Group-Assisted Purification Chemistry for Asymmetric Mannich-type Reaction of Chiral N-Phosphonyl Imines with Azlactones Leading to Syntheses of α-Quaternary α,ß-Diamino Acid Derivatives †.

An asymmetric Mannich-type reaction between chiral N-phosphonyl imines and azlactones [oxazol-5(4H)-ones] has been established under convenient conditions at room temperature. The reaction was performed without using any bases, additives, or catalysts to achieve up to excellent chemical yields and diastereoselectivity for 32 examples. The α-quaternary syn-α,ß-diamino acid products were purified simply by washing the crude mixtures with cosolvents, following the group-assisted purification chemistry/technology, without involving traditional chromatography or recrystallization methods. The auxiliary can be readily removed and recycled for reuse. The absolute configuration was unambiguously assigned by X-ray structural analysis.

Blood-based screening for HPV-associated cancers.

Background: HPV-associated oropharyngeal cancer (HPV+OPSCC) is the most common HPV-associated cancer in the United States yet unlike cervical cancer lacks a screening test. HPV+OPSCCs are presumed to start developing 10-15 years prior to clinical diagnosis. Circulating tumor HPV DNA (ctHPVDNA) is a sensitive and specific biomarker for HPV+OPSCC. Taken together, blood-based screening for HPV+OPSCC may be feasible years prior to diagnosis. Methods: We developed an HPV whole genome sequencing assay, HPV-DeepSeek, with 99% sensitivity and specificity at clinical diagnosis. 28 plasma samples from HPV+OPSCC patients collected 1.3-10.8 years prior to diagnosis along with 1:1 age and gender-matched controls were run on HPV-DeepSeek and an HPV serology assay. Results: 22/28 (79%) of cases and 0/28 controls screened positive for HPV+OPSCC with 100% detection within four years of diagnosis and a maximum lead time of 7.8 years. We next applied a machine learning model classifying 27/28 cases (96%) with 100% detection within 10 years. Plasma-based PIK3CA gene mutations, viral genome integration events and HPV serology were used to orthogonally validate cancer detection with 68% (19/28) of the cohort having multiple cancer signals detected. Molecular fingerprinting of HPV genomes was performed across patients demonstrating that each viral genome was unique, ruling out contamination. In patients with tumor blocks from diagnosis (15/28), molecular fingerprinting was performed within patients confirming the same viral genome across time. Conclusions: We demonstrate accurate blood-based detection of HPV-associated cancers with lead times up to 10 years before clinical cancer diagnosis and in doing so, highlight the enormous potential of ctDNA-based cancer screening.

Double trouble: Synchronous and metachronous primaries confound ctHPVDNA monitoring.

BACKGROUND: Human papillomavirus-associated head and neck squamous cell carcinoma (HPV + HNSCC) occurs in the oropharynx (HPV + OPSCC), sinonasal cavity (HPV + SNSCC), and nasopharynx (HPV + NPC). Circulating tumor HPV DNA (ctHPVDNA) is an accurate tool for diagnosis, treatment monitoring, and recurrence detection. An emerging challenge with ctHPVDNA is that ~7.4% of HPV + HNSCC patients develop synchronous or metachronous HPV+ primaries, which could confound ctHPVDNA monitoring. METHODS: We describe a 65-year-old patient with T2N1M0 HPV16 + OPSCC and a 55-year-old patient with T2N2M0 HPV16 + OPSCC. Both patients were enrolled in our prospective observational ctHPVDNA study with longitudinal blood collections throughout treatment. Both patients developed multiple HPV+ primaries. RESULTS: Detailed discussion of the patients' treatment courses, the subsequent diagnoses of their second HPV+ primaries, and their ctHPVDNA monitoring is presented. CONCLUSIONS: As ctHPVDNA use becomes more prevalent, it is important to recognize that an increase in ctHPVDNA can come not only from the primary tumor or metastatic clones, but also from synchronous or metachronous second primaries.

Identification of cancer prognosis-associated functional modules using differential co-expression networks.

The rapid accumulation of cancer-related data owing to high-throughput technologies has provided unprecedented choices to understand the progression of cancer and discover functional networks in multiple cancers. Establishment of co-expression networks will help us to discover the systemic properties of carcinogenesis features and regulatory mechanisms of multiple cancers. Here, we proposed a computational workflow to identify differentially co-expressed gene modules across 8 cancer types by using combined gene differential expression analysis methods and a higher-order generalized singular value decomposition. Four co-expression modules were identified; and oncogenes and tumor suppressors were significantly enriched in these modules. Functional enrichment analysis demonstrated the significantly enriched pathways in these modules, including ECM-receptor interaction, focal adhesion and PI3K-Akt signaling pathway. The top-ranked miRNAs (mir-199, mir-29, mir-200) and transcription factors (FOXO4, E2A, NFAT, and MAZ) were identified, which play an important role in deregulating cellular energetics; and regulating angiogenesis and cancer immune system. The clinical significance of the co-expressed gene clusters was assessed by evaluating their predictability of cancer patients' survival. The predictive power of different clusters and subclusters was demonstrated. Our results will be valuable in cancer-related gene function annotation and for the evaluation of cancer patients' prognosis.

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.