identify putative regulatory (switch) genes involved in drastic changes to the cell phenotype, to gene expression profiles of different BRAF^V600E mutant cancers and their normal counterparts in order to identify the switch genes that could potentially explain the heterogeneity of these tumours' responses to vemurafenib.

RESULTS

We identified lung adenocarcinoma as the tumour with the highest number of switch genes (298) compared to its normal counterpart. By looking for switch genes encoding for kinases with homology sequences similar to known vemurafenib targets, we found that thyroid cancer and lung adenocarcinoma have a similar number of putative targetable switch gene kinases (5 and 6, respectively) whereas colorectal cancer has just one.

CONCLUSIONS

We are persuaded that our network analysis may aid in the comprehension of molecular mechanisms underlying the different responses to vemurafenib in BRAF^V600E mutant tumours.

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data

BACKGROUND

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model.

RESULTS

We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database.

CONCLUSION

In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

Role of contrast-enhanced ultrasound to define prognosis and predict response to biotherapy in pancreatic neuroendocrine tumors

PURPOSE

The incidence of neuroendocrine tumors (NETs) is progressively increasing. Most cases arise from the digestive system, where ileum, rectum and pancreas represent the commonest site of origin. Liver metastases are frequently detected at diagnosis or during the follow-up. Contrast-enhanced ultrasound (CEUS) is used in patients with pancreatic NETs (P-NETs) and liver metastases from P-NET but its role has not been standardized. The aim of this retrospective study was to investigate CEUS in patients with P-NETs and liver metastases from P-NET both as prognostic factor and predictor of response to therapy with somatostatin analogues (SSAs).

METHODS

CEUS was performed at the diagnosis of NET and 3, 6 and 12 months after the beginning of SSAs. CEUS pattern was compared with contrast-enhanced computed tomography (CT) pattern.

RESULTS

There was a significant association between CEUS and CT pattern (X ² = 79.0; p < 0.0001). A significant association was found between CEUS pattern and Ki-67 index (X ² = 24.6; p < 0.0001). The hypervascular homogeneous CEUS typical pattern was associated with low tumor grading (G1 or G2) (X ² = 24.0; p < 0.0001). CEUS pattern changed from hypervascular homogeneous in baseline to hypovascular/hypervascular inhomogeneous after SSA therapy, with a significant association between tumor response at CT scan and appearance of hypervascular inhomogeneous pattern at CEUS evaluation (6 months: X ² = 57.0; p < 0.0001; 12 months: X ² = 49.8; p < 0.0001).

CONCLUSIONS

In patients with P-NET, CEUS pattern correlates with tumor grading, being homogeneous in G1-G2 but not in G3 tumors. After therapy with SSAs, CEUS is predictive of response to SSAs. These findings seem to support a role of CEUS as prognostic and predictive factor of response.

MicroRNAs miR-7 and miR-340 predict response to neoadjuvant chemotherapy in breast cancer

PURPOSE

miRNAs have been linked to chemosensitivity of breast cancer cells in vitro. In patients, however, there is no clinically validated method for predicting chemotherapy response. The aim of this study was to assess whether (I) a specific pattern of miRNA expression in pretherapeutic biopsies can predict response to neoadjuvant chemotherapy, and (II) differential miRNA expression in residual tumor after completion of chemotherapy allows further prognostic stratification of non-responding patients.

METHODS

Sixty-four patients with newly diagnosed large (≥3 cm) or locally advanced primary breast cancers who underwent neoadjuvant anthracycline/taxane-based chemotherapy were included. Relative expression of 10 miRNAs likely to be associated with chemotherapy response (miR-7,-21,-29a,-29b,-34a,-125b,-155,-200c,-340,-451) was determined by quantitative RT-PCR from pretherapeutic biopsies (n = 64) and residual invasive tumor after chemotherapy (n = 42). Pathologic complete response (pCR) defined by absence of invasive tumor served as reference standard. In addition, miRNA expression was compared with disease-free and overall survival.

RESULTS

Nine (14%) of 64 patients achieved pCR. High expression of miR-7 and low expression of miR-340 in pretherapeutic biopsies predicted pCR with a negative predictive value of 96 and 97%, respectively (specificity 54 and 57%). The combined profile of miR-7^high/miR-340^low demonstrated improved specificity of 86% while maintaining a high negative predictive value (96%) to identify non-responders. Pretherapeutic expression of miR-200c and miR-155 showed prognostic information, and low expression was associated with increased overall survival (115 vs. 90 months, p ≤ 0.03). After chemotherapy, the overall survival of patients with residual invasive tumor was better for those demonstrating low miR-7 or high miR-125b (p = 0.01).

CONCLUSIONS

Intratumoral expression of miR-7 and miR-340 prior to neoadjuvant chemotherapy could be used to predict pCR and a profile of miR-7^low or miR-340^high identified patients unlikely to achieve pCR who might benefit from alternative treatment options including earlier surgery. Our study identifies miRNAs as promising predictive biomarkers, which could aid in optimization of breast cancer management and treatment stratification.

Guided parent-delivered cognitive behavioral therapy for childhood anxiety: Predictors of treatment response

BACKGROUND

Guided Parent-delivered Cognitive Behaviour Therapy (GPD-CBT) is a brief, effective treatment for childhood anxiety disorders, however not all children respond favourably.

AIMS

To examine predictors of response to GPD-CBT.

METHODS

Parents of 125 children (7-12 years) with an anxiety disorder received GPD-CBT over 2.6 or 5.3h. Recovery was measured post treatment and six months later.

RESULTS

Younger children and those with primary Generalised Anxiety Disorder (GAD) improved more post treatment, but older children and those without primary GAD had better outcomes at six month follow up. Fewer children allocated to 2.6h had recovered post treatment compared to those allocated to the 5.2h intervention, but did not differ significantly six months later.

CONCLUSIONS

The identification of predictors of short and longer-term treatment outcomes can guide treatment decisions following this low-intensity approach.

Prediction of anti-angiogenesis escape

Many clinical trials have demonstrated the benefit of anti-angiogenesis therapy in the treatment of gynecologic cancer. However, these benefits have often been in terms of progression-free rather than overall survival and in some cases, the magnitude of benefit demonstrated in the pivotal phase 3 trials has been disappointing when compared with the percentage of patients who responded in earlier phase 2 trials. Two potential explanations for this are the current inability to stratify patients according to chance of benefit and the development of resistance mechanisms within the tumor. In this article, we review the prediction of response and the proposed resistance and escape mechanisms involved in anti-angiogenesis therapy, including the up-regulation of alternative proangiogenic pathways, vascular co-option, and resistance to hypoxia. These insights may offer a personalized strategy for anti-angiogenesis therapy and help us to consider the best selection of other therapies that should be combined with anti-angiogenesis therapy to improve the outcome of patients with gynecologic cancer.

Predicting response to cognitive therapy and interpersonal therapy, with or without antidepressant medication, for major depression: a pragmatic trial in routine practice

BACKGROUND

Identifying patient characteristics that predict response within treatments (prognostic) or between treatments (prescriptive) can inform clinical decision-making. In this study, we sought to identify predictors of response to evidence-based treatments in a sample of depressed patients seeking help in routine practice.

METHODS

Data come from a pragmatic trial of 174 patients with major depression who received an evidence-based treatment of their own choice: cognitive therapy (CT), interpersonal therapy (IPT), antidepressant medication (ADM) alone or in combination with either of the two psychotherapies. Patient characteristics measured at baseline were examined to see if they predicted subsequent response as measured with the Beck Depression Inventory (BDI) over the course of 26 weeks of treatment, using mixed regression modeling.

RESULTS

Higher agoraphobia scores at baseline predicted more change in depression scores across treatments, irrespective of the treatment received. Physical functioning moderated the response to treatment: patients with high physical functioning fared better in combined treatment than patients with low physical functioning, whereas physical functioning did not predict a differential response in the psychotherapy group. Moreover, the lowest levels of physical functioning predicted an increase of depressive symptoms in combined treatment.

LIMITATIONS

A relatively small sample size, and selection of several predictors that were less theory-driven, which hampers the translation to clinical practice.

CONCLUSIONS

If replicated, the prognostic and prescriptive indices identified in this study could guide decision-making in routine practice. Development of more uniform requirements for the analysis and reporting of prediction studies is recommended.

HER2 status for prognosis and prediction of treatment efficacy in adenocarcinomas: a review

The past few years have seen flourish new biologic parameters for cancer prognosis that are revolutionizing therapeutic strategies. HER-2 is in this perspective a striking example, as it is now a key element for the care of 15-20% of breast cancer. HER-2 overexpression has first been reported as a prognostic factor before its consideration as a main parameter to predict treatment efficacy. However, although HER-2 status is now also used as a prognostic factor for many cancers, its ability to predict the action of trastuzumab in these new contexts is much lower than in breast cancer. In this literature review, we aimed to discuss HER-2 overexpression as a prognostic factor and as a predictive parameter of treatment response in selected solid tumors with a focus on adenocarcinomas.

FDG-PET Evaluation of Response to Treatment

This article discusses evaluating response after and during therapy in various settings and for the types of cancers for which ample evidence demonstrates that PET imaging with flourodeoxyglucose provides a valuable surrogate for response to therapy. It also briefly discusses pitfalls in obtaining an optimal assessment of response and issues that need further attention for this modality to become established as an independent predictor of response to anticancer therapy.