tbuckley/cabot-search huggingface dataset:
Abstract CT235: A phase 1b/2 study of onvansertib (PCM-075) in combination with FOLFIRI and bevacizumab for second line treatment of patients with KRAS-mutated metastatic colorectal cancer (mCRC)
Authors: Afsaneh Barzi, Heinz-Josef Lenz, Errin Samuëlsz, Maya Ridinger, Mark Erlander, Tanios S. Bekaii-Saab, Daniel H. Ahn
Journal: Cancer Research (2020)

Abstract Background: Chemotherapy in combination with targeted agents are standard-of-care options for patients for mCRC with response rates &amp;gt;50% in first line. In the second line setting, efficacy of chemotherapy and targeted agents are much lower with response rates of 5% for FOLFIRI (5-fluorouracil, leucovorin, irinotecan) + bevacizumab (anti-VEGF). New treatment options are urgently needed in particular for the 50 % of patients harboring a KRAS mutation. Polo-like kinase 1 (PLK1) is a serine/threonine kinase, master regulator of the mitotic checkpoint and cell division. PLK1 is overexpressed in CRC and its overexpression is associated with poor prognostic. A genome wide RNAi screen identified PLK1 as a synthetic lethal target in KRAS mutant CRC cells, inducing cell cycle arrest and apoptosis upon inhibition. Onvansertib is an oral, highly selective PLK1 inhibitor that demonstrates single agent and synergistic activity with irinotecan in preclinical CRC models. Additionally, KRAS mutated vs wild-type cells showed higher sensitivity to onvansertib. PLK1 inhibition is a potential target in KRAS-mutated mCRC and onvansertib + FOLFIRI + bevacizumab may provide a new second-line treatment option. Trial design: The primary objective of this single-arm Phase 1b/2 study is to assess the safety and preliminary efficacy of onvansertib in combination with FOLFIRI and bevacizumab in the second line setting for KRAS-mutated mCRC patients. For the Phase 1b segment, a standard 3 + 3 dose-escalation design is used to determine the maximum tolerated dose or recommended phase 2 dose (RP2D) of onvansertib. As of January 24th 2020, enrollment in the second dose level is ongoing. Efficacy will be determined by objective response rate (ORR) according to RECIST v1.1 (primary endpoint), progression-free survival and reduction in KRAS allelic burden in liquid biopsies (secondary endpoints). In the phase 2, based on a one-sided one sample log-rank test with 10% Type I error, there will be at least 90% power to detect an improvement in ORR from 5% to 20% with 26 patients. Exploratory endpoints include genomic studies of circulating tumor cells and ctDNA to evaluate altered pathways that correlate with patient response. Clinical trial identification NCT03829410. Citation Format: Afsaneh Barzi, Heinz-Josef Lenz, Errin Samuëlsz, Maya Ridinger, Mark Erlander, Tanios S. Bekaii-Saab, Daniel H. Ahn. A phase 1b/2 study of onvansertib (PCM-075) in combination with FOLFIRI and bevacizumab for second line treatment of patients with KRAS-mutated metastatic colorectal cancer (mCRC) [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 CT235.
Abstract 1154: AFPep mitigates the risk of breast cancer
Authors: Samuel B. Fordyce, Thomas T. Andersen, James A. Bennett
Journal: Cancer Research (2020)

Abstract Women whose risk of breast cancer is elevated (based on Gail or Tyrer-Cuzik models) have only limited options to intervene in attempt to alter disease progression, and most options are of limited efficacy or have unwanted side effects. Options include increased surveillance, lifestyle changes, drugs such as tamoxifen or aromatase inhibitors, or removal of target tissue. Options that are more efficacious, better tolerated, and more empowering are needed for women in high-risk cohorts. For this purpose, we have developed AFPep, a homobiotic, selective anti-estrogenic/anti-breast cancer active site-analog of alpha-fetoprotein (AFP). AFPep stops growth of human breast cancers growing in xenograft models and prevents development of either estrogen-induced or MNU-induced mammary cancers in rats. In all models, AFPep is effective against tamoxifen-resistant breast cancer, is additive with tamoxifen (allowing dose reduction of tamoxifen), and reduces the uterine hyperplasia induced by tamoxifen. AFPep does not disrupt the estrous cycle of rats, nor does it alter fertility or fecundity and does not induce teratogenicity. The mechanism of action of AFPep differs from that of tamoxifen in that AFPep inhibits phosphorylation of the estrogen receptor leading to a downstream biomarker profile consistent with inhibition of tumor growth. AFPep is orally bioavailable and well tolerated in mice, rats, dogs and primates. The therapeutic index of AFPep in rats shows a substantially broader safety margin than that of currently used agents. AFPep may offer an effective, well tolerated option for women in high risk cohorts who wish to mitigate their risk of breast cancer. Citation Format: Samuel B. Fordyce, Thomas T. Andersen, James A. Bennett. AFPep mitigates the risk of breast cancer [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 1154.
Abstract 5131: Differentially expressed proteins in esophageal squamous cell carcinoma tissue
Authors: Fuchun Si
Journal: Cancer Research (2020)

Abstract Purpose: To identify differentially expressed proteins among the esophageal squamous cell carcinoma(ESCC) carcinoma tissue, adjacent normal tissue, normal esophageal mucosa, in order to look out the protein related to different stages and study the molecular mechanism and occurrence of the esophageal squamous cell carcinoma. Methods: Tissue specimens of ESCC and the corresponding clinical data database of ESCC patients were established, a total of 69 patients were selected according to distribution of their sex and age. Two-dimensional gel electrophoresis technology to electrophoresis and get the separation protein of cancerous tissues, adjacent tissues and normal esophageal tissues,then ImageMaster 2D Platinum software was used to search, quantify, match and statistical analysise the proteins on the gel image. 4700 Proteomics Analyzer (TOF/TOFTM) was used to analyze protein spots, in which differentially expressed proteins meet standards were further searched in the Matrix Science, London, UK protein database. Results: Totle 44 and 29 kinds of differentially expressed proteins were screened from esophageal cancer tissues, adjacent noncancerous tissues and normal tissues, among which, there were 13 differentially expressed proteins between esophageal cancer tissues and adjacent noncancerous tissues, 14 differentially expressed proteins between esophageal cancer tissues and normal tissues, 17 between esophageal adjacent noncancerous tissues and normal tissues. These differential expression proteins mainly involved in PKC-catalyzed phosphorylation of inhibitory phosphoprotein of myosin phosphatase, nuclear factor of activated T cells(NFAT) pathway, Integrin Signaling Pathway, Cell to Cell Adhesion Signaling, Calmodulin(CaM) nuclear factor of activated T cells, Mineral absorption of calcium binding protein path way(S100), Path way of vascular smooth muscle contraction affe cted by caldesmon, Apoptotic Signaling in Response to DNA Damage path way, which had the function of cytoskeleton structure, protein phosphorylation, acetylation of aminoend, regulation of calcium combination,tumor cell movement, signal transduction, cell differentiation, cell apoptosis, muscle contraction, translation regulation, gene expression, energy metabolism, cell proliferation. Conclusion: This study study achieved differentially expressed protein in ESCC tissue patients, provides new basis for the study of esophageal cancer carcinogenesis mechanism. Citation Format: Fuchun Si. Differentially expressed proteins in esophageal squamous cell carcinoma tissue [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 5131.
Abstract 5836: Autophagy inhibition as a new therapeutic strategy for cancers with transcription elongation defects
Authors: Belal Muhammad, Lisa Vinnedge, KAkajan Komurov
Journal: Cancer Research (2020)

Abstract Increasing evidence suggests that the tumorigenic process is driven by various forms of transcriptional and epigenetic defects beyond somatic mutations. We recently described a novel non-genomic phenomenon associated with tumorigenesis that we call Transcription Elongation Defects (TEdeff), which is characterized by high levels of truncated transcripts, intron retention, and loss of gene body exon expression. The TEdeff phenotype exists in more than 20% of all human tumors and is linked to immunotherapy resistance (Modur et al, 2018). The aim of this study was to find a new therapeutic strategy for these types of tumors by targeting cellular pathways that are essential for TEdeff tumor survival. Computational analyses of RNA-sequencing data indicate that TEdeff tumors have extensive overexpression of the cytoplasmic protein homeostasis machinery, including several components of autophagy and proteasome degradation pathways. This strongly suggests that these cells may have increased dependency on cellular protein quality control due to elevated proteotoxic stress. Accordingly, we experimentally show that TEdeff cancer cell lines have a profound accumulation of protein aggregates (aggresomes) that appear to be associated with autophagy for clearance. Importantly, TEdeff cells, but not control cells with proficient transcription elongation (TEprof), are highly sensitive to autophagy inhibitors like chloroquine (CQ), but not proteasome inhibitors, which indicates their increased dependency on autophagy for survival. We also show evidence that the proteasomal degradation system may be defective in TEdeff cells, further supporting the hypothesis that these cells are dependent on alternative protein quality control mechanisms. Mechanistically, we propose that blocking the autophagy pathway maximizes proteotoxic stress, due to the accumulation of toxic aggresomes and proteins generated from defective transcripts, leading to a strong synthetic lethality in TEdeff cells. Therefore, autophagy inhibition may be a potent treatment strategy for TEdeff tumors, which are inherently resistant to current immunotherapies. Citation Format: Belal Muhammad, Lisa Vinnedge, KAkajan Komurov. Autophagy inhibition as a new therapeutic strategy for cancers with transcription elongation defects [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 5836.
Abstract 1759: KDM5 inhibitor regulates growth of early stage and advanced human prostate cancer cells
Authors: Tunde Smith, Zhenbang Chen, LaMonica V. Stewart
Journal: Cancer Research (2020)

Abstract Prostate cancer (PCa) remains a leading cause of cancer deaths because many patients ultimately develop an incurable castration-resistant form of metastatic PCa. While chromatin alterations are linked to PCa, the role of histone demethylases in the PCa progression has not been fully elucidated. Members of the Jumonji AT-rich interactive domain 1 (JARID1) or lysine demethylase 5 (KDM5) family of lysine demethylases are known for their ability to demethylate H3K4me3. However, the genes encoding these proteins are amplified in metastatic PCa. Literature suggests members of the KDM5 family enhance expression and function of androgen receptor (AR), a protein that drives growth of human PCa cells. The KDM5 family inhibitor 2-4(4-methylphenyl)-1,2-benzisothiazol-3(2H)-one (PBIT) has been suggested as a potential lead compound for cancer therapy since it suppresses proliferation of human breast cancer cells. However, to date no studies have tested the effects of PBIT in PCa. The goal of this study was to characterize the effects of PBIT alone and in combination with a PPARγ agonist, 15-Deoxy-Δ-12,14-prostaglandin J2 (15-dPGJ2) on PCa cells. Presto blue analyses were conducted in AR positive, castration-sensitive LNCaP cells and the castration-resistant PC3 cells expressing little to no AR. Our studies revealed that PBIT treatment decreased the proliferation of both LNCaP and PC3 cells in a dose-dependent manner. Significant decreases in cell proliferation were detected when PBIT concentrations were greater than or equal to 5 μM. In addition, 15-dPGJ2 was able to decrease the proliferation of PCa cells. Importantly, the combination of PBIT and 15-dPGJ2 significantly suppressed the growth of PC3 and LNCaP cells. The anti-proliferative effect of the combined inhibition was at least additive, as the combination index was less than 1. Furthermore, PBIT at concentrations ≥ 5 μM also reduced migration of PC3 cells. Together, our data suggest that PBIT and other KDM5 inhibitors can reduce the proliferation and migration of multiple types of PCa cells. Our findings also provide evidence that the combination of KDM5 family inhibitors and PPARγ agonists might serve as an effective therapy for advanced human PCa. Citation Format: Tunde Smith, Zhenbang Chen, LaMonica V. Stewart. KDM5 inhibitor regulates growth of early stage and advanced human prostate cancer cells [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 1759.
Abstract 2108: Application of random forest machine learning techniques on mixed data from breast cancer studies
Authors: Jelmar Quist, Lawson Taylor, Johan Staaf, Anita Grigoriadis
Journal: Cancer Research (2020)

Abstract Integrative analysis of diverse high-dimensional molecular, histopathological and clinical data provides an effective way to identify biologically and clinically relevant subclasses across multi-level data. However, unbiased integrative methods to identify distinctive features and group structure in such data remains problematic. We propose a machine learning clustering technique based on random forest methods that enables unbiased integration. Using a permutation-based framework for the tree construction procedure and measuring of feature importance, robust and pure clusters can be produced. The performance of standard, regularised, and conditional inference random forest methods was evaluated using the adjusted Rand index, the Calinski-Harabasz index, and cluster and feature purity. In simulations studies, random forest clustering techniques were able to identify clusters of high purity. Using datasets from the UCI Machine Learning Repository as a proof of concept, all three techniques were able to identify clusters in mixed data, whereby the conditional inference method produced clusters with the highest feature purity. Next, we applied our clustering techniques to high-dimensional data obtained from two independent breast cancer studies: (i) International Cancer Genome Consortium (ICGC), consisting of 560 cases and 147 features, and (ii) Sweden Cancerome Analysis Network - Breast (SCAN-B), incorporating 241 cases and 53 features. Features included rearrangement and mutational signatures, somatic mutation in cancer drivers, germline mutations in BRCA1/2, genomic instability measures, intrinsic molecular breast cancer subtypes, and clinico-pathological characteristics. Despite dissimilarities in the breast cancer subtype composition between these two datasets, the conditional inference random forest method was able to identify concordant subgroups between the studies supported by molecular and histopathological characteristics. Moreover, novel relationships amongst molecular features with potential clinical relevance were revealed. For example, one cluster was enriched for BRCA2-deficient breast cancer cases with MYC amplifications, while another predominantly consisted of non-basal-like triple-negative breast cancers with PIK3CA mutations. Together, these results support the use of our machine learning clustering technique based on random forest methods to identify robust and biologically relevant group structures using complex high-dimensional mixed data. Citation Format: Jelmar Quist, Lawson Taylor, Johan Staaf, Anita Grigoriadis. Application of random forest machine learning techniques on mixed data from breast cancer studies [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 2108.
Abstract 5321: Novel imipridone ONC206 inhibits cell proliferation and induces apoptosis in uterine serous cancer through altering MAPK/AKT signaling network and metabolic reprogramming
Authors: Chi Lam Au Yeung, Wen Hu, Li Zhang, Sammy Ferri-Borgogno, Rohinton S. Tarapore, Joshua E. Allen, Karen H. Lu, Samuel C. Mok
Journal: Cancer Research (2020)

Abstract Uterine serous carcinoma (USC) is the most aggressive subtype of endometrial cancer. Patients with advanced stage USC have poor survival rates and new treatment regimens are lacking. ONC201 is the first clinical bitopic antagonist of dopamine receptor D2 (DRD2), that is well tolerated and currently being investigated in several clinical trials for oncology. ONC206 is a chemical derivative of ONC201 with the same impridone core structure, which is also a DRD2 antagonist that exhibits distinct receptor pharmacology and nanomolar potency in various preclinical cancer models. However, the effects of ONC206 on USC progression and the mechanism of action have not been thoroughly explored. Using both in vitro and in vivo models, and multiple USC cell lines, the effects of ONC206 on cell proliferation and apoptosis were determined. Reverse phase protein arrays (RPPAs) and Western blot analyses were used to determine the effect of ONC206 on the expression of key proteins in various signaling networks in ONC206 treated USC cells. The results showed that ONC206 suppressed USC cell proliferation and induces apoptosis in a dose-dependent manner. Luciferase labeled USC cell ARK1-bearing mice treated with 100mg/kg ONC206 had significantly lower chemiluminescent signals than those treated with the control buffer. RPPA data showed that ONC206 treated USC cells had markedly lower expression signals in p38MAPK, p-AKT, p-S6, and multiple mitochondrial proteins associated with mitochondrial ATP synthesis including MTCO1, and TFAM than the control cells did. Significantly lower ATP levels and cytochrome c oxidase activities in ONC206 treated USC cells than in control cells were demonstrated by luminescent ATP detection assay kit and the cytochrome c oxidase assay kit. These data suggest that ONC206 suppresses USC progression through inhibiting MAPK/AKT network, which subsequently leads to metabolic reprogramming and increased apoptosis. Moreover, by knocking-out DRD2, USC cells became more resistant to ONC206 treatment. This suggested that the effect of ONC206 is likely mediated through its binding to DRD2. ONC206 also showed a synergistic effect with paclitaxel in vitro. Further studies which demonstrate the optimal dosage and the efficacy of treatment of USC using ONC206 are warranted. Citation Format: Chi Lam Au Yeung, Wen Hu, Li Zhang, Sammy Ferri-Borgogno, Rohinton S. Tarapore, Joshua E. Allen, Karen H. Lu, Samuel C. Mok. Novel imipridone ONC206 inhibits cell proliferation and induces apoptosis in uterine serous cancer through altering MAPK/AKT signaling network and metabolic reprogramming [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 5321.
Abstract 3217: An integrated web server for multi-omics data deconvolution
Authors: Xiaoyu Lu, Pengtao Dang, Szu-Wei Tu, Wennan Chang, Changlin Wan, Chi Zhang, Sha Cao
Journal: Cancer Research (2020)

Abstract Tissue data deconvolution has been broadly utilized to characterize the variation of cell components and their relative abundance in cancer tumor microenvironment. Numerous deconvolution methods have been developed with different mathematical and biological considerations. Our previous studies have derived a rigorous mathematical condition for a less biased deconvolution. We have demonstrated a cell type can be reliably inferred from an omics data only of the cell type is with distinct signature genes, i.e., the cell type specific expression signature of the selected marker genes of all cell types are orthogonal or at least non-positively associated with each other, which is named as strong or weak “identifiable” conditions. Our analysis suggests the identifiability of a certain cell type is mostly determined by the significance of its gene markers, which is data set and tissue microenvironment dependent. However, such data set specificity has been commonly ignored in existing methods. In this study, we developed a comprehensive web server, namely ISMD (an Integrated web Server for Multi-omics data Deconvolution), to recommend a best deconvolution method and optimize the gene markers and cell types in analyzing a specific data set. The server solved to two fundamental challenges in the practical application of deconvolution methods, namely (1) method selection and (2) parameter optimization. Specifically, ISMD utilized a semi-supervised approach for method recommendation and parameter optimization, which first evaluate the species, sample size, and tissue type of a given data set and test the low rank structure of commonly used cell type specific markers for cell type and signature gene selection. ISMD can handle the blood, normal, inflammatory, and cancer tissue, and central nervous and hematopoietic systems of human and mouse. The semi-supervised knowledge transfer encoded in ISMD enables its application to multi-omics data including general transcriptomics, ATAC-seq and DNA-methylation data. The method has been validated by using a large collection of tissue samples. A user friend web-server has been released through https://github.com/xiaoyulu95/ISMD. We anticipate ISMD can help to increase the specificity and rigorousness in the application of tissue deconvolution methods, to bring more insights of the role of different cell types and their interactions and stimulate the discovery of new therapeutic targets in a tumor micro-environment. Citation Format: Xiaoyu Lu, Pengtao Dang, Szu-Wei Tu, Wennan Chang, Changlin Wan, Chi Zhang, Sha Cao. An integrated web server for multi-omics data deconvolution [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 3217.