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Introduction Breast cancer (BC) is the most common cancer in women, with a high disease burden, especially in the advanced disease stages. Our study investigated the metabolomic profile of breast cancer patients’ serum with the aim of identifying novel diagnostic biomarkers that could be used, especially for early disease detection. Materials and Methods Using targeted metabolomic serum profiling based on high-performance liquid chromatography mass spectrometry, women with BC (n = 39) and a control group (n = 21) were examined for 232 endogenous metabolites. Results The top performing biomarkers included acylcarnitines (ACs) and 9,12-linoleic acid. A combined panel of the top 4 biomarkers achieved 83% sensitivity and 81% specificity, with an area under the curve (AUC) of 0.839 (95% confidence interval, 0.811-0.867). Individual markers also provided significant predictive values: AC 12:0, sensitivity of 72%, specificity of 67%, and AUC of 0.71; AC 14:2, sensitivity of 74%, specificity of 71%, and AUC of 0.73; AC 14:0: sensitivity of 67%, specificity of 81%, and AUC of 0.73; and 9,12-linoleic acid, sensitivity of 69%, specificity of 67%, and AUC of 0.71. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination. Conclusion Using mass spectrometry-targeted metabolomic profiling, ACs and 9,12-linoleic acid were identified as potential diagnostic biomarkers for breast cancer. Additionally, these identified metabolites could provide additional insight into cancer cell metabolism.

3606 Background: Colorectal cancer (CRC) screening programs suffer from poor uptake and biomarkers have limited diagnostic accuracy. The measurement of the methylation status of tumor-derived cell-free DNA in plasma may address these challenges. We used a targeted methylation panel, tumor-derived signal deduction and machine learning algorithm to refine a blood test for the detection of early-stage CRC. Methods: This was a prospective, international multicenter observational cohort study. Plasma samples were collected either prior to a scheduled colonoscopy as part of standard colorectal cancer screening or prior to colonic surgery for primary CRC. Differentially methylated regions (DMRs) were initially selected by analyzing CRC and control tissue samples with whole genome bisulfite sequencing. A targeted sequencing assay was designed to capture these DMRs in plasma ctDNA. Individual sequencing reads were evaluated for cancer-specific methylation signal and scores calculated for each DMR in a sample. A panel of methylation scores originating from 203 DMRs was used in a prediction model building and validated in a test cohort of patients. Results: Calculated scores were used to train a machine learning model on 68 ctDNA samples from 18 early stage (I-II) and 16 late-stage (III-IV) CRC patients and 34 age, BMI, gender and country of origin matched neoplasia-free controls (median age 63 [50-74], mean BMI 27 [19.5-37], female 50%, Spanish and Ukrainian population, distal cancers 50%). This model was then applied to an independent set of subjects from Spanish, Ukraine and Germany, including 36 stage I-IV cancer patients (median age 61.5 [55-82], BMI 28 [16-39], female 47%, 42% of the tumors were distal) and 159 age and sex matched controls. 87 of the control patients had a negative colonoscopy finding (cNEG), 19 had hyperplastic polyps (HP), 37 had small non-advanced adenomas (NAA) and 16 were diagnosed with other benign gastrointestinal diseases (GID). The model correctly classified 92% (33/36) of CRC patients. Sensitivity per cancer stage ranged from 83% (5/6) for stage I, 92% (11/12) for stage II, 92% (12/13) for stage III to 100% (5/5) for stage IV. Specificity of the model was 97% (154/159), with 100% (37/37) NAA, 94% (15/16) GID, 95% (18/19) HP and 97% cNEG patients correctly identified. Lesion location, gender, BMI, age and country of origin were not significantly correlated to prediction outcome. Conclusions: Methylation sequencing data analyzed using read-wise scoring approach combined with a machine-learning algorithm is highly diagnostic for early-stage (I-II) CRCs (89% sensitivity at 97% specificity). This method could serve as the basis for a highly accurate and minimally invasive blood-based CRC screening test with significant implications for the clinical utility of ctDNA in early-stage cancer detection.

Background: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, and its incidence is increasing in younger persons. Established CRC screening methods include fecal immunochemical testing, which requires handling of stool, and colonoscopy, which is invasive. Many persons who remain unscreened might accept a blood-based screening test, which could have profound public health impact. DNA methylation is a stable, early, and tissue-specific event in cancer development and progression. Measuring the methylation status of tumor-derived cell-free DNA in plasma could enable identification of early-stage CRC. We first developed a plasma-based methylated DNA panel for all stages of CRC (Study 1). We then validated this panel in a subsequent study with independent samples (Study 2). Methods: Differentially methylated regions (DMRs) were initially selected by analyzing tissue data from The Cancer Genome Atlas (TCGA) database. In Study 1, candidate regions were evaluated in plasma samples of 215 patients (93 CRC, 122 controls including 31 with non-advanced adenoma) using methylation-sensitive restriction enzyme qPCR (MSRE-qPCR). This training set was used for marker evaluation and construction of a prediction model. The most promising methylation markers were selected by a support vector machine (SVM)-based machine learning classifier using a random forest algorithm. In Study 2, the selected markers were validated in an independent validation set of 724 samples collected in Spain, Ukraine, the UK, and the US (152 CRC, 622 controls including 148 with non-advanced adenoma and 52 with non-CRC cancer). Results: Tissue data analysis yielded 180 potential DMRs. In Study 1, a panel consisting of the top 12 methylation markers was selected. In Study 2, the prediction model based on this panel correctly classified 117/152 CRC (77%) patients. Sensitivity improved with CRC stage, ranging from 71% (25/35) for stage I, 76% (37/49) for stage II, 77% (37/48) for stage III, to 100% (14/14) for stage IV. Specificity of the model was 88% (544/622). Among non-CRC cancer cases, specificity was 83% overall (73% [19/26] for lung cancer and 92% [24/26] for breast cancer). Conclusions: We developed a prediction model based on a novel plasma 12-marker methylation panel that demonstrated highly promising test performance characteristics for early-stage CRC detection in a validation study using independent samples. This method could serve as the basis for a highly accurate and minimally invasive blood-based CRC screening test. Citation Format: Kristi Kruusmaa, Marko Bitenc, Walter Pulverer, Andreas Weinhaeusel, José Luis Rodrigo Agudo, Andrés Barrientos Delgado, Andrés Sánchez Yagüe, José Manuel Rodríguez Laiz, Marko Chersicola, Primož Knap, Silvia Schönthaler, Uri Ladabaum. Development and clinical performance of an accurate cell-free DNA (cfDNA) methylation assay for early detection of colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A05.

Introduction Epithelial ovarian cancer (EOC) is a disease with a poor survival rate mostly due to its discovery in late stages. The aim of this study was to investigate the metabolomic profile of ovarian cancer with the intention of identifying and describing novel biomarkers with diagnostic potential. Material and methods Targeted serum metabolomic profiling was performed on 15 patients with ovarian cancer, 21 healthy controls and 21 patients with benign ovarian conditions, using HPLC–TQ/MS. Results Panel of 49 top performing biomarkers shows separation between EOC and controls with 87% specificity and 87% sensitivity with AUC of 93% (CI 90%–95%). Using only 5 top biomarkers, specificity of 80% and sensitivity of 83% was achieved on extended sample set. Most significant biomarkers belong to sphingolipid classes, especially long chain ceramides and sphingomyelins. Different concentrations of various fatty acid chain lengths ceramides and sphingomyelins are also implying their respective roles in cell proliferation and growth inhibition. Conclusion Long chain ceramides and sphingomyelins may serve as a novel biomarker for epithelial ovarian cancer detection and may also offer insight into the role of sphingolipid metabolism in cell proliferation.

In the period 1988-1992, 74 consecutive radically resected patients with NSCLC were randomised to postoperative radiotherapy or surgery alone in order to evaluate the influence of postoperative radiotherapy on survival. There were 61 males and 13 females, aged 35-80 years, median 59 years. Their distribution by stage was as follows: pT1N2 = 19, pT2N2 = 54, pT3N2 = one patient; histology: 32 squamous, 32 adeno and 10 large cell carcinomas; surgery: atypical resection in six, lobectomy in 27, bilobectomy in ten, and pneumonectomy in 31 patients. In 27 patients, only one lymph node in a single mediastinal lymph node site was affected; in 31 patients more than one lymph node in one site; in 16 patients more sites were affected. In 35/74 patients radiotherapy of hilar and mediastinal sites with 3000 cGy in 2 weeks was performed. On December 31, 1994, 19 patients (26%) were still alive; 39/55 patients died of the following causes: locoregional failure-10(26%), distant metastases- 25 (64%), other tumor-unrelated causes-four patients (10%). Five-year survival rates did not show statistically significant differences between the irradiated and surgically treated patients only with respect to sex, pTNM stage, histology and frequency of locoregional failure. The number of metastatic mediastinal lymph nodes was the only significant prognostic factor (P < 0.005) in both randomised groups.