bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 6223: PD3D®models: New age in cancer research and clinical diagnostics
Authors: Pflaume Alina , Exner Samantha , Herrera-Glomm Katja , Loskutov Jürgen , Pfohl Ulrike , Regenbrecht Manuela , Sankarasubramanian Sushmitha , Wedeken Lena , Finkler Sabine , Ruhe Larissa , Adelmann Quirin Graf , Reinhard Christoph , Stroebel Philipp , Regenbrecht Christian R. 
Published: June 2022

Abstract
Patient-derived 3D cell culture models (PD3D®) developed as a powerful tool for disease modelling, biomarker and drug discovery. Currently, they are gaining increasing significance in the field of personalized oncology, as they recapitulate the histopathology of the original tumor tissue and preserve its genetic markup. PD3D® can be used to model intratumoral heterogeneity and for medium and high throughput drug screens. Using a reverse clinical engineering approach, PD3D® models allow identification of chemoresistance/sensitivity signatures (i.e., biomarkers) and can be applied in personalized oncology to identify treatment for an individual patient. We successfully established PD3D® models from more than 300 tumor tissue samples, ranging from more prevalent cancers like colorectal, breast and pancreas carcinoma, to rare tumor entities including various sarcoma types and thymoma. PD3D® models from different tumor entities differ in morphology and culture media requirements. When treating PD3D® from the same tumor entity with standard of care drugs, we found that their response differed, as does clinical response of patients. Furthermore, we successfully used PD3D® models to identify a biomarker for predicting chemosensitivity towards a targeted drug. For application of PD3D® in truly personalized oncology, we developed a protocol that allows us to generate a PD3D® culture and perform a drug sensitivity assay for an individual patient within a therapy-relevant timeframe. Using this protocol, we identified a combination therapy for a pretreated, metastasized appendix carcinoma within 29 days, that resulted in stable disease of the patient. In conclusion, PD3D® models can be derived from various cancer entities and used to analyze drug response in cohorts of models for drug development or identification of signatures related to drug resistance or sensitivity. Furthermore, PD3D® models can be used to predict a patient tumor’s drug response in a personalized manner, supporting the oncologist to identify the best treatment option for the patient.
Citation Format: Alina Pflaume, Samantha Exner, Katja Herrera-Glomm, Jürgen Loskutov, Ulrike Pfohl, Manuela Regenbrecht, Sushmitha Sankarasubramanian, Lena Wedeken, Sabine Finkler, Larissa Ruhe, Quirin Graf Adelmann, Christoph Reinhard, Philipp Stroebel, Christian R. Regenbrecht. PD3D®models: New age in cancer research and clinical diagnostics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6223.
Abstract 6227: An optimized preclinical antibody-drug conjugate against cancers with cKIT overexpression or activating mutations
Authors: Kim Jin-Ock , Ko Han-Jik , Kim Jae-Won , Park Sang Gyu 
Published: June 2022

Abstract
cKIT overexpression and activating mutations have been observed in many types of cancer, including small cell lung cancer (SCLC), gastrointestinal stromal tumor (GIST), and acute myeloid leukemia (AML), and are known to be predictive of poor prognosis. In previously reported work, NN2101-DM1, a fully human, anti-cKIT antibody-drug conjugate (ADC), was developed as a proof-of-concept (PoC) entity by conjugating the site-nonspecific and uncleavable linker SMCC with the microtubule inhibitory payload DM1. This linker-payload system is the same used in ado-trastuzumab emtansine, an approved ADC for HER2-positive breast cancer. NN2101-DM1 showed therapeutic efficacy against SCLC and GIST both in vitro and in xenograft models in vivo, demonstrating the feasibility of ADC development using NN2101. Despite the potent efficacy of NN2101-DM1, there are inherent limitations to SMCC linker and DM1 payload systems. The nonspecific attachment of the linker to the antibody produces highly heterogeneous drug-antibody ratios (DARs) with unpredictable attachment. Additionally, impaired lysosomal proteolytic activity has been shown to contribute to resistance to SMCC-DM1 cleavage. To optimize the ADC configuration for advancement to a clinical candidate, four types of ADC with different linker-payload combinations and DARs were designed, and their efficacy and safety were evaluated in vitro and in vivo. Among them, NN3201, a conjugation of the antimitotic payload MMAE to the cKIT-targeting antibody NN2101 via a site-specific, cleavable peptide linker, with a homogenous (&gt;95%) DAR of 4 was selected as a lead candidate. This linker system utilizes a cysteine re-bridging technology to produce site-specific conjugation with high DAR homogeneity. NN3201 showed consistent sub-nanomolar IC50s in several in vitro cell lines and demonstrated complete remission as monotherapy in SCLC (H526), GIST (GIST-T1), AML (Kasumi-1), and mastocytoma (HMC 1.2) cell-derived xenograft models in doses ranging from 1.0-3.0 mg/kg. There were no significant toxicities detected at doses up to 60 mg/kg in normal mice, suggesting a therapeutic index greater than 20 times. In conclusion, NN3201 showed potent anti-tumor activity and was selected as preclinical candidate.
Citation Format: Jin-Ock Kim, Han-Jik Ko, Jae-Won Kim, Sang Gyu Park. An optimized preclinical antibody-drug conjugate against cancers with cKIT overexpression or activating mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6227.
Abstract 6253: RNA-seq based somatic variant calling and gene expression analysis reveals tumor heterogeneity and metastatic potential in colorectal cancers
Authors: Choi Ji Won , Ahn Kwangsung , Kim Sangsoo , Park Dong-Il , Park Soo-kyung 
Published: June 2022

Abstract
Colorectal cancer (CRC) is the third common malignant tumor and the second most responsible for worldwide cancer deaths. Also, RNAseq technology has been used for two purposes: to find exonic regions on the genome and quantify the expression level of the gene. We tried to validate the pipeline for identifying somatic variants from RNA-seq data, mainly following GATK4 somatic calling pipelines with some optimizing modifications. It is intended to examine whether somatic mutations driven from RNAseq data are associated with the biological behaviors of the individual colorectal cancer cells. We found that key genes (i.e., tumor suppressor genes such as APC and p53) are mostly mutated by T to C (in the non-coding region) or C to T (in the coding region) transitions, so that causes missense and nonsense mutations. However, highly mutated genes did not show significant expressional changes compared to the normal tissue. Otherwise, genes related to the metastatic potentials were observed to have highly increased expression. Our results can substantially verify the reliability of the somatic mutation calling approach using RNAseq data to call cancer-driving mutation and confirm an increase of extracellular matrix metabolism in the CRC.
Citation Format: Ji Won Choi, Kwangsung Ahn, Sangsoo Kim, Dong-Il Park, Soo-kyung Park. RNA-seq based somatic variant calling and gene expression analysis reveals tumor heterogeneity and metastatic potential in colorectal cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6253.
Abstract 6275: Evaluation of FRα/TRPV6 expression in gynecological cancer
Authors: Li Ning , Li Yifan , Tan Wei , Teng Yan , Huang Baohua , Wu Lingying 
Published: June 2022

Abstract
Purpose:Folate Receptor Alpha (FRα) is highly expressed in multiple malignancies, including ovarian, NSCLC and breast cancer. Transient Receptor Potential Vanilloid 6(TRPV6) is upregulated in breast, ovarian and prostate cancer. However, there is no report on expression and correlation between expression of the two markers. Since both markers are highly expressed in ovarian cancer, it is of great interest to evaluate expression of FRα and TRPV6 in a panel of gynecological cancer, including those major subtypes.
Method:We have collected 200+ FFPE samples achieved in pathology core at National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, including high grade and low grade serous ovarian cancer and clear cell ovarian cancer et al. Using validated IHC assays, expression of both FRα and TRPV6 are evaluated.
Result:Various levels of FRα and TRPV6 express in different histological subtypes of ovarian cancer. High grade serous ovarian cancer expresses the highest levels of FOLR1, which is about 2.8 and 2 folds over that of low-grade ovarian cancer and that of clear cancer ovarian cancer, respectively. The highest levels of TRPV6 expression are observed in clear cell ovarian cancer, which is 2.6 and 1.6 folds over that observed in low grade ovarian cancer and that observed in high grade ovarian cancer, respectively. The two biomarker expression is overlapped in about 45% of all samples analyzed. A comparison of samples from patients with or without good response to neoadjuvant therapy reveals samples with poor responses tend to express higher levels of FRα than those with good responses do. Addition evaluation and analysis are still on-going.
Conclusion: FRα and TRPV6 are highly express in gynecological cancer. Evaluation of their expression is of great value for molecularly-targeted therapies for those two targets and provide a precision medicine option for the clinical development for such drugs.
Citation Format: Ning Li, Yifan Li, Wei Tan, Yan Teng, Baohua Huang, Lingying Wu. Evaluation of FRα/TRPV6 expression in gynecological cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6275.
Abstract 6308: Antitumor effects of RBPJ Inhibitor-1 on osteosarcoma cells
Authors: Callahan Dakota , Hinojosa Leetoria , Torres Haydee M. , Tao Jianning 
Published: June 2022

Abstract
Osteosarcoma (OS) is the most common primary malignancy of the bone in adolescents and children with limited treatment options. Drug resistance to current therapy together with severe side effects urgently calls for alternative treatment strategies targeting critical cancer signaling pathways. Recently, our and other studies suggest that hyperactivation of Notch signaling contributes to the pathogenesis of human and murine OS, and its inhibition may be a therapeutic approach for the treatment of OS. Notch pathophysiological activity involves activated Notch (i.e. NICD, the notch intracellular domain) forming a nuclear complex with DNA-binding factors and coactivators such as recombination signal binding protein for immunoglobulin kappa J region (RBPJ). This evolutionary conserved mechanism is involved in the maintenance and survival of cancer stem cells, which can govern various cell activities like metastasis and chemoresistance. This study aims to examine the in vitro effects of targeting the NICD-RBPJ interaction using a small molecular inhibitor of the RBPJ (RIN1) on osteosarcoma cell lines. We found that treatment of 10 µM RIN1 on SJSA-1 (ATCC, CRL-2098), a human osteosarcoma cell line with a high Notch activity, can significantly decrease cell proliferation at 48 and 72 hours (h). We also found that at 10 µM of RIN1 affects SJSA-1 migration using a Boyden chamber-based cell system at 24 h, a time point where we find minimal contribution from an effect on cell proliferation. Treatment with RIN1 can block SJSA-1 clonogenicity, but we showed that it has limited ability to block SJSA-1 cell invasion. Treatment with RIN1 does not initiate or enhance the osteoblast-like differentiation of SJSA-1. Notch target genes HEY1 and CyclinD1 was significantly modulated in SJSA-1 cells upon treatment in a Notch-independent manner. Interestingly, treatment with RIN1 on SJSA-1 decreases SOX2 protein expression, a well-known cancer stem cell marker, and upregulates tumor suppressor RB1 protein expression. Ongoing studies include additional cellular function assays and RNA-seq transcriptomic analysis of RIN1’s effect on two Notch-dependent cancer cell lines including MB 157 (ATCC, CRL-7721) and T29 in its activating context. In summary, RIN1 has been shown to have a significant effect on the different cell behaviors of human osteosarcoma, which may eventually facilitate with the treatment of patients that have metastatic disease.
Citation Format: Dakota Callahan, Leetoria Hinojosa, Haydee M. Torres, Jianning Tao. Antitumor effects of RBPJ Inhibitor-1 on osteosarcoma cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6308.
Abstract 6315: Are a subset of patients at higher risk for mortality in stage III colon cancer: An analysis of the SEER database
Authors: Hajirawala Luv N. , Yi Yong , Bergeron Michelle A. , Dooley Danielle A. , Orangio Guy R. , Bevier-Rawls Elyse R. , Davis Kurt G. , Barton Jeffrey S. 
Published: June 2022

Abstract
Background: High-risk features (HRF) increase mortality for Stage II colon cancer (CC). These HRF include pathologic T4 disease, perineural invasion (PNI), lymphovascular invasion (LVI), an inadequate lymphadenectomy (less than 12 lymph nodes harvested) and poorly differentiated tumors. The presence of two or more HRF has an additive impact on survival for patients with Stage II CC. While T4 and disease greater than N1a raise the risk of mortality in Stage III CC, the effect of the other accepted HRF in patients with Stage III CC has not been studied.
Materials and Methods: Surveillance, Epidemiology and End Results (SEER) (2010 - 2017) was used to identify patients with Stage III and IV CC. Patients with Stage III CC were then further classified by the presence of zero, 1, or 2 or more of the following HRF: pathologic T4, PNI, inadequate lymphadenectomy, or poor differentiation. Overall (OS) and cause-specific (CS) survival were calculated.
Results: 57,640 patients with Stage III or IV CC were identified. 35,760 (62%) patients had Stage III disease and 21,880 (38%) had Stage IV disease. Among patients with Stage III disease, 16,733 (46.8%) had no HRF, 12,703 (35.5%) had 1 HRF and 6,324 (17.7%) had 2 or more HRF. For patients with 1 HRF, 4,610 (36.3%) had poor differentiation, 2,339 (18.4%) had PNI, 3,673 (28.9%) had T4 disease, and 2,078 (16.4%) had an inadequate nodal harvest. Patients with Stage III CC without HRF had the best OS (73.3%) and CS (81.4%,). This was followed by Stage III with 1 HRF (OS 63.7%, CS 71.6%) and Stage III with 2 or more HRF (OS 44%, CS 51%). Patients with Stage IV disease had the worst OS (18.1%) and CS (20.4%) (p&lt;0.05). For patients with 1 HRF, pathologic T4 conferred worse OS (57.2%) and CS (63.2%), compared to PNI (OS 66.4%, CS 73.7%), inadequate lymphadenectomy (OS 64.6%, CS 74.9%) and poor differentiation (OS 66.6%, CS 75.4%) (p&lt;0.01).
Conclusion: HRF have a cumulative influence on OS and CS in patients with Stage III CC. The presence of multiple HRF should be considered higher risk for patients with Stage III disease.
Citation Format: Luv N. Hajirawala, Yong Yi, Michelle A. Bergeron, Danielle A. Dooley, Guy R. Orangio, Elyse R. Bevier-Rawls, Kurt G. Davis, Jeffrey S. Barton. Are a subset of patients at higher risk for mortality in stage III colon cancer: An analysis of the SEER database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6315.
Abstract 6337: Assessing genomic literacy among medical trainees and practitioners in Pakistan
Authors: Barakzai Madina , Rehmat Shagufta , Khan Faisal F. 
Published: June 2022

Abstract
Genomic literacy has a significant impact on the successful application and deployment of new practices such as precision medicine. It can be described as the ability to achieve, process, understand, and apply genomic knowledge to health-related decision-making. The aim of this study is to assess the level of genomic literacy, including the knowledge of genetic testing, its outcomes, and its significance among medical trainees and practitioners in Pakistan. This study also offers recommendations for improving genomic literacy within the medical community in Pakistan. A cross-sectional study for which an online questionnaire was designed in the English language using Google Forms. The questionnaire was distributed among trainees and practitioners across Pakistan through online social media platforms. The survey comprised of 36 questions categorized into three sections namely demographics, quantitative questions, and knowledge section. A total of 219 medical professionals (56% trained in medicine while 44% in Dentistry) responded to the survey. Medical practitioners from 27 different cities across Pakistan participated in the survey but with a clear bias towards the city of Peshawar (90%). The majority of the participants (57.5%) were trainee medical officers. Most of the participants in this cohort were aware of genetics, the genome, and PCR technology but 74.9% were not familiar with genetic tests in their specialty. The mean total score for the multiple-choice questions among the participants was 62.5% graduates of medicine (65%) outperforming those in dentistry (59%). Participants were asked about their interest in further learning opportunities about genomics, for which 71% answered in affirmation, however, the remaining were not overly interested. Genomic literacy level can be considered satisfactory at the level of classical genetics, yet there is a clear need for training in molecular biology concepts such as protein synthesis, gene expression, and genome structure within the medical community. With a limited sample size and diversity of respondents, the subject warrants a broader survey to assess genomic literacy which can help in better planning for wider adoption of genomic testing in the medical community. Findings of this survey also highlight the need for assessing medical communities in other low-resource and underprivileged settings to help enhance our understanding and avoid a looming ‘genomics divide’.
Citation Format: Madina Barakzai, Shagufta Rehmat, Faisal F. Khan. Assessing genomic literacy among medical trainees and practitioners in Pakistan [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6337.
Abstract 6340: Deep, unbiased and peptide-centric plasma proteomics with differential analysis of proteoforms enabling proteogenomic studies of NSCLC at scale
Authors: Donovan Margaret , Huang Henry , Blume John , Ko Marwin , Benz Ryan , Platt Theodore , Cuevas Juan , Batzoglou Serafim , Siddiqui Asim , Farokhzad Omid 
Published: June 2022

Abstract
Introduction: Comprehensive assessment of the proteome remains elusive because of proteoforms arising from alternative splicing, allelic variation, and protein modifications. Characterization of the variable protein forms, or proteoforms will expand our understanding of the molecular mechanisms underlying diseases, however requires unbiased protein coverage at sufficient scale. Scalable, deep and unbiased proteomics studies have been impractical due to cumbersome and lengthy workflows required for complex samples, like blood plasma. Here, we demonstrate the power of Proteograph in a proof-of-concept proteogenomic analysis of 80 healthy controls and 61 early-stage non-small-cell lung cancer (NSCLC) samples to dissect differences between protein isoforms arising from alternative gene splicing, as well as the identification of novel peptides arising from allelic variation.
Materials, Methods and Results: Processing the 141 plasma samples with Proteograph yielded 21,959 peptides corresponding to 2,499 protein groups. Using peptides with significant abundance differences (p &lt; 0.05; Benjamini-Hochberg corrected), we extracted proteins comprised of peptides where at least one peptide had significantly higher plasma abundance, and another significantly lower plasma abundance in controls vs. cancer, resulting in a set of putative proteoforms. For three of these proteins, the abundance variation is possibly explained by underlying protein isoforms. To identify protein variants, we performed exome sequencing on 29 individuals from the NSCLC study, created personalized mass spectrometry search libraries for each individual, and identified 464 protein variants.
Conclusions: Proteograph can generate unbiased and deep plasma proteome profiles that enable identification of protein variants and peptides present in plasma, at a scale sufficient to enable population-scale proteomic studies.
Citation Format: Margaret Donovan, Henry Huang, John Blume, Marwin Ko, Ryan Benz, Theodore Platt, Juan Cuevas, Serafim Batzoglou, Asim Siddiqui, Omid Farokhzad. Deep, unbiased and peptide-centric plasma proteomics with differential analysis of proteoforms enabling proteogenomic studies of NSCLC at scale [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6340.
Abstract 636: Prediction of therapeutic response via data-driven maps of tumor cell architecture
Authors: Silva Erica N. , Singhal Akshat , Park Sungjoon , Kreisberg Jason , Ideker Trey 
Published: June 2022

Abstract
Machine learning is revolutionizing the analysis of biological and biomedical data. One continued challenge is to build models that are not only predictive but also provide mechanistic explanations. Directly incorporating explainability into the model’s design has emerged as a possible solution. Recently, we reported several “visible” machine learning systems - DCell and DrugCell - which predict cellular growth based on genetic perturbations. In these visible models, the structure of the underlying neural network mirrors known aspects of cell biology. Here we designed a cancer-specific model of therapeutic response. First, we selected NeST (Nested Systems in Tumors; Zheng et al., Science 2021), a data-driven hierarchy of tumor cell systems under selection in cancer, as the structure for our visible neural network. Second, we restricted input features to genes currently assayed on clinical cancer gene panels. We considered training schemes that assess the model’s ability to generalize to novel genotypes. Most importantly, we assessed performance on patient data from the AACR Project GENIE clinical trial where we discriminated between patients who are likely to respond to selected drug treatments. Finally, we examined model explanations within the visible NeST architecture to highlight cellular mechanisms of response to drug therapy. Overall, we found that by considering a tumor cell architecture optimized for clinical application, we can design an interpretable deep learning model that both accurately stratifies patient responses and explains the drug mechanism in cancer cells.
Citation Format: Erica N. Silva, Akshat Singhal, Sungjoon Park, Jason Kreisberg, Trey Ideker. Prediction of therapeutic response via data-driven maps of tumor cell architecture [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 636.