PET Metabolic Biomarkers for Cancer

Towards personalised dosimetry in patients with liver malignancy treated with 90Y-SIRT using in vivo-driven radiobiological parameters

BACKGROUND

The prediction of response is one of the major challenges in radiation-based therapies. Although the selection of accurate linear-quadratic model parameters is essential for the estimation of radiation response and treatment outcome, there is a limited knowledge about these radiobiological parameters for liver tumours using radionuclide treatments.

METHODS

The "clinical radiobiological" parameters ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) for twenty-five patients were derived using the generalised linear-quadratic model, the diagnostic ([¹⁸F] FDG PET/CT) and therapeutic ([⁹⁰Y]-SIR-Spheres PET/CT) images to compute the biological effective dose and tumour control probability (TCP) for each patient.

RESULTS

It was estimated that the values for [Formula: see text] and [Formula: see text] parameters range in ≈ 0.001-1 Gy^-1 and ≈ 1-49 Gy, respectively. We have demonstrated that the time factors, [Formula: see text], [Formula: see text] and [Formula: see text] are the key parameters when evaluating liver malignancy lesional response to [⁹⁰Y]SIR-Spheres treatment. Patients with cholangiocarcinoma have been shown to have the longest average [Formula: see text] (≈ 236 ± 67 d), highest TCP (≈ 53 ± 17%) and total liver lesion glycolysis response ([Formula: see text] ≈ 64%), while patients with metastatic colorectal cancer tumours have the shortest average [Formula: see text] (≈ 129 ± 19 d), lowest TCP (≈ 28 ± 13%) and [Formula: see text] ≈ 8%, respectively.

CONCLUSIONS

Tumours with shorter [Formula: see text] have shown a shorter [Formula: see text] and thus poorer TCP and [Formula: see text]. Therefore, these results suggest for such tumours the [⁹⁰Y]SIR-Spheres will be only effective at higher initial dose rate (e.g.  > 50 Gy/day).

The metabolic addiction of cancer stem cells

Cancer stem cells (CSC) are the minor population of cancer originating cells that have the capacity of self-renewal, differentiation, and tumorigenicity (when transplanted into an immunocompromised animal). These low-copy number cell populations are believed to be resistant to conventional chemo and radiotherapy. It was reported that metabolic adaptation of these elusive cell populations is to a large extent responsible for their survival and distant metastasis. Warburg effect is a hallmark of most cancer in which the cancer cells prefer to metabolize glucose anaerobically, even under normoxic conditions. Warburg's aerobic glycolysis produces ATP efficiently promoting cell proliferation by reprogramming metabolism to increase glucose uptake and stimulating lactate production. This metabolic adaptation also seems to contribute to chemoresistance and immune evasion, a prerequisite for cancer cell survival and proliferation. Though we know a lot about metabolic fine-tuning in cancer, what is still in shadow is the identity of upstream regulators that orchestrates this process. Epigenetic modification of key metabolic enzymes seems to play a decisive role in this. By altering the metabolic flux, cancer cells polarize the biochemical reactions to selectively generate "onco-metabolites" that provide an added advantage for cell proliferation and survival. In this review, we explored the metabolic-epigenetic circuity in relation to cancer growth and proliferation and establish the fact how cancer cells may be addicted to specific metabolic pathways to meet their needs. Interestingly, even the immune system is re-calibrated to adapt to this altered scenario. Knowing the details is crucial for selective targeting of cancer stem cells by choking the rate-limiting stems and crucial branch points, preventing the formation of onco-metabolites.

Metabolomics and the Multi-Omics View of Cancer

Cancer is widely regarded to be a genetic disease. Indeed, over the past five decades, the genomic perspective on cancer has come to almost completely dominate the field. However, this genome-only view is incomplete and tends to portray cancer as a disease that is highly heritable, driven by hundreds of complex genetic interactions and, consequently, difficult to prevent or treat. New evidence suggests that cancer is not as heritable or purely genetic as once thought and that it really is a multi-omics disease. As highlighted in this review, the genome, the exposome, and the metabolome all play roles in cancer’s development and manifestation. The data presented here show that >90% of cancers are initiated by environmental exposures (the exposome) which lead to cancer-inducing genetic changes. The resulting genetic changes are, then, propagated through the altered DNA of the proliferating cancer cells (the genome). Finally, the dividing cancer cells are nourished and sustained by genetically reprogrammed, cancer-specific metabolism (the metabolome). As shown in this review, all three “omes” play roles in initiating cancer. Likewise, all three “omes” interact closely, often providing feedback to each other to sustain or enhance tumor development. Thanks to metabolomics, these multi-omics feedback loops are now much more evident and their roles in explaining the hallmarks of cancer are much better understood. Importantly, this more holistic, multi-omics view portrays cancer as a disease that is much more preventable, easier to understand, and potentially, far more treatable.

Cancer cell metabolic plasticity in migration and metastasis

Metabolic reprogramming is a hallmark of cancer metastasis in which cancer cells manipulate their metabolic profile to meet the dynamic energetic requirements of the tumor microenvironment. Though cancer cell proliferation and migration through the extracellular matrix are key steps of cancer progression, they are not necessarily fueled by the same metabolites and energy production pathways. The two main metabolic pathways cancer cells use to derive energy from glucose, glycolysis and oxidative phosphorylation, are preferentially and plastically utilized by cancer cells depending on both their intrinsic metabolic properties and their surrounding environment. Mechanical factors in the microenvironment, such as collagen density, pore size, and alignment, and biochemical factors, such as oxygen and glucose availability, have been shown to influence both cell migration and glucose metabolism. As cancer cells have been identified as preferentially utilizing glycolysis or oxidative phosphorylation based on heterogeneous intrinsic or extrinsic factors, the relationship between cancer cell metabolism and metastatic potential is of recent interest. Here, we review current in vitro and in vivo findings in the context of cancer cell metabolism during migration and metastasis and extrapolate potential clinical applications of this work that could aid in diagnosing and tracking cancer progression in vivo by monitoring metabolism. We also review current progress in the development of a variety of metabolically targeted anti-metastatic drugs, both in clinical trials and approved for distribution, and highlight potential routes for incorporating our recent understanding of metabolic plasticity into therapeutic directions. By further understanding cancer cell energy production pathways and metabolic plasticity, more effective and successful clinical imaging and therapeutics can be developed to diagnose, target, and inhibit metastasis.

Flavonoids against the Warburg phenotype-concepts of predictive, preventive and personalised medicine to cut the Gordian knot of cancer cell metabolism

The Warburg effect is characterised by increased glucose uptake and lactate secretion in cancer cells resulting from metabolic transformation in tumour tissue. The corresponding molecular pathways switch from oxidative phosphorylation to aerobic glycolysis, due to changes in glucose degradation mechanisms known as the 'Warburg reprogramming' of cancer cells. Key glycolytic enzymes, glucose transporters and transcription factors involved in the Warburg transformation are frequently dysregulated during carcinogenesis considered as promising diagnostic and prognostic markers as well as treatment targets. Flavonoids are molecules with pleiotropic activities. The metabolism-regulating anticancer effects of flavonoids are broadly demonstrated in preclinical studies. Flavonoids modulate key pathways involved in the Warburg phenotype including but not limited to PKM2, HK2, GLUT1 and HIF-1. The corresponding molecular mechanisms and clinical relevance of 'anti-Warburg' effects of flavonoids are discussed in this review article. The most prominent examples are provided for the potential application of targeted 'anti-Warburg' measures in cancer management. Individualised profiling and patient stratification are presented as powerful tools for implementing targeted 'anti-Warburg' measures in the context of predictive, preventive and personalised medicine.

Integrating the Tumor Microenvironment into Cancer Therapy

Tumor progression is mediated by reciprocal interaction between tumor cells and their surrounding tumor microenvironment (TME), which among other factors encompasses the extracellular milieu, immune cells, fibroblasts, and the vascular system. However, the complexity of cancer goes beyond the local interaction of tumor cells with their microenvironment. We are on the path to understanding cancer from a systemic viewpoint where the host macroenvironment also plays a crucial role in determining tumor progression. Indeed, growing evidence is emerging on the impact of the gut microbiota, metabolism, biomechanics, and the neuroimmunological axis on cancer. Thus, external factors capable of influencing the entire body system, such as emotional stress, surgery, or psychosocial factors, must be taken into consideration for enhanced management and treatment of cancer patients. In this article, we review prognostic and predictive biomarkers, as well as their potential evaluation and quantitative analysis. Our overarching aim is to open up new fields of study and intervention possibilities, within the framework of an integral vision of cancer as a functional tissue with the capacity to respond to different non-cytotoxic factors, hormonal, immunological, and mechanical forces, and others inducing stroma and tumor reprogramming.

Anti-signal Recognition Particle Antibody-positive Necrotizing Myopathy with Secondary Cardiomyopathy: The First Myocardial Biopsy- and Multimodal Imaging-proven Case

A 69-year-old Japanese woman was admitted to our hospital with progressive muscle weakness and dysphagia. She was taking pitavastatin for dyslipidemia. Her serum creatine kinase was 6,300 U/L. Pitavastatin was stopped, but her symptoms deteriorated, and cardiac congestion appeared. A muscle biopsy showed necrotizing myopathy (NM), and anti-signal recognition particle (SRP) antibody was positive. ¹⁸F-fluorodeoxyglucose-positron emission tomography showed an abnormal uptake, and magnetic resonance imaging showed abnormal gadolinium enhancement in the left ventricular wall. An endomyocardial biopsy revealed inflammatory cardiomyopathy. Steroid, tacrolimus, and intravenous immunoglobulins were effective against the symptoms. This is the first case of biopsy-proven secondary cardiomyopathy due to anti-SRP-positive NM.

Guidance on the use of PET for treatment planning in radiotherapy clinical trials

The aim of this article is to propose meaningful guidance covering the practical and technical issues involved when planning or conducting clinical trials involving positron emission tomography (PET)-guided radiotherapy. The complexity of imaging requirements will depend on the study aims, design and PET methods used. Where PET is used to adapt radiotherapy, a high level of accuracy and reproducibility is required to ensure effective and safe treatment delivery. The guidance in this document is intended to assist researchers designing clinical trials involving PET-guided radiotherapy to provide sufficient information about the appropriate methods to complete PET-CT imaging to a consistent standard at participating centres. The guidance is divided into six categories: application of PET in radiotherapy, resource requirements, quality assurance, imaging protocol design, data management and image processing. Each section provides an overview of the recent literature to support the specific recommendations. This guidance builds on previous recommendations from the National Cancer Research Institute PET Research Network and has been produced in collaboration with the National Radiotherapy Trials Quality Assurance Group.

Artificial Intelligence in Lung Cancer Pathology Image Analysis

OBJECTIVE

Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection.

MATERIALS AND METHODS

In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer.

RESULTS

We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis.

DISCUSSION AND CONCLUSION

With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Metabolism of Stem and Progenitor Cells: Proper Methods to Answer Specific Questions

Stem cells can stay quiescent for a long period of time or proliferate and differentiate into multiple lineages. The activity of stage-specific metabolic programs allows stem cells to best adapt their functions in different microenvironments. Specific cellular phenotypes can be, therefore, defined by precise metabolic signatures. Notably, not only cellular metabolism describes a defined cellular phenotype, but experimental evidence now clearly indicate that also rewiring cells towards a particular cellular metabolism can drive their cellular phenotype and function accordingly. Cellular metabolism can be studied by both targeted and untargeted approaches. Targeted analyses focus on a subset of identified metabolites and on their metabolic fluxes. In addition, the overall assessment of the oxygen consumption rate (OCR) gives a measure of the overall cellular oxidative metabolism and mitochondrial function. Untargeted approach provides a large-scale identification and quantification of the whole metabolome with the aim to describe a metabolic fingerprinting. In this review article, we overview the methodologies currently available for the study of invitro stem cell metabolism, including metabolic fluxes, fingerprint analyses, and single-cell metabolomics. Moreover, we summarize available approaches for the study of in vivo stem cell metabolism. For all of the described methods, we highlight their specificities and limitations. In addition, we discuss practical concerns about the most threatening steps, including metabolic quenching, sample preparation and extraction. A better knowledge of the precise metabolic signature defining specific cell population is instrumental to the design of novel therapeutic strategies able to drive undifferentiated stem cells towards a selective and valuable cellular phenotype.

Is the measurement of the size of uterine lesions with positron emission tomography consistent in pre- and postmenopausal periods in endometrioid-type endometrial cancer?

Objective

We aimed to investigate the correlation of the size and volume of uterine tumors obtained using positron emission tomography/computed tomography (PET/CT) and pathology specimens in patients with endometrioid-type endometrial cancer (EEC) in the premenopausal period, and to compare the results with those of postmenopausal women. In the premenopausal period, the endometrium uses more glucose than in the postmenopausal period. Therefore, the measurement of uterine tumor size using PET/CT in the premenopausal period may normally be different.

Materials and Methods

In this retrospective study, we reviewed the records of patients who were diagnosed as having EEC and underwent hysterectomy. Only patients who underwent preoperative PET/CT imaging were included in the study. The thickness and volume of the uterine lesion, and its maximum standardized uptake value as obtained using PET/CT and hysterectomy pathology specimens were recorded.

Results

Tumor size (p=0.051) and volume (p=0.404) were not found to be correlated with the imaging method used in premenopausal women and pathologic specimens. However, there was a correlation in postmenopausal women (p<0.001 for tumor size and p<0.001 for tumor volume). PET/CT has higher sensitivity, specificity, and positive predictive value in the postmenopausal period in the detection of >20 mm uterine tumors.

Conclusion

PET/CT has a limited role in the measurement of the size of uterine lesions in all patients, especially in the premenopausal period; therefore, we recommend that frozen-section examinations be used intraoperatively to decide on lymph node dissection.