Search Results (58)

A hydatidiform mole (HM) is the most common form of gestational trophoblastic disease (GTD). Differentiating hydatidiform moles (HMs) from non-molar pregnancies and distinguishing complete HMs (CHMs) from partial HMs (PHMs) remains challenging due to overlapping morphological features and a high rate of misclassification. This study aimed to evaluate reliable immunohistochemical markers for improving diagnostic accuracy and addressing the limitations of current molecular techniques. We retrospectively analyzed 64 cases of HMs and hydropic abortions (HAs), diagnosed in women aged 17–36 years between 2010 and 2024, at the Pathology Department of “Elena Doamna” Clinical Hospital, Iași, Romania. Routine histology was supplemented with immunohistochemistry (IHC) using p57, Ki-67, β-hCG, and E-cadherin, with semiquantitative immunoscores applied. Histology revealed 38 PHMs (59.37%), 16 CHMs (23.88%), and 10 HAs (15.62%). p57 was positive in 100% of PHMs and HAs but only in 18% of CHMs. Ki-67 expression was predominantly strong in CHMs, variable in PHMs, and weak in all HAs. β-hCG showed the highest expression in CHMs, followed by PHMs and HAs, while E-cadherin was strongest in HAs. Morphological features alone are insufficient for HM diagnosis; thus, ancillary techniques like p57 IHC and DNA genotyping are crucial to differentiate complete, partial moles, and non-molar specimens by revealing unique genetic patterns, especially p57 absence in CHMs and ploidy/parental origin in PHMs. In this context, an algorithmic approach integrating histology, immunohistochemistry, and genotyping reduces interobserver variability and refines diagnostic precision. Full article

Non-muscle invasive bladder cancer (NMIBC) accounts for the majority of bladder cancer diagnoses and remains a clinical challenge due to its high recurrence and progression rates despite intravesical Bacillus Calmette–Guérin (BCG) therapy. In recent years, tumor-infiltrating lymphocytes (TILs) have emerged as promising biomarkers, reflecting the interplay between the tumor and host immune system. However, the evidence regarding their prognostic and predictive role is still conflicting, largely due to methodological heterogeneity, lack of standardized evaluation criteria, and limited prospective validation. This narrative review summarizes the current knowledge on TILs in NMIBC, focusing on their compartmental distribution (stromal, intraepithelial, and tumor–stroma interface), compositional diversity (CD4⁺, CD8⁺, Treg, B cells), and spatial dynamics. Special attention is given to their role in predicting response to BCG immunotherapy, the contribution of tumor-associated macrophages and tertiary lymphoid structures, and the emergence of immune escape pathways, including Programmed Death-Ligand 1 (PD-L1) and the HLA-E/NKG2A axis. Advances in digital pathology, spatial transcriptomics, and integrated immunoscore models provide more accurate metrics compared to simple cell counts, highlighting the importance of functional and spatial signatures. Despite encouraging progress, TILs are not yet ready for routine incorporation into histopathological reporting. Future directions include standardized assessment, integration with molecular biomarkers, and prospective multicenter validation to enable their translation into risk stratification and personalized therapeutic decision-making. Full article

Background: Angiosarcomas (ASs) represent a heterogeneous and highly aggressive subset of tumors that respond poorly to systemic treatments and are associated with short progression-free survival (PFS) and overall survival (OS). The aim of this study was to develop and validate an immune-related prognostic model—termed the AS score—using data from two independent sarcoma cohorts. Methods: A prognostic model was developed using a previously characterized cohort of 25 angiosarcoma samples. Candidate genes were identified via the Maxstat algorithm (Maxstat v0.7-25 for R), combined with log-rank testing. The AS score was then computed by weighing normalized gene expression levels according to Cox regression coefficients. For external validation, transcriptomic data from TCGA Sarcoma cohort (n = 253) were analyzed. The Immunoscore—which reflects the tumor immune microenvironment—was inferred using the ESTIMATE package (v1.0.13) in R. All statistical analyses were performed in RStudio (v 4.0.3). Results: Four genes—IGF1R, MAP2K1, SERPINE1, and TCF12—were ultimately selected to construct the prognostic model. The resulting AS score enabled the classification of angiosarcoma cases into two prognostically distinct groups (p = 0.00012). Cases with high AS score values, which included both cutaneous and non-cutaneous forms, exhibited significantly poorer outcomes, whereas cases with low AS scores were predominantly cutaneous. A significant association was observed between the AS score and the Immunoscore (p = 0.025), with higher Immunoscore values found in high-AS score tumors. Validation using TCGA sarcoma cohort confirmed the prognostic value of both the AS score (p = 0.0066) and the Immunoscore (p = 0.0029), with a strong correlation between their continuous values (p = 2.9 × 10⁻⁸). Further survival analysis, integrating categorized scores into four groups, demonstrated robust prognostic significance (p = 0.00021). Notably, in tumors with a low Immunoscore, AS score stratification was not prognostic. In contrast, among cases with a high Immunoscore, the AS score effectively distinguished outcomes (p < 0.0001), identifying a subgroup with poor prognosis but potential sensitivity to immunotherapy. Conclusions: This combined classification using the AS score and Immunoscore has prognostic relevance in sarcoma, suggesting that angiosarcomas with an immunologically active microenvironment (high Immunoscore) and poor prognosis (high AS score) may be prime candidates for immunotherapy and this approach warrants prospective validation. Full article

Lung cancer with the highest number of new cases diagnosed in Europe and in Poland, remains an example of malignancy with a very poor prognosis despite the recent progress in medicine. Different treatment strategies are now available for cancer therapy based on its type, molecular subtype and other factors including overall health, the stage of disease and cancer molecular profile. Immunotherapy is emerging as a potential addition to surgery, chemotherapy, radiotherapy or other targeted therapies, but also considered a mainstay therapy mode. This combination is an area of active investigation in order to enhance efficacy and overcome resistance. Due to the complexity and dynamic of cancer’s ecosystem, novel therapeutic targets and strategies need continued research into the cellular and molecular mechanisms within the tumour microenvironment. From the genetic point of view, several signatures ranging from a few mutated genes to hundreds of them have been identified and associated with therapy resistance and metastatic potential. ML techniques and AI can enhance the predictive potential of genetic signatures and model the prognosis. Here, we present the overview of already existing treatment approaches, the current findings of key aspects of immunotherapy, such as immune checkpoint inhibitors (ICIs), existing molecular biomarkers like PD-L1 expression, tumour mutation burden, immunoscore, and neoantigens, as well as their roles as predictive markers for treatment response and resistance. Full article

Background/Objectives: Predictive tools are needed to assess the response to treatment and guide treatment decisions for locally advanced rectal cancer (LARC). This study explores the value of combining the immunoscore (IS) and magnetic resonance imaging tumor regression grade (mrTRG) with pathologic and radiologic neoadjuvant rectal (NAR) scores in predicting pathologic complete response (pCRs). Methods: The scores were assessed for patients with LARC enrolled in the Averectal study (NCT03503630), who received five fractions of short-course radiotherapy, followed by six cycles of mFOLFOX-6 plus avelumab, and total mesorectal excision. The IS was calculated using the mean density percentiles of CD3- and CD8-positive T-cells on baseline biopsy samples. Baseline and post-treatment MRIs were reviewed to measure the mrTRG. NAR scores were calculated using the pre-treatment T stage and post-treatment pathologic and radiologic N and T stages. Results: Fifteen out of thirty-five patients whose data were available achieved pCR (42.8%), and seven out of fourteen patients with mrTRG = 1 (complete response) attained pCR. In patients with both a mrTRG = 1 and high IS, the pCR rate was 66.7% (6/9). All of the patients who achieved pCR had a low or intermediate pathologic NAR score with a significant correlation between pCR and pathologic NAR scores (p < 0.0001). Both pathologic and radiologic NAR scores were correlated with overall survival and disease-free survival. Conclusions: The IS can supplement the mrTRG to better predict TNT outcomes, along with the use of the NAR score. This combination could potentially help with patient selection for non-operative management and guide treatment strategies for those with different recurrence risks. Full article

Background/Objectives: Although immunotherapy is the primary treatment option for intermediate-stage hepatocellular carcinoma (HCC), its efficacy varies. This study aimed to identify non-invasive imaging biomarkers predictive of the immunoscore linked to dynamic contrast-enhanced computed tomography (CECT). Methods: We performed immunohistochemical staining with CD3⁺ and CD8⁺ antibodies and counted the positive cells in the invasive margin (IM) and central tumor (CT), converting them to an immunoscore of 0 to 4 points. We assessed the dynamic CECT findings obtained from 96 patients who underwent hepatectomy for HCC and evaluated the relationship between dynamic CECT findings and immunoscores. For validation, we assessed the treatment effects on 81 nodules using the Response Evaluation Criteria in Solid Tumors in another cohort of 41 patients who received combined immunotherapy with atezolizumab and bevacizumab (n = 27) and durvalumab and tremelizumab (n = 14). Results: HCCs with peritumoral enhancement in the arterial phase (p < 0.001) and rim APHE (p = 0.009) were associated with the immunoscore in univariate linear regression analysis and peritumoral enhancement in the arterial phase (p = 0.004) in multivariate linear regression analysis. The time to nodular progression in HCCs with peritumoral enhancement in the arterial phase was significantly longer than that in HCCs without this feature (p < 0.001). Conclusions: We identified HCCs with peritumoral enhancement in the arterial phase as a noninvasive imaging biomarker to predict immune-inflamed HCC with a high immunoscore tendency. These HCCs were most likely to respond to combined immunotherapy. Full article

Background and Objectives: There is no reliable immune scoring system that can help us predict the postoperative outcomes of colorectal cancer patients with peritoneal metastases after cytoreductive surgery. In this cohort, the aims were (1) to evaluate the postoperative morbidity, mortality and surgical oncological outcomes in colorectal cancer patients with peritoneal metastasis; (2) to compare oncological and postoperative outcomes of colon cancer patients with peritoneal metastasis and rectal cancer patients with peritoneal metastasis; and (3) to assess the prognostic value of the modified Glasgow Prognostic Score (mGPS) and the CRP–albumin ratio (CAR). Materials and Methods: A prospectively maintained database of 258 patients who underwent cytoreductive surgery for peritoneal metastases of colorectal origin between 2007 and 2024 was analyzed. According to the anatomical location of the primary tumor, two different groups were created: rectum cancer patients with peritoneal metastasis (Group A) and colon cancer patients with peritoneal metastasis (Group B). All standard clinico-pathological characteristics, operative findings, morbi-mortality results, and final oncologic outcomes were compared between Groups A and B. We evaluated whether CAR and mGPS could predict postoperative morbi-mortality and overall survival in the two groups or not. Results: No significant difference was detected between Groups A and B in terms of clinical–demographic characteristics. In both groups, the preoperative mGPS and CAR values were statistically significantly higher in those who developed postoperative high-grade complications (C-D grade III/IV) (p < 0.001) and those who died perioperatively (p = 0.001 and p = 0.002). Conclusions: In multivariate Cox analysis, the CAR was found to be an independent prognostic factor for overall survival in this cohort. CAR and mGPS predicted high-grade complications and postoperative mortality in both groups. Full article

Colorectal cancer (CRC) remains a significant global health burden, necessitating a thorough understanding of prognostic and predictive factors to enhance patient outcomes. This systematic review aims to comprehensively evaluate prognostic and predictive determinants in CRC, encompassing both traditional and emerging biomarkers. A systematic search of major electronic databases was conducted to identify relevant studies published from 1995 up to 2024. Eligible articles were critically appraised, and data extraction was performed according to predefined criteria. The prognostic determinants examined included clinicopathological features such as tumor stage, grade, and lymph node involvement, as well as molecular biomarkers including RAS, BRAF, and MSI status. Predictive determinants encompassed biomarkers influencing response to targeted therapies and immunotherapy, such as HER2 and Immunoscore. The review also explores novel prognostic and predictive markers, including tumor microenvironment characteristics and liquid biopsy-based biomarkers. Synthesizing evidence from diverse studies, this review provides insights into the prognostic and predictive landscape of CRC, highlighting the potential clinical implications of identified determinants. Understanding the multifaceted nature of prognostic and predictive factors in CRC is imperative for the advancement of personalized treatment strategies and improvement of patient outcomes. Full article

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin–eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists’ evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy. Full article

Currently, therapy response cannot be accurately predicted in HER2-negative breast cancer (BC). Measuring stromal tumour-infiltrating lymphocytes (sTILs) and mediators of the tumour microenvironment and characterizing tumour-infiltrating immune cells (TIICs) may improve treatment response in the neoadjuvant setting. Tumour tissue and peripheral blood samples were retrospectively collected from 118 patients, and sTILs were evaluated. Circulating exosomes and myeloid-derived suppressor cells were determined by flow cytometry. TIICs markers (CD4, CD8, CD20, CD1a, and CD68) were assessed immunohistochemically. High sTILs were significantly associated with pathological complete response (pCR; p = 0.048) and event-free survival (EFS; p = 0.027). High-CD68 cells were significantly associated with pCR in triple-negative (TN, p = 0.027) and high-CD1a cells with EFS in luminal-B (p = 0.012) BC. Cluster analyses of TIICs revealed two groups of tumours (C1 and C2) that had different immune patterns and clinical outcomes. An immunoscore based on clinicopathological variables was developed to identify high risk (C1) or low-risk (C2) patients. Additionally, cluster analyses revealed two groups of tumours for both luminal-B and TNBC. Our findings support the association of sTILs with pCR and show an immunological component in a subset of patients with HER2-negative BC. Our immunoscore may be useful for future escalation or de-escalation treatments. Full article

Ovarian cancer (OC) still registers a high prevalence in female gynecological pathology. Given the aggressiveness of the tumor and the lack of response to conventional therapies, a current research interest is the identification of new prognostic markers. Gal-8, a member of the galectin family of molecules, involved in tumorigenesis, disease progression, and metastasis, has been assigned as a valuable tumor prognostic factor, and its inhibition may open new perspectives in cancer therapeutic management. Few studies have been carried out so far to evaluate OCs’ galectin profiles. Our study aimed to characterize the Gal-8 profile in different types of ovarian neoplasia and to demonstrate its prognostic value. Our study group comprised 46 cases of OCs that were histologically and immunohistochemically investigated, introduced to Gal-8 immunoreactivity, qualitatively and semi-quantitatively evaluated, and correlated with clinicopathological characteristics. Gal-8 immunoexpression was identified in tumor epithelial cells, showing a dominant nuclear labeling, followed by cytoplasmic and mixed, nuclear, and cytoplasmic labeling. Significant differences between tumor histotypes were found in the statistical analysis between low and high Gal-8 immunoscore levels and clinicopathological features: HGSC (eng.= high-grade serous carcinoma) vs. LGSC (eng. = low-grade serous carcinoma), pathogenic types (type I vs. type II), and tumor grades. Our results reflect Gal-8 expression variability depending on the histological type and subtype, the progression stages, and the degree of differentiation of ovarian tumors, supporting its value as a prognostic factor. Our findings open perspectives for larger studies to validate our results, along with a potential Gal-8 transformation into a future therapeutic target. Full article

The aim of this study was to identify Artemis as a predictive biomarker for guiding preoperative chemoradiotherapy in locally advanced rectal cancer. The resection specimens were collected from 50 patients with rectal cancer who underwent preoperative chemoradiotherapy. Artemis expression in biopsy tissues was evaluated using immunohistochemical staining according to the percentage of positively stained cells combined with staining intensity. Among the 50 patients, 36 (72%) had a weakly positive Artemis protein expression, 10 (20%) had a moderately positive expression, and 4 (8%) showed a strongly positive expression. The criteria of magnetic resonance imaging tumor regression grade (mrTRG) and pathological rectal cancer regression grade (RCRG) were used to assess the tumor response to chemoradiotherapy. Correlation analysis shows that there is a significant negative correlation between high Artemis immunoscore and treatment response (r = −0.532, p < 0.001). The results imply that high Artemis expression was associated with poor treatment response. Our study suggested a potential role of Artemis as a predictive biomarker of the tumor response to preoperative chemoradiotherapy in patients with locally advanced rectal cancer. Full article

Background: This study aims to understand the clinical and pathological importance of intratumoral budding (ITB) in colorectal cancer (CRC) and its relationship with tumor-infiltrating lymphocytes (TILs). CRCs can be classified into hot (high immunoscore (IS)) and cold (low IS) tumors. Methods: We investigated the number of ITBs in a hotspot area and categorized them into high-ITB (≥5) and low-ITB (<5) groups. The clinicopathological significance of ITB in human CRCs was evaluated, and a detailed analysis based on tumor-infiltrating lymphocytes (TILs) was also performed. Results: High ITB was identified in 59 of 266 CRC cases (22.2%). High ITB significantly correlated with a poorly differentiated tumor, lympho-vascular invasion, perineural invasion, higher pT stage, lymph node metastasis, and higher metastatic lymph node ratio. High ITB was also significantly correlated with a low IS and low CD8-positive lymphocytic infiltrate. The number of ITBs was substantially higher in the low-IS group than in the high-IS group (3.28 ± 3.31 vs. 2.19 ± 2.59; p = 0.005). High ITB significantly correlated with worse overall survival (p = 0.004). In the low-IS group, CRCs with high ITB had a significantly worse prognosis than those with low ITB (p = 0.021). However, there was no significant difference in prognosis between the high- and low-ITB groups in the high-IS group (p = 0.498). Conclusions: Taken together, high ITB was significantly correlated with aggressive tumor behaviors and worse survival in patients with CRCs. In addition, ITB can be useful for the prognostic stratification of CRCs with low IS. Full article

Background: We aimed to determine the prognostic value of an immunoscore reflecting CD3+ and CD8+ T cell density estimated from real-world transcriptomic data of a patient cohort with advanced malignancies treated with immune checkpoint inhibitors (ICIs) in an effort to validate a reference for future machine learning-based biomarker development. Methods: Transcriptomic data was collected under the Total Cancer Care Protocol (NCT03977402) Avatar^® project. The real-world immunoscore for each patient was calculated based on the estimated densities of tumor CD3+ and CD8+ T cells utilizing CIBERSORTx and the LM22 gene signature matrix. Then, the immunoscore association with overall survival (OS) was estimated using Cox regression and analyzed using Kaplan–Meier curves. The OS predictions were assessed using Harrell’s concordance index (C-index). The Youden index was used to identify the optimal cut-off point. Statistical significance was assessed using the log-rank test. Results: Our study encompassed 522 patients with four cancer types. The median duration to death was 10.5 months for the 275 participants who encountered an event. For the entire cohort, the results demonstrated that transcriptomics-based immunoscore could significantly predict patients at risk of death (p-value < 0.001). Notably, patients with an intermediate–high immunoscore achieved better OS than those with a low immunoscore. In subgroup analysis, the prediction of OS was significant for melanoma and head and neck cancer patients but did not reach significance in the non-small cell lung cancer or renal cell carcinoma cohorts. Conclusions: Calculating CD3+ and CD8+ T cell immunoscore using real-world transcriptomic data represents a promising signature for estimating OS with ICIs and can be used as a reference for future machine learning-based biomarker development. Full article

Background: The Immunoscore (IS) is a quantitative digital pathology assay that evaluates the immune response in cancer patients. This study reports on the reproducibility of pathologists’ visual assessment of CD3+- and CD8+-stained colon tumors, compared to IS quantification. Methods: An international group of expert pathologists evaluated 540 images from 270 randomly selected colon cancer (CC) cases. Concordance between pathologists’ T-score, corresponding hematoxylin–eosin (H&E) slides, and the digital IS was evaluated for two- and three-category IS. Results: Non-concordant T-scores were reported in more than 92% of cases. Disagreement between semi-quantitative visual assessment of T-score and the reference IS was observed in 91% and 96% of cases before and after training, respectively. Statistical analyses showed that the concordance index between pathologists and the digital IS was weak in two- and three-category IS, respectively. After training, 42% of cases had a change in T-score, but no improvement was observed with a Kappa of 0.465 and 0.374. For the 20% of patients around the cut points, no concordance was observed between pathologists and digital pathology analysis in both two- and three-category IS, before or after training (all Kappa < 0.12). Conclusions: The standardized IS assay outperformed expert pathologists’ T-score evaluation in the clinical setting. This study demonstrates that digital pathology, in particular digital IS, represents a novel generation of immune pathology tools for reproducible and quantitative assessment of tumor-infiltrated immune cell subtypes. Full article