Drug Response In Patients Research Articles

Abstract B069: Drug response variability between luminal and basal prostate cancer tumors

Abstract Introduction: Prostate cancer (PCa) is a genomically heterogeneous disease that has been subtyped into molecularly distinct subtypes. Over the past several years, multiple drugs have demonstrated improvements in overall survival in men with advanced prostate cancer, prompting further investigations of these drugs. However, characterizing drug response in the localized disease setting and in the context of PCa molecular subtypes needs further investigation. Here in a large prospective cohort of men with adverse pathology we explore the heterogeneity of patient drug response between basal, luminal, and neuroendocrine prostate cancer subtypes. Methods: Whole-transcriptome RNA expression profiles of 9,640 radical prostatectomy (RP) samples from prospective use of the Decipher test were obtained from the GRID registry database. Patients were subtyped into basal, luminal A, luminal B, and neuroendocrine subtypes using the PAM50 and small cell gene expression signatures. Drug response scores (DRS) predicting patient sensitivity for 89 oncology drugs were determined using in vitro drug sensitivity and microarray data from the NCI-60 panel. Pearson’s chi squared test was used to determine significant differences in drug sensitivity among PCa subtypes. Results: Applying the subtype signatures to the cohort, we classified 43% of samples as basal, 26% as luminal A, 30% as luminal B, and 2% as neuroendocrine. DRS was highly variable across the subtypes. Basal tumors showed a distinct drug response profile where basal tumors were more sensitive to kinase inhibitors (e.g., cabozantanib, dasatnib, erlotinib), mTOR inhibitors (e.g., everolimus, temsirolumus), DNA repair inhibitors (e.g., olaparib, mitoxantrone), and alkylating (e.g., cisplatin, carboplatin) chemotherapy. Luminal A and B were more sensitive to steroid inhibition (e.g., abiraterone, tamoxifen) and anti-microtuble (e.g., docetaxel, paclitaxel, vinorelbine) chemotherapy, whereas neuroendocrine had highest DRS for antiproliferative agents (e.g., mitomycin, cytabarine, carmustine, topotecan), Significant differences in average DRS scores in subtypes were observed for all 89 drugs (p<0.001). Conclusion: Prostate cancer subtypes in the localized disease setting have distinct drug response profiles, suggesting that subtyping and DRS scores may be useful for selecting candidates for systemic therapy trials. Citation Format: Robert Den, Jonathan Lehrer, Mandeep Takhar, Mohammed Alshalalfa, Nicholas Erho, Elai Davicioni, Felix Feng. Drug response variability between luminal and basal prostate cancer tumors [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B069.

Clinically important neutralizing anti-drug antibodies detected with an in-house competitive ELISA.

Therapeutic drug monitoring of TNF-alpha inhibitors is crucial for evaluating patients with inflammatory diseases on a personalized level. It has been clinically observed that many patients receiving TNF-alpha inhibitors, with negative drug and anti-drug antibody results from bridging ELISA (bELISA), lose their drug response over time, despite dose optimization. Our aims were to develop innovative in-house competitive ELISAs (cELISAs) for the detection of neutralizing antibodies against infliximab and adalimumab and compare their results to reporter gene assay (RGA) and in-house bELISA. Furthermore, we aimed to evaluate patient anti-drug antibody results in regard to their clinical records and potential benefits of therapeutic drug monitoring with the novel cELISAs. Sera of patients treated with infliximab (n = 46) or adalimumab (n = 31), having undetectable drug levels, were tested with our in-house cELISA. Briefly, samples were incubated with a fixed amount of drug and the neutralizing capacity of the samples was determined. The cELISA results were compared to RGA and bELISA results using Spearman's correlation coefficient. Additionally, patient clinical data were evaluated in line with the results of cELISA, bELISA, and RGA using the Kaplan-Meier analysis and the Log Rank test. Both anti-infliximab and anti-adalimumab cELISAs showed very good correlation to RGA (r = 0.932, p < 0.0001 and r = 0.947, p < 0.0001, respectively). Furthermore, a positive result in anti-infliximab cELISA can predict treatment failure in 100% of patients with negative bELISA, while a positive result in anti-adalimumab cELISA can predict treatment failure in 80% of patients with negative bELISA. Taken together, we developed innovative cELISAs enabling quantification of functional and neutralizing anti-drug antibodies, comparable to RGA. The association between cELISA results and loss of drug response in patients identified clinically important anti-drug antibodies, as measured by cELISA.

Trimodal distribution of arylamine N-acetyltransferase 1 mRNA in breast cancer tumors: association with overall survival and drug resistance

BackgroundArylamine N-acetyltransferase 1 (NAT1) is a drug metabolizing enzyme that has been associated with cancer cell proliferation in vitro and with survival in vivo. NAT1 expression has been associated with the estrogen receptor and it has been proposed as a prognostic marker for estrogen receptor positive cancers. However, little is known about the distribution of NAT1 mRNA across an entire patient population or its effects on outcomes. To address this, gene expression data from breast cancer patient cohorts were investigated to identify sub-populations based on the level of NAT1 expression. Patient survival and drug response was examined to determine whether NAT1 mRNA levels influenced any of these parameters.ResultsNAT1 expression showed a trimodal distribution in breast cancer samples (n = 1980) but not in tumor tissue from ovarian, prostate, cervical or colorectal cancers. In breast cancer, NAT1 mRNA in each sub-population correlated with a separate set of genes suggesting different mechanisms of NAT1 gene regulation. Kaplan-Meier plots showed significantly better survival in patients with highest NAT1 mRNA compared to those with intermediate or low expression. While NAT1 expression was elevated in estrogen receptor-positive patients, it did not appear to be dependent on estrogen receptor expression. Overall survival was analyzed in patients receiving no treatment, hormone therapy or chemotherapy. NAT1 expression correlated strongly with survival in the first 5 years in those patients receiving chemotherapy but did not influence survival in the other two groups. This suggests that low NAT1 expression is associated with chemo-resistance. The sensitivity of NAT1 mRNA levels as a single parameter to identify non-responders to chemotherapy was 0.58 at a log(2) < 6.5.ConclusionsNAT1 mRNA can be used to segregate breast cancer patients into sub-populations that demonstrate different overall survival. Moreover, low NAT1 expression shows a distinct poor response to chemotherapy. Analysis of NAT1 expression may be useful for identifying specific individuals who would benefit from alternative therapy or drug combinations. However, additional information is required to increase the sensitivity of identifying non-responders.

Abstract 1052: Identification of signal transduction pathway activity in patient-derived xenograft models in comparison with their originating clinical samples of a variety of human cancer types

Abstract Background. Targeted drugs are directed against cellular signal transduction pathways, such as the PI3K pathway, and efficacy depends on the active pathway in the tumor. Development and clinical application of targeted drugs for personalized cancer treatment require tests which identify pathway activity in cancer cells/tissue. Patient-derived xenograft (PDX) models are becoming the cornerstone of preclinical drug development. Characterization of PDX models and clinical samples for pathway activity is needed for successful preclinical drug testing and patient drug response prediction. Inference of signal transduction pathway activity from cancer genomics information is often unreliable. A novel analysis method has been developed which enables identification and quantification of activity of signal transduction pathways, based on Bayesian reasoning models which infer an activity score from mRNA levels of pathway target genes (Verhaegh et al, Cancer Res 2014:2936-45). For the AR, ER, PI3K, Hedgehog, TGFbeta, Wnt, and NFkB pathways, models have been biologically validated on a number of different cell/tissue types. Methods. For a number of different cancer types (among which colon, breast, lung, ovarian, bladder, osteosarcoma, melanoma, esophagus), AR, ER, PI3K, Hedgehog, TGFbeta, Wnt, and NFkB pathway activity was inferred from Affymetrix HG-U133Plus2.0 microarray data, available from clinical studies (GEO database) and from PDX models (Charles River). Clinical and xenograft pathway activity scores were compared and where possible correlated to genomic datato identify associated pathway driving alterations. Results. Different cancer types were analyzed and compared. Pathway activity patterns for different cancer types in PDX models resemble pathway activity in clinical patient cohorts. Furthermore, known associated cancer mutations are reproduced in PDX models, such as APC mutations in colon PDX and ovary PDX, and an activating beta-catenin mutation in breast PDX, that show an activated Wnt pathway. Conclusion. Signal transduction pathway activity in PDX models resembles pathway activity in patient cohorts with corresponding cancer types and corresponds with known driving mutations. Functional pathway activity testing in PDX models is expected to increase xenograft model utility for (1) further validation of pathway tests for clinical diagnostic use and for (2) preclinical testing of targeted drug efficacy. Citation Format: Wim Verhaegh, Anja van de Stolpe, Nevisa Caushaj, Manuel Landesfeind, Angelika Zaremba. Identification of signal transduction pathway activity in patient-derived xenograft models in comparison with their originating clinical samples of a variety of human cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1052.

Abstract 1047: Advanced Lung Adenocarcinoma Cell Bank (ALACB) : A comprehensive preclinical platform

Abstract Purpose: Patients with advanced lung adenocarcinoma often lack large clinical specimens required for molecular testing, and are limited in next therapeutic options. Patient-derived cells (PDC) derived from malignant effusion (ME) of the patient can predict patient drug responses and provide a valuable tool for studying mechanisms of drug resistance. In this study, we created an Advanced Lung Adenocarcinoma Cell Bank (ALACB) consisting of 27 unique PDCs established from patients who progressed on various TKIs including osimertinib. Experimental Design: ME samples were tested for malignancy, processed, observed by light microscopy, and cultured. The establishment of a PDC was considered complete when it was free of normal cells, maintained known patient tumor mutation, and recapitulated patient's response. PDCs were cryopreserved to maintain a cell bank and analyzed by next-generation sequencing before passage number 25. Results: A total of 110 malignant effusion samples were obtained from 89 patients with advanced lung adenocarcinoma. Sixty-seven patients had EGFR mutations, 9 with ALK rearrangements, 1 with both EGFR mutation and ALK rearrangement, 7 with ROS1 rearrangements, and 5 with unknown driver oncogenes. 62 (56%) samples were positive for tumor. A high correlation between malignancy and spheroid positivity was observed (47/62; 76%). Most PDCs (26/27) were established from malignancy-positive/spheroid-positive samples, 12 of which were serially established from 5 patients along the clinical course. Of 36 cases that were positive for malignancy but failed in the establishment, 27 (75%) were lost due to a paucity of tumor cells, and 8 (22%) lost due to rapid cell senescence before passage number 10. Establishment of ROS1-positive PDCs showed a higher success rate (53%) than PDCs with EGFR-mutations (21%) or ALK rearrangements (23%). Median passage number of all PDCs was 24, while 3 EGFR-mutant PDCs displayed slow growth rates. Of 15 EGFR-mutant PDCs, 7 were derived from gefitinib-resistant tumors, 1 from afatinib-resistant tumor, and 6 from osimertinib or olmutinib-resistant tumors. Three PDCs harbored rare EGFR mutations including exon 20 insertion, L861Q, or G719X/S768I, respectively. Three ALK-positive PDCs were derived from crizotinib-resistant, alectinib-resistant, or ceritinib-resistant tumor. Of 8 ROS1-positive PDCs, 3 were derived from TKI-naïve tumors, and 5 from crizotinib-resistant tumors. Whole-exome sequencing and RNA sequencing were performed on 13 and 6 cases, respectively. Conclusions: Cytology positivity and spheroid formation were crucial factors in the successful establishment of ALACB, which reflects genomic profiles and drug response of advanced lung adenocarcinoma. ALACB is a renewable resource for biologic assays, generating in vitro drug-resistant models, and evaluation of novel drugs that will advance lung cancer translational research. Citation Format: Seok Young Kim, Dong Hwi Kim, Hyeong-Seok Joo, Ji Yeon Lee, Mi Ran Yun, Han Na Kang, Byoung Chul Cho, Hye Ryun Kim. Advanced Lung Adenocarcinoma Cell Bank (ALACB) : A comprehensive preclinical platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1047.

Individual responses of rodents in modelling of affective disorders and in their treatment: prospective review.

IntroductionLack of good animal models for affective disorders, including major depression and bipolar disorder, is noted as a major bottleneck in attempts to study these disorders and develop better treatments. We suggest that an important approach that can help in the development and use of better models is attention to variability between model animals. RESULTS: Differences between mice strains were studied for some decades now, and sex differences get more attention than in the past. It is suggested that one factor that is mostly neglected, individual variability within groups, should get much more attention. The importance of individual differences in behavioral biology and ecology was repeatedly mentioned but its application to models of affective illness or to the study of drug response was not heavily studied. The standard approach is to overcome variability by standardization and by increasing the number of animals per group. CONCLUSIONS: Possibly, the individuality of specific animals and their unique responses to a variety of stimuli and drugs, can be helpful in deciphering the underlying biology of affective behaviors as well as offer better prediction of drug responses in patients.

Sinecatechins 10% ointment for genital warts: Case report of a beneficial reaction in an HIV-positive woman.

Patients with HIV infection are more likely to develop anogenital warts compared to HIV-negative people and are susceptible to treatment failures and recurrences. We report a case of extensive vulvar warts in an HIV-positive woman successfully treated with sinecatechins ointment. After the failure of a combination of cryotherapy and imiquimod 5% cream, we started therapy with sinecatechins 10% ointment. The patient developed an intense local inflammatory reaction after three weeks that induced the discontinuation of the therapy. After two weeks, we observed a complete regression of inflammation and a reduction of genital warts. The lesions completely regressed within a few weeks, with no relapse after eight months. Sinecatechins is a standardized extract of green tea leaves, effective in the treatment of external genital and perianal warts in immunocompetent patients, but their role has not been yet studied for immunocompromised people. Our case may represent a starting point for further studies, in order to evaluate the relation between treatment dosage, side effects, and drug response in immunocompromised patients.

Orosomucoid 2 serves for predicting but not for monitoring drug response in patients with knee osteoarthritis treated with chondroitin sulfate/glucosamine hydrochloride

Orosomucoid 2 serves for predicting but not for monitoring drug response in patients with knee osteoarthritis treated with chondroitin sulfate/glucosamine hydrochloride

Budget impact model for oncopharmacogenetics from the perspective of mandatory basic health insurance in Switzerland using the example of breast cancer.

Single-nucleotide polymorphisms (SNPs) can severely impact individual drug response and health outcomes in cancer patients. Genetic tests to screen for marker SNPs are available to adjust the drug dose of oncologicals to the patient’s needs. However, it is unclear whether the positive effects outbalance the increased costs or even lead to an overall cost reduction. This very pragmatic analysis used three frequently used oncologicals for the treatment of breast cancer to evaluate whether preemptive pharmacogenetic testing may have a cost-reducing impact on health care spending in the Swiss health care system. Our results indicate that oncopharmacogenetics might help to reduce health care costs (ie, by avoiding adverse drug effects) and to increase efficiency of drugs in oncologic patients.

The IgG Galactosylation Ratio is Higher in Spondyloarthritis Patients and Associated with the MRI and the Response of TNF Blocker

Objectives: MRI is an important tool for evaluating inflammation levels and assessing treatment response in patients with ankylosing spondylitis (AS). However, it is expensive, and requires experienced physicians. The goal of this study was to identify a biomarker correlated with MRI score in assessing treatment response of AS. Methods: A total of 558 spondyloarthritis (SpA) patients including 527 AS patients, 10 Psoriasis (PsA) and 21 non-radigographic SpA (nrSpA,) and 725 controls were enrolled for the studies. Plasma IgG galactosylation (IgG-Gal) level was measured by mass spectrometry. Clinical indexes such as Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and C-reactive protein (CRP) were measured in all AS patients. MRIs and X-rays were obtained from 65 AS patients who were treated with a TNF blocker and longitudinally followed up for 6 months. Results: The IgG-Gal ratio was twice as high in the AS patients compared to the controls. It correlated with sacroiliac joint (SIJ) and spine inflammation indices (Pearson coefficient/p value was 0.52/2E10-4 and 0.29/1E-3, respectively). In addition, AS patients with a higher IgG-Gal ratio at baseline tended to show greater improvement in SIJ inflammation scores by MRI, and the IgG-Gal ratio of AS patients was able to predict the response of TNF blocker measured by Assessment of SpondyloArthritis international Society (ASAS40) (AUC = 0.85). Conclusion: IgG-Gal ratio was significantly increased in AS patients. In clinical care, it may be used as a potential biomarker for diagnosis and prediction of drug response of AS patients in the future. Funding Statement: The study was supported by research grants from the National Basic Research Program (2014CB541801), National key research and development program (2016YFA0501303), National Natural Science Foundation of China (31521003, 31630088), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), International S&T Cooperation Program of China (2013DFA30870), 111 Project (B13016) and US NIH NIAID U01 (1U01AI090909), and China Medical Foundation (CMF). Computational support was provided by the High-End Computing Center located at Fudan University. Declaration of Interests: All authors declare no conflicts of interest. Ethics Approval Statement: This study was approved by the Ethical Committees of the School of Life Sciences of Fudan University and all participants provided written informed consent.

Deep learning of mutation-gene-drug relations from the literature

BackgroundMolecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature.ResultsHere, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature. The first method uses the Biomedical Entity Search Tool (BEST) scoring results as some of the features to train the machine learning classifiers. The second method uses not only the BEST scoring results, but also word vectors in a deep convolutional neural network model that are constructed from and trained on numerous documents such as PubMed abstracts and Google News articles. Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks.Our methods achieved better results compared with the state-of-the-art methods. We used our proposed features in a simple machine learning model, and obtained F1-scores of 0.96 and 0.82 for mutation-gene and mutation-drug relation classification, respectively. We also developed a deep learning classification model using convolutional neural networks, BEST scores, and the word embeddings that are pre-trained on PubMed or Google News data. Using deep learning, the classification accuracy improved, and F1-scores of 0.96 and 0.86 were obtained for the mutation-gene and mutation-drug relations, respectively.ConclusionWe believe that our computational methods described in this research could be used as an important tool in identifying molecular biomarkers that predict drug responses in cancer patients. We also built a database of these mutation-gene-drug relations that were extracted from all the PubMed abstracts. We believe that our database can prove to be a valuable resource for precision medicine researchers.

Generation of matched patient-derived xenograft in vitro-in vivo models using 3D macroporous hydrogels for the study of liver cancer

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide, often manifesting at the advanced stage when cure is no longer possible. The discrepancy between preclinical findings and clinical outcome in HCC is well-recognized. So far, sorafenib is the only targeted therapy approved as first-line therapy for patients with advanced HCC. There is an urgent need for improved preclinical models for the development of HCC-targeted therapies. Patient-derived xenograft (PDX) tumor models have been shown to closely recapitulate human tumor biology and predict patient drug response. However, the use of PDX models is currently limited by high costs and low throughput. In this study, we engineered in vitro conditions conducive for the culture of HCC-PDX organoids derived from a panel of 14 different HCC-PDX lines through the use of a three-dimensional macroporous cellulosic sponge system. To validate the in vitro HCC-PDX models, both in vivo and in vitro HCC-PDX models were subjected to whole exome sequencing and RNA-sequencing. Correlative studies indicate strong concordance in genomic and transcriptomic profiles as well as intra-tumoral heterogeneity between each matched in vitro-in vivo HCC-PDX pairs. Furthermore, we demonstrate the feasibility of using these in vitro HCC-PDX models for drug testing, paving the way for more efficient preclinical studies in HCC drug development.

Abstract B107: Antitumor activity of new antibody-drug conjugate with eribulin in triple-negative breast cancer PDX

Abstract Trastuzumab-emtansine (T-DM1) is an ADC that combines the HER2-targeting properties of trastuzumab with intracellular delivery of DM1 (a highly potent derivative of the anti-microtubule agent maytansine). A new biomarker of recent interest in the cancer field is folate receptor alpha (FRA), a membrane-bound protein with high affinity for binding and transporting folate into cells. Overexpression of FRA may confer a growth advantage to tumors by increasing folate uptake and affect cell proliferation via alternative cell signaling pathways. FRA levels have been found to be elevated in tumors of epithelial origin compared to normal tissue, including TNBC. Due to an absence of potential targeted therapy for this breast cancer subtype, the finding that a significant number of TNBCs express abundant FRA suggests an important population of patients may benefit from FRA-targeting therapy. Eighty-five percent of preclinical agents entering oncology clinical trials fail to demonstrate sufficient safety or efficacy to gain regulatory approval. This failure rate shows a weak understanding of the complexity of human cancer, the continued limitations of the predictive value of existing preclinical models, and the scale at which cancer models are interrogated in the preclinical setting. There is a need for new experimental models that better replicate the diversity of human tumor biology in a preclinical setting. It is now evidenced that PDXs recapitulate human tumor biology and predict patient drug response by directly comparing drug responses in patients and their corresponding xenografts. To extend such observations to a greater number of human cancers, we have generated in collaboration with Eisai an extensive collection of breast PDXs. In this study, MORAb-202, a novel folate receptor-targeting eribulin conjugate created through a Morphotek and Eisai collaboration, was tested in FRA-expressing TNBC PDXs. Compared to free eribulin, MORAb-202 showed higher and specific antitumor activity on FRA-positive PDXs. When injected once, MORAb-202 at 5 mg/kg (0.1 mg/kg eq eribulin) showed a treatment group/control group percent (T/C %) between 4 to 17%, whereas eribulin injected at 0.1 mg/kg following the same equivalent dose and schedule of administration showed a T/C % between 74 to 93%. On non-FRA-expressing PDXs, antitumor activities of MORAb-202 and eribulin were similar. Interestingly, to find similar antitumor activity, the dose of eribulin without vectorization should be enhanced to 32-fold compared to targeted eribulin. At this dose of eribulin, close to the maximum tolerated dose, a tumor relapse is observed (100%), whereas no relapse was observed for mice treated with MORAb-202. These encouraging results lead to a proof of concept of the use of MORAb-202 in TNBC. Citation Format: Marc Hillairet de Boisferon, Caroline Mignard, Coralie Durix, Toshimitsu Uenaka, Katherine Rybinski, Xin Cheng, Keiji Furuuchi, Eart Albone. Antitumor activity of new antibody-drug conjugate with eribulin in triple-negative breast cancer PDX [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr B107.

Abstract B65: Measuring intratumoral heterogeneity with a mutant-allele frequency score across distinct cancer types

Abstract Intratumoral heterogeneity (ITH) is a phenomenon where a tumor, over space and time, branches into a set of genetically distinct clones, which have unique somatic branch mutations, although sharing several somatic trunk mutations. Clonal populations expand or regress in response to selective pressures exerted by the tumor microenvironment. Therefore, ITH has already been linked to several cancer features such as the development of metastases and resistance against a broad range of therapies. Through whole exome sequencing, we can access the number of reads covering the alleles of somatic mutations in tumor samples. Thus, mutant-allele frequency (MAF), i.e., the proportion of reads reporting the mutant allele over all reads mapped to the locus, is informative about the number of clones within a tumor bearing this genetic variant. However, MAF estimations can be impaired by tumor purity, copy number alteration, or sampling approaches. It is proposed that the analysis of multiple samples of a tumor improve accuracy in quantifying clone populations, but single sample is cheaper and more feasible for the clinicians. Mutant-Allele Tumor Heterogeneity (MATH) is a method capable of trustworthily scoring ITH in single samples, as it is based on the range of the MAF distribution of a tumor. The higher the MATH score, the more heterogeneous is a tumor. The goal of this project is to evaluate the ITH levels of 6,390 tumor samples of 20 distinct tumor types, for which genomic data is publicly available through GDAC Firehose repository. We accessed the MAF files and filtered somatic variants: (i) selected only single nucleotide variants, (ii) mutant alleles reported by at least three reads, (iii) total coverage equal to or higher than 30 reads. We estimated MATH scores using R (v3.2.4). The distribution of MATH scores across distinct tumor types exhibited a wide variability (ranging from 12 to 128). Uterine corpus endometrial carcinoma had the lowest MATH scores, whereas prostate adenocarcinoma and testicular germ cell tumors presented the highest MATH scores. The wide ITH variability among nine distinct tumor types has already been assessed in previous studies by using another scoring methodology. High MATH score was significantly associated with decreased overall survival in head and neck squamous cell carcinoma and predicted the development of metastasis in individuals with colorectal cancer. We will further explore the correlation between MATH scores and clinical data to all the 20 tumors here described by exploring the corresponding public clinical data from the cBioPortal for Cancer Genomics. Should MATH score prove to be an optimal scoring method for single biopsies, capable of predicting patient's prognosis, drug response, or disease outcome, it will be a valuable tool for clinicians for a broad spectrum of cancer treatments. Citation Format: Cibele Masotti, Filipe F. Santos, Anamaria A. Camargo, Pedro A.F. Galante. Measuring intratumoral heterogeneity with a mutant-allele frequency score across distinct cancer types [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr B65.

Quantitation of JAK2 V617F Allele Burden by Using the QuantStudio™ 3D Digital PCR System.

The JAK2 V617F mutation is highly prevalent in patients with myeloproliferative neoplasms (MPN). Furthermore, it has been shown that its allelic burden correlates with hematologic characteristics, drug response, and clinical endpoints in MPN patients. Digital PCR is an emerging technology for sensitive mutation detection and quantitation, based on dilution and high-grade partitioning of a sample. Here, we describe the use of the nanofluidic chip-based QuantStudio™ 3D Digital PCR System for quantitation of the JAK2 V617F mutation.

Abstract A188: Harnessing synthetic lethality to predict the response to cancer treatments

Abstract Synthetic lethality (SL) describes an interaction between a pair of genes whereby their double knockout is lethal, while their respective knockout is not. The identification of SL interactions (SLi) via large-scale genomic screens offers promising opportunities for developing selective therapies in cancer. However, our analysis of the TCGA cohort shows that many of the interactions do not carry predictive signal of patient survival or drug response. Here we present a data-driven approach termed ISLE (Identification of clinically relevant Synthetic LEthality) that mines the TCGA cohort to identify a subset of clinically relevant SL interactions (cSLi). ISLE consists of the following inference steps, analysis of tumor, cell line, and gene evolutionary data. We first create an initial pool of SL pairs identified through direct double knockout screens/isogenic cell line screens or inferred from large-scale shRNA/sgRNA single-gene knockout screens. Starting from this initial SL pool, ISLE first identifies putative SL gene pairs whose co-inactivation is under-represented in tumors, testifying that it is selected against. Second, it prioritizes candidate SL pairs whose co-inactivation is associated with improved patient’s prognosis, testifying that it may hamper tumor progression. Finally, it prioritizes SL-gene pairs with similar evolutionary phylogenetic profiles based on the notion that SL interactions are conserved across multiple species. We validate the identified SL pairs using an unseen large-scale in vitro drug response screen by showing the SL pairs marks a decent prediction accuracy (AUC~0.8). We compare ISLE’s performance to the standard supervised drug response prediction approaches in DREAM challenges, and our prediction based on generic pretreatment tumor samples (from TCGA) was within top 3 in prediction accuracy among the top predictors. ISLE-based approach also successfully distinguishes responders vs nonresponders to drug treatment (for &amp;gt;70% of drugs) in mouse xenografts using the activity profile of the drug target’s SL-partners. We then experimentally show the utility of SL in predicting synergistic drug combinations in patient-derived cell lines based on the notion that the two drugs whose targets have SL interactions are synergistic. Most importantly, we demonstrate for the first time that an SL network can successfully predict the treatment outcome in cancer patients in multiple large-scale patient datasets including TCGA, where cSLi are successfully predict patients’ response for more than 70% of cancer drugs. ISLE is predictive of patients’ response for the majority of current cancer drugs without any drug-specific training. Of paramount importance, the predictions of ISLE are based on SLi between (potentially) all genes in the cancer genome, thus prioritizing treatments for patients whose tumors do not bear specific actionable mutations in cancer driver genes, offering a novel approach to precision-based cancer therapy. Citation Format: Joo S. Lee, Avinash Das, Livnat Jerby-Arnon, Rand Arafeh, Matthew Davidson, Arnaud Amzallag, Seung Gu Park, Kuoyuan Cheng, Welles Robinson, Dikla Atias, Chani Stossel, Ella Buzhor, Gidi Stein, Joshua J. Waterfall, Paul S. Meltzer, Talia Golan, Sridhar Hannenhalli, Eyal Gottlieb, Cyril H. Benes, Yardena Samuels, Emma Shanks, Eytan Ruppin. Harnessing synthetic lethality to predict the response to cancer treatments [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A188.

Clinical predictors of response and discontinuation of belimumab in patients with systemic lupus erythematosus in real life setting. Results of a large, multicentric, nationwide study

ObjectiveTo investigate efficacy, safety and survival of belimumab and to identify predictors of drug response and drug discontinuation in patients with active SLE in clinical practice. Patients and methodsData of SLE patients, treated with belimumab, from 11 Italian prospective cohorts were analyzed. SLEDAI-2K, anti-dsDNA, C3, C4, prednisone daily dose, DAS-28, 24-h proteinuria, CLASIa (Cutaneous LE Disease Area and Severity Index Activity) were recorded at baseline and every 6 months. SLE Responder Index-4 (SRI-4) was calculated at 12 and 24 months. Demographic and clinical features and comorbidities were included in the univariate and multivariate analysis. Adverse events were recorded at each visit. Statistics was performed using the SPSS software. ResultsWe studied 188 SLE patients, mean follow-up 17.5 ± 10.6 months. The most frequent manifestations, which required the use of belimumab, were polyarthritis (45.2%) and skin rashes (25.5%). SRI-4 was achieved by 77.0% and 68.7% of patients at 12 and 24-months. Independent predictors of 12-month response were SLEDAI-2K ≥ 10 (OR 40.46, p = 0.001) and polyarthritis (OR 12.64, p = 0.001) and of 24-month response were SLEDAI-2K ≥ 10 (OR 15.97, p = 0.008), polyarthritis (OR 32.36, p = 0.006), and prednisone ≥7.5 mg/day (OR 9.94, p = 0.026). We observed a low rate of severe adverse events. Fifty-eight patients (30.8%) discontinued belimumab after a mean follow-up of 10.4 ± 7.5 months. The drug survival was 86.9%, 76.9%, 69.4%, 67.1%, and 61.9% at 6, 12, 18, 24, and 30 months, respectively. No factors associated with drug discontinuation were found. ConclusionBelimumab is effective and safe when used in clinical practice setting.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

Screening of antigenic vesicular fluid proteins of Echinococcus multilocularis as potential viability biomarkers to monitor drug response in alveolar echinococcosis patients.

The only drugs available to treat alveolar echinococcosis (AE) are mostly parasitostatic and in many cases prescribed for life. Decision criteria for discontinuation rely on the absence of parasitic viability. The aim of the present study is to search for candidate proteins that may exhibit good potential as biomarkers for viability. Sixteen serum samples (five healthy controls, 11 patients with AE), are used. AE-patients are classified into three groups "Cured" (n = 2), "ABZ-responders" (n = 4) and "ABZ-nonresponders" (n = 5). Immunoreactive proteins from vesicular fluid (VF) are identified and quantified by LC-MS/MS analysis after immunoprecipitation (IP) using all 16 serum samples. Shotgun analysis of VF lead to the identification of 107 E. multilocularis proteins. Comparative proteomics reveal nine proteins more abundant in IP eluates from ABZ-nonresponder patients (cathepsin b, prosaposin a preprotein, actin modulator protein, fucosidase alpha L1 tissue, gluthatione-S-tranferase, beta galactosidase, elongation factor 2, H17g protein tegumental antigen, and NiemannPick C2 protein). Detection of antibodies against these proteins by ELISA could be helpful to monitor the course of alveolar echinococcosis under albendazole (ABZ) treatment.

Abstract 2607: The cBioPortal for Cancer Genomics: an open source platform for accessing and interpreting complex cancer genomics data in the era of precision medicine

Abstract The cBioPortal for Cancer Genomics is an open-access portal (http://cbioportal.org) that enables interactive, exploratory analysis of large-scale cancer genomics data. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including cohort and patient-level visualization, mutation visualization, survival analysis, enrichment analysis, and network analysis. The user interface is user-friendly, responsive, and makes genomic data easily accessible to translational scientists, biologists, and clinicians. The cBioPortal is a fully open source platform. All code is available on GitHub (https://github.com/cBioPortal/) under GNU Affero GPL license. The code base is maintained by multiple groups, including Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, and The Hyve, an open source bioinformatics company based in the Netherlands. More than 30 academic centers as well as multiple pharmaceutical and biotech companies maintain private instances of the cBioPortal. This includes the recently launched cBioPortal instance at the NCI Genomic Data Commons (https://cbioportal.gdc.nci.nih.gov/), and two large cBioPortal instances hosting genomic and clinical data at MSK and DFCI, supporting the MSK-IMPACT and DFCI Profile projects, two of the largest clinical sequencing efforts in the world. Our multi-institutional software team has accelerated the progress of evolving the core architectural technologies and developing new features to keep pace with the rapidly advancing fields of cancer genomics and precision cancer medicine. For example, we have integrated multi-platform genomics data with extensive clinical data including patient demographics, treatment history, and survival data. We have also developed a patient-centric view that visualizes both clinical and genomic data with annotation from OncoKB knowledge base. In the next few years, the development team will focus on the following areas: (1) Implementing major architectural changes to ensure future scalability and performance. (2) New features to support precision medicine, including (i) improved integration of knowledge base annotation, (ii) enhanced visualization of patient timeline, drug response, and tumor evolution, (iii) new patient similarity metrics, (iv) improved support for immunogenomics and immunotherapy, and (v) new visualization and analysis features for understanding response to therapy. (3) New analysis and target discovery features for large cohorts, including (i) supporting user-defined virtual cohort by selecting samples from multiple studies, and (ii) comparison of genomic or clinical characteristics of two or more selected cohorts. (4) Expanding community outreach, user support and training, and documentation. Citation Format: Jianjiong Gao, Ersin Ciftci, Pichai Raman, Pieter Lukasse, Istemi Bahceci, Adam Abeshouse, Hsiao-Wei Chen, Ino de Bruijn, Benjamin Gross, Zachary Heins, Ritika Kundra, Aaron Lisman, Angelica Ochoa, Robert Sheridan, Onur Sumer, Yichao Sun, Jiaojiao Wang, Manda Wilson, Hongxin Zhang, James Xu, Andy Dufilie, Priti Kumari, James Lindsay, Anthony Cros, Karthik Kalletla, Fedde Schaeffer, Sander Tan, Sjoerd van Hagen, Jorge Reis-Filho, Kees van Bochove, Ugur Dogrusoz, Trevor Pugh, Adam Resnick, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for Cancer Genomics: an open source platform for accessing and interpreting complex cancer genomics data in the era of precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2607. doi:10.1158/1538-7445.AM2017-2607