Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients

Potential biomarkers develop for predicting the prognosis of patients with esophageal squamous cell carcinoma after optimized chemoradiotherapy using serum metabolomics

BACKGROUND

Esophageal squamous cell carcinoma (ESCC), the most common type of esophageal cancer, characterized by low five-year survival rate, and concurrent chemoradiotherapy (CCRT) has been proposed to treat ESCC, while potential biomarkers for prognostic monitoring after optimized CCRT remains unknown.

METHODS

Serum samples from 45 patients with ESCC were collected and categorized into three groups: Control (pre-CCRT), CCRT (during CCRT), and CCRT-1 M (one-month post-CCRT). The therapeutic effect was evaluated using CT imaging and established evaluation criteria. Untargeted metabolomic analysis was performed on the serum samples to identify differential metabolites caused by CCRT treatment, assessing their potential for prognostic monitoring.

RESULTS

CCRT had significant therapeutic efficacy in patients with ESCC, as indicated by CT imaging and RECIST 1.1 solid tumor evaluation criteria. Notably, several metabolic markers were identified through non-targeted metabolomic analysis, highlighting changes following CCRT treatment. These differential metabolites are involved in the dysregulation of phenylalanine, tyrosine, and tryptophan biosynthesis, as well as histidine, arginine, and proline metabolism, and glycine, serine, and threonine metabolism, suggesting a reduction in glucose metabolism in patients with ESCC after CCRT. Additionally, ROC analysis indicated that the AUC of these metabolites exceeded 0.661, underscoring their diagnostic value for assessing CCRT efficacy and their potential use in prognostic monitoring. Comparative metabolomic analysis identified L-phenylalanine and lysine as promising serum biomarkers for predicting therapeutic outcomes.

CONCLUSIONS

CCRT shows considerable therapeutic benefit in patients with ESCC, with observed reductions in glucose metabolism post-treatment. L-phenylalanine and lysine may serve as potential serum biomarkers to predict CCRT efficacy.

Application of spatial omics in gastric cancer

Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer

BACKGROUND

Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data.

METHODS

Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively.

RESULTS

The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance.

CONCLUSION

We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma

MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.

Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis.

Lipid metabolism analysis in esophageal cancer and associated drug discovery

Esophageal cancer is an upper gastrointestinal malignancy with a bleak prognosis. It is still being explored in depth due to its complex molecular mechanisms of occurrence and development. Lipids play a crucial role in cells by participating in energy supply, biofilm formation, and signal transduction processes, and lipid metabolic reprogramming also constitutes a significant characteristic of malignant tumors. More and more studies have found esophageal cancer has obvious lipid metabolism abnormalities throughout its beginning, progress, and treatment resistance. The inhibition of tumor growth and the enhancement of antitumor therapy efficacy can be achieved through the regulation of lipid metabolism. Therefore, we reviewed and analyzed the research results and latest findings for lipid metabolism and associated analysis techniques in esophageal cancer, and comprehensively proved the value of lipid metabolic reprogramming in the evolution and treatment resistance of esophageal cancer, as well as its significance in exploring potential therapeutic targets and biomarkers.

State-of-the-art mass spectrometry imaging applications in biomedical research

Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.

Neoadjuvant PD-1 blockade combined with chemotherapy is not superior to neoadjuvant chemotherapy alone in resectable locally advanced esophageal carcinoma

BACKGROUND

Neoadjuvant chemotherapy (nCT) or neoadjuvant chemoradiotherapy followed by surgery has been recommended as standard treatment in patients with locally advanced esophageal cancer (LAEC). But the risk of tumor recurrence still remained, and many patients refused or abandoned radiotherapy because of the intolerable adverse effects in China. Neoadjuvant immunochemotherapy (nICT) followed by surgery has become an emerging treatment in patients with esophageal cancer. There was still no consensus on whether nICT was superior to nCT alone in patients with esophageal cancer.

METHODS

In this retrospective study, patients with resectable esophageal cancer who received surgery after nICT (n=26, 40%) or nCT alone (n=39, 60%) were included. The patients were classified as nICT or nCT arm. The primary endpoints were pathological tumor response (PTR) and event-free survival (EFS). The different clinic-pathological features were compared by the Kruskal-Wallis test for continuous variables and the Chi-square (χ2) test for categorical variables. Kaplan-Meier curves were used to estimate EFS from the date of treatment to recurrence or death. All tests were 2-sided with a significative P-value defined <.05.

RESULTS

Three (11.5%) of the 26 patients achieved pathological complete remission (pCR) in the nICT group, and four (10.3%) of the 39 patients achieved pCR in the nCT group, respectively (P=1.000). Six (23.1%) of the 26 patients achieved major pathological response (MPR) in the nICT group, and 11 (28.2%) of the 39 patients achieved MPR in the nCT group, respectively (P=0.645). Downstaging was achieved in 13 (44.8%) patients in the nICT group and 16 (55.2%) patients in the nCT group, respectively (P=0.732). To verify the tumor regression grade (TRG) results, we compared them with MPR and pCR, which showed a significant dependency (P< 0.001). Patients who achieved downgrading showed better MPR and pCR rates (P<0.001 and P =0.010). There was no significant difference in EFS between the nICT and nCT groups (HR=1.011, 95% CI: 0.421-2.425, P = 0.981).

CONCLUSIONS

Neoadjuvant PD-1 blockade combined with chemotherapy was not superior to chemotherapy alone for patients with resectable locally advanced esophageal carcinoma. However, more studies with long-term follow-up were needed to confirm this result.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with esophageal cancer

Metabolomics has been reported as an efficient tool to screen biomarkers that are related to esophageal cancer. However, the metabolic biomarkers identifying malignant degrees and therapeutic efficacy are still largely unknown in the disease. Here, GC-MS-based metabolomics was used to understand metabolic alteration in 137 serum specimens from patients with esophageal cancer, which is approximately two- to fivefold as many plasma specimens as the previous reports. The elevated amino acid metabolism is in sharp contrast to the reduced carbohydrate as a characteristic feature of esophageal cancer. Comparative metabolomics showed that most metabolic differences were determined between the early stage (0–II) and the late stage (III and IV) among the 0–IV stages of esophageal cancer and between patients who received treatment and those who did not receive treatment. Glycine, serine, and threonine metabolism and glycine were identified as the potentially overlapped metabolic pathway and metabolite, respectively, in both disease progress and treatment effect. Glycine, fructose, ornithine, and threonine can be a potential array for the evaluation of disease prognosis and therapy in esophageal cancer. These results highlight the means of identifying previously unknown biomarkers related to esophageal cancer by a metabolomics approach.

Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients

BACKGROUND

The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non-small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read-out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning.

METHODS

Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high-resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy-naïve patients with NSCLC (n = 85).

RESULTS

The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716-8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004-4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445-1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977-1.550) were not independent prognostic factors. Using Kaplan-Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non-responders (P < 0.001).

CONCLUSIONS

Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology-based assessment of tumor response.