Ufd1-Npl4 recruit Cdc48 for disassembly of ubiquitylated CMG helicase at the end of chromosome replication

Disassembly of the Cdc45-MCM-GINS (CMG) DNA helicase is the key regulated step during DNA replication termination in eukaryotes, involving ubiquitylation of the Mcm7 helicase subunit, leading to a disassembly process that requires the Cdc48 “segregase”. Here, we employ a screen to identify partners of budding yeast Cdc48 that are important for disassembly of ubiquitylated CMG helicase at the end of chromosome replication. We demonstrate that the ubiquitin-binding Ufd1-Npl4 complex recruits Cdc48 to ubiquitylated CMG. Ubiquitylation of CMG in yeast cell extracts is dependent upon lysine 29 of Mcm7, which is the only detectable site of ubiquitylation both in vitro and in vivo (though in vivo other sites can be modified when K29 is mutated). Mutation of K29 abrogates in vitro recruitment of Ufd1-Npl4-Cdc48 to the CMG helicase, supporting a model whereby Ufd1-Npl4 recruits Cdc48 to ubiquitylated CMG at the end of chromosome replication, thereby driving the disassembly reaction

Activated leukocyte cell adhesion molecule in breast cancer: prognostic indicator

INTRODUCTION: Activated leukocyte cell adhesion molecule (ALCAM) (CD166) is an immunoglobulin molecule that has been implicated in cell migration. The present study examined the expression of ALCAM in human breast cancer and assessed its prognostic value. METHODS: The immunohistochemical distribution and location of ALCAM was assessed in normal breast tissue and carcinoma. The levels of ALCAM transcripts in frozen tissue (normal breast, n = 32; breast cancer, n = 120) were determined using real-time quantitative PCR. The results were then analyzed in relation to clinical data including the tumor type, the grade, the nodal involvement, distant metastases, the tumor, node, metastasis (TNM) stage, the Nottingham Prognostic Index (NPI), and survival over a 6-year follow-up period. RESULTS: Immunohistochemical staining on tissue sections in ducts/acini in normal breast and in breast carcinoma was ALCAM-positive. Differences in the number of ALCAM transcripts were found in different types of breast cancer. The level of ALCAM transcripts was lower (P = 0.05) in tumors from patients who had metastases to regional lymph nodes compared with those patients without, in higher grade tumors compared with Grade 1 tumors (P < 0.01), and in TNM Stage 3 tumors compared with TNM Stage 1 tumors (P < 0.01). Tumors from patients with poor prognosis (with NPI > 5.4) had significantly lower levels (P = 0.014) of ALCAM transcripts compared with patients with good prognosis (with NPI < 3.4), and tumors from patients with local recurrence had significantly lower levels than those patients without local recurrence or metastases (P = 0.04). Notably, tumors from patients who died of breast cancer had significantly lower levels of ALCAM transcripts (P = 0.0041) than those with primary tumors but no metastatic disease or local recurrence. Patients with low levels of ALCAM transcripts had significantly (P = 0.009) more incidents (metastasis, recurrence, death) compared with patients with primary breast tumors with high levels of ALCAM transcripts. CONCLUSIONS: In the present panel of breast cancer specimens, decreased levels of ALCAM correlated with the nodal involvement, the grade, the TNM stage, the NPI, and the clinical outcome (local recurrence and death). The data suggest that decreased ALCAM expression is of clinical significance in breast cancer, and that reduced expression indicates a more aggressive phenotype and poor prognosis

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer

Image-based multiplex immune profiling of cancer tissues : translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer.Gilead Breast Cancer Research Grant; Breast Cancer Research Foundation; Susan G Komen Leadership; Interne Fondsen KU Leuven/Internal Funds KU Leuven; Swedish Society for Medical Research; Swedish Breast Cancer Association; Cancer Research Program; US Department of Defense; Mayo Clinic Breast Cancer; Marie Sklodowska Curie; NHMRC; National Institutes of Health; Cancer Research UK; Japan Society for the Promotion of Science; Horizon 2020 European Union Research and Innovation Programme National Cancer Institute; National Heart, Lung and Blood Institute; National Institute of Biomedical Imaging and Bioengineering; VA Merit Review Award; US Department of Veterans Affairs Biomedical Laboratory Research Breast Cancer Research Program; Prostate Cancer Research Program; Lung Cancer Research Program; Kidney Precision Medicine Project (KPMP) Glue Grant; EPSRC; Melbourne Research Scholarship; Peter MacCallum Cancer Centre; KWF Kankerbestrijding; Dutch Ministry of Health, Welfare and Sport the Breast Cancer Research Foundation; Agence Nationale de la Recherche; Q-Life; National Breast Cancer Foundation of Australia; National Health and Medical Council of Australia; All-Island Cancer Research Institute; Irish Cancer Society; Science Foundation Ireland Investigator Programme; Science Foundation Ireland Strategic Partnership Programme. Open access funding provided by IReL.https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896hj2024ImmunologySDG-03:Good heatlh and well-bein

Spatial analyses of immune cell infiltration in cancer : current methods and future directions. A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.http://www.thejournalofpathology.com/hj2024ImmunologySDG-03:Good heatlh and well-bein