Correlating and Combining Genomic and Proteomic Assessment with In Vivo Molecular Functional Imaging: Will This Be the Future Roadmap for Personalized Cancer Management?

Pancreatic neuroendocrine neoplasms: a 2022 update for radiologists

Pancreatic neuroendocrine neoplasms (PaNENs) are a unique group of pancreatic neoplasms with a wide range of clinical presentations and behaviors. Given their heterogeneous appearance and increasing detection on cross-sectional imaging, it is essential that radiologists understand the variable presentation and distinctions PaNENs display compared to other pancreatic neoplasms. Additionally, some of these neoplasms may be hormonally functional, and it is imperative that radiologists be aware of the common clinical presentations of hormonally active PaNENs. Knowledge of PaNEN pathology and treatments may influence which imaging modality is optimal for each patient. Each imaging modality used for PaNENs has distinct advantages and disadvantages, particularly in different treatment settings. Thus, the focus of this manuscript is to provide an update for the radiologist on PaNEN pathology, imaging, and treatments.

New frontiers in imaging including radiomics updates for pancreatic neuroendocrine neoplasms

OBJECTIVE

To illustrate the applications of various imaging tools including conventional MDCT, MRI including DWI, CT & MRI radiomics, FDG & DOTATATE PET-CT for diagnosis, staging, grading, prognostication, treatment planning and assessing treatment response in cases of pancreatic neuroendocrine neoplasms (PNENs).

BACKGROUND

Gastroenteropancreatic neuroendocrine neoplasms (GEP NENs) are very diverse clinically & biologically. Their treatment and prognosis depend on staging and primary site, as well as histological grading, the importance of which is also reflected in the recently updated WHO classification of GEP NENs. Grade 3 poorly differentiated neuroendocrine carcinomas (NECs) are aggressive & nearly always advanced at diagnosis with poor prognosis; whereas Grades-1 and 2 well-differentiated neuroendocrine tumors (NETs) can be quite indolent. Grade 3 well-differentiated NETs represent a new category of neoplasm with an intermediate prognosis. Importantly, the evidence suggest grade heterogeneity can occur within a given tumor and even grade progression can occur over time. Emerging evidence suggests that several non-invasive qualitative and quantitative imaging features on CT, dual-energy CT (DECT), MRI, PET and somatostatin receptor imaging with new tracers, as well as texture analysis, may be useful to grade, prognosticate, and accurately stage primary NENs. Imaging features may also help to inform choice of treatment and follow these neoplasms post-treatment.

CONCLUSION

GEP NENs treatment and prognosis depend on the stage as well as histological grade of the tumor. Traditional ways of imaging evaluation for diagnosis and staging does not yet yield sufficient information to replace operative and histological evaluation. Recognition of important qualitative imaging features together with quantitative features and advanced imaging tools including functional imaging with DWI MRI, DOTATATE PET/CT, texture analysis with radiomics and radiogenomic features appear promising for more accurate staging, tumor risk stratification, guiding management and assessing treatment response.

Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms

Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis, hormonal syndromes produced, biological behavior and consequently, in their requirement for and/or response to specific chemotherapeutic agents and molecular targeted therapies. Various imaging techniques are available for functional and morphological evaluation of these neoplasms and the selection of investigations performed in each patient should be customized to the clinical question. Also, with the increased availability of cross sectional imaging, these neoplasms are increasingly being detected incidentally in routine radiology practice. This article is a review of the various imaging modalities currently used in the evaluation of neuroendocrine neoplasms, along with a discussion of the role of advanced imaging techniques and a glimpse into the newer imaging horizons, mostly in the research stage.

Peptide Receptor Radionuclide Therapy in the Management of Neuroendocrine Tumors (Neoplasms): Fundamentals and Salient Clinical Practice Points for Medical Oncologists

This editorial commentary is an expert summary of “Peptide Receptor Radionuclide Therapy (PRRT),” encompassing the essential fundamentals and salient clinical practice points, deliberated and designed in a point-wise manner with theme-based subheadings. Emphasis has been laid on the topics of practical relevance to the referring oncologists with relevant finer points where necessary. A part of the presented overview has been generated from the authors' own practical experience of more than 3500 successful therapies delivered over the last 9 years at a large tertiary care PRRT setting by the joint efforts of Radiation Medicine Centre (RMC), Bhabha Atomic Research Centre (BARC), and Gastrointestinal services of Tata Memorial Hospital (TMH) at the TMH-RMC premises. While the technical indigenization is beyond the scope of this treatise, we must mention here that India had been one of the frontrunners in this treatment modality, and the PRRT services in this country were developed purely as an indigenous effort right from the production of the radionuclide (177-Lutetium) at the reactor and radiolabeling and production of the radiopharmaceutical (177Lu-DOTATATE) by the radiopharmaceutical scientists at the BARC and RMC; such an endeavor allowed this very specialized therapy to be delivered at a very affordable cost in our setting which could be viewed as a major societal contribution of the atomic energy research in this country.

Ensemble outlier detection and gene selection in triple-negative breast cancer data

Background

Learning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares - Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals’ outlierness based on the Cook’s distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level.

Results

We applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified.

Conclusions

The proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2149-7) contains supplementary material, which is available to authorized users.

Advanced bioanalytics for precision medicine

Precision medicine is a new paradigm that combines diagnostic, imaging, and analytical tools to produce accurate diagnoses and therapeutic interventions tailored to the individual patient. This approach stands in contrast to the traditional "one size fits all" concept, according to which researchers develop disease treatments and preventions for an "average" patient without considering individual differences. The "one size fits all" concept has led to many ineffective or inappropriate treatments, especially for pathologies such as Alzheimer's disease and cancer. Now, precision medicine is receiving massive funding in many countries, thanks to its social and economic potential in terms of improved disease prevention, diagnosis, and therapy. Bioanalytical chemistry is critical to precision medicine. This is because identifying an appropriate tailored therapy requires researchers to collect and analyze information on each patient's specific molecular biomarkers (e.g., proteins, nucleic acids, and metabolites). In other words, precision diagnostics is not possible without precise bioanalytical chemistry. This Trend article highlights some of the most recent advances, including massive analysis of multilayer omics, and new imaging technique applications suitable for implementing precision medicine. Graphical abstract Precision medicine combines bioanalytical chemistry, molecular diagnostics, and imaging tools for performing accurate diagnoses and selecting optimal therapies for each patient.

Beyond imaging: The promise of radiomics

The domain of investigation of radiomics consists of large-scale radiological image analysis and association with biological or clinical endpoints. The purpose of the present study is to provide a recent update on the status of this rapidly emerging field by performing a systematic review of the literature on radiomics, with a primary focus on oncologic applications. The systematic literature search, performed in Pubmed using the keywords: "radiomics OR radiomic" provided 97 research papers. Based on the results of this search, we describe the methods used for building a model of prognostic value from quantitative analysis of patient images. Then, we provide an up-to-date overview of the results achieved in this field, and discuss the current challenges and future developments of radiomics for oncology.

Advances in the Diagnosis of Neuroendocrine Neoplasms

Somatostatin receptor PET/CT using (68)Ga-labeled somatostatin analogs, is a mainstay for the evaluation of the somatostatin receptor status in neuroendocrine neoplasms. In addition, the assessment of glucose metabolism by (18)F-FDG PET/CT at diagnosis can overcome probable shortcomings of histopathologic grading. This offers a systematic theranostic approach for the management of neuroendocrine neoplasms, that is, patient selection for the appropriate treatment-surgery, somatostatin analogs, peptide receptor radionuclide therapy, targeted therapies like everolimus and sunitinib, or chemotherapy-and also for therapy response monitoring. Novel targets, for example, the chemokine receptor CXCR4 in higher-grade tumors and glucagon like peptide-1 receptor in insulinomas, appear promising for imaging. Scandium-44 and Copper-64, especially on account of their longer half-life (for pretherapeutic dosimetry) and cyclotron production (which favors mass production), might be the potential alternatives to (68)Ga for PET/CT imaging. The future of molecular imaging lies in Radiomics, that is, qualitative and quantitative characterization of tumor phenotypes in correlation with tumor genomics and proteomics, for a personalized cancer management.

MIB-1 Index-Stratified Assessment of Dual-Tracer PET/CT with 68Ga-DOTATATE and 18F-FDG and Multimodality Anatomic Imaging in Metastatic Neuroendocrine Tumors of Unknown Primary in a PRRT Workup Setting

Our aim was to comparatively assess dual-tracer PET/CT (⁶⁸Ga-DOTATATE and ¹⁸F-FDG) and multimodality anatomic imaging in studying metastatic neuroendocrine tumors (NETs) of unknown primary (CUP-NETs) scheduled for peptide receptor radionuclide therapy for divergence of tracer uptake on dual-tracer PET/CT, detection of primary, and overall lesion detection vis-a-vis tumor proliferation index (MIB-1/Ki-67). Methods: Fifty-one patients with CUP-NETs (25 men, 26 women; age, 22-74 y), histopathologically proven and thoroughly investigated with conventional imaging modalities (ultrasonography, CT/contrast-enhanced CT, MRI, and endoscopic ultrasound, wherever applicable), were retrospectively analyzed. Patients were primarily referred for deciding on feasibility of peptide receptor radionuclide therapy (except 2 patients), and all had undergone ⁶⁸Ga-DOTATATE and ¹⁸F-FDG PET/CT as part of pretreatment workup. The sites of metastases included liver, lung/mediastinum, skeleton, abdominal nodes, and other soft-tissue sites. Patients were divided into 5 groups on the basis of MIB-1/Ki-67 index on a 5-point scale: group I (1%-5%) (n = 35), group II (6%-10%) (n = 8), group III (11%-15%) (n = 4), group IV (16%-20%) (n = 2), and group V (>20%) (n = 2). Semiquantitative analysis of tracer uptake was undertaken by SUV_max of metastatic lesions and the primary (when detected). The SUV_max values were studied over increasing MIB-1/Ki-67 index. The detection sensitivity of ⁶⁸Ga-DOTATATE for primary and metastatic lesions was assessed and compared with other imaging modalities including ¹⁸F-FDG PET/CT. Results: Unknown primary was detected on ⁶⁸Ga-DOTATATE in 31 of 51 patients, resulting in sensitivity of 60.78% whereas overall lesion detection sensitivity was 96.87%. The overall lesion detection sensitivities (individual groupwise from group I to group V) were 97.75%, 87.5%, 100%, 100%, and 66.67%, respectively. As MIB-1/Ki-67 index increased, ⁶⁸Ga-DOTATATE uptake decreased in metastatic and primary lesions (mean SUV_max, 43.5 and 22.68 g/dL in group I to 22.54 and 16.83 g/dL in group V, respectively), whereas ¹⁸F-FDG uptake showed a gradual rise (mean SUV_max, 3.66 and 2.86 g/dL in group I to 7.53 and 9.58 g/dL in group V, respectively). There was a corresponding decrease in the ⁶⁸Ga-DOTATATE-to-¹⁸F-FDG uptake ratio with increasing MIB-1/Ki-67 index (from 11.89 in group I to 2.99 in group V). Conclusion: In CUP-NETs, the pattern of uptake on dual-tracer PET (⁶⁸Ga-DOTATATE and ¹⁸F-FDG) correlates well with tumor proliferation index with a few outliers; combined dual-tracer PET/CT with MIB-1/Ki-67 index would aid in better whole-body assessment of tumor biology in CUP-NETs.

WOMEN IN CANCER THEMATIC REVIEW: Systemic therapies in neuroendocrine tumors and novel approaches toward personalized medicine

Neuroendocrine tumors (NETs) are a group of heterogenous neoplasms. Evidence-based treatment options for antiproliferative therapy include somatostatin analogues, the mTOR inhibitor everolimus, the multiple tyrosine kinase inhibitor sunitinib and peptide receptor radionuclide therapy with 177-Lu-octreotate. In the absence of definite predictive markers, therapeutic decision making follows clinical and pathological criteria. As objective response rates with targeted drugs are rather low, and response duration is limited in most patients, numerous combination therapies targeting multiple pathways have been explored in the field. Upfront combination of drugs, however, is associated with increasing toxicity and has shown little benefit. Major advancements in the molecular understanding of NET based on genomic, epigenomic and transcriptomic analysis have been achieved with prognostic and therapeutic impact. New insight into molecular alterations has paved the way to biomarker-driven clinical trials and may facilitate treatment stratification toward personalized medicine in the near future. However, an improved understanding of the complexity of pathway interactions is required for successful treatment. A systems biology approach is one of the tools that may help to achieve this endeavor.