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Colorectal cancer (CRC) is the second cause of cancer-related deaths in both sexes globally and presents different clinical outcomes that are described by a range of genomic and epigenomic alterations. Despite the advancements in CRC screening plans and treatment strategies, the prognosis of CRC is dismal. In the last two decades, molecular biomarkers predictive of prognosis have been identified in CRC, although biomarkers predictive of treatment response are only available for specific biological drugs used in stage IV CRC. Translational clinical trials mainly based on “omic” strategies allowed a better understanding of the biological heterogeneity of CRCs. These studies were able to classify CRCs into subtypes mainly related to prognosis, recurrence risk, and, to some extent, also to treatment response. Accordingly, the comprehensive molecular characterizations of CRCs, including The Cancer Genome Atlas (TCGA) and consensus molecular subtype (CMS) classifications, were presented to improve the comprehension of the genomic and epigenomic landscapes of CRCs for a better patient management. The CMS classification obtained by the CRC subtyping consortium categorizes CRC into four consensus molecular subtypes (CMS1–4) characterized by different prognoses. In this review, we discussed the CMS classification in different settings with a focus on its relationships with precursor lesions, tumor immunophenotype, and gut microbiota, as well as on its role in predicting prognosis and/or response to pharmacological treatments, as a crucial step towards precision medicine.

Immune checkpoint inhibitors (ICIs) have substantially improved clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC). However, the cutaneous immune-related adverse events (cirAEs) they elicit-being the most frequent and earliest-emerging toxicities-not only compromise treatment adherence but also exhibit a distinct positive association with systemic immune activation and antitumor efficacy. Given these characteristics, elucidating the pathogenic mechanisms of cirAEs and identifying predictive biomarkers are critical for the early detection and intervention of cirAEs, as well as for forecasting the onset of other immune-related adverse events (irAEs) and assessing ICI therapeutic prognosis. This review systematically summarizes recent advances in the pathological mechanisms of cirAEs and predictive biomarkers. Mechanistically, cirAEs result from multifactorial interplay, including genetic predisposition, shared antigen-driven cross-reactivity, and breakdown of cutaneous immune tolerance. For predictive biomarkers, strategies span traditional predictors (eg, demographic and immunological features) and their clinical translation challenges to emerging methods leveraging multi-omics integration and radiomics. Finally, this review addresses future challenges and directions in cirAEs research: Specifically, the positive association between cirAEs and efficacy demands accurate differentiation of \"manageable toxicities\" from \"high-risk toxicities\"; furthermore, future studies must validate causal biomarkers via prospective multi-omics cohorts and develop AI-driven dynamic prediction models to enable toxicity-stratified management and optimization of personalized immunotherapy.

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

The prognosis of breast cancer (BC) in young women (BCYW) aged ≤40 years tends to be poorer than that in older patients due to aggressive phenotypes, late diagnosis, distinct biologic, and poorly understood genomic features of BCYW. Considering the estimated predisposition of only approximately 15% of the BC population to BC-promoting genes, the underlying reasons for an increased occurrence of BCYW, at large, cannot be completely explained based on general risk factors for BC. This underscores the need for the development of next-generation of tissue- and body fluid-based prognostic and predictive biomarkers for BCYW. Here, we identified the genes associated with BCYW with a particular focus on the age, intrinsic BC subtypes, matched normal or normal breast tissues, and BC laterality. In young women with BC, we observed dysregulation of age-associated cancer-relevant gene sets in both cancer and normal breast tissues, sub-sets of which substantially affected the overall survival (OS) or relapse-free survival (RFS) of patients with BC and exhibited statically significant correlations with several gene modules associated with cellular processes such as the stroma, immune responses, mitotic progression, early response, and steroid responses. For example, high expression of COL1A2, COL5A2, COL5A1, NPY1R, and KIAA1644 mRNAs in the BC and normal breast tissues from young women correlated with a substantial reduction in the OS and RFS of BC patients with increased levels of these exemplified genes. Many of the genes upregulated in BCYW were overexpressed or underexpressed in normal breast tissues, which might provide clues regarding the potential involvement of such genes in the development of BC later in life. Many of BCYW-associated gene products were also found in the extracellular microvesicles/exosomes secreted from breast and other cancer cell-types as well as in body fluids such as urine, saliva, breast milk, and plasma, raising the possibility of using such approaches in the development of non-invasive, predictive and prognostic biomarkers. In conclusion, the findings of this study delineated the pathogenomics of BCYW, providing clues for future exploration of the potential predictive and prognostic importance of candidate BCYW molecules and research strategies as well as a rationale to undertake a prospective clinical study to examine some of testable hypotheses presented here. In addition, the results presented here provide a framework to bring out the importance of geographical disparities, to overcome the current bottlenecks in BCYW, and to make the next quantum leap for sporadic BCYW research and treatment.

Breast cancer is a common malignancy among women, with triple-negative breast cancer (TNBC) representing a subtype with poor prognosis. Due to the lack of expression of targetable receptors, traditional hormone therapy and HER2-targeted therapy are ineffective against TNBC. Moreover, TNBC typically exhibits more aggressive biological behavior, with a high propensity for recurrence and metastasis, further exacerbating its poor prognosis. While chemotherapy remains the primary treatment modality, its efficacy is limited, and patients readily develop resistance. Consequently, exploring novel therapeutic strategies and targets is crucial for improving the prognosis of patients with TNBC. Tumor-infiltrating lymphocytes (TILs) are promising prognostic and predictive biomarkers of TNBC. Multiple studies have demonstrated that a higher number of TILs in early-stage TNBC is correlated with favorable outcomes. Furthermore, clinical trials have demonstrated that TIL therapy is effective in solid tumors. This review outlines the current understanding of the TIL role in TNBC, elucidates the mechanisms and clinical efficacy of TIL therapy, and discusses future research directions and challenges for TILs.

Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.

This corrects the article DOI: 10.1038/bjc.2017.85

Great advances in immune checkpoint blockade have resulted in a paradigm shift in patients with lung cancer. Immune-checkpoint inhibitor (ICI) treatment, either as monotherapy or combination therapy, has been established as the standard of care for patients with locally advanced/metastatic non-small cell lung cancer without EGFR/ALK alterations or extensive-stage small cell lung cancer. An increasing number of clinical trials are also ongoing to further investigate the role of ICIs in patients with early-stage lung cancer as neoadjuvant or adjuvant therapy. Although PD-L1 expression and tumor mutational burden have been widely studied for patient selection, both of these biomarkers are imperfect. Due to the complex cancer-immune interactions among tumor cells, the tumor microenvironment and host immunity, collaborative efforts are needed to establish a multidimensional immunogram to integrate complementary predictive biomarkers for personalized immunotherapy. Furthermore, as a result of the wide use of ICIs, managing acquired resistance to ICI treatment remains an inevitable challenge. A deeper understanding of the underlying biological mechanisms of acquired resistance to ICIs is helpful to overcome these obstacles. In this review, we describe the cutting-edge progress made in patients with lung cancer, the optimal duration of ICI treatment, ICIs in some special populations, the unique response patterns during ICI treatment, the emerging predictive biomarkers, and our understanding of primary and acquired resistance mechanisms to ICI treatment.

Programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) is a negative modulatory signaling pathway for activation of T cell. It is acknowledged that PD-1/PD-L1 axis plays a crucial role in the progression of tumor by altering status of immune surveillance. As one of the most promising immune therapy strategies, PD-1/PD-L1 inhibitor is a breakthrough for the therapy of some refractory tumors. However, response rate of PD-1/PD-L1 inhibitors in overall patients is unsatisfactory, which limits the application in clinical practice. Therefore, biomarkers which could effectively predict the efficacy of PD-1/PD-L1 inhibitors are crucial for patient selection. Biomarkers reflecting tumor immune microenvironment and tumor cell intrinsic features, such as PD-L1 expression, density of tumor infiltrating lymphocyte (TIL), tumor mutational burden, and mismatch-repair (MMR) deficiency, have been noticed to associate with treatment effect of anti-PD-1/anti-PD-L1 therapy. Furthermore, gut microbiota, circulating biomarkers, and patient previous history have been found as valuable predictors as well. Therefore establishing a comprehensive assessment framework involving multiple biomarkers would be meaningful to interrogate tumor immune landscape and select sensitive patients.

Although the clinical development of immune checkpoint inhibitors (ICIs) therapy has ushered in a new era of anti-tumor therapy, with sustained responses and significant survival advantages observed in multiple tumors, most patients do not benefit. Therefore, more and more attention has been paid to the identification and development of predictive biomarkers for the response of ICIs, and more in-depth and comprehensive understanding has been continuously explored in recent years. Predictive markers of ICIs efficacy have been gradually explored from the expression of intermolecular interactions within tumor cells to the expression of various molecules and cells in tumor microenvironment, and been extended to the exploration of circulating and host systemic markers. With the development of high-throughput sequencing and microarray technology, a variety of biomarker strategies have been deeply explored and gradually achieved the process from the identification of single marker to the development of multifactorial synergistic predictive markers. Comprehensive predictive-models developed by integrating different types of data based on different components of tumor-host interactions is the direction of future research and will have a profound impact in the field of precision immuno-oncology. In this review, we deeply analyze the exploration course and research progress of predictive biomarkers as an adjunctive tool to tumor immunotherapy in effectively identifying the efficacy of ICIs, and discuss their future directions in achieving precision immuno-oncology.