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1. Understanding the Cognitive Complexity in Language Elicited by Product Images

Author

Chen, Yan-Ying, Hakimi, Shabnam, Van, Monica, Chen, Francine, Hong, Matthew, Klenk, Matt, and Wu, Charlene

Subjects
Computer Science - Computation and Language
Abstract

Product images (e.g., a phone) can be used to elicit a diverse set of consumer-reported features expressed through language, including surface-level perceptual attributes (e.g., "white") and more complex ones, like perceived utility (e.g., "battery"). The cognitive complexity of elicited language reveals the nature of cognitive processes and the context required to understand them; cognitive complexity also predicts consumers' subsequent choices. This work offers an approach for measuring and validating the cognitive complexity of human language elicited by product images, providing a tool for understanding the cognitive processes of human as well as virtual respondents simulated by Large Language Models (LLMs). We also introduce a large dataset that includes diverse descriptive labels for product images, including human-rated complexity. We demonstrate that human-rated cognitive complexity can be approximated using a set of natural language models that, combined, roughly capture the complexity construct. Moreover, this approach is minimally supervised and scalable, even in use cases with limited human assessment of complexity.

Published
2024

2. The PD-1- and LAG-3-targeting bispecific molecule tebotelimab in solid tumors and hematologic cancers: a phase 1 trial.

Author

Luke, Jason, Patel, Manish, Blumenschein, George, Hamilton, Erika, Chmielowski, Bartosz, Ulahannan, Susanna, Connolly, Roisin, Santa-Maria, Cesar, Wang, Jie, Bahadur, Shakeela, Weickhardt, Andrew, Asch, Adam, Mallesara, Girish, Clingan, Philip, Dlugosz-Danecka, Monika, Tomaszewska-Kiecana, Monika, Pylypenko, Halyna, Hamad, Nada, Kindler, Hedy, Sumrow, Bradley, Kaminker, Patrick, Chen, Francine, Zhang, Xiaoyu, Shah, Kalpana, Smith, Douglas, De Costa, Anushka, Li, Jonathan, Li, Hua, Sun, Jichao, and Moore, Paul

Subjects
Humans, Programmed Cell Death 1 Receptor, Antibodies, Monoclonal, Humanized, Neoplasms, Hematologic Neoplasms, Immunoconjugates
Abstract

Tebotelimab, a bispecific PD-1×LAG-3 DART molecule that blocks both PD-1 and LAG-3, was investigated for clinical safety and activity in a phase 1 dose-escalation and cohort-expansion clinical trial in patients with solid tumors or hematologic malignancies and disease progression on previous treatment. Primary endpoints were safety and maximum tolerated dose of tebotelimab when administered as a single agent (n = 269) or in combination with the anti-HER2 antibody margetuximab (n = 84). Secondary endpoints included anti-tumor activity. In patients with advanced cancer treated with tebotelimab monotherapy, 68% (184/269) experienced treatment-related adverse events (TRAEs; 22% were grade ≥3). No maximum tolerated dose was defined; the recommended phase 2 dose (RP2D) was 600 mg once every 2 weeks. There were tumor decreases in 34% (59/172) of response-evaluable patients in the dose-escalation cohorts, with objective responses in multiple solid tumor types, including PD-1-refractory disease, and in LAG-3+ non-Hodgkin lymphomas, including CAR-T refractory disease. To enhance potential anti-tumor responses, we tested margetuximab plus tebotelimab. In patients with HER2+ tumors treated with tebotelimab plus margetuximab, 74% (62/84) had TRAEs (17% were grade ≥3). The RP2D was 600 mg once every 3 weeks. The confirmed objective response rate in these patients was 19% (14/72), including responses in patients typically not responsive to anti-HER2/anti-PD-1 combination therapy. ClinicalTrials.gov identifier: NCT03219268 .

Published
2023

4. Can Behavioral Experts Predict Outcome Heterogeneity?

Author

Iliev, Rumen, Filipowicz, Alexandre L. S., Sumner, Emily Sarah, Chen, Francine, Arechiga, Nikos, Carter, Scott, Harinen, Totte, Sieck, Kate A, and Wu, Charlene C.

Subjects
Anthropology, Psychology, Behavioral Science, Human Factors
Abstract

Recently Milkman et al. (2021) reported that heterogeneity of outcomes is extremely difficult to predict in advance and that behavioral experts, despite their educational background or their extensive applied research experience, might not be better in their predictions than non-experts. Such findings are rather concerning and deserve more attention. In the current project we present three studies aimed at assessing the quality of human predictions in respect to outcome heterogeneity of behavioral interventions. We borrow methods from anthropology and cognitive psychology to asses both consensus and accuracy of such predictions as well as to evaluate the potential role of behavioral expertise. Our results are more optimistic than previous findings and reveal that experts’ predictions are significantly better than a chance level. These findings have implications for applied behavioral research and further advance our theoretical understanding of expertise.

Published
2023

6. Understanding and Shifting Preferences for Battery Electric Vehicles

Author

Arechiga, Nikos, Chen, Francine, Iliev, Rumen, Sumner, Emily, Carter, Scott, Filipowicz, Alex, Bravo, Nayeli, Van, Monica, Glazko, Kate, Murakami, Kalani, Denoue, Laurent, Hogan, Candice, Sieck, Katharine, Wu, Charlene, and Lyons, Kent

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Identifying personalized interventions for an individual is an important task. Recent work has shown that interventions that do not consider the demographic background of individual consumers can, in fact, produce the reverse effect, strengthening opposition to electric vehicles. In this work, we focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs). One of the constraints in building models to suggest interventions for shifting preferences is that each intervention can influence the effectiveness of later interventions. This, in turn, requires many subjects to evaluate effectiveness of each possible intervention. To address this, we propose to identify personalized factors influencing BEV adoption, such as barriers and motivators. We present a method for predicting these factors and show that the performance is better than always predicting the most frequent factors. We then present a Reinforcement Learning (RL) model that learns the most effective interventions, and compare the number of subjects required for each approach., Comment: 5 pages, 5 figures

Published
2022

7. Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression

Author

Arechiga, Nikos, Chen, Francine, Chen, Yan-Ying, Zhang, Yanxia, Iliev, Rumen, Toyoda, Heishiro, and Lyons, Kent

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

We consider the problem of learning free-form symbolic expressions from raw data, such as that produced by an experiment in any scientific domain. Accurate and interpretable models of scientific phenomena are the cornerstone of scientific research. Simple yet interpretable models, such as linear or logistic regression and decision trees often lack predictive accuracy. Alternatively, accurate blackbox models such as deep neural networks provide high predictive accuracy, but do not readily admit human understanding in a way that would enrich the scientific theory of the phenomenon. Many great breakthroughs in science revolve around the development of parsimonious equational models with high predictive accuracy, such as Newton's laws, universal gravitation, and Maxwell's equations. Previous work on automating the search of equational models from data combine domain-specific heuristics as well as computationally expensive techniques, such as genetic programming and Monte-Carlo search. We develop a deep neural network (MACSYMA) to address the symbolic regression problem as an end-to-end supervised learning problem. MACSYMA can generate symbolic expressions that describe a dataset. The computational complexity of the task is reduced to the feedforward computation of a neural network. We train our neural network on a synthetic dataset consisting of data tables of varying length and varying levels of noise, for which the neural network must learn to produce the correct symbolic expression token by token. Finally, we validate our technique by running on a public dataset from behavioral science.

Published
2021

9. Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data

Author

Karn, Sanjeev Kumar, Chen, Francine, Chen, Yan-Ying, Waltinger, Ulli, and Schuetze, Hinrich

Subjects
Computer Science - Computation and Language
Abstract

Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overview of the discussions. Existing systems first disentangle the posts by threads and then extract summaries from those threads. A major issue with such systems is error propagation from the disentanglement component. While end-to-end trainable summarization system could obviate explicit disentanglement, such systems require a large amount of labeled data. To address this, we propose to pretrain an end-to-end trainable hierarchical encoder-decoder system using synthetic interleaved texts. We show that by fine-tuning on a real-world meeting dataset (AMI), such a system out-performs a traditional two-step system by 22%. We also compare against transformer models and observed that pretraining with synthetic data both the encoder and decoder outperforms the BertSumExtAbs transformer model which pretrains only the encoder on a large dataset., Comment: Adapt-NLP: The Second Workshop on Domain Adaptation for NLP

Published
2021

10. Neoadjuvant enoblituzumab in localized prostate cancer: a single-arm, phase 2 trial

Author

Shenderov, Eugene, De Marzo, Angelo M., Lotan, Tamara L., Wang, Hao, Chan, Sin, Lim, Su Jin, Ji, Hongkai, Allaf, Mohamad E., Chapman, Carolyn, Moore, Paul A., Chen, Francine, Sorg, Kristina, White, Andrew M., Church, Sarah E., Hudson, Briana, Fields, Paul A., Hu, Shaohui, Denmeade, Samuel R., Pienta, Kenneth J., Pavlovich, Christian P., Ross, Ashley E., Drake, Charles G., Pardoll, Drew M., and Antonarakis, Emmanuel S.

Published
2023
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11. A Visual Analytics Framework for Contrastive Network Analysis

Author

Fujiwara, Takanori, Zhao, Jian, Chen, Francine, and Ma, Kwan-Liu

Subjects
Computer Science - Social and Information Networks, Computer Science - Graphics, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other. For example, when comparing protein interaction networks derived from normal and cancer tissues, one essential task is to discover protein-protein interactions unique to cancer tissues. However, this task is challenging when the networks contain complex structural (and semantic) relations. To address this problem, we design ContraNA, a visual analytics framework leveraging both the power of machine learning for uncovering unique characteristics in networks and also the effectiveness of visualization for understanding such uniqueness. The basis of ContraNA is cNRL, which integrates two machine learning schemes, network representation learning (NRL) and contrastive learning (CL), to generate a low-dimensional embedding that reveals the uniqueness of one network when compared to another. ContraNA provides an interactive visualization interface to help analyze the uniqueness by relating embedding results and network structures as well as explaining the learned features by cNRL. We demonstrate the usefulness of ContraNA with two case studies using real-world datasets. We also evaluate through a controlled user study with 12 participants on network comparison tasks. The results show that participants were able to both effectively identify unique characteristics from complex networks and interpret the results obtained from cNRL., Comment: To appear in IEEE Conference on Visual Analytics Science and Technology (VAST) 2020

Published
2020

12. Thoracic Disease Identification and Localization using Distance Learning and Region Verification

Author

Zhang, Cheng, Chen, Francine, and Chen, Yan-Ying

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The identification and localization of diseases in medical images using deep learning models have recently attracted significant interest. Existing methods only consider training the networks with each image independently and most leverage an activation map for disease localization. In this paper, we propose an alternative approach that learns discriminative features among triplets of images and cyclically trains on region features to verify whether attentive regions contain information indicative of a disease. Concretely, we adapt a distance learning framework for multi-label disease classification to differentiate subtle disease features. Additionally, we feed back the features of the predicted class-specific regions to a separate classifier during training to better verify the localized diseases. Our model can achieve state-of-the-art classification performance on the challenging ChestX-ray14 dataset, and our ablation studies indicate that both distance learning and region verification contribute to overall classification performance. Moreover, the distance learning and region verification modules can capture essential information for better localization than baseline models without these modules., Comment: British Machine Vision Conference (BMVC) 2020

Published
2020

13. Network Comparison with Interpretable Contrastive Network Representation Learning

Author

Fujiwara, Takanori, Zhao, Jian, Chen, Francine, Yu, Yaoliang, and Ma, Kwan-Liu

Subjects
Computer Science - Machine Learning, Computer Science - Social and Information Networks, Statistics - Machine Learning
Abstract

Identifying unique characteristics in a network through comparison with another network is an essential network analysis task. For example, with networks of protein interactions obtained from normal and cancer tissues, we can discover unique types of interactions in cancer tissues. This analysis task could be greatly assisted by contrastive learning, which is an emerging analysis approach to discover salient patterns in one dataset relative to another. However, existing contrastive learning methods cannot be directly applied to networks as they are designed only for high-dimensional data analysis. To address this problem, we introduce a new analysis approach called contrastive network representation learning (cNRL). By integrating two machine learning schemes, network representation learning and contrastive learning, cNRL enables embedding of network nodes into a low-dimensional representation that reveals the uniqueness of one network compared to another. Within this approach, we also design a method, named i-cNRL, which offers interpretability in the learned results, allowing for understanding which specific patterns are only found in one network. We demonstrate the effectiveness of i-cNRL for network comparison with multiple network models and real-world datasets. Furthermore, we compare i-cNRL and other potential cNRL algorithm designs through quantitative and qualitative evaluations., Comment: To appear in Journal of Data Science, Statistics, and Visualisation. The previous preprint version was titled "Interpretable Contrastive Learning for Networks" (arXiv:2005.12419v1)

Published
2020

14. Addressing Data Bias Problems for Chest X-ray Image Report Generation

Author

Harzig, Philipp, Chen, Yan-Ying, Chen, Francine, and Lienhart, Rainer

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bias machine learning models. Previous attempts did not focus on isolating the generation of the abnormal and normal sentences in order to increase the variability of generated paragraphs. To address this, we propose to separate abnormal and normal sentence generation by using two different word LSTMs in a hierarchical LSTM model. We conduct an analysis on the distinctiveness of generated sentences compared to the BLEU score, which increases when less distinct reports are generated. We hope our findings will help to encourage the development of new metrics to better verify methods of automatic medical report generation., Comment: Oral at BMVC 2019

Published
2019

15. A Hierarchical Decoder with Three-level Hierarchical Attention to Generate Abstractive Summaries of Interleaved Texts

Author

Karn, Sanjeev Kumar, Chen, Francine, Chen, Yan-Ying, Waltinger, Ulli, and Schütze, Hinrich

Subjects
Computer Science - Computation and Language
Abstract

Interleaved texts, where posts belonging to different threads occur in one sequence, are a common occurrence, e.g., online chat conversations. To quickly obtain an overview of such texts, existing systems first disentangle the posts by threads and then extract summaries from those threads. The major issues with such systems are error propagation and non-fluent summary. To address those, we propose an end-to-end trainable hierarchical encoder-decoder system. We also introduce a novel hierarchical attention mechanism which combines three levels of information from an interleaved text, i.e, posts, phrases and words, and implicitly disentangles the threads. We evaluated the proposed system on multiple interleaved text datasets, and it out-performs a SOTA two-step system by 20-40%.

Published
2019

19. Video D from Preclinical Development of MGC018, a Duocarmycin-based Antibody–drug Conjugate Targeting B7-H3 for Solid Cancer

Author

Scribner, Juniper A., primary, Brown, Jennifer G., primary, Son, Thomas, primary, Chiechi, Michael, primary, Li, Pam, primary, Sharma, Sharad, primary, Li, Hua, primary, De Costa, Anushka, primary, Li, Ying, primary, Chen, Yan, primary, Easton, Ann, primary, Yee-Toy, Nicholas C., primary, Chen, Francine Z., primary, Gorlatov, Sergey, primary, Barat, Bhaswati, primary, Huang, Ling, primary, Wolff, Christina R., primary, Hooley, Jeff, primary, Hotaling, Tim E., primary, Gaynutdinov, Timur, primary, Ciccarone, Valentina, primary, Tamura, James, primary, Koenig, Scott, primary, Moore, Paul A., primary, Bonvini, Ezio, primary, and Loo, Deryk, primary

Published
2023
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20. Data from Development of MGD007, a gpA33 x CD3-Bispecific DART Protein for T-Cell Immunotherapy of Metastatic Colorectal Cancer

Author

Moore, Paul A., primary, Shah, Kalpana, primary, Yang, Yinhua, primary, Alderson, Ralph, primary, Roberts, Penny, primary, Long, Vatana, primary, Liu, Daorong, primary, Li, Jonathan C., primary, Burke, Steve, primary, Ciccarone, Valentina, primary, Li, Hua, primary, Fieger, Claudia B., primary, Hooley, Jeff, primary, Easton, Ann, primary, Licea, Monica, primary, Gorlatov, Sergey, primary, King, Kathy L., primary, Young, Peter, primary, Adami, Arash, primary, Loo, Deryk, primary, Chichili, Gurunadh R., primary, Liu, Liqin, primary, Smith, Douglas H., primary, Brown, Jennifer G., primary, Chen, Francine Z., primary, Koenig, Scott, primary, Mather, Jennie, primary, Bonvini, Ezio, primary, and Johnson, Syd, primary

Published
2023
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21. Data from Preclinical Evaluation of IMGC936, a Next-Generation Maytansinoid-based Antibody–drug Conjugate Targeting ADAM9-expressing Tumors

Author

Scribner, Juniper A., primary, Hicks, Stuart W., primary, Sinkevicius, Kerstin W., primary, Yoder, Nicholas C., primary, Diedrich, Gundo, primary, Brown, Jennifer G., primary, Lucas, Jacquelynn, primary, Fuller, Megan E., primary, Son, Thomas, primary, Dastur, Anahita, primary, Hooley, Jeff, primary, Espelin, Christopher, primary, Themeles, Marian, primary, Chen, Francine Z., primary, Li, Ying, primary, Chiechi, Michael, primary, Lee, Jenny, primary, Barat, Bhaswati, primary, Widjaja, Lusiana, primary, Gorlatov, Sergey, primary, Tamura, James, primary, Ciccarone, Valentina, primary, Ab, Olga, primary, McEachem, Kerry A., primary, Koenig, Scott, primary, Westin, Eric H., primary, Moore, Paul A., primary, Chittenden, Thomas, primary, Gregory, Richard J., primary, Bonvini, Ezio, primary, and Loo, Deryk, primary

Published
2023
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22. Video A from Preclinical Development of MGC018, a Duocarmycin-based Antibody–drug Conjugate Targeting B7-H3 for Solid Cancer

Author

Scribner, Juniper A., primary, Brown, Jennifer G., primary, Son, Thomas, primary, Chiechi, Michael, primary, Li, Pam, primary, Sharma, Sharad, primary, Li, Hua, primary, De Costa, Anushka, primary, Li, Ying, primary, Chen, Yan, primary, Easton, Ann, primary, Yee-Toy, Nicholas C., primary, Chen, Francine Z., primary, Gorlatov, Sergey, primary, Barat, Bhaswati, primary, Huang, Ling, primary, Wolff, Christina R., primary, Hooley, Jeff, primary, Hotaling, Tim E., primary, Gaynutdinov, Timur, primary, Ciccarone, Valentina, primary, Tamura, James, primary, Koenig, Scott, primary, Moore, Paul A., primary, Bonvini, Ezio, primary, and Loo, Deryk, primary

Published
2023
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23. Supplementary Figure from Preclinical Evaluation of IMGC936, a Next-Generation Maytansinoid-based Antibody–drug Conjugate Targeting ADAM9-expressing Tumors

Author

Scribner, Juniper A., primary, Hicks, Stuart W., primary, Sinkevicius, Kerstin W., primary, Yoder, Nicholas C., primary, Diedrich, Gundo, primary, Brown, Jennifer G., primary, Lucas, Jacquelynn, primary, Fuller, Megan E., primary, Son, Thomas, primary, Dastur, Anahita, primary, Hooley, Jeff, primary, Espelin, Christopher, primary, Themeles, Marian, primary, Chen, Francine Z., primary, Li, Ying, primary, Chiechi, Michael, primary, Lee, Jenny, primary, Barat, Bhaswati, primary, Widjaja, Lusiana, primary, Gorlatov, Sergey, primary, Tamura, James, primary, Ciccarone, Valentina, primary, Ab, Olga, primary, McEachem, Kerry A., primary, Koenig, Scott, primary, Westin, Eric H., primary, Moore, Paul A., primary, Chittenden, Thomas, primary, Gregory, Richard J., primary, Bonvini, Ezio, primary, and Loo, Deryk, primary

Published
2023
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24. Video E from Preclinical Development of MGC018, a Duocarmycin-based Antibody–drug Conjugate Targeting B7-H3 for Solid Cancer

Author

Scribner, Juniper A., primary, Brown, Jennifer G., primary, Son, Thomas, primary, Chiechi, Michael, primary, Li, Pam, primary, Sharma, Sharad, primary, Li, Hua, primary, De Costa, Anushka, primary, Li, Ying, primary, Chen, Yan, primary, Easton, Ann, primary, Yee-Toy, Nicholas C., primary, Chen, Francine Z., primary, Gorlatov, Sergey, primary, Barat, Bhaswati, primary, Huang, Ling, primary, Wolff, Christina R., primary, Hooley, Jeff, primary, Hotaling, Tim E., primary, Gaynutdinov, Timur, primary, Ciccarone, Valentina, primary, Tamura, James, primary, Koenig, Scott, primary, Moore, Paul A., primary, Bonvini, Ezio, primary, and Loo, Deryk, primary

Published
2023
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25. Tables S1-S5; Figures S1-S5 from Development of MGD007, a gpA33 x CD3-Bispecific DART Protein for T-Cell Immunotherapy of Metastatic Colorectal Cancer

Author

Moore, Paul A., primary, Shah, Kalpana, primary, Yang, Yinhua, primary, Alderson, Ralph, primary, Roberts, Penny, primary, Long, Vatana, primary, Liu, Daorong, primary, Li, Jonathan C., primary, Burke, Steve, primary, Ciccarone, Valentina, primary, Li, Hua, primary, Fieger, Claudia B., primary, Hooley, Jeff, primary, Easton, Ann, primary, Licea, Monica, primary, Gorlatov, Sergey, primary, King, Kathy L., primary, Young, Peter, primary, Adami, Arash, primary, Loo, Deryk, primary, Chichili, Gurunadh R., primary, Liu, Liqin, primary, Smith, Douglas H., primary, Brown, Jennifer G., primary, Chen, Francine Z., primary, Koenig, Scott, primary, Mather, Jennie, primary, Bonvini, Ezio, primary, and Johnson, Syd, primary

Published
2023
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26. Supplementary Materials and Methods from Preclinical Development of MGC018, a Duocarmycin-based Antibody–drug Conjugate Targeting B7-H3 for Solid Cancer

Author

Scribner, Juniper A., primary, Brown, Jennifer G., primary, Son, Thomas, primary, Chiechi, Michael, primary, Li, Pam, primary, Sharma, Sharad, primary, Li, Hua, primary, De Costa, Anushka, primary, Li, Ying, primary, Chen, Yan, primary, Easton, Ann, primary, Yee-Toy, Nicholas C., primary, Chen, Francine Z., primary, Gorlatov, Sergey, primary, Barat, Bhaswati, primary, Huang, Ling, primary, Wolff, Christina R., primary, Hooley, Jeff, primary, Hotaling, Tim E., primary, Gaynutdinov, Timur, primary, Ciccarone, Valentina, primary, Tamura, James, primary, Koenig, Scott, primary, Moore, Paul A., primary, Bonvini, Ezio, primary, and Loo, Deryk, primary

Published
2023
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27. Video C from Preclinical Development of MGC018, a Duocarmycin-based Antibody–drug Conjugate Targeting B7-H3 for Solid Cancer

Author

Scribner, Juniper A., primary, Brown, Jennifer G., primary, Son, Thomas, primary, Chiechi, Michael, primary, Li, Pam, primary, Sharma, Sharad, primary, Li, Hua, primary, De Costa, Anushka, primary, Li, Ying, primary, Chen, Yan, primary, Easton, Ann, primary, Yee-Toy, Nicholas C., primary, Chen, Francine Z., primary, Gorlatov, Sergey, primary, Barat, Bhaswati, primary, Huang, Ling, primary, Wolff, Christina R., primary, Hooley, Jeff, primary, Hotaling, Tim E., primary, Gaynutdinov, Timur, primary, Ciccarone, Valentina, primary, Tamura, James, primary, Koenig, Scott, primary, Moore, Paul A., primary, Bonvini, Ezio, primary, and Loo, Deryk, primary

Published
2023
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28. Supplementary Data from Preclinical Evaluation of IMGC936, a Next-Generation Maytansinoid-based Antibody–drug Conjugate Targeting ADAM9-expressing Tumors

Author

Scribner, Juniper A., primary, Hicks, Stuart W., primary, Sinkevicius, Kerstin W., primary, Yoder, Nicholas C., primary, Diedrich, Gundo, primary, Brown, Jennifer G., primary, Lucas, Jacquelynn, primary, Fuller, Megan E., primary, Son, Thomas, primary, Dastur, Anahita, primary, Hooley, Jeff, primary, Espelin, Christopher, primary, Themeles, Marian, primary, Chen, Francine Z., primary, Li, Ying, primary, Chiechi, Michael, primary, Lee, Jenny, primary, Barat, Bhaswati, primary, Widjaja, Lusiana, primary, Gorlatov, Sergey, primary, Tamura, James, primary, Ciccarone, Valentina, primary, Ab, Olga, primary, McEachem, Kerry A., primary, Koenig, Scott, primary, Westin, Eric H., primary, Moore, Paul A., primary, Chittenden, Thomas, primary, Gregory, Richard J., primary, Bonvini, Ezio, primary, and Loo, Deryk, primary

Published
2023
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29. Supplementary Data from Preclinical Development of MGC018, a Duocarmycin-based Antibody–drug Conjugate Targeting B7-H3 for Solid Cancer

Author

Scribner, Juniper A., primary, Brown, Jennifer G., primary, Son, Thomas, primary, Chiechi, Michael, primary, Li, Pam, primary, Sharma, Sharad, primary, Li, Hua, primary, De Costa, Anushka, primary, Li, Ying, primary, Chen, Yan, primary, Easton, Ann, primary, Yee-Toy, Nicholas C., primary, Chen, Francine Z., primary, Gorlatov, Sergey, primary, Barat, Bhaswati, primary, Huang, Ling, primary, Wolff, Christina R., primary, Hooley, Jeff, primary, Hotaling, Tim E., primary, Gaynutdinov, Timur, primary, Ciccarone, Valentina, primary, Tamura, James, primary, Koenig, Scott, primary, Moore, Paul A., primary, Bonvini, Ezio, primary, and Loo, Deryk, primary

Published
2023
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30. Supplementary Table 1 from Development of an Fc-Enhanced Anti–B7-H3 Monoclonal Antibody with Potent Antitumor Activity

Author

Loo, Deryk, primary, Alderson, Ralph F., primary, Chen, Francine Z., primary, Huang, Ling, primary, Zhang, Wenjun, primary, Gorlatov, Sergey, primary, Burke, Steve, primary, Ciccarone, Valentina, primary, Li, Hua, primary, Yang, Yinhua, primary, Son, Tom, primary, Chen, Yan, primary, Easton, Ann N., primary, Li, Jonathan C., primary, Rillema, Jill R., primary, Licea, Monica, primary, Fieger, Claudia, primary, Liang, Tony W., primary, Mather, Jennie P., primary, Koenig, Scott, primary, Stewart, Stanford J., primary, Johnson, Syd, primary, Bonvini, Ezio, primary, and Moore, Paul A., primary

Published
2023
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31. Supplementary Figure 3 from Development of an Fc-Enhanced Anti–B7-H3 Monoclonal Antibody with Potent Antitumor Activity

Author

Loo, Deryk, primary, Alderson, Ralph F., primary, Chen, Francine Z., primary, Huang, Ling, primary, Zhang, Wenjun, primary, Gorlatov, Sergey, primary, Burke, Steve, primary, Ciccarone, Valentina, primary, Li, Hua, primary, Yang, Yinhua, primary, Son, Tom, primary, Chen, Yan, primary, Easton, Ann N., primary, Li, Jonathan C., primary, Rillema, Jill R., primary, Licea, Monica, primary, Fieger, Claudia, primary, Liang, Tony W., primary, Mather, Jennie P., primary, Koenig, Scott, primary, Stewart, Stanford J., primary, Johnson, Syd, primary, Bonvini, Ezio, primary, and Moore, Paul A., primary

Published
2023
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32. Supplementary Figure 2 from Development of an Fc-Enhanced Anti–B7-H3 Monoclonal Antibody with Potent Antitumor Activity

Author

Loo, Deryk, primary, Alderson, Ralph F., primary, Chen, Francine Z., primary, Huang, Ling, primary, Zhang, Wenjun, primary, Gorlatov, Sergey, primary, Burke, Steve, primary, Ciccarone, Valentina, primary, Li, Hua, primary, Yang, Yinhua, primary, Son, Tom, primary, Chen, Yan, primary, Easton, Ann N., primary, Li, Jonathan C., primary, Rillema, Jill R., primary, Licea, Monica, primary, Fieger, Claudia, primary, Liang, Tony W., primary, Mather, Jennie P., primary, Koenig, Scott, primary, Stewart, Stanford J., primary, Johnson, Syd, primary, Bonvini, Ezio, primary, and Moore, Paul A., primary

Published
2023
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33. Supplementary Figure Legend from Development of an Fc-Enhanced Anti–B7-H3 Monoclonal Antibody with Potent Antitumor Activity

Author

Loo, Deryk, primary, Alderson, Ralph F., primary, Chen, Francine Z., primary, Huang, Ling, primary, Zhang, Wenjun, primary, Gorlatov, Sergey, primary, Burke, Steve, primary, Ciccarone, Valentina, primary, Li, Hua, primary, Yang, Yinhua, primary, Son, Tom, primary, Chen, Yan, primary, Easton, Ann N., primary, Li, Jonathan C., primary, Rillema, Jill R., primary, Licea, Monica, primary, Fieger, Claudia, primary, Liang, Tony W., primary, Mather, Jennie P., primary, Koenig, Scott, primary, Stewart, Stanford J., primary, Johnson, Syd, primary, Bonvini, Ezio, primary, and Moore, Paul A., primary

Published
2023
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34. Supplementary Table 2 from Development of an Fc-Enhanced Anti–B7-H3 Monoclonal Antibody with Potent Antitumor Activity

Author

Loo, Deryk, primary, Alderson, Ralph F., primary, Chen, Francine Z., primary, Huang, Ling, primary, Zhang, Wenjun, primary, Gorlatov, Sergey, primary, Burke, Steve, primary, Ciccarone, Valentina, primary, Li, Hua, primary, Yang, Yinhua, primary, Son, Tom, primary, Chen, Yan, primary, Easton, Ann N., primary, Li, Jonathan C., primary, Rillema, Jill R., primary, Licea, Monica, primary, Fieger, Claudia, primary, Liang, Tony W., primary, Mather, Jennie P., primary, Koenig, Scott, primary, Stewart, Stanford J., primary, Johnson, Syd, primary, Bonvini, Ezio, primary, and Moore, Paul A., primary

Published
2023
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35. Supplementary Figure 4 from Development of an Fc-Enhanced Anti–B7-H3 Monoclonal Antibody with Potent Antitumor Activity

Author

Loo, Deryk, primary, Alderson, Ralph F., primary, Chen, Francine Z., primary, Huang, Ling, primary, Zhang, Wenjun, primary, Gorlatov, Sergey, primary, Burke, Steve, primary, Ciccarone, Valentina, primary, Li, Hua, primary, Yang, Yinhua, primary, Son, Tom, primary, Chen, Yan, primary, Easton, Ann N., primary, Li, Jonathan C., primary, Rillema, Jill R., primary, Licea, Monica, primary, Fieger, Claudia, primary, Liang, Tony W., primary, Mather, Jennie P., primary, Koenig, Scott, primary, Stewart, Stanford J., primary, Johnson, Syd, primary, Bonvini, Ezio, primary, and Moore, Paul A., primary

Published
2023
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36. Supplementary Figure 1 from Development of an Fc-Enhanced Anti–B7-H3 Monoclonal Antibody with Potent Antitumor Activity

Author

Loo, Deryk, primary, Alderson, Ralph F., primary, Chen, Francine Z., primary, Huang, Ling, primary, Zhang, Wenjun, primary, Gorlatov, Sergey, primary, Burke, Steve, primary, Ciccarone, Valentina, primary, Li, Hua, primary, Yang, Yinhua, primary, Son, Tom, primary, Chen, Yan, primary, Easton, Ann N., primary, Li, Jonathan C., primary, Rillema, Jill R., primary, Licea, Monica, primary, Fieger, Claudia, primary, Liang, Tony W., primary, Mather, Jennie P., primary, Koenig, Scott, primary, Stewart, Stanford J., primary, Johnson, Syd, primary, Bonvini, Ezio, primary, and Moore, Paul A., primary

Published
2023
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37. Network Comparison with Interpretable Contrastive Network Representation Learning

Author

Fujiwara, Takanori, Zhao, Jian, Chen, Francine, Yu, Yaoliang, and Ma, Kwan-Liu

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), Computer Science - Social and Information Networks, Machine Learning (cs.LG)
Abstract

Identifying unique characteristics in a network through comparison with another network is an essential network analysis task. For example, with networks of protein interactions obtained from normal and cancer tissues, we can discover unique types of interactions in cancer tissues. This analysis task could be greatly assisted by contrastive learning, which is an emerging analysis approach to discover salient patterns in one dataset relative to another. However, existing contrastive learning methods cannot be directly applied to networks as they are designed only for high-dimensional data analysis. To address this problem, we introduce a new analysis approach called contrastive network representation learning (cNRL). By integrating two machine learning schemes, network representation learning and contrastive learning, cNRL enables embedding of network nodes into a low-dimensional representation that reveals the uniqueness of one network compared to another. Within this approach, we also design a method, named i-cNRL, which offers interpretability in the learned results, allowing for understanding which specific patterns are only found in one network. We demonstrate the effectiveness of i-cNRL for network comparison with multiple network models and real-world datasets. Furthermore, we compare i-cNRL and other potential cNRL algorithm designs through quantitative and qualitative evaluations., To appear in Journal of Data Science, Statistics, and Visualisation. The previous preprint version was titled "Interpretable Contrastive Learning for Networks" (arXiv:2005.12419v1)

Published
2022

38. Abstract 330: Targeting B7-H3 in prostate cancer: Preclinical proof of concept with MGC018, an investigational anti-B7-H3 antibody-drug conjugate

Author

Scribner, Juniper A., primary, Chen, Francine Z., additional, De Costa, Anushka, additional, Li, Ying, additional, Chiechi, Michael, additional, Son, Thomas, additional, Hooley, Jeff, additional, Li, Jonathan, additional, Koenig, Scott, additional, Bohac, Chet, additional, Bonvini, Ezio, additional, Moore, Paul A., additional, and Loo, Deryk, additional

Published
2022
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40. Targeting B7-H3 in prostate cancer: Phase 2 trial in localized prostate cancer using the anti-B7-H3 antibody enoblituzumab, with biomarker correlatives.

Author

Shenderov, Eugene, primary, De Marzo, Angelo M, additional, Lotan, Tamara L., additional, Wang, Hao, additional, Lim, Su Jin, additional, Allaf, Mohamad E., additional, Moore, Paul A., additional, Chen, Francine, additional, Sorg, Kristina, additional, White, Andrew M, additional, Hudson, Briana, additional, Fields, Paul A., additional, Hu, Shaohui, additional, Denmeade, Samuel R., additional, Pienta, Kenneth J., additional, Pavlovich, Christian P., additional, Drake, Charles G., additional, Pardoll, Drew M., additional, and Antonarakis, Emmanuel S., additional

Published
2022
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41. Preclinical Evaluation of IMGC936, a Next-Generation Maytansinoid-based Antibody–drug Conjugate Targeting ADAM9-expressing Tumors

Author

Scribner, Juniper A., primary, Hicks, Stuart W., additional, Sinkevicius, Kerstin W., additional, Yoder, Nicholas C., additional, Diedrich, Gundo, additional, Brown, Jennifer G., additional, Lucas, Jacquelynn, additional, Fuller, Megan E., additional, Son, Thomas, additional, Dastur, Anahita, additional, Hooley, Jeff, additional, Espelin, Christopher, additional, Themeles, Marian, additional, Chen, Francine Z., additional, Li, Ying, additional, Chiechi, Michael, additional, Lee, Jenny, additional, Barat, Bhaswati, additional, Widjaja, Lusiana, additional, Gorlatov, Sergey, additional, Tamura, James, additional, Ciccarone, Valentina, additional, Ab, Olga, additional, McEachem, Kerry A., additional, Koenig, Scott, additional, Westin, Eric H., additional, Moore, Paul A., additional, Chittenden, Thomas, additional, Gregory, Richard J., additional, Bonvini, Ezio, additional, and Loo, Deryk, additional

Published
2022
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42. Growing calls by experts in New Zealand for 'circuit breaker' lockdown

Author

Chen, Francine

Subjects
Vaccination, News, opinion and commentary
Abstract

New Zealand's government is facing growing calls from public health experts to enact a short, sharp 'circuit breaker' lockdown, a week after it shifted away from its much-vaunted coronavirus elimination [...]

Published
2021

43. Interplay Between Checkpoint Molecule B7-H3 and Human Leucocyte Antigen (HLA) Class I Expression: Relevance to the Clinical Course of Pancreatic Ductal Adenocarcinoma (PDAC)

Author

Michelakos, Theodoros, primary, Kontos, Filippos, additional, Sadagopan, Ananthan, additional, Cai, Lei, additional, Villani, Vincenzo, additional, Sabbatino, Francesco, additional, Moore, Paul, additional, Chen, Francine, additional, Ferrone, Soldano, additional, and Ferrone, Cristina R., additional

Published
2021
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44. Abstract 950: Targeting B7-H3 in squamous cell carcinoma of the head and neck: Preclinical proof-of-concept with the investigational anti-B7-H3 antibody-drug conjugate, MGC018

Author

Scribner, Juniper A., primary, Chen, Francine Z., additional, Li, Ying, additional, Chiechi, Michael, additional, Son, Thomas, additional, Hooley, Jeff, additional, Koenig, Scott, additional, Moore, Paul A., additional, Bonvini, Ezio, additional, Bohac, Chet, additional, and Loo, Deryk, additional

Published
2021
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45. Abstract 1841: IMGC936, an investigational ADAM9-targeting antibody drug conjugate, is active against patient-derived ADAM9-expressing xenograft models

Author

Ab, Olga, primary, Scribner, Juniper A., additional, Sinkevicius, Kerstin, additional, Loo, Deryk, additional, Hicks, Stuart W., additional, Watkins, Krystal, additional, Chen, Francine Z., additional, Espelin, Christopher, additional, Themeles, Marian, additional, Li, Ying, additional, Bohac, Chet, additional, McKay, Patrick Zweidler, additional, Moore, Paul A., additional, Sloss, Callum M., additional, Bonvini, Ezio, additional, and Westin, Eric H., additional

Published
2021
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48. A Phase 1, Open-Label Study of MGD013, a Bispecific DART® Molecule Binding PD-1 and LAG-3 in Patients with Relapsed or Refractory Diffuse Large B-Cell Lymphoma

Author

Wang, Jie, primary, Asch, Adam S., additional, Hamad, Nada, additional, Weickhardt, Andrew, additional, Tomaszewska-Kiecana, Monika, additional, Dlugosz-Danecka, Monika, additional, Pylypenko, Halyna, additional, Bahadur, Shakeela, additional, Ulahannan, Susanna, additional, Koucheki, Janine, additional, Sun, Jichao, additional, Li, Hua, additional, Chen, Francine, additional, Zhang, Xiaoyu, additional, Muth, John, additional, Kaminker, Patrick, additional, Moore, Paul, additional, and Sumrow, Bradley James, additional

Published
2020
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49. 313 A phase 1 evaluation of tebotelimab, a bispecific PD-1 x LAG-3 DART® molecule, in combination with margetuximab in patients with advanced HER2+ neoplasms

Author

Patel, Manish, primary, Luke, Jason, additional, Hamilton, Erika, additional, Chmielowski, Bartosz, additional, Blumenschein, George, additional, Kindler, Hedy, additional, Bahadur, Shakeela, additional, Santa-Maria, Cesar, additional, Koucheki, Janine, additional, Sun, Jichao, additional, Kaul, Sanjeev, additional, Chen, Francine, additional, Zhang, Xiaoyu, additional, Muth, John, additional, Kaminker, Patrick, additional, Moore, Paul, additional, Sumrow, Bradley, additional, and Ulahannan, Susanna, additional

Published
2020
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50. 682 Antibody-mediated blockade of interleukin-10 receptor-alpha promotes the activation of immune cells from in vitro dissociated tumor samples

Author

Berezhnoy, Alexey, primary, Shah, Kalpana, additional, Liu, Daorong, additional, Lung, Peter, additional, Long, Vatana, additional, Stahl, Kurt, additional, Smith, Douglas, additional, Chen, Francine, additional, Li, Hua, additional, Li, Jonathan, additional, Alderson, Ralph, additional, Ciccarone, Valentina, additional, Tamura, James, additional, Bonvini, Ezio, additional, Diedrich, Gundo, additional, and Moore, Paul, additional

Published
2020
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