Metabolic Imaging in Prostate Cancer: Where We Are

Targeting cancer-specific metabolic pathways for developing novel cancer therapeutics

Cancer is a heterogeneous disease characterized by various genetic and phenotypic aberrations. Cancer cells undergo genetic modifications that promote their proliferation, survival, and dissemination as the disease progresses. The unabated proliferation of cancer cells incurs an enormous energy demand that is supplied by metabolic reprogramming. Cancer cells undergo metabolic alterations to provide for increased energy and metabolite requirement; these alterations also help drive the tumor progression. Dysregulation in glucose uptake and increased lactate production via "aerobic glycolysis" were described more than 100 years ago, and since then, the metabolic signature of various cancers has been extensively studied. However, the extensive research in this field has failed to translate into significant therapeutic intervention, except for treating childhood-ALL with amino acid metabolism inhibitor L-asparaginase. Despite the growing understanding of novel metabolic alterations in tumors, the therapeutic targeting of these tumor-specific dysregulations has largely been ineffective in clinical trials. This chapter discusses the major pathways involved in the metabolism of glucose, amino acids, and lipids and highlights the inter-twined nature of metabolic aberrations that promote tumorigenesis in different types of cancer. Finally, we summarise the therapeutic interventions which can be used as a combinational therapy to target metabolic dysregulations that are unique or common in blood, breast, colorectal, lung, and prostate cancer.

Carbon sources and pathways for citrate secreted by human prostate cancer cells determined by NMR tracing and metabolic modeling

The human prostate accumulates high luminal citrate levels to serve sperm viability. There is only indirect qualitative evidence about metabolic pathways and carbon sources maintaining these levels. Human citrate-secreting prostate cancer cells were supplied with ¹³C-labeled substrates, and NMR spectra of extracellular fluid were recorded. We report absolute citrate production rates of prostate cells and direct evidence that glucose is the main carbon source for secreted citrate. Pyruvate carboxylase provides sufficient anaplerotic carbons to support citrate secretion. Glutamine carbons exchange with carbons for secreted citrate but are likely not involved in its net synthesis. Moreover, we developed metabolic models employing the ¹³C distribution in extracellular citrate as input to assess intracellular pathways followed by carbons toward citrate.

Prostate epithelial cells have the unique capacity to secrete large amounts of citrate, but the carbon sources and metabolic pathways that maintain this production are not well known. We mapped potential pathways for citrate carbons in the human prostate cancer metastasis cell lines LNCaP and VCaP, for which we first established that they secrete citrate (For LNCaP 5.6 ± 0.9 nmol/h per 10⁶ cells). Using ¹³C-labeled substrates, we traced the incorporation of ¹³C into citrate by NMR of extracellular fluid. Our results provide direct evidence that glucose is a main carbon source for secreted citrate. We also demonstrate that carbons from supplied glutamine flow via oxidative Krebs cycle and reductive carboxylation routes to positions in secreted citrate but likely do not contribute to its net synthesis. The potential anaplerotic carbon sources aspartate and asparagine did not contribute to citrate carbons. We developed a quantitative metabolic model employing the ¹³C distribution in extracellular citrate after ¹³C glucose and pyruvate application to assess intracellular pathways of carbons for secreted citrate. From this model, it was estimated that in LNCaP about 21% of pyruvate entering the Krebs cycle is converted via pyruvate carboxylase as an anaplerotic route at a rate more than sufficient to compensate carbon loss of this cycle by citrate secretion. This model provides an estimation of the fraction of molecules, including citrate, leaving the Krebs cycle at every turn. The measured ratios of ¹³C atoms at different positions in extracellular citrate may serve as biomarkers for (malignant) epithelial cell metabolism.

Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients

After primary treatment of localized prostate carcinoma (PC), up to a third of patients have disease recurrence. Different predictive models have already been used either for initial stratification of PC patients or to predict disease recurrence. Recently, artificial intelligence has been introduced in the diagnosis and management of PC with a potential to revolutionize this field. The aim of this study was to analyze machine learning (ML) classifiers in order to predict disease progression in the moment of prostate-specific antigen (PSA) elevation during follow-up. The study cohort consisted of 109 PC patients treated with external beam radiotherapy alone or in combination with androgen deprivation therapy. We developed and evaluated the performance of two ML algorithms based on artificial neural networks (ANN) and naïve Bayes (NB). Of all patients, 72.5% was randomly selected for a training set while the remaining patients were used for testing of the models. The presence/absence of disease progression was defined as the output variable. The input variables for models were conducted from the univariate analysis preformed among two groups of patients in the training set. They included two pretreatment variables (UICC stage and Gleason's score risk group) and five posttreatment variables (nadir PSA, time to nadir PSA, PSA doubling time, PSA velocity, and PSA in the moment of disease reevaluation). The area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy was calculated to test the models' performance. The results showed that specificity was similar for both models, while NB achieved better sensitivity then ANN (100.0% versus 94.4%). The ANN showed an accuracy of 93.3%, and the matching for NB model was 96.7%. In this study, ML classifiers have shown potential for application in routine clinical practice during follow-up when disease progression was suspected.

Metabolic Phenotypes, Dependencies, and Adaptation in Lung Cancer

Lung cancer is a heterogeneous disease that is subdivided into histopathological subtypes with distinct behaviors. Each subtype is characterized by distinct features and molecular alterations that influence tumor metabolism. Alterations in tumor metabolism can be exploited by imaging modalities that use metabolite tracers for the detection and characterization of tumors. Microenvironmental factors, including nutrient and oxygen availability and the presence of stromal cells, are a critical influence on tumor metabolism. Recent technological advances facilitate the direct evaluation of metabolic alterations in patient tumors in this complex microenvironment. In addition, molecular alterations directly influence tumor cell metabolism and metabolic dependencies that influence response to therapy. Current therapeutic approaches to target tumor metabolism are currently being developed and translated into the clinic for patient therapy.

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

Simple Summary

Prostate Cancer is one of the main threats to men’s health. Its accurate diagnosis is crucial to properly treat patients depending on the cancer’s level of aggressiveness. Tumor risk-stratification is still a challenging task due to the difficulties met during the reading of multi-parametric Magnetic Resonance Images. Artificial Intelligence models may help radiologists in staging the aggressiveness of the equivocal lesions, reducing inter-observer variability and evaluation time. However, these algorithms need many high-quality images to work efficiently, bringing up overfitting and lack of standardization and reproducibility as emerging issues to be addressed. This study attempts to illustrate the state of the art of current research of Artificial Intelligence methods to stratify prostate cancer for its clinical significance suggesting how widespread use of public databases could be a possible solution to these issues.

Abstract

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

Allele-Specific MicroRNA-Mediated Regulation of a Glycolysis Gatekeeper PDK1 in Cancer Metabolism

BACKGROUND

Emerging evidence has revealed that genetic variations in microRNA (miRNA) binding sites called miRSNPs can alter miRNA binding in an allele-specific manner and impart prostate cancer (PCa) risk. Two miRSNPs, rs1530865 (G > C) and rs2357637 (C > A), in the 3' untranslated region of pyruvate dehydrogenase kinase 1 (PDK1) have been previously reported to be associated with PCa risk. However, these results have not been functionally validated.

METHODS

In silico analysis was used to predict miRNA-PDK1 interactions and was tested using PDK1 knockdown, miRNA overexpression and reporter gene assay.

RESULTS

PDK1 expression was found to be upregulated in PCa metastasis. Further, our results show that PDK1 suppression reduced the migration, invasion, and glycolysis of PCa cells. Computational predictions showed that miR-3916, miR-3125 and miR-3928 had a higher binding affinity for the C allele than the G allele for the rs1530865 miRSNP which was validated by reporter gene assays. Similarly, miR-2116 and miR-889 had a higher affinity for the A than C allele of the rs2357637 miRSNP. Overexpression of miR-3916 and miR-3125 decreased PDK1 protein levels in cells expressing the rs1530865 SNP C allele, and miR-2116 reduced in cells with the rs2357637 SNP A allele.

CONCLUSIONS

The present study is the first to report the regulation of the PDK1 gene by miRNAs in an allele-dependent manner and highlights the role of PDK1 in metabolic adaption associated with PCa progression.

The inhibitory effect of melatonin on human prostate cancer

Prostate cancer (PCa) is one of the most commonly diagnosed human cancers in males. Nearly 191,930 new cases and 33,330 new deaths of PCa are estimated in 2020. Androgen and androgen receptor pathways played essential roles in the pathogenesis of PCa. Androgen depletion therapy is the most used therapies for primary PCa patients. However, due to the high relapse and mortality of PCa, developing novel noninvasive therapies have become the focus of research. Melatonin is an indole-like neurohormone mainly produced in the human pineal gland with a prominent anti-oxidant property. The anti-tumor ability of melatonin has been substantially confirmed and several related articles have also reported the inhibitory effect of melatonin on PCa, while reviews of this inhibitory effect of melatonin on PCa in recent 10 years are absent. Therefore, we systematically discuss the relationship between melatonin disruption and the risk of PCa, the mechanism of how melatonin inhibited PCa, and the synergistic benefits of melatonin and other drugs to summarize current understandings about the function of melatonin in suppressing human prostate cancer. We also raise several unsolved issues that need to be resolved to translate currently non-clinical trials of melatonin for clinic use. We hope this literature review could provide a solid theoretical basis for the future utilization of melatonin in preventing, diagnosing and treating human prostate cancer.

Revisiting prostate cancer metabolism: From metabolites to disease and therapy

Prostate cancer (PCa), one of the most commonly diagnosed cancers worldwide, still presents important unmet clinical needs concerning treatment. In the last years, the metabolic reprogramming and the specificities of tumor cells emerged as an exciting field for cancer therapy. The unique features of PCa cells metabolism, and the activation of specific metabolic pathways, propelled the use of metabolic inhibitors for treatment. The present work revises the knowledge of PCa metabolism and the metabolic alterations that underlie the development and progression of the disease. A focus is given to the role of bioenergetic sources, namely, glucose, lipids, and glutamine sustaining PCa cell survival and growth. Moreover, it is described as the action of oncogenes/tumor suppressors and sex steroid hormones in the metabolic reprogramming of PCa. Finally, the status of PCa treatment based on the inhibition of metabolic pathways is presented. Globally, this review updates the landscape of PCa metabolism, highlighting the critical metabolic alterations that could have a clinical and therapeutic interest.

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.

Localising occult prostate cancer metastasis with advanced imaging techniques (LOCATE trial): a prospective cohort, observational diagnostic accuracy trial investigating whole-body magnetic resonance imaging in radio-recurrent prostate cancer

BACKGROUND

Accurate whole-body staging following biochemical relapse in prostate cancer is vital in determining the optimum disease management. Current imaging guidelines recommend various imaging platforms such as computed tomography (CT), Technetium 99 m (99mTc) bone scan and 18F-choline and recently 68Ga-PSMA positron emission tomography (PET) for the evaluation of the extent of disease. Such approach requires multiple hospital attendances and can be time and resource intensive. Recently, whole-body magnetic resonance imaging (WB-MRI) has been used in a single visit scanning session for several malignancies, including prostate cancer, with promising results, providing similar accuracy compared to the combined conventional imaging techniques. The LOCATE trial aims to investigate the application of WB-MRI for re-staging of patients with biochemical relapse (BCR) following external beam radiotherapy and brachytherapy in patients with prostate cancer.

METHODS/DESIGN

The LOCATE trial is a prospective cohort, multi-centre, non-randomised, diagnostic accuracy study comparing WB-MRI and conventional imaging. Eligible patients will undergo WB-MRI in addition to conventional imaging investigations at the time of BCR and will be asked to attend a second WB-MRI exam, 12-months following the initial scan. WB-MRI results will be compared to an enhanced reference standard comprising all the initial, follow-up imaging and non-imaging investigations. The diagnostic performance (sensitivity and specificity analysis) of WB-MRI for re-staging of BCR will be investigated against the enhanced reference standard on a per-patient basis. An economic analysis of WB-MRI compared to conventional imaging pathways will be performed to inform the cost-effectiveness of the WB-MRI imaging pathway. Additionally, an exploratory sub-study will be performed on blood samples and exosome-derived human epidermal growth factor receptor (HER) dimer measurements will be taken to investigate its significance in this cohort.

DISCUSSION

The LOCATE trial will compare WB-MRI versus the conventional imaging pathway including its cost-effectiveness, therefore informing the most accurate and efficient imaging pathway.

TRIAL REGISTRATION

LOCATE trial was registered on ClinicalTrial.gov on 18th of October 2016 with registration reference number NCT02935816.

Cancer metabolism in a snapshot: MRS(I)

The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by ¹³ C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-¹³ C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr ¹³ C-labelled ratio with respect to untreated or non-responding tumour. For therapeutic agents inducing tumour hypoxia, the ¹³ C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. ³¹ P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole ¹ H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits.

Radiolabelled choline versus PSMA PET/CT in prostate cancer restaging: a meta-analysis

Both radiolabelled choline and prostate specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) could be used in patients with biochemical recurrent prostate cancer (BRPCa). We aimed to perform a meta-analysis about the head-to-head comparison of detection rate (DR) between these methods in BRPCa. A comprehensive literature search of studies listed in PubMed/MEDLINE, EMBASE and Cochrane library databases through October 2018 and regarding the head-to-head comparison of DR between radiolabelled choline and PSMA PET/CT in BRPCa was carried out. Overall pooled DR was calculated on a per patient-based analysis; subgroup analyses taking into account different prostate-specific antigen (PSA) cut-off values were performed. Five studies (257 BRPCa patients) were included. The meta-analysis provided the following overall DR: 56% [95% confidence interval (95% CI): 37-75%] for radiolabelled choline PET/CT and 78% (95% CI: 70-84%) for radiolabelled PSMA PET/CT. Significant difference of DR was found only in patients with PSA ≤ 1 ng/ml [the DR of radiolabelled choline and PSMA PET/CT were 27% (95% CI: 17-39%) and 54% (95% CI: 43-65%), respectively]. Radiolabelled PSMA PET/CT proved to be clearly superior in detecting BRPCa lesions at low PSA levels (≤ 1 ng/ml) when compared to radiolabelled choline PET/CT. On the other hand, the superiority of radiolabelled PSMA PET/CT was less evident in patients with PSA > 1 ng/ml. More studies and in particular cost-effectiveness analyses comparing these imaging methods are warranted.

Prostate imaging features that indicate benign or malignant pathology on biopsy

Accurate diagnosis of clinically significant prostate cancer is essential in identifying patients who should be offered treatment with curative intent. Modifications to the Gleason grading system in recent years show that accurate grading and reporting at needle biopsy can improve identification of clinically significant prostate cancers. Extracapsular extension of prostate cancer has been demonstrated to be an adverse prognostic factor with greater risk of metastatic spread than organ-confined disease. Tumor volume may be an independent prognostic factor and should be considered in conjunction with other factors. Multi-parametric magnetic resonance imaging (MP-MRI) has become an increasingly important tool in the diagnosis and characterization of prostate cancer. MP-MRI allows T2-weighted (T2W) anatomical imaging to be combined with functional and physiological assessment. Diffusion-weighted imaging (DWI) has shown greater sensitivity, specificity and negative predictive value compared to prostate specific antigen (PSA) testing and T2W imaging alone and has a more positive correlation with Gleason score and tumour volume. Dynamic gadolinium contrast-enhanced (DCE) imaging can exhibit difficulties in distinguishing prostatitis from malignancy in the peripheral zone, and between benign prostatic hyperplasia (BPH) and malignancies in the transition zone (TZ). Computer aided diagnosis utilizes software to aid radiologists in detecting and diagnosing abnormalities from diagnostic imaging. New techniques of quantitative MRI, such as VERDICT MRI use tissue-specific factors to delineate different cellular and microstructural phenotypes, characterizing tissue properties with greater detail. Proton MR spectroscopic imaging (MRSI) is a more technically challenging imaging modality than DCE and DWI MRI. Over the last decade, choline and prostate-specific membrane antigen (PSMA) positron emission tomography (PET) have developed as better tools for staging than conventional imaging. While hyperpolarized MRI shows promise in improving the imaging and differentiation of benign and malignant lesions there is further work required. Accurate reading and interpretation of diagnostic investigations is key to accurate identification of abnormal areas requiring biopsy, sparing those in whom benign or indolent disease can be managed by non-invasive means. Embracing and advancing existing technologies is essential in furthering this process.

Imaging Cancer Metabolism

It is widely accepted that altered metabolism contributes to cancer growth and has been described as a hallmark of cancer. Our view and understanding of cancer metabolism has expanded at a rapid pace, however, there remains a need to study metabolic dependencies of human cancer in vivo. Recent studies have sought to utilize multi-modality imaging (MMI) techniques in order to build a more detailed and comprehensive understanding of cancer metabolism. MMI combines several in vivo techniques that can provide complementary information related to cancer metabolism. We describe several non-invasive imaging techniques that provide both anatomical and functional information related to tumor metabolism. These imaging modalities include: positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS) that uses hyperpolarized probes and optical imaging utilizing bioluminescence and quantification of light emitted. We describe how these imaging modalities can be combined with mass spectrometry and quantitative immunochemistry to obtain more complete picture of cancer metabolism. In vivo studies of tumor metabolism are emerging in the field and represent an important component to our understanding of how metabolism shapes and defines cancer initiation, progression and response to treatment. In this review we describe in vivo based studies of cancer metabolism that have taken advantage of MMI in both pre-clinical and clinical studies. MMI promises to advance our understanding of cancer metabolism in both basic research and clinical settings with the ultimate goal of improving detection, diagnosis and treatment of cancer patients.

Skeletal muscle metastasis from the most common carcinomas orthopedic surgeons deal with. A systematic review of the literature

INTRODUCTION

There is scarce information in the literature dealing with the clinical presentation, management and oncologic outcomes of skeletal muscle metastases (SMM). We sought to perform a systematic review of the literature to investigate: (1) tumor characteristics of SMM, (2) therapeutic approach, and (3) oncological outcomes.

METHODS

A systematic review of the literature was performed using PubMed and EMBASE search engines. A total of 3231 references were reviewed and 49 studies were included. Demographic data, presentation characteristics, and oncological outcomes were recorded. Statistical analysis was performed using SPSS 22.0 software (IBM; Armonk, New York) and Comprehensive Meta-Analysis software version 3 (Biostat, Inc.), with p < 0.05 as statistically significant.

RESULTS

A total of 231 patients were included. These tumors presented more commonly on males 58.4% (135/231), with a mean age of 60.08 ± 10.6 years, and in the axial area 39.6% (88/222). The most common carcinoma type was lung 41.1% (95/231). Resection of a single metastases did not change survival significantly (p = 0.992). LRR was higher within the group of patients that underwent WLE compared with non-WLE [31.3% (23/74) vs. 8.7% (2/23), p ≤ 0.001]. Kaplan-Meier survival analysis for the entire cohort showed an estimate of 15.3 months [95% confidence interval (CI) 11.6-19; standard error (SE) 0.432], with lung carcinoma carrying the worst prognosis 6.7 months (95% CI 5.4-8.07; SE 0.68). Patients with a single SMM showed a worse estimate mean survival time compared to patients with multiple metastases limited to muscles [8.6 months (95% CI 4.7-12.5; SE 2.0) vs 25.4 months (95% CI 19.8-31.05; SE 2.8; p ≤ 0.001)].

CONCLUSIONS

Overall survival is poor and is driven mainly by the type of carcinoma. An Increased LRR might be present due to the systemic nature of the condition, and degree of control of the primary carcinoma.

Glucose Metabolism in the Progression of Prostate Cancer

Prostate cancer is one of the most common types of cancer in western country males but the mechanisms involved in the transformation processes have not been clearly elucidated. Alteration in cellular metabolism in cancer cells is recognized as a hallmark of malignant transformation, although it is becoming clear that the biological features of metabolic reprogramming not only differ in different cancers, but also among different cells in a type of cancer. Normal prostate epithelial cells have a peculiar and very inefficient energy metabolism as they use glucose to synthesize citrate that is secreted as part of the seminal liquid. During the transformation process, prostate cancer cells modify their energy metabolism from inefficient to highly efficient, often taking advantage of the interaction with other cell types in the tumor microenvironment that are corrupted to produce and secrete metabolic intermediates used by cancer cells in catabolic and anabolic processes. We recapitulate the metabolic transformations occurring in the prostate from the normal cell to the metastasis, highlighting the role of the microenvironment and summarizing what is known on the molecular mechanisms involved in the process.

Iron granules in plasma cells

Resistance of cancer cells to chemotherapy is the first cause of cancer-associated death. Thus, new strategies to deal with the evasion of drug response and to improve clinical outcomes are needed. Genetic and epigenetic mechanisms associated with uncontrolled cell growth result in metabolism reprogramming. Cancer cells enhance anabolic pathways and acquire the ability to use different carbon sources besides glucose. An oxygen and nutrient-poor tumor microenvironment determines metabolic interactions among normal cells, cancer cells and the immune system giving rise to metabolically heterogeneous tumors which will partially respond to metabolic therapy. Here we go into the best-known cancer metabolic profiles and discuss several studies that reported tumors sensitization to chemotherapy by modulating metabolic pathways. Uncovering metabolic dependencies across different chemotherapy treatments could help to rationalize the use of metabolic modulators to overcome therapy resistance.