Exploring Gut Microbiome in Predicting the Efficacy of Immunotherapy in Non-Small Cell Lung Cancer

Targeting bacterial metabolites in tumor for cancer therapy: An alternative approach for targeting tumor-associated bacteria

Emerging evidence reveal that tumor-associated bacteria (TAB) can facilitate the initiation and progression of multiple types of cancer. Recent work has emphasized the significant role of intestinal microbiota, particularly bacteria, plays in affecting responses to chemo- and immuno-therapies. Hence, it seems feasible to improve cancer treatment outcomes by targeting intestinal bacteria. While considering variable richness of the intestinal microbiota and diverse components among individuals, direct manipulating the gut microbiota is complicated in clinic. Tumor initiation and progression requires the gut microbiota-derived metabolites to contact and reprogram neoplastic cells. Hence, directly targeting tumor-associated bacteria metabolites may have the potential to provide alternative and innovative strategies to bypass the gut microbiota for cancer therapy. As such, there are great opportunities to explore holistic approaches that incorporates TAB-derived metabolites and related metabolic signals modulation for cancer therapy. In this review, we will focus on key opportunistic areas by targeting TAB-derived metabolites and related metabolic signals, but not bacteria itself, for cancer treatment, and elucidate future challenges that need to be addressed in this emerging field.

Gut microbiome model predicts response to neoadjuvant immunotherapy plus chemoradiotherapy in rectal cancer

BACKGROUND

Accurate evaluation of the response to preoperative treatment enables the provision of a more appropriate personalized therapeutic schedule for locally advanced rectal cancer (LARC), which remains an enormous challenge, especially neoadjuvant immunotherapy plus chemoradiotherapy (nICRT).

METHODS

This prospective, multicenter cohort study enrolled patients with LARC from 6 centers who received nICRT. The dynamic variation in the gut microbiome during nICRT was evaluated. A species-level gut microbiome prediction (SPEED) model was developed and validated to predict the pathological complete response (pCR) to nICRT.

FINDINGS

A total of 50 patients were enrolled, 75 fecal samples were collected from 33 patients at different time points, and the pCR rate reached 42.4% (14/33). Lactobacillus and Eubacterium were observed to increase after nICRT. Additionally, significant differences in the gut microbiome were observed between responders and non-responders at baseline. Significantly higher abundances of Lachnospiraceaebacterium and Blautiawexlerae were found in responders, while Bacteroides, Prevotella, and Porphyromonas were found in non-responders. The SPEED model showcased a superior predictive performance with areas under the curve of 98.80% (95% confidence interval [CI]: 95.67%-100%) in the training cohort and 77.78% (95% CI: 65.42%-88.29%) in the validation cohort.

CONCLUSIONS

Programmed death 1 (PD-1) blockade plus concurrent long-course CRT showed a favorable pCR rate and is well tolerated in microsatellite-stable (MSS)/mismatch repair-proficient (pMMR) patients with LARC. The SPEED model can be used to predict the pCR to nICRT based on the baseline gut microbiome with high robustness and accuracy, thereby assisting clinical physicians in providing individualized management for patients with LARC.

FUNDING

This research was funded by the China National Natural Science Foundation (82202884).

Gut microbial metabolites in lung cancer development and immunotherapy: Novel insights into gut-lung axis

Metabolic derivatives of numerous microorganisms inhabiting the human gut can participate in regulating physiological activities and immune status of the lungs through the gut-lung axis. The current well-established microbial metabolites include short-chain fatty acids (SCFAs), tryptophan and its derivatives, polyamines (PAs), secondary bile acids (SBAs), etc. As the study continues to deepen, the critical function of microbial metabolites in the occurrence and treatment of lung cancer has gradually been revealed. Microbial derivates can enter the circulation system to modulate the immune microenvironment of lung cancer. Mechanistically, oncometabolites damage host DNA and promote the occurrence of lung cancer, while tumor-suppresive metabolites directly affect the immune system to combat the malignant properties of cancer cells and even show considerable application potential in improving the efficacy of lung cancer immunotherapy. Considering the crosstalk along the gut-lung axis, in-depth exploration of microbial metabolites in patients' feces or serum will provide novel guidance for lung cancer diagnosis and treatment selection strategies. In addition, targeted therapeutics on microbial metabolites are expected to overcome the bottleneck of lung cancer immunotherapy and alleviate adverse reactions, including fecal microbiota transplantation, microecological preparations, metabolite synthesis and drugs targeting metabolic pathways. In summary, this review provides novel insights and explanations on the intricate interplay between gut microbial metabolites and lung cancer development, and immunotherapy through the lens of the gut-lung axis, which further confirms the possible translational potential of the microbiome metabolome in lung cancer treatment.

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Too Much of a Good Thing: The Association of Elevated Vitamin B12 Levels and Outcomes in Patients With Cancer Treated With Immunotherapy

Metabolic pathways may regulate responses to cancer immunotherapy (IO). Due to its immunomodulatory properties, we sought to examine the association between serum vitamin B12 (VitB12) and survival in individuals with cancer treated with immune checkpoint inhibitors, compared with biological and chemotherapy. We collected data on patients with advanced cancer initiating intravenous antineoplastic treatment and a concomitant VitB12 measurement (elevated: >820 ng/L), between January 2010 and January 2022. Patients on IO and other regimens (control) were compared using the Mann-Whitney test for continuous variables, χ 2 test or Fisher test for categorical variables, and multivariate Cox regression models assessed the effect of VitB12 on overall survival and progression-free survival, adjusting for confounders. Patient groups (control: n = 408; IO: n = 93) were balanced for the treatment line and VitB12 (elevated 29.9% vs 23.7%; mean 762.4 vs 687.6 ng/L). In multivariate analysis, overall survival in all patients was negatively associated with VitB12 [control: hazard ratio (HR): 1.4, 95% CI: 1.01-1.96, P = 0.04, false discovery rate (FDR): 0.069; IO: HR: 2.74 as sum of linear baseline and interaction effects, log scale], age (HR: 1.03, 95% CI: 1.02-1.04, P < 0.01), male sex (HR: 0.66, 95% CI: 0.50-0.88, P < 0.01), and neutrophil-to-lymphocyte ratio (HR: 1.05, 95% CI: 0.48-0.99, P = 0.01). However, VitB12 was significantly negatively associated with progression-free survival only in the IO group ( P < 0.001, FDR < 0.001, calculated HR: 8.34; biological treatment P = 0.08; FDR: 0.111; neutrophil-to-lymphocyte ratio, P = 0.07; FDR: 0.09). Taken together, elevated VitB12 was a negative predictor for outcomes on IO, independently of other known prognostic factors. Further research is needed to elucidate the immune-metabolic interplay and its interaction with the gut microbiome, as well as interventional strategies to enhance IO responses.

Exploring the human archaeome: its relevance for health and disease, and its complex interplay with the human immune system

This Review aims to coalesce existing knowledge on the human archaeome, a less-studied yet critical non-bacterial component of the human microbiome, with a focus on its interaction with the immune system. Despite a largely bacteria-centric focus in microbiome research, archaea present unique challenges and opportunities for understanding human health. We examine the archaeal distribution across different human body sites, such as the lower gastrointestinal tract (LGT), upper aerodigestive tract (UAT), urogenital tract (UGT), and skin. Variability in archaeal composition exists between sites; methanogens dominate the LGT, while Nitrososphaeria are prevalent on the skin and UAT. Archaea have yet to be classified as pathogens but show associations with conditions such as refractory sinusitis and vaginosis. In the LGT, methanogenic archaea play critical metabolic roles by converting bacterial end-products into methane, correlating with various health conditions, including obesity and certain cancers. Finally, this work looks at the complex interactions between archaea and the human immune system at the molecular level. Recent research has illuminated the roles of specific archaeal molecules, such as RNA and glycerolipids, in stimulating immune responses via innate immune receptors like Toll-like receptor 8 (TLR8) and 'C-type lectin domain family 4 member E' (CLEC4E; also known as MINCLE). Additionally, metabolic by-products of archaea, specifically methane, have demonstrated immunomodulatory effects through anti-inflammatory and anti-oxidative pathways. Despite these advancements, the mechanistic underpinnings of how archaea influence immune activity remain a fertile area for further investigation.

Melanoma and microbiota: Current understanding and future directions

Over the last decade, the composition of the gut microbiota has been found to correlate with the outcomes of cancer patients treated with immunotherapy. Accumulating evidence points to the various mechanisms by which intestinal bacteria act on distal tumors and how to harness this complex ecosystem to circumvent primary resistance to immune checkpoint inhibitors. Here, we review the state of the microbiota field in the context of melanoma, the recent breakthroughs in defining microbial modes of action, and how to modulate the microbiota to enhance response to cancer immunotherapy. The host-microbe interaction may be deciphered by the use of "omics" technologies, and will guide patient stratification and the development of microbiota-centered interventions. Efforts needed to advance the field and current gaps of knowledge are also discussed.

The artificial intelligence and machine learning in lung cancer immunotherapy

Since the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy. It is imperative to accurately and intelligently select appropriate patients for immunotherapy or predict the immunotherapy efficacy. In recent years, machine learning (ML)-based artificial intelligence (AI) was developed in the area of medical-industrial convergence. AI can help model and predict medical information. A growing number of studies have combined radiology, pathology, genomics, proteomics data in order to predict the expression levels of programmed death-ligand 1 (PD-L1), tumor mutation burden (TMB) and tumor microenvironment (TME) in cancer patients or predict the likelihood of immunotherapy benefits and side effects. Finally, with the advancement of AI and ML, it is believed that "digital biopsy" can replace the traditional single assessment method to benefit more cancer patients and help clinical decision-making in the future. In this review, the applications of AI in PD-L1/TMB prediction, TME prediction and lung cancer immunotherapy are discussed.

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient's blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.

The Microbiome-TIME Axis: A Host of Possibilities

Cancer continues to be a significant source of mortality and morbidity worldwide despite progress in cancer prevention, early detection, and treatment. Fortunately, immunotherapy has been a breakthrough in the treatment of many cancers. However, the response to immunotherapy treatment and the experience of associated side effects varies significantly between patients. Recently, attention has been given to understanding the role of the tumor immune microenvironment (TIME) in the development, progression, and treatment response of cancer. A new understanding of the role of the microbiota in the modulation of the TIME has further complicated the story but also unlocked a new area of adjuvant therapeutic research. The complex balance of tumor-permissive and tumor-suppressive immune environments requires further elucidation in order to be harnessed as a therapeutic target. Because both the TIME and the microbiome show importance in these areas, we propose here the concept of the "microbiome-TIME axis" to review the current field of research and future directions.