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PURPOSE With the growing number of available targeted therapeutics and molecular biomarkers, the optimal care of patients with cancer now depends on a comprehensive understanding of the rapidly evolving landscape of precision oncology, which can be challenging for oncologists to navigate alone. METHODS We developed and implemented a precision oncology decision support system, GI TARGET, (Gastrointestinal Treatment Assistance Regarding Genomic Evaluation of Tumors) within the Gastrointestinal Cancer Center at the Dana-Farber Cancer Institute. With a multidisciplinary team, we systematically reviewed tumor molecular profiling for GI tumors and provided molecularly informed clinical recommendations, which included identifying appropriate clinical trials aided by the computational matching platform MatchMiner, suggesting targeted therapy options on or off the US Food and Drug Administration–approved label, and consideration of additional or orthogonal molecular testing. RESULTS We reviewed genomic data and provided clinical recommendations for 506 patients with GI cancer who underwent tumor molecular profiling between January and June 2019 and determined follow-up using the electronic health record. Summary reports were provided to 19 medical oncologists for patients with colorectal (n = 198, 39%), pancreatic (n = 124, 24%), esophagogastric (n = 67, 13%), biliary (n = 40, 8%), and other GI cancers. We recommended ≥ 1 precision medicine clinical trial for 80% (406 of 506) of patients, leading to 24 enrollments. We recommended on-label and off-label targeted therapies for 6% (28 of 506) and 25% (125 of 506) of patients, respectively. Recommendations for additional or orthogonal testing were made for 42% (211 of 506) of patients. CONCLUSION The integration of precision medicine in routine cancer care through a dedicated multidisciplinary molecular tumor board is scalable and sustainable, and implementation of precision oncology recommendations has clinical utility for patients with cancer.

What would life on our planet be like if the Moon were to vanish; or perhaps if we had multiple moons? Earth wouldn’t be the same without its constant lunar companion, as Ethan Siegel explains

With the advent of next generation sequencing in cancer care, patients' tumors can be genomically profiled and specific genetic alterations can be targeted with precision medicine drugs. However, the abundance of patient sequencing data coupled with complex clinical trial eligibility has made it challenging to match patients to precision medicine trials. To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials. MatchMiner supports two distinct workflows: (1) patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. In MatchMiner at DFCI, there are currently 330+ precision medicine trials and genomic and genomic and clinical data from 39,000+ patients. Although MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017, its impact on patient care has not yet been extensively studied. In this study, we analyzed temporal trends of 170 MatchMiner-driven trial enrollments. We compared these 170 MatchMiner-driven trial enrollments to non-MatchMiner-driven trial enrollments to determine how MatchMiner has impacted patient enrollments. To compare MatchMiner-driven trial enrollments to non-MatchMiner-driven enrollments, we limited the non-MatchMiner group by choosing patients who enrolled on the same trials. We also ensured that all patients in both enrollment groups had a genomic report present in MatchMiner before their consent date. We then analyzed temporal trends between genomic report dates, patient consent and on-study dates, and patient views in MatchMiner. MatchMiner-driven enrollments had a significant decrease in time from genomic report date to consent date compared to non-MatchMiner-driven enrollments. Thus, clinical use of MatchMiner decreased time to enroll in a precision medicine study, and suggests that use of precision medicine trial matching tools such as MatchMiner are important for the future of patient care. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). We are committed to supporting MatchMiner as an open-source software; to our knowledge, at least five cancer centers are implementing MatchMiner. Citation Format: Harry Klein, Tali Mazor, Priti Kumari, James Lindsay, Andrea Ovalle, Ethan Siegel, Pavel Trukhanov, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform that accelerates patient enrollment on to precision medicine trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1198.

To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials based on clinical and genomic features. MatchMiner supports two distinct workflows: (1) patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017. There are currently 275+ trials curated in the system and genomic data from 26,000+ patients. Over 80% of living patients match to at least one open clinical trial, with an average of 6 trial matches per patient. At least 98 patients have enrolled on a clinical trial as a result of MatchMiner. To enable computational matching, we developed clinical trial markup language (CTML), a structured format to encode detailed information about a trial. CTML utilizes boolean logic to define clinical (e.g. cancer type), demographic (e.g. age) and genomic (e.g. specific mutations, copy number alterations, structural variants or mutational signatures) eligibility, which can be applied to individual arms of a trial. MatchMiner is an open-source two-tier web application with a Python-based REST API server and an AngularJS front-end. MatchMiner utilizes Security Assertion Markup Language (SAML)-based authentication and is fully HIPAA-compliant when hosted behind a secure institutional firewall. MatchMiner ingests clinical and genomic data, and connects to existing clinical systems, including clinical trial management systems for real-time trial status. We recently refactored the core matching algorithm (the matchengine), which improves upon the original matchengine in several ways: (1) increased granularity in reporting the reason for a match; (2) can match all patients/trials or individual patients/trials; (3) easily extensible to match based on additional data types. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). We are committed to supporting MatchMiner as an open-source software; to our knowledge, at least five cancer centers are implementing MatchMiner at their own institutions. In summary, we have defined a standard for encoding clinical trial information in a structured and computable form, and we have developed an open-source computational trial matching platform to support patient-specific trial identification as well as trial-specific patient recruitment. We are actively collaborating with clinical groups at Dana-Farber Cancer Institute and other institutions to understand the role of MatchMiner in their clinical workflows, and we are committed to continuing to evolve MatchMiner to meet clinical needs. Citation Format: Tali Mazor, Rachel B. Keller, Priti Kumai, James Lindsay, Eric Marriott, Andrea Ovalle, Ethan Siegel, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3382.

To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials based on clinical and genomic features. MatchMiner supports two distinct workflows: (1) a patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) a trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017. There are currently 250+ trials curated in the system and genomic data from 24,000+ patients. Over 82% of living patients match to at least one open clinical trial, with an average of 6 trial matches per patient. At least 85 patients have enrolled on a clinical trial as a result of MatchMiner. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). MatchMiner is a two-tier web application with a Python-based REST application programming interface (API) server and an AngularJS front-end. MatchMiner utilizes Security Assertion Markup Language (SAML)-based authentication and, when hosted behind a secure institutional firewall, is fully HIPAA-compliant. MatchMiner can connect to existing clinical systems, including clinical trial management systems for real-time trial status. To enable computational matching to clinical trials, we developed clinical trial markup language (CTML), a structured format to encode detailed information about a trial. CTML utilizes boolean logic to define clinical (e.g. cancer type), demographic (e.g. age) and genomic (e.g. specific mutations, copy number alterations, structural variants or mutational signatures) eligibility, which can be applied to individual arms of a trial. We recently refactored the core matching algorithm (the matchengine), which improves upon the original matchengine in several ways. While the original matchengine reported the reason for a patient-trial match, the refactored matchengine provides additional, more granular details. In addition, the original matchengine ran at the cohort level, matching all patients to all trials, whereas the refactored matchengine can also run against individual patients or trials, speeding up the matching process. The refactored matchengine is also easily extensible to match based on additional data types. In summary, we have defined a standard for encoding clinical trial information in a structured and computable form, and we have developed an open-source computational trial matching platform to support patient-specific trial identification as well as trial-specific patient recruitment. We are actively collaborating with clinical groups at Dana-Farber Cancer Institute to understand the role of MatchMiner in their clinical workflows, and we are committed to continuing to evolve MatchMiner to meet clinical needs. Citation Format: Tali Mazor, Rachel B Keller, Priti Kumari, James Lindsay, Eric Marriott, Andrea Ovalle, Ethan Siegel, Elizabeth H Williams, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A024. doi:10.1158/1535-7163.TARG-19-A024

Oxytocin (OT) and arginine-vasopressin (AVP) are involved in the regulation of complex social behaviors across a wide range of taxa. Despite this, little is known about the neuroanatomy of the OT and AVP systems in most non-human primates, and less in humans. The effects of OT and AVP on social behavior, including aggression, mating, and parental behavior, may be mediated primarily by the extensive connections of OT- and AVP-producing neurons located in the hypothalamus with the basal forebrain and amygdala, as well as with the hypothalamus itself. However, OT and AVP also influence social cognition, including effects on social recognition, cooperation, communication, and in-group altruism, which suggests connectivity with cortical structures. While OT and AVP V1a receptors have been demonstrated in the cortex of rodents and primates, and intranasal administration of OT and AVP has been shown to modulate cortical activity, there is to date little evidence that OT-and AVP-containing neurons project into the cortex. Here, we demonstrate the existence of OT- and AVP-containing fibers in cortical regions relevant to social cognition using immunohistochemistry in humans, chimpanzees, and rhesus macaques. OT-immunoreactive fibers were found in the straight gyrus of the orbitofrontal cortex as well as the anterior cingulate gyrus in human and chimpanzee brains, while no OT-immunoreactive fibers were found in macaque cortex. AVP-immunoreactive fibers were observed in the anterior cingulate gyrus in all species, as well as in the insular cortex in humans, and in a more restricted distribution in chimpanzees. This is the first report of OT and AVP fibers in the cortex in human and non-human primates. Our findings provide a potential mechanism by which OT and AVP might exert effects on brain regions far from their production site in the hypothalamus, as well as potential species differences in the behavioral functions of these target regions.

We present high signal-to-noise measurements of the acoustic scale in the presence of nonlinear growth and redshift distortions using 320(Gpc/h)^3 of cosmological PM simulations. Using simple fitting methods, we obtain robust measurements of the acoustic scale with scatter close to that predicted by the Fisher matrix. We detect and quantify the shift in the acoustic scale by analyzing the power spectrum: we detect at greater than 5 sigma a decrease in the acoustic scale in the real-space matter power spectrum of 0.2% at z=1.5, growing to 0.45% at z=0.3. In redshift space, the shifts are about 25% larger: we detect a decrease of 0.25% of at z=1.5 and 0.54% at z=0.3. Despite the nonzero amounts, these shifts are highly predictable numerically, and hence removable within the standard ruler analysis of clustering data. Moreover, we show that a simple density-field reconstruction method substantially reduces the scatter and nonlinear shifts of the acoustic scale measurements: the shifts are reduced to less than 0.1% at z=0.3-1.5, even in the presence of non-negligible shot noise. Finally, we show that the ratio of the cosmological distance to the sound horizon that would be inferred from these fits is robust to variations in the parameterization of the fitting method and reasonable differences in the template cosmology., Comment: Accepted for publication in the ApJ, 14 emulated apj pages with 8 figures and 6 tables, small changes to match the accepted version