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526 Robust prediction of patient outcomes with immune checkpoint blockade therapy for cancer using common clinical, pathologic, and genomic features
  1. Tiangen Chang1,
  2. Yingying Cao1,
  3. Hannah Sfreddo2,
  4. Saugato R Dhruba1,
  5. Se-Hoon Lee3,
  6. Cristina Valero2,
  7. Seong-Keun Yoo2,
  8. Diego Chowell2,
  9. Luc Morris2 and
  10. Eytan Ruppin1
  1. 1National Institutes of Health, Bethesda, MD, USA
  2. 2Memorial Sloan Kettering Cancer Center, New York, NY, USA
  3. 3Sungkyunkwan University, Seoul, Republic of Korea
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.

Abstract

Background Immune checkpoint blockade (ICB) has revolutionized our approach to cancer treatment. However, the response rate of immune checkpoint blockade (ICB) is still low. With the accumulation of large-scale ICB data, efforts to use these data to build machine learning predictors of ICB response are rising. However, there are several shared concerns about these models, including their black box nature that limits interpretability and the potential risk of overfitting during model training, which have so far impeded their clinical translation.

Methods Here we analyzed ~ 3000 samples across 18 solid tumor types from multiple cohorts with more than 20 clinical, pathologic, and genomic features measured. We developed, trained, and evaluated 20 machine-learning models to identify the most predictive model for ICB response by comparing their performance on test sets and importantly, performance difference between training vs test sets, using a repeated cross-validation procedure. The machine learning models include decision trees, Gaussian processes, support vector machine, XGBoost, and deep neural networks, among others. Finally, we developed the LOgistic Regression-based Immunotherapy-response Score (LORIS) using a transparent, compact 6-feature logistic LASSO regression model. This approach was validated for developing both pan-cancer and NSCLC-specific models across multiple independent datasets (figure 1).

Results The linear LASSO regression model outperforms all other models and biomarkers by having the highest performance on cross-validation sets and notably, the smallest performance difference between training and cross-validation sets (figure 2). LORIS outperforms previous signatures in ICB response prediction and can identify patients more likely to respond to ICB treatment, importantly, even those with low TMB or tumor PD-L1 expression levels (figure 3). LORIS consistently predicts both the short-term and the long-term survival across almost all cancer types (figure 3). Most importantly, ICB response probability increases near-monotonically (from 0% to 100%) with the LORIS, which can be used in both patient inclusion and exclusion. In contrast, ICB response probability is ~20% in low TMB patients and not always higher with higher TMB (figure 4). Finally, this approach is also effective in developing cancer-type-specific models for predicting ICB response (figure 5).

Conclusions Our study identifies important clinical features linked to ICB response and survival, allowing for more accurate and interpretable predictions using just a few clinically readily measurable features. We expect that this method will help improve clinical decision-making practices in precision medicine to maximize patient benefit.

Abstract 526 Figure 1

Overview of the study

Abstract 526 Figure 2

Robust prediction of pan-cancer objective response to immunotheraphy by a 6-variable logistic LASSO regression model.

Abstract 526 Figure 3

LORIS predicts patient outcomes following immunotherapy for both pan-cancer and individual cancer types.

Abstract 526 Figure 4

Monotonic relationship between LORIS and patient objective response probability & survival following immunotherapy.

Abstract 526 Figure 5

Robust prediction of response to immunotherapy in NSCLC with logistic LASSO regression.

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This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

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