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

Tumour immune microenvironment biomarkers predicting cytotoxic chemotherapy efficacy in colorectal cancer

The role of the local tumour and stromal immune landscape is increasingly recognised to be important in cancer development, progression and response to therapy. The composition, function, spatial orientation and gene expression profile of the infiltrate of the innate and adaptive immune system at the tumour and surrounding tissue has an established prognostic role in colorectal cancer (CRC). Multiple studies have confirmed that a tumour immune microenvironment (TIME) reflective of a type 1 adaptive immune response is associated with improved prognosis. There have been significant efforts to evolve these observations into validated, histopathology-based prognostic biomarkers, such as the Immunoscore. However, the clinical need lies much more in the development of predictive, not prognostic, biomarkers which have the potential to improve patient outcomes. This is particularly pertinent to help guide cytotoxic chemotherapy use in CRC, which remains the standard of care. Cytotoxic chemotherapy has recognised immunomodulatory activity distinct from its antimitotic effects, including mechanisms such as immunogenic cell death (ICD) and induction/inhibition of key immune players. Response to chemotherapy may differ with regard to molecular subtype of CRC, which are strongly associated with immune phenotypes. Thus, immune markers are potentially useful, though under-reported, predictive biomarkers. In this review, we discuss the impact of the TIME on response to cytotoxic chemotherapy in CRC, with a focus on baseline immune markers, and associated genomic and transcriptomic signatures

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

OBJECTIVE Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Patient-centric trials for therapeutic development in precision oncology

An enhanced understanding of the molecular pathology of disease gained from genomic studies is facilitating the development of treatments that target discrete molecular subclasses of tumours. Considerable associated challenges include how to advance and implement targeted drug-development strategies. Precision medicine centres on delivering the most appropriate therapy to a patient on the basis of clinical and molecular features of their disease. The development of therapeutic agents that target molecular mechanisms is driving innovation in clinical-trial strategies. Although progress has been made, modifications to existing core paradigms in oncology drug development will be required to realize fully the promise of precision medicine

Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review

Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.Comment: 20 pages, 2 figure

A dualistic model of primary anal canal adenocarcinoma with distinct cellular origins, etiologies, inflammatory microenvironments and mutational signatures: implications for personalised medicine.

Primary adenocarcinoma of the anal canal is a rare and aggressive gastrointestinal disease with unclear pathogenesis. Because of its rarity, no clear clinical practice guideline has been defined and a targeted therapeutic armamentarium has yet to be developed. The present article aimed at addressing this information gap by in-depth characterising the anal glandular neoplasms at the histologic, immunologic, genomic and epidemiologic levels. In this multi-institutional study, we first examined the histological features displayed by each collected tumour (n = 74) and analysed their etiological relationship with human papillomavirus (HPV) infection. The intratumoural immune cell subsets (CD4, CD8, Foxp3), the expression of immune checkpoints (PD-1, PD-L1), the defect in mismatch repair proteins and the mutation analysis of multiple clinically relevant genes in the gastrointestinal cancer setting were also determined. Finally, the prognostic significance of each clinicopathological variable was assessed. Phenotypic analysis revealed two region-specific subtypes of anal canal adenocarcinoma. The significant differences in the HPV status, density of tumour-infiltrating lymphocytes, expression of immune checkpoints and mutational profile of several targetable genes further supported the separation of these latter neoplasms into two distinct entities. Importantly, anal gland/transitional-type cancers, which poorly respond to standard treatments, displayed less mutations in downstream effectors of the EGFR signalling pathway (i.e., KRAS and NRAS) and demonstrated a significantly higher expression of the immune inhibitory ligand-receptor pair PD-1/PD-L1 compared to their counterparts arising from the colorectal mucosa. Taken together, the findings reported in the present article reveal, for the first time, that glandular neoplasms of the anal canal arise by HPV-dependent or independent pathways. These etiological differences leads to both individual immune profiles and mutational landscapes that can be targeted for therapeutic benefits

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision