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Serial circulating tumor DNA (ctDNA) monitoring is emerging as a non-invasive strategy to predict and monitor immune checkpoint blockade (ICB) therapeutic efficacy across cancer types. Yet, limited data exist to show the relationship between ctDNA dynamics and tumor genome and immune microenvironment in patients receiving ICB. Here, we present an in-depth analysis of clinical, whole-exome, transcriptome, and ctDNA profiles of 73 patients with advanced solid tumors, across 30 cancer types, from a phase II basket clinical trial of pembrolizumab (NCT02644369) and report changes in genomic and immune landscapes (primary outcomes). Patients stratified by ctDNA and tumor burden dynamics correspond with survival and clinical benefit. High mutation burden, high expression of immune signatures, and mutations in BRCA2 are associated with pembrolizumab molecular sensitivity, while abundant copy-number alterations and B2M loss-of-heterozygosity corresponded with resistance. Upon treatment, induction of genes expressed by T cell, B cell, and myeloid cell populations are consistent with sensitivity and resistance. We identified the upregulated expression of PLA2G2D, an immune-regulating phospholipase, as a potential biomarker of adaptive resistance to ICB. Together, these findings provide insights into the diversity of immunogenomic mechanisms that underpin pembrolizumab outcomes., Although circulating tumour DNA (ctDNA) can predict immune checkpoint blockade (ICB) responses, its association with tumour biomarkers remains unknown. Here, the authors use ctDNA to inform exome and transcriptome profiling of >100 patients with 30 cancer types on a single clinical ICB trial and identify tumour microenvironment features associated with response.

Background International Metastatic Renal Cell Carcinoma (mRCC) Database Consortium (IMDC) risk groups are important when considering therapeutic options for first-line treatment. Materials and Methods Adult patients with clear cell mRCC initiating first-line sunitinib between 2010 and 2018 were included in this retrospective database study. Median time to treatment discontinuation (TTD) and overall survival (OS) were estimated using Kaplan-Meier analysis. Outcomes were stratified by IMDC risk groups and evaluated for those in the combined intermediate and poor risk group and separately for those in the intermediate risk group with one versus two risk factors. Results Among 1,769 patients treated with first-line sunitinib, 318 (18%) had favorable, 1,031 (58%) had intermediate, and 420 (24%) had poor IMDC risk. Across the three risk groups, patients had similar age, gender, and sunitinib initiation year. Median TTD was 15.0, 8.5, and 4.2 months in the favorable, intermediate, and poor risk groups, respectively, and 7.1 months in the combined intermediate and poor risk group. Median OS was 52.1, 31.5, and 9.8 months in the favorable, intermediate, and poor risk groups, respectively, and 23.2 months in the combined intermediate and poor risk group. Median OS (35.1 vs. 21.9 months) and TTD (10.3 vs. 6.6 months) were significantly different between intermediate risk patients with one versus two risk factors. Conclusion This real-world study found a median OS of 52 months for patients with favorable IMDC risk treated with first-line sunitinib, setting a new benchmark on clinical outcomes of clear cell mRCC. Analysis of intermediate risk group by one or two risk factors demonstrated distinct clinical outcomes. Implications for Practice This analysis offers a contemporary benchmark for overall survival (median, 52.1 months; 95% confidence interval, 43.4–61.2) among patients with clear cell metastatic renal cell carcinoma who were treated with sunitinib as first-line therapy in a real-world setting and classified as favorable risk according to International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk group classification. This study demonstrates that clinical outcomes differ between IMDC risk groups as well as within the intermediate risk group based on the number of risk factors, thus warranting further consideration of risk group when counseling patients about therapeutic options and designing clinical trials.

The increased use of immune checkpoint inhibitors across cancer programs has created the need for standardized patient assessment, education, monitoring, and management of immune-related adverse events (irAEs). At William Osler Health System in Brampton, Ontario, a practical step-wise approach detailing the implementation of cancer immunotherapy in routine practice was developed. The approach focuses on four key steps: (1) identification of patient educators; (2) development of patient education materials; (3) development of patient monitoring tools; (4) involvement and education of multidisciplinary teams. Here, we provide an in-depth description of what was included in each step and how we integrated the different elements of the program. For each step, resources, tools, and materials that may be useful for patients, healthcare providers, and multidisciplinary teams were developed or modified based on existing materials. At our centre, the program led to improved patient comprehension of irAEs, the ability to act on symptoms (patient self-efficacy), and low rates of emergency room visits at first presentation for irAEs. We recognize that centres may need to tailor the approaches to their institutional policies and encourage centres to adapt and modify the forms and tools according to their needs and requirements.

Background and aims Immune checkpoint inhibitors (ICI) are increasingly used in cancer therapy. Elevated liver enzymes frequently occur in patients treated with ICI but evaluation is poorly described. We sought to better understand causes of liver enzyme elevation, investigation and management. Methods Patients treated with anti-PD-1, PDL-1 or CTLA-4 therapy in Phase I/II clinical trials between August 2012 and December 2018 were included. Clinical records of patients with significant liver enzyme elevations were retrospectively reviewed. Results Of 470 ICI-treated patients, liver enzyme elevation occurred in 102 (21.6%), attributed to disease progression (56; 54.9%), other drugs/toxins (7; 6.9%), other causes (22; 21.6%) and ICI immunotoxicity (17; 16.7%; 3.6% of total cohort). Immunotoxicity was associated with higher peak ALT than other causes of enzyme elevation (N = 17; M = 217, 95% CI 145–324 for immunotoxicity, N = 103; M = 74, 95% CI 59–92 for other causes; ratio of means 0.34, 95% CI 0.19–0.60, p = Conclusions Liver enzyme elevation is common in patients receiving ICI, but often has a cause other than immunotoxicity. A biochemical signature with higher ALT and ALT/AST ratio, a history of prior ICI exposure and other organ immunotoxicities may help to identify patients at a higher likelihood of immunotoxicity. Liver biopsy can be safely deferred in most patients. We propose an approach to diagnostic evaluation in patients with liver enzyme elevations following ICI exposure.

Background Human leukocyte antigen class 1 (HLA-1)–dependent immune activity is linked to autoimmune diseases. HLA-1–dependent CD8+ T cells are required for immune checkpoint blockade antitumor activity. It is unknown if HLA-1 genotype is predictive of toxicity to immune checkpoint blockade. Methods Patients with advanced solid tumors stratified into 5 cohorts received single agent pembrolizumab (anti-programmed cell death-1) 200 mg intravenously every 3 weeks in an investigator-initiated phase II trial (Investigator-Initiated Phase II Study of Pembrolizumab Immunological Response Evaluation study, NCT02644369). Germline whole-exome sequencing of peripheral blood mononuclear cells was performed using the Illumina HiSeq2500 platform. HLA-1 haplotypes were predicted from whole-exome sequencing using HLAminer and HLAVBSeq. Heterozygosity of HLA-A, -B, and -C, individual HLA-1 alleles, and HLA haplotype dimorphism at positions −21 M and −21 T of the HLA-A and -B leader sequence were analyzed as predictors of toxicity defined as grade 2 or greater immune-related adverse events and clinical benefit defined as complete or partial response, or stable disease for 6 or more cycles of pembrolizumab. Statistical significance tests were 2-sided. Results In the overall cohort of 101 patients, the frequency of toxicity and clinical benefit from pembrolizumab was 22.8% and 25.7%, respectively. There was no association between any of the HLA-1 loci or alleles with toxicity. HLA-C heterozygosity had an association with decreased clinical benefit relative to HLA-C homozygosity when controlling for cohort (odds ratio = 0.28, 95% confidence interval = 0.09 to 0.91, P = .04). HLA-A and -B haplotype −21 M/T dimorphism and heterozygosity of HLA-A, -B, and -C were not predictive of outcomes. Conclusions HLA-C heterozygosity may predict decreased response to pembrolizumab. Prospective validation is required.

Background No validated molecular biomarkers exist to help guide diagnosis of renal cell carcinoma (RCC) patients. We seek to evaluate the quality of published RCC circulating diagnostic biomarker manuscripts using the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines. Methods The phrase “(renal cell carcinoma OR renal cancer OR kidney cancer OR kidney carcinoma) AND circulating AND (biomarkers OR cell free DNA OR tumor DNA OR methylated cell free DNA OR methylated tumor DNA)” was searched in Embase, MEDLINE, and PubMed in March 2018. Relevant manuscripts were scored using 41 STARD subcriteria for a maximal score of 26 points. All tests of statistical significance were 2 sided. Results The search identified 535 publications: 27 manuscripts of primary research were analyzed. The median STARD score was 11.5 (range = 7-16.75). All manuscripts had appropriate abstracts, introductions, and distribution of alternative diagnoses. None of the manuscripts stated how indeterminant data were handled or if adverse events occurred from performing the index test or reference standard. Statistically significantly higher STARD scores were present in manuscripts reporting receiver operator characteristic curves (P < .001), larger sample sizes (P = .007), and after release of the original STARD statement (P = .005). Conclusions Most RCC circulating diagnostic biomarker manuscripts poorly adhere to the STARD guidelines. Future studies adhering to STARD guidelines may address this unmet need.

BACKGROUND: In metastatic renal-cell carcinoma (mRCC), recent data have shown efficacy of first-line ipilimumab and nivolumab (ipi-nivo) as well as immuno-oncology (IO)/vascular endothelial growth factor (VEGF) inhibitor combinations. Comparative data between these strategies are limited.OBJECTIVE: To compare the efficacy of ipi-nivo versus IO-VEGF (IOVE) combinations in mRCC, and describe practice patterns and effectiveness of second-line therapies.DESIGN, SETTING, AND PARTICIPANTS: Using the International Metastatic Renal-cell Carcinoma Database Consortium (IMDC) dataset, patients treated with any first-line IOVE combination were compared with those treated with ipi-nivo.INTERVENTION: All patients received first-line IO combination therapies.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: First- and second-line response rates, time to treatment failure (TTF), time to next treatment (TNT), and overall survival (OS) were analysed. Hazard ratios were adjusted for IMDC risk factors.RESULTS AND LIMITATIONS: In total, 113 patients received IOVE combinations and 75 received ipi-nivo. For IOVE combinations versus ipi-nivo, first-line response rates were 33% versus 40% (between-group difference 7%, 95% confidence interval [CI] -8% to 22%, p = 0.4), TTF was 14.3 versus 10.2 mo (p = 0.2), TNT was 19.7 versus 17.9 mo (p = 0.4), and median OS was immature but not statistically different (p = 0.17). Adjusted hazard ratios for TTF, TNT, and OS were 0.71 (95% CI 0.46-1.12, p = 0.14), 0.65 (95% CI 0.38-1.11, p = 0.11), and 1.74 (95% CI 0.82-3.68, p = 0.14), respectively. Sixty-four (34%) patients received second-line treatment. In patients receiving subsequent VEGF-based therapy, second-line response rates were lower in the IOVE cohort than in the ipi-nivo cohort (15% vs 45%; between-group difference 30%, 95% CI 3-57%, p = 0.04; n = 40), though second-line TTF was not significantly different (3.7 vs 5.4 mo; p = 0.4; n = 55). Limitations include the study's retrospective design and sample size.CONCLUSIONS: There were no significant differences in first-line outcomes between IOVE combinations and ipi-nivo. Most patients received VEGF-based therapy in the second line. In this group, second-line response rate was greater in patients who received ipi-nivo initially.PATIENT SUMMARY: There were no significant differences in key first-line outcomes for patients with metastatic renal-cell carcinoma receiving immuno-oncology/vascular endothelial growth factor inhibitor combinations versus ipilimumab and nivolumab.

Immune-checkpoint blockade (ICB) uses antibody targeting of specific inhibitory receptors and ligands. The major limitations of ICB, such as high cost, limited success rate, and immune-related adverse events (irAE), highlight the need for predictive biomarkers. We analyzed pre-immunotherapy and post-immunotherapy serum samples of 24 patients treated with pembrolizumab for changes in PD-1 and over 1,000 additional protein markers using a multiplex proximity extension assay (PEA) to identify potential predictive biomarkers of response and/or toxicity. Candidates were selected based on the criteria that at least 2 patients within any of 3 patient groups (responders without irAEs, responders with irAEs, or nonresponders with irAEs) had either a ≥4-fold increase or 4-fold decrease in expression post-immunotherapy. Female and male control samples were used as technical duplicates. A patient group with no response and no irAEs was used to exclude candidates. Following treatment with pembrolizumab, there was a relative increase of PD-1 in the serum of all patients, compared with controls (average 4.4-fold). We identified 7 additional serum proteins that met our candidate selection criteria. These candidate markers did not have any significant association with response or toxicity to pembrolizumab. Overall, we show that serum PD-1 increases post-therapy with pembrolizumab treatment but has no predictive value for response or toxicity in this small set of patients.

Background No validated molecular biomarkers exist to help guide prognosis of renal cell carcinoma (RCC) patients. We seek to evaluate the quality of published prognostic circulating RCC biomarker manuscripts using the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) guidelines. Methods The phrase "(renal cell carcinoma OR renal cancer OR kidney cancer OR kidney carcinoma) AND circulating AND (biomarkers OR cell free DNA OR tumor DNA OR methylated cell free DNA OR methylated tumor DNA)" was searched in Embase, Medline and PubMed March 2018. Relevant manuscripts were scored using 48 REMARK sub-criteria for a maximal score of 20 points. Results The search identified 535 publications: 33 were manuscripts of primary research and were analyzed. The mean REMARK score was 10.6 (range 6.42-14.2). All manuscripts stated their biomarker, study objectives and method of case selection. The lowest scoring criteria: time lapse between storage of blood/serum and marker assay (n = 2) and lack of flow diagram (n = 2). REMARK scores were significantly higher in publications stating adherence to REMARK guidelines (p = 0.0307) and reporting statistically significant results (p = 0.0318). Conclusions Most RCC prognostic biomarker manuscripts poorly adhere to the REMARK guidelines. Better designed studies and appropriate reporting are required to address this urgent unmet need.

Background: Neoantigens arising from somatic mutations are attractive targets for cancer immunotherapy as they may be recognized as foreign by the immune system. RO7198457, a systemically administered RNA-Lipoplex iNeST was designed to stimulate T cell responses against neoantigens. A first-in-human Phase Ib study of RO7198457, in combination with the aPD-L1 antibody atezolizumab is being conducted in patients with locally advanced or metastatic solid tumors. Methods: RO7198457 is manufactured on a per-patient basis and contains up to 20 tumor-specific neoepitopes. Nine doses of RO7198457 were administered i.v. in weekly and bi-weekly intervals during the 12-week induction stage and every 24 weeks during the maintenance stage. Atezolizumab 1200 mg was administered on Day 1 of each 21-day cycle. Results: In total, 132 patients enrolled in cohorts with doses ranging from 15-50 μg RO7198457 in combination with atezolizumab. Most common tumor types were NSCLC, TNBC, melanoma and CRC. The median number of prior therapies was 3 (range 1-11). 39% of patients received prior immunotherapy. Most patients had low levels of PD-L1 expression (93% patients with Citation Format: Juanita S. Lopez, Ross Camidge, Marco Iafolla, Sylvie Rottey, Martin Schuler, Matthew Hellmann, Ani Balmanoukian, Luc Dirix, Michael Gordon, Ryan Sullivan, Brian S. Henick, Charles Drake, Kit Wong, Patricia LoRusso, Patrick Ott, Lawrence Fong, Aglaia Schiza, Jeffery Yachnin, Christian Ottensmeier, Fadi Braiteh, Johanna Bendell, Rom Leidner, George Fisher, Guy Jerusalem, Laura Molenaar-Kuijsten, Marcus Schmidt, Scott A. Laurie, Raid Aljumaily, Achim Rittmeyer, Eelke Gort, Ignacio Melero, Lars Mueller, Rachel Sabado, Patrick Twomey, Jack Huang, Manesh Yadav, Jingbin Zhang, Felicitas Mueller, Evelyna Derhovanessian, Ugur Sahin, Özlem Türeci, Thomas Powles. A phase Ib study to evaluate RO7198457, an individualized Neoantigen Specific immunoTherapy (iNeST), in combination with atezolizumab in patients with locally advanced or metastatic solid tumors [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 CT301.

Background Limited data exist in the clonal dynamics of serial ctDNA as a predictive biomarker in advanced solid tumor pts receiving immune checkpoint blockade. Methods Pts with mixed solid tumors received single agent P (anti-PD-1) 200mg IV Q3wks in the investigator-initiated phase II INSPIRE trial (NCT02644369). ctDNA was assayed at baseline (B) and start of cycle 3 (C3) using a pt-specific amplicon-based NGS assay (Signatera™). Samples were considered ctDNA positive if≥2 of 16pt-specific targets met the qualifying confidence score threshold.Table: 113PTable: 113PEndpointORR N=72*CBR N=72*EndpointPFS N=73*OS N=73*SubgroupCR/PR N=15SD/PD N=57CR/PR/SD≥6 cycles N=22CR/PR/SD Results Of 94 pts are presented. Demographics: male 38%; median age=55 yrs (range 21–81); triple negative breast (19%), ovarian (19%) and head and neck (17%) cancers comprised the major malignancies. Median no. of P cycles=3 (range 1–35); follow up was 14m (range 0.6-35.4); RECIST responses: CR 3.2% (n=3), PR 14% (n=13), CBR (CR+PR+SD>6 cycles) 28% (n=26), RECIST/clinical PD (n=61/18; 65%/19%). Median PFS=2.5m and median OS=14m. In all 94 pts, ctDNAB correlated with PFS (adjusted HR 0.53, 95% CI 0.34-0.84, p=0.01) and OS (adjusted HR 0.47, 95% CI 0.28-0.8, p=0.01). Among 74 pts with both ctDNAB and ctDNAC3, the change (ΔctDNA) correlated with clinical efficacy parameters (Table). Conclusions Strong correlations exist between both ctDNAB and ΔctDNA with clinical outcome, suggesting both prognostic and predictive values in pts with mixed solid tumors. Clinical trial identification NCT02644369, December 31, 2015. Legal entity responsible for the study University Health Network, Toronto. Funding Merck, Netera, Terry Fox Research Institute, Princess Margaret Cancer Foundation. Disclosure S. Dashner: Shareholder / Stockholder / Stock options, Full / Part-time employment: Netera. A.R. Hansen: Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Merck; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): GlaxoSmithKline; Research grant / Funding (institution): Genentech/Roche; Research grant / Funding (institution): Bristol-Myers Squibb; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): Boston Biomedical; Research grant / Funding (institution): Boehringer-Ingelheim; Research grant / Funding (institution): AstraZeneca/Medimmune; Research grant / Funding (institution): Janssen; Research grant / Funding (institution): Karyopharm. P. Bedard: Honoraria (institution), Advisory / Consultancy: Sanofi; Advisory / Consultancy, Research grant / Funding (institution): Roche/Genentech; Advisory / Consultancy, Research grant / Funding (institution): Bristol-Myers Squibb; Advisory / Consultancy, Research grant / Funding (institution): Pfizer; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): GlaxoSmithKline; Research grant / Funding (institution): AstraZeneca/Medimmune; Research grant / Funding (institution): Merck; Research grant / Funding (institution): Nektar Therapeutics; Research grant / Funding (institution): Servier; Research grant / Funding (institution): Seattle Genetics; Research grant / Funding (institution): PTC Therapeutics; Research grant / Funding (institution): Oncothyreon. S. Lheureux: Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Merck; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): AstraZeneca/Medimmune; Research grant / Funding (institution): Roche/Genentech; Research grant / Funding (institution): Tesaro. A. Spreafico: Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Merck; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Bristol-Myers Squibb; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Novartis; Honoraria (self), Advisory / Consultancy: Oncorus; Research grant / Funding (institution): Symphogen; Research grant / Funding (institution): AstraZeneca/Medimmune; Research grant / Funding (institution): Bayer; Research grant / Funding (institution): Surface Oncology; Research grant / Funding (institution): Northern Biologics; Research grant / Funding (institution): Janssen; Research grant / Funding (institution): Oncology/Johnson & Johnson; Research grant / Funding (institution): Array Biopharma. A.A. Razak: Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Boehringer Ingelheim; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Eli Lilly; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Merck; Research grant / Funding (institution): CASI Pharmaceuticals; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): Deciphera; Research grant / Funding (institution): Karyopharm Therapeutics; Research grant / Funding (institution): Pfizer; Research grant / Funding (institution): Roche/Genentech; Research grant / Funding (institution): Boston Biomedical; Research grant / Funding (institution): Bristol-Myers Squibb; Research grant / Funding (institution): AstraZeneca/Medimmune; Research grant / Funding (institution): Amgen; Research grant / Funding (institution): GlaxoSmithKline; Research grant / Funding (institution): Blueprint Medicines; Research grant / Funding (institution): Merck; Research grant / Funding (institution): Abbvie; Research grant / Funding (institution): Adaptimmune. H. Wu: Shareholder / Stockholder / Stock options, Full / Part-time employment: Netera. S. Shchegrova: Shareholder / Stockholder / Stock options, Full / Part-time employment: Netera. P.S. Ohashi: Honoraria (self), Advisory / Consultancy: Symphogen Inc; Honoraria (self), Advisory / Consultancy: Providence Therapeutics. M. Louie: Shareholder / Stockholder / Stock options, Full / Part-time employment: Netera. H. Sethi: Shareholder / Stockholder / Stock options, Full / Part-time employment: Netera. A. Aleshin: Shareholder / Stockholder / Stock options, Full / Part-time employment: Netera. L.L. Siu: Honoraria (self), Advisory / Consultancy: Merck; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Pfiser; Honoraria (self), Advisory / Consultancy: Celgene; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): AstraZeneca/Medimmune; Honoraria (self), Advisory / Consultancy: Morphosys; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Roche/Genentech; Honoraria (self), Advisory / Consultancy: GeneSeeq; Honoraria (self), Advisory / Consultancy: Loxo; Honoraria (self), Advisory / Consultancy: Oncorus; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Symphogen; Honoraria (self), Advisory / Consultancy: Seattle Genetics; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): Bristol-Myers Squibb; Research grant / Funding (institution): Boerhinger-Ingelheim; Research grant / Funding (institution): GlaxoSmithKline; Research grant / Funding (institution): Karyopharm; Research grant / Funding (institution): Astellas; Research grant / Funding (institution): Bayer; Research grant / Funding (institution): Abbvie; Research grant / Funding (institution): Amgen; Research grant / Funding (institution): Intensity Therapeutics; Research grant / Funding (institution): Mirati; Research grant / Funding (institution): Shattucks; Spouse / Financial dependant: Agios. S. Bratman: Research grant / Funding (self): Nektar Therapeutics; Licensing / Royalties, Co-inventor of a patent relating to circulating tumor DNA detection technology: Roche Molecular Diagnostics. T.J. Pugh: Honoraria (self): Merck; Honoraria (self): Prosigna; Honoraria (self): Chrysalis Medical Advisors; Advisory / Consultancy: DynaCare; Research grant / Funding (self): Boehringer Ingelheim. All other authors have declared no conflicts of interest.

Biological systems often involve chemical reactions occurring in low-molecule-number regimes, where fluctuations are not negligible and thus stochastic models are required to capture the system behaviour. The resulting models are generally quite large and complex, involving many reactions and species. For clarity and computational tractability, it is important to be able to simplify these systems to equivalent ones involving fewer elements. While many model simplification approaches have been developed for deterministic systems, there has been limited work on applying these approaches to stochastic modelling. Here, we describe a method that reduces the complexity of stochastic biochemical network models, and apply this method to the reduction of a mammalian signalling cascade and a detailed model of the process of bacterial gene expression. Our results indicate that the simplified model gives an accurate representation for not only the average numbers of all species, but also for the associated fluctuations and statistical parameters.

Background Simulating the major molecular events inside an Escherichia coli cell can lead to a very large number of reactions that compose its overall behaviour. Not only should the model be accurate, but it is imperative for the experimenter to create an efficient model to obtain the results in a timely fashion. Here, we show that for many parameter regimes, the effect of the host cell genome on the transcription of a gene from a plasmid-borne promoter is negligible, allowing one to simulate the system more efficiently by removing the computational load associated with representing the presence of the rest of the genome. The key parameter is the on-rate of RNAP binding to the promoter (k_on), and we compare the total number of transcripts produced from a plasmid vector generated as a function of this rate constant, for two versions of our gene expression model, one incorporating the host cell genome and one excluding it. By sweeping parameters, we identify the k_on range for which the difference between the genome and no-genome models drops below 5%, over a wide range of doubling times, mRNA degradation rates, plasmid copy numbers, and gene lengths. Results We assess the effect of the simulating the presence of the genome over a four-dimensional parameter space, considering: 24 min Conclusion Exclusion of the genome is shown to yield less than 5% difference in transcript numbers over wide ranges of values, and computational speed is improved by two to 24 times by excluding explicit representation of the genome.

Recent developments in molecular biology have made it feasible to carry out experimental verification of mathematical models for biochemical processes, offering the eventual prospect of creating a detailed, validated picture of gene expression. A persistent difficulty with this long-term goal is the incompleteness of the kinetic information available in the literature: Many rate constants cannot or have not yet been measured. Here, we present a method of filling in missing parameters using an approach conceptually analogous to mean-field approaches in statistical mechanics: When studying a particular gene, we extract key parameters by considering the averaged effect of all other genes in the system, analogously to considering the averaged magnetic field in a physical spin model. This methodology has been applied to account for the effect of the presence of the Escherichia coli genome on the availability of key enzymes involved in gene expression (RNA polymerases and ribosomes), yielding the number of free enzymes as a function of cellular growth rate. These conclusions have been obtained by deriving genome-wide averages and matching them to bulk literature values of E. coli K-12 and B/r. Average rate constants have been found for RNA polymerases and ribosomes binding to promoter and ribosome-binding sites, respectively; these results suggest that cells vary not only their production rates of RNA polymerase and ribosomes under different growth-rate conditions but also change their global level of transcriptional/translational activation and repression, thus altering the average binding rate constants for these enzymes. To test the mean-field method, the results from the genome-wide averages have been applied to the induced lac operon, where our derived on-rate for binding of RNA polymerase to the promoter is in good agreement with previous experimental results.