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(1) Background: molecular tumor boards (MTBs) are crucial instruments for discussing and allocating targeted therapies to suitable cancer patients based on genetic findings. Currently, limited evidence is available regarding the regional impact and the outreach component of MTBs; (2) Methods: we analyzed MTB patient data from four neighboring Bavarian tertiary care oncology centers in Würzburg, Erlangen, Regensburg, and Augsburg, together constituting the WERA Alliance. Absolute patient numbers and regional distribution across the WERA-wide catchment area were weighted with local population densities; (3) Results: the highest MTB patient numbers were found close to the four cancer centers. However, peaks in absolute patient numbers were also detected in more distant and rural areas. Moreover, weighting absolute numbers with local population density allowed for identifying so-called white spots—regions within our catchment that were relatively underrepresented in WERA MTBs; (4) Conclusions: investigating patient data from four neighboring cancer centers, we comprehensively assessed the regional impact of our MTBs. The results confirmed the success of existing collaborative structures with our regional partners. Additionally, our results help identifying potential white spots in providing precision oncology and help establishing a joint WERA-wide outreach strategy.
Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.
Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).
Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.
Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.
Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.
Background
Multimodal treatment strategies – perioperative chemotherapy (CTx) and radical surgery – are currently accepted as treatment standard for locally advanced gastric cancer. However, the role of adjuvant postoperative CTx (postCTx) in addition to neoadjuvant preoperative CTx (preCTx) in this setting remains controversial.
Methods
Between 4/2006 and 12/2013, 116 patients with locally advanced gastric cancer were treated with preCTx. 72 patients (62 %), in whom complete tumor resection (R0, subtotal/total gastrectomy with D2-lymphadenectomy) was achieved, were divided into two groups, one of which receiving adjuvant therapy (n = 52) and one without (n = 20). These groups were analyzed with regard to survival and exclusion criteria for adjuvant therapy.
Results
Postoperative complications, as well as their severity grade, did not correlate with fewer postCTx cycles administered (p = n.s.). Long-term survival was shorter in patients receiving postCTx in comparison to patients without postCTx, but did not show statistical significance. In per protocol analysis by excluding two patients with perioperative death, a shorter 3-year survival rate was observed in patients receiving postCTx compared to patients without postCTx (3-year survival: 71.2 % postCTx group vs. 90.0 % non-postCTx group; p = 0.038).
Conclusion
These results appear contradicting to the anticipated outcome. While speculative, they question the value of post-CTx. Prospectively randomized studies are needed to elucidate the role of postCTx.
Background
The management of rectal cancer (RC) has substantially changed over the last decades with the implementation of neoadjuvant chemoradiotherapy, adjuvant therapy and improved surgery such as total mesorectal excision (TME). It remains unclear in which way these approaches overall influenced the rate of local recurrence and overall survival.
Methods
Clinical, histological and survival data of 658 out of 662 consecutive patients with RC were analyzed for treatment and prognostic factors from a prospectively expanded single-institutional database. Findings were then stratified according to time of diagnosis in patient groups treated between 1993 and 2001 and 2002 and 2010.
Results
The study population included 658 consecutive patients with rectal cancer between 1993 and 2010. Follow up data was available for 99.6% of all 662 treated patients. During the time period between 2002 and 2010 significantly more patients underwent neoadjuvant chemoradiotherapy (17.6% vs. 60%) and adjuvant chemotherapy (37.9% vs. 58.4%). Also, the rate of reported TME during surgery increased. The rate of local or distant metastasis decreased over time, and tumor related 5-year survival increased significantly with from 60% to 79%.
Conclusion
In our study population, the implementation of treatment changes over the last decade improved the patient’s outcome significantly. Improvements were most evident for UICC stage III rectal cancer.