The curvature of proton‐inventory functions (rate constants in mixtures of protium and deuterium oxides) for hydrolysis of substrates of varying structure by trypsins from four different sources has been used as a signal of transition‐state stabilization by a short, strong hydrogen bonds (“low‐barrier hydrogen bonds”) at the histidine‐aspartate site of the catalytic triad. Previous estimates suggested contributions as large as 85 kJ mol−1 but the current findings are consistent with contributions of no more than a few kJ mol−1.

Background: The advent of Next-Generation Sequencing (NGS), and other molecular diagnostic technologies, has enabled the use of genomic information to guide targeted treatment in cancer patients. While this precision oncology approach can yield exciting clinical outcomes, the innumerable genomic variants identified in individual tumors effectively establishes each case as a unique N=1 clinical presentation. This scenario is contrary to a basic dogma of medical practice where historical cases and treatment outcomes guide future management and therapeutic decisions. Aggregation of large data sets, on a multi-institutional basis, has the potential to overcome the N=1 paradox and yield management insights in the implementation of precision oncology. Methods: We have formed the Oncology Precision Network (OPeN), an oncology data sharing consortium, to aggregate big data sets consisting of clinical, genomic, pharmacological, and treatment response data from diverse patient cases. Data from Intermountain Healthcare, Stanford University, and Swedish Cancer Institute-Providence St. Joseph Health, as well as other institutions, comprises the database and is derived from 79 hospitals, over 800 physician clinics and more than 50,000 annual cases. Results: The OPeN database can be interrogated by variant type, specific therapeutics, clinical outcomes, and by grouped variables, in a structured data format. The overarching IT platform is a cloud based, open source, triple store precision oncology solution, Syapse. These data are yielding valuable insights, including tumor mutational burden (TMB) scores and their correlation to immunotherapy response, clinical response in various drug-gene combinations, and therapy-specific adverse events. Conclusions: We anticipate this resource will be used by the Molecular Tumor Boards of contributing institutions for clinical interpretation, and by treating providers to overcome the N=1 challenge associated with precision oncology. Citation Format: Lincoln Nadauld, Derrick Haslem, Paul D. Tittel, Mariko Tameishi, Thomas Brown, James Ford. OPeN: the oncology precision network data sharing consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 998. doi:10.1158/1538-7445.AM2017-998

Background: Cancer care is evolving to a model of precision medicine where genomic changes in a patient's tumor are used to inform individualized management (mgmt). The optimal approach and impact of tumor profiling on cancer care remain important research questions. We report the impact on clinical decision-making by results from a PMRP in a research practice. Methods: A custom designed next generation sequencing (NGS) 68 gene alteration (GA) panel, covering clinically relevant genes and regions was developed in 2014. The NGS results were used to: 1) prioritize standard therapies; 2) match patients (pts) with clinical trials (CT); and 3) serve as a data mining resource. NGS testing was offered early in the course of mgmt. An Institutional Review Board approved prospective registration protocol (PMRP) was activated in 2014, with the objective of establishing a centralized longitudinal, molecular phenotypic, and research data repository. Primary endpoints include proportion of pts where NGS impacted mgmt, to include enrollment onto CT. A cloud-based informatics platform was developed to: manage PMRP; facilitate CT matching; perform quality assurance/quality improvement; pursue research initiatives. Results: As of 11/15/2016, 869 pts gave informed consent, with 844 pts enrolled. The top primary sites included: breast (115); colorectal (111); central nervous system (103); lung (91); ovary (49); hematologic malignancies (46); pancreas (37); uterus (28); esophagus (25); skin (21). Of solid tumor pts with documented clinical stage, 130 (40%) pts had early stage cancer (I, II and III), and 193 (60%) pts had advanced stage (IV) cancer. NGS results: 739 (88%) pts with GA found; 27 (3%) pts without GA. Of pts with GA, 178 (24%) pts had actionable (on-label drugs) GA and 476 (64%) pts had applicable (off-label or CT) GA, for a total of 546 (74%) pts with actionable and/or applicable GA. The top actionable GA were: KRAS (125); PIK3CA (17); BRAF (13); EGFR (12); NRAS (11); AKT1 (3); TET2 (2); ERBB2 (2); HRAS (2). The top applicable GA, included: TP53 (225); TPMT (78); TYMS (78); PIK3CA (77); APC (56); PTEN (52); IDH1 (34); CDNK2A (22); CTNNB1 (18); TET2 (16). Care mgmt impact was reported by physicians for 508 pts with actionable/applicable GA. Physicians reported mgmt impact, at time of reporting, for 105 (21%) pts, to include: new treatment (Tx) in 30 (6%) pts; no Tx given in 18 (4%) pts; Tx changed in 12 (2%) pts; Tx stopped in 1 (<1%) pt; 6 (1%) pts enrolled onto CT. In 403 (79%) pts, physicians reported no mgmt impact, to include: insufficient evidence in 294 (58%) pts; drugs/CT access in 91 (18%) pts; refused Tx in 17 (3%) pts. Conclusions: NGS profiling of tumors with this 68 GA panel has an impact on clinical decision- making in a minority, though substantial number, of pts. Impact on CT participation remains modest. Access to drugs and CT remains an important barrier. Citation Format: Thomas D. Brown, Paul D. Tittel, Philip J. Gold, Charles W. Drescher, John M. Pagel, J D. Beatty, Patra Grevstad, Desiree Iriarte, Shlece Alexander, Madeleine Brindle, Xiaoyu Liu, Donielle O'connor, Mariko Tameishi, Danbin Xu, Anna B. Berry. Impact of a personalized medicine research program (PMRP), using targeted tumor profiling and a cloud based clinical trials matching platform, on clinical decision-making [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 997. doi:10.1158/1538-7445.AM2017-997

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