# Precision Immuno-Oncology in NSCLC: Challenges in machine learning biomarkers research

Sébastien Benzekry

► To cite this version:

Sébastien Benzekry. Precision Immuno-Oncology in NSCLC: Challenges in machine learning biomarkers research. EORTC PAMM 2024 - 44th Meeting of the European Organization of Research and Treatment of Cancer (EORTC) – Pharmacology and Molecular Mechanisms (PAMM) Group, Feb 2024, Marseille, France. hal-04990474

HAL Id: hal-04990474

https://inria.hal.science/hal-04990474v1

Submitted on 13 Mar 2025

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Distributed under a Creative Commons Attribution 4.0 International License
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THE
PIONEER
PROJECT

# Precision Immuno-Oncology in NSCLC

*Challenges in machine learning biomarkers research*

S. Benzekry, Inria – Inserm COMPO, Marseille, PI: F. Barlesi
PIONEER

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THE
PIONEER
PROJECT

# PRECISION IMMUNO-ONCOLOGY FOR ADVANCED NON-SMALL CELL LUNG CANCER PATIENTS WITH PD-1 ICI RESISTANCE

450 pts

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24,287 variables
(w/o NGS)

1,581 markers

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## P = 442 PRE-TREATMENT (VS) BIOMARKERS

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*R: RECIST 1.1 evaluation every 6 weeks

Barles, F. et al. WCLC 2019 (P1.04-30);

Barles, F. et al. ESMO 2020 (LBA53),

Greillier, L. et al., AACR 2022,

Barles, F. et al. ESMO-IO 2022,

Leca et al. SITC 2021,

Ghezali et al. SITC 2022 (p 701)
Data lock #2, N = 200

... Data lock #5, N = 439

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≥ 2^nd^ line, N = 170

anti-PD(L) 1 monotherapy

1^st^ line, N = 269

CT + anti-PD(L) 1 combotherapy

* Preprocess, data cleaning, **monitoring**

* focus on **VS**

* **Statistical analyses** : (single markers)

* EP/LR/PFS/OS

* Large, comprehensive, automated reports available online

* **Integrative analysis/machine learning**

* NA imputation

* Unsupervised learning

* Variable selection

* Supervised learning

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# OUTCOME DEFINITION

2ND+ LINE = EARLY-PROGRESSION, 1ST LINE = PFS

* Progression = RECIST-based progression (≥ 20 % increase of target lesions SLD from nadir) or death

* EP = progression ≤ 3 months

* Similar (but not equivalent) to progression before or at second scan

* There are 79 EP and 91 non-EP

* PFS = time to progression or loss of follow-up

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THE
PIONEER
PROJECT

# DATA ANALYSIS FRAMEWORK
# DATA WRANGLING, CLEANING, PREPROCESSING AND DOCUMENTATION

## Project organised around Epics :

* **Preprocess Data 2**
  * Nov 6, 2021 - Jun 24, 2022 : 8 months
  * 24 issues

* **Preprocess Data 3**
  * May 9 - Nov 30, 2022 : 7 months
  * 74 issues

* **Stats analysis data 3**
  * May 9 - Dec 16, 2022 : 8 months
  * 71 issues

* **Preprocess Data 4**
  * May 5 - Sep, 2023 : 5 months
  * 26 issues

* **Stats Data 4**
  * Jun 5 - Sep, 2023 : 4 months
  * 16 issues

* **Merge requests : 662 Total**

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Human.months involved: **29 h.m**

(MK (6), KA (6), LB (6), CB (3), PD (1), SB (6)) + interns (12)
# AUTOMATIC REPORTING, PACKAGE DEVELOPMENT, CONTINUOUS INTEGRATION / CONTINUOUS DEVELOPMENT

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PIONeeR

Project ID: 44

Leave project

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4,787 Commits

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18 Branches

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31 Tags

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43.1 GB Project Storage

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3 Releases

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## Online website

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Human.months involved: MK (8), KA (2), CB (5), PD (8), SB (8), MB (2) = 28 h.m
Welcome to the PIONEER project insiders' webpage

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COMPUTATIONAL ONCOLOGY

325 first line ORR DCR PFS OS

325 first line PFS

325 first line VS to V2

325 second line ORR DCR PFS OS

325 second line early regressors

325 second line summary of the results

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Statistical analysis of early progressors clinical and biomarkers
data

Second-line patients from data lock 3

© Karthik S. Venkatesh
© Completed on 19 Jan, 2021

1 Description of the data

1.1 Pre-processing

Details about the processing of the variables can be found at the
www.compo.gitlabpages.inria.fr

1.2 Overview of variables

COMPO

Follow the link to our public data website

Some external links

• PIONEER Marrow Immunoanalyse website

Reports

• Slides

• Statistical analysis reports

• Technical reports

• READINGS

correlations

correlations activated T cells

focus_biopsy.html

monitoring RECIST.html

na_exploratory_analysis

preprocess.html

supervised

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Explorations of circulating activated T cells

2 Correlation with intra-tumor lymphocytes

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# THE PIONEER PROJECT: CODE OVERVIEW

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will be released open source
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THE
PIONEER
PROJECT

STATISTICAL ANALYSIS
**EARLY PROGRESSION:** classification (0 or 1)

* Boxplots and t-tests, barplots and $\chi^2$-tests

* Logistic regression and confidence intervals: *univariate (uv)* and *multivariate (mv)*

* AUC

**Progression-free survival (PFS):** survival regression (time-to-event, right-censored data)

* Kaplan-Meier analysis

* Cox regression (uv and mv)

* C-index

**Control variables:** PDL1 (1%), age, histology, ECOG, gender, smoking

- Adjusted p-values to account for multiple hypothesis testing (False Discovery Rate, Benjamini-Hochberg correction)

- variables with more than 85% missing values are disregarded (only one at VS: Serum_IFNg, has only one value)
STATS 2+ LINE: EARLY PROGRESSION

All variables with *univariate* p < 0.05, display is the *multivariate* OR

<table><thead><tr><th>Sample</th><th>Label</th><th>Level</th><th>NA</th><th>N</th><th>Odds ratio</th><th>OR (95% CI)</th></tr></thead><tbody><tr><td></td><td>ECOG</td><td>1</td><td>0.0%</td><td>160</td><td></td><td>3.06 (1.27 - 7.34)</td></tr><tr><td>blood</td><td>M MDSC (% among CD45+ cells)</td><td></td><td>35.5%</td><td>105</td><td></td><td>2.59 (1.02 - 6.55)</td></tr><tr><td>ffpe</td><td>PD-L1 expression (1 (Pos)/0 (Neg))</td><td>&lt;1%</td><td>13.6%</td><td>139</td><td></td><td>2.53 (1.14 - 5.61)</td></tr><tr><td>blood</td><td>CD8 ILT2+ T cells (%) (% among lymphocytes)</td><td></td><td>36.7%</td><td>102</td><td></td><td>2.21 (1.22 - 4.03)</td></tr><tr><td>blood</td><td>PLR (Log10 NA)</td><td>7.7%</td><td>149</td><td></td><td></td><td>2.17 (1.34 - 3.52)</td></tr><tr><td>blood</td><td>IgA+ B cells (%) (% among CD19+ cells)</td><td>33.1%</td><td>108</td><td></td><td></td><td>2.13 (0.934 - 4.86)</td></tr><tr><td>blood</td><td>Cytotoxic T cells (%) (% among lymphocytes)</td><td>13.6%</td><td>139</td><td></td><td></td><td>2.09 (1.33 - 3.28)</td></tr><tr><td>blood</td><td>Activated T cells (%) (% among lymphocytes)</td><td>13.6%</td><td>139</td><td></td><td></td><td>1.99 (1.24 - 3.17)</td></tr><tr><td>blood</td><td>Alkaline phosphatase (Log10 IU/L)</td><td>9.5%</td><td>147</td><td></td><td></td><td>1.89 (1.17 - 3.05)</td></tr><tr><td>blood</td><td>Number of metastases at baseline</td><td>1.8%</td><td>158</td><td></td><td></td><td>1.85 (1.2 - 2.84)</td></tr><tr><td>blood</td><td>NLR (Log10 NA)</td><td></td><td>3.6%</td><td>156</td><td></td><td>1.82 (1.1 - 3.01)</td></tr><tr><td>blood</td><td>PD-L1+ inflammatory monocytes (%) (among inflammatory monocytes)</td><td>39.6%</td><td>98</td><td></td><td></td><td>1.77 (0.976 - 3.21)</td></tr><tr><td>blood</td><td>C reactive protein (Log10 MG/L)</td><td>25.4%</td><td>120</td><td></td><td></td><td>1.71 (1.02 - 2.86)</td></tr><tr><td>blood</td><td>Platelets (10^9/L)</td><td>7.7%</td><td>149</td><td></td><td></td><td>1.69 (1.09 - 2.64)</td></tr><tr><td>blood</td><td>Committed B cells (%) (% among CD19+ cells)</td><td>33.1%</td><td>108</td><td></td><td></td><td>1.47 (0.859 - 2.51)</td></tr><tr><td>blood</td><td>T cells (%) (% among lymphocytes)</td><td>13.6%</td><td>139</td><td></td><td></td><td>1.42 (0.919 - 2.2)</td></tr><tr><td>ffpe</td><td>MDSC Neutrophilia Stroma Tumor (Log10 Cells/mm³)</td><td>23.1%</td><td>126</td><td></td><td></td><td>1.37 (0.907 - 2.06)</td></tr><tr><td>ffpe</td><td>ISIC CD8 prox40 tumor (Log10 Cells/mm³)</td><td>16.6%</td><td>137</td><td></td><td></td><td>0.904 (0.592 - 1.38)</td></tr><tr><td>ffpe</td><td>ISIC PDL1 prox40 tumor (Log10 Cells/mm³)</td><td>16.6%</td><td>137</td><td></td><td></td><td>0.878 (0.569 - 1.36)</td></tr><tr><td>ffpe</td><td>IDI Parench Tumor (Log10 Cells/mm³)</td><td>19.5%</td><td>132</td><td></td><td></td><td>0.812 (0.545 - 1.21)</td></tr><tr><td>blood</td><td>NKG2D+ NK cells (Log10 Cells/mm³)</td><td>34.3%</td><td>107</td><td></td><td></td><td>0.757 (0.448 - 1.28)</td></tr><tr><td>ffpe</td><td>Quantification perc PD L1 per/50 (1 (Pos)/0 (Neg))</td><td>1</td><td>13.6%</td><td>139</td><td></td><td>0.731 (0.203 - 2.63)</td></tr><tr><td>blood</td><td>Quantification perc PD L1 SG (%)</td><td></td><td>13.6%</td><td>139</td><td></td><td>0.715 (0.407 - 1.25)</td></tr><tr><td>ffpe</td><td>Chlorine (MMOL/L)</td><td></td><td>17.2%</td><td>134</td><td></td><td>0.707 (0.449 - 1.11)</td></tr><tr><td>blood</td><td>CD4 FOXP3 dens Stroma Tumor (Log10 Cells/mm³)</td><td>23.7%</td><td>125</td><td></td><td></td><td>0.665 (0.441 - 1)</td></tr><tr><td>blood</td><td>KLGR1+ NK cells (Log10 Cells/mm³)</td><td>30.8%</td><td>113</td><td></td><td></td><td>0.661 (0.395 - 1.1)</td></tr><tr><td>blood</td><td>ILT2+ NK cells (Log10 Cells/mm³)</td><td>30.8%</td><td>113</td><td></td><td></td><td>0.602 (0.361 - 1)</td></tr><tr><td>blood</td><td>NK cells (Log10 Cells/mm³)</td><td>13.6%</td><td>139</td><td></td><td></td><td>0.595 (0.382 - 0.926)</td></tr><tr><td>blood</td><td>Hemoglobin (%)</td><td>4.1%</td><td>155</td><td></td><td></td><td>0.589 (0.382 - 0.908)</td></tr><tr><td>blood</td><td>NKG2A+ NK cells (Log10 Cells/mm³)</td><td>31.4%</td><td>112</td><td></td><td></td><td>0.583 (0.343 - 0.99)</td></tr><tr><td>blood</td><td>Albumin (G/L)</td><td>14.8%</td><td>138</td><td></td><td></td><td>0.583 (0.375 - 0.907)</td></tr><tr><td>blood</td><td>Sodium (MMOL/L)</td><td>4.7%</td><td>155</td><td></td><td></td><td>0.578 (0.376 - 0.888)</td></tr><tr><td>blood</td><td>Hemoglobin (G/L)</td><td>5.9%</td><td>153</td><td></td><td></td><td>0.54 (0.341 - 0.854)</td></tr></tbody><tfoot><tr><td colspan="7">Non-early prod. Early prod.</td></tr></tfoot></table>

* **Control variables**

* **Clinical markers**

* Inflammation (NLR, PLR)

* Hemoglobin

* **Blood immune cells**

* Activated T cells (CD45+ CD3+ HLA-DR+) in cells/mm³

* NK cells, NKG2D+ NK cells, NKG2A+ NK cells, KLRG1+ NK cells in cells/mm³

* cytotoxic T cells, CD8 NKG2A+ T cells in %

* M MDSC in %

* **Tumor**

* PDL1

* ID1 (CD3+) and ID7 (CD3+CD8+LAG3+) parench tumor

* CD4 FOXP3 stroma
# STATS 2+ LINE: DIFFERENCE EARLY PROGRESSION VS PRIMARY RESISTANCE

## EARLY PROG

<table><thead><tr><th>Sample</th><th>Label</th><th>Level</th><th>NA</th><th>N</th><th>Odds ratio</th><th>OR (95% CI)</th></tr></thead><tbody><tr><td></td><td>ECOG</td><td>1</td><td>0.0%</td><td>160</td><td></td><td>3.06 (1.27 - 7.34)</td></tr><tr><td>blood</td><td>M MDSC (% among CD45+ cells)</td><td></td><td>35.5%</td><td>105</td><td></td><td>2.59 (1.02 - 6.55)</td></tr><tr><td>flpe</td><td>PD-L1 expression (1 (Pos) / 0 (Neg))</td><td>&lt;1%</td><td>13.6%</td><td>139</td><td></td><td>2.53 (1.14 - 5.61)</td></tr><tr><td>blood</td><td>CD11b I/II+ T cells (%) (% among lymphocytes)</td><td></td><td>36.7%</td><td>102</td><td></td><td>2.21 (1.22 - 4.03)</td></tr><tr><td>blood</td><td>PLR (Log10 NA)</td><td>7.7%</td><td>149</td><td>149</td><td></td><td>2.17 (1.34 - 3.52)</td></tr><tr><td>blood</td><td>IgA+ B cells (%) (% among CD19+ cells)</td><td></td><td>33.1%</td><td>108</td><td></td><td>2.13 (0.934 - 4.86)</td></tr><tr><td>blood</td><td>Cytotoxic T cells (%) (% among lymphocytes)</td><td></td><td>13.6%</td><td>139</td><td></td><td>2.09 (1.33 - 3.28)</td></tr><tr><td>blood</td><td>Activated T cells (%) (% among lymphocytes)</td><td></td><td>13.6%</td><td>139</td><td></td><td>1.99 (1.24 - 3.17)</td></tr><tr><td>blood</td><td>Alkaline phosphatase (Log10 IU/L)</td><td></td><td>9.5%</td><td>147</td><td></td><td>1.89 (1.17 - 3.05)</td></tr><tr><td>blood</td><td>Number of metastases at baseline</td><td></td><td>1.8%</td><td>158</td><td></td><td>1.85 (1.2 - 2.84)</td></tr><tr><td>blood</td><td>NLR (Log10 NA)</td><td></td><td>3.6%</td><td>156</td><td></td><td>1.82 (1.1 - 3.01)</td></tr><tr><td>blood</td><td>PD-L1+ inflammatory monocytes (% among inflammatory monocytes)</td><td></td><td>39.6%</td><td>98</td><td></td><td>1.77 (0.976 - 3.21)</td></tr><tr><td>blood</td><td>C reactive protein (Log10 MGL)</td><td></td><td>25.4%</td><td>120</td><td></td><td>1.71 (1.02 - 2.86)</td></tr><tr><td>blood</td><td>Platelets (10^9/L)</td><td></td><td>7.7%</td><td>149</td><td></td><td>1.69 (1.09 - 2.64)</td></tr><tr><td>blood</td><td>Committed B cells (%) (% among CD19+ cells)</td><td></td><td>33.1%</td><td>108</td><td></td><td>1.47 (0.859 - 2.51)</td></tr><tr><td>blood</td><td>T cells (%) (% among lymphocytes)</td><td></td><td>13.6%</td><td>139</td><td></td><td>1.42 (0.919 - 2.2)</td></tr><tr><td>flpe</td><td>MDSC Neutrophilis Stroma Tumor (Log10 Cells/mm³)</td><td></td><td>23.1%</td><td>126</td><td></td><td>1.37 (0.907 - 2.06)</td></tr><tr><td>flpe</td><td>ISIC CD8 prox40 tumor (Log10 Cells/mm³)</td><td></td><td>16.6%</td><td>137</td><td></td><td>0.904 (0.592 - 1.38)</td></tr><tr><td>flpe</td><td>ISIC PDL1 prox40 tumor (Log10 Cells/mm³)</td><td></td><td>16.6%</td><td>137</td><td></td><td>0.878 (0.569 - 1.36)</td></tr><tr><td>flpe</td><td>IDI Parench Tumor (Log10 Cells/mm³)</td><td></td><td>19.4%</td><td>132</td><td></td><td>0.812 (0.545 - 1.21)</td></tr><tr><td>blood</td><td>NKG2D+ NK cells (Log10 Cells/mm³)</td><td></td><td>34.3%</td><td>107</td><td></td><td>0.757 (0.448 - 1.28)</td></tr><tr><td>flpe</td><td>Quantification perc: PD L1 prox.1 (1 (Pos) / 0 (Neg))</td><td>1</td><td>13.6%</td><td>139</td><td></td><td>0.731 (0.203 - 2.63)</td></tr><tr><td>flpe</td><td>Quantification perc PD L1 SG (%)</td><td></td><td>13.6%</td><td>139</td><td></td><td>0.715 (0.407 - 1.25)</td></tr><tr><td>flpe</td><td>Chlorine (MMOLL)</td><td></td><td>17.2%</td><td>134</td><td></td><td>0.707 (0.449 - 1.11)</td></tr><tr><td>flpe</td><td>C4D FOXP3+ Stroma Tumor (Log10 Cells/mm³)</td><td></td><td>23.7%</td><td>125</td><td></td><td>0.665 (0.441 - 1.01)</td></tr><tr><td>blood</td><td>KLRG1+ NK cells (Log10 Cells/mm³)</td><td></td><td>30.8%</td><td>113</td><td></td><td>0.661 (0.395 - 1.1)</td></tr><tr><td>blood</td><td>ILT2+ NK cells (Log10 Cells/mm³)</td><td></td><td>30.8%</td><td>113</td><td></td><td>0.602 (0.361 - 1)</td></tr><tr><td>blood</td><td>NK cells (Log10 Cells/mm³)</td><td></td><td>13.6%</td><td>139</td><td></td><td>0.595 (0.382 - 0.926)</td></tr><tr><td>blood</td><td>Hematoxin (%)</td><td></td><td>4.1%</td><td>155</td><td></td><td>0.589 (0.382 - 0.908)</td></tr><tr><td>blood</td><td>NKG2A+ NK cells (Log10 Cells/mm³)</td><td></td><td>31.4%</td><td>112</td><td></td><td>0.583 (0.343 - 0.99)</td></tr><tr><td>blood</td><td>Albumin (GLL)</td><td></td><td>14.8%</td><td>138</td><td></td><td>0.583 (0.375 - 0.907)</td></tr><tr><td>blood</td><td>Sodium (MMOLL)</td><td></td><td>4.7%</td><td>155</td><td></td><td>0.578 (0.376 - 0.888)</td></tr><tr><td>blood</td><td>Hemoglobin (GLL)</td><td></td><td>5.9%</td><td>153</td><td></td><td>0.54 (0.341 - 0.854)</td></tr></tbody></table>

## PRIMARY RESISTANCE

<table><thead><tr><th>Sample</th><th>Label</th><th>Level</th><th>NA</th><th>N</th><th>Odds ratio</th><th>OR (95% CI)</th></tr></thead><tbody><tr><td>blood</td><td>Non classical monocytes (%) among non classical monocytes</td><td>19%</td><td>137</td><td>137</td><td></td><td>2.89 (1.924 - 3.78)</td></tr><tr><td>blood</td><td>gamma/delta T cells (Log10 Cells/mm³)</td><td>37.1%</td><td>104</td><td>105</td><td></td><td>1.94 (1.09 - 3.44)</td></tr><tr><td>blood</td><td>CD3 ILT2+ T cells (Log10 Cells/mm³)</td><td>35.9%</td><td>105</td><td>104</td><td></td><td>1.92 (0.994 - 3.71)</td></tr><tr><td>blood</td><td>KLRG1+ gamma/delta T cells (Log10 Cells/mm³)</td><td>36.5%</td><td>104</td><td>104</td><td></td><td>1.89 (0.986 - 3.63)</td></tr><tr><td>blood</td><td>Activated T cells (Log10 Cells/mm³)</td><td>13.9%</td><td>143</td><td>143</td><td></td><td>1.82 (1.16 - 2.84)</td></tr><tr><td>blood</td><td>CD39+ gamma/delta T cells (Log10 Cells/mm³)</td><td>36.5%</td><td>104</td><td>104</td><td></td><td>1.78 (0.998 - 3.16)</td></tr><tr><td>flpe</td><td>PD-L1 expression 1% (1 (Pos) / 0 (Neg))</td><td>&lt;1%</td><td>13.5%</td><td>143</td><td></td><td>1.75 (0.799 - 3.82)</td></tr><tr><td>flpe</td><td>ISIC CD8 d prox20 tumor (Log10 Cells/mm³)</td><td>17.1%</td><td>137</td><td>137</td><td></td><td>0.849 (0.55 - 1.31)</td></tr><tr><td>flpe</td><td>ISIC PDL1 d prox20 tumor (Log10 Cells/mm³)</td><td>17.1%</td><td>137</td><td>137</td><td></td><td>0.839 (0.542 - 1.3)</td></tr><tr><td>flpe</td><td>ISIC CD8 prox40 tumor (Log10 Cells/mm³)</td><td>17.1%</td><td>137</td><td>137</td><td></td><td>0.831 (0.546 - 1.26)</td></tr><tr><td>flpe</td><td>ISIC PDL1 prox40 tumor (Log10 Cells/mm³)</td><td>17.1%</td><td>137</td><td>137</td><td></td><td>0.809 (0.526 - 1.25)</td></tr><tr><td>flpe</td><td>IDI Tumor (Log10 Cells/mm³)</td><td>20.9%</td><td>132</td><td>132</td><td></td><td>0.762 (0.503 - 1.15)</td></tr><tr><td>flpe</td><td>Quantification perc PD L1 SG (%)</td><td>13.5%</td><td>143</td><td>143</td><td></td><td>0.757 (0.452 - 1.27)</td></tr><tr><td>flpe</td><td>IDI Parench Tumor (Log10 Cells/mm³)</td><td>20.9%</td><td>132</td><td>132</td><td></td><td>0.733 (0.448 - 1.1)</td></tr><tr><td>flpe</td><td>MDSC M MDSC Tumor (Log10 Cells/mm³)</td><td>22.9%</td><td>127</td><td>127</td><td></td><td>0.673 (0.445 - 1.02)</td></tr><tr><td>flpe</td><td>C4D FOXP3+ Stroma Tumor (Log10 Cells/mm³)</td><td>24.1%</td><td>125</td><td>125</td><td></td><td>0.393 (0.392 - 0.895)</td></tr><tr><td>flpe</td><td>PD-L1+ cells den. in Tumor (Log10 Cells/mm³)</td><td>17.1%</td><td>137</td><td>137</td><td></td><td>0.59 (0.357 - 0.975)</td></tr></tbody><tfoot><tr><td colspan="7">Non-primary resistant Primary resistant</td></tr></tfoot></table>

only 4 variables in both (uv):
"CD4 FOXP3 dens Stroma Tumor"
"MDSC M MDSC Tumor"
"Quantification perc PD L1 SG"
"Activated T cells"
"ISIC PDL1 prox40 tumor"
"PD-L1 expression 1%"

All variables with *univariate* *p* < 0.05, display is the *multivariate* OR
# STATS 1ST LINE: PFS, POSITIVE IMPACT

<table><thead><tr><th>Sample</th><th>Marker</th><th>Ref. Lvl</th><th>NA</th><th>N</th><th>Hazard ratio</th><th>HR (95% CI)</th></tr></thead><tbody><tr><td>Blood</td><td>PD-L1+ CD14Low CD16+ monocytosis</td><td></td><td>18.8%</td><td>221</td><td>+</td><td>0.869 (0.687 - 1.1)</td></tr><tr><td>Tumor</td><td>Isig 15g</td><td></td><td>34.6%</td><td>178</td><td>+</td><td>0.858 (0.693 - 1.06)</td></tr><tr><td>Tumor</td><td>ID2 Parenchym Tumor</td><td></td><td>28.3%</td><td>195</td><td>+</td><td>0.844 (0.675 - 1.06)</td></tr><tr><td>Tumor</td><td>ID2 Tumor</td><td></td><td>28.3%</td><td>195</td><td>+</td><td>0.838 (0.666 - 1.05)</td></tr><tr><td>Blood</td><td>CD86+ B cells (%)</td><td></td><td>14.3%</td><td>233</td><td>+</td><td>0.833 (0.666 - 1.04)</td></tr><tr><td>Tumor</td><td>ID1 Tumor</td><td></td><td>28.3%</td><td>195</td><td>+</td><td>0.827 (0.659 - 1.04)</td></tr><tr><td>Tumor</td><td>ISig 21g</td><td></td><td>34.6%</td><td>178</td><td>+</td><td>0.822 (0.659 - 1.03)</td></tr><tr><td>Tumor</td><td>ID8 Tumor</td><td></td><td>28.3%</td><td>195</td><td>+</td><td>0.796 (0.578 - 1.1)</td></tr><tr><td>Blood</td><td>LWR</td><td></td><td>4.8%</td><td>259</td><td>+</td><td>0.778 (0.636 - 0.952)</td></tr><tr><td>Blood</td><td>Hemoglobin</td><td></td><td>3.7%</td><td>262</td><td>+</td><td>0.769 (0.653 - 0.906)</td></tr><tr><td>Blood</td><td>Sodium</td><td></td><td>6.2%</td><td>255</td><td>+</td><td>0.756 (0.624 - 0.916)</td></tr><tr><td>Tumor</td><td>Quantification perc PD L1 SG</td><td></td><td>27.2%</td><td>198</td><td>+</td><td>0.756 (0.558 - 1.02)</td></tr><tr><td>Tumor</td><td>MDSC TIM Parenchym Tumor</td><td></td><td>29.0%</td><td>193</td><td>+</td><td>0.752 (0.582 - 0.972)</td></tr><tr><td>Blood</td><td>CD86+ B cells</td><td></td><td>14.3%</td><td>233</td><td>+</td><td>0.742 (0.581 - 0.949)</td></tr><tr><td>Tumor</td><td>MDSC M MDSC Parenchym Tumor</td><td></td><td>29.0%</td><td>193</td><td>+</td><td>0.737 (0.571 - 0.951)</td></tr><tr><td>Tumor</td><td>ISIC CD8low 2.0 tumor</td><td></td><td>24.6%</td><td>205</td><td>+</td><td>0.73 (0.55 - 0.97)</td></tr><tr><td>Tumor</td><td>MDSC TIM Tumor</td><td></td><td>29.0%</td><td>193</td><td>+</td><td>0.717 (0.548 - 0.939)</td></tr><tr><td>Blood</td><td>Prothrombin ratio</td><td></td><td>20.6%</td><td>216</td><td>+</td><td>0.712 (0.568 - 0.991)</td></tr><tr><td>Tumor</td><td>MDSC TIM Stromal Tumor</td><td></td><td>29.0%</td><td>193</td><td>+</td><td>0.709 (0.544 - 0.923)</td></tr><tr><td>Blood</td><td>Red cells</td><td></td><td>7.7%</td><td>251</td><td>+</td><td>0.705 (0.58 - 0.857)</td></tr><tr><td>Tumor</td><td>ISIC CD8 dms Tumor</td><td></td><td>24.6%</td><td>205</td><td>+</td><td>0.703 (0.525 - 0.943)</td></tr><tr><td>Blood</td><td>Albumin</td><td></td><td>17.6%</td><td>224</td><td>+</td><td>0.698 (0.563 - 0.865)</td></tr><tr><td>Blood</td><td>Hematocrit</td><td></td><td>4.0%</td><td>261</td><td>+</td><td>0.695 (0.572 - 0.843)</td></tr><tr><td>Tumor</td><td>PD-L1+ cell density in Tumor</td><td></td><td>24.6%</td><td>205</td><td>+</td><td>0.681 (0.495 - 0.943)</td></tr><tr><td>Tumor</td><td>ISIC CD8 pms40 tumor</td><td></td><td>24.6%</td><td>205</td><td>+</td><td>0.669 (0.474 - 0.943)</td></tr><tr><td>Tumor</td><td>ISIC CD8 pms20 tumor</td><td></td><td>24.6%</td><td>205</td><td>+</td><td>0.645 (0.444 - 0.936)</td></tr><tr><td>Tumor</td><td>MDSC M MDSC Tumor</td><td></td><td>29.0%</td><td>193</td><td>+</td><td>0.644 (0.484 - 0.858)</td></tr><tr><td>Tumor</td><td>VS CD16</td><td></td><td>62.5%</td><td>102</td><td>+</td><td>0.644 (0.44 - 0.942)</td></tr><tr><td>Tumor</td><td>ISIC PDL1 d.pms20 tumor</td><td></td><td>24.6%</td><td>205</td><td>+</td><td>0.635 (0.435 - 0.928)</td></tr><tr><td>Blood</td><td>Chlorine</td><td></td><td>15.8%</td><td>229</td><td>+</td><td>0.629 (0.513 - 0.772)</td></tr><tr><td>Tumor</td><td>ISIC PDL1 pms40 tumor</td><td></td><td>24.6%</td><td>205</td><td>+</td><td>0.628 (0.437 - 0.903)</td></tr><tr><td>Tumor</td><td>Presence of mutations on TP53</td><td>1</td><td>23.9%</td><td>207</td><td>+</td><td>0.608 (0.408 - 0.908)</td></tr><tr><td>Tumor</td><td>MDSC M MDSC Stromal Tumor</td><td></td><td>29.0%</td><td>193</td><td>+</td><td>0.6 (0.449 - 0.801)</td></tr><tr><td>Tumor</td><td>CD45+ CD16+ tumor</td><td></td><td>77.9%</td><td>60</td><td>+</td><td>0.563 (0.342 - 0.924)</td></tr><tr><td>Tumor</td><td>Quantification perc PD L1 perc50</td><td>1</td><td>18.8%</td><td>221</td><td>+</td><td>0.543 (0.283 - 1.04)</td></tr><tr><td>Other</td><td>Stroma density TIL-Beam</td><td></td><td>80.1%</td><td>54</td><td>+</td><td>0.486 (0.281 - 0.839)</td></tr><tr><td>Tumor</td><td>CD45+ CD16+ stroma</td><td></td><td>77.6%</td><td>61</td><td>+</td><td>0.473 (0.241 - 0.93)</td></tr></tbody></table>

## Multiplex IHC +++

* Co-localization of PDL1+ cells to lymphocytes

* ID1 (CD3+), ID2 (CD3+CD8+), ID8 (TIM3+, LAG3+)

* CD8+ far from PDL1+

* M MDSC

* CD16

* CD45+ CD16+ tumor and stroma

## Blood immune cells --
# STATS 1ST LINE: PFS, NEGATIVE IMPACT

## ⑩ Mutations (detrimental):

ANTI-CYTO (1 pt), CTNNB1 (5), KEAP1 (13), STK11 (19)

## ⑩ Blood tests

* Inflammation: CRP, PLR, NLR

* LDH

* Others: alkaline phosphatase, ferritin,...

* Histology

## ⑩ Tumor (Innate / VCT)

* NKp46 NKG2A PD1

## ⑩ Blood immune cells

* Activated T cells

* no NK

* CD8 and CD4 PD1+ cells

* Non classical PDL1+ and inflammatory monocytes

* Dendritic cells

<table><thead><tr><th>Sample</th><th>Marker</th><th>Ref. Level</th><th>NA</th><th>N</th><th>Hazard ratio</th><th>HR (95% CI)</th></tr></thead><tbody><tr><td>Tumor</td><td>Presence of mutations on ANTI-CYTOKERATIN1</td><td>43.4%</td><td>154</td><td></td><td></td><td>32.8 (2.67 - 404)</td></tr><tr><td>Blood</td><td>PLR</td><td>5.1%</td><td>258</td><td></td><td></td><td>3.47 (1.68 - 7.14)</td></tr><tr><td>Tumor</td><td>Presence of mutations on CTNNB1</td><td>23.9%</td><td>207</td><td></td><td></td><td>2.98 (1.38 - 6.43)</td></tr><tr><td>Tumor</td><td>ISIC Three Groups</td><td>Low</td><td>24.6%</td><td>205</td><td></td><td>2.06 (1.1 - 3.86)</td></tr><tr><td>Tumor</td><td>PD-L1 expression</td><td>&lt;1%</td><td>18.8%</td><td>221</td><td></td><td>1.82 (1.24 - 2.66)</td></tr><tr><td>Blood</td><td>C-reactive protein</td><td>3.7%</td><td>212</td><td></td><td></td><td>1.67 (1.39 - 2.01)</td></tr><tr><td>Blood</td><td>NLR</td><td>3.7%</td><td>262</td><td></td><td></td><td>1.62 (0.953 - 2.75)</td></tr><tr><td>Tumor</td><td>IS21 Two Groups</td><td>Low</td><td>34.6%</td><td>178</td><td></td><td>1.6 (1.03 - 2.49)</td></tr><tr><td>Tumor</td><td>ISIC Three Groups</td><td>Int</td><td>24.6%</td><td>205</td><td></td><td>1.59 (0.899 - 2.8)</td></tr><tr><td>Tumor</td><td>Gender</td><td>M</td><td>0.0%</td><td>272</td><td></td><td>1.57 (1.04 - 2.36)</td></tr><tr><td>Tumor</td><td>Presence of mutations on KEAP1</td><td>1</td><td>23.9%</td><td>207</td><td></td><td>1.49 (0.926 - 2.41)</td></tr><tr><td>Tumor</td><td>Presence of mutations on STK11</td><td>1</td><td>23.9%</td><td>207</td><td></td><td>1.45 (0.916 - 2.29)</td></tr><tr><td>Blood</td><td>LDH</td><td>30.5%</td><td>189</td><td></td><td></td><td>1.42 (1.14 - 1.78)</td></tr><tr><td>Tumor</td><td>ISIC Two Groups</td><td>Low</td><td>24.6%</td><td>205</td><td></td><td>1.42 (0.936 - 2.15)</td></tr><tr><td>Blood</td><td>Ferritin</td><td>50.4%</td><td>135</td><td></td><td></td><td>1.41 (1.1 - 1.82)</td></tr><tr><td>Tumor</td><td>V5 NKp46 NKG2A PD1</td><td>62.5%</td><td>102</td><td></td><td></td><td>1.39 (1.06 - 1.81)</td></tr><tr><td>Blood</td><td>Platelets</td><td>4.0%</td><td>261</td><td></td><td></td><td>1.39 (1.16 - 1.65)</td></tr><tr><td>Blood</td><td>Alkaline phosphatase</td><td>13.2%</td><td>236</td><td></td><td></td><td>1.37 (1.21 - 1.56)</td></tr><tr><td>Blood</td><td>ANG2</td><td>64.7%</td><td>98</td><td></td><td></td><td>1.36 (1.04 - 1.78)</td></tr><tr><td>Blood</td><td>Histology</td><td>Squamous</td><td>0.0%</td><td>272</td><td></td><td>1.35 (0.808 - 2.25)</td></tr><tr><td>Tumor</td><td>CD45, CD39+ stroma</td><td>77.6%</td><td>61</td><td></td><td></td><td>1.26 (0.958 - 1.67)</td></tr><tr><td>Blood</td><td>PD-L1+ non classical monocytes</td><td>19.9%</td><td>218</td><td></td><td></td><td>1.26 (1.07 - 1.49)</td></tr><tr><td>Blood</td><td>Non classical monocytes</td><td>13.2%</td><td>236</td><td></td><td></td><td>1.24 (1.09 - 1.41)</td></tr><tr><td>Blood</td><td>Inflammatory monocytes</td><td>13.2%</td><td>236</td><td></td><td></td><td>1.24 (1.08 - 1.42)</td></tr><tr><td>Blood</td><td>PMN MDSC</td><td>13.6%</td><td>235</td><td></td><td></td><td>1.23 (1.08 - 1.41)</td></tr><tr><td>Blood</td><td>DCendritic cells</td><td>13.2%</td><td>236</td><td></td><td></td><td>1.23 (1.07 - 1.41)</td></tr><tr><td>Blood</td><td>mMDSC</td><td>13.2%</td><td>236</td><td></td><td></td><td>1.23 (1.07 - 1.41)</td></tr><tr><td>Blood</td><td>CD4+Low CD16+ monocytes</td><td>13.2%</td><td>236</td><td></td><td></td><td>1.22 (1.05 - 1.42)</td></tr><tr><td>Blood</td><td>CD8 PD1+ T cells (%)</td><td>11.8%</td><td>240</td><td></td><td></td><td>1.21 (1.02 - 1.42)</td></tr><tr><td>Blood</td><td>ASAT</td><td>5.9%</td><td>256</td><td></td><td></td><td>1.2 (1.05 - 1.38)</td></tr><tr><td>Blood</td><td>CD4 PD1+ T cells</td><td>12.1%</td><td>239</td><td></td><td></td><td>1.18 (1.1 - 1.4)</td></tr><tr><td>Blood</td><td>Monocytes</td><td>4.4%</td><td>260</td><td></td><td></td><td>1.15 (1.03 - 1.28)</td></tr><tr><td>Blood</td><td>Activated T cells (%)</td><td>9.6%</td><td>246</td><td></td><td></td><td>1.1 (0.92 - 1.31)</td></tr><tr><td>Blood</td><td>CD8 ILT2+ T cells (%)</td><td>14.3%</td><td>233</td><td></td><td></td><td>1.07 (0.877 - 1.31)</td></tr></tbody></table>

<figure><img src="image_33.png" /></figure>
## DIFFERENCE BETWEEN PFS AND PRIM_RES (PUV)

### PFS - PRIM_RES

<table><tr><td>Heart frequency (bpm)</td></tr><tr><td>Marginal zone-like B cells (%)</td></tr><tr><td>Marginal zone-like B cells</td></tr><tr><td>C5aR+ CD14Low CD16- monocytes</td></tr><tr><td>NK cells</td></tr><tr><td>NKp80+ NK cells</td></tr><tr><td>KLRG1+ NK cells</td></tr><tr><td>ILT2+ NK cells</td></tr></table>

<table><tr><td>Total CD8+ CD45RO+ T cells (%)</td></tr><tr><td>CD3 CD73+ T cells (%)</td></tr><tr><td>CD4 PD1+ T cells (%)</td></tr><tr><td>CD8 CD73+ T cells (%)</td></tr><tr><td>CD3 PD1+ T cells</td></tr><tr><td>CD8 PD1+ T cells</td></tr><tr><td>LDH</td></tr><tr><td>dNLR</td></tr></table>

### PFS - PRIM_RES

<table><tr><td>Gender</td></tr><tr><td>CD86+ B cells (%)</td></tr><tr><td>CD86+ B cells</td></tr><tr><td>Non classical monocytes</td></tr><tr><td>PD-L1+ CD14Low CD16- monocytes</td></tr><tr><td>PD-L1+ non classical monocytes</td></tr><tr><td>CD8 ILT2+ T cells (%)</td></tr><tr><td>CD8 NKG2A+ T cells (%)</td></tr><tr><td>Quantification perc PDL1 perc50</td></tr></table>

<table><tr><td>ISign 21g</td></tr><tr><td>Isign 15g</td></tr><tr><td>MDSC TIM Tumor</td></tr><tr><td>MDSC M MDSC Tumor</td></tr><tr><td>MDSC TIM Parench Tumor</td></tr><tr><td>MDSC M MDSC Parench Tumor</td></tr><tr><td>MDSC TIM Stroma Tumor</td></tr><tr><td>MDSC M MDSC Stroma Tumor</td></tr><tr><td>ID2 Tumor</td></tr><tr><td>ID3 Tumor</td></tr></table>

<table><tr><td>ID4 Tumor</td></tr><tr><td>ID5 Tumor</td></tr><tr><td>ID6 Tumor</td></tr><tr><td>ID7 Tumor</td></tr><tr><td>ID9 Tumor</td></tr><tr><td>ID1 Parench Tumor</td></tr><tr><td>ID3 Parench Tumor</td></tr><tr><td>ID4 Parench Tumor</td></tr><tr><td>ID3 Stroma Tumor</td></tr><tr><td>ID4 Stroma Tumor</td></tr><tr><td>ID6 Stroma Tumor</td></tr><tr><td>ISIC PDL1 d.prox20 tumor</td></tr><tr><td>ISIC PDL1 prox40 tumor</td></tr></table>

<table><tr><td>ISIC CD8 dens Tumor</td></tr><tr><td>ISIC CD8 d.prox20 tumor</td></tr><tr><td>ISIC CD8free d.20 tumor</td></tr><tr><td>ISIC CD8 prox40 tumor</td></tr><tr><td>ISIC Two Groups</td></tr><tr><td>ID10 Stroma Tumor</td></tr><tr><td>IS21 Two Groups</td></tr><tr><td>Monocytes</td></tr><tr><td>MLR</td></tr><tr><td>Presence of mutations on TP53</td></tr><tr><td>Presence of mutations on STK11</td></tr><tr><td>Presence of mutations on ANTI-CYTOKERATINE</td></tr><tr><td>Presence of mutations on KEAP1</td></tr></table>
<figure><img src="image_34.png" /></figure>

THE
PIONEER
PROJECT

MACHINE LEARNING
# ML WORKFLOW

<figure><img src="image_35.png" /></figure>

<table><thead><tr><td>Model</td><td>Accuracy</td><td>ROC AUC</td><td>PPV</td></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></tr><tr><td>Logistic Regression</td><td>0.67 ± 0.04</td><td>0.73 ± 0.03</td><td>0.69 ± 0.08</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></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></tr><tr><td>Linear SVM</td><td>0.58 ± 0.09</td><td>0.73 ± 0.03</td><td>0.72 ± 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></tr></tbody></table>

<figure><img src="image_36.png" /></figure>

NA imputation

Feature selection

Machine Learning

Cross-validation

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

External validation
# CHALLENGES IN PIONEER ANALYSIS

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

Tackle these problems one-by-one. Use theory if available, do simulations otherwise.
<figure><img src="image_40.png" /></figure>

SO

DISCOURAGED!

Effect (50)

Effect (50)
<figure><img src="image_41.png" /></figure>

THE
PIONEER
PROJECT

# MACHINE LEARNING
## MISSING DATA
## Reasons for missing data

- Samples lost

- Samples expired

- Patient refusal

- Test not conclusive

- There must be others

All these **carry some information**

So missingness is probably **not random**
(MNAR)

Also, position of missing values are **highly correlated**

<figure><img src="image_42.png" /></figure>

PIONeeR technical report, NA analysis, link to website
# MISSING DATA: SIMULATION STUDIES
## DEEP LEARNING SURVIVAL MODELS

Simulation data with 2 groups, interaction impacting survival, control of the rate of missing data

<figure><img src="image_43.png" /></figure>

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

$C$ = Harrel's
C-statistic

⇒ simple median imputation is no worse than more complicated methods
## DERIVATION OF A ROBUST DATASET

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

170 patients, 360 variables, 35% NA

Patients

<figure><img src="image_46.png" /></figure>

Variables

Value is filled

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

FALSE

TRUE

Basic clinical data

Basic serum markers

Immune-monitoring

Pathology

Multiplex immunohistochemistry

151 patients, 287 variables, 21% NA

Remove variables and
patients with > 60% NA

<figure><img src="image_48.png" /></figure>

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

Variables
<figure><img src="image_50.png" /></figure>

THE
PIONEER
PROJECT

# MACHINE LEARNING
## FEATURE SELECTION
## CORRELATED FEATURES
# HIERARCHICAL STRUCTURE

Two (or more) biomarkers are highly related

**Consequences :**

* **Unstable** feature selection

* **Bad** parameter estimates

* **Even** bad signs

**Remedy :**

* Ad hoc preprocessing (bad)

* PCA (bad)

* Use regularization

* Ridge / Tikhonov

* Lasso (bad)

* Use specialized FS method

<figure><img src="image_51.png" /><figcaption>Correlation of circulating immune cells biomarkers</figcaption></figure>

<figure><img src="image_52.png" /></figure>
CLASSICAL LASSO IS NOT STABLE

BOOTSTRAP ANALYSIS

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

<figure><img src="image_54.png" /></figure>
# 3 FAMILIES OF FEATURE SELECTION METHODS

## FILTERS

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

Rank features based on
Statistical information (e.G., T-test)
or similarity-based criteria

## WRAPPERS

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

Rely on the performance of a
predictive model

## EMBEDDED

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

Feature selection is embedded
within the learning process
double bootstrap LASSO : BOBO

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

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

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

<figure><img src="image_61.png" /><figcaption>A. Bakhmach, MSc</figcaption></figure>
# EARLY PROGRESSORS SIGNATURE
## INCREMENTAL LASSO-BASED FEATURE SELECTION

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

<figure><img src="image_63.png" /></figure>

[ Greillier, L et al. AACR 2022 ]
<figure><img src="image_64.png" /></figure>

THE
PIONEER
PROJECT

MACHINE LEARNING
RESULTS
# MOST SIGNIFICANT VARIABLES

## PERFORMANCE METRICS OF THE 8-FEATURES SIGNATURE

### Cross-validation on train

<figure><img src="image_65.png" /></figure>

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

### Test

<figure><img src="image_67.png" /></figure>

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

* Rizvi et al., JCO 2018: PD-L1 AUC = 0.65

* Good AUC

* Slight overfitting in CV

* Balanced PPV / NPV

* Mitigated Specificity in test

Caution: train/test set is fixed in this analysis
# OTHER ML MODELS

## DID NOT OUTPERFORM LOGISTIC REGRESSION

Nested ML models performance on line sup 2

<figure><img src="image_69.png" /></figure>
# MOST SIGNIFICANT VARIABLES

## FIRST INTERPRETATION OF THE 8-FEATURES SIGNATURE

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

High M MDSC are early
progressors

NK to Activated ratio is a
biomarker

PD-L1++ patients

High PLR or high number of meta
favors early progression
RESULTS: MACHINE LEARNING
1ST LINE

A 15-biomarkers signature

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

● Significant for PFS association

Training set (n=116, 75%), Testing set (n=39, 25%)

Cross validation on Train

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

Train + Test

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

Test

<figure><img src="image_74.png" /></figure>

Prediction of the 1-year PFS with 83% sensitivity

Train

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

Test

<figure><img src="image_76.png" /></figure>
<figure><img src="image_77.png" /></figure>

THE
PIONEER
PROJECT

# MACHINE LEARNING
## DATA AUGMENTATION
## SYNTHETIC DATA IN ML LOOP

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

Data augmentation

*Including survival time and censorship
<figure><img src="image_79.png" /></figure>

Learn better discriminative models
(ML efficacy > 1)

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

Predict OS from PIONeeR clinical biomarkers, all treatment lines

ML efficacy < 1

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

Predict OS from German Breast Cancer Study group dataset. (Open data)

ML efficacy ≈ 1

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

Predict OS from PIONeeR basic clinical variables, all treatment lines
<figure><img src="image_83.png" /></figure>

THE
PIONEER
PROJECT

SUMMARY
# SUMMARY

Classical statistical analysis is still informative and
the first thing to do

## Second+ line patients

* Few tumor markers, probably due to timing of biopsy, multiple circulating markers

* Counter-intuitive discoveries:

* circulating activated and cytotoxic T cells are positively associated with early progression

* Tumor Tregs have a positive impact

* Circulating monocytic MDSCs have negative impact

* NK cells have positive impact

## First line patients

* Classical inflammation markers play a significant prognostic role

* Presence of mutations is detrimental

* Many more tumor markers related to T cells infiltration

Machine learning lessons for biomarkers

* Detecting effects (and generalize) is hard:

* few, possibly **missing** observations,

* But learned models **still predict well**

* many **correlated** biomarkers,

* possibly **complex interactions**

* Many tests, methods, models to try

* Need for **reproducibility** and **benchmarking**

* **Generative modeling** can help

* Specific constraints of biomarker discovery

* Need for **interpretability**

* Need for **minimal signature**
# The QUANTIC project: methodology

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

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

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

## Methodological challenges

* To date, no validated model of tumor-immune-drug interaction on clinical data

* Deal with data heterogeneity

* Integrate high-dimensional data:

* at baseline

* longitudinally

## Leads

* Mechanistic models on reduced number of key immune players

* Include individual lesion kinetics and appearance

* For high-dimensional static data: use ML algorithms as covariate models in mixed-effects models

* For high-dimensional dynamic data: combine dimension reduction, mechanistic modeling and mixed-effects

* Use Bayesian and joint modeling
# ACKNOWLEDGEMENTS

## Patients & Families

## Investigators and Study teams

### Marseille (& area)

* APHM Mult. Oncology & Therap Innov. Dep.: Pr L. Greillier, Dr P Tomasini

* St Joseph Private Hospital: Dr. C. Foa

* Europ. Hospital: Dr. J. Le Treut

* Ste Musse CHI, Toulon: Dr. C. Audigier-Valette

* Maynard Private Hospital, Bastia, Corsica: Dr. A. Frikha

* Bastia Hospital: Dr. P. Bory

* Aix-Pertuis Hospital: Dr S. Martinez

* OncoGard: Dr. A. Serre

* Nimes Hospital: Dr. S. Van Hulst

* Avignon Hospital: Dr. N. Cloarec

### Lyon (& area)

* Léon Bérard Center Medical Oncology Department: Dr. M. Pérol

* Villefranche Hospital: Drs. L. Falcheri, L. Odier

* Annecy Hospital: Dr. S. Hominal

### Toulouse (& area)

* Toulouse CHU Thoracic Oncology Department: Pr J. Mazières

* Cahors Hospital: Dr. P. Barre

* Montauban Hospital: Dr. S. Zahi

* CHIVA center: Drs. F. Romagné, L. Lebas

### Aix-Marseille Université

* M. Roumieux, Pr. J. Ciccolini, M. Hamimed

### Assistance Publique Hôpitaux de Marseille (trials sponsor)

* Health & Research Dep.: A. Giuliani

* Oncology Phase I Unit CLIP² labeled (CEPCM): M. Le Ray, Dr. A. Pelletier

* Pathology Dep.: Drs. JP. Dales, L. Juquel & team

* ImmunoProfiling Platform: Dr. F. Vély, A. Pouchin

* VasculoMonitoring Platform: Pr. F. Sabatier, L. Arnaud

* Data Management: R. Malkoun

### Veracyte

* J. Fieschi, F. Monville, L. Ghezali & team

### Innate Pharma

* E. Vivier, S. Bokobza, C. Laporte

### Inserm - Inria (COMPO Team)

* Biostatistics - Machine Learning: S. Benzekry, A. Bakhmad, M. Boussena, M. Karlsen, P. Dufosse, A. Vaglio

### Institut Paoli Calmettes - CRCM & ImCheck Therapeutics

* D. Olive & teams

### Léon Bérard Center

* Methodologists: Dr. D. Pérol, S. Chabaud, C. Cropet

Inria

COMPO
COMPUTATIONAL ONCOLOGY

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

innate pharma

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

ImCheck

Aix-Marseille
Hôpitaux
université
dé de Marseille

ap-
hm

Inserm
UNIVERSITE
DE MARSEILLE

cnrs

CENTRE LEON
BERARD

GUSTAVE/
ROUSSY

INSTITUT PAOLI-CALMETTES
Institut Hôpital Bérard

AstraZeneca

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

veracyte.

Ciml

CRCM

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

mAbs

Ciphe

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

Hôpitaux de Toulouse