The Clinical Impact of Time-restricted Eating on Cancer: A Systematic Review.

CONTEXT: In the face of the growing global burden of cancer, there is increasing interest in dietary interventions to mitigate its impacts. Pre-clinical evidence suggests that time-restricted eating (TRE), a type of intermittent fasting, induces metabolic effects and alterations in the gut microbiome that may impede carcinogenesis. Research on TRE in cancer has progressed to human studies, but the evidence has yet to be synthesized. OBJECTIVE: The objective of this study was to systematically evaluate the clinical and/or metabolomic effects of TRE compared with ad libitum eating or alternative diets in people with cancer. DATA SOURCES: Ovid MEDLINE, Ovid Embase, CINAHL, Ovid Cochrane Central Register of Control Trials (CENTRAL), Web of Science Core Collection (ESCI, CPCI-SSH, CPCI-S), and SCOPUS were searched up to January 4, 2023, using the core concepts of "intermittent fasting" and "cancer." Original study designs, protocols, and clinical trial registries were included. DATA EXTRACTION: After evaluating 13 900 results, 24 entries were included, consisting of 8 full articles, 2 abstracts, 1 published protocol and 13 trial registries. All data were extracted, compared, and critically analyzed. DATA ANALYSIS: There was heterogeneity in the patient population (eg, in tumor sites), TRE regimens (eg, degree of restriction, duration), and clinical end points. A high rate (67-98%) of TRE adherence was observed, alongside improvements in quality of life. Four articles assessed cancer markers and found a reduction in tumor marker carcinoembryonic antigen, reduced rates of recurrence, and a sustained major molecular response, following TRE. Five articles demonstrated modified cancer risk factors, including beneficial effects on body mass index, adiposity, glucoregulation, and inflammation in as short a period as 8 weeks. None of the completed studies assessed the effect of TRE on the microbiome, but analysis of the microbiome is a planned outcome in 2 clinical trials. CONCLUSIONS: Preliminary findings suggest that TRE is feasible and acceptable by people with cancer, may have oncological benefits, and improves quality of life. REGISTRATION: PROSPERO registration No. CRD42023386885.

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Mass spectrometry imaging methods for visualizing tumor heterogeneity.

Profiling spatial distributions of lipids, metabolites, and proteins in tumors can reveal unique cellular microenvironments and provide molecular evidence for cancer cell dysfunction and proliferation. Mass spectrometry imaging (MSI) is a label-free technique that can be used to map biomolecules in tumors in situ. Here, we discuss current progress in applying MSI to uncover molecular heterogeneity in tumors. First, the analytical strategies to profile small molecules and proteins are outlined, and current methods for multimodal imaging to maximize biological information are highlighted. Second, we present and summarize biological insights obtained by MSI of tumor tissue. Finally, we discuss important considerations for designing MSI experiments and several current analytical challenges.

Metabolic engineering for optimized CAR-T cell therapy.

The broad effectiveness of T cell-based therapy for treating solid tumour cancers remains limited. This is partly due to the growing appreciation that immune cells must inhabit and traverse a metabolically demanding tumour environment. Accordingly, recent efforts have centred on using genome-editing technologies to augment T cell-mediated cytotoxicity by manipulating specific metabolic genes. However, solid tumours exhibit numerous characteristics restricting immune cell-mediated cytotoxicity, implying a need for metabolic engineering at the pathway level rather than single gene targets. This emerging concept has yet to be put into clinical practice as many questions concerning the complex interplay between metabolic networks and T cell function remain unsolved. This Perspective will highlight key foundational studies that examine the relevant metabolic pathways required for effective T cell cytotoxicity and persistence in the human tumour microenvironment, feasible strategies for metabolic engineering to increase the efficiency of chimeric antigen receptor T cell-based approaches, and the challenges lying ahead for clinical implementation.

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network.

Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures associated with tumour radioresistance may assist with the development of targeted treatment strategies to improve clinical outcomes. Raman spectroscopy (RS) can monitor post-irradiation biomolecular changes and signatures of radiation response in tumour cells in a label-free manner. Convolutional Neural Networks (CNN) perform feature extraction directly from data in an end-to-end learning manner, with high classification performance. Furthermore, recently developed CNN explainability techniques help visualize the critical discriminative features captured by the model. In this work, a CNN is developed to characterize tumour response to radiotherapy based on its degree of radioresistance. The model was trained to classify Raman spectra of three human tumour cell lines as radiosensitive (LNCaP) or radioresistant (MCF7, H460) over a range of treatment doses and data collection time points. Additionally, a method based on Gradient-Weighted Class Activation Mapping (Grad-CAM) was used to determine response-specific salient Raman peaks influencing the CNN predictions. The CNN effectively classified the cell spectra, with accuracy, sensitivity, specificity, and F1 score exceeding 99.8%. Grad-CAM heatmaps of H460 and MCF7 cell spectra (radioresistant) exhibited high contributions from Raman bands tentatively assigned to glycogen, amino acids, and nucleic acids. Conversely, heatmaps of LNCaP cells (radiosensitive) revealed activations at lipid and phospholipid bands. Finally, Grad-CAM variable importance scores were derived for glycogen, asparagine, and phosphatidylcholine, and we show that their trends over cell line, dose, and acquisition time agreed with previously established models. Thus, the CNN can accurately detect biomolecular differences in the Raman spectra of tumour cells of varying radiosensitivity without requiring manual feature extraction. Finally, Grad-CAM may help identify metabolic signatures associated with the observed categories, offering the potential for automated clinical tumour radiation response characterization.

Strategies for uncovering stable isotope tracing patterns between cell populations.

Despite practical complexities, isotope tracing studies in humans are becoming increasingly feasible. However, several technological challenges need to be addressed in order to take full advantage of human tracing studies. First, absolute metabolic flux measurements in mice are not so easily applied to human models, given that tissue resection is restricted to a single surgical time point. Second, isotope tracing has yet to be employed to detect metabolic differences between cells types in vivo. Here, we discuss the current models and propose an alternative, liquid tumor environment, that could overcome these limitations. Furthermore, we highlight current strategies used to maintain isotopolog enrichment following cell isolation techniques to facilitate cell-type-specific analysis.

Decision Aids for Patients With Head and Neck Cancer: Qualitative Elicitation of Design Recommendations From Patient End Users.

BACKGROUND: Patients with head and neck cancer (HNC) carry a clinically significant symptom burden, have alterations in function (eg, impaired ability to chew, swallow, and talk), and decrease in quality of life. Furthermore, treatment impacts social activities and interactions as patients report reduced sexuality and shoulder the highest rates of depression across cancer types. Patients suffer undue anxiety because they find the treatment incomprehensible, which is partially a function of limited, understandable information. Patients' perceptions of having obtained adequate information prior to and during treatment are predictive of positive outcomes. Providing patient-centered decision support and utilizing visual images may increase understanding of treatment options and associated risks to improve satisfaction with their decision and consultation, while reducing decisional conflict. OBJECTIVE: This study aims to gather requirements from survivors of HNC on the utility of key visual components to be used in the design of an electronic decision aid (eDA) to assist with decision-making on treatment options. METHODS: Informed by a scoping review on eDAs for patients with HNC, screens and visualizations for an eDA were created and then presented to 12 survivors of HNC for feedback on their utility, features, and further requirements. The semistructured interviews were video-recorded and thematically analyzed to inform co-design recommendations. RESULTS: A total of 9 themes were organized into 2 categories. The first category, eDAs and decision support, included 3 themes: familiarity with DAs, support of concept, and versatility of the prototype. The second category, evaluation of mock-up, contained 6 themes: reaction to the screens and visualizations, favorite features, complexity, preference for customizability, presentation device, and suggestions for improvement. CONCLUSIONS: All participants felt an eDA, used in the presence of their oncologist, would support a more thorough and transparent explanation of treatment or augment the quality of education received. Participants liked the simple design of the mock-ups they were shown but, ultimately, desired customizability to adapt the eDA to their individual information needs. This research highlights the value of user-centered design, rooted in acceptability and utility, in medical health informatics, recognizing cancer survivors as the ultimate knowledge holders. This research highlights the value of incorporating visuals into technology-based innovations to engage all patients in treatment decisions.

CD8 + T cell infiltration is associated with improved survival and negatively correlates with hypoxia in clear cell ovarian cancer.

Unlike other histological types of epithelial ovarian carcinoma, clear cell ovarian carcinoma (CCOC) has poor response to therapy. In many other carcinomas, expression of the hypoxia-related enzyme Carbonic anhydrase IX (CAIX) by cancer cells is associated with poor prognosis, while the presence of CD8 + tumor-infiltrating lymphocytes (TIL) is positively prognostic. We employed [18F]EF5-PET/CT imaging, transcriptome profiling, and spatially-resolved histological analysis to evaluate relationships between CAIX, CD8, and survival in CCOC. Tissue microarrays (TMAs) were evaluated for 218 cases in the Canadian COEUR study. Non-spatial relationships between CAIX and CD8 were investigated using Spearman rank correlation, negative binomial regression and gene set enrichment analysis. Spatial relationships at the cell level were investigated using the cross K-function. Survival analysis was used to assess the relationship of CAIX and CD8 with patient survival for 154 cases. CD8 + T cell infiltration positively predicted survival with estimated hazard ratio 0.974 (95% CI 0.950, 1000). The negative binomial regression analysis found a strong TMA effect (p-value < 0.0001). It also indicated a negative association between CD8 and CAIX overall (p-value = 0.0171) and in stroma (p-value = 0.0050) but not in tumor (p-value = 0.173). Examination of the spatial association between the locations of CD8 + T cells and CAIX cells found a significant amount of heterogeneity in the first TMA, while in the second TMA there was a clear signal indicating negative spatial association in stromal regions. These results suggest that hypoxia may contribute to immune exclusion, primarily mediated by effects in stroma.

Reconstruction of Raman Spectra of Biochemical Mixtures Using Group and Basis Restricted Non-Negative Matrix Factorization.

Raman spectroscopy is a useful tool for obtaining biochemical information from biological samples. However, interpretation of Raman spectroscopy data in order to draw meaningful conclusions related to the biochemical make up of cells and tissues is often difficult and could be misleading if care is not taken in the deconstruction of the spectral data. Our group has previously demonstrated the implementation of a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework as an alternative to more widely used dimensionality reduction techniques such as principal component analysis (PCA) for the deconstruction of Raman spectroscopy data as related to radiation response monitoring in both cellular and tissue data. While this method provides better biological interpretability of the Raman spectroscopy data, there are some important factors which must be considered in order to provide the most robust GBR-NMF model. We here evaluate and compare the accuracy of a GBR-NMF model in the reconstruction of three mixture solutions of known concentrations. The factors assessed include the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal to noise thresholds, and how different groups of biochemicals compare to each other. The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. We also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component. Overall, we found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a significant effect on the deconstruction, on the condition that all biochemicals in the mixture were included as bases chemicals in the model. We also report that some groups of biochemicals achieve a more accurate deconstruction using GBR-NMF than others, likely due to similarity in the individual bases spectra.

Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts.

Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.

Assessing the Immune Regulatory Role of Metabolites In Vitro.

One method of immune evasion that cancer cells employ is the secretion of immune regulatory metabolites into the tumor microenvironment (TME). These metabolites can promote immunosuppressive cell subsets, while inhibiting key tumor-killing subsets, such as T cells. Thus, the identification of these metabolites may help develop methods for improving cell-based therapy. However, after identifying a potential immune regulatory metabolite, it is crucial to assess the impacts of the metabolite on T cell immunobiology. In this chapter, we describe an in vitro method of testing and analyzing the influence of a specific metabolite on T cell proliferation and function.

Radiation treatment response and hypoxia biomarkers revealed by machine learning assisted Raman spectroscopy in tumour cells and xenograft tissues.

Recent advancements in anatomical imaging of tumours as treatment targets have led to improvements in RT. However, it is unlikely that improved anatomical imaging alone will be the sole driver for new advances in personalised RT. Biochemically based radiobiological information is likely to be required for next-generation improvements in the personalisation of radiotherapy dose prescriptions to individual patients. In this paper, we use Raman spectroscopy (RS), an optical technique, to monitor individual biochemical response to radiation within a tumour microenvironment. We spatially correlate individual biochemical responses to augmentatively derived hypoxic maps within the tumour microenvironment. Furthermore, we pair RS with a data analytical framework combining (i) group and basis restricted non-negative matrix factorization (GBR-NMF), (ii) a random forest (RF) classifier, (iii) and a feature metric importance calculation method, Shapley Additive exPlanations (SHAP), in order to ascertain the relative importance of individual biochemicals in describing the overall biological response as observed with RS. The current study found that the GBR-NMF-RF-SHAP model helped identify a wide range of radiation response biomarkers and hypoxia indicators (e.g., glycogen, lipids, DNA, amino acids) in H460 human lung cancer cells and H460 xenografts. Correlations between the hypoxic regions and Raman chemical biomarkers (e.g., glycogen, alanine, and arginine) were also identified in H460 xenografts. To summarize, GBR-NMF-RF-SHAP combined with RS can be applied to monitor the RT-induced biochemical response within cellular and tissue environments. Individual biochemicals were identified that (i) contributed to overall biological response to radiation, and (ii) spatially correlated with hypoxic regions of the tumour. RS combined with our analytical pipeline shows promise for further understanding of individual biochemical dynamics in radiation response for use in cancer therapy.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study.

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Methionine cycle-dependent regulation of T cells in cancer immunity.

The methionine cycle comprises a series of reactions that catabolizes and regenerates methionine. This process is crucial to many cellular functions, including polyamine synthesis, DNA synthesis, redox balance, and DNA and histone methylation. In response to antigens, T cells activate the methionine cycle to support proliferation and differentiation, indicating the importance of the methionine cycle to T cell immunity. In cancer, T cells serve as important effectors of adaptive immunity by directly killing cancerous cells. However, the tumor microenvironment can induce a state of T cell exhaustion by regulating the methionine metabolism of T cells, posing a barrier to both endogenous T cell responses and T cell immunotherapy. Here we review the role of methionine cycle metabolites in regulating the activation and effector function of T cells and explore the mechanism by which tumor cells exploit the methionine pathway as a means of immune evasion. Finally, we discuss new perspectives on reprogramming the methionine cycle of T cells to enhance anti-tumor immunotherapy.

Principles of reproducible metabolite profiling of enriched lymphocytes in tumors and ascites from human ovarian cancer.

Identifying metabolites and delineating their immune-regulatory contribution in the tumor microenvironment is an area of intense study. Interrogating metabolites and metabolic networks among immune cell subsets and host cells from resected tissues and fluids of human patients presents a major challenge, owing to the specialized handling of samples for downstream metabolomics. To address this, we first outline the importance of collaborating with a biobank for coordinating and streamlining workflow for point of care, sample collection, processing and cryopreservation. After specimen collection, we describe our 60-min rapid bead-based cellular enrichment method that supports metabolite analysis between T cells and tumor cells by mass spectrometry. We also describe how the metabolic data can be complemented with metabolic profiling by flow cytometry. This protocol can serve as a foundation for interrogating the metabolism of cell subsets from primary human ovarian cancer.

The effects of glutamine supplementation on markers of apoptosis and autophagy in sickle cell disease peripheral blood mononuclear cells.

OBJECTIVES: L-Glutamine was FDA-approved for sickle cell disease (SCD) in 2017, yet the mechanism(s)-of-action are poorly understood. This study investigates the potential activation of autophagy as a previously unexplored mechanism-of-benefit. DESIGN: Prospective, open-label, 8-week, phase-2 trial of oral L-glutamine (10 g TID) in patients with SCD at risk for pulmonary hypertension identified by Doppler-echocardiography by an elevated tricuspid-regurgitant-jet-velocity (TRV)≥ 2.5 m/s. Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples taken from SCD patients at baseline, two, four, six and eight weeks of glutamine therapy, and from controls at baseline; BAX (pro-apoptotic marker) and LC3-II/LC3-I (autophagy marker) were measured via western blot analysis to assess apoptosis and autophagy respectively. SETTING: Comprehensive SCD Center in Oakland, California. RESULTS: Patients with SCD (n = 8) had a mean age of 44 ± 16, 50% were male; 63% Hb-SS, and mean TRV= 3.1 ± 0.7 m/s. Controls' mean age (n = 5) was 32 ± 12% and 57% were male; all were Hb-AA with a mean TRV= 1.8 ± 0.6. At baseline, SCD-PBMCs had 2-times higher levels of BAX and LC3-I versus controls (both p = 0.03). Levels of BAX expression increased by 300% after 8-weeks of glutamine supplementation (p = 0.005); LC3-I protein levels decreased while LC3-II levels increased by 70%, giving a significant increase in the LC3-II/LC3-I ratio (p = 0.02). CONCLUSION: PBMCs from glutamine-supplemented SCD patients have upregulated apoptotic and autophagy proteins. The parallel increase in BAX and the LC3-II / LC3-I ratio with glutamine supplementation suggest a possible role of autophagic cell death. The increase in apoptotic markers provide insight into a possible mechanism used by peripheral PBMCs during glutamine supplementation in patients with SCD.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer.

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Ubiquitinating the way to T cell metabolism.

In this issue, Harris et al. (2022. J. Cell Biol.https://doi.org/10.1083/jcb.202203095) show that phosphofructokinase is a substrate for ubiquitination by Fbxo7, a key protein in the ubiquitination pathway. Their findings point to a new interplay between metabolic enzyme degradation in the regulation of T cells.

Intermittent Fasting in Cancer: a Role in Survivorship?

PURPOSE OF REVIEW: To discuss the historical development of intermittent fasting, its potential underlying mechanisms, and the state of clinical trials, and to reflect on considerations for practice and future recommendations. RECENT FINDINGS: Preclinical studies consistently show the robust disease-modifying efficacy of intermittent fasting in various metabolic diseases which may hold implications for cancer prevention and survivorship. Twenty-one clinical trials have or are being conducted on fasting in cancer, utilizing various fasting regimens across different tumor types as a stand-alone intervention or in adjunct to anticancer treatment, with heterogenous outcome variables. Though there are no known, reproducible diets, to cure or prevent cancer recurrence, preliminary research on the underlying mechanisms, tolerance, and safety of intermittent fasting in cancer warrants further investigation. The inherent flexibility of intermittent fasting to accommodate all types of diets is of necessity in oncology.