Search Results (122)

Hepatocellular carcinoma (HCC) presents significant intertumoral heterogeneity, complicating prognosis and treatment. To address this, we performed an integrated single-cell RNA-sequencing analysis of HCC specimens using Seurat and identified malignant cells via Infercnv. Through a systematic evaluation of 101 machine learning algorithms used in combination, we developed tumor-cell-specific gene signatures (TCSGs) that demonstrated strong predictive performance, with area under the curve (AUC) values ranging from 0.72 to 0.74 in independent validation cohorts. Risk stratification based on these signatures revealed distinct therapeutic vulnerabilities: high-risk patients showed increased sensitivity to sorafenib, while low-risk patients exhibited enhanced responses to immunotherapy and transarterial chemoembolization (TACE). Pharmacogenomic analysis with Oncopredict identified four chemotherapeutic agents, including sapitinib and dinaciclib, with risk-dependent efficacy patterns. Furthermore, CRISPR/Cas9-dependency screening prioritized SRSF7 as essential for HCC cell survival, a finding confirmed by the identification of protein-level overexpression in tumors via immunohistochemistry. This multi-omics framework bridges single-cell characterization to clinical decision-making, offering a clinically actionable prognostic system that can be used to optimize therapeutic selection in HCC management. Full article

Breast cancer is a highly heterogeneous disease, with tumors capable of adapting to shifting conditions, making the development of effective personalized therapies particularly challenging. Patient-derived models, such as patient-derived organoids (PDOs) and circulating tumor cell (CTC) cultures, have emerged as powerful tools for investigating intra- and inter-tumor heterogeneity. These models largely retain the genetic, phenotypic, and microenvironmental features of the original tumors, providing valuable insights into disease progression, drug response, and resistance mechanisms. Furthermore, by enabling tumors’ spatiotemporal molecular profiling, PDOs and CTCs offer a dynamic approach to assess treatment efficacy over time. However, to fully capture the complexity of breast cancer heterogeneity, it is required to develop models from multiple tumor and blood samples collected throughout the course of treatment. This review explores the potential of integrating PDOs and CTC models to better understand intra-tumor heterogeneity while addressing key challenges in developing patient-derived models that accurately recapitulate patients’ tumors to advance personalized care. The integration of PDOs and CTCs could represent a paradigm shift in the personalized management of metastatic breast cancer. Full article

Circulating tumor cells (CTCs) are multifaceted biomarkers with significant potential for precision oncology, offering opportunities to refine diagnoses and personalize treatments across various cancer types, including colorectal and breast cancer. CTC assays serve as reliable prognostic indicators, even during chemotherapy and/or molecularly targeted therapies. Notably, CTCs exhibit heterogeneity that gradually develops during carcinogenesis and becomes more pronounced in advanced disease stages. These intra- and intertumoral heterogeneities pose challenges, particularly when drug-resistant clones emerge following therapy. The dynamic behavior of CTCs provides valuable insights into treatment response and prognosis. Extensive efforts have led to the development of technologies for effective CTC isolation, accelerating their clinical implementation. While both CTC and circulating tumor DNA (ctDNA) tests offer prognostic value, they reflect different aspects of tumor biology: CTC counts indicate tumor progression, while ctDNA levels correlate with tumor burden. The combined analysis is expected to yield complementary insights. CTC tests are feasible in general hospitals and may serve as tumor markers comparable to, or even superior to, conventional markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) for colorectal cancer, and CA15-3 for breast cancer. Early incorporation of CTC tests into routine blood panels appears to be a rational and promising approach. Full article

Glioblastoma (GBM) is a highly aggressive primary brain cancer with dismal prognoses despite current standards of care. Immunotherapy is being explored for GBM, given its promising results in other solid malignancies; however, the results from early clinical studies in GBM are disappointing. It has been discovered that GBM has numerous mechanisms of immune resistance, including the physical blood–brain barrier, high intratumoral and intertumoral heterogeneity, and numerous cellular and molecular components in the tumor microenvironment (TME) that promote immunosuppression. Furthermore, GBM utilizes numerous metabolic pathways to establish a survival advantage in the TME. Recently, it has begun to become evident that these complex metabolic pathways that promote GBM growth and invasion also contribute to tumor immune resistance. Aerobic glycolysis provides tumor cells with ample ATP while depleting key glucose and increasing acidity in the TME. Increased glutamine, tryptophan, and arginine metabolism deprives T cells of these necessary amino acids for proper anti-tumor function. Sphingolipid metabolism promotes an immunosuppressive phenotype in the TME and affects immune cell trafficking. This review will discuss, in detail, the key metabolic pathways relevant to GBM pathophysiology which also modulate host immunosuppression. Full article

Breast cancer (BC) is among the most common neoplasms globally and is the leading cause of cancer-related mortality in women. Despite significant advancements in prevention, early diagnosis, and treatment strategies made over the past two decades, breast cancer continues to pose a significant global health challenge. One of the major obstacles in the clinical management of breast cancer patients is the high intertumoral and intratumoral heterogeneity that influences disease progression and therapeutic outcomes. The inability of preclinical experimental models to replicate this diversity has hindered the comprehensive understanding of BC pathogenesis and the development of new therapeutic strategies. An ideal experimental model must recapitulate every aspect of human BC to maintain the highest predictive validity. Therefore, a thorough understanding of each model’s inherent characteristics and limitations is essential to bridging the gap between basic research and translational medicine. In this context, omics technologies serve as powerful tools for establishing comparisons between experimental models and human tumors, which may help address BC heterogeneity and vulnerabilities. This review examines the BC models currently used in preclinical research, including cell lines, patient-derived organoids (PDOs), organ-on-chip technologies, carcinogen-induced mouse models, genetically engineered mouse models (GEMMs), and xenograft mouse models. We emphasize the advantages and disadvantages of each model and outline the most important applications of omics techniques to aid researchers in selecting the most relevant model to address their specific research questions. Full article

Background/Objectives: The heterogeneity of the tumor immune microenvironment is a key determinant of tumor oncogenesis. This study aims to evaluate the composition of seven immune cells across 5323 samples from 14 cancers using DNA methylation data. Methods: A deconvolution algorithm was proposed to estimate the composition of seven immune cells using 1256 immune cell population-specific methylation genes. Based on the immune infiltration features of seven immune cell fractions, 42 subtypes of 14 tumors (2–5 subtypes per tumor) were identified. Results: Significant differences in immune cells between subtypes were revealed for each cancer. The study found that the methylation values of the selected specific sites correlated with gene expression in most tumor subtypes. Immune infiltration results were integrated with phenotypic data, including survival data and tumor stages, revealing significant correlations between immune infiltration and phenotypes in some tumors. Subtypes with high proportions of CD4+ T cells, CD8+ T cells, CD56+ NK cells, CD19+ B cells, CD14+ monocytes, neutrophils, and eosinophils were identified, with subtype counts of 9, 24, 22, 13, 19, 9, and 11, respectively. Additionally, 2412 differentially expressed genes between these subtypes and normal tissues were identified. Pathway enrichment analysis revealed that these genes were mainly enriched in pathways related to drug response and chemical carcinogens. Differences in ESTIMATE scores for subtypes of seven tumors and TIDE scores for eight tumors were also observed. Conclusions: This study demonstrates the intra-tumor and inter-tumor immune heterogeneity of pan-cancer through DNA methylation analysis, providing assistance for tumor diagnosis. Full article

Glioblastoma (GBM) is a primary central nervous system malignancy with a median survival of 15–20 months. The presence of both intra- and intertumoral heterogeneity limits understanding of biological mechanisms leading to tumor resistance, including immune escape. An attractive field of research to examine treatment resistance are immune signatures composed of cluster of differentiation (CD) markers and cytokines. CD markers are surface markers expressed on various cells throughout the body, often associated with immune cells. Cytokines are the effector molecules of the immune system. Together, CD markers and cytokines can serve as useful biomarkers to reflect immune status in patients with GBM. However, there are gaps in the understanding of the intricate interactions between GBM and the peripheral immune system and how these interactions change with standard and immune-modulating treatments. The key to understanding the true nature of these interactions is through multi-omic analysis of tumor progression and treatment response. This review aims to identify potential non-invasive blood-based biomarkers that can contribute to an immune signature through multi-omic approaches, leading to a better understanding of immune involvement in GBM. Full article

Cancer is a therapeutically challenging and genomically complicated disease. Pioneering studies have uncovered multifaceted aspects of cancer, ranging from intra- and inter-tumor heterogeneity, drug resistance, and genetic/epigenetic mutations. Loss of apoptosis is another critical aspect that makes cancer cells resistant to death. A substantial fraction of mechanistic information gleaned from cutting-edge studies has enabled researchers to develop near-to-complete resolution of the apoptotic pathway. Within the exciting frontiers of apoptosis, TRAIL (tumor necrosis factor-related apoptosis-inducing ligand) has garnered phenomenal appreciation by interdisciplinary researchers principally because of its unique capability to target cancer cells. TRAIL-based monotherapies and combinatorial therapies have reached phase II and phase III clinical trials. Rapidly upgrading the list of clinical trials substantiates the clinically valuable role of TRAIL-based therapeutics in cancer therapy. However, there is a growing concern about the poor bioavailability and rapid clearance of TRAIL-based therapeutics. Excitingly, the charismatic field of nanotechnology offers solutions for different problems, and we have witnessed remarkable breakthroughs in the efficacy of TRAIL-based therapeutics using nanotechnological approaches. In this review, we have attempted to provide a summary about different nanotechnologically assisted delivery methods for TRAIL-based therapeutics in cell culture studies and animal model studies for the inhibition/prevention of cancer. Full article

Acute lymphoblastic leukemia (ALL) is a hematological neoplasm characterized by the clonal expansion of abnormal lymphoid precursors in bone marrow, which leads to alterations in the processes of cell differentiation and maturation as a consequence of genetic alterations. The integration of conventional methods, such as cytogenetics and immunophenotyping, and next-generation sequencing (NGS) has led to significant improvements at diagnosis and patient stratification; this has also allowed the discovery of several novel molecular entities with specific genetic variants that may drive the processes of leukemogenesis. Nevertheless, the understanding of the process of leukemogenesis remains a challenge since this disease persists as the most frequent cancer in children; it accounts for approximately one-quarter of adult acute leukemias, and the patient management may take into consideration the high intra- and inter-tumor heterogeneity and the relapse risk due to the various molecular events that can occur during clonal evolution. Some germline variants have been identified as risk factors or have been found to be related to the response to treatment. Therefore, better knowledge of the genetic alterations in B-ALL will have a prognostic impact from the perspective of personalized medicine. This review aims to compare, synthesize, and highlight recent findings concerning ALL obtained through NGS that have led to a better understanding of new molecular subtypes based on immunophenotypic characteristics, mutational profiles, and expression profiles. Full article

Background/Objective: In this study, for the first time, we examined and compared the sensitivity of four patient-derived cutaneous melanoma cell lines to alpha radiation in vitro and analyzed it in view of cell nucleus area and the formation of double-strand breaks (DSB). Melanoma cells sensitivity to alpha radiation was compared to photon radiation effects. Furthermore, we compared the sensitivity of the melanoma cells to squamous cell carcinoma. Methods: Human melanoma cell lines YDFR.C, DP.C, M12.C, and M16.C, and the squamous cell carcinoma cell line, CAL 27, were irradiated in vitro using Americium-241 as alpha-particle source. Cells were irradiated with doses of 0 to 2.8 gray (Gy). Cell viability, DNA DSB, and nuclear size were measured. Results: 1. Alpha radiation caused death and proliferation arrest of all four melanoma cell lines, but inter-tumor heterogeneity was observed. 2. The most sensitive cell line (DP.C) had a significantly larger nucleus area (408 µm²) and the highest mean number of DSB per cell (9.61) compared to more resistant cells. 3. The most resistant cell, M16.C, had a much lower nucleus area (236.99 µm²) and DSB per cell (6.9). 4. Alpha radiation was more lethal than photon radiation for all melanoma cells. 5. The SCC cell, CAL 27, was more sensitive to alpha radiation than all melanoma cells but had a similar number of DSB (6.67) and nucleus size (175.49 µm²) as the more resistant cells. 6. The cytotoxic effect of alpha radiation was not affected by proliferation arrest after serum starvation. 7. Killing of cells by alpha radiation was marginally elevated by ATR or topoisomerase 1 inhibition. Conclusions: This study demonstrates that various human melanoma cells can be killed by alpha radiation but exhibit variance in sensitivity to alpha radiation. Alpha radiation applied using the Intra-tumoral Diffusing alpha-emitters Radiation Therapy (Alpha DaRT) methodology may serve as an efficient treatment for human melanoma. Full article

Glioblastoma (GBM) is the most prevalent central nervous system tumour (CNS). Patients with GBM have a dismal prognosis of 15 months, despite an intensive treatment schedule consisting of surgery, chemoradiation and concurrent chemotherapy. In the last decades, many trials have been performed investigating small molecule inhibitors, which target specific genes involved in tumorigenesis. So far, these trials have been unsuccessful, and standard of care for GBM patients has remained the same since 2005. This review gives an overview of trials investigating small molecule inhibitors on their own, combined with chemotherapy or other small molecule inhibitors. We discuss possible resistance mechanisms in GBM, focussing on intra- and intertumoral heterogeneity, bypass mechanisms and the influence of the tumour microenvironment. Moreover, we emphasise how combining inhibitors can help overcome these resistance mechanisms. We also address strategies for improving trial outcomes through modifications to their design. In summary, this review aims to elucidate different resistance mechanisms against small molecule inhibitors, highlighting their significance in the search for novel therapeutic combinations to improve the overall survival of GBM patients. Full article

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization. Full article

Liquid biopsies can accurately identify molecular alterations in patients with colorectal cancer with high concordance with tissue analysis and shorter turnaround times. Circulating tumor (ct) DNA analysis can be used for diagnosing and monitoring tumor evolution in patients with metastatic colorectal cancer who are treated with EGFR inhibitors. In this article, we reported three clinical cases to illustrate the relevance of RAS mutations identified in ctDNA samples of patients with wild-type metastatic colorectal cancer who received an EGFR inhibitor plus chemotherapy as first-line treatment. The identification of RAS mutations in these patients is one of the most frequently identified mechanisms of acquired resistance. However, detecting a KRAS mutation via liquid biopsy can be caused by inter-tumor heterogeneity or it can be a false positive due to clonal hematopoiesis. More research is needed to determine whether ctDNA monitoring may help guide therapy options in metastatic colorectal cancer patients. We performed a literature review to assess the technologies that are used for analysis of RAS mutations on ctDNA, the degree of agreement between tissue and plasma and the importance of tissue/plasma discordant cases. Full article

This study aimed to evaluate the potential of pre-treatment CT-based radiomics features (RFs) derived from single and multiple tumor sites, and state-of-the-art machine-learning survival algorithms, in predicting progression-free survival (PFS) for patients with metastatic lung adenocarcinoma (MLUAD) receiving first-line treatment including immune checkpoint inhibitors (CPIs). To do so, all adults with newly diagnosed MLUAD, pre-treatment contrast-enhanced CT scan, and performance status ≤ 2 who were treated at our cancer center with first-line CPI between November 2016 and November 2022 were included. RFs were extracted from all measurable lesions with a volume ≥ 1 cm³ on the CT scan. To capture intra- and inter-tumor heterogeneity, RFs from the largest tumor of each patient, as well as lowest, highest, and average RF values over all lesions per patient were collected. Intra-patient inter-tumor heterogeneity metrics were calculated to measure the similarity between each patient lesions. After filtering predictors with univariable Cox p < 0.100 and analyzing their correlations, five survival machine-learning algorithms (stepwise Cox regression [SCR], LASSO Cox regression, random survival forests, gradient boosted machine [GBM], and deep learning [Deepsurv]) were trained in 100-times repeated 5-fold cross-validation (rCV) to predict PFS on three inputs: (i) clinicopathological variables, (ii) all radiomics-based and clinicopathological (full input), and (iii) uncorrelated radiomics-based and clinicopathological variables (uncorrelated input). The Models’ performances were evaluated using the concordance index (c-index). Overall, 140 patients were included (median age: 62.5 years, 36.4% women). In rCV, the highest c-index was reached with Deepsurv (c-index = 0.631, 95%CI = 0.625–0.647), followed by GBM (c-index = 0.603, 95%CI = 0.557–0.646), significantly outperforming standard SCR whatever its input (c-index range: 0.560–0.570, all p < 0.0001). Thus, single- and multi-site pre-treatment radiomics data provide valuable prognostic information for predicting PFS in MLUAD patients undergoing first-line CPI treatment when analyzed with advanced machine-learning survival algorithms. Full article

Medulloblastoma is the most frequently encountered malignant brain tumor in the pediatric population. The standard of care currently consists of surgical resection, craniospinal irradiation, and multi-agent chemotherapy. However, despite this combination of multiple aggressive modalities, recurrence of the disease remains a substantial concern, and treatment resistance is a rising issue. The development of this resistance results from the interplay of a myriad of anatomical properties, cellular processes, molecular pathways, and genetic and epigenetic alterations. In fact, several efforts have been directed towards this domain and characterizing the major contributors to this resistance. Herein, this review highlights the different mechanisms that drive relapse and are implicated in the occurrence of treatment resistance and discusses them in the context of the latest molecular-based classification of medulloblastoma. These mechanisms include the impermeability of the blood-brain barrier to drugs, the overactivation of specific molecular pathways, the resistant and multipotent nature of cancer stem cells, intratumoral and intertumoral heterogeneity, and metabolic plasticity. Subsequently, we build on that to explore potential strategies and targeted agents that can abrogate these mechanisms, undermine the development of treatment resistance, and augment medulloblastoma’s response to therapeutic modalities. Full article