Reinforcement learning for individualized lung cancer screening schedules: A nested case-control study.

BACKGROUND: The current guidelines for managing screen-detected pulmonary nodules offer rule-based recommendations for immediate diagnostic work-up or follow-up at intervals of 3, 6, or 12 months. Customized visit plans are lacking. PURPOSE: To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL-based policy models. METHODS: Using a nested case-control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer-free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL-based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack-years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL-based policy models with rule-based follow-up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer). RESULTS: We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack-years, beyond those considered in guideline protocols) and the selection of follow-up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL-based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule-based protocols, the three best-performing RL-based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes. CONCLUSIONS: This study highlights the potential of using an RL-based approach that is both clinically interpretable and performance-robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system.

Human Leukocyte Antigen-Allelic Variations May Influence the Age at Cancer Diagnosis in Lynch Syndrome.

Lynch syndrome (LS) is an inherited cancer predisposition disorder associated with an elevated risk of developing various solid cancers, but mostly colorectal cancer (CRC). Despite having the same germline pathogenic variant (PV) in one of the mis-match repair genes or the EPCAM gene, Lynch syndrome variant heterozygotes (LSVH) exhibit a remarkable phenotypic variability in the risk of developing cancer. The role of human leukocyte antigen (HLA) in modifying cancer development risk prompted our hypothesis into whether HLA variations act as potential genetic modifiers influencing the age at cancer diagnosis in LSVH. To investigate this, we studied a unique cohort of 426 LSVH carrying the same germline PV in the hMLH1 gene (MLH1:c.1528C > T) in South Africa. We intuitively selected 100 LSVH with the greatest diversity in age at cancer diagnosis (N = 80) and the oldest cancer unaffected LSVH (N = 20) for a high-throughput HLA genotyping of 11 HLA class I and class II loci using the shotgun next-generation sequencing (NGS) technique on the Illumina MiSeq platform. Statistical analyses employed Kaplan-Meier survival analyses with log-rank tests, and Cox proportional hazards using binned HLA data to minimize type I error. Significant associations were observed between young age at cancer diagnosis and HLA-DPB1*04:02 (mean age: 37 y (25-50); hazard ratio (HR) = 3.37; corrected p-value (q) = 0.043) as well as HLA-DPB1 binned alleles (including HLA-DPB1*09:01, HLA-DPB1*10:01, HLA-DPB1*106:01, HLA-DPB1*18:01, HLA-DPB1*20:01, HLA-DPB1*26:01, HLA-DPB1*28:01, HLA-DPB1*296:01, and HLA-DPB1*55:01) (mean age: 37 y (17-63); HR = 2.30, q = 0.045). The involvement of HLA-DPB1 alleles in the age at cancer diagnosis may highlight the potential role of HLA class II in the immune response against cancer development in LSVH. When validated in a larger cohort, these high-risk HLA-DPB1 alleles could be factored into cancer risk prediction models for personalized cancer screening in LSVH.

Drug-Resistant Epithelial Ovarian Cancer: Current and Future Perspectives.

Epithelial ovarian cancer (EOC) is a complex disease with diverse histological subtypes, which, based on the aggressiveness and course of disease progression, have recently been broadly grouped into type I (low-grade serous, endometrioid, clear cell, and mucinous) and type II (high-grade serous, high-grade endometrioid, and undifferentiated carcinomas) categories. Despite substantial differences in pathogenesis, genetics, prognosis, and treatment response, clinical diagnosis and management of EOC remain similar across the subtypes. Debulking surgery combined with platinum-taxol-based chemotherapy serves as the initial treatment for High Grade Serous Ovarian Carcinoma (HGSOC), the most prevalent one, and for other subtypes, but most patients exhibit intrinsic or acquired resistance and recur in short duration. Targeted therapies, such as anti-angiogenics (e.g., bevacizumab) and PARP inhibitors (for BRCA-mutated cancers), offer some success, but therapy resistance, through various mechanisms, poses a significant challenge. This comprehensive chapter delves into emerging strategies to address these challenges, highlighting factors like aberrant miRNAs, metabolism, apoptosis evasion, cancer stem cells, and autophagy, which play pivotal roles in mediating resistance and disease relapse in EOC. Beyond standard treatments, the focus of this study extends to alternate targeted agents, including immunotherapies like checkpoint inhibitors, CAR T cells, and vaccines, as well as inhibitors targeting key oncogenic pathways in EOC. Additionally, this chapter covers disease classification, diagnosis, resistance pathways, standard treatments, and clinical data on various emerging approaches, and advocates for a nuanced and personalized approach tailored to individual subtypes and resistance mechanisms, aiming to enhance therapeutic outcomes across the spectrum of EOC subtypes.

Methods behind neoantigen prediction for personalized anticancer vaccines.

Next to conventional cancer therapies, immunotherapies such as immune checkpoint inhibitors have broadened the cancer treatment landscape over the past decades. Recent advances in next generation sequencing and bioinformatics technologies have made it possible to identify a patient's own immunogenic neoantigens. These cancer neoantigens serve as important targets for personalized immunotherapy which has the benefit of being more active and effective in targeting cancer cells. This paper is a step-by-step guide discussing the different analyses and challenges encountered during in-silico neoantigen prediction. The protocol describes all the tools and steps required for the identification of immunogenic neoantigens.

Navigating Precision Oncology: Insights from an Integrated Clinical Data and Biobank Repository Initiative across a Network Cancer Program.

Advancing cancer treatment relies on the rapid translation of new scientific discoveries to patient care. To facilitate this, an oncology biobank and data repository program, also referred to as the "Moonshot" program, was launched in 2021 within the Integrated Network Cancer Program of the Allegheny Health Network. A clinical data program (CDP) and biospecimen repository were established, and patient data and blood and tissue samples have been collected prospectively. To date, the study has accrued 2920 patients, predominantly female (61%) and Caucasian (90%), with a mean age of 64 ± 13 years. The most common cancer sites were the endometrium/uterus (12%), lung/bronchus (12%), breast (11%), and colon/rectum (11%). Of patients diagnosed with cancer, 34% were diagnosed at stage I, 25% at stage II, 26% at stage III, and 15% at stage IV. The CDP is designed to support our initiative in advancing personalized cancer research by providing a comprehensive array of patient data, encompassing demographic characteristics, diagnostic details, and treatment responses. The "Moonshot" initiative aims to predict therapy responses and clinical outcomes through cancer-related biomarkers. The CDP facilitates this initiative by fostering data sharing, enabling comparative analyses, and informing the development of novel diagnostic and therapeutic methods.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.

BACKGROUND: Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. RESULTS: Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. CONCLUSIONS: LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Neoadjuvant personalized cancer vaccines: the final frontier?

INTRODUCTION: Clinical trials of personalized cancer vaccines have shown that on-demand therapies that are manufactured for each patient, result in activated T cell responses against individual tumor neoantigens. However, their use has been traditionally restricted to adjuvant settings and late-stage cancer therapy. There is growing support for the implementation of PCV earlier in the cancer therapy timeline, for reasons that will be discussed in this review. AREAS COVERED: The efficacy of cancer vaccines may be to some extent dependent on treatment(s) given prior to vaccine administration. Tumors can undergo radical immunoediting following treatment with immunotherapies, such as checkpoint inhibitors, which may affect the presence of the very mutations targeted by cancer vaccines. This review will cover the topics of neoantigen cancer vaccines, tumor immunoediting, and therapy timing. EXPERT OPINION: Therapy timing remains a critical topic to address in optimizing the efficacy of personalized cancer vaccines. Most personalized cancer vaccines are being evaluated in late-stage cancer patients and after treatment with checkpoint inhibitors, but they may offer a greater benefit to the patient if administered in earlier clinical settings, such as the neoadjuvant setting, where patients are not facing T cell exhaustion and/or a further compromised immune system.

Advances and challenges in clinical applications of tumor cell-derived extracellular vesicles.

Extracellular vesicles (EVs) are a class of substances that feature vesicle-like structures. Initially deemed to be "biological waste", recent studies have highlighted the crucial role of EVs in mediating information communication between cells by transporting bioactive components. Specifically, tumor cell-derived extracellular vesicles (TEVs) contain components that can be utilized for disease diagnosis and as vaccines to activate the immune system. Moreover, since TEVs have a phospholipid bilayer shell and can transport exogenous substances, they are being increasingly explored as drug delivery vehicles in anti-tumor therapy. TEVs have proven highly compatible with their corresponding tumor cells, allowing for efficient drug delivery and exerting killing effects on tumor cells through various mechanisms such as domino effects, lysosomal pathways, and inhibition of drug efflux from tumor tissues. Despite these promising developments, challenges remain in the clinical applications of EVs derived from tumor cells. This paper outlines the current advances and limitations in this field, highlighting the potential of TEVs as a powerful tool for combating cancer.

Optimizing cancer therapy for individuals based on tumor-immune-drug system interaction.

BACKGROUND AND AIM: Chemotherapy is a crucial component of cancer therapy, albeit with significant side effects. Chemotherapy either damages or inhibits the immune system; therefore, its efficacy varies according to the patient's immune state. Currently, there is no efficient model that incorporates tumor-immune-drug (TID) interactions to guide clinical medication strategies. In this study, we compared five different types of existing TID models with the aim to integrate them into a single, comprehensive model; our goal was to accurately reflect the reality of TID interactions to guide personalized cancer therapy. METHODS: We studied four different drug treatment profiles: direct function, normal distribution function, sine function, and trapezoid function. We developed a platform capable of plotting all combinations of parameter sets and their corresponding treatment efficiency scores. Subsequently, we generated 10,000 random parameter combinations for an individual case and plotted two polygon graphs using a seismic colormap to depict efficacy of treatment. Then, we developed a platform providing treatment suggestions for all stages of tumors and varying levels of self-immunity. We created polygons demonstrating successful treatments according to parameters related to tumor and immune status. RESULTS: The trapezoid drug treatment function achieved the best inhibitory effect on the tumor cell density. The treatment can be optimized with a high score indicating that the drug delivery interval had exceeded a specific value. More efficient parameter combinations existed when the immunity was strong compared to when it was weak, thus indicating that increasing the patient's self-immunity can make treatment much more effective. CONCLUSIONS: In summary, we created a comprehensive model that can provide quantitative recommendations for a gentle, yet efficient, treatment customized according to the individual's tumor and immune system characteristics.

Personalized Cancer Vaccines Go Viral: Viral Vectors in the Era of Personalized Immunotherapy of Cancer.

The aim of personalized cancer vaccines is to elicit potent and tumor-specific immune responses against neoantigens specific to each patient and to establish durable immunity, while minimizing the adverse events. Over recent years, there has been a renewed interest in personalized cancer vaccines, primarily due to the advancement of innovative technologies for the identification of neoantigens and novel vaccine delivery platforms. Here, we review the emerging field of personalized cancer vaccination, with a focus on the use of viral vectors as a vaccine platform. The recent advancements in viral vector technology have led to the development of efficient production processes, positioning personalized viral vaccines as one of the preferred technologies. Many clinical trials have shown the feasibility, safety, immunogenicity and, more recently, preliminary evidence of the anti-tumor activity of personalized vaccination, fostering active research in the field, including further clinical trials for different tumor types and in different clinical settings.

Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction.

The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous T cells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.

Tumor Vaccines: Unleashing the Power of the Immune System to Fight Cancer.

This comprehensive review delves into the rapidly evolving arena of cancer vaccines. Initially, we examine the intricate constitution of the tumor microenvironment (TME), a dynamic factor that significantly influences tumor heterogeneity. Current research trends focusing on harnessing the TME for effective tumor vaccine treatments are also discussed. We then provide a detailed overview of the current state of research concerning tumor immunity and the mechanisms of tumor vaccines, describing the complex immunological processes involved. Furthermore, we conduct an exhaustive analysis of the contemporary research landscape of tumor vaccines, with a particular focus on peptide vaccines, DNA/RNA-based vaccines, viral-vector-based vaccines, dendritic-cell-based vaccines, and whole-cell-based vaccines. We analyze and summarize these categories of tumor vaccines, highlighting their individual advantages, limitations, and the factors influencing their effectiveness. In our survey of each category, we summarize commonly used tumor vaccines, aiming to provide readers with a more comprehensive understanding of the current state of tumor vaccine research. We then delve into an innovative strategy combining cancer vaccines with other therapies. By studying the effects of combining tumor vaccines with immune checkpoint inhibitors, radiotherapy, chemotherapy, targeted therapy, and oncolytic virotherapy, we establish that this approach can enhance overall treatment efficacy and offset the limitations of single-treatment approaches, offering patients more effective treatment options. Following this, we undertake a meticulous analysis of the entire process of personalized cancer vaccines, elucidating the intricate process from design, through research and production, to clinical application, thus helping readers gain a thorough understanding of its complexities. In conclusion, our exploration of tumor vaccines in this review aims to highlight their promising potential in cancer treatment. As research in this field continues to evolve, it undeniably holds immense promise for improving cancer patient outcomes.

Personalized neoantigen cancer vaccines: An analysis of the clinical and commercial potential of ongoing development programs.

Neoantigen cancer vaccines harbor promise as next-generation immuno-oncology therapies, whereby cancer vaccines are tailored to the patient's tumor antigen and represent the future of personalized cancer therapy. While several biotech companies have ongoing development programs, little has been published about the true commercial potential of these innovative therapies and the challenges these products will face upon regulatory approval. In this paper, we provide an overview of neoantigen cancer vaccine development programs and discuss the commercial environment these therapies will face upon launch.

Clinical Trial on the Safety and Tolerability of Personalized Cancer Vaccines Using Human Platelet Lysate-Induced Antigen-Presenting Cells.

Research and development of personalized cancer vaccines as precision medicine are ongoing. We predicted human leukocyte antigen (HLA)-compatible cancer antigen candidate peptides based on patient-specific cancer genomic profiles and performed a Phase I clinical trial for the safety and tolerability of cancer vaccines with human platelet lysate-induced antigen-presenting cells (HPL-APCs) from peripheral monocytes. Among the five enrolled patients, two patients completed six doses per course (2-3 × 107 cells per dose), and an interim analysis was performed based on the immune response. An immune response was detected by enzyme-linked immunosorbent spot (ELISpot) assays to HLA-A*33:03-matched KRASWT, HLA-DRB1*09:01-compliant KRASWT or G12D, or HLA-A*31:01-matched SMAD4WT, and HLA-DRB1*04:01-matched SMAD4G365D peptides in two completed cases, respectively. Moreover, SMAD4WT-specific CD8+ effector memory T cells were amplified. However, an attenuation of the acquired immune response was observed 6 months after one course of cancer vaccination as the disease progressed. This study confirmed the safety and tolerability of HPL-APCs in advanced and recurrent cancers refractory to standard therapy and is the first clinical report to demonstrate the immunoinducibility of personalized cancer vaccines using HPL-APCs. Phase II clinical trials to determine immune responses with optimized adjuvant drugs and continued administration are expected to demonstrate efficacy.

Usefulness of Docking and Molecular Dynamics in Selecting Tumor Neoantigens to Design Personalized Cancer Vaccines: A Proof of Concept.

Personalized cancer vaccines based on neoantigens are a new and promising treatment for cancer; however, there are still multiple unresolved challenges to using this type of immunotherapy. Among these, the effective identification of immunogenic neoantigens stands out, since the in silico tools used generate a significant portion of false positives. Inclusion of molecular simulation techniques can refine the results these tools produce. In this work, we explored docking and molecular dynamics to study the association between the stability of peptide-HLA complexes and their immunogenicity, using as a proof of concept two HLA-A2-restricted neoantigens that were already evaluated in vitro. The results obtained were in accordance with the in vitro immunogenicity, since the immunogenic neoantigen ASTN1 remained bound at both ends to the HLA-A2 molecule. Additionally, molecular dynamic simulation suggests that position 1 of the peptide has a more relevant role in stabilizing the N-terminus than previously proposed. Likewise, the mutations may have a "delocalized" effect on the peptide-HLA interaction, which means that the mutated amino acid influences the intensity of the interactions of distant amino acids of the peptide with the HLA. These findings allow us to propose the inclusion of molecular simulation techniques to improve the identification of neoantigens for cancer vaccines.

Reigniting hope in cancer treatment: the promise and pitfalls of IL-2 and IL-2R targeting strategies.

Interleukin-2 (IL-2) and its receptor (IL-2R) are essential in orchestrating immune responses. Their function and expression in the tumor microenvironment make them attractive targets for immunotherapy, leading to the development of IL-2/IL-2R-targeted therapeutic strategies. However, the dynamic interplay between IL-2/IL-2R and various immune cells and their dual roles in promoting immune activation and tolerance presents a complex landscape for clinical exploitation. This review discusses the pivotal roles of IL-2 and IL-2R in tumorigenesis, shedding light on their potential as diagnostic and prognostic markers and their therapeutic manipulation in cancer. It underlines the necessity to balance the anti-tumor activity with regulatory T-cell expansion and evaluates strategies such as dose optimization and selective targeting for enhanced therapeutic effectiveness. The article explores recent advancements in the field, including developing genetically engineered IL-2 variants, combining IL-2/IL-2R-targeted therapies with other cancer treatments, and the potential benefits of a multidimensional approach integrating molecular profiling, immunological analyses, and clinical data. The review concludes that a deeper understanding of IL-2/IL-2R interactions within the tumor microenvironment is crucial for realizing the full potential of IL-2-based therapies, heralding the promise of improved outcomes for cancer patients.

Generation of colon cancer-derived tumor-infiltrating T cells (TILs) for adoptive cell therapy.

Adoptive cell therapy (ACT) using specific immune cells and stem cells has emerged as a promising treatment option that could complement traditional cancer therapies in the future. In particular, tumor-infiltrating lymphocytes (TILs) have been shown to be effective against solid tumors in various clinical trials. Despite the enormous disease burden and large number of premature deaths caused by colorectal cancer (CRC), studies on TILs isolated from tumor tissue of patients with CRC are still rare. To date, studies on ACT often lack controlled and comparable expansion processes as well as selected ACT-relevant T-cell populations. We describe a procedure for generating patient-specific TILs, which are prerequisites for clinical trials of ACT in CRC. The manufacturing and characteristics of these TILs differ in important modalities from TILs commonly used for this therapeutic approach. Tumor tissue samples were obtained from 12 patients undergoing surgery for primary CRC, predominantly with low microsatellite instability (pMMR-MSI-L). Tumors in the resected specimens were examined pathologically, and an approved volume of tumor tissue was transferred to a disposable perfusion bioreactor. Tissue samples were subjected to an automatically controlled and highly reproducible cultivation process in a GMP-conform, closed perfusion bioreactor system using starting medium containing interleukin-2 and interleukin-12. Outgrowth of TIL from tissue samples was initiated by short-term supplementation with a specific activation cocktail. During subsequent expansion, TILs were grown in interleukin-2-enriched medium. Expansion of TILs in a low-scaled, two-phase process in the Zellwerk ZRP bioreactor under hyperoxic conditions resulted in a number of approximately 2 × 109 cells. The expanded TILs consisted mainly (73%) of the ACT-relevant CD3+/CD8+ effector memory phenotype (CD45RO+/CCR7-). TILs harvested under these conditions exhibited high functional potential, which was confirmed upon nonspecific stimulation (interferon-γ, tumor necrosis factor-α cytokine assay).

Studying the importance of regulatory T cells in chemoimmunotherapy mathematical modeling and proposing new approaches for developing a mathematical dynamic of cancer.

Studying the mathematical dynamics of cancer has gained the attention of bioengineers in the past three decades. Different kinds of modelling considering various aspects of treatment have been proposed. In this paper, the key role of Regulatory T cells is discussed and a model in ordinary differential equation (ODE) form is proposed by adding this state to the system dynamics considering chemoimmunotherapy treatment. Regulatory T cells are considered as one of the main tumor cells' tactics to deceive the body's immune system. The improved model is verified mathematically and biologically and fits all criteria in both fields. The results show that entering Regulatory T cells state on cancer mathematical modelling for simulating body cells for chemoimmunotherapy provides a way to identify critical cases more carefully, which a simplified model is unable to accomplish. This point emphasizes the fact that this state must be present in cancer modelling to anticipate immune response more accurately. The advanced system fixed points are obtained by the Newton method and bifurcation diagrams are derived and discussed. New features and remarks are proposed during the journey of developing more accurate models that have the best fit with laboratory data. The sensitivity chart of the model is illustrated and novel aspects of discussions are made with the aim of personalizing a model for a patient and identifying critical conditions based on the chart before any treatment begins. This point enables physicians to determine whether critical conditions have occurred for a patient in a specific treatment or not.

A Highly Sensitive Flow Cytometric Approach to Detect Rare Antigen-Specific T Cells: Development and Comparison to Standard Monitoring Tools.

Personalized vaccines against patient-unique tumor-associated antigens represent a promising new approach for cancer immunotherapy. Vaccine efficacy is assessed by quantification of changes in the frequency and/or the activity of antigen-specific T cells. Enzyme-linked immunosorbent spot (ELISpot) and flow cytometry (FCM) are methodologies frequently used for assessing vaccine efficacy. We tested these methodologies and found that both ELISpot and standard FCM [monitoring CD3/CD4/CD8/IFNγ/Viability+CD14+CD19 (dump)] demonstrate background IFNγ secretion, which, in many cases, was higher than the antigen-specific signal measured by the respective methodology (frequently ranging around 0.05-0.2%). To detect such weak T-cell responses, we developed an FCM panel that included two early activation markers, 4-1BB (CD137) and CD40L (CD154), in addition to the above-cited markers. These two activation markers have a close to zero background expression and are rapidly upregulated following antigen-specific activation. They enabled the quantification of rare T cells responding to antigens within the assay well. Background IFNγ-positive CD4 T cell frequencies decreased to 0.019% ± 0.028% and CD8 T cells to 0.009% ± 0.013%, which are 19 and 13 times lower, respectively, than without the use of these markers. The presented methodology enables highly sensitive monitoring of T-cell responses to tumor-associated antigens in the very low, but clinically relevant, frequencies.

Glycolysis Inhibition of Autophagy Drives Malignancy in Ovarian Cancer: Exacerbation by IL-6 and Attenuation by Resveratrol.

Cancer cells drive the glycolytic process towards the fermentation of pyruvate into lactate even in the presence of oxygen and functioning mitochondria, a phenomenon known as the "Warburg effect". Although not energetically efficient, glycolysis allows the cancer cell to synthesize the metabolites needed for cell duplication. Autophagy, a macromolecular degradation process, limits cell mass accumulation and opposes to cell proliferation as well as to cell migration. Cancer cells corrupt cancer-associated fibroblasts to release pro-inflammatory cytokines, which in turn promote glycolysis and support the metastatic dissemination of cancer cells. In mimicking in vitro this condition, we show that IL-6 promotes ovarian cancer cell migration only in the presence of glycolysis. The nutraceutical resveratrol (RV) counteracts glucose uptake and metabolism, reduces the production of reactive oxygen species consequent to excessive glycolysis, rescues the mitochondrial functional activity, and stimulates autophagy. Consistently, the lack of glucose as well as its metabolically inert analogue 2-deoxy-D-glucose (2-DG), which inhibits hexokinase 2 (HK2), trigger autophagy through mTOR inhibition, and prevents IL-6-induced cell migration. Of clinical relevance, bioinformatic analysis of The Cancer Genome Atlas dataset revealed that ovarian cancer patients bearing mutated TP53 with low expression of glycolytic markers and IL-6 receptor, together with markers of active autophagy, display a longer overall survival and are more responsive to platinum therapy. Taken together, our findings demonstrate that RV can counteract IL-6-promoted ovarian cancer progression by rescuing glycolysis-mediated inhibition of autophagy and support the view that targeting Warburg metabolism can be an effective strategy to limit the risk for cancer metastasis.