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

BACKGROUND: Field 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. OBJECTIVE: To 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 PARTICIPANTS: We 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 ANALYSIS: Endpoints included the rates of recurrence and progression. Cox regression and Wilcoxon rank-sum and Fisher's exact tests were used. RESULTS AND LIMITATIONS: A 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. CONCLUSIONS: Field 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 SUMMARY: Molecular changes in the tissue lining the bladder result in tumor recurrence. Urinary measurements may be used to monitor bladder cancer status and treatment responses.

Toxicity and therapy outcome associations in LIG3, SLCO1B3, ABCB1, OPRM1 and GSTP1 in high-grade serous ovarian cancer.

Adverse effects are the major limiting factors in combinatorial chemotherapies. To identify genetic associations in ovarian cancer chemotherapy-induced toxicities and therapy outcomes, we examined a cohort of 101 patients receiving carboplatin-paclitaxel treatment with advanced high-grade serous ovarian cancers. Based on literature and database searches, we selected 19 candidate polymorphisms, designed a multiplex single nucleotide polymorphism-genotyping assay and applied Cox regression analysis, case-control association statistics and the log-rank Mantel-Cox test. In the Cox regression analysis, the SLCO1B3 rs1052536 AA-genotype was associated with a reduced risk of any severe toxicity (hazard ratio = 0.35, p = 0.023). In chi-square allelic test, the LIG3 rs1052536 T-allele was associated with an increased risk of neuropathy (odds ratio [OR] = 2.79, p = 0.031) and GSTP1 rs1695 G allele with a poorer response in the first-line chemotherapy (OR = 2.65, p = 0.026). In Kaplan-Meier survival analysis, ABCB1 rs2032582 TT-genotype was associated with shorter overall survival (uncorrected p = 0.025) and OPRM1 rs544093 GG and GT genotypes with shorter platinum-free interval (uncorrected p = 0.027) and progression-free survival (uncorrected p = 0.012). Results suggest that SLCO1B3 and LIG3 variants are associated with the risk of adverse effects in patients receiving carboplatin-paclitaxel treatment, the GSTP1 variant may affect the treatment response and ABCB1 and OPRM1 variants may influence the prognosis.

A Novel Two-Lipid Signature Is a Strong and Independent Prognostic Factor in Ovarian Cancer.

Epithelial ovarian cancer (EOC) generally responds well to oncological treatments, but the eventual development of a refractory disease is a major clinical problem. Presently, there are no prognostic blood-based biomarkers for the stratification of EOC patients at the time of diagnosis. We set out to assess and validate the prognostic utility of a novel two-lipid signature, as the lipidome is known to be markedly aberrant in EOC patients. The study consisted of 499 women with histologically confirmed EOC that were prospectively recruited at the university hospitals in Turku (Finland) and Charité (Berlin, Germany). Lipidomic screening by tandem liquid chromatography-mass spectrometry (LC-MS/MS) was performed for all baseline serum samples of these patients, and additionally for 20 patients of the Turku cohort at various timepoints. A two-lipid signature, based on the ratio of the ceramide Cer(d18:1/18:0) and phosphatidylcholine PC(O-38:4), showed consistent prognostic performance in all investigated study cohorts. In the Turku cohort, the unadjusted hazard ratios (HRs) per standard deviation (SD) (95% confidence interval) were 1.79 (1.40, 2.29) for overall and 1.40 (1.14, 1.71) for progression-free survival. In a Charité cohort incorporating only stage III completely resected patients, the corresponding HRs were 1.59 (1.08, 2.35) and 1.53 (1.02, 2.30). In linear-mixed models predicting progression of the disease, the two-lipid signature showed higher performance (beta per SD increase 1.99 (1.38, 2.97)) than cancer antigen 125 (CA-125, 1.78 (1.13, 2.87)). The two-lipid signature was able to identify EOC patients with an especially poor prognosis at the time of diagnosis, and also showed promise for the detection of disease relapse.

HE4 in the evaluation of tumor load and prognostic stratification of high grade serous ovarian carcinoma.

OBJECTIVE: Human epididymis protein 4 (HE4) is a validated, complementary biomarker to cancer antigen 125 (CA125) for high grade serous ovarian carcinoma (HGSC). Currently, there are insufficient data on the utility of longitudinal HE4 measurement during HGSC treatment and follow up. We set to provide a comprehensive analysis on the kinetics and prognostic performance of HE4 with serial measurements during HGSC treatment and follow up. METHODS: This prospective study included 143 patients with advanced HGSC (ClinicalTrials.gov identifier: NCT01276574). Serum CA125 and HE4 were measured at baseline, before each cycle of chemotherapy and during follow up until first progression. Baseline biomarker values were compared to the tumor load assessed during surgery and to residual disease. Biomarker nadir values and concentrations at progression were correlated to survival. RESULTS: The baseline HE4 concentration distinguished patients with a high tumor load from patients with a low tumor load assessed during surgery (p<.0001). The baseline CA125 level was not associated with tumor load to a similar extent (p=.067). At progression, the HE4 level was an independent predictor of worse survival in the multivariate analysis (p=.002). All patients that were alive 3 years post-progression had a serum HE4 concentration below 199.20 pmol/l at the 1st recurrence. CONCLUSION: HE4 is a feasible biomarker in the treatment monitoring and prognostic stratification of patients with HGSC. Specifically, the serum level of HE4 at first relapse was associated with the survival of patients and it may be a useful complementary tool in the selection of second line treatments. This is to the best of our knowledge the first time this finding has been reported.

Exploratory Analysis of CA125-MGL and -STn Glycoforms in the Differential Diagnostics of Pelvic Masses.

BACKGROUND: The cancer antigen 125 (CA125) immunoassay (IA) does not distinguish epithelial ovarian cancer (EOC) from benign disease with the sensitivity needed in clinical practice. In recent studies, glycoforms of CA125 have shown potential as biomarkers in EOC. Here, we assessed the diagnostic abilities of two recently developed CA125 glycoform assays for patients with a pelvic mass. Detailed analysis was further conducted for postmenopausal patients with marginally elevated conventionally measured CA125 levels, as this subgroup presents a diagnostic challenge in the clinical setting. METHODS: Our study population contained 549 patients diagnosed with EOC, benign ovarian tumors, and endometriosis. Of these, 288 patients were postmenopausal, and 98 of them presented with marginally elevated serum levels of conventionally measured CA125 at diagnosis. Preoperative serum levels of conventionally measured CA125 and its glycoforms (CA125-MGL and CA125-STn) were determined. RESULTS: The CA125-STn assay identified EOC significantly better than the conventional CA125-IA in postmenopausal patients (85% vs. 74% sensitivity at a fixed specificity of 90%, P = 0.0009). Further, both glycoform assays had superior AUCs compared to the conventional CA125-IA in postmenopausal patients with marginally elevated CA125. Importantly, the glycoform assays reduced the false positive rate of the conventional CA125-IA. CONCLUSIONS: The results indicate that the CA125 glycoform assays markedly improve the performance of the conventional CA125-IA in the differential diagnosis of pelvic masses. This result is especially valuable when CA125 is marginally elevated.

A longitudinal analysis of CA125 glycoforms in the monitoring and follow up of high grade serous ovarian cancer.

OBJECTIVE: Cancer antigen 125 (CA125) is generally considered the gold standard of biomarkers in the diagnosis and monitoring of high grade serous ovarian carcinoma (HGSC). We recently reported, that two CA125 glycoforms (CA125-STn and CA125-MGL) have a high specificity to HGSC and further hypothesized, that these cancer specific glycoforms are feasible candidates as biomarkers in HGSC treatment and follow up. METHODS: Our cohort consisted of 122 patients diagnosed with HGSC. Serum samples were collected longitudinally at the time of diagnosis, during treatment and follow up. Serum levels of CA125, CA125-STn and CA125-MGL were determined and compared or correlated with different end points (tumor load assessed intraoperatively, residual disease, treatment response, progression free survival). RESULTS: Serum CA125-STn levels at diagnosis differentiated patients with low tumor load and high tumor load (p = 0,030), indicating a favorable detection of tumor volume. Similarly, the CA125-STn levels at diagnosis were significantly lower in patients with subsequent complete cytoreduction than in patients with suboptimal cytoreduction (p = 0,025). Conventional CA125 did not differentiate these patients (p = 0,363 and p = 0,154). The CA125-STn nadir value predicted the progression free survival of patients. The detection of disease relapse was improved with CA125-STn, which presented higher fold increase in 80,0% of patients and earlier increase in 37,0% of patients. CONCLUSIONS: CA125-STn showed promise as a useful biomarker in the monitoring and follow up of patients with HGSC utilizing a robust and affordable technique. Our findings are topical as a suitable indicator of tumor load facilitates patient selection in an era of new targeted therapies.

Open Source Infrastructure for Health Care Data Integration and Machine Learning Analyses.

PURPOSE: We have created a cloud-based machine learning system (CLOBNET) that is an open-source, lean infrastructure for electronic health record (EHR) data integration and is capable of extract, transform, and load (ETL) processing. CLOBNET enables comprehensive analysis and visualization of structured EHR data. We demonstrate the utility of CLOBNET by predicting primary therapy outcomes of patients with high-grade serous ovarian cancer (HGSOC) on the basis of EHR data. MATERIALS AND METHODS: CLOBNET is built using open-source software to make data preprocessing, analysis, and model training user friendly. The source code of CLOBNET is available in GitHub. The HGSOC data set was based on a prospective cohort of 208 patients with HGSOC who were treated at Turku University Hospital, Finland, from 2009 to 2019 for whom comprehensive clinical and EHR data were available. RESULTS: We trained machine learning (ML) models using clinical data, including a herein developed dissemination score that quantifies the disease burden at the time of diagnosis, to identify patients with progressive disease (PD) or a complete response (CR) on the basis of RECIST (version 1.1). The best performance was achieved with a logistic regression model, which resulted in an area under receiver operating characteristic curve (AUROC) of 0.86, with a specificity of 73% and a sensitivity of 89%, when it classified between patients who experienced PD and CR. CONCLUSION: We have developed an open-source computational infrastructure, CLOBNET, that enables effective and rapid analysis of EHR and other clinical data. Our results demonstrate that CLOBNET allows predictions to be made on the basis of EHR data to address clinically relevant questions.

Prospective Longitudinal ctDNA Workflow Reveals Clinically Actionable Alterations in Ovarian Cancer.

PURPOSE: Circulating tumor DNA (ctDNA) detection is a minimally invasive technique that offers dynamic molecular snapshots of genomic alterations in cancer. Although ctDNA markers can be used for early detection of cancers or for monitoring treatment efficacy, the value of ctDNA in guiding treatment decisions in solid cancers is controversial. Here, we monitored ctDNA to detect clinically actionable alterations during treatment of high-grade serous ovarian cancer, the most common and aggressive form of epithelial ovarian cancer with a 5-year survival rate of 43%. PATIENTS AND METHODS: We implemented a clinical ctDNA workflow to detect clinically actionable alterations in more than 500 cancer-related genes. We applied the workflow to a prospective cohort consisting of 78 ctDNA samples from 12 patients with high-grade serous ovarian cancer before, during, and after treatment. These longitudinal data sets were analyzed using our open-access ctDNA-tailored bioinformatics analysis pipeline and in-house Translational Oncology Knowledgebase to detect clinically actionable genomic alterations. The alterations were ranked according to the European Society for Medical Oncology scale for clinical actionability of molecular targets. RESULTS: Our results show good concordance of mutations and copy number alterations in ctDNA and tumor samples, and alterations associated with clinically available drugs were detected in seven patients (58%). Treatment of one chemoresistant patient was changed on the basis of detection of ERBB2 amplification, and this ctDNA-guided decision was followed by significant tumor shrinkage and complete normalization of the cancer antigen 125 tumor marker. CONCLUSION: Our results demonstrate a proof of concept for using ctDNA to guide clinical decisions. Furthermore, our results show that longitudinal ctDNA samples can be used to identify poor-responding patients after first cycles of chemotherapy. We provide what we believe to be the first comprehensive, open-source ctDNA workflow for detecting clinically actionable alterations in solid cancers.