bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 679: Quantitative systems pharmacology modeling of immunotherapies in B-cell acute lymphoblastic leukemia
Authors: Demin Oleg , Nikitich Antonina , Demin Oleg 
Published: August 2019

Abstract
Introduction: Multiple T-cell engaging antibody-based molecules and CAR-T therapies are in development for the treatment of B-cell acute lymphoblastic leukemia (B-ALL). Moreover, two therapies have already been approved by FDA: blinatumomab (CD19/CD3 bispecific T-cell engager) and tisagenlecleucel (anti-CD19 CAR-T therapy). This work employs quantitative systems pharmacology (QSP) modeling approach to investigate factors affecting outcomes of administration of such types of therapy.
Methods: Model was developed on the basis of Immune Response Template (QSP model of immune system). It describes leukemic blasts, hematopoiesis of normal B cells, CD4+ and CD8+ T-cell subsets, and cytokines (IL-6, TNFα, IFNγ, TGFβ, etc.) in bone marrow and blood. The model takes into account the different level of expression of CD19 on various subsets of normal B cells and leukemic cells. Model parameters were estimated and fitted against in vitro, in vivo human data. Small part of clinical data on blinatumomab and anti-CD19 CAR-T therapies was used for model calibration, other part was used for model validation.
Results: Our model reproduced clinically relevant blinatumomab and anti-CD19 CAR-T hallmark data including depletion of CD19+ cells, T-cell subset dynamics and cytokine release post infusion. The model successfully described the clinically observed phenomenon on progressive cytokine release attenuation with subsequent blinatumomab treatment cycles. It confirmed the dose step strategy for decreasing initial cytokine release. Model showed that treatment outcome is very sensitive to target expression, level of immune cells, disease progression rate and expression of PD-L1 on leukemic cells. The proportion of CD19+ and CD19- leukemic blasts before treatment can be used as a biomarker to predict probability of relapse in patients to stratify patients.
Conclusions: This work provides a quantitative modeling framework to predict responses to immunotherapies in B-ALL on the basis of realistic baseline conditions and reveal the factors affecting treatment outcome.
Citation Format: Oleg Demin Jr, Antonina Nikitich, Oleg Demin. Quantitative systems pharmacology modeling of immunotherapies in B-cell acute lymphoblastic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 679.
Abstract 687: Implementation of inter-patient variability in semi-mechanistic model of melanoma treatment with vemurafenib
Authors: Shchelokov Dmitry , Demin Oleg , Demin Oleg 
Published: August 2019

Abstract
Introduction: The number of parameters in quantitative systems pharmacology (QSP), mechanistic and semi-mechanistic models is greater than in conventional PK/PD models. That is why it is not possible to implement inter-patient variability using only clinical data. The objective of this study was to describe inter-patient variability in the semi-mechanistic model of melanoma treatment with vemurafenib, a BRAF inhibitor, on the basis of published human in vitro, in vivo and clinical data, and to predict overall response from phase 2 clinical trial.
Methods: Model described melanoma progression and its treatment with vemurafenib. It includes (i) proliferation, apoptosis, and transition between states (c-MET negative/positive and sensitive/resistant to BRAF inhibitor) of cancer cells; (ii) HGF effect on proliferation of melanoma cells; (iii) PK/PD of vemurafenib. Various types of data were used to extract inter-patient variability, for example: (a) in vitro data on effect of vemurafenib on melanoma cell lines [1]; (b) in vivo data on tumor growth in melanoma patients without treatment [2]; (c) in vivo data on HGF level in blood plasma of melanoma patients [3].
Results: Different virtual patients developed by the model reproduce all types of tumor response to vemurafenib monotherapy according to RECIST criteria: complete response, partial response, stable disease and progressive disease. To evaluate the predictive ability of the model in a quantitative manner we compared results of simulation of virtual patients population with clinical outcomes. An overall response rate was 70% in the model versus 53% (95% CI, 44 to 62) observed in phase 2 clinical trial [4]. Overestimation can be explained by intermittent administration and dose reduction in case of adverse events in clinical trials which were not taken into account in the model.
Conclusions: Our work establishes the benefit of using of in vitro and in vivo data in addition to clinical data to predict the variability in treatment outcomes of cancer patients.
[1] Stones et al. Front Genet. 2013 May 8;4:66
[2] Hartung et al. PLoS One. 2017 May 4;12(5):e0176080
[3] Hügel et al. Melanoma Res. 2016 Aug;26(4):354-60
[4] Sosman et al. N Engl J Med. 2012 Feb 23;366(8):707-14
Citation Format: Dmitry Shchelokov, Oleg Demin Jr, Oleg Demin. Implementation of inter-patient variability in semi-mechanistic model of melanoma treatment with vemurafenib [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 687.
Abstract 691: Simulating TTFields-induced temperature changes within a patient’s brain - a proof of concept study
Authors: Naveh Ariel , Bomzon Ze'ev 
Published: August 2019

Abstract
Introduction: TTFields is an antimitotic cancer treatment that utilizes low intensity (1-3 V/cm) alternating electric fields in the intermediate frequency (100-300 kHz) that are delivered in two orthogonal directions using 2 pairs of transducer arrays. TTFields are currently approved for Glioblastoma Multiforme (GBM). Preclinical studies show that the effect of TTFields is intensity-dependent with a therapeutic threshold of 1 V/cm, and that as the intensity rises so does the efficacy. However, delivery of high field intensities could result in temperature increases within the body that lead to thermal damage to tissue. In practice, during treatment with TTFields, tissue heating is limited by a closed loop that adjusts the delivery of TTFields to ensure that skin temperature remains within the safety limits. Since heating limits the intensity at which TTFields is delivered, accurate models of tissue heating during TTFields therapy could provide insights into new approaches for the delivery of TTFields. Since measuring the temperature spatial distribution within a patient’s brain is extremely challenging, simulating the heat flow within the head during treatment is a more practical approach. Here, we present preliminary results from a study aimed at developing a valid computational model of heat transfer in the head during TTFields therapy.
Method: A realistic computational head model of a healthy male was used in this study. Virtual transducer arrays were placed on the head. To simulate the delivery of TTFields to the brain, we used ZMT's Sim4Life v4.0 electro-quasi-static solver. The results of the electric field simulation were then used as heat source for the heat simulation. The heat simulation solves the Pennes bio-heat model, taking into account tissue heat capacity and conductivity, and also accounting for heat convection through blood perfusion. Sensitivity analysis was performed to establish the sensitivity of the temperature distribution within the brain to the thermal properties of the various tissue types.
Results: Average temperatures within the brain tissue were well below 38° C for all simulations. Temperature distributions within the brain were highly sensitive to the perfusion coefficient assigned to the cerebrospinal fluid as well as other tissues.Conclusions: We have established a computational framework for investigating heat transfer in the brain during TTFields therapy. Further work is needed to correctly establish the thermal properties of the various tissue types. This may require experimental measurements in which the heat transfer model is fine-tuned and validated.
Citation Format: Ariel Naveh, Ze'ev Bomzon. Simulating TTFields-induced temperature changes within a patient’s brain - a proof of concept study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 691.
Abstract 693: Deep learning analysis of circulating tumor DNA identified a pan-tumor molecular subtype with enhanced response to durvalumab (anti-PDL1)
Authors: Wu Song , Sridhar Sriram , Si Han , Kuziora Michael , Englert Judson , Abdullah Shaad E. , Dennis Philip , Xiao Feng , Gao Guozhi , Ranade Koustubh , Higgs Brandon W. 
Published: August 2019

Abstract
Tumor mutation burden (TMB) has emerged as a promising predictive biomarker for anti-PD1/L1; however, whether somatic mutations in specific genes can sensitize to immunotherapy is poorly understood. We used deep learning to identify molecular subtypes from mutations detectable in circulating tumor DNA (ctDNA) and associated these with outcomes with anti-PDL1 (durvalumab) and durvalumab + anti-CTLA4 (tremelimumab) therapy.
Patient blood samples from 4 nonrandomized phase 2 trials evaluating durvalumab (NCT01693562, 1L+ solid tumors; NCT02087423, 3L+ NSCLC) or durvalumab+ tremelimumab (NCT02000947, 2L+ NSCLC; NCT02261220, 2L+ solid tumors) were sequenced with the Guardant 360 assay of 73 genes. CtDNA mutations were examined pre-treatment in a discovery cohort of 818 patients composed of &gt;15 tumor types and validated in a cohort of 94 patients composed of &gt;5 tumor types. Deep learning neuron network was computed using Tensorflow to identify 4 unique molecular subgroups. Kaplan-Meier analysis was calculated and objective response rates (ORR) were assessed with RECISIST v1.1.
The subgroups consisted of: DS1 (N = 215/818, 26%) enriched in RB1 mutations (17%) and low TMB, DS2 (N = 237/818, 29%) enriched in ARID1A (33%), NFE2L2 (8%), and cKIT (13%) mutations, DS3 (N = 251/818, 31%) enriched in EGFR, ERBB2, BRAF and STK11 mutations, and DS4 (N = 115/818, 14%) enriched in TERT, FGFR3, and PIK3CA mutations. In the discovery cohort, the DS2 subgroup had most improved outcomes (mOS=24.6 months, mPFS=3.7 months, ORR=27%) compared to the other 3 subgroups. DS1 subgroup had the worst outcomes (mOS=6.3 months, mPFS=1.6 months, ORR=10%), while the biomarker evaluable population (BEP, n=818) had ORR=17%. In the validation cohort, DS2 also had the most improved outcomes (mOS=not reached, mPFS=13.2 months, and ORR=64%), with the BEP (n=94) having an ORR=37%. PD-L1 was not correlated with any specific molecular subgroup, while an IFNg mRNA signature trended with correlation with subgroup DS2.
Deep learning analysis of ctDNA identified a molecular subgroup with enhanced response to anti-PDL1 treatment in multiple tumor types.
Citation Format: Song Wu, Sriram Sridhar, Han Si, Michael Kuziora, Judson Englert, Shaad E. Abdullah, Philip Dennis, Feng Xiao, Guozhi Gao, Koustubh Ranade, Brandon W. Higgs. Deep learning analysis of circulating tumor DNA identified a pan-tumor molecular subtype with enhanced response to durvalumab (anti-PDL1) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 693.
Abstract 694: Mathematical modeling of stromal fibroblast-mediated adaptive resistance to bortezomib in multiple myeloma
Authors: Eton Elliot , Kaveh Kamran , Khiste Sachin , Fernandes Andrea , Agarwal Ashima , Sengupta Shiladitya , Goldman Aaron 
Published: August 2019

Abstract
Understanding intrinsic and adaptive resistance is crucial to overcoming multiple myeloma chemotherapy failure. We recently demonstrated that standard chemotherapy can induce cancer cells to phenotypically transition to a transient state of dormancy and drug tolerance. Further, targeting cells precisely during this transition can thwart the emergence of adaptive resistance (Goldman, 2015). However, it is not yet understood how the stromal contexture may contribute to this chemotherapy-induced drug tolerance.
We developed a deterministic mathematical model containing a system of differential equations to examine how stromal fibroblasts temporally affect phenotypic switching between active and dormant, bortezomib-tolerant cancer cell states. The equations describe the total growth rate of each cell state population, which is influenced by that population’s count and baseline growth rate, the frequencies of transitions to and from the other cell state, the drug treatment concentration, and the population carrying capacity. The model also establishes that the presence of fibroblasts affects the frequency of transitions between the cell states. This framework is calibrated to in vitro data, and model simulations predict that bortezomib-tolerant myeloma cells may escape more quickly from dormancy and proliferate when they are incubated in the presence of fibroblasts than in their absence. This prediction is supported by preliminary experimental observations. The mathematical model could be adapted to predict how stromal fibroblasts, or the tumor microenvironment generally, may influence phenotypic switching in other cancers.
Citation Format: Elliot Eton, Kamran Kaveh, Sachin Khiste, Andrea Fernandes, Ashima Agarwal, Shiladitya Sengupta, Aaron Goldman. Mathematical modeling of stromal fibroblast-mediated adaptive resistance to bortezomib in multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 694.
Abstract 696: High-throughput screening of therapeutic combinations against ovarian cancer
Authors: Zhou Peng , Wong Alan S. 
Published: August 2019

Abstract
Applying synergistic drug combinations can increase efficacy of cancer treatment. However, conventional methods for identifying those combinations involve testing of every possible combination including different dosages for each agent, are difficult to scale due to the huge amount of efforts and costs needed. To tackle this problem, we have developed CombiGEM-CRISPR (combinatorial genetics en masse-CRISPR) to enable high-throughput assembly and screening of pair-wise gene combinations. By applying CombiGEM-CRISPR,phenotypes of combinatorial gene knockouts could be used to predict the effects of drug inhibitor combinations targeting the matching gene products. We designed a pair-wise sgRNA library comprising 25,281 combinations for interrogating 52 genes that are overexpressed in ovarian cancers. By pooled evaluation of their effects on cell proliferation, our screening identified new combinations that inhibit ovarian cancer cell growth. Therefore, our work accelerates the discovery of new therapeutic options against ovarian cancer through applying CombiGEM-CRISPR.
Citation Format: Peng Zhou, Alan S. Wong. High-throughput screening of therapeutic combinations against ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 696.
Abstract 78: IGF-1R inhibition activates p70S6K1 to promote survival via MEK1/2 activation in colon cancer cells
Authors: Wang Qing , Yang Hsin-Sheng 
Published: August 2019

Abstract
The insulin-like growth factor 1 receptor (IGF-1R) is a heterotetramer with tyrosine kinase activity, composed of two α subunits containing the ligand-binding site and two β subunits harboring the tyrosine kinase activity. Upon binding with ligands (IGF-1, IGF-2, and insulin), IGF-1R leads to activation of downstream pathways including PI3-K/AKT and Ras/MAPK, two frequently activated pathways for promoting cell survival and proliferation in various cancers including colorectal cancer. Immunohistochemical studies revealed that IGF-1R is over-expressed in colorectal cancer tissues compared to the adjacent normal tissues. Most importantly, a higher expression of IGF-1R is associated with a higher grade and stage in colorectal cancer patients. Since the IGF-1R signaling pathway plays an essential role in development, maintenance, and survival of tumors, many small molecule compounds and antibodies targeting IGF-1R have been developed. Unfortunately, these anti-IGF-1R agents demonstrate very limited efficacy in clinical trials, suggesting the existence of a mechanism to overcome the inhibitory effect of IGF-1R in cancer cells. In this study, we aim to understand the resistance mechanism of IGF-1R inhibition in colon cancer cells. Prolonged treatment of IGF-1R resistant colon cancer cells with IGF-1R inhibitors (OSI-906, BMS-754807, and GSK1838705A) stimulates p70S6K1 activation, a well-known kinase signaling for cell survival. We also found that p70S6K1 activation is independent of mutation of K-RAS and PIK3CA, frequently occurring in colorectal cancer. Genetic knockdown or pharmacologic inhibition of p70S6K1 efficiently suppresses cell viability in response to IGF-1R inhibition, indicating that p70S6K1 activation contributes to the survival of IGF-1R inhibitor-treated cells. In addition to the increased phosphorylation of p70S6K1, phosphorylation of MEK1/2 is also elevated in colon cancer cells with prolonged inhibition of IGF-1R. Knockdown of MEK1/2 results in reduction of p70S6K1 activation in response to IGF-1R inhibition and subsequently increases apoptosis, suggesting that MEK1/2 is involved in p70S6K1 activation for survival. Furthermore, the combination of BMS754807 and U0126, a MEK1/2 inhibitor, effectively decreases the cell viability and increases apoptosis. Our data suggest that inhibition of MEK1/2 enhances the anti-proliferation effects of IGF-1R inhibitors, pointing out a new direction for overcoming the resistance of IGF-1R inhibition in colorectal cancer.
Citation Format: Qing Wang, Hsin-Sheng Yang. IGF-1R inhibition activates p70S6K1 to promote survival via MEK1/2 activation in colon cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 78.
Abstract 863: Functions of FRAS1 as a driver gene of liver metastasis from gastric cancer analyzed by the genome editing technology
Authors: Umeda Shinichi , Kanda Mitsuro , Tanaka Haruyoshi , Sonohara Fuminori , Takami Hideki , Hattori Norifumi , Hayashi Masamichi , Tanaka Chie , Kobayashi Daisuke , Yamada Suguru , Nakayama Goro , Koike Masahiko , Fujiwara Michitaka , Kodera Yasuhiro 
Published: August 2019

Abstract
Background: The prognosis of patients with liver metastasis from gastric cancer (GC) is dismal and it remains a serious problem in the management of GC. To elucidate complex molecular mechanisms underlying formation of liver metastasis is necessary for development of novel therapeutic modality.
Methods: Transcriptome analysis of 57749 molecules with 4 groups (no recurrence, peritoneal recurrence, liver recurrence and nodal recurrence) was conducted to screen out the candidate molecules specific for liver metastasis. Subsequently, the stable knockout (KO) cells were established using the CRISPR/Cas9 system and cell functions, apoptosis, cell cycle, oxidative stress and cancer stemness were analyzed. PCR and antibody array analyses were conducted to identify the co-regulated molecules. Mouse subcutaneous xenograft models and liver metastasis models were generated to evaluate tumorigenicity of the KO cells in vivo. Tissue mRNA expression levels were determined by qRT-PCR in patients with stage II/III GC and correlated with recurrence patterns.
Results: Fraser extracellular matrix complex subunit 1 (FRAS1) was identified as a candidate molecule overexpressed specifically in the liver metastasis group. Proliferation, adhesion, invasion and migration abilities were suppressed in FRAS1-KO cells compared to the control cells. Cell cycle alternation, increased proportions of apoptotic cells, increased oxidative stress and decreased cancer stemness were also observed in FRAS1-KO cells. PCR array analysis revealed that FRAS1 mRNA expression was positively correlated with TGFB1, MAP1B, AHNKA and negatively correlated with BMP2. Pathway analysis demonstrated that expression of MUC1, Survivin, Ki67 and Met proteins were decreased in FRAS1-KO cells. The tumorigenicity of FRAS1-KO cells was reduced in a mouse subcutaneous model and more clearly in liver metastasis models. Patients with high FRAS1 mRNA expression had a significantly higher incidence of liver recurrences after curative gastrectomy.
Conclusion: Our results indicated that FRAS1 contributes to formation of liver metastasis from GC and is a potential therapeutic target.
Citation Format: Shinichi Umeda, Mitsuro Kanda, Haruyoshi Tanaka, Fuminori Sonohara, Hideki Takami, Norifumi Hattori, Masamichi Hayashi, Chie Tanaka, Daisuke Kobayashi, Suguru Yamada, Goro Nakayama, Masahiko Koike, Michitaka Fujiwara, Yasuhiro Kodera. Functions of FRAS1 as a driver gene of liver metastasis from gastric cancer analyzed by the genome editing technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 863.