Immune checkpoint blockade

Hi everyone!

Please note the date and location change! Thank you to Clarifai (https://www.clarifai.com/) for hosting us! Food and drinks will be provided :)

Excited to host our 2nd meetup in the series on cancer immunotherapy! For context, check out this NYT article, Harnessing the Immune System to Fight Cancer . This event is open to all, whether you are a computational wizard, cancer researcher, or if you are just generally curious about the topic! We hope everyone will be able to walk away having learned something new :) Last time, we talked about cancer vaccines, with a focus on personalized, tumor-specific, antigen vaccines, created and optimized using using the patient’s own cancer genome and machine learning. This time, we will focus on immune checkpoint blockade, a way cancer evades detection by our immune system.

Immune checkpoint blockade therapies rely on interrupting a so-called checkpoint that cancer cells exploit to signal the immune system to leave them alone. In two connected talks, we will explain how these therapies work and how we can make them better, as well as how we can use statistical modeling to evaluate biomarkers for response to checkpoint blockade.

An introduction to immune checkpoint blockade in cancer immunotherapy | Isaac Hodes (https://github.com/ihodes)
Isaac will focus on some key interactions between the immune system and cancer cells, and talk about how we take advantage of these interactions with various targeted drugs. He will also cover how we might combine multiple therapies in search of even better outcomes.

Evaluating biomarkers for response to checkpoint blockade | Jacki Novik (http://github.com/jburos)
Jacki will give an overview of the current methods for evaluating biomarkers for response to checkpoint blockade, including some examples from our current research. Key challenges include (1) heterogeneity of responses to therapy, (2) number of potential biomarkers to consider, and (3) small sample sizes. In addition, many of the biomarkers we aim to evaluate are themselves the end products of complex computational pipelines. Hence, evaluating data elements in the context of checkpoint blockade poses a unique set of challenges and opportunities. Jacki will conclude with some examples of how we might improve upon current models for response to therapy.

About Hammer Lab (http://www.hammerlab.org/)
Hammer Lab, within the Icahn Institute at Mount Sinai, is a team of 15 software developers and data scientists working to develop novel cancer immunotherapies and to make existing immunotherapies more effective. MIT Technology Review mentioned our work as one of their 10 Breakthrough Technologies of 2016 (https://www.technologyreview.com/s/600763/10-breakthrough-technologies-2016-immune-engineering/). Our lab is unique in that nearly all of our lab members come from industry rather than academia. We make use of recent developments in programming language theory, distributed data processing, and machine learning, and we strive to make high quality, maintainable software. We are committed to open source software and open and reproducible science.