Cancer Treatment Response Research Articles

Field Cancerization Is Associated with Tumor Development, T-cell Exhaustion, and Clinical Outcomes in Bladder Cancer

BackgroundField cancerization is characterized by areas of normal tissue affected by mutated clones. Bladder field cancerization may explain the development and recurrence of bladder cancer and may be associated with treatment outcomes. ObjectiveTo investigate the predictive and prognostic roles of field cancerization in patients with high-risk non–muscle-invasive bladder cancer (NMIBC) treated with bacillus Calmette-Guérin (BCG). Design, setting, and participantsWe conducted comprehensive genomic and proteomic analyses for 751 bladder biopsies and 234 urine samples from 136 patients with NMIBC. The samples were collected at multiple time points during the disease course. Field cancerization in normal-appearing bladder biopsies was measured using deep-targeted sequencing and error correction models. Outcome measurements and statistical analysisEndpoints included the rates of recurrence and progression. Cox regression and Wilcoxon rank-sum and Fisher’s exact tests were used. Results and limitationsA high level of field cancerization was associated with high tumor mutational burden (p = 0.007), high tumor neoantigen load (p = 0.029), and high tumor-associated CD8 T-cell exhaustion (p = 0.017). In addition, high field cancerization was associated with worse short-term outcomes (p = 0.029). Nonsynonymous mutations in bladder cancer–associated genes such as KDM6A, ARID1A, and TP53 were identified as early disease drivers already found in normal-appearing bladder biopsies. Urinary tumor DNA (utDNA) levels reflected the bladder tumor burden and originated from tumors and field cancerization. High levels of utDNA after BCG were associated with worse clinical outcomes (p = 0.027) and with disease progression (p = 0.003). High field cancerization resulted in high urinary levels of proteins associated with angiogenesis and proliferation. Limitations include variation in the number of biopsies and time points analyzed. ConclusionsField cancerization levels are associated with tumor development, immune responses, and clinical outcomes. utDNA measurements can be used to monitor disease status and treatment response. Patient summaryMolecular changes in the tissue lining the bladder result in tumor recurrence. Urinary measurements may be used to monitor bladder cancer status and treatment responses.

Targeting collagen XVIII improves the efficiency of ErbB inhibitors in breast cancer models.

The tumor extracellular matrix (ECM) critically regulates cancer progression and treatment response. Expression of the basement membrane component collagen XVIII (ColXVIII) is induced in solid tumors, but its involvement in tumorigenesis has remained elusive. We show here that ColXVIII was markedly upregulated in human breast cancer (BC) and was closely associated with a poor prognosis in high-grade BCs. We discovered a role for ColXVIII as a modulator of epidermal growth factor receptor tyrosine kinase (ErbB) signaling and show that it forms a complex with ErbB1 and -2 (also known as EGFR and human epidermal growth factor receptor 2 [HER2]) and α6-integrin to promote cancer cell proliferation in a pathway involving its N-terminal portion and the MAPK/ERK1/2 and PI3K/AKT cascades. Studies using Col18a1 mouse models crossed with the mouse mammary tumor virus-polyoma virus middle T antigen (MMTV-PyMT) mammary carcinogenesis model showed that ColXVIII promoted BC growth and metastasis in a tumor cell-autonomous manner. Moreover, the number of mammary cancer stem cells was significantly reduced in the MMTV-PyMT and human cell models upon ColXVIII inhibition. Finally, ablation of ColXVIII substantially improved the efficacy of ErbB-targeting therapies in both preclinical models. In summary, ColXVIII was found to sustain the stemness properties of BC cells and tumor progression and metastasis through ErbB signaling, suggesting that targeting ColXVIII in the tumor milieu may have important therapeutic potential.

Elucidating the Associated Biological Function and Clinical Significance of RHOJ Expression in Urothelial Carcinoma.

Urothelial cancer, a common urinary system malignancy, often presents treatment challenges due to metastasis and chemotherapy side effects. Angiogenesis, crucial for tumor growth, has become a target for drug development. This study explores the expression, prognostic value, and clinical correlation of RHOJ in the TCGA BLCA, GSE31684, and GSE32894 datasets. We identify common differentially expressed genes across these databases and utilize g:Profiler and Cytoscape ClueGO for functional assessment. Further, we perform a gene set enrichment analysis (GSEA) using Hallmark gene sets and use the imsig package for immune cell infiltration analysis. Our analysis indicates that RHOJ expression levels significantly impact survival rates, tumor progression, and immune response in urothelial tumors. High RHOJ expression correlated with poor prognosis, advanced disease stages, and an increase in monocyte population within the tumor microenvironment. This aligns with current literature indicating a key role of immune infiltration in bladder cancer progression and treatment response. Moreover, the GSEA and imsig results further suggest a potential mechanistic link between RHOJ expression and immune-related pathways. Considering the increasing emphasis on immunotherapeutic strategies in bladder cancer management, our findings on RHOJ's potential as a diagnostic biomarker and its association with immune response open new avenues for therapeutic interventions.

First-in-human study of a novel cell death tracer [99mTc]Tc-Duramycin: safety, biodistribution and radiation dosimetry in healthy volunteers

BackgroundImaging of cell death can provide an early indication of treatment response in cancer. [99mTc]Tc-Duramycin is a small-peptide SPECT tracer that recognizes both apoptotic and necrotic cells by binding to phosphatidylethanolamine present in the cell membrane. Preclinically, this tracer has shown to have favorable pharmacokinetics and selective tumor accumulation early after the onset of anticancer therapy. In this first-in-human study, we report the safety, biodistribution and internal radiation dosimetry of [99mTc]Tc-Duramycin in healthy human volunteers.ResultsSix healthy volunteers (3 males, 3 females) were injected intravenously with [99mTc]Tc-Duramycin (dose: 6 MBq/kg; 473 ± 36 MBq). [99mTc]Tc-Duramycin was well tolerated in all subjects, with no serious adverse events reported. Following injection, a 30-min dynamic planar imaging of the abdomen was performed, and whole-body (WB) planar scans were acquired at 1, 2, 3, 6 and 23 h post-injection (PI), with SPECT acquisitions after each WB scan and one low-dose CT after the first SPECT. In vivo 99mTc activities were determined from semi-quantitative analysis of the images, and time-activity curves were generated. Residence times were calculated from the dynamic and WB planar scans. The mean effective dose was 7.61 ± 0.75 µSv/MBq, with the kidneys receiving the highest absorbed dose (planar analysis: 43.82 ± 4.07 µGy/MBq, SPECT analysis: 19.72 ± 3.42 μGy/MBq), followed by liver and spleen. The median effective dose was 3.61 mSv (range, 2.85–4.14). The tracer cleared slowly from the blood (effective half-life of 2.0 ± 0.4 h) due to high plasma protein binding with < 5% free tracer 3 h PI. Excretion was almost exclusively renal.Conclusion[99mTc]Tc-Duramycin demonstrated acceptable dosimetry (< 5 mSv) and a favorable safety profile. Due to slow blood clearance, optimal target-to-background ratios are expected 5 h PI. These data support the further assessment of [99mTc]Tc-Duramycin for clinical treatment response evaluation.Trial registration: NCT05177640, Registered April 30, 2021, https://clinicaltrials.gov/study/NCT05177640.

Integrated variant allele frequency analysis pipeline and R package: easyVAF.

Somatic sequence variants are associated with cancer diagnosis, prognostic stratification, and treatment response. Variant allele frequency (VAF), the percentage of sequence reads with a specific DNA variant over the read depth at that locus, has been used as a metric to quantify mutation rates in these applications. VAF has the potential for feature detection by reflecting changes in tumor clonal composition across treatments or time points. Although there are several packages, including Genome Analysis Toolkit and VarScan, designed for variant calling and rare mutation identification, there is no readily available package for comparing VAFs among and between groups to identify loci of interest. To this end, we have developed the R package easyVAF, which includes parametric and nonparametric tests to compare VAFs among multiple groups. It is accompanied by an interactive R Shiny app. With easyVAF, the investigator has the option between three statistical tests to maximize power while maintaining an acceptable type I error rate. This paper presents our proposed pipeline for VAF analysis, from quality checking to group comparison. We evaluate our method in a wide range of simulated scenarios and show that choosing the appropriate test to limit the type I error rate is critical. For situations where data is sparse, we recommend comparing VAFs with the beta-binomial likelihood ratio test over Fisher's exact test and Pearson's χ2 test.

Promising Marker for Treatment Response in Prostate Cancer

Promising Marker for Treatment Response in Prostate Cancer

A gene signature of cancer-associated fibroblasts predicts prognosis and treatment response in bladder cancer.

Due to the pivotal role cancer-associated fibroblasts (CAFs) play in tumor progression, our study aimed to develop a signature of CAFs-related gene (CRG) to predict the survival outcomes and treatment response of bladder cancer (BLCA). The transcriptome data and relevant clinical information about BLCA were collected from publicly available databases, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Weighted gene co-expression network analysis was utilized to uncover CAFs-associated hub genes, and subsequently, a risk model for survival prognosis was constructed using LASSO-Cox regression. The immune microenvironment, immune infiltration, immunotherapy response, and drug sensitivity were explored using ESTIMATE, CIBERSORT, TIDE, and oncoPredict algorithms. To verify the expression of the CRGs, additional analyses were performed using online databases (HPA, CCLE, TIMER, cBioPortal, and TISCH). Our study developed a CRG signature and constructed a prognostic model. Significant differences in overall survival were observed between the two risk stratifications. The risk score increased with the infiltration of CAFs and tumor staging progression, while closely correlating with immune checkpoint expression and infiltration of CD8 T cells, follicular helper T cells, regulatory T cells, activated dendritic cells, M0 macrophages, M2 macrophages, and resting mast cells. Furthermore, a higher proportion of patients in the low-risk stratification exhibited responsiveness to immunotherapy, and significant variances in sensitivity to multiple chemotherapy medications were observed between the two risk stratifications. The construction of the risk model based on the CRG signature offers new avenues for the prognosis evaluation and development of personalized treatment strategies for BLCA.

Immune dysfunction revealed by digital spatial profiling of immuno-oncology markers in progressive stages of renal cell carcinoma and in brain metastases

BackgroundThe tumor microenvironment (TME) contributes to cancer progression and treatment response to therapy, including in renal cell carcinoma (RCC). Prior profiling studies, including single-cell transcriptomics, often involve limited sample sizes...

PACT: a pipeline for analysis of circulating tumor DNA.

Detection of genomic alterations in circulating tumor DNA (ctDNA) is currently used for active clinical monitoring of cancer progression and treatment response. While methods for analysis of small mutations are more developed, strategies for detecting structural variants (SVs) in ctDNA are limited. Additionally, reproducibly calling small-scale mutations, copy number alterations, and SVs in ctDNA is challenging due to the lack to unified tools for these different classes of variants. We developed a unified pipeline for the analysis of ctDNA [Pipeline for the Analysis of ctDNA (PACT)] that accurately detects SVs and consistently outperformed similar tools when applied to simulated, cell line, and clinical data. We provide PACT in the form of a Common Workflow Language pipeline which can be run by popular workflow management systems in high-performance computing environments. PACT is freely available at https://github.com/ChrisMaherLab/PACT.

Integrative multi-omics analyses unravel the immunological implication and prognostic significance of CXCL12 in breast cancer.

CXCL12 is a vital factor in physiological and pathological processes, by inducing migration of multiple cells. We aimed to comprehensively detect the role of CXCL12 in breast cancer, and explore novel CXCL12-related biomarkers through integrative multi-omics analyses to build a powerful prognostic model for breast cancer patients. Immunohistochemistry analysis of the tissue microarray was performed to evaluate the correlation between CXCL12 expression levels and breast cancer patient outcomes. Combined single-nucleus and spatial transcriptomics data was used to uncover the expression distribution of CXCL12 in breast cancer microenvironment. CXCL12-related genes were identified by WGCNA analysis. Univariate Cox and LASSO regression analyses were then conducted to screen prognostic genes from above CXCL12-related genes, followed by the construction of the CXCL12-related prognostic signature, identification of risk groups, and external validation of the prognostic signature. Analyses of biological function, mutation landscape, immune checkpoint genes and immune cells, were performed to further reveal the differences between high/low-risk groups. Paired single-cell RNA-seq and bulk RNA-seq were analyzed to further disclose the association between the risk score and the complex tumor immune microenvironment. To screen potential therapeutic agents for breast cancer patients, analyses of gene-drug correlation and sensitivity to immunotherapy were conducted. High expression of CXCL12 was linked with a prolonged survival in breast cancer. A total of 402 genes were identified by WGCNA analysis and 11 genes, covering VAT1L, TMEM92, SDC1, RORB, PCSK9, NRN1, NACAD, JPH3, GJA1, BMP8B and ADAMTS2, were screened as the candidate prognostic genes. Next, the prognostic signature was built and validated using these genes to predict the outcomes of breast cancers. The high-risk group patients exhibited significantly inferior prognoses. The combination of the risk score and tumor mutational burden (TMB) had remarkably improved performance in predicting patient outcomes. Besides, high-risk group patients showed higher infiltration of M2-like macrophages. Finally, several potential anticancer drugs were identified. The high-risk group patients were more sensitive to immunotherapy but resistant to docetaxel. CXCL12 has important immunological implication and prognostic significance in breast cancer. The CXCL12-related prognostic model could well predict the prognosis and treatment response of breast cancers.

Spatial heterogeneity of tumor microenvironment influences the prognosis of clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is an immunologically and histologically diverse tumor. However, how the structural heterogeneity of tumor microenvironment (TME) affects cancer progression and treatment response remains unclear. Hence, we characterized the TME architectures of ccRCC tissues using imaging mass cytometry (IMC) and explored their associations with clinical outcome and therapeutic response.MethodsUsing IMC, we profiled the TME landscape of ccRCC and paracancerous tissue by measuring 17 markers involved in tissue architecture, immune cell and immune activation. In the ccRCC tissue, we identified distinct immune architectures of ccRCC tissue based on the mix score and performed cellular neighborhood (CN) analysis to subdivide TME phenotypes. Moreover, we assessed the relationship between the different TME phenotypes and ccRCC patient survival, clinical features and treatment response.ResultsWe found that ccRCC tissues had higher levels of CD8+ T cells, CD163− macrophages, Treg cells, endothelial cells, and fibroblasts than paracancerous tissues. Immune infiltrates in ccRCC tissues distinctly showed clustered and scattered patterns. Within the clustered pattern, we identified two subtypes with different clinical outcomes based on CN analysis. The TLS-like phenotype had cell communities resembling tertiary lymphoid structures, characterized by cell–cell interactions of CD8+ T cells-B cells and GZMB+CD8+ T cells-B cells, which exhibited anti-tumor features and favorable outcomes, while the Macrophage/T-clustered phenotype with macrophage- or T cell-dominated cell communities had a poor prognosis. Patients with scattered immune architecture could be further divided into scattered-CN-hot and scattered-CN-cold phenotypes based on the presence or absence of immune CNs, but both had a better prognosis than the macrophage/T-clustered phenotype. We further analyzed the relationship between the TME phenotypes and treatment response in five metastatic ccRCC patients treated with sunitinib, and found that all three responders were scattered-CN-hot phenotype while both non-responders were macrophage/T-clustered phenotype.ConclusionOur study revealed the structural heterogeneity of TME in ccRCC and its impact on clinical outcome and personalized treatment. These findings highlight the potential of IMC and CN analysis for characterizing TME structural units in cancer research.

Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer

ObjectivesTo develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset.MethodsBaseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1–2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups:(1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval(2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation(3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length(4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC)Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97).ResultsAfter external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48–0.72) to predict complete response and 0.65 (95%CI=0.53–0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables.ConclusionsOverall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset).Clinical relevance statementPredicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization.Key PointsThis multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer.Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates.No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.

Serum Interleukin 6 Level is Associated With Overall Survival and Treatment Response in Gastric and Gastroesophageal Junction Cancer.

To identify novel prognostic and predictive biomarkers for gastric and gastroesophageal junction (G+GEJ) adenocarcinoma. There are few biomarkers to guide treatment for G+GEJ. The systemic inflammatory response of G+GEJ patients is associated with survival. In this study, we evaluated the relationship of circulating serum cytokine levels with overall survival (OS) and pathologic tumor regression grade (TRG) in G+GEJ patients. We queried the UT Southwestern gastric cancer biobank to identify consecutive patients diagnosed with G+GEJ from 2016 to 2022; these patients had pretreatment serum collected at diagnosis. For patients who received neoadjuvant therapy, an additional serum sample was collected immediately before surgical resection. An unbiased screen of 17 cytokines was measured in a discovery cohort. A multivariable Cox proportional hazards model was used to assess the association of cytokine concentration with OS. Findings were validated in additional patients. In patients who received neoadjuvant therapy, we assessed whether the change in interleukin 6 (IL-6) after therapy was associated with TRG. Sixty-seven patients were included in the discovery cohort, and IL-6 was the only pretreatment cytokine associated with OS; this was validated in 134 other patients (hazard ratio: 1.012 per 1pg/mL increase, 95% CI: 1.006-1.019, P = 0.0002). Patients in the top tercile of IL-6 level had worse median OS (10.6 months) compared with patients in the intermediate (17.4 months) and bottom tercile (35.8 months, P < 0.0001). Among patients who underwent neoadjuvant therapy (n = 50), an unchanged or decrease in IL-6 level from pretreatment to posttreatment, had a sensitivity and specificity of 80% for predicting complete or near-complete pathologic tumor regression (TRG 0-1). Pretreatment serum level of IL-6 is a promising prognostic biomarker for G+GEJ patients. Comparing pre and post-neoadjuvant IL-6 levels may predict pathologic response to neoadjuvant therapy.

EVALUATING HUMAN MICROBIOME SIGNATURES ASSOCIATED WITH DEVELOPMENT, OUTCOME, AND TREATMENT RESPONSE IN GLIOMA AND BRAIN METASTASIS: A SCOPING REVIEW

Abstract The human microbiome is made up of over 500 species of bacteria in the body. Microbiome dysbiosis has been implicated in cancer development and treatment response, including in both primary brain tumors and brain metastases, by acting through the gut-brain axis. We conducted a scoping review of the current evidence surrounding the human microbiome and brain tumors. METHODS: A systematic search of relevant studies and abstracts from 5 electronic databases from 1946-2023 was conducted based on a search strategy developed by an information specialist. Eligible studies included human, in vivo, or in vitro studies that focused on the relationship between microbiome and glioma and brain metastasis development, response to therapies, and outcomes. RESULTS: Of 383 citations, 31 studies met inclusion criteria, including 15 articles and 16 conference abstracts. There were 19 human studies and 18 mouse studies, of which 24 studies focused on glioma and 7 studies focused on brain metastases. They either characterized the microbiome in patients with brain tumors (n=19) or its correlation to systemic therapies for glioma (n=12). Changes in the Firmicutes/Bacteroidetes ratio, which is a marker of dysbiosis in patients with brain tumors was observed in 7 studies; however, the direction of change varied. Microbiome dysbiosis seemed to occur in response to both immunotherapy (5 studies) and temozolomide (3 studies), with variable impacts on treatment response. CONCLUSION: Evidence surrounding the impact of the gut-brain axis on brain tumors remains in its infancy with a large amount of heterogeneity.

The Local Microbiome in Esophageal Cancer and Treatment Response: A Review of Emerging Data and Future Directions.

peer-reviewed, published studies describing the role of local, gastrointestinal-specific microbiota or the role of the gut microbiome in treatment responses were reviewed. PubMed was searched from 1 September 2022 to 1 November 2022, using the following terms in combination: "microbiome", "tumor microbiome", "esophageal cancer", "cancer", "cancer treatment", and "immunotherapy". Original research articles were considered, and other reviews or editorials were discarded. In total, approximately 250 articles were considered. over 70 studies describing microbiome research in either gastrointestinal carcinogenesis or the systemic treatment response were identified and reviewed. a growing body of evidence supports the role of the esophageal microbiome in both esophageal tumorigenesis and the immune checkpoint inhibitor response. More well-designed, comprehensive studies are required to collect the appropriate clinical, microbial, and immunophenotype data that are needed to clarify the precise role of the microbiome in esophageal carcinogenesis and treatment.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer.

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

Using Synthetic MRI and Radiomics to Predict Treatment Response in Triple-Negative Breast Cancer.

Using Synthetic MRI and Radiomics to Predict Treatment Response in Triple-Negative Breast Cancer.

Molecular portraits of cell cycle checkpoint kinases in cancer evolution, progression, and treatment responsiveness.

Cell cycle dysregulation is prerequisite for cancer formation. However, it is unknown whether the mode of dysregulation affects disease characteristics. Here, we conduct comprehensive analyses of cell cycle checkpoint dysregulation using patient data and experimental investigations. We find that ATM mutation predisposes the diagnosis of primary estrogen receptor (ER)+/human epidermal growth factor (HER)2- cancer in older women. Conversely, CHK2 dysregulation induces formation of metastatic, premenopausal ER+/HER2- breast cancer (P = 0.001) that is treatment-resistant (HR = 6.15, P = 0.01). Lastly, while mutations in ATR alone are rare, ATR/TP53 co-mutation is 12-fold enriched over expected in ER+/HER2- disease (P = 0.002) and associates with metastatic progression (HR = 2.01, P = 0.006). Concordantly, ATR dysregulation induces metastatic phenotypes in TP53 mutant, not wild-type, cells. Overall, we identify mode of cell cycle dysregulation as a distinct event that determines subtype, metastatic potential, and treatment responsiveness, providing rationale for reconsidering diagnostic classification through the lens of the mode of cell cycle dysregulation..

Supraphysiological Androgens Promote the Tumor Suppressive Activity of the Androgen Receptor through cMYC Repression and Recruitment of the DREAM Complex.

Determining the molecular pathways by which supraphysiological androgens promote growth arrest and treatment responses in prostate cancer provides opportunities for biomarker-selected clinical trials and the development of strategies to augment responses.

ACSM6 overexpression indicates a non-inflammatory tumor microenvironment and predicts treatment response in bladder cancer: results from multiple real-world cohorts.

Background: ACSMs play critical roles in lipid metabolism; however, their immunological function within the tumor microenvironment (TME) remains unclear, especially that of ACSM6. In this study, we investigate the latent effect of ACSM6 on bladder cancer (BLCA). Methods: Several real-world cohorts, including the Xiangya (in-house), The Cancer Genome Atlas (TCGA-BLCA), and IMvigor210 cohorts, with TCGA-BLCA cohort serving as the discovery cohort were compared. We investigated the potential immunological effects of ACSM6 in regulating the BLCA tumor microenvironment by analyzing its correlation with immunomodulators, anti-cancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, we assessed the precision of ACSM6 in predicting BLCA molecular subtypes and responses to several treatments using ROC analysis. To ensure the robustness of our findings, all results were confirmed in two independent external cohorts: the IMvigor210 and Xiangya cohorts. Results: ACSM6 expression was markedly upregulated in BLCA. Our analysis suggests that ACSM6 might have significant impact to promote the formation of a non-inflamed tumor microenvironment because of its negative correlation with immunomodulators, anticancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, high ACSM6 expression levels in BLCA may predict the luminal subtype, which is typically associated with resistance to chemotherapy, neoadjuvant chemotherapy, and radiotherapy. These findings were consistent across both the IMvigor210 and Xiangya cohorts. Conclusion: ACSM6 has the potential to serve as a valuable predictor of the tumor microenvironment phenotypes and treatment outcomes in BLCA, thereby contributing to more precise treatment.