Cancer immunotherapy seeks to harness the body’s immune system, and most often T cells, to recognize and kill cancer cells while leaving healthy cells alone. In the last decade, there have been many breakthroughs in cancer immunotherapy, yet treatments still only work for some cancer patients some of the time.
To address this gap, the Schmidt Center held a machine learning competition in January 2023, in which participants developed algorithms to uncover new ways to modify, or “perturb,” T cells to make them better cancer-cell killers.
Scientists in the Hacohen Lab at the Broad then tested their predictions in mouse models, making this the first challenge that the Schmidt Center knows of in which new experiments were performed based on the output of machine-learning models developed in the challenge. More than 1,000 people from 87 countries registered for the competition.
Hacohen Lab, Harvard’s Laboratory for Innovation Science (LISH), the MIT Department of Electrical Engineering and Computer Science, Topcoder, Gordian Biotechnology, and Saturn Cloud.
Previously, the Broad Institute’s Hacohen Lab ran experiments testing the effects of 73 gene knockouts in T cells on mice with cancer. Given that it took months to test a fraction of the 20,000 potential gene knockouts, Broad researchers wanted a way to zero in on the most promising perturbations. Enter the Cancer Immunotherapy Machine Learning Competition.
In Part 1 of the competition, participants received gene expression data from 66 of the 73 T-cell-gene knockouts from the Hacohen Lab experiments as training data. They then had to develop an algorithm that could predict how knocking out the seven “held-out” genes would affect T cells.
Part 2 participants used their algorithms from the first part to propose new gene knockouts (picking from any of the 20K genes in the entire genome) to shift as many T cells as possible into a cancer-fighting state. In Part 3, participants proposed a metric for ranking how well a particular gene knockout would bring about this desired shift in T cells.
First place: Marios Gavrielatos and Konstantinos Kyriakidis, Greece
Second place: Yuzhou Gu, Anzo Teh, Yanjun Han, and Brandon Wang, U.S. (MIT)
Third place: Peter Novotný, Poland
First place: Brody Langille, Jordan Trajkovski, and Elizabeth Hudson
Second place: mglettig (username)*
Third place: Ai Vu Hong, researcher at Genethon, France
Fourth place: Saket Kunwar, independent researcher, Nepal
Fifth place: lxastro0 (username)*
Sixth place: John Gardner, freelance data scientist
Seventh place: agilsoft (username)*
Eighth place: Basak Eraslan, postdoctoral researcher holding a joint position at the Regev Lab in Genentech and Kundaje Lab at Stanford University
Ninth place: Haoyue Dai, Kun Zhang, Ignavier Ng, Yujia Zheng, Xinshuai Dong, and Yewen Fan from Carnegie Mellon University; Petar Stojanov, postdoctoral fellow at the Eric and Wendy Schmidt Center; Gongxu Luo, Mohamed bin Zayed University of Artificial Intelligence; and Biwei Huang, University of California, San Diego
Ninth place: Liu Xindi, freelance programmer
Ninth place: Johnson Zhou, Camille Sayoc, and Yi-Cheng Peng, Master’s students of the Faculty of Engineering and IT at the University of Melbourne, Victoria, Australia
First place: Dariusz Brzeziński and Wojciech Kotlowski from Poznań University of Technology in Poland
Second place: Salil Bhate, postdoctoral fellow at the Eric and Wendy Schmidt Center
Third place: Irene Bonafonte Pardàs, Artur Szalata, and Benjamin Schubert from Helmholtz Center Munich and Miriam Lyzotte from Mila - Quebec AI Institute