Motivation Advancements in cancer genetics have facilitated the development of therapies with actionable mutations. Although mutated genes have been studied extensively, their chaotic behavior has not been appreciated. Thus, in contrast to naïve DNA, mutated DNA sequences can display characteristics of unpredictability and sensitivity to the initial conditions that may be dictated by the environment, expression patterns and presence of other genomic alterations. Employing a DNA walk as a form of 2D analysis of the nucleotide sequence, we demonstrate that chaotic behavior in the sequence of a mutated gene can be predicted. Results Using fractal analysis for these DNA walks, we have determined the complexity and nucleotide variance of commonly observed mutated genes in non-small cell lung cancer, and their wild-type counterparts. DNA walks for wild-type genes demonstrate varying levels of chaos, with BRAF, NTRK1 and MET exhibiting greater levels of chaos than KRAS, paxillin and EGFR. Analyzing changes in chaotic properties, such as changes in periodicity and linearity, reveal that while deletion mutations indicate a notable disruption in fractal ‘self-similarity’, fusion mutations demonstrate bifurcations between the two genes. Our results suggest that the fractals generated by DNA walks can yield important insights into potential consequences of these mutated genes. Availability and implementation Introduction to Turtle graphics in Python is an open source article on learning to develop a script for Turtle graphics in Python, freely available on the web at https://docs.python.org/2/library/turtle.html. cDNA sequences were obtained through NCBI RefSeq database, an open source database that contains information on a large array of genes, such as their nucleotide and amino acid sequences, freely available at https://www.ncbi.nlm.nih.gov/refseq/. FracLac plugin for Fractal analysis in ImageJ is an open source plugin for the ImageJ program to perform fractal analysis, free to download at https://imagej.nih.gov/ij/plugins/fraclac/FLHelp/Introduction.html. Supplementary information Supplementary data are available at Bioinformatics online.

Mathematical cancer models are immensely powerful tools that are based in part on the fractal nature of biological structures, such as the geometry of the lung. Cancers of the lung provide an opportune model to develop and apply algorithms that capture changes and disease phenotypes. We reviewed mathematical models that have been developed for biological sciences and applied them in the context of small cell lung cancer (SCLC) growth, mutational heterogeneity, and mechanisms of metastasis. The ultimate goal is to develop the stochastic and deterministic nature of this disease, to link this comprehensive set of tools back to its fractalness and to provide a platform for accurate biomarker development. These techniques may be particularly useful in the context of drug development research, such as combination with existing omics approaches. The integration of these tools will be important to further understand the biology of SCLC and ultimately develop novel therapeutics.

e20713 Background: The prevalence of next-generation sequencing and the availability of a large number of targeted therapies in the clinics, has complicated treatment decision-making in lung cancer. While national guidelines and commercial pathways offer a method to improve the oncologists’ adherence to appropriate testing and treatment modalities available, more effort is required to solidify this as a standard of care model at academic and community sites. A better understanding of the improved durable survival of targeted therapy assignment compared with non-targeted therapy outside of the clinical trial setting is needed to understand the efficacy and accuracy of precision medicine. Methods: We perform an in-depth analysis of a series of lung AD patients (n = 798) with genomic and clinical data in a recently created thoracic patient registry, who were treated at COH between 2009-2018 period. Results: 798 individuals with lung AD were identified in the Thoracic Oncology Registry who were treated or were intended to be treated at COH; 662 (83%) of the patients had genomic testing performed at the request of their treating oncologist and 460 (58%) of whom received a 1st-line targeted therapy decision (including clinical trial assignment based on bio-marker). Oncogenic alterations were detected in 653 (82%) patients with the majority presenting with EGFR (47%), who were mostly treated with erlotinib (78%). 462/653 (70%) patients had an alteration detected with an available FDA approved therapy and 90% (416/462) of the patients were appropriately matched to a targeted therapy based on the oncologist’s decision. Several decision-making algorithms were tested and fast-and-frugal trees (FFTs) proved superior with a positive predictive value (PPV) of 90% and only required two important cues in informing the decision of the type of treatment to give to the patient. Furthermore, a targeted therapy treatment decision showed a significant benefit with a median OS of 38 months as compared to 22 months in the non-targeted therapy decision-making group (p < 0.00001). This was also evident in the PFS analysis where targeted therapy decision-making had a median survival of 9 months as compared with 5 months in the other groups (p < 0.00001). Conclusions: FFTs are a novel tool to test the efficacy of precision medicine in a real-world setting and can provide a more streamlined method for clinical guidance and decision-making. FFTs were able to predict with 90% PPV a precision medicine decision that was correlated with improved PFS (9 vs 5 months) and OS (38 vs 22 months).

e18192 Background: Immunotherapy related adverse events (irAEs) and hospital admissions with Immune Checkpoint Inhibitors (ICIs) in thoracic malignancies remain poorly characterized. Methods: Admissions after ICIs in all thoracic malignancies patients received ICIs at City of Hope (total 384) were identified as of 11/8/2018. IrAEs, outcomes, pathology and next-generation sequencing (NGS) data were collected, including Tumor mutation burden (TMB) and PD-L1 (22C3). Length of stay (LOS) and overall survival (OS) was calculated. Unpaired T-tests if data passed normality test, Chi-square and Fisher’s exact test, Gehan-Breslow-Wilcoxon test were used for comparison between 2 groups (irAEs VS no irAEs) for LOS, demographics and genetics, and survival respectively. Results: 100 patients had hospital admissions after ICIs therapy and 90 patients (41 women, 49 men) had stage IV disease (63 lung adenocarcinomas, 14 squamous cell lung cancer, 5 small cell lung cancer, 8 others). 28 out of 90 patients had irAEs (10 pneumonitis/pneumonia, 4 adrenal insufficiencies, 4 colitis, 3 liver toxicities, 2 nephritis, 1 heart failure, 1 pancreatitis, 1 diabetic ketone acidosis, and others including multiple irAEs). There was no difference between the patients who had irAEs VS no irAEs in LOS (median 7 days VS 6 days, P = 0.57). Patients with irAEs had more invasive diagnostic procedures than no irAEs (53.6% VS 25.8%, P = 0.02). There was a trend of longer OS in irAEs patients (median 16.4 months VS 6.8 months, P = 0.13) than no irAEs. Male gender (71.4% (20/28) VS 46.8% (29/62), OR = 2.85, P = 0.04) and smoking exposure (89.3% (25/28) VS 58.1% (36/62), OR = 6.0, P < 0.01) were associated with irAEs patients. Genetic alterations of LRP1B gene (83.3% (5/6) VS 26.9% (7/26), OR = 13.6, P = 0.02) and MLL3 gene (66.7% (4/6) VS 19.2% (5/26), OR = 8.4, P = 0.04) were associated with patients who had irAES. No difference was found in age, lines of therapy, TP53, KRAS, EGFR, STK11, PIK3CA, TMB, PD-L1 between the irAEs and no irAEs patients. Conclusions: Hospitalized patients who had irAEs had similar LOS compared with patients without irAEs but potentially longer OS. Gender, smoking status and genes associated with irAEs and ICIs outcomes were explored. Larger dataset for molecular and clinical features was planned.

e20707 Background: In recent years, healthcare centers have become more reliant on the advent of new technology, testing methods and a greater use of electronic medical records, which provide a plethora of information available to physicians, researchers, and the medical community. This aggregate of information stands to be a powerful tool that can enable one to perform highly complex investigations more easily, as well as aid physicians in their efforts to provide personalized medicine to patients. However, in order to take advantage of vital clinical and research data, we must initially make an effort to create an effective infrastructure for the collection, storage, utilization, and protection of this information with uniquely designed disease-specific registries that have adequate informatics support to allow for the collection of a large number of patients. Methods: In this study, we perform an in-depth analysis of a series of lung adenocarcinoma patients (n = 415) with genomic and clinical data in a recently created thoracic patient registry. Results: Of the 415 patients in the analysis, 59% (n = 245) were female; the median age was 64 (range, 22-92) years with a median OS of 33.29 months (95% CI, 29.77-39.48). The frequency of the most commonly occurring oncogenes was 50% EGFR (n = 207/415), 28% KRAS (n = 97/352), and 7% ALK rearrangement (n = 28/377), while the most commonly occurring tumor suppressor genes consisted of TP53 (n = 140/283 [49%]), LRP1B (n = 63/228 [28%]), and STK11 (n = 39/278 [14%]). The most common actionable alterations were identified in EGFR L858R/exon 19 deletion (n = 177/415 [42.7%]), ALK rearrangement (n = 28/377 [7.4%]), ROS1 rearrangement (n = 3/257 [1.2%]), BRAF V600E (n = 7/288 [2.4%]) and MET exon 14 splice site/deletion (n = 7/287 [2.4%]). While there were no median OS differences in patients who were tested under a broad-panel vs small-panel (33.4 months vs 33.5 months; 95% CI; P = 0.36), there was a discernible difference in survival for 222 patients, who had an actionable alteration, with a median OS of 39.8 months as compared to 193 patients who were wild-type with a median OS of 26.0 months (95% CI; P < 0.001). In addition, we identified an unprecedented number of patients with actionable alterations [53.5% (222/415)], including distinct individual alteration rates, as compared with 15.0% and 22.3% in TCGA and GENIE respectively. Conclusions: The use of patient registries, focused genomic panels and the appropriate use of clinical guidelines in community and academic settings may influence cohort selection for clinical trials and improve survival outcomes.