Predictive models of tumour response to treatment using functional imaging techniques

Editorial, abstract not included.Loredana G. Marcu, Eva Bezak, Iuliana Toma-Dasu, and Alexandru Das

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

© 2017 The Author(s). Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis

The potential for liquid biopsies in the precision medical treatment of breast cancer.

Currently the clinical management of breast cancer relies on relatively few prognostic/predictive clinical markers (estrogen receptor, progesterone receptor, HER2), based on primary tumor biology. Circulating biomarkers, such as circulating tumor DNA (ctDNA) or circulating tumor cells (CTCs) may enhance our treatment options by focusing on the very cells that are the direct precursors of distant metastatic disease, and probably inherently different than the primary tumor's biology. To shift the current clinical paradigm, assessing tumor biology in real time by molecularly profiling CTCs or ctDNA may serve to discover therapeutic targets, detect minimal residual disease and predict response to treatment. This review serves to elucidate the detection, characterization, and clinical application of CTCs and ctDNA with the goal of precision treatment of breast cancer

Evaluation of the current knowledge limitations in breast cancer research: a gap analysis

BACKGROUND A gap analysis was conducted to determine which areas of breast cancer research, if targeted by researchers and funding bodies, could produce the greatest impact on patients. METHODS Fifty-six Breast Cancer Campaign grant holders and prominent UK breast cancer researchers participated in a gap analysis of current breast cancer research. Before, during and following the meeting, groups in seven key research areas participated in cycles of presentation, literature review and discussion. Summary papers were prepared by each group and collated into this position paper highlighting the research gaps, with recommendations for action. RESULTS Gaps were identified in all seven themes. General barriers to progress were lack of financial and practical resources, and poor collaboration between disciplines. Critical gaps in each theme included: (1) genetics (knowledge of genetic changes, their effects and interactions); (2) initiation of breast cancer (how developmental signalling pathways cause ductal elongation and branching at the cellular level and influence stem cell dynamics, and how their disruption initiates tumour formation); (3) progression of breast cancer (deciphering the intracellular and extracellular regulators of early progression, tumour growth, angiogenesis and metastasis); (4) therapies and targets (understanding who develops advanced disease); (5) disease markers (incorporating intelligent trial design into all studies to ensure new treatments are tested in patient groups stratified using biomarkers); (6) prevention (strategies to prevent oestrogen-receptor negative tumours and the long-term effects of chemoprevention for oestrogen-receptor positive tumours); (7) psychosocial aspects of cancer (the use of appropriate psychosocial interventions, and the personal impact of all stages of the disease among patients from a range of ethnic and demographic backgrounds). CONCLUSION Through recommendations to address these gaps with future research, the long-term benefits to patients will include: better estimation of risk in families with breast cancer and strategies to reduce risk; better prediction of drug response and patient prognosis; improved tailoring of treatments to patient subgroups and development of new therapeutic approaches; earlier initiation of treatment; more effective use of resources for screening populations; and an enhanced experience for people with or at risk of breast cancer and their families. The challenge to funding bodies and researchers in all disciplines is to focus on these gaps and to drive advances in knowledge into improvements in patient care

Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer

INTRODUCTION Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice. METHODS More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer 'stem' cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account. RESULTS The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working. CONCLUSIONS With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

Radiological evaluation of biomarkers for renal cell carcinoma

Role of MRI DWI sequences in the evaluation of early response to neo- angiogenesis inhibitors in metastatic renal cell carcinoma Purpose: Angiogenesis inhibitors have a potential role in treating metastatic renal cell carcinoma, but it is still not clear why some patients don't respond. Our objective was to look for DWI parameters able to identify patients with metastatic renal cell carcinoma who would not benefit from target therapy. RECIST1.1 was considered as Reference Standard. Methods & Materials: We prospectively enrolled 43 patients candidate to start angiogenesis inhibitors with at least one target lesion and who underwent 1,5T MRI examination with multiple bvalues DWI sequences (0,40,200,300,600): one week before (t0), 2 weeks after (t2) and 8 weeks (t8) after treatment beginning. ADC value was calculated drawing ROIs on 3 different planes. 33 patients with 38 lesions had suitable data for comparative evaluation. Results: At T8 follow-up 9 patients had partial response (PR), 20 table disease (SD), 4 progression disease (PD); average progression free survival was 272 days. PD group, as compared to DC or to PR showed significantly lower ADC values at b40 at t0 (p<0.05): we can assess that more vascularised lesions are more responsive to treatment. PD group have significantly lower ADC values then both other groups, at t0, t2 and t8, for all b-values (p<0.05). PFS and OS correlates well with ADC, in particular OS with ADC b40 at t0 (r=0,69). Coclusions: Results show that PD group has significantly lower ADC values than PR or DC everytime (t0, t2, t8) At t0 there is a better correlation between PFS or OS & ADC than PFS & dimensional criteria. ADC at t0 may help selecting patients with promising good response to angiogenesis inhibitors. Moreover at t0 and at t2 ADC has the potential to select patients who wouldn't benefit from angiogenesis inhibitors Nowadays, in the era of target therapy, it is crucial to select patients potentially responders. We have to look at cost/benefit ratio and at increasing costs of treatment options. DWI has the potential role to identify patients whose's tumor wouldn't benefit from target therapy, adding a value (ADC) to other imaging (e.g. DCE-MRI, texture imaging) and clinical parameters (e.g. miRNA) in a hypothetic multiparametric analysis.CT Texture Analysis in Clear Cell Renal Cell Carcinoma: a Radiogenomics Prospective Purpose: The aim of this study was to investigate whether quantitative parameters obtained from CT Texture Analysis (CTTA) correlate with expression of miRNA in clear cell Renal Cell Carcinoma (ccRCC). Methods and Materials: In a retrospective single centre study, multiphasic CT examination (with arterial, portal, equilibrium and urographic phases) was performed on 20 patients with clear cell renal carcinomas (14 men and 6 women; mean age 65 years ± 13). Measures of heterogeneity were obtained in post-processing by placing a ROI on the entire tumour and CTTA parameters such as entropy, kurtosis, skewness, mean, mean of positive pixels, and SD of pixel distribution histogram were measured using multiple filter settings. Quantitative data were correlated with the expression of miRNAs obtained from the same cohort of patients: 8 fresh frozen samples and 12 formalin-fixed paraffin-embedded samples (miR-21-5p, miR-210-3p, miR-185-5p, miR-221-3p, miR-145-5p). Both evaluations (miRNAs and CTTA) were performed on tumour tissues as well as on normal cortico-medullar tissues. Analysis of Variance with linear multiple regression model methods were obtained with SPSS statistic software. For all comparisons, statistical significance was assumed p<0.05 Results: We evidenced that CTTA has robust parameters (e.g. entropy, mean, sd) to distinguish normal from pathological tissues. Moreover, a higher coefficient of determination between entropy and miR-21-5p expression (R2 =0,25) was evidenced in tumour tissues as compared to normal tissues (R2 =0,15). Interestingly, excluding four patients with extreme over-expression of miR-21-5p, excellent relation between entropy and miR21-5p levels was found specifically in tumour samples (R2= 0,64; p<0.05). Conclusion: Entropy and miRNA-21-5p show promising correlation in ccRCC; in addiction CTTA features, in particular mean and entropy show a statistically significant increase in ccRCC as compared with normal renal parenchyma

Multiparametric image modelling:predicting treatment response in rectal cancer

Patients with advanced rectal cancer are typically treated with chemo- and radiation therapy (chemoradiation) followed by a major surgery where the entire rectum is removed. Some patients respond so well to chemoradiation that here is hardly any tumor present after treatment. This is why major surgery is increasingly omitted in patients that respond well to chemoradiation and instead these patients are treated “organ-saving” – i.e. with only a minor intervention or even without surgery. In this thesis, the possibility to predict treatment outcome to chemoradiation beforehand based on the available clinical data and imaging data has been studied using prediction models. If it is known upfront whether a patient will respond well to chemoradiation, this may offer possibilities to further personalize treatment to a specific patient. The predictive values of various imaging techniques were compared. In addition, the influence of data variations on reproducibility was investigated. The results of these studies offer important insights that are valuable for the development of future prediction models

Computer simulation of glioma growth and morphology

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion