# Machine learning for prediction of immunotherapy efficacy in non-small cell lung cancer from simple clinical and biological data

Sébastien Benzekry, Mathieu Grangeon, Mélanie Karlsen, Pauline Fleury, Maria Alexa, Isabella Bicalho-Frazeto, Solène Chaleat, Pascale Tomasini, Dominique Barbolosi, Fabrice Barlesi, et al.

► To cite this version:

Sébastien Benzekry, Mathieu Grangeon, Mélanie Karlsen, Pauline Fleury, Maria Alexa, et al. Machine learning for prediction of immunotherapy efficacy in non-small cell lung cancer from simple clinical and biological data. Colloque Sciences numériques et Intelligence Artificielle pour la Santé à Aix-Marseille Université, Nov 2021, Marseille, France. 2021. [hal-03467615](https://hal.science/hal-03467615)

HAL Id: hal-03467615

https://hal.science/hal-03467615

Submitted on 6 Dec 2021

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# Machine learning for prediction of immunotherapy efficacy in nonsmall cell lung cancer from simple clinical and biological data

Sébastien Benzekry <fnref n="1" />, *, Mathieu Grangeon <fnref n="2" />, *, Mélanie Karlsen <fnref n="1" />, Pauline Fleury <fnref n="1" />, Maria Alexa <fnref n="1" />, Isabella Bicalho-Frazeto <fnref n="1" />, Solène Chaleat <fnref n="2" />, Pascale Tomasini <fnref n="2" />,<fnref n="3" />, Dominique Barbolosi <fnref n="1" />, Fabrice Barlesi <fnref n="3" />,<fnref n="4" />, Laurent Greillier <fnref n="1" />,<fnref n="2" />

## BACKGROUND

* Immune checkpoint inhibitors (ICIs) are now a therapeutic standard in stage IV non-small cell lung cancer (NSCLC).

* However, 4 out of 5 patients don't respond to single agent ICI.

* Strong predictive markers for ICIs efficiency are still lacking.

## MATERIAL AND METHODS

### Data

* 298 2nd line or more stage IV NSCLC patients

* Pre-treatment blood count + clinical variables

* 12 variables :

- hemoglobin

- platelets

- leukocytes

- neutrophils

- lymphocytes

- eosinophils

- monocytes

- basophils

- NLR (neutrophils to lymphocytes ratio)

- PLR (platelets to lymphocytes ratio)

- derived NLR (absolute neutrophils count / (white blood count – absolute neutrophils count))

- BMI

- patient status (dichotomized into < 2 or ≥ 2)

* Disease control (DCR) : patients with complete or partial response, or stable disease.

### Machine Learning

* **Statistical analysis :**
  - univariable and multivariable logistic regression

* **Feature selection :**
  - Feature importance from random forest classification algorithm (no tuning, 1,000 trees).
  - Sort by mean decrease accuracy
  - incremental logistic regression
  - Optimal set of features = maximal before observing a decrease in 10-fold cross-validated accuracy

* **ML :**

- Models :

- logistic regression

- random forest

- single layer neural network

- naïve Bayes

- k-nearest neighbors

- support vector models

- tuned and trained on the train set (3-fold cross-validation, N=200)

- predictions assessed on the test set (N=100)

- decision tree for interpretability

## OBJECTIVES

* Study the predictive power of **blood counts** before the start of immunotherapy

* Assess the utility of classical machine learning algorithms to **establish a predictive model of response**

## RESULTS

### Logistic regression analysis for disease control

<table><caption>A</caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Univariable logistic regression</th><th colspan="3">Multivariable logistic regression</th></tr><tr><th>Odds ratio [95% IC]</th><th>p</th><th>signif</th><th>Odds ratio [95% IC]</th><th>p</th><th>signif</th></tr></thead><tbody><tr><td>Lymphocytes</td><td>1.1 [0.83, 1.4]</td><td>0.678</td><td></td><td>0.98 [0.15, 5.2]</td><td>0.984</td><td></td></tr><tr><td>NLR</td><td>0.49 [0.31, 0.73]</td><td>0.000879</td><td>***</td><td>0.68 [0.098, 1.9]</td><td>0.651</td><td></td></tr><tr><td>Platelets</td><td>1 [0.82, 1.3]</td><td>0.762</td><td></td><td>1.3 [0.72, 2.4]</td><td>0.404</td><td></td></tr><tr><td>PLR</td><td>0.84 [0.64, 1.1]</td><td>0.156</td><td></td><td>1.1 [0.5, 2.4]</td><td>0.788</td><td></td></tr><tr><td>Leukocytes</td><td>0.68 [0.5, 0.89]</td><td>0.00791</td><td>**</td><td>0.6 [0.0022, 3e+02]</td><td>0.847</td><td></td></tr><tr><td>Hemoglobin</td><td>1.9 [1.5, 2.5]</td><td>9.26e-07</td><td>***</td><td>1.8 [1.3, 2.4]</td><td>0.000122</td><td>***</td></tr><tr><td>dNLR</td><td>0.63 [0.47, 0.83]</td><td>0.00155</td><td>**</td><td>0.8 [0.33, 2.7]</td><td>0.689</td><td></td></tr><tr><td>Neutrophils</td><td>0.62 [0.45, 0.83]</td><td>0.00232</td><td>**</td><td>1.5 [0.0047, 2.7e+02]</td><td>0.863</td><td></td></tr><tr><td>Monocytes</td><td>0.87 [0.69, 1.1]</td><td>0.226</td><td></td><td>0.86 [0.5, 1.4]</td><td>0.545</td><td></td></tr><tr><td>Eosinophils</td><td>1.3 [0.97, 1.9]</td><td>0.139</td><td></td><td>1.1 [0.75, 1.8]</td><td>0.582</td><td></td></tr><tr><td>Basophils</td><td>1.2 [0.95, 1.8]</td><td>0.177</td><td></td><td>1.2 [0.89, 1.8]</td><td>0.321</td><td></td></tr><tr><td>BMI</td><td>1.2 [0.95, 1.5]</td><td>0.123</td><td></td><td>1 [0.76, 1.3]</td><td>0.997</td><td></td></tr><tr><td>Performance status</td><td>0.5 [0.39, 0.64]</td><td>6.21e-08</td><td>***</td><td>0.58 [0.44, 0.75]</td><td>7.79e-05</td><td>***</td></tr></tbody></table>

<figure><img src="image_1.png" /><figcaption>Variable selection</figcaption></figure>

<figure><img src="image_2.png" /><figcaption>B</figcaption></figure>

### Machine learning algorithms predictive performances

<figure><img src="image_3.png" /></figure>

### Summary of machine learning algorithms predictive performances

<table><thead><tr><th>Model</th><th>Accuracy</th><th>ROC AUC</th><th>PPV</th><th>NPV</th><th>Sensitivity</th><th>Specificity</th></tr></thead><tbody><tr><td>Random Forest</td><td>0.68 ± 0.04</td><td>0.74 ± 0.03</td><td>0.70 ± 0.08</td><td>0.68 ± 0.06</td><td>0.58 ± 0.08</td><td>0.78 ± 0.06</td></tr><tr><td>Logistic Regression</td><td>0.67 ± 0.04</td><td>0.73 ± 0.03</td><td>0.69 ± 0.08</td><td>0.67 ± 0.06</td><td>0.57 ± 0.09</td><td>0.77 ± 0.07</td></tr><tr><td>Naive Bayes</td><td>0.67 ± 0.04</td><td>0.73 ± 0.03</td><td>0.72 ± 0.07</td><td>0.65 ± 0.06</td><td>0.49 ± 0.07</td><td>0.83 ± 0.05</td></tr><tr><td>Single Layer Neural Network</td><td>0.66 ± 0.03</td><td>0.72 ± 0.03</td><td>0.69 ± 0.09</td><td>0.66 ± 0.06</td><td>0.54 ± 0.09</td><td>0.78 ± 0.07</td></tr><tr><td>k-Nearest Neighbour</td><td>0.66 ± 0.04</td><td>0.69 ± 0.04</td><td>0.65 ± 0.07</td><td>0.66 ± 0.06</td><td>0.58 ± 0.07</td><td>0.73 ± 0.07</td></tr><tr><td>Linear SVM</td><td>0.58 ± 0.09</td><td>0.73 ± 0.03</td><td>0.72 ± 0.09</td><td>0.58 ± 0.10</td><td>0.19 ± 0.25</td><td>0.94 ± 0.09</td></tr><tr><td>Polynomial SVM</td><td>0.55 ± 0.08</td><td>0.73 ± 0.03</td><td>0.61 ± 0.13</td><td>0.58 ± 0.13</td><td>0.19 ± 0.29</td><td>0.89 ± 0.23</td></tr><tr><td>Radial basis SVM</td><td>0.55 ± 0.08</td><td>0.73 ± 0.03</td><td>0.67 ± 0.17</td><td>0.56 ± 0.06</td><td>0.20 ± 0.28</td><td>0.88 ± 0.25</td></tr></tbody></table>

## CONCLUSION AND PERSPECTIVES

**Blood counts prior to ICIs** (elevation of hemoglobin, decrease of NLR, leukocytes or neutrophils) and clinical status (good PS) were **significantly associated with better DCR** in multivariable analysis. The practical application of these associations using machine learning algorithms was able to **accurately predict individual response to treatment**. This could be improved further by increasing the number of variables in the model and should be further validated in an independent cohort.

## References

1. Benzekry, S.; Grangeon, M.; ... ; Barlesi, F.; Greillier, L.; et al. Machine learning for prediction of immunotherapy efficacy in non-small cell lung cancer from simple clinical and biological data.

2. Benzekry, S.; Grangeon, M.; Karlsen, M.; Alexa, M.; Bicalho-Frazeto, I.; Chaleat, S.; Tomasini, P.; Barbolosi, D.; Barlesi, F.; Greillier, L. Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data. Cancers 2021, 13, x.

<footnote n="1">¹ Computational pharmacology and clinical Oncology Team, Inria Sophia Antipolis - Méditerranée, Cancer Research Center of Marseille, Inserm, CNRS, Aix Marseille University, Marseille, France.</footnote>

<footnote n="2">² Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France.</footnote>

<footnote n="3">³ Aix Marseille University, CNRS, INSERM, CRCM, Marseille, France</footnote>

<footnote n="4">⁴ Gustave Roussy Cancer Campus, International Center of Thoracic Cancers, Université Paris-Saclay, Villejuif, France.</footnote>

<footnote>* = these authors contributed equally</footnote>