In patients with advanced rectal cancer (cUICC II and III) multimodality therapy resulted in better long-term local tumor control. Ongoing clinical trials are focusing on therapy intensification to improve disease-free (DFS) and cancer-specific survival (CSS), the integration of biomarkers for prediction of individual recurrence risk, and the identification of new targets. In this context, we investigated HER-2, a member of the epidermal growth factor receptor family, whose expression pattern and role was unclear in rectal cancer. A total of 264 patients (192 male, 72 female; median age 64 y) received standardized multidisciplinary treatment according to protocols of phase II/III trials of the German Rectal Cancer Study Group. HER-2 status was determined in pretherapeutic biopsies and resection specimens using immunohistochemistry scoring and detection of silver in situ hybridization amplification. Tumors with an immunohistochemistry score of 3 or silver in situ hybridization ratios of ≥2.0 were classified HER-2 positive; these results were correlated with clinicopathologic parameters [eg, resection (R) status, nodal status ((y)pN)], DFS, and CSS. Positive HER-2 status was found in 12.4% of biopsies and in 26.7% of resected specimens. With a median follow-up of 46.5 months, patients with HER-2 positivity showed in trend a better DFS (P=0.1) and a benefit in CSS (P=0.03). The 5-year survival rate was 96.0% (HER-2 positive) versus 80.0% (HER-2 negative). In univariate and multivariate analyses, HER-2 was an independent predictor for CSS (0.02) along with the (y)pN status (P<0.00001) and R status (P=0.011). HER-2 amplification is detectable in a relevant proportion (26.7%) of rectal cancer patients. For the development of innovative new therapies, HER-2 may represent a promising target and should be further assessed within prospective clinical trials.

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The aim of this study was to discriminate malignant and benign clinical T1 renal masses on routinely acquired computed tomography (CT) images using radiomics and machine learning techniques.Adult patients undergoing surgical resection and histopathological analysis of clinical T1 renal masses were included. Preoperative CT studies in venous phase from multiple referring centers were included, without restriction to specific CT scanners, slice thickness, or degrees of artifacts. Renal masses were segmented and 120 standardized radiomic features extracted. Machine learning algorithms were used to predict malignancy of renal masses using radiomics features and cross-validation. Diagnostic accuracy of machine learning models and assessment by independent blinded radiologists were compared based on the gold standard of histopathologic diagnosis.A total of 94 patients met inclusion criteria (benign renal masses: n = 18; malignant: n = 76). CT studies from 18 different scanners were assessed with median slice thickness of 2.5 mm and artifacts in 15 cases (15.9%).Area under the receiver-operating-characteristics curve (AUC) of random forest (random forest [RF], AUC = 0.83) was significantly higher compared to the radiologists (AUC = 0.68, P = .047). Sensitivity was significantly higher for RF versus radiologists (0.88 vs 0.80, P = .045), whereas specificity was numerically higher for RF (0.67 vs 0.50, P = .083).Although limited by an overall small sample size and few benign renal tumors, a radiomic features and machine learning approach suggests a high diagnostic accuracy for discrimination of malignant and benign clinical T1 renal masses on venous phase CT. The presented algorithm robustly outperforms human readers in a real-life scenario with nonstandardized imaging studies from various referring centers. Abbreviations: AML = angiomyolipoma, AUC = area under the ROC curve, CD = cluster of differentiation, CK = cytokeratin, CT = computed tomography, HE = hematoxylin-eosin, HMB = human melanoma black, ICC = interobserver correlation coefficient, IQR = interquartile range, KNN = k-nearest neighbor, NN = neural network, POM = probability of malignancy, RCC = renal cell carcinoma, RF = random forest, RFE = recursive feature elimination, ROC = receiver operating characteristics, ROI = region of interest, SVM = c, US = United States, XG boost = extreme gradient boosting.

Intrinsic and acquired resistance to the monoclonal antibody drug trastuzumab is a major problem in the treatment of HER2-positive breast cancer. A deeper understanding of the underlying mechanisms could help to develop new agents. Our intention was to detect genes and single nucleotide polymorphisms (SNPs) affecting trastuzumab efficiency in cell culture. Three HER2-positive breast cancer cell lines with different resistance phenotypes were analyzed. We chose BT474 as model of trastuzumab sensitivity, HCC1954 as model of intrinsic resistance, and BTR50, derived from BT474, as model of acquired resistance. Based on RNA-Seq data, we performed differential expression analyses on these cell lines with and without trastuzumab treatment. Differentially expressed genes between the resistant cell lines and BT474 are expected to contribute to resistance. Differentially expressed genes between untreated and trastuzumab treated BT474 are expected to contribute to drug efficacy. To exclude false positives from the candidate gene set, we removed genes that were also differentially expressed between untreated and trastuzumab treated BTR50. We further searched for SNPs in the untreated cell lines which could contribute to trastuzumab resistance. The analysis resulted in 54 differentially expressed candidate genes that might be connected to trastuzumab efficiency. 90% of 40 selected candidates were validated by RT-qPCR. ALPP, CALCOCO1, CAV1, CYP1A2 and IGFBP3 were significantly higher expressed in the trastuzumab treated than in the untreated BT474 cell line. GDF15, IL8, LCN2, PTGS2 and 20 other genes were significantly higher expressed in HCC1954 than in BT474, while NCAM2, COLEC12, AFF3, TFF3, NRCAM, GREB1 and TFF1 were significantly lower expressed. Additionally, we inferred SNPs in HCC1954 for CAV1, PTGS2, IL8 and IGFBP3. The latter also had a variation in BTR50. 20% of the validated subset have already been mentioned in literature. For half of them we called and analyzed SNPs. These results contribute to a better understanding of trastuzumab action and resistance mechanisms.