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# Machine Learning to Predict Survival and Resistance to Immune Checkpoint Inhibition

Sébastien Benzekry

► To cite this version:

Sébastien Benzekry. Machine Learning to Predict Survival and Resistance to Immune Checkpoint Inhibition. Twelfth International Workshop on Pharmacodynamics of Anticancer Agents, Sep 2024, Ponta Delgada (Açores), Portugal. hal-04990492

HAL Id: hal-04990492

https://inria.hal.science/hal-04990492v1

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Distributed under a Creative Commons Attribution 4.0 International License
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COMPO
COMPUTATIONAL ONCOLOGY

Instituts thématiques Inserm
Institut national de la santé et de la recherche médicale

# Machine Learning to Predict Survival and Resistance to Immune Checkpoint Inhibition

S. Benzekry

Twelfth International Workshop on Pharmacodynamics of Anticancer Agents

Ponta Delgada, September 2024

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# Mechanistic learning

<figure><img src="image_9.png" /></figure>
Long term responders / survivors to anti-PD(L)1 in advanced NSCLC

<figure><img src="image_10.png" /></figure>
# Current state of the art

## Baseline

* Response: PDL1 (AUC = 0.601¹), TMB (AUC = 0.646¹)

* ROPRO score for survival (27 basic clinical variables) : C-index = 0.69²

## On treatment

* Tumor kinetics : KG has important predictive power of OS hazard ratio (HR)<fnref n="3" />

<figure><img src="image_11.png" /></figure>

# Objectives = predict survival (phase 3 OS) and individual resistance

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

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<footnote n="1">¹ Rizvi, H. et al. J. Clin. Oncol. 2018.</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 (2018).</footnote>
<figure><img src="image_15.png" /></figure>

# kinetics – Machine Learning (kML) for prediction of OS

## Raw baseline data

Clinical characteristics
p = 73

Genomic and Transcriptomic
p = 395 and 58,311

ML Feature
selection

## Minimal signature

Clinical
characteristics
p = 11

Genomic and
Transcriptomic

## Raw longitudinal data

Tumor Kinetics (TK)
k = 8,635

Blood markers Kinetics (BK)
k = 108,551
Albumin, CRP, Neutrophils, LDH

Mixed-
effects

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

<figure><img src="image_17.png" /></figure>

## Model-based
individual parameters

TK
p = 3

BK
p = 12

<figure><img src="image_18.png" /></figure>

## Survival

### Individual prognosis

* C-index

* Survival

### Individual digital twin

## Machine
Learning

### Clinical trial

* Survival curve

* Hazard ratio

### Trial digital arm

CRP = C reactive protein, LDH = lactate dehydrogenase
# Data for model development and external validation

## Baseline data

* Patients' and disease characteristics
  **p = 73 parameters**

* Transcriptomic and mutational data
  **p = 58,311 and 395**

## Longitudinal data

* Tumor kinetics (TK, SLD)
  **5,570/3,065 observations**

* 4 BK markers (Albumin, CRP, LDH and Neutrophils)
  **61,296/47,255 observations**

<table><thead><tr><th>Study</th><th>Description</th><th>N</th></tr></thead><tbody><tr><td>FIR<br>GO28625</td><td>Phase 2 study for the efficacy and safety of ATZ in advanced NSCLC</td><td>133</td></tr><tr><td>POPLAR<br>GO28753</td><td>Phase 2 randomised controlled trial of ATZ versus docetaxel in NSCLC</td><td>134</td></tr><tr><td>BIRCH<br>GO28754</td><td>Phase 2 study of ATZ in advanced or metastatic NSCLC</td><td>595</td></tr><tr><td>Train</td><td></td><td>862</td></tr><tr><td>Test - OAK<br>GO28915</td><td>Phase 3 RCT of ATZ versus docetaxel (DTX) in patients with previously treated NSCLC</td><td>553 (ATZ)<br>521 (DTX)</td></tr><tr><td>Train + Test</td><td></td><td>1936</td></tr></tbody></table>

Train

Test

NSCLC: Non-Small Cell Lung Cancer; p = number of parameters, N: number of patients treated with atezolizumab (patients from French centers were excluded for legal reasons (N=118)); In total, data from 1074 patients from OAK were used as Test set (553 from the ATZ arm, 521 from the DTX arm); PD: Pharmacodynamic; SLD: Sum of the Largest Diameters. CRP: C Reactive Protein; LDH: Lactate Dehydrogenase.

1. Fehrenbacher L *et al.* Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet (2016)

2. Fehrenbacher L *et al.* Updated Efficacy Analysis Including Secondary Population Results for OAK: A Randomized Phase III Study of Atezolizumab versus Docetaxel in Patients with Previously Treated Advanced Non-Small Cell Lung Cancer. Journal of Thoracic Oncology (2018)

3. Solange Peters *et al.* Phase II Trial of Atezolizumab As First-Line or Subsequent Therapy for Patients With Programmed Death-Ligand 1-Selected Advanced Non-Small-Cell Lung Cancer (BIRCH). JCO (2017)

4. Spigel D.R *et al.* FIR: Efficacy, Safety, and Biomarker Analysis of a Phase II Open-Label Study of Atezolizumab in PD-L1-Selected Patients With NSCLC. Journal of Thoracic Oncology (2018)
# Blood markers kinetics (BK)

## Spaghetti plot

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## Early cycles (normalized)

<figure><img src="image_20.png" /></figure>

* Is there any kinetic pattern ?
* Does it carry predictive value ?
* Can we describe dynamics with a mechanistic model ?
# Is there any kinetic pattern? Yes !

Corrected Bayesian Information Criterion (BICc x 10³) for four empirical kinetic models of BK. b : standard deviation of the proportional error model

<table><thead><tr><th rowspan="2">Model</th><th colspan="2">Albumin</th><th colspan="2">CRP</th><th colspan="2">LDH</th><th colspan="2">Neutrophils</th></tr><tr><th>BICc</th><th>b</th><th>BICc</th><th>b</th><th>BICc</th><th>b</th><th>BICc</th><th>b</th></tr></thead><tbody><tr><td>Double-exponential</td><td>48.4</td><td>0.058</td><td>28.8</td><td>0.21</td><td>39.9</td><td>0.56</td><td>102</td><td>0.14</td></tr><tr><td>Hyperbolic</td><td>48.0</td><td>0.056</td><td>29.7</td><td>0.22</td><td>40.9</td><td>0.62</td><td>103</td><td>0.14</td></tr><tr><td>Linear</td><td>49.4</td><td>0.063</td><td>30.0</td><td>0.23</td><td>42.4</td><td>0.70</td><td>105</td><td>0.17</td></tr><tr><td>Constant</td><td>49.7</td><td>0.065</td><td>31</td><td>0.25</td><td>43.0</td><td>0.74</td><td>106</td><td>0.18</td></tr></tbody></table>

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Does it carry predictive value? Yes !

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⇒ Individual TK and BK model parameters (without baseline) = ML inputs
# kML model development and validation

## Model development

### Phase 2 trials

* FIR

* BIRCH

* POPLAR

Atezolizumab

ML

Feature selection

$$N = 533 \\ p = 26$$

NLME

Population prior

Bayesian Estimation

ML

Train

Trained model

ML

Predict

Individual predictions

$\hat{S}^i(t)$

## Model validation

### Phase 3 trial OAK

Atezolizumab arm

Bayesian Estimation

$$N = 377 \\ p = 26$$

ML

Predict

Individual predictions

Trial prediction

Docetaxel arm

$$N = 354 \\ p = 26$$

Individual predictions
# Minimal baseline (mBSL) signature kinetics-ML (kML) model

## Minimal baseline signature

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* All features sorted using LASSO

* Incremental models with increasing number of features

* Minimal set of features that reaches the plateau

⇒ 11 mBSL variables

⇒ easily clinically accessible

⇒ RNAseq does not carry predictive value

⇒ discarded
# kML individual predictions

mBSL = minimal baseline
model = random survival forest

## Cross-val on train

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6 months

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12 months

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c-index = 0.79

AUC-12 months = 0.86

## Test set

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24 months

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PDL1 : AUC = 0.65

RoPro: c-index = 0.69 AUC-3 months = 0.817

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Study
starts

<figure><img src="image_33.png" /></figure>

Interim

10 weeks
(2.5 months)

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25 weeks
(6.25 months)

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60 weeks
(15 months)

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Full kinetics
(end of study)

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Hazard ratio

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kML predicts phase 3 trial results
from early data
Mechanistic model

$$ \frac{dL}{dt} = k_L \left(\frac{L_{eq}}{L}\right)^{\gamma_L} + c(d_1S_1 + d_2S_2) + c_0 \mathbf{1}_{S'(t-r)>0} S'(t) - k_L L $$

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$$ \left\{ \begin{aligned} \frac{dP}{dt} &= k_{PROL} \left(1 + n_{INFL} \frac{S}{1+S}\right) P \left(\frac{N_{eq}}{N}\right)^{\gamma_N} - k_{TR} P \\ \frac{dT_i}{dt} &= k_{TR} T_{i-1} - k_{TR} T_i \\ \frac{dN}{dt} &= k_{TR} T_3 - k_{CIRC} N \end{aligned} \right. $$

Data: clinical trials for
atezolizumab (ATZ) :

* 3 phase 2 **monotherapy**
**862 patients**
44,911 data points

* 1 phase 3 **combotherapy**
ATZ, Bevacizumab and
Carboplatin + Paclitaxel
**1115 patients**
67,507 data points
Individual fits (Combotherapy)

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* Fits heterogenous BK profiles

• Albumin early drops

- LDH early peaks

* Late sharp increase of LDH
(death shortly after)

- Identifiability

- All RSEs < 35%

- despite the large number of
parameters (14)

* Very large variability of some
parameters

- Small η - shrinkage < 11%
Post-cycle 4 prediction of individual survival

<figure><img src="image_44.png" /></figure>

Longitudinal
data
considered

10 fold Cross-validation, AUC = accuracy at 12 months

<figure><img src="image_45.png" /></figure>

COMBO
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# Model-based outperforms data-based relative change for prediction

*Compared CV c-indices for C3D1 relative change*

<figure><img src="image_47.png" /></figure>

What is the **added value** of NLME modeling compared to just observed data-based kinetic metrics?

* Model-based metrics clearly have both **better predictive power** and **narrower uncertainty**

* Dynamic modeling allows to **capture the kinetics and correct for intra-individual stochasticity** (noise)

Note: 10-fold cross-validation on FIR, BIRCH and POPLAR (train data set). Only relative change from baseline (at cycle 3 pre-dose) used as model metric. Other model-based parameters ignored here for fair comparison
# Summary

* Kinetic models based on simple, routinely available blood markers have prognostic value

* Could be used as non-invasive surrogate markers of response

* Modeling >> data only

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

* NLME to account for longitudinal data

* ML and data science methods to build multivariable models from a large number of features

* **Minimal signature**: 11 baseline clinical features + TK (1 variable – 2 parameters) + BK (4 variables – 8 parameters)

* **Mechanistic modeling** was shown to improve predictions

* Application to clinical oncology could improve **personalized health care**

* Application to **early prediction of trial outcome** could:

* Save a lot of effort (..... **and money**!)

* Be used to better stratify patients

* Identify signal in early-phase trials
Beyond classical biomarkers:
PIONeeR – QUANTIC and SChISM
# THE PIONEER AND QUANTIC PROJECTS

## Precision Immuno-Oncology for advanced Non-small cell lung cancer patients with PD-1 ICI Resistance

<figure><img src="image_48.png" /></figure>
# 2+ LINE RESULTS (CROSS-VALIDATION ON THE TRAIN SET)

## DATA LOCK 2 - N = 137 OUTCOME - EARLY PROGRESSION

Predictive markers

BUT.... results vanished on the test set !

markers selected by LASSO

<figure><img src="image_49.png" /></figure>

Color code

Circulating blood cells

Tumor biomarker

Vascular origin

Routine blood tests

Soluble immune marker

... partly due to the small number of patients added at data 3 (28)
# OUTCOME DEFINITION
## WHAT ENDPOINT?

<table><thead><tr><td>Outcome</td><td>Time of PD (mths)</td><td>Non-evaluable</td><td>Resistant / Non-resistant (%)</td></tr></thead><tbody><tr><td>Early progression (EP)</td><td>&lt; 3</td><td>18</td><td>111/310 (26/74)</td></tr><tr><td>Primary resistance<fnref n="1" /></td><td>≤ 6</td><td>4</td><td>172/263 (40/60)</td></tr><tr><td>Secondary resistance<fnref n="1" /></td><td>&gt; 6</td><td>272</td><td>58/109 (35/65)</td></tr></tbody></table>

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<figure><img src="image_51.png" /></figure>

**PDL1 does not explain everything**
In PDL1 < 1% patients, 50% of primary resistant

Non-evaluable = patients with not enough follow-up (< 6 weeks) or censored

<footnote n="1">¹2023 SITC guidelines. Tawbi, H. A. et al., Rizvi et al., J. Immunother. Cancer, 2023</footnote>
# PIONEER BIOMARKERS
## NARRATIVE

* 439 patients

* 446 markers

* Outcomes
  Primary resistance (6 mths)
  PFS

Companion
website

Dashboard

<figure><img src="image_52.png" /></figure>

Apparent (full)

<figure><img src="image_53.png" /></figure>

Data VS

<figure><img src="image_54.png" /></figure>

Univariable
Biostats

<figure><img src="image_55.png" /></figure>

Multivariable
Signature

<figure><img src="image_56.png" /></figure>

Predictive
Modeling

<figure><img src="image_57.png" /></figure>

CD4 FOXP3 dens Stroma Tumor (Log10 Cells/mm³)

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

CD4 FOXP3 dens Stroma Tumor (Log10 Cells/mm³)

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>
# CHALLENGES IN PIONEER BM ANALYSIS
## MACHINE LEARNING

<table><thead><tr><td>Difficulties</td><td>Remedies</td></tr></thead><tbody><tr><td>Overfitting</td><td>Optimism-correction</td></tr><tr><td>Many, correlated data</td><td>Feature selection</td></tr><tr><td>A priori complex interactions</td><td>Nonlinear models</td></tr><tr><td>Missing data</td><td>Imputation</td></tr><tr><td>Small n, Large p</td><td>Generative modelling</td></tr></tbody></table>

Tackle these problems one-by-one. Use theory if available, do simulations otherwise.
⇒ Reproducibility and
traceability

Human.months involved for preprocessing only = **28 h.m**
# METHODS ML

## Problem = overfitting in train / test

* Because of $p \sim n$ ($N = 269 + 170 = 439$, $p = 447$)

* Because of **NA imputation** ?

* Because of **feature selection** ?

Collins et al., Evaluation of clinical prediction models (part 1): from development to external validation, BMJ, 2024

> Splitting data at the moment of model development should generally be avoided as it discards data leading to a more unreliable model, whilst leaving too few data to reliably evaluate its performance

* Solution = **drop the train / test approach**

⇒ Define a method (FS + ML)

⇒ Apply to the **entire dataset** (apparent metrics)

⇒ Evaluate and report **optimism** (= overfitting) using bootstraps

CV train

<figure><img src="image_64.png" /></figure>

Test

<figure><img src="image_65.png" /></figure>
# SIGNATURE
## METHODOLOGY

* **Problem = signature instability**

* **Reasons: missing values, correlations and $p \sim N$**

Benchmark of 18 FS methods: all failing 😞

<figure><img src="image_66.png" /></figure>

* Need **more stringent filtering** of variables for the signature

* At the same time, don't discard variables that are strong in but have high NA rate

Best solution = **simple** = use **univariate** stats and **FDR adjustment**

**p adjustable mv < 0.5 PRIM_RES + PFS + AUC > 0.6 + C-index > 0.6**

LASSO

<figure><img src="image_67.png" /></figure>
# PREDICTIVE MODEL

## RESULTS

Apparent (full)

<figure><img src="image_68.png" /></figure>

Optimism-corrected

<figure><img src="image_69.png" /></figure>

<table><thead><tr><th>Classifier</th><th>AUC corr</th><th>AUC bias</th><th>SE corr</th><th>SE bias</th><th>PPV corr</th><th>PPV bias</th></tr></thead><tbody><tr><td>RandomForestClassifier</td><td>0.721</td><td>0.06</td><td>0.669</td><td>0.121</td><td>0.553</td><td>0.035</td></tr><tr><td>HistGradientBoostingClassifier</td><td>0.72</td><td>0.17</td><td>0.557</td><td>0.211</td><td>0.593</td><td>0.193</td></tr><tr><td>ExtraTreesClassifier</td><td>0.718</td><td>0.028</td><td>0.737</td><td>0.007</td><td>0.544</td><td>0.049</td></tr><tr><td>LinearDiscriminantAnalysis</td><td>0.715</td><td>0.055</td><td>0.7</td><td>0.079</td><td>0.536</td><td>0.034</td></tr><tr><td>MLPClassifier</td><td>0.714</td><td>0.154</td><td>0.611</td><td>0.296</td><td>0.564</td><td>0.073</td></tr><tr><td>LogisticRegression</td><td>0.713</td><td>0.057</td><td>0.691</td><td>0.111</td><td>0.533</td><td>0.023</td></tr><tr><td>GradientBoostingClassifier</td><td>0.712</td><td>0.224</td><td>0.538</td><td>0.299</td><td>0.596</td><td>0.246</td></tr><tr><td>ExtraTreesClassifier</td><td>0.709</td><td>0.087</td><td>0.643</td><td>0.14</td><td>0.546</td><td>0.079</td></tr><tr><td>BaggingClassifier</td><td>0.707</td><td>0.293</td><td>0.289</td><td>0.711</td><td>0.68</td><td>0.32</td></tr></tbody></table>

* 14 ML models tested (linear, tree ensembles, neural networks)

* Best = random forest
## AUC AND POSITIVE PREDICTIVE VALUE

<figure><img src="image_70.png" /></figure>

$$F1 = \frac{2}{\frac{1}{SE} + \frac{1}{PPV}}$$

single markers

only potentially actionable markers (5)
# PREDICTIVE MODEL
## PFS STRATIFICATION

Train

<figure><img src="image_71.png" /></figure>

Test

<figure><img src="image_72.png" /></figure>
<figure><img src="image_73.png" /></figure>

cutpoint defined at the proportion of PRIM_RES on the full data set
# LESSONS

* Preprocessing / **data cleaning** is essential but **time consuming**

* **Software development** agile methods are very useful to handle large data science projects

* Feature selection for biomarker studies is a **hard problem**

* ML literature: a lot of methods but datasets are either synthetic or with $p << N$

* Biomedical literature: $p >> N$ (e.g., omics) but **stability** rarely assessed and **reproducibility** is an issue

* Even in large studies, we have $p \sim N$: **big fat data**

* Don't waste data for model development: **drop the train / test** approach

* Use **optimism-correction**

* Validate the model in a subsequent study (ideally performed by another group)

* Results with **brute force** methods were disappointing. Methodological advances are needed

* Feature selection

* NA imputation

* Causal inference

<figure><img src="image_74.png" /></figure>
# SChISM
## Size CfDNA Immunotherapies Signature Monitoring

**APHM: S. Salas, Inria / Inserm: S. Benzekry, L. Nguyen, Adelis**

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /></figure>

Patient 700-13 (Total N = 300)

<figure><img src="image_77.png" /></figure>
Predictive value of baseline cfDNA markers

<figure><img src="image_78.png" /></figure>

Relative concentration of
cfDNA >1650 bp (%)

<figure><img src="image_79.png" /></figure>
<figure><img src="image_80.png" /></figure>

Different processes of cfDNA release

$$ \frac{dS_S}{dt} = -\epsilon_C \cdot 1_C \cdot S_S - \epsilon_I \cdot I \cdot S_S $$

$$ \frac{dS_R}{dt} = K_G \cdot S_R $$

$$ \frac{dD_S}{dt} = \lambda_X \cdot (\epsilon_C \cdot 1_C \cdot S_S + \epsilon_I \cdot I \cdot S_S) + \lambda_S \cdot S - k_D \cdot D_S + k_n \cdot D_L $$

$$ \frac{dD_L}{dt} = \lambda_L \cdot S' \cdot 1_{S'>0} + \lambda_{AS} \cdot S - k_D \cdot D_L - k_n \cdot D_S $$

$$ \frac{dI}{dt} = \beta \cdot D_L \cdot I - k_I \cdot I $$

<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /></figure>

<figure><img src="image_83.png" /></figure>

<figure><img src="image_84.png" /></figure>

<figure><img src="image_85.png" /></figure>

<figure><img src="image_86.png" /></figure>

<figure><img src="image_87.png" /></figure>

Patient
701-34

<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /></figure>

<figure><img src="image_90.png" /></figure>

<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

<figure><img src="image_93.png" /></figure>

Patient
701-1
Roche / Genentech

R. Bruno

P. Chanu

F. Mercier

C. Jamois

<figure><img src="image_94.png" /></figure>

INSTITUT
NATIONAL
DU CANCER

Open
postdoc position

sebastien.benzekry@inria.fr

# Acknowledgments

Thank you for your attention!

<figure><img src="image_95.png" /></figure>

https://team.inria.fr/compo/

CRCM

Centre de Recherche
en Cancérologie de Marseille

<figure><img src="image_96.png" /></figure>

cnrs

<figure><img src="image_97.png" /></figure>

INSTITUT PAOLI-CALMETTES

<figure><img src="image_98.png" /></figure>

unicancer

<figure><img src="image_99.png" /></figure>

Aix*Marseille
université

Instituts
thématiques

<figure><img src="image_100.png" /></figure>

Inserm

Institut national
de la santé et de la recherche médicale

COMPO

COMPUTATIONAL ONCOLOGY

Patients and families

## Computational

A. Bakhmach

C. Bigarre

M. Boussena

P. Dufosse

M. Karlsen

L. Nguyen - Phuong

R. Taieb

A. Vaglio

## Clinical oncology

D. Boulate

J. Ciccolini

L. Greillier

X. Muracciole

S. Salas

F. Barlesi

<figure><img src="image_101.png" /></figure>

Assistance Publique
Hôpitaux de Marseille

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