Immuno-Oncology Biomarkers for Personalized Immunotherapy in Breast Cancer

Targeting Dectin-1 and or VISTA enhances anti-tumor immunity in melanoma but not colorectal cancer model

PURPOSE

Acquired resistance to immune checkpoint blockers (ICBs) is a major barrier in cancer treatment, emphasizing the need for innovative strategies. Dectin-1 (gene Clec7a) is a C-type lectin receptor best known for its ability to recognize β-glucan-rich structures in fungal cell walls. While Dectin-1 is expressed in myeloid cells and tumor cells, its significance in cancer remains the subject of controversy.

METHODS

Using Celc7a-/- mice and curdlan administration to stimulate Dectin-1 signaling, we explored its impact. VISTA KO mice were employed to assess VISTA's role, and bulk RNAseq analyzed curdlan effects on neutrophils.

RESULTS

Our findings reveal myeloid cells as primary Dectin-1 expressing cells in the tumor microenvironment (TME), displaying an activated phenotype. Strong Dectin-1 co-expression/co-localization with VISTA and PD-L1 in TME myeloid cells was observed. While Dectin-1 deletion lacked protective effects, curdlan stimulation significantly curtailed B16-F10 tumor progression. RNAseq and pathway analyses supported curdlan's role in triggering a cascade of events leading to increased production of pro-inflammatory mediators, potentially resulting in the recruitment and activation of immune cells. Moreover, we identified a heterogeneous subset of Dectin-1+ effector T cells in the TME. Similar to mice, human myeloid cells are the prominent cells expressing Dectin-1 in cancer patients.

CONCLUSION

Our study proposes Dectin-1 as a potential adjunctive target with ICBs, orchestrating a comprehensive engagement of innate and adaptive immune responses in melanoma. This innovative approach holds promise for overcoming acquired resistance to ICBs in cancer treatment, offering avenues for further exploration and development.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.

Novel Strategies Using Sagacious Targeting for Site-Specific Drug Delivery in Breast Cancer Treatment: Clinical Potential and Applications

For more than a decade, researchers have been working to achieve new strategies and smart targeting drug delivery techniques and technologies to treat breast cancer (BC). Nanotechnology presents a hopeful strategy for targeted drug delivery into the building of new therapeutics using the properties of nanomaterials. Nanoparticles are of high regard in the field of diagnosis and the treatment of cancer. The use of these nanoparticles as an encouraging approach in the treatment of various cancers has drawn the interest of researchers in recent years. In order to achieve the maximum therapeutic effectiveness in the treatment of BC, combination therapy has also been adopted, leading to minimal side effects and thus an enhancement in the quality of life for patients. This review article compares, discusses and criticizes the approaches to treat BC using novel design strategies and smart targeting of site-specific drug delivery systems.

Unveiling the Immune Microenvironment's Role in Breast Cancer: A Glimpse into Promising Frontiers

Breast cancer (BC), one of the most widespread and devastating diseases affecting women worldwide, presents a significant public health challenge. This review explores the emerging frontiers of research focused on deciphering the intricate interplay between BC cells and the immune microenvironment. Understanding the role of the immune system in BC is critical as it holds promise for novel therapeutic approaches and precision medicine strategies. This review delves into the current literature regarding the immune microenvironment's contribution to BC initiation, progression, and metastasis. It examines the complex mechanisms by which BC cells interact with various immune cell populations, including tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs). Furthermore, this review highlights the impact of immune-related factors, such as cytokines and immune checkpoint molecules. Additionally, this comprehensive analysis sheds light on the potential biomarkers associated with the immune response in BC, enabling early diagnosis and prognostic assessment. The therapeutic implications of targeting the immune microenvironment are also explored, encompassing immunotherapeutic strategies and combination therapies to enhance treatment efficacy. The significance of this review lies in its potential to pave the way for novel therapeutic interventions, providing clinicians and researchers with essential knowledge to design targeted and personalized treatment regimens for BC patients.

MCM10: An effective treatment target and a prognostic biomarker in patients with uterine corpus endometrial carcinoma

Molecular profiling has been applied for uterine corpus endometrial carcinoma (UCEC) management for many years. The aim of this study was to explore the role of MCM10 in UCEC and construct its overall survival (OS) prediction models. Data from TCGA, GEO, cbioPotal and COSMIC databases and the methods, such as GO, KEGG, GSEA, ssGSEA and PPI, were employed to bioinformatically detect the effects of MCM10 on UCEC. RT-PCR, Western blot and immunohistochemistry were used to validate the effects of MCM10 on UCEC. Based on Cox regression analysis using the data from TCGA and our clinical data, two OS prediction models for UCEC were established. Finally, the effects of MCM10 on UCEC were detected in vitro. Our study revealed that MCM10 was variated and overexpressed in UCEC tissue and involved in DNA replication, cell cycle, DNA repair and immune microenvironment in UCEC. Moreover, silencing MCM10 significantly inhibited the proliferation of UCEC cells in vitro. Importantly, based on MCM10 expression and clinical features, the OS prediction models were constructed with good accuracy. MCM10 could be an effective treatment target and a prognostic biomarker for UCEC patients. The OS prediction models might help establish the strategies of follow-up and treatment for UCEC patients.

The role of blood cholesterol quality in patients with advanced cancer receiving immune checkpoint inhibitors

INTRODUCTION

Immune checkpoint inhibitors (ICIs) became the standard of care for several solid tumors. A limited fraction of patients (pts) achieves a long-term benefit. Plasmatic and intracellular cholesterol levels have emerged as promising biomarkers. The aim of the present study was to determine whether cholesterol efflux capacity (CEC), mediated by serum transporters (ABCA1 and ABCG1) and passive diffusion (PD), impacts on clinical outcome of advanced non-small cell lung cancer (NSCLC) and metastatic renal cell carcinoma (mRCC) pts treated with ICIs.

MATERIAL AND METHODS

We retrospectively enrolled advanced NSCLC and mRCC pts consecutively treated with ICIs between October 2013 and October 2018. CEC and cholesterol loading capacity (CLC) were assessed by well-established specific cell models. As primary endpoint, CEC, PD and CLC were correlated with overall survival (OS) while the effects of these parameters on progression-free survival (PFS) and clinical benefit (CB), defined as complete/partial response or stable disease, represented secondary endpoints.

RESULTS

NSCLC accounted for 94.2% of 70 enrolled cases, and serum sample suitable for CEC and PD determination was available in 68. Blood cholesterol and serum ABCA1, ABCG1, PD and CLC were associated with outcomes (OS, PFS and CB) at univariate analysis. At the multivariate analysis, only PD confirmed its positive prognostic value in terms of OS, PFS and CB.

CONCLUSION

The favorable impact of cholesterol PD on clinical outcome might reflect its main conformation in mature HDL particles which potentially shape an inflamed context, ultimately promoting ICI efficacy. Further prospective studies are needed to support our findings and uncover targetable pathways.

Immune Checkpoint Inhibitors and Novel Immunotherapy Approaches for Breast Cancer

PURPOSE OF REVIEW

To critically review the existing evidence on immune checkpoint inhibitors (ICIs) in early-stage and metastatic breast cancer and discuss emerging strategies in the different breast cancer subtypes.

RECENT FINDINGS

Immunotherapy has become one of the major milestones in contemporary oncology, revolutionizing the treatment of multiple solid tumors. ICI agents combined with chemotherapy have demonstrated significant efficacy in both early-stage and metastatic triple-negative breast cancer. However, only a subgroup of patients responds to those agents and some associated toxicities, although infrequent, can be life-disabling. Emerging data from immunotherapy studies in advanced hormone receptor-positive (HR +) breast cancer as well as HER2-positive disease are arising with mixed results. Although breast cancer has not classically been considered a hot tumor, ICIs have proven to be effective in a subset of breast cancer patients. However, much remains to be learned, and the identification of new biomarkers beyond PD-L1 expression is essential not only to improve the efficacy of ICI but also to identify patients who can avoid them, together with their toxicities and costs.

Pro-Inflammatory and Anti-Inflammatory Salivary Cytokines in Breast Cancer: Relationship with Clinicopathological Characteristics of the Tumor

The aim of the work was to compare the salivary cytokine profile of breast cancer patients with the clinicopathological characteristics of the tumor. The study included 113 patients with breast cancer (main group, mean age 54.1 years) and 111 patients with breast fibroadenomas (control group, mean age 56.7 years). Before treatment, saliva samples were collected from all patients and the content of cytokines (IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-18, MCP-1, and TNF-α) was determined. The content of cytokines in saliva correlates well with the clinicopathological characteristics of breast cancer. The level of all salivary cytokines increases at advanced stages of breast cancer and at a low degree of tumor differentiation. The exception is MCP-1, for which there is an extremely high content for well-differentiated breast cancer. A statistically significant increase in the content of MCP-1, IL-1β, IL-2, IL-4, and IL-10 was found in triple-negative breast cancer. For the first time, the correlation of salivary levels of TNF-α, IL-1β, and IL-6 with HER2 status, MCP-1, IL-1β, IL-2, and IL-4 with the hormonal status of the tumor was shown. The relationship between the level of IL-2, IL-10, and IL-18 in saliva with the level of Ki-67 expression has been established.

Predictive biomarkers for personalized medicine in breast cancer

Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.

Guiding immunotherapy combinations: Who gets what?

Although PD-1 and CTLA-4 inhibitors have proven successful in a range of malignancies, there are subsets of patients that do not respond to these agents due to upregulation of adaptive and innate resistance mechanisms by the tumor and its surrounding microenvironment. As new immunotherapeutic strategies are developed, there is a need for rational implementation of novel immunotherapy combinations that target complementary mechanisms of immunotherapy resistance intrinsic to each patient and tumor type. In this short review, we cover mechanisms by which tumors evade the immune system, as well as summarize available clinical data on emerging therapeutic agents that target these defense mechanisms. Rational implementation of combination immunotherapy targeting patient- and malignancy-specific immune evasion mechanisms may thus lead to enhanced response rates and allow immunotherapy to be effective even in tumors that are historically considered poorly responsive to immunotherapy.

Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immunotherapy for the treatment of breast cancer

Breast cancer has historically been a disease for which immunotherapy was largely unavailable. Recently, the use of immune checkpoint inhibitors (ICIs) in combination with chemotherapy for the treatment of advanced/metastatic triple-negative breast cancer (TNBC) has demonstrated efficacy, including longer progression-free survival and increased overall survival in subsets of patients. Based on clinical benefit in randomized trials, ICIs in combination with chemotherapy for the treatment of some patients with advanced/metastatic TNBC have been approved by the United States (US) Food and Drug Administration (FDA), expanding options for patients. Ongoing questions remain, however, about the optimal chemotherapy backbone for immunotherapy, appropriate biomarker-based selection of patients for treatment, the optimal strategy for immunotherapy treatment in earlier stage disease, and potential use in histological subtypes other than TNBC. To provide guidance to the oncology community on these and other important concerns, the Society for Immunotherapy of Cancer (SITC) convened a multidisciplinary panel of experts to develop a clinical practice guideline (CPG). The expert panel drew upon the published literature as well as their clinical experience to develop recommendations for healthcare professionals on these important aspects of immunotherapeutic treatment for breast cancer, including diagnostic testing, treatment planning, immune-related adverse events (irAEs), and patient quality of life (QOL) considerations. The evidence-based and consensus-based recommendations in this CPG are intended to give guidance to cancer care providers treating patients with breast cancer.

The emerging role of immune checkpoint inhibitors in the treatment of triple-negative breast cancer

Triple-negative breast cancers (TNBCs) form a heterogeneous group of breast carcinomas that lack expression of estrogen receptor, progesterone receptor and epidermal growth factor receptor 2. In the past decade, immune checkpoint inhibitors (ICIs) have revolutionized the arena of cancer immunotherapy. Early results are now accumulating from trials involving the treatment of TNBCs with radical ICIs therapies, including combinational therapies that include ICI technologies. In this review, we provide a broad overview of the progress of immunotherapy-based treatments and discuss future opportunities for their use in TNBC.

Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer

Background

Immune checkpoint blockade therapy has clearly shown clinical activity in patients with triple-negative breast cancer, but less than half of the patients benefit from the treatments. While a number of ongoing clinical trials are investigating different combinations of checkpoint inhibitors and chemotherapeutic agents, predictive biomarkers that identify patients most likely to benefit remains one of the major challenges. Here we present a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that incorporates detailed mechanisms of immune–cancer cell interactions to make efficacy predictions and identify predictive biomarkers for treatments using atezolizumab and nab-paclitaxel.

Methods

A QSP model was developed based on published data of triple-negative breast cancer. With the model, we generated a virtual patient cohort to conduct in silico virtual clinical trials and make retrospective analyses of the pivotal IMpassion130 trial that led to the accelerated approval of atezolizumab and nab-paclitaxel for patients with programmed death-ligand 1 (PD-L1) positive triple-negative breast cancer. Available data from clinical trials were used for model calibration and validation.

Results

With the calibrated virtual patient cohort based on clinical data from the placebo comparator arm of the IMpassion130 trial, we made efficacy predictions and identified potential predictive biomarkers for the experimental arm of the trial using the proposed QSP model. The model predictions are consistent with clinically reported efficacy endpoints and correlated immune biomarkers. We further performed a series of virtual clinical trials to compare different doses and schedules of the two drugs for simulated therapeutic optimization.

Conclusions

This study provides a QSP platform, which can be used to generate virtual patient cohorts and conduct virtual clinical trials. Our findings demonstrate its potential for making efficacy predictions for immunotherapies and chemotherapies, identifying predictive biomarkers, and guiding future clinical trial designs.

Lights and Shadows in Immuno-Oncology Drug Development

The rapidly evolving landscape of immuno-oncology (IO) is redefining the treatment of a number of cancer types. IO treatments are becoming increasingly complex, with different types of drugs emerging beyond checkpoint inhibitors. However, many of the new drugs either do not progress from phase I-II clinical trials or even fail in late-phase trials. We have identified at least five areas in the development of promising IO treatments that should be redefined for more efficient designs and accelerated approvals. Here we review those critical aspects of IO drug development that could be optimized for more successful outcome rates in all cancer types. It is important to focus our efforts on the mechanisms of action, types of response and adverse events of these novel agents. The use of appropriate clinical trial designs with robust biomarkers of response and surrogate endpoints will undoubtedly facilitate the development and subsequent approval of these drugs. Further research is also needed to establish biomarker-driven strategies to select which patients may benefit from immunotherapy and identify potential mechanisms of resistance.

Pre-treatment neutrophil-lymphocyte and platelet-lymphocyte ratios as additional markers for breast cancer progression: A retrospective cohort study

Background

Breast cancer is the most prevalent cancer that causes significant morbidity and loss of productivity. Around a third of all breast cancer patients are potentially develop distant metastases albeit the current implementation of multidisciplinary treatment. A simple but effective marker to predict the risks of cancer progression is very important for clinicians to improve treatment and surveillance.

Methods

We recruited 1083 non-metastatic patients and analyzed the ratios of neutrophil to lymphocyte (NLR) and platelet to lymphocyte (PLR) in relation to progression-free survivals (PFS) and risks of distant metastases.

Results

Baseline clinicopathological variables were not significantly different in the pretreatment NLR and PLRs. Using maximum points of sensitivity and specificity of the Receiver Operating Characteristic (ROC) curve, cut-off values were determined 2.8 for NLR and 170 for PLR. Higher NLR was associated with skin and chest wall cancer infiltration (T4, P = 0.0001). Elevated PLR was associated with more advanced stages at diagnosis (P = 0.03). High NLR values were significantly associated with risks of disease progression (OR 1.555, 95% CI: 1.206–2.005, P = 0.001). Patients with high NLR had shorter PFS (34.9 vs 53.5 months, Log-rank test = 0.001) and shorter time to develop recurrent distant metastatic disease (66.6 vs 104.6 months, Log-rank test = 0.027).

Conclusion

High NLR is significantly associated with higher risk of disease progression and shorter time to develop metastases particularly among breast cancer patients diagnosed in the advanced stages.

•

Elevated NLR is associated with higher risks of breast cancer disease progression.

•

High NLR is associated with shorter time to develop distant metastases.

•

NLR might be useful as an additional prognostic marker in breast cancer.

Determining Factors in the Therapeutic Success of Checkpoint Immunotherapies against PD-L1 in Breast Cancer: A Focus on Epithelial-Mesenchymal Transition Activation

Breast cancer is the most common neoplasm diagnosed in women around the world. Checkpoint inhibitors, targeting the programmed death receptor-1 or ligand-1 (PD-1/PD-L1) axis, have dramatically changed the outcome of cancer treatment. These therapies have been recently considered as alternatives for treatment of breast cancers, in particular those with the triple-negative phenotype (TNBC). A further understanding of the regulatory mechanisms of PD-L1 expression is required to increase the benefit of PD-L1/PD-1 checkpoint immunotherapy in breast cancer patients. In this review, we will compile the most recent studies evaluating PD-1/PD-L1 checkpoint inhibitors in breast cancer. We review factors that determine the therapeutic success of PD-1/PD-L1 immunotherapies in this pathology. In particular, we focus on pathways that interconnect the epithelial-mesenchymal transition (EMT) with regulation of PD-L1 expression. We also discuss the relationship between cellular metabolic pathways and PD-L1 expression that are involved in the promotion of resistance in TNBC.

The Biological Function and Therapeutic Potential of Exosomes in Cancer: Exosomes as Efficient Nanocommunicators for Cancer Therapy

Cancer therapeutics must be delivered to their targets for improving efficacy and reducing toxicity, though they encounter physiological barriers in the tumor microenvironment. They also face limitations associated with genetic instability and dynamic changes of surface proteins in cancer cells. Nanosized exosomes generated from the endosomal compartment, however, transfer their cargo to the recipient cells and mediate the intercellular communication, which affects malignancy progression, tumor immunity, and chemoresistance. In this review, we give an overview of exosomes' biological aspects and therapeutic potential as diagnostic biomarkers and drug delivery vehicles for oncotherapy. Furthermore, we discuss whether exosomes could contribute to personalized cancer immunotherapy drug design as efficient nanocommunicators.