Dosing Oncology Therapeutics in Combination Therapy for Renal Dysfunction: The University of California San Diego Study of Personalized Cancer Therapy to Determine Response and Toxicity (UCSD-PREDICT) Experience.

Introduction Dose reductions are often required to avoid toxicity in combination therapy for advanced cancers, but information on appropriate dose reductions in renal dysfunction is lacking. This study assessed dose reductions of renally cleared oncology agents given in combination therapy in the setting of renal dysfunction. Methods A database of 1,072 patients was screened to identify patients with renal dysfunction (glomerular filtration rate < 60 mL/min) receiving oncology combination therapy with at least one agent requiring dose reduction for renal insufficiency. The dose of the renal agent was compared to the single-agent renal dosing recommendations to calculate a dose percentage. Tolerability was determined from electronic medical records review. Results Thirty-three regimens (n = 25 patients) were identified: 11 included at least one targeted agent (n = 8 patients) and 22 had only cytotoxic chemotherapy (n = 18 patients). The renal agent was given at the recommended single-agent renal dose in ~50% of combinations; ~50% of all regimens were tolerated, and only six combinations had dose reductions for toxicity. The median final dose percentage was 100% of the recommended renal dose (range: 25% - 333%); no significant differences were seen between groups (cytotoxic - tolerated, cytotoxic - not tolerated, targeted - tolerated, targeted - not tolerated; p = 0.38). No significant differences were observed between tolerated vs. non-tolerated (p = 0.97) or targeted vs. cytotoxic (p = 0.80) regimens. Conclusions Dose reductions of renally cleared agents are highly variable in oncology patients with renal dysfunction. Additional studies are needed to determine appropriate dosing adjustments in this population

Review of precision cancer medicine: Evolution of the treatment paradigm.

In recent years, biotechnological breakthroughs have led to identification of complex and unique biologic features associated with carcinogenesis. Tumor and cell-free DNA profiling, immune markers, and proteomic and RNA analyses are used to identify these characteristics for optimization of anticancer therapy in individual patients. Consequently, clinical trials have evolved, shifting from tumor type-centered to gene-directed, histology-agnostic, with innovative adaptive design tailored to biomarker profiling with the goal to improve treatment outcomes. A plethora of precision medicine trials have been conducted. The majority of these trials demonstrated that matched therapy is associated with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To improve the implementation of precision medicine, this approach should be used early in the course of the disease, and patients should have complete tumor profiling and access to effective matched therapy. To overcome the complexity of tumor biology, clinical trials with combinations of gene-targeted therapy with immune-targeted approaches (e.g., checkpoint blockade, personalized vaccines and/or chimeric antigen receptor T-cells), hormonal therapy, chemotherapy and/or novel agents should be considered. These studies should target dynamic changes in tumor biologic abnormalities, eliminating minimal residual disease, and eradicating significant subclones that confer resistance to treatment. Mining and expansion of real-world data, facilitated by the use of advanced computer data processing capabilities, may contribute to validation of information to predict new applications for medicines. In this review, we summarize the clinical trials and discuss challenges and opportunities to accelerate the implementation of precision oncology

Perspectives - Cannon Design’s Open Hand Studio

Not only can architects create great space, they can also inspire better connections between the built environment and the social sector. John Syvertsen, Chris Lambert, and Ashley Marsh talk with Sahar Nikanjam and Professor James Hagy of The Rooftops Project about their work with not-for-profit organizations through architectural firm Cannon Design’s Open Hand Studio initiative.https://digitalcommons.nyls.edu/rooftops_project/1012/thumbnail.jp

Perspectives - Cannon Design’s Open Hand Studio

Not only can architects create great space, they can also inspire better connections between the built environment and the social sector. John Syvertsen, Chris Lambert, and Ashley Marsh talk with Sahar Nikanjam and Professor James Hagy of The Rooftops Project about their work with not-for-profit organizations through architectural firm Cannon Design’s Open Hand Studio initiative.https://digitalcommons.nyls.edu/rooftops_project/1012/thumbnail.jp

Profiles - UCAN’s New Campus Construction Project, Chicago, Illinois

Funding and constructing a new $41 million facility may be a once-in-a-generation, if ever, event, for many social service not-for-profits. Choosing a site that invests directly in the neighborhood and the people served can have ripple effects far beyond the central purpose of the delivery of services the buildings are designed to support. The Rooftops Project’s Sahar Nikanjam and Professor James Hagy walked the site of UCAN’s new campus construction under way in the Lawndale neighborhood of Chicago.https://digitalcommons.nyls.edu/rooftops_project/1027/thumbnail.jp

Efficacy and safety of anticancer drug combinations: a meta-analysis of randomized trials with a focus on immunotherapeutics and gene-targeted compounds.

Hundreds of trials are being conducted to evaluate combination of newer targeted drugs as well as immunotherapy. Our aim was to compare efficacy and safety of combination versus single non-cytotoxic anticancer agents. We searched PubMed (01/01/2001 to 03/06/2018) (and, for immunotherapy, ASCO and ESMO abstracts (2016 through March 2018)) for randomized clinical trials that compared a single non-cytotoxic agent (targeted, hormonal, or immunotherapy) versus a combination with another non-cytotoxic partner. Efficacy and safety endpoints were evaluated in a meta-analysis using a linear mixed-effects model (guidelines per PRISMA Report).We included 95 randomized comparisons (single vs. combination non-cytotoxic therapies) (59.4%, phase II; 41.6%, phase III trials) (29,175 patients (solid tumors)). Combinations most frequently included a hormonal agent and a targeted small molecule (23%). Compared to single non-cytotoxic agents, adding another non-cytotoxic drug increased response rate (odds ratio [OR]=1.61, 95%CI 1.40-1.84)and prolonged progression-free survival (hazard ratio [HR]=0.75, 95%CI 0.69-0.81)and overall survival (HR=0.87, 95%CI 0.81-0.94) (all p<0.001), which was most pronounced for the association between immunotherapy combinations and longer survival. Combinations also significantlyincreased the risk of high-grade toxicities (OR=2.42, 95%CI 1.98-2.97) (most notably for immunotherapy and small molecule inhibitors) and mortality at least possibly therapy related (OR: 1.33, 95%CI 1.15-1.53) (both p<0.001) (absolute mortality = 0.90% (single agent) versus 1.31% (combinations)) compared to single agents. In conclusion, combinations of non-cytotoxic drugs versus monotherapy in randomized cancer clinical trials attenuated safety, but increased efficacy, with the balance tilting in favor of combination therapy, based on the prolongation in survival

Relationship between protein biomarkers of chemotherapy response and microsatellite status, tumor mutational burden and PD-L1 expression in cancer patients.

Chemotherapy and checkpoint inhibitor immunotherapies are increasingly used in combinations. We determined associations between the presence of anti-PD-1/PD-L1 therapeutic biomarkers and protein markers of potential chemotherapy response. Data were extracted from a clinical-grade testing database (Caris Life Sciences; February 2015 through November 2017): immunotherapy response markers (microsatellite instability-high [MSI-H], tumor mutational burden-high [TMB-H], and PD-L1 protein expression) and protein chemotherapy response markers (excision repair complementation group 1 [ERCC1], topoisomerase 1 [TOPO1], topoisomerase 2 [TOP2A], thymidylate synthase [TS], tubulin beta 3 [TUBB3], ribonucleotide reductase regulatory subunit M1 [RRM1] and O-6-methyl guanine DNA methyltransferase [MGMT]). Relationships were determined by the Mantel-Haenszel chi-squared test or Fischer's exact tests. Overall, 28,034 patients representing a total of 40 tumor types were assessed. MSI-H was found in 3.3% of patients (73% were also TMB-H), TMB-H, 8.4% (28.3% were also MSI-H) and PD-L1 expression in 11.0% of patients (5.1% were also MSI-H; 16.4% were also TMB-H). Based on concurrent biomarker expression, combinations of immunotherapy with platinum (ERCC1 negativity) or with doxorubicin, epirubicin or etoposide (TOP2A positivity) have a higher probability of response, whereas combinations with irinotecan or topotecan (TOPO1 positivity), with gemcitabine (RRM1 negativity), and fluorouracil, pemetrexed or capecitabine (TS negativity) may be of less benefit. The potential for immunotherapy and taxane (TUBB3 negativity) combinations is present for MSI-H but not TMB-H or PD-L1-expressing tumors; for temozolomide and dacarbazine (MGMT negative), PD-L1 is frequently coexpressed, but MSI-H and TMB-H are not associated. Protein markers of potential chemotherapy response along with next-generation sequencing for immunotherapy response markers can help support rational combinations as part of an individualized, precision oncology approach

A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks

Software testing activities scrutinize the artifacts and the behavior of a software product to find possible defects and ensure that the product meets its expected requirements. Recently, Deep Reinforcement Learning (DRL) has been successfully employed in complex testing tasks such as game testing, regression testing, and test case prioritization to automate the process and provide continuous adaptation. Practitioners can employ DRL by implementing from scratch a DRL algorithm or using a DRL framework. DRL frameworks offer well-maintained implemented state-of-the-art DRL algorithms to facilitate and speed up the development of DRL applications. Developers have widely used these frameworks to solve problems in various domains including software testing. However, to the best of our knowledge, there is no study that empirically evaluates the effectiveness and performance of implemented algorithms in DRL frameworks. Moreover, some guidelines are lacking from the literature that would help practitioners choose one DRL framework over another. In this paper, we empirically investigate the applications of carefully selected DRL algorithms on two important software testing tasks: test case prioritization in the context of Continuous Integration (CI) and game testing. For the game testing task, we conduct experiments on a simple game and use DRL algorithms to explore the game to detect bugs. Results show that some of the selected DRL frameworks such as Tensorforce outperform recent approaches in the literature. To prioritize test cases, we run experiments on a CI environment where DRL algorithms from different frameworks are used to rank the test cases. Our results show that the performance difference between implemented algorithms in some cases is considerable, motivating further investigation.Comment: Accepted for publication at EMSE (Empirical Software Engineering journal) 202

Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach

A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep Reinforcement Learning (DRL) is the application of DL in the domain of Reinforcement Learning (RL). Like any software systems, DRL applications can fail because of faults in their programs. In this paper, we present the first attempt to categorize faults occurring in DRL programs. We manually analyzed 761 artifacts of DRL programs (from Stack Overflow posts and GitHub issues) developed using well-known DRL frameworks (OpenAI Gym, Dopamine, Keras-rl, Tensorforce) and identified faults reported by developers/users. We labeled and taxonomized the identified faults through several rounds of discussions. The resulting taxonomy is validated using an online survey with 19 developers/researchers. To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations. The execution flow of DRLinter consists in parsing a DRL program to generate a model conforming to our meta-model and applying detection rules on the model to identify faults occurrences. The effectiveness of DRLinter is evaluated using 15 synthetic DRLprograms in which we injected faults observed in the analyzed artifacts of the taxonomy. The results show that DRLinter can successfully detect faults in all synthetic faulty programs

Automatic Fault Detection for Deep Learning Programs Using Graph Transformations

Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning (DL) to solve complex real-world problems. A DL program encodes the network structure of a desirable DL model and the process by which the model learns from the training dataset. Like any software, a DL program can be faulty, which implies substantial challenges of software quality assurance, especially in safety-critical domains. It is therefore crucial to equip DL development teams with efficient fault detection techniques and tools. In this paper, we propose NeuraLint, a model-based fault detection approach for DL programs, using meta-modelling and graph transformations. First, we design a meta-model for DL programs that includes their base skeleton and fundamental properties. Then, we construct a graph-based verification process that covers 23 rules defined on top of the meta-model and implemented as graph transformations to detect faults and design inefficiencies in the generated models (i.e., instances of the meta-model). First, the proposed approach is evaluated by finding faults and design inefficiencies in 28 synthesized examples built from common problems reported in the literature. Then NeuraLint successfully finds 64 faults and design inefficiencies in 34 real-world DL programs extracted from Stack Overflow posts and GitHub repositories. The results show that NeuraLint effectively detects faults and design issues in both synthesized and real-world examples with a recall of 70.5 % and a precision of 100 %. Although the proposed meta-model is designed for feedforward neural networks, it can be extended to support other neural network architectures such as recurrent neural networks. Researchers can also expand our set of verification rules to cover more types of issues in DL programs