Kevin Hu

Eradication of large established tumors in mice by combination immunotherapy that engages innate and adaptive immune responses

Checkpoint blockade with antibodies specific for cytotoxic T lymphocyte–associated protein (CTLA)-4 or programmed cell death 1 (PDCD1; also known as PD-1) elicits durable tumor regression in metastatic cancer, but these dramatic responses are confined to a minority of patients. This suboptimal outcome is probably due in part to the complex network of immunosuppressive pathways present in advanced tumors, which are unlikely to be overcome by intervention at a single signaling checkpoint. Here we describe a combination immunotherapy that recruits a variety of innate and adaptive immune cells to eliminate large tumor burdens in syngeneic tumor models and a genetically engineered mouse model of melanoma; to our knowledge tumors of this size have not previously been curable by treatments relying on endogenous immunity. Maximal antitumor efficacy required four components: a tumor-antigen-targeting antibody, a recombinant interleukin-2 with an extended half-life, anti-PD-1 and a powerful T cell vaccine. Depletion experiments revealed that CD8+ T cells, cross-presenting dendritic cells and several other innate immune cell subsets were required for tumor regression. Effective treatment induced infiltration of immune cells and production of inflammatory cytokines in the tumor, enhanced antibody-mediated tumor antigen uptake and promoted antigen spreading. These results demonstrate the capacity of an elicited endogenous immune response to destroy large, established tumors and elucidate essential characteristics of combination immunotherapies that are capable of curing a majority of tumors in experimental settings typically viewed as intractable.

Links that speak: The global language network and its association with global fame

Significance People have long debated about the global influence of languages. The speculations that fuel this debate, however, rely on measures of language importance—such as income and population—that lack external validation as measures of a language’s global influence. Here we introduce a metric of a language’s global influence based on its position in the network connecting languages that are co-spoken. We show that the connectivity of a language in this network, after controlling for the number of speakers of a language and their income, remains a strong predictor of a language’s influence when validated against two independent measures of the cultural content produced by a language’s speakers. Languages vary enormously in global importance because of historical, demographic, political, and technological forces. However, beyond simple measures of population and economic power, there has been no rigorous quantitative way to define the global influence of languages. Here we use the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures. We find that the structure of these three global language networks (GLNs) is centered on English as a global hub and around a handful of intermediate hub languages, which include Spanish, German, French, Russian, Portuguese, and Chinese. We validate the measure of a language’s centrality in the three GLNs by showing that it exhibits a strong correlation with two independent measures of the number of famous people born in the countries associated with that language. These results suggest that the position of a language in the GLN contributes to the visibility of its speakers and the global popularity of the cultural content they produce.

Pantheon 1.0, a manually verified dataset of globally famous biographies

We present the Pantheon 1.0 dataset: a manually curated dataset of individuals that have transcended linguistic, temporal, and geographic boundaries. The Pantheon 1.0 dataset includes the 11,341 biographies present in more than 25 languages in Wikipedia and is enriched with: (i) manually curated demographic information (place of birth, date of birth, and gender), (ii) a cultural domain classification categorizing each biography at three levels of aggregation (i.e. Arts/Fine Arts/Painting), and (iii) measures of global visibility (fame) including the number of languages in which a biography is present in Wikipedia, the monthly page-views received by a biography (2008-2013), and a global visibility metric we name the Historical Popularity Index (HPI). We validate our measures of global visibility (HPI and Wikipedia language editions) using external measures of accomplishment in several cultural domains: Tennis, Swimming, Car Racing, and Chess. In all of these cases we find that measures of accomplishments and fame (HPI) correlate with an

Pantheon: Visualizing historical cultural production

R^2 \geq 50%

DIVE: A Mixed-Initiative System Supporting Integrated Data Exploration Workflows

, suggesting that measures of global fame are appropriate proxies for measures of accomplishment.We present the Pantheon 1.0 dataset: a manually verified dataset of individuals that have transcended linguistic, temporal, and geographic boundaries. The Pantheon 1.0 dataset includes the 11,341 biographies present in more than 25 languages in Wikipedia and is enriched with: (i) manually verified demographic information (place and date of birth, gender) (ii) a taxonomy of occupations classifying each biography at three levels of aggregation and (iii) two measures of global popularity including the number of languages in which a biography is present in Wikipedia (L), and the Historical Popularity Index (HPI) a metric that combines information on L, time since birth, and page-views (2008–2013). We compare the Pantheon 1.0 dataset to data from the 2003 book, Human Accomplishments, and also to external measures of accomplishment in individual games and sports: Tennis, Swimming, Car Racing, and Chess. In all of these cases we find that measures of popularity (L and HPI) correlate highly with individual accomplishment, suggesting that measures of global popularity proxy the historical impact of individuals.

Abstract A52: Eradication of large established tumors with combination immunotherapy engaging innate and adaptive immunity

We introduce Pantheon, a dataset and visualization platform quantifying cultural accomplishments that have broken the barriers of space, time and language. The Pantheon dataset connects the 11,340 biographies available in more than 25 languages in Wikipedia with a cultural domain, place of birth, and time period. We present this data through an online data visualization platform supporting the exploration of the dataset. In this poster we describe the Pantheon dataset and visualization platform, both of which are available at http://pantheon.media.mit.edu.

Reply to Biersteker: When methods matter

Generating knowledge from data is an increasingly important activity. This process of data exploration consists of multiple tasks: data ingestion, visualization, statistical analysis, and storytelling. Though these tasks are complementary, analysts often execute them in separate tools. Moreover, these tools have steep learning curves due to their reliance on manual query specification. Here, we describe the design and implementation of DIVE, a web-based system that integrates state-of-the-art data exploration features into a single tool. DIVE contributes a mixed-initiative interaction scheme that combines recommendation with point-and-click manual specification, and a consistent visual language that unifies different stages of the data exploration workflow. In a controlled user study with 67 professional data scientists, we find that DIVE users were significantly more successful and faster than Excel users at completing predefined data visualization and analysis tasks.

Pantheon: A Dataset for the Study of Global Cultural Production.

Checkpoint blockade against CTLA-4 or PD-1 has demonstrated that an endogenous immune response can be stimulated to elicit durable regressions in advanced cancer, but these dramatic responses are currently confined to a minority of patients. This outcome is probably due in part to the complex network of immunosuppressive pathways present in advanced tumors, which are unlikely to be overcome by intervention at a single signaling checkpoint, requiring a counter-directed network of pro-immunity signals. Here we demonstrate a combination immunotherapy that recruits a diverse set of innate and adaptive immune effectors, enabling robust elimination of tumor burdens that to our knowledge have not previously been curable by treatments relying on endogenous immunity. Maximal anti-tumor efficacy required four components: a tumor antigen targeting antibody, an extended half-life IL-2, anti-PD-1, and a powerful T-cell vaccine. This combination elicited durable cures in a majority of animals, formed immunological memory in multiple transplanted tumor models, and induced sustained tumor regression in an autochthonous BRrafV600E/Pten-/- melanoma model. Multiple innate immune cell subsets, CD8+ T-cells, and cross-presenting dendritic cells were critical to successful therapy. Treatment induced high levels of intratumoral inflammatory cytokines and immune cell infiltration, enhanced antibody-mediated tumor antigen uptake, and promoted antigen spreading. These results demonstrate the capacity of an elicited endogenous immune response to destroy large, established tumors and elucidate essential characteristics of combination immunotherapies capable of curing a majority of tumors in experimental settings typically viewed as intractable. Citation Format: Kelly Dare Moynihan, Cary Opel, Gregory Szeto, Alice Tzeng, Zhu Eric, Jesse Engreitz, Williams Robert, Kavya Rakhra, Michael Zhang, Adrienne Rothschilds, Sudha Kumari, Ryan L. Kelly, Byron Kwan, Wuhbet Abraham, Kevin Hu, Naveen Mehta, Monique Kauke, Heikyung Suh, Douglas A. Lauffenburger, K. Dane Wittrup, Darrell J. Irvine. Eradication of large established tumors with combination immunotherapy engaging innate and adaptive immunity. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr A52.

VizML: A Machine Learning Approach to Visualization Recommendation.

We appreciate Biersteker’s comments (1) on our research (2). Moreover, we agree with many of her points so wholeheartedly that our paper addresses them in detail: We devote whole sections in the main text and supporting information to the incompleteness of the Index Translationum, the imperfect quality of the language detector, and the limitations of the Wikipedia dataset, among others.