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# Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

Sébastien Benzekry, Mélanie Karlsen, Abdessamad Elkaoutari, Suresh Vatakuti, Petter Curle, Candice Jamois

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## ► To cite this version:

Sébastien Benzekry, Mélanie Karlsen, Abdessamad Elkaoutari, Suresh Vatakuti, Petter Curle, et al. Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer. World Conference on Pharmacometrics, Mar 2022, Cape Town, South Africa. [hal-03935768](https://hal-03935768)

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Submitted on 12 Jan 2023

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# Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

S. Benzekry<fnref n="1" />

M. Karlsen<fnref n="1" />

A. El Kaoutari<fnref n="1" />

S. Vatakuti<fnref n="2" />

P. Curle<fnref n="3" />

C. Jamois<fnref n="2" />

¹COMPO, Inria Méditerranée, Centre de Recherche sur le Cancer de Marseille, Inserm, CNRS, IPC, Aix-Marseille University, Marseille, France; ²Safety and Early Development Informatics and <fnref n="3" /> Clinical Pharmacometrics, Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel, Switzerland

## BACKGROUND

* Treatment of advanced non-small cell lung cancer by atezolizumab-based therapy

* Prediction of survival from baseline or early on-treatment data could

* guide treatment decision during drug development

* inform personalized health care

* Current predictive biomarker: programmed death-ligand 1 (PD-L1)<fnref n="1" />

* State of the art from baseline clinical and biological data: ROPRO score<fnref n="2" />

* Predictive value of transcriptomic and mutation data is unclear

* Tumor kinetics (TK) model parameter growth rate (KG) has important predictive power of hazard ratio (HR)<fnref n="3" />

* Predictive value of kinetics of pharmacodynamic biomarkers is unclear

## STUDIES, PATIENTS AND DATA

<table><thead><tr><td>Studies</td><td>Study Description</td><td>Population</td><td>Patients treated with atezolizumab</td></tr></thead><tbody><tr><td>FIR GO28625</td><td>Phase 2 study that evaluated the efficacy and safety of anti-programmed death-ligand 1 (PD-L1) atezolizumab in advanced NSCLC selected by tumor cell (TC) or tumor-infiltrating immune cell (IC) PD-L1 expression</td><td>PD-L1 positive locally advanced or metastatic NSCLC (lines 1 and 2+)</td><td>133</td></tr><tr><td>POPLAR GO28753</td><td>Phase 2 randomised controlled trial (RCT) of atezolizumab versus docetaxel for patients with previously treated NSCLC</td><td>Locally advanced or metastatic NSCLC who failed platinum therapy</td><td>134</td></tr><tr><td>BIRCH GO28754</td><td>Phase 2 Study of Atezolizumab in participants with PD-L1 positive locally advanced or metastatic NSCLC</td><td>Locally advanced or metastatic NSCLC (lines 1, 2 or 3)</td><td>595</td></tr><tr><td>Total</td><td></td><td></td><td>862</td></tr></tbody></table>

### 5 sources of data:

#### Baseline

* clinical and biological characteristics (73 variables)

* RNAseq (~58k variables)

* FMI (mutation data on 395 genes)

* Tumor mutational burden (TMB)

#### Longitudinal

* kinetics of tumor size (TK, sum of largest diameters)

* kinetics of 4 pharmacodynamic markers (PD):

* albumin, C-reactive protein (CRP), lactate deshydrogenase (LDH), neutrophils

<figure><img src="image_4.png" /><figcaption>Number of patients with data available for each dataset</figcaption></figure>

→ FMI and TMB disregarded because they would have highly reduced the number of patients

## METHODS

<figure><img src="image_5.png" /></figure>

* Best survival model = random survival forest

* Multiple methods of feature selection were assessed

* Evaluation of performances with 10-fold cross-validation

<figure><img src="image_6.png" /></figure>

* Results using full time courses suffer from immortal time bias

→ We used truncated data sets

* At cycles 3, 5 and 10 pre-dose

* i.e. after 6, 12 and 27 weeks of treatment

## RESULTS: NONLINEAR MIXED-EFFECTS MODELING (NLME)

### Tumor kinetics

$$S(t) = S_0 (e^{-KS \cdot t} + e^{KG \cdot t} - 1)$$

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

### Albumin kinetics

$$y(t) = p + e^{l} \frac{q - p}{t + e^{l}}$$

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>CRP, LDH and neutrophils kinetics were described using the above double-exponential model</figcaption></figure>

<figure><img src="image_12.png" /></figure>

## OBJECTIVE

To provide a digital **decision-enabling tool** by predicting **overall survival** based on early tumor size and longitudinal PD biomarker data using the strengths of **pharmacometrics** (PHMx) and machine learning (ML)

## RESULTS MACHINE LEARNING: FULL TIME COURSE

<table><thead><tr><td>Minimal clinical signature</td></tr></thead><tbody><tr><td>C-reactive protein</td></tr><tr><td>Heart rate</td></tr><tr><td>Neutrophils-to-lymphocytes ratio</td></tr><tr><td>Neutrophils</td></tr><tr><td>Lymphocytes-to-leukocytes ratio</td></tr><tr><td>Liver metastases</td></tr><tr><td>ECOG (0 vs 1)</td></tr><tr><td>PDL1 (≥ 50%)</td></tr><tr><td>Hemoglobin</td></tr><tr><td>Baseline sum of longest diameters</td></tr><tr><td>Lactate deshydrogenase</td></tr></tbody></table>

<figure><img src="image_13.png" /></figure>

* A minimal signature achieves (almost) the same performances as the full model

* RNAseq only brings negligible additional predictive power → dropped in the final model

* Substantial predictive power, except for PPV (ability to predict death, 67.3% ± 13.1)

<figure><img src="image_14.png" /><figcaption>C-index as a function of feature sets</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Positive (1) = death, Negative (0) = alive
All classification metrics computed for survival at 12 months</figcaption></figure>

## RESULTS MACHINE LEARNING: TRUNCATED TIME COURSE

<figure><img src="image_16.png" /></figure>

* At least **4 completed cycles** of treatment (≥ 12 weeks) of data are required to achieve substantial individual predictive power

* PD model derived metrics are more informative of individual survival than TK metrics from 12 weeks onwards

* Best individual kinetic markers: CRP and neutrophils

## DISCUSSION

### Conclusion

* The **combination of NLME and ML** allowed to take the best of the two approaches in order to predict individual survival

* NLME for **longitudinal TK and PD** data

* ML to build **multivariable** models from a large number of features

* A model was established based on a 26 features **minimal signature**: 11 baseline clinical features + longitudinal TK (3 variables) + longitudinal PD (12 variables)

$$C\text{-index} = 0.818 \pm 0.029, \text{AUC} = 0.905 \pm 0.0414$$

* RNAseq data did not yield substantial predictive power

### Perspectives

* **External validation** on the phase 3 OAK trial

* Prediction of study-level overall survival in multiple arms, from early on-study data

<footnote n="1">1 Shukuya, T. & Carbone, D. P. Predictive Markers for the Efficacy of Anti-PD-1/PD-L1 Antibodies in Lung Cancer. Journal of Thoracic Oncology 11, 976–988 (2016).</footnote>

<footnote n="2">2 Becker, T. et al. An enhanced prognostic score for overall survival of patients with cancer derived from a large real-world cohort. Ann Oncol 31, 1561–1568 (2020).</footnote>

<footnote n="3">3 Claret, L. et al. A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics. Clin Cancer Res 24, 3292–3298 (2018).</footnote>