A strategy for early detection of response to chemotherapy drugs based on treatment-related changes in the metabolome

The rational modulation of autophagy sensitizes colorectal cancer cells to 5-fluouracil and oxaliplatin

Colorectal cancer (CRC) is the third most common and deadliest cancer globally. Regimens using 5-fluorouracil (5FU) and Oxaliplatin (OXA) are the first-line treatment for CRC, but tumor recurrence is frequent. It is plausible to hypothesize that differential cellular responses are triggered after treatments depending on the genetic background of CRC cells and that the rational modulation of cell tolerance mechanisms like autophagy may reduce the regrowth of CRC cells. This study proposes investigating the cellular mechanisms triggered by CRC cells exposed to 5FU and OXA using a preclinical experimental design mimicking one cycle of the clinical regimen (i.e., 48 h of treatment repeated every 2 weeks). To test this, we treated CRC human cell lines HCT116 and HT29 with the 5FU and OXA, combined or not, for 48 h, followed by analysis for two additional weeks. Compared to single-drug treatments, the co-treatment reduced tumor cell regrowth, clonogenicity and stemness, phenotypes associated with tumor aggressiveness and poor prognosis in clinics. This effect was exerted by the induction of apoptosis and senescence only in the co-treatment. However, a week after treatment, cells that tolerated the treatment had high levels of autophagy features and restored the proliferative phenotype, resembling tumor recurrence. The pharmacologic suppression of early autophagy during its peak of occurrence, but not concomitant with chemotherapeutics, strongly reduced cell regrowth. Overall, our experimental model provides new insights into the cellular mechanisms that underlie the response and tolerance of CRC cells to 5FU and OXA, suggesting optimized, time-specific autophagy inhibition as a new avenue for improving the efficacy of current treatments.

The multikinase inhibitor axitinib in the treatment of advanced hepatocellular carcinoma: the current clinical applications and the molecular mechanisms

Advanced hepatocellular carcinoma (HCC) is a formidable public health problem with limited curable treatment options. Axitinib, an oral tyrosine kinase inhibitor, is a potent and selective second-generation inhibitor of vascular endothelial growth factor receptor (VEGFR) 1, 2, and 3. This anti-angiogenic drug was found to have promising activity in various solid tumors, including advanced HCC. At present, however, there is no relevant review article that summarizes the exact roles of axitinib in advanced HCC. In this review, 24 eligible studies (seven studies in the ClinicalTrials, eight experimental studies, and nine clinical trials) were included for further evaluation. The included randomized or single-arm phase II trials indicated that axitinib could not prolong the overall survival compared to the placebo for the treatment of advanced HCC, but improvements in progression free survival and time to tumor progression were observed. Experimental studies showed that the biochemical effects of axitinib in HCC might be regulated by its associated genes and affected signaling cascades (e.g. VEGFR2/PAK1, CYP1A2, CaMKII/ERK, Akt/mTor, and miR-509-3p/PDGFRA). FDA approved sorafenib combined with nivolumab (an inhibitor of PD-1/PD-L1) as the first line regimen for the treatment of advanced HCC. Since both axitinib and sorafenib are tyrosine kinase inhibitors as well as the VEGFR inhibitors, axitinib combined with anti-PDL-1/PD-1 antibodies may also exhibit tremendous potential in anti-tumoral effects for advanced HCC. The present review highlights the current clinical applications and the molecular mechanisms of axitinib in advanced HCC. To move toward clinical applications by combining axitinib and other treatments in advanced HCC, more studies are still warranted in the near future.

Mass spectrometry-based metabolomics approach and in vitro assays revealed promising role of 2,3-dihydroquinazolin-4(1H)-one derivatives against colorectal cancer cell lines

Colorectal cancer (CRC) is the most frequent form of gastrointestinal cancer and one of the major causes of human mortality worldwide. Many of the current CRC therapies have limitations due to multidrug resistance and/or severe side effects. Quinazoline derivatives are promising lead compounds with a wide range of pharmacological actions. In this study, the effect of seven synthesized 2,3-dihydroquinazolin-4(1H)-one analogues as potential anticancer agents against two CRC cell lines (HCT116 and SW480) was investigated using cell viability proliferation, migration, adhesion and invasion assays. A liquid chromatography-mass spectrometry (LC-MS/MS) metabolomics approach was used to identify the underlying biochemical pathways disturbed in treated-HCT116 cells. Cell viability proliferation assay revealed that four compounds (C2, C3, C5, and C7) had IC₅₀ < 10 µM with C5 displaying the most potent cytotoxic effect (IC₅₀ 1.4 and 0.3 µM against HCT116 and SW480, respectively). Additionally, the compounds showed suppression of wound closure after 72 h, and both C2 and C5 significantly decreased the number of adherent cells and suppressed HCT116 cells invasion. Metabolomics study revealed that C5 induced significant perturbations in the level of several metabolites including spermine, polyamines, glutamine, creatine and carnitine, and altered biochemical processes essential for cell proliferation and progression such as amino acids biosynthesis and metabolism, redox homeostasis, energy related processes (e.g., fatty acid oxidation, second Warburg like effect) and one-carbon metabolism. Our findings indicate that 2,3-dihydroquinazolin-4(1H)-one analogues, particularly C5, have promising anticancer properties, and shed light on the role of metabolomics in identifying new therapeutic targets and providing better understanding of the pathways altered in treated cancer cells.

Sunitinib versus Pazopanib Dilemma in Renal Cell Carcinoma: New Insights into the In Vitro Metabolic Impact, Efficacy, and Safety

Sunitinib and pazopanib are tyrosine kinase inhibitors (TKIs) used as first-line therapy for metastatic renal cell carcinoma (RCC). Although these TKIs are associated with similar survival outcomes, some differences have been reported in their safety profiles. In this work, traditional toxicological endpoints (cell viability and growth, oxidative stress, and nuclear morphology) and 1H NMR spectroscopy-based metabolomics analysis were used to provide new insights into the cytotoxicity and metabolic mechanisms underlying sunitinib and pazopanib treatments. Tumoral (Caki-1) and non-tumoral (HK-2) human renal cells were exposed to clinically relevant concentrations of sunitinib (2 µM) or pazopanib (50 µM). Sunitinib showed selectivity for cancer cells, inhibiting proliferation, and inducing apoptotic death of Caki-1 cells, whereas pazopanib had a similar cytotoxic effect in both tumoral and non-tumoral cells. 1H-NMR metabolomics unveiled a higher impact of sunitinib on the levels of intracellular metabolites of Caki-1 cells (seven dysregulated metabolites), suggesting dysregulations on amino acid, glutathione and glycerophospholipid metabolisms. In contrast, pazopanib had a higher impact on the levels of extracellular metabolites of Caki-1 cells (seven dysregulated metabolites in culture medium), unveiling alterations on amino acid and energetic metabolisms. In HK-2 cells, sunitinib caused only a minor increase in intracellular isoleucine levels, whereas pazopanib induced several alterations on the intracellular (three dysregulated metabolites) and extracellular (three dysregulated metabolites) compartments suggesting changes on amino acid, glycerophospholipid, and energy metabolisms. Our results demonstrate that these TKIs elicit distinct cellular and metabolic responses, with sunitinib showing better in vitro efficacy against target RCC cells and lesser nephrotoxic potential than pazopanib.

Metabolic Pathways Associated With Psychoneurological Symptoms in Children With Cancer Receiving Chemotherapy

CONTEXT

Children with cancer undergoing chemotherapy experience a cluster of psychoneurological symptoms (PNS), including pain, fatigue, anxiety, and depressive symptoms. Metabolomics is promising to differentiate metabolic pathways associated with the PNS cluster.

OBJECTIVES

Identify metabolic pathways associated with the PNS cluster in children with cancer before and after chemotherapy.

METHODS

Pain, fatigue, anxiety, and depressive symptoms were assessed using the Pediatric PROMIS scales. T-scores were computed and divided dichotomously by a cutoff point of 50; the PNS cluster was a sum of the four symptoms ranging from 0 (all T-scores <50) to 4 (all T-scores ≥50). Serum metabolites were processed using liquid chromatography mass-spectrometry untargeted metabolomics approach. Linear regression models examined metabolites associated with the PNS cluster. Metabolic pathway enrichment analysis was performed.

RESULTS

Participant demographics (n = 40) were 55% female, 60% white, 62.5% aged 13-19 years, and 62.5% diagnoses of Hodgkin's lymphoma and B-cell acute lymphocytic leukemia. Among 9276 unique metabolic features, 454 were associated with pain, 281 with fatigue, 596 with anxiety, 551 with depressive symptoms, and 300 with the PNS cluster across one chemotherapy cycle. Fatty acids pathways were associated with pain: de novo fatty acid biosynthesis (p < .001), fatty acid metabolism (p = .001), fatty acid activation (p = .004), and omega-3 fatty acid metabolism (p = .009). Tryptophan amino acid pathway was associated with fatigue (p < .001), anxiety (p = .015), and the PNS cluster (p = .037). Carnitine shuttle was associated with the PNS cluster (p = .015).

CONCLUSION

Fatty acids and amino acids pathways were associated with PNS in children undergoing chemotherapy. These findings require further investigation in a larger sample.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

Early detection of treatment futility in patients with metastatic colorectal cancer

PURPOSE

Chemotherapy options for treating CRC have rapidly expanded in recent years, and few have predictive biomarkers. Oncologists are challenged with evidence-based selection of treatments, and response is evaluated retrospectively based on serial imaging beginning after 2-3 months. As a result, cumulative toxicities may appear in patients who will not benefit. Early recognition of non-benefit would reduce cumulative toxicities. Our objective was to determine treatment-related changes in the circulating metabolome corresponding to treatment futility.

METHODS

Metabolomic studies were performed on serial plasma samples from patients with CRC in a randomized controlled trial of cetuximab vs. cetuximab + brivanib (N = 188). GC-MS quantified named 94 metabolites and concentrations were evaluated at baseline, Weeks 1, 4 and 12 after treatment initiation. In a discovery cohort (N = 68), a model distinguishing changes in metabolites associated with radiographic disease progression and response was generated using OPLS-DA. A cohort of 120 patients was used for validation of the model.

RESULTS

By one week after treatment, a stable model of 21 metabolites could distinguish between progression and partial response (R2Y = 0.859; Q2Y = 0.605; P = 5e-4). In the validation cohort, patients with the biomarker had a significantly shorter OS (P < 0.0001). In a separate cohort of patients with HCC on axitinib, appearance of the biomarker also signified a shorter PFS (1.7 months vs. 9.2 months, P = 0.001).

CONCLUSION

We have identified changes in the metabolome that appear within 1 week of starting treatment associated with treatment futility. The novel approach described is applicable to future efforts in developing a biomarker for early assessment of treatment efficacy.

Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

Stratifying nutritional restriction in cancer therapy: Next stop, personalized medicine

Dietary interventions combined with cancer drugs represent a clinically valid polytherapy. In particular nutrient restriction (NR) in the form of varied fasting or caloric restriction regimens holds great clinical promise, conceptually due to the voracious anabolic appetite of cancer cells. This metabolic dependency is driven by a strong selective pressure to increasingly acquire biomass of a proliferating tumor and can be therapeutically exploited as vulnerability. A host of preclinical data suggest that NR can potentiate the efficacy of, or alleviate resistance to, cancer drugs. However, complicating clinical implementation are the many variables involved, such as host biology, cancer stage and type, oncogenic mutation landscape, tumor heterogeneity, variations in treatment modalities, and patient compliance to NR protocols. This calls for systematic preclinical screens and co-clinical studies to predict effective combinations of NR with cancer drugs and to allow for patient stratification regarding responsiveness to polytherapy. Such screen-and-stratify pipelines should consider tumor heterogeneity as well as the role of immune effectors in the tumor microenvironment and may lead to biomarker discovery advancing the oncology field toward personalized options with improved translatability to clinical settings. This opinion-based review provides a critical overview of recent literature investigating NR for cancer treatment, pinpoints limitations of current studies, and suggests standardizations and refinements for future studies and trials. The proposed measures aim to increase the translational value of preclinical data and effectively harness the vast potential of NR as adjuvant for cancer therapy.