The impact of demographic change on the age at diagnosis in German head and neck cancer (HNC) patients is unclear. Here we present an evaluation of aging trends in HNC at a tertiary referral center.
...Retrospective cohort study on aging trends at the initial diagnosis of newly diagnosed patients with HNC between 2004 and 2018 at the head and neck cancer center Ulm in relation to demographic data of the catchment area.
The study population consisted of 2450 individuals diagnosed with HNC with a mean age of 62.84 (±11.67) years. We observed a significant increase in annual incidence rates and mean age over time. Mean age among HNC patients increased significantly more than among the population in the catchment area. Whereas the incidence rate of patients <50 years did not change, the incidence of HNC patients aged ≥70 years increased the most. The mean patient age in the main tumor sites increased significantly. Surprisingly, HPV-positive patients were not younger than HPV-negative patients, but showed a non-significant trend towards a higher mean age (63.0 vs. 60.7 years).
Increasing incidence rates in older patients pose a challenge for health care systems. A nationwide study is needed to assess the dynamics and impact of aging on the incidence of HNC.
For a growing number of tumors the BRAF V600E mutation carries therapeutic relevance. In histiocytic proliferations the distribution of BRAF mutations and their relevance has not been clarified. Here ...we present a retrospective genotyping study and a prospective observational study of a patient treated with a BRAF inhibitor. Genotyping of 69 histiocytic lesions revealed that 23/48 Langerhans cell lesions were BRAF-V600E-mutant whereas all non-Langerhans cell lesions (including dendritic cell sarcoma, juvenile xanthogranuloma, Rosai-Dorfman disease, and granular cell tumor) were wild-type. A metareview of 29 publications showed an overall mutation frequency of 48.5% and with N=653 samples this frequency is well defined. The BRAF mutation status cannot be predicted based on clinical parameters and outcome analysis showed no difference. Genotyping identified a 45 year-old woman with an aggressive and treatment-refractory, ultrastructurally confirmed systemic BRAF-mutant LCH. Prior treatments included glucocorticoid/vinblastine and cladribine-monotherapy. Treatment with vemurafenib over 3 months resulted in a dramatic metabolic response by FDG-PET and stable radiographic disease; the patient experienced progression after 6 months. In conclusion, BRAF mutations in histiocytic proliferations are restricted to lesions of the Langerhans-cell type. While for most LCH-patients efficient therapies are available, patients with BRAF mutations may benefit from the BRAF inhibitor vemurafenib.
Suppressor of cytokine signaling 1 (SOCS1) mutations are among the most frequent somatic mutations in classical Hodgkin lymphoma (cHL), yet their prognostic relevance in cHL is unexplored. Here, we ...performed laser-capture microdissection of Hodgkin/Reed-Sternberg (HRS) cells from tumor samples in a cohort of 105 cHL patients. Full-length SOCS1 gene sequencing showed mutations in 61% of all cases (n = 64/105). Affected DNA-motifs and mutation pattern suggest that many of these SOCS1 mutations are the result of aberrant somatic hypermutation and we confirmed expression of mutant alleles at the RNA level. Contingency analysis showed no significant differences of patient-characteristics with HRS-cells containing mutant vs. wild-type SOCS1. By predicted mutational consequence, mutations can be separated into those with non-truncating point mutations ('minor' n = 49/64 = 77%) and those with length alteration ('major'; n = 15/64 = 23%). Subgroups did not differ in clinicopathological characteristics; however, patients with HRS-cells that contained SOCS1 major mutations suffered from early relapse and significantly shorter overall survival (P = 0.03). The SOCS1 major status retained prognostic significance in uni-(P = 0.016) and multivariate analyses (P = 0.005). Together, our data indicate that the SOCS1 mutation type qualifies as a single-gene prognostic biomarker in cHL.
Over the past two decades, there has been a rising trend in malignant melanoma incidence worldwide. In 2008, Germany introduced a nationwide skin cancer screening program starting at age 35. The aims ...of this study were to analyse the distribution of malignant melanoma tumour stages over time, as well as demographic and regional differences in stage distribution and survival of melanoma patients.
Pooled data from 61 895 malignant melanoma patients diagnosed between 2002 and 2011 and documented in 28 German population-based and hospital-based clinical cancer registries were analysed using descriptive methods, joinpoint regression, logistic regression and relative survival.
The number of annually documented cases increased by 53.2% between 2002 (N = 4 779) and 2011 (N = 7 320). There was a statistically significant continuous positive trend in the proportion of stage UICC I cases diagnosed between 2002 and 2011, compared to a negative trend for stage UICC II. No trends were found for stages UICC III and IV respectively. Age (OR 0.97, 95% CI 0.97-0.97), sex (OR 1.18, 95% CI 1.11-1.25), date of diagnosis (OR 1.05, 95% CI 1.04-1.06), 'diagnosis during screening' (OR 3.24, 95% CI 2.50-4.19) and place of residence (OR 1.23, 95% CI 1.16-1.30) had a statistically significant influence on the tumour stage at diagnosis. The overall 5-year relative survival for invasive cases was 83.4% (95% CI 82.8-83.9%).
No distinct changes in the distribution of malignant melanoma tumour stages among those aged 35 and older were seen that could be directly attributed to the introduction of skin cancer screening in 2008.
Prognostication in pancreatic ductal adenocarcinoma (PDAC) remains a challenge. Recently, a link between mutated KRAS and glutamic-oxaloacetic transaminase (GOT1/AST1) has been described as part of ...the metabolic reprogramming in PDAC. The clinical relevance of this novel metabolic KRAS-GOT1 link has not been determined in primary human patient samples. Here we studied the GOT1 expression status as a prognostic biomarker in PDAC. We employed three independent PDAC cohorts with clinicopathological- and follow-up data: a) ICGC, comprising 57 patients with whole-exome sequencing and genome-wide expression profiling; b) ULM, composed of 122 surgically-treated patients with tissue-samples and KRAS status; c) a validation cohort of 140 primary diagnostic biopsy samples. GOT1 expression was assessed by RNA level (ICGC) or immunolabeling (ULM/validation cohort). GOT1 expression varied (ICGC) and correlation with the KRAS mutation- and expression status was imperfect (P = 0.2, ICGC; P = 0.8, ULM). Clinicopathological characteristics did not differ when patients were separated based on GOT1 high vs. low (P = 0.08-1.0); however, overall survival was longer in patients with GOT1-expressing tumors (P = 0.093, ICGC; P = 0.049, ULM). Multivariate analysis confirmed GOT1 as an independent prognostic marker (P = 0.009). Assessment in univariate (P = 0.002) and multivariate models in the validation cohort (P = 0.019), containing 66% stage IV patients, confirmed the independency of GOT1. We propose the GOT1 expression status as a simple and reliable prognostic biomarker in pancreatic ductal adenocarcinoma.
Evading apoptosis is a hallmark of pancreatic cancer. In pancreatic cancer models, chemotherapy down-regulates the antiapoptotic protein cellular FLICE inhibitory protein (c-FLIP), which renders ...cells sensitive to apoptosis. Currently, the relevance of c-FLIP expression as a biomarker in pancreatic cancer is unknown, and here we assessed the prognostic significance of the c-FLIP expression status in a large cohort of pancreatic cancer patients with clinical follow-up.
Cellular FLICE inhibitory protein expression levels were determined by immunohistochemistry in 120 surgically resected ductal pancreatic adenocarcinomas. Survival analysis by c-FLIP status was compared with established clinicopathologic biomarkers as well as Ki-67 and cyclooxygenase 2 expression levels as 2 other established independent prognostic biomarkers in pancreatic cancer.
Of 120 tumors, 111 (91%) were c-FLIP positive, whereas 9 (9%) were completely c-FLIP negative. Cyclooxygenase 2 was positive in 59 cases (52%), and Ki-67 was positive in more than 10% of tumor cells in 51 cases (44%). Univariate and multivariate survival analysis (correcting for stage, grade, and proliferation index) showed that c-FLIP is an independent prognostic factor. Specifically, c-FLIP negativity identifies 9% of patients with a highly aggressive disease course (P = 0.0001).
Cellular FLICE inhibitory protein expression status is a valuable prognostic biomarker in pancreatic cancer.
•Relatively simple machine learning (ML) methods can be highly accurate with GCxGC.•Chemical fingerprints from smart templates capture information-rich patterns.•Untargeted TIC profiles can be nearly ...as useful as target quantifier ions.
Machine learning (ML) has been used previously to recognize particular patterns of constituent compounds. Here, ML is used with comprehensive chemical fingerprints that capture the distribution of all constituent compounds to flexibly perform various pattern recognition tasks. Such pattern recognition requires a sequence of chemical analysis, data analysis, and pattern analysis. Chemical analysis with comprehensive multidimensional chromatography is a maturing approach for highly effective separations of complex samples and so provides a solid foundation for undertaking comprehensive chemical fingerprinting. Data analysis with smart templates employs marker peaks and chemical logic for chromatographic alignment and peak-regions to delineate chromatographic windows in which analytes are quantified and matched consistently across chromatograms to create chemical profiles that serve as complete fingerprints. Pattern analysis uses ML techniques with the resulting fingerprints to recognize sample characteristics, e.g., for classification. Our experiments evaluated the effectiveness of seventeen different ML techniques for various classification problems with chemical fingerprints from a rich data set from 126 wine samples of different varieties, geographic regions, vintages, and wineries. Results of these experiments showed an accuracy range from 58% to 88% for different ML methods on the most difficult classification problems and 96% to 100% for different ML methods on the least difficult classification problems. Averaged over 14 classification problems, accuracy for the different methods ranged from 80% to 90%, with some relatively simple ML techniques among the top-performing methods.
PDF
