A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma

Genome-wide omics technology boosts deep interrogation into the clinical prognosis and inherent mechanism of pancreatic oncology. Classic LASSO methods coequally treat all candidates, ignoring individual characteristics, thus frequently deteriorating performance with comparatively more predictors. Here, we propose a wavelet-based deep learning method in variable selection and prognosis formulation for PAAD with small samples and multisource information. With the genomic, epigenomic, and clinical cohort information from The Cancer Genome Atlas, the constructed five-molecule model is validated via Kaplan-Meier survival estimate, rendering significant prognosis capability on high- and low-risk subcohorts ( p value < 0.0001), together with three predictors manifesting the individual prognosis significance ( p value: 0.0012~0.024). Moreover, the performance of the prognosis model has been benchmarked against the traditional LASSO and wavelet-based methods in the 3- and 5-year prediction AUC items, respectively. Specifically, the proposed model with discrete stationary wavelet base (bior1.5) overwhelmingly outperformed traditional LASSO and wavelet-based methods (AUC: 0.787 vs. 0.782 and 0.721 for the 3-year case; AUC: 0.937 vs. 0.802 and 0.859 for the 5-year case). Thus, the proposed model provides a more accurate perspective, but with less predictor burden for clinical prognosis in the pancreatic carcinoma study.

Molecular Prognosis of Endometrial Adenocarcinoma by Expression Patterns of the V-ATPase C1 Subunit

Comparative analysis of expression patterns of ATP6V1C1 encoding C1 subunit of V-H+-ATPase revealed that molecular alterations correlated with endometrial cancer of better prognosis were grouped with low ATP6V1C1 expression while those correlated with worse prognosis were clustered with high ATP6V1C1 expression levels. Expression patterns of C1 subunit in endometrial adenocarcinoma are associated with molecular malignancy signatures shared by histological subtypes of highest mortality rate and suggest that G3 adenocarcinoma exhibit molecular changes resembling endometrial serous carcinoma. ATP6V1C1 might serve as novel prognostic marker allowing identification of targetable pathways for high-risk endometrial cancer.

Descriptive and Functional Genomics in Acute Myeloid Leukemia (AML): Paving the Road for a Cure

Over the past decades, genetic advances have allowed a more precise molecular characterization of AML with the identification of novel oncogenes and tumor suppressors as part of a comprehensive AML molecular landscape. Recent advances in genetic sequencing tools also enabled a better understanding of AML leukemogenesis from the preleukemic state to posttherapy relapse. These advances resulted in direct clinical implications with the definition of molecular prognosis classifications, the development of treatment recommendations based on minimal residual disease (MRD) measurement and the discovery of novel targeted therapies, ultimately improving AML patients’ overall survival. The more recent development of functional genomic studies, pushed by novel molecular biology technologies (short hairpin RNA (shRNA) and CRISPR-Cas9) and bioinformatics tools design on one hand, along with the engineering of humanized physiologically relevant animal models on the other hand, have opened a new genomics era resulting in a greater knowledge of AML physiopathology. Combining descriptive and functional genomics will undoubtedly open the road for an AML cure within the next decades.

Comprehensive Molecular Characterizations of Chinese Patients With Different Subtypes of Lung Squamous Cell Carcinoma

BackgroundThis study aims to profile integrative genomic spectra of Chinese patients with different subtypes of lung squamous cell carcinoma (LUSC) and explore potential molecular prognosis factors.MethodsWe retrospectively identified 204 surgically resected LUSC patients in Shanghai Chest Hospital who underwent capture-based targeted next-generation sequencing (NGS) with a panel of 68 lung cancer‐related genes from September 2017 to January 2019. NGS was used to profile comprehensive molecular characterizations.ResultsOf 204 cases, 114 (55.9%) were keratinizing squamous cell carcinoma (KSCC), 77 (37.7%) were non-keratinizing squamous cell carcinoma (NKSCC), 13 (6.4%) were basaloid squamous cell carcinoma (BSCC), respectively. All subtypes presented similarly high proportions of mutations, including TP53, CDKN2A, and NOTCH1. A comparable prevalence of FGFR1 amplifications was identified between KSCC and NKSCC (11.4 versus 26.9%, p = 0.007). Compared with NKSCC, IGF1R amplifications were more frequent in BSCC (0 versus 15.4%, p = 0.019). We found cases with TP53 alterations had less EGFR alterations in KSCC (P = 0.013, OR = 0.158). Compared with TCGA cohorts, our Chinese cohorts exhibited statistic differences in both somatic mutations and signaling pathways. We found that STK 11 alterations and TOP2A alterations were significantly associated with higher risk of recurrence in patients with LUSC.ConclusionsSignificant differences exist among three subtypes of LUSC in molecular characterizations.