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Background A limitation of positive selection strategies to enrich for circulating tumor cells (CTCs) is that there might be CTCs with insufficient expression of the surface target marker which may be missed by the procedure. We optimized a method for enrichment, subsequent detection and characterization of CTCs based on depletion of the leukocyte fraction. Methods The 2-step protocol was developed for processing 20 mL blood and based on red blood cell lysis followed by leukocyte depletion. The remaining material was stained with the epithelial markers EpCAM and cytokeratin (CK) 7/8 or for the melanoma marker HMW-MAA/MCSP. CTCs were detected by flow cytometry. CTCs enriched from blood of patients with carcinoma were defined as EpCAM+CK+CD45-. CTCs enriched from blood of patients with melanoma were defined as MCSP+CD45-. One-hundred-sixteen consecutive blood samples from 70 patients with metastatic carcinomas (n = 48) or metastatic melanoma (n = 22) were analyzed. Results CTCs were detected in 47 of 84 blood samples (56%) drawn from carcinoma patients, and in 17 of 32 samples (53%) from melanoma patients. CD45-EpCAM-CK+ was detected in pleural effusion specimens, as well as in peripheral blood samples of patients with NSCLC. EpCAM-CK+ cells have been successfully cultured and passaged longer than six months suggesting their neoplastic origin. This was confirmed by CGH. By defining CTCs in carcinoma patients as CD45-CK+ and/or EpCAM+, the detection rate increased to 73% (61/84). Conclusion Enriching CTCs using CD45 depletion allowed for detection of epithelial cancer cells not displaying the classical phenotype. This potentially leads to a more accurate estimation of the number of CTCs. If detection of CTCs without a classical epithelial phenotype has clinical relevance need to be determined.

Background Socioeconomic status' (SES) impact on distress during cancer survivorship has been insufficiently studied, although the consequences of low SES can be cumulative and adversely impact a person's ability to access resources required for improved health and quality of life. Patients and Methods We conducted a prospective study involving newly diagnosed patients within 2 months of diagnosis (t1), and at 6‐, 12‐, and 18‐month follow‐up (t2–t4) using the Distress Thermometer (DT). Generalized Linear Mixed Models (GLMM) were used to test for changes in distress over time, with fixed effects of time, SES, and age. Results Out of 1702 eligible patients, 965 completed the baseline DT (53% men, 60.5 years); 779, 681, and 626 participated at follow‐ups. Out of 554 completers, 9% were chronically distressed, while 40.8% were never distressed. Distress decreased in 21.3%, increased in 11.0%, and 17.8% fluctuated over time. Low‐SES patients consistently had the highest rates of distress. Distress scores and the frequency of distress (DT ≥ 5) decreased over time in all SES and age groups: For DT mean scores, GLMM revealed a significant effect of time (χ2(3) = 72.0, p < 0.001), but not of SES (χ2(2) = 5.9, p = 0.052). For frequency of distress, there was a main effect of time (χ2(3) = 41.4, p < 0.001) and SES (χ2(2) = 15.5, p < 0.001). Younger patients (< 65 years) consistently experienced more distress than older patients (≥ 65 years). For DT mean scores, GLMM showed an effect of time (χ2(3) = 72.1, p < 0.001) and age (χ2(1) = 66.2, p < 0.001). Similarly, for frequency of distress we found an effect of time (χ2(3) = 41.7, p < 0.001) and age (χ2(1) = 52.8, p < 0.001). Conclusion Effective psychosocial interventions require a customized approach to decrease distress in vulnerable groups. Trial Registration This study was registered in the International Clinical Trials Registry (NCT04620564, https://clinicaltrials.gov/)

Colorectal cancer (CRC) is the second most common cause of all cancer deaths in Europe and the Western world with a lifetime risk of approximately 5%. Despite several improvements in the treatment of patients with unresectable CRC prognosis is poor and there is the need of developing new treatment strategies for patients with metastatic chemorefractory disease. The S100 calcium binding protein A4 (S100A4) predicts metastasis formation and reduced CRC patient survival. S100A4 was previously identified as transcriptional target of the Wnt/β-catenin signaling pathway. The Food and Drug Administration (FDA)-approved anti-helminthic drug niclosamide is known to intervene in the Wnt/β-catenin pathway signaling, leading to reduced expression of S100A4 linked to restricted in vivo metastasis formation. Thus, we aim at translation of our findings on restricting S100A4-driven metastasis into clinical practice for treating metastasized CRC patients progressing after standard therapy.BACKGROUNDColorectal cancer (CRC) is the second most common cause of all cancer deaths in Europe and the Western world with a lifetime risk of approximately 5%. Despite several improvements in the treatment of patients with unresectable CRC prognosis is poor and there is the need of developing new treatment strategies for patients with metastatic chemorefractory disease. The S100 calcium binding protein A4 (S100A4) predicts metastasis formation and reduced CRC patient survival. S100A4 was previously identified as transcriptional target of the Wnt/β-catenin signaling pathway. The Food and Drug Administration (FDA)-approved anti-helminthic drug niclosamide is known to intervene in the Wnt/β-catenin pathway signaling, leading to reduced expression of S100A4 linked to restricted in vivo metastasis formation. Thus, we aim at translation of our findings on restricting S100A4-driven metastasis into clinical practice for treating metastasized CRC patients progressing after standard therapy.NIKOLO is a phase II, single center, one-arm open-label clinical trial to investigate the safety and efficacy of niclosamide tablets in patients with metastasized CRC progressing under standard therapy. Eligible patients will receive 2 g of orally applied niclosamide once a day and will continue with the treatment once daily till disease progression or toxicity. Toxicities will be graded according to National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE) v4.03. The primary objective of this trial is to assess the progression free survival after 4 months, secondary objectives are overall survival, time to progression, disease control rate (remission + partial remission + stable disease), and safety. Furthermore, pharmacokinetic analysis will be conducted to evaluate niclosamide plasma concentration.METHODS/DESIGNNIKOLO is a phase II, single center, one-arm open-label clinical trial to investigate the safety and efficacy of niclosamide tablets in patients with metastasized CRC progressing under standard therapy. Eligible patients will receive 2 g of orally applied niclosamide once a day and will continue with the treatment once daily till disease progression or toxicity. Toxicities will be graded according to National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE) v4.03. The primary objective of this trial is to assess the progression free survival after 4 months, secondary objectives are overall survival, time to progression, disease control rate (remission + partial remission + stable disease), and safety. Furthermore, pharmacokinetic analysis will be conducted to evaluate niclosamide plasma concentration.This study is expected to provide evidence of the feasibility, toxicity and efficacy of niclosamide in the treatment of patients with metastasized CRC and could help to establish a new treatment option.DISCUSSIONThis study is expected to provide evidence of the feasibility, toxicity and efficacy of niclosamide in the treatment of patients with metastasized CRC and could help to establish a new treatment option.The study is registered with ClinicalTrials.gov (NCT02519582) and the European Clinical Trials Database (EudraCT 2014-005151-20).TRIAL REGISTRATIONThe study is registered with ClinicalTrials.gov (NCT02519582) and the European Clinical Trials Database (EudraCT 2014-005151-20).

The circadian clock, a fundamental biological regulator, governs essential cellular processes in health and disease. Circadian-based therapeutic strategies are increasingly gaining recognition as promising avenues. Aligning drug administration with the circadian rhythm can enhance treatment efficacy and minimize side effects. Yet, uncovering the optimal treatment timings remains challenging, limiting their widespread adoption. In this work, we introduce a high-throughput approach integrating live-imaging and data analysis techniques to deep-phenotype cancer cell models, evaluating their circadian rhythms, growth, and drug responses. We devise a streamlined process for profiling drug sensitivities across different times of the day, identifying optimal treatment windows and responsive cell types and drug combinations. Finally, we implement multiple computational tools to uncover cellular and genetic factors shaping time-of-day drug sensitivity. Our versatile approach is adaptable to various biological models, facilitating its broad application and relevance. Ultimately, this research leverages circadian rhythms to optimize anti-cancer drug treatments, promising improved outcomes and transformative treatment strategies. The circadian rhythm has been linked to cancer cell sensitivity to therapy but tools to understand this further are limited. Here, by combining live-cell imaging and computational tools, the authors develop a high-throughput deep-phenotyping approach to evaluate circadian rhythms and use it to determine time-of-day drug sensitivity in cancer cell lines.

Genetic investigation of tumor heterogeneity and clonal evolution in solid cancers could be assisted by the analysis of liquid biopsies. However, tumors of various entities might release different quantities of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) into the bloodstream, potentially limiting the diagnostic potential of liquid biopsy in distinct tumor histologies. Patients with advanced colorectal cancer (CRC), head and neck squamous cell carcinoma (HNSCC), and melanoma (MEL) were enrolled in the study, representing tumors with different metastatic patterns. Mutation profiles of cfDNA, CTCs, and tumor tissue were assessed by panel sequencing, targeting 327 cancer-related genes. In total, 30 tissue, 18 cfDNA, and 7 CTC samples from 18 patients were sequenced. Best concordance between the mutation profile of tissue and cfDNA was achieved in CRC and MEL, possibly due to the remarkable heterogeneity of HNSCC (63%, 55% and 11%, respectively). Concordance especially depended on the amount of cfDNA used for library preparation. While 21 of 27 (78%) tissue mutations were retrieved in high-input cfDNA samples (30–100 ng, N  = 8), only 4 of 65 (6%) could be detected in low-input samples (<30 ng, N  = 10). CTCs were detected in 13 of 18 patients (72%). However, downstream analysis was limited by poor DNA quality, allowing targeted sequencing of only seven CTC samples isolated from four patients. Only one CTC sample reflected the mutation profile of the respective tumor. Private mutations, which were detected in CTCs but not in tissue, suggested the presence of rare subclones. Our pilot study demonstrated superiority of cfDNA- compared to CTC-based mutation profiling. It was further shown that CTCs may serve as additional means to detect rare subclones possibly involved in treatment resistance. Both findings require validation in a larger patient cohort.

Background Targeting the epidermal growth factor receptor (EGFR) pathway is an important approach for a variety of tumors. This study assessed the effect of cetuximab, an anti-EGFR monoclonal antibody, on three gastric cancer cell lines with different phenotypes in vitro and in a therapeutic orthotopic murine gastric cancer model. Methods Three human gastric cancer cell lines (AGS, MKN-45, NCI-N87) were evaluated for cell surface EGFR expression, and K-ras and BRAF mutations. In vitro, the effects of cetuximab, carboplatin, irinotecan, and docetaxel were investigated. Orthotopic tumors derived from MKN-45 and NCI-N87 were established in nude mice. After 4 weeks, the animals received cetuximab (1 mg/kg, weekly i.p.) or carboplatin (20 mg/kg, weekly i.p.), or both agents. The volume of the primary tumor and local and systemic tumor spread were determined at autopsy at 14 weeks. Tumor sections were immunostained for EGFR, as well as stained for CD31 to analyze microvessel density. Results Cell surface expression of EGFR was found only in AGS and NCI-N87 cells. AGS cells displayed a codon 12 K-ras mutation, and all three cell lines were BRAF wild-type. In vitro, cetuximab significantly reduced cell viability and proliferation only in EGFR-positive/K-ras wild-type NCI-N87 cells (−48%). In vivo, cetuximab in combination with carboplatin synergistically reduced tumor volume (−75%), dissemination (−63%), and vascularization (−47%) in NCI-N87 xenografts. Tumors derived from EGFR-negative MKN-45 cells were unaffected by cetuximab. Conclusions Cetuximab is effective in K-ras wild-type, EGFR-expressing gastric cancer cell lines and xenografts. In vivo, the combination of cetuximab with carboplatin displayed synergistic antitumor activity.

Organoid cultures derived from colorectal cancer (CRC) samples are increasingly used as preclinical models for studying tumor biology and the effects of targeted therapies under conditions capturing in vitro the genetic make-up of heterogeneous and even individual neoplasms. While 3D cultures are initiated from surgical specimens comprising multiple cell populations, the impact of tumor heterogeneity on drug effects in organoid cultures has not been addressed systematically. Here we have used a cohort of well-characterized CRC organoids to study the influence of tumor heterogeneity on the activity of the KRAS/MAPK-signaling pathway and the consequences of treatment by inhibitors targeting EGFR and downstream effectors. MAPK signaling, analyzed by targeted proteomics, shows unexpected heterogeneity irrespective of RAS mutations and is associated with variable responses to EGFR inhibition. In addition, we obtained evidence for intratumoral heterogeneity in drug response among parallel \"sibling\" 3D cultures established from a single KRAS-mutant CRC. Our results imply that separate testing of drug effects in multiple subpopulations may help to elucidate molecular correlates of tumor heterogeneity and to improve therapy response prediction in patients.

Salivary gland cancers are rare, diverse malignancies characterized by poor response to immunotherapy. The tumor immune environment in these cancers remains poorly understood. To address this, we perform an integrative analysis of the tumor immune microenvironment in a large cohort of advanced salivary gland cancer samples. Most tumors exhibit low immune activity with limited immune cell infiltration. Inflammation is linked to higher tumor mutational burden in non-adenoid cystic carcinoma histologies. Subtype specific expression of immune checkpoints is identified with prominent expression of VTCN1 in luminal-like cells within adenoid cystic carcinoma. Macrophages with immunosuppressive properties dominate the immune microenvironment across subtypes. Responses to immunotherapy are limited and associated with a higher ratio of T-cells relative to macrophages in individual cases, warranting further investigation. Here, we show an immunosuppressive environment in salivary gland cancers and identify subtype-specific immune vulnerabilities that could inform tailored therapeutic strategies. Salivary gland cancers (SGC) respond poorly to immunotherapies and new treatment strategies are needed. Here, the authors develop an integrated analysis of advanced SGC to characterize the immune microenvironment and identify potential therapeutic vulnerabilities.

The introduction of adjuvant systemic treatment for patients with high-risk melanomas necessitates accurate staging of disease. However, inconsistencies in outcomes exist between disease stages as defined by the American Joint Committee on Cancer (8th edition). We aimed to develop a tool to predict patient-specific outcomes in people with melanoma rather than grouping patients according to disease stage. Patients older than 13 years with confirmed primary melanoma who underwent sentinel lymph node biopsy (SLNB) between Oct 29, 1997, and Nov 11, 2013, at four European melanoma centres (based in Berlin, Germany; Amsterdam and Rotterdam, the Netherlands; and Warsaw, Poland) were included in the development cohort. Potential predictors of recurrence-free and melanoma-specific survival assessed were sex, age, presence of ulceration, primary tumour location, histological subtype, Breslow thickness, sentinel node status, number of sentinel nodes removed, maximum diameter of the largest sentinel node metastasis, and Dewar classification. A prognostic model and nomogram were developed to predict 5-year recurrence-free survival on a continuous scale in patients with stage pT1b or higher melanomas. This model was also calibrated to predict melanoma-specific survival. Model performance was assessed by discrimination (area under the time-dependent receiver operating characteristics curve [AUC]) and calibration. External validation was done in a cohort of patients with primary melanomas who underwent SLNB between Jan 30, 1997, and Dec 12, 2013, at the Melanoma Institute Australia (Sydney, NSW, Australia). The development cohort consisted of 4071 patients, of whom 2075 (51%) were female and 1996 (49%) were male. 889 (22%) had sentinel node-positive disease and 3182 (78%) had sentinel node-negative disease. The validation cohort comprised 4822 patients, of whom 1965 (41%) were female and 2857 (59%) were male. 891 (18%) had sentinel node-positive disease and 3931 (82%) had sentinel node-negative disease. Median follow-up was 4·8 years (IQR 2·3–7·8) in the development cohort and 5·0 years (2·2–8·9) in the validation cohort. In the development cohort, 5-year recurrence-free survival was 73·5% (95% CI 72·0–75·1) and 5-year melanoma-specific survival was 86·5% (85·3–87·8). In the validation cohort, the corresponding estimates were 66·1% (64·6–67·7) and 83·3% (82·0–84·6), respectively. The final model contained six prognostic factors: sentinel node status, Breslow thickness, presence of ulceration, age at SLNB, primary tumour location, and maximum diameter of the largest sentinel node metastasis. In the development cohort, for the model's prediction of recurrence-free survival, the AUC was 0·80 (95% CI 0·78–0·81); for prediction of melanoma-specific survival, the AUC was 0·81 (0·79–0·84). External validation showed good calibration for both outcomes, with AUCs of 0·73 (0·71–0·75) and 0·76 (0·74–0·78), respectively. Our prediction model and nomogram accurately predicted patient-specific risk probabilities for 5-year recurrence-free and melanoma-specific survival. These tools could have important implications for clinical decision making when considering adjuvant treatments in patients with high-risk melanomas. Erasmus Medical Centre Cancer Institute.

Risk stratification remains a critical challenge in non-small cell lung cancer patients for optimal therapy selection. In this study, we develop an artificial intelligence-powered spatial cellomics approach that combines histology, multiplex immunofluorescence imaging and multimodal machine learning to characterize the complex cellular relationships of 43 cell phenotypes in the tumor microenvironment in a real-world retrospective cohort of 1168 non-small cell lung cancer patients from two large German cancer centers. The model identifies cell niches associated with survival and achieves a 14% and 47% improvement in risk stratification in the two main non-small cell lung cancer subtypes, lung adenocarcinoma and squamous cell carcinoma, respectively, combining niche patterns with conventional cancer staging. Our results show that complex immune cell niche patterns identify potentially undertreated high-risk patients qualifying for adjuvant therapy. Our approach highlights the potential of artificial intelligence powered multiplex imaging analyses to better understand the contribution of the tumor microenvironment to cancer progression and to improve risk stratification and treatment selection in non-small cell lung cancer. Risk stratification in non-small cell lung cancer (NSCLC) remains challenging. By combining multiplex immunofluorescence, H&E histology, and AI, the study identifies spatial “cell-niche” patterns that enhance survival prediction beyond UICC8 staging. These patterns reclassify many stage I patients as high risk, revealing potentially undertreated cases and establishing spatial tumor microenvironment features as clinically actionable biomarkers.