Genomic aberrations associated with triple-negative
breast cancer (TNBC) molecular subtypes based on
Lehmann classification.
Thèse présentée par Yacine BARECHE
en vue de l’obtention du grade académique de docteur en Sciences
Biomédicales et Pharmaceutiques
Année académique 2019-2020
Sous la direction du Professeur Christos SOTIRIOU
Breast Cancer Translational Research Laboratory, J.-C. Heuson Department of Medical Oncology, Institut Jules Bordet, Université Libre de Bruxelles
Jury de thèse :
o Carine MAENAUT (Université libre de Bruxelles, Présidente) o Christos SOTIRIOU (Université libre de Bruxelles, Secrétaire) o Vincent DETOURS (Université libre de Bruxelles)
o Pierre HEIMANN (Université libre de Bruxelles)
o Alexandra VAN KEYMEULEN (Université libre de Bruxelles) o Vincent BOURS (Université de Liège)
Remerciements
Je tiens à remercier toutes les personnes qui de près ou de loin m’ont soutenu et permis de réaliser ce travail de recherche. Je souhaite remercier également le Télévie ainsi que le Fonds National de la Recherche Scientifique qui ont financé cette thèse de doctorat.
Merci au professeur Christos Sotiriou, mon promoteur, qui m’a permis de réaliser cette thèse de doctorat dans son laboratoire. Je le remercie pour tout ce que j’ai pu apprendre à son contact mais aussi pour sa confiance et son enthousiasme qui m’ont fait murir en tant que scientifique.
Je tiens à remercier tous mes collègues du laboratoire de recherche translationnelle en cancérologie mammaire (le BCTL) de l’Institut Jules Bordet : Françoise, Alphy, Vinu, Samira, Jeanne, Mariana, Delphine, David, Dominique, Fréderic, Julie et Christine, pour leur bonne humeur, leurs conseils et toutes les connaissances qu’ils ont pu m’inculquer au cours de ces 4 années. Je remercie aussi tout particulièrement :
Floriane, avec qui j’ai partagé mon bureau et notre vie de thésard au quotidien. Ghizlane, Guilia, Danai, Xiao et Mattia, qui ont supporté mes plaisanteries répétées.
Luis, Florian et Sébastian qui, malgré leur court séjour dans le laboratoire, ont toujours su être présent pour répondre à mes questions en tout genre.
Laurence, Marion et François pour leur soutien, leur discussion et leur amitié qui m’ont permis d’avancer tout au long de ce périple.
Je remercie ma famille et mes amis pour leurs soutiens et leurs encouragements.
i
Table of contents
Table of Tables ... ii
List of Additional Data... v
List of Abbreviations ... vi
Summary in French ... viii
Introduction ... 1
1. Introduction and rationale ... 1
1.1. Breast cancer classification... 1
1.2. Clinical management of breast cancer heterogeneity ... 4
2. Breast cancer biological insights through gene expression and next-generation sequencing ... 4
2.1. Clinical implications of gene-expression-based assays ... 4
2.2. The genomic landscape of breast cancer ... 6
2.3. The breast cancer era of precision medicine ... 8
3. Triple Negative Breast Cancer (TNBC): a distinct and heterogeneous disease . 10 3.1. The molecular heterogeneity of TNBC ... 12
3.2. The need for better stratification ... 15
4. The tumor microenvironment in TNBC ... 18
4.1. The composition of the tumor micro-environment ... 18
4.2. The prognostic and predictive value of tumor infiltrating lymphocytes ... 19
4.3. Promising success of cancer immunotherapy ... 22
Justification and aims of the thesis ... 26
Chapter 1: Characterization of TNBC genomic and transcriptomic
heterogeneity ... 28
Chapter 2: Characterization of TNBC tumor microenvironment
heterogeneity: towards an optimized treatment approach ... 55
Concluding remarks and perspectives ... 101
References ... 106
ii
Table of Tables
Table 1.1 : The breast cancer classification. ... 3
Table 1.2 : Potentially targetable pathway in TNBC based on NGS studies. ... 12
Table 1.3 : Prognostic and predictive value of TILs in TNBC. ... 21
Table 1.4 : Reported Trials of Anti–CTLA-4 or Anti–PD1/PD-L1 ICB in Metastatic TNBC. ... 25
Table 2.1 : Patient and tumour clinic-pathological characteristics within each TNBC molecular subtype. ... 36
Table S2.1 : Types of genomic data available for each patient. ... 46
Table S2.2 : Patient, PAM50 and TNBC molecular subtypes before and after reclassification. ... 46
Table S3.1 : Clinico-pathological characteristics within each TNBC & TIME molecular subtypes. ... 82
Table S3.2 : Tumor Microenvironment gene expression signatures. ... 82
Table S3.3: Association between TME signatures & molecular subtypes. ... 82
Table S3.4: Prognostic value of TME signatures. ... 82
Table S3.5: Tumor Immune cell composition associations with TNBC molecular subtypes. ... 82
Table S3.6: Spearman correlation between gene expression and CIN scores in cohort A. ... 82
Table S3. 7: Spearman correlation between gene expression and CIN scores in cohort B. ... 82
Table S3.8: Associations of copy number loss genes with CYT. ... 82
Table S3.9: Therapeutic immune targets grouped by signaling pathways. ... 83
iii
Table of Figures
Figure 1.1 : The natural disease evolution of breast cancer. ... 2
Figure 1.2 : Overall survival of BC patients according to PAM50 intrinsic molecular subtypes. ... 6
Figure 1.3 : Molecular portrait of PAM50 intrinsic BC subtypes. ... 8
Figure 1.4 : TNBC and BL molecular heterogeneity. ... 13
Figure 1.5 : Molecular portrait of TNBC molecular heterogeneity. ... 14
Figure 1.6 : Molecular alteration characterized within TNBC with potential target inhibitors. ... 16
Figure 1.7 : The tumor micro-environment contexture. ... 18
Figure 1.8 : The cancer immunity cycle. ... 20
Figure 1.9: Immune check inhibitors in cancer treatment. ... 23
Figure 2.1 : Mutational landscape of TNBC molecular subtypes. ... 38
Figure 2.2 : Genomic instability within each TNBC molecular subtype. ... 41
Figure 2.3 : Altered signaling pathways and deregulated hallmarks of cancer signatures within each TNBC molecular subtype. ... 42
Figure S2.1: Unsupervised consensus clustering of the METABRIC TNBC cohort. . 47
Figure S2.2 : Agreement between Lehmann’s molecular subtype and the unsupervised consensus clustering. ... 48
Figure S2.3 : Association of TNBC molecular subtypes with different clinic-pathological variables and clinical outcome. ... 49
Figure S2.4 : Mutation distribution across each TNBC molecular subtypes. ... 51
Figure S2.5 : Chromosomal instability within each TNBC molecular subtype. ... 52
Figure S2.6 : Altered pathways in the TNBC molecular subtype. ... 54
Figure 3.1: Tumor Micro-Environment features associated with TNBC molecular subtypes and overall survival. ... 67
Figure 3.2: Characterization of the spatial immune landscape and immune composition in TNBC... 69
Figure 3.3: Chromosomal instability association with immune escape. ... 74
Figure 3.4: Therapeutic immune targets according to TNBC molecular and TIME subtypes. ... 76
iv Figure S3.1: CONSORT Diagram of the study. ... 84 Figure S3.2: Validation of the TIME classification using TCGA H&E slides. ... 86 Figure S3.3: 5q and 15q region loss is associated with high CIN and reduced CYT levels. ... 88 Figure S3.4: Chromosomal instability and chromosome 5q/15q region losses are independently associated with CYT. ... 89 Figure S3. 5 Specific 5q/15q region losses associated with reduced CYT.
Associations between chromosome 5q. ... 90 Figure S3.6: Tumor Micro-Environment features associated with TNBC molecular subtypes and relapse-free survival. ... 93 Figure S3.7: Characterization of the spatial immune landscape and immune
composition in TNBC... 94 Figure S3.8: Therapeutic immune targets according to TNBC molecular and TIME subtypes. ... 96 Figure S3.9: Relationship and Survival association of several TNBC molecular
v
List of Additional Data
Supporting materials that cannot be included in the printed version of this thesis for reasons of space are available online.
Supplementary Data File 1
Available online at : https://doi.org/10.6084/m9.figshare.8869172
• Table S2.1 : Types of genomic data available for each patient.
• Table S2.2 : Patient, PAM50 and TNBC molecular subtypes before and after
reclassification.
Supplementary Data File 2
Available online at : https://doi.org/10.6084/m9.figshare.9104483
• Table S3.1 : Clinico-pathological characteristics within each TNBC & TIME
molecular subtypes.
• Table S3.2 : Tumor Microenvironment gene expression signatures. • Table S3.3: Association between TME signatures & molecular subtypes. • Table S3.4: Prognostic value of TME signatures.
• Table S3.5: Tumor Immune cell composition associations with TNBC molecular
subtypes.
• Table S3.6: Spearman correlation between gene expression and CIN scores in
cohort A.
• Table S3. 7: Spearman correlation between gene expression and CIN scores in
cohort B.
• Table S3.8: Associations of copy number loss genes with CYT.
• Table S3.9: Therapeutic immune targets grouped by signaling pathways. • Table S3.10 : Associations between targetable immune molecules expression
vi
List of Abbreviations
Abbreviation DescriptionAMP CNA amplification AR Androgen receptor
AURORA Aiming to Understand the Molecular Aberrations in Metastatic Breast Cancer BAF Bi-allele frequency
BC Breast cancer BL Basal-like BL1 Basal-like 1 BL2 basal-like 2
BLIA Basal-like immune-activated BLIS Basal-like immune-suppressed
CAF Cancer-associated fibroblast CBS Circular Binary Segmentation CDF Cumulative distribution function
CIN Chromosomal instability CNA Copy-number alterations
COSMIC Catalogue of Somatic Mutation In Cancer CT Chemotherapy
CTLA-4 Cytotoxic T-lymphocyte antigen 4 CYT Cytotoxic immune activity
DC Dendritic cells DFS Disease-free survival
EBI European Bioinformatics Institute
ECM Extra-cellular matrix
EGA European Genome-Phenome Archive
EMT Epithelial to mesenchymal transition ER Estrogen receptor
ExAC Exome Aggregation Consortium FDR False discovery rate
FFPE Formalin fixed paraffin embedded FI Fully Inflamed
FISH Fluorescence in situ hybridization GAIN CNA gain
GE Gene expression
GENIE AACR Project Genomics Evidence Neoplasia Information Exchange GEO Gene Expression Omnibus
GISTIC Genomic Identification of Significant Targets in Cancer GO Gene Ontology
H&E Hematoxylin and eosin
HER2 Human epidermal growth factor 2 HER2-E HER2-enriched
HETD Hemizygous deletion HOMD Homozygous deletion
HR Hormonal receptor
HRD Homologous Recombination Deficiency IC Immune Cold
ICB Immune checkpoint blockade IDC Invasive ductal carcinoma IHC Immunohistochemistry
vii IM Immunomodulatory
indels Insertions/deletions
ITH Intra-Tumor Heterogeneity LAR Luminal androgen receptor LogR Log ratio
LOH Loss of heterozygosity LST Large-scale state transition LumA Luminal-A
LumB Luminal-B M Mesenchymal
M1 Anti-tumorigenic macrophages M2 Pro-tumorigenic macrophages mAbs Monoclonal antibody
Mb Megabase
MBCproject Metastatic Breast Cancer Project MDSC Myeloid-derived suppressor cell
MES Mesenchymal
METABRIC Molecular Taxonomy of Breast Cancer International Consortium MHC Major histocompatibility
MR Margin Restricted MSL Mesenchymal stem-like
mTNBC Metastatic triple-negative breast cancer NEUT Neutral
NGS Next-generation sequencing NK Natural killer cells
NODE National Omics Data Encyclopedia
NST Invasive mammary carcinoma of no special type NtAI Allelic imbalance extending to the telomere ORR Objective response rate
OS Overall survival
PAM50 Intrinsic molecular subtype pCR Pathological complete response PD-1 Programmed cell death-1
PD-L1 Programmed cell death-1 ligand PR Progesterone receptor
RFS Relapse free survival SD Standard deviation SR Stroma Restricted
TAM Tumor-associated macrophage TCGA The Cancer Genome Atlas
TCR T cell receptor Th T helper cells
TILs Tumor infiltrating lymphocytes TIME Tumor Immune Micro-Environment
TMB Tumor mutational burden TME Tumor microenvironment TNBC Triple negative breast cancer TNBCtype TNBC molecular subtype
Treg Regulatory T cell UNS Unstable
viii
Summary in French
Le cancer du sein (BC) est la tumeur maligne la plus répandue et la deuxième cause de mortalité due au cancer chez les femmes dans le monde. Les récents progrès en matière de diagnostic précoce et de traitements anticancéreux efficaces en BC ont nettement amélioré la survie des patientes atteintes de cette maladie. Cependant, le cancer du sein triple négatif (TNBC), qui est le sous-type le plus agressif de cancer du sein, est associé à un taux élevé de récidive et à un plus mauvais pronostic. Les TNBC, représentant 10 à 20% des cancers du sein, sont définis par l’absence des récepteurs à l’œstrogène (ER), à la progestérone (PR) ainsi que du récepteur aux facteurs de croissance épidermiques (HER2). À ce jour, les options de traitement pour les patientes atteintes d’un TNBC restent principalement limitées à la chimiothérapie. Les premières études cliniques évaluant les inhibiteurs de points de contrôle immunitaire PD-1/PD-L1 dans le cancer du sein ont démontré un effet favorable dans le traitement des TNBC. La combinaison anti-PD-L1 et chimiothérapie est depuis peu approuvée comme thérapie dans le TNBC à un stade avancé. Cependant, à l'instar d'autres thérapies ciblées, une proportion importante de patientes ne tire pas profit de cette approche. Une meilleure caractérisation de l’hétérogénéité de la tumeur et de son microenvironnement devient donc nécessaire afin d’optimiser la stratification des patientes atteintes d’un TNBC afin d’élaborer des stratégies de traitement personnalisées.
En 2011, les avancées dans les technologies de séquençage à haut débit ont permis de mettre en évidence l’hétérogénéité présente au sein de cette maladie en distinguant la présence de six sous-types moléculaires au niveau transcriptomique : deux sous-types basal-like (BL1 & BL2), un sous-type immunomodulateur (IM), un sous-type luminal androgen receptor (LAR), un sous-type mesenchymal (M) et un sous-type mesenchymal stem-like (MSL). L’objectif de cette thèse consiste à étudier ces sous-types moléculaires en fonction des informations clinico-pathologiques, de la survie mais aussi des altérations génomiques et transcriptomiques obtenues au sein des larges cohortes rétrospectives de patientes TNBC.
ix démontre d’importantes distinctions tant au niveau génomique qu’au niveau transcriptomique entre les sous-types moléculaires. Cette hétérogénéité entre les sous-types moléculaires se retrouve aussi au niveau des caractéristiques clinico-pathologiques, de l’évolution clinique et en matière de pronostic de survie.
La seconde partie de cette thèse consiste en une caractérisation de l’hétérogénéité du microenvironnement tumoral ouvrant de possibles applications immuno-thérapeutiques pour traiter cette maladie. En comparaison aux autres sous-types de cancer du sein, les TNBC sont caractérisés par un taux élevé d’infiltration lymphocytaire et l’immunothérapie est apparue comme une stratégie de traitement prometteuse. Les dernières études cliniques ont démontré des résultats encourageants pour les inhibiteurs de points de contrôle immunitaire en monothérapie et en combinaison avec de la chimiothérapie. Dans le but de mieux sélectionner les patients qui pourraient bénéficier de ces traitements, nous avons étudié différents processus reflétant le microenvironnement tumoral en fonction des sous-types moléculaires. Notre analyse démontre que cette classification moléculaire présente différents profils d’expression de marqueurs ciblés par l’immunothérapie, ainsi qu'une composition et une localisation immunitaire spécifique à chaque sous-type.
1
Introduction
1. Introduction and rationale
Breast cancer (BC) is the most frequently diagnosed cancer and the second most common cause of cancer mortality in women worldwide. In 2018, the estimated number of new cases of invasive malignancies among women was approximately 878,980 in the United States, with BC in the lead at a proportion of 30% (Siegel, Miller, and Jemal 2018). Despite the slight increase in BC incidence from 2005 to 2014 (+0.4%), survival has also been increasing during the same period of time (+1.8%), reaching a 90% overall survival rate. Development in early diagnosis and new treatment strategies have led to this continuous survival improvement in BC over the last decades.
1.1.
Breast cancer classification
BC clinical development is a stepwise progression from normal tissue to atypical hyperplasia, leading to carcinoma in situ and finally to invasive breast cancer (Figure
1.1A). BC is essentially a genetic disease, evolving and progressing over time through
2 “Western-style diet” are also well-known BC risk factors (McTiernan 2003; Senkus et al. 2015).
BC is a complex and heterogeneous disease characterized by distinct pathological features and diverse response to treatment as well as long-term patient survival. Breast malignancies are mostly composed of invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) representing, respectively, 80% and 10% of invasive BC (Ci et al. 2003) (Figure 1.1B). Of note, IDC tumors are also now called invasive mammary carcinoma of no special type (NST) (Sinn and Kreipe 2013). BC can also be composed of other less commonly found morphological subtypes namely, medullary, metaplastic, mucinous, papillary, tubular and inflammatory carcinomas.
Figure 1.1 : The natural disease evolution of breast cancer.
(A) Breast carcinoma progressive stage as defined by histopathology. Figure adapted
from Casasent et al. 2017. (B) Illustration of female breast anatomy, on the left with healthy and abnormal ductal and lobular tissue, on the right. Figure adapted from
https://www.teresewinslow.com/#/breast/.
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3 These morphological subtypes of breast cancer can be further classified based on the expression of three hormonal receptors (HR) namely, the estrogen (ER), the progesterone (PR) and the human epidermal growth factor 2 (HER2) receptors. Despite this complexity, clinical decisions still rely primarily on the assessment of these markers using immunohistochemistry (IHC) and/or fluorescence in situ hybridization (FISH). BC patients can be divided into three clinically distinct subpopulations: ER-positive/PR-positive (also called HR+), HER2-positive (HER2+) or ER-negative/PR-negative/HER2-negative (also called triple negative BC, TNBC). Based on their histological grade and proliferation marker (Ki67) level, HR+ tumor can be further subdivided into Luminal A and B subtypes. Luminal A tumors are defined as HR+/HER2- with low Ki67 whereas Luminal B tumors are defined as HR+ with either high HER2 or Ki67 expression. Finally, HER2+ and TNBC are usually highly proliferative and aggressive tumors (Table 1.1). All these subtypes exhibit distinct biology, prognosis, treatment strategies and metastasis pattern.
Table 1.1 : The breast cancer classification.
Abbreviations: ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; mAbs, monoclonal antibody. Figure adapted from
http://www.pathophys.org/breast-cancer/.
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4
1.2.
Clinical management of breast cancer heterogeneity
Initially developed in the 60s to serve as a strong ER stimulant for fertility control in women, tamoxifen has, in fact, showed the opposite activity. A decade later, this unsuccessful drug was used for the first time against BC (Cole, Jones, and Todd 1971). It was later shown that the response to tamoxifen was driven by the presence of ER on the tumor surface (Mourldsen et al. 1978). In the 90s, it was discovered that 25 to 30% of BC over-expressed the HER2 protein and this over-expression led to an aggressive phenotype (Paolo et al. 1987; Hudziak, Schlessingert, and Ullrich 1987; Guy et al. 1992). This discovery led to the development of a humanized anti-HER2 monoclonal antibody (mAb) that will later be known as Trastuzumab (Herceptin®) and
will change the way we treat HER2+ BC patients (B. J. Baselga et al. 1996; Cobleigh et al. 1999; Hortobagyi 2005). These therapeutic targeted treatment approaches have drastically improved clinical response to treatment and survival in women with BC either positive for ER or HER2. However, since TNBC lack both ER and HER2 receptors, treatment option remains limited (Senkus et al. 2015) and tend to develop a disease with poor prognosis when compared to other subtypes (Fallahpour et al. 2017; Howlader et al. 2018; van Maaren et al. 2019).
2. Breast cancer biological insights through gene expression and
next-generation sequencing
Over the last two decades, the reducing sequencing cost and the increasing data output obtained from each newer next-generation sequencing (NGS) technology generation have changed the way cancer research was performed. This rapid accumulation of sequencing data has led to tremendous advances in our understanding of BC biology, through detailed characterization of the cancer genome and transcriptome.
2.1.
Clinical implications of gene-expression-based assays
5 and supervised statistical approaches. The former has helped researchers and clinicians to highlight the existence of distinct biological signatures characterizing distinct molecular subtype while the latter has allowed the identification of several gene expression signatures predicting either or both survival and response to treatment.
Unsupervised hierarchical clustering of BC whole transcriptome profiling has unveiled the presence of four distinct “intrinsic” molecular subtypes (Perou et al. 2000; Sorlie et al. 2001; C. Sotiriou et al. 2003):
• Luminal-A (LumA), which is mostly HR-positive and histologically low-grade/low proliferative.
• Luminal-B (LumB), which is mostly HR-positive but may express low levels of hormone receptors and are often high-grade/high proliferative.
• HER2-enriched (HER2-E), which show amplification and high expression of HER2.
• Basal-like (BL), which mostly correspond to HR-negative and HER2-negative tumors (TNBC).
6
Figure 1.2 : Overall survival of BC patients according to PAM50
intrinsic molecular subtypes.
Outcome predictions according to intrinsic BC subtypes (PAM50) in a set of (A) 295 untreated BC patients and (B) 1311 node-positive BC patients treated with adjuvant anthracycline- and taxane-based chemotherapy. Figures adapted and reprinted from Fan C. et al. 2006 & Liu MC et al. 2016.
Survival and response to treatment driven supervised analysis have led to the identification and commercialization of several prognostic signatures. Such methods like the PAM50 risk of relapse score (Parker et al. 2009), the OncoType DX® (Paik et
al. 2004), the Mammaprint® (Van’t Veer et al. 2002), the Breast Cancer IndexSM (Zhang
et al. 2013) or the MapQuant DxTM Genomic Grade Index (Christos Sotiriou et al. 2006)
have been shown to improve clinical outcome prediction in retrospective and prospective studies (Sparano et al. 2015; Cardoso et al. 2016; Sparano et al. 2018; Brandao, Ponde, and Piccart-gebhart 2018). These assays are now helping clinicians to distinguish between the high and low genomic risk of relapse patients and most importantly identifying those that might not require adjuvant chemotherapy.
2.2.
The genomic landscape of breast cancer
The 21st century has been characterized by the genomic revolution and was led by
the first major biological and medical international research community: The Human Genome Project. $3 billions and 15 years were needed for this consortium to be able to sequence the first entire human genome (F. S. Collins, Morgan, and Patrinos 2003) and have paved the way for subsequent successes.
7 In 2012, a series of multiple BC related reports appeared in Nature, highlighting for the first time the substantial heterogeneity of somatic mutation pattern in thousands of BC samples (Shah et al. 2012; Curtis et al. 2012; Stephens et al. 2012; Ellis et al. 2012; Koboldt et al. 2012). Two of these reports, from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International consortia (METABRIC) (Curtis et al. 2012; Koboldt et al. 2012), now perceived as landmark studies, combined large-scale investigation of somatic mutations with CNA and gene expression profiles, thus providing a comprehensive catalogue of likely genomic drivers of human BC. The unsupervised analysis of 2000 BC paired DNA–RNA profiles, from the METABRIC study, revealed 10 novel subgroups associated with distinct clinical outcomes. Their study also investigated associations between CNA and gene expression, thus identifying new putative BC driver genes, such as, deletion in
PPP2R2A, MTAP and MAP2K4. The TCGA study went a step further by adding DNA
methylation, proteomics and miRNA information into a more complete picture of BC heterogeneity, highlighting the convergence of all these molecular alterations into the four main PAM50 intrinsic BC molecular subtypes (Figure 1.3). Overall, the LumA subtype had the lowest mutation rate while the highest was observed for HER2-Enriched and Basal-likes subtypes. The LumA subtype was mainly associated with
PIK3CA mutations (45%) while LumB tumors exhibited a diversity of significantly
mutated genes, with TP53 and PIK3CA (29% each) being the most frequent. Finally, Basal-like tumors were mainly associated with TP53 mutations, whereas the HER2-Enriched subtype showed high HER2 amplification (80%) with enrichment of TP53 (72%) and PIK3CA (39%) mutations.
8
Figure 1.3 : Molecular portrait of PAM50 intrinsic BC subtypes.
PAM50 subtype association with genetic and genomic alteration as well as clinical data in 501 BC patients. Reprinted from Kokoldt et al. 2012.
2.3.
The breast cancer era of precision medicine
Precision medicine has been defined by the National Health Institute as “an emerging approach for disease treatment and prevention that considers individual variability in genes, environment, and lifestyle for each person.” Toward this end, oncology, and more specifically the BC field, have benefitted immensely from the proliferation of worldwide efforts to characterize the genomic and transcriptomic heterogeneity from thousands of cases (Garraway and Lander 2013; Garraway, Verweij, and Ballman 2013). Together with the development of cancer ‘driver genes’ catalog and the increasing compendium of targeted agents available, oncology has served as a proving field of precision medicine. However, with the increasing amount of information generated by the routine practice of genomic tumor sequencing, clinicians no longer have the capacity to analyze, interpret and deliver an appropriate treatment without the development of bioinformatics tools along with centralized knowledge databases of accurately clinically interpreted tumor somatic mutation (Good et al. 2014; Griffith et al. 2017).
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9 Advances in computational analysis pipelines over the past decade have led to the development of new bioinformatic algorithms specifically dedicated to rapidly and precisely identifying somatic alterations from a variety of samples such as, blood, plasma, fresh frozen or FFPE tissues (Ding et al. 2014). Meanwhile, all these genomic sequencing efforts led to the rising of several consortiums aiming to associate genomic alterations with molecular and clinical consequences such as, dbSNP (Sherry et al. 2000), The Exome Aggregation Consortium (ExAC) (Lek et al. 2016) or the Catalogue of Somatic Mutation In Cancer (COSMIC) (Bamford et al. 2004). Other joint efforts from the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) or the Metastatic Breast Cancer Project (MBCproject) ambition to provide the statistical power necessary to improve clinical decision-making (The AACR Project GENIE Consortium Cancer 2017; Wagle et al. 2017).
As previously shown, BC has been at the forefront of precision medicine with the early use of therapies targeting ER and HER2 receptors, such as, tamoxifen and trastuzumab in ER+ and HER2+ BC patients together with the use of gene expression classifiers, such as, MammaPrint and OncotypeDX for the optimal management of treatment in ER+ BC patients (Cardoso et al. 2016; Sparano et al. 2018; Brandao, Ponde, and Piccart-gebhart 2018). However, ER+ BC patients tend to develop resistance to endocrine therapy, especially in the metastatic setting where resistance is almost inevitable (Osborne et al. 2009; Yeo, Turner, and Jones 2014). Genomic-based studies helped clinicians to shed light into anti-estrogen resistance mechanisms by comparing the mutational landscape of previously untreated primary tumors versus metastatic tumors treated with anti-estrogen therapy (Toy et al. 2013; Robinson et al. 2013). These studies identified several recurrent activating ESR1 mutations in metastatic resistant patients and showed that ER+ patients with these mutations will unlikely response to endocrine therapy. Furthermore, these same studies also highlighted that patients developing resistance under aromatase inhibitors (blocking the conversion of the androgens into estrogen), will still benefit from direct ER antagonists such as tamoxifen.
10 A couple years ago, several large scale genomic studies revealed the existence of at least 30 mutational signatures, heterogeneously present across 40 cancer types (Nik-Zainal et al. 2012; Alexandrov, Nik-Zainal, Wedge, Campbell, et al. 2013; Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013; Nik-Zainal et al. 2016). These mutation signatures, obtained from somatic driver and passenger mutations, can inform on the endogenous or exogenous processes generating these mutations and could have potential implications for the understanding of cancer etiology, prevention and therapy. Using this computational approach, Nik-Zainal et al. demonstrated that BC patients without a BRCA mutation could still, through combined mutational, CNA and genomic rearrangement signatures, exhibit a BRCAness phenotype (Davies et al. 2017). Approximately, ~1-5% of BC patients present a BRCA germline mutations and are selectively sensitive to PARP inhibitors, but this study proposed that up to 20% of BC patients with BRCAness phenotype may also have selective therapeutic sensitivity to PARP inhibitors.
Taken together, these insightful studies and large consortiums have changed the way BC clinical practice is and will be performed in the near future, with international efforts, such as, the Pan-European AURORA (Aiming to Understand the Molecular Aberrations in Metastatic Breast Cancer) program (Zardavas et al. 2014; Maetens et al. 2017; Aftimos et al. 2019), aiming at integrating next-generation sequencing and big data science into a more precise medicine.
3. Triple Negative Breast Cancer (TNBC): a distinct and
heterogeneous disease
11 Kiyamova 2008; M. V. Dieci, Orvieto, et al. 2014). As compared to HR-positive and/or HER2-positive, TNBC is a highly aggressive disease, more prevalent in younger (<40 years of age) and in African-American or Hispanic women (Dent et al. 2007; Bauer et al. 2007; L. Carey et al. 2010; Foulkes, Smith, and Reis-Filho 2010). In contrast to other BC subtypes, which disseminate to bones and soft tissues, TNBC tumors tend to metastasize preferentially to the brain and lung (Fulford et al. 2007; Dent et al. 2007). Germline BRCA mutations have also been associated with TNBC, thus raising the question of the BRCA deficiency mechanism in the pathogenesis of this aggressive disease (Turner and Reis-Filho 2006). The characterization of BC subtype heterogeneity has enabled the identification of several genomic and transcriptomic alterations in TNBC (Table 1.2) (Koboldt et al. 2012; Curtis et al. 2012; Balko et al. 2014; Pereira et al. 2016). However, intra- and inter-patient heterogeneity, as well as acquired treatment resistance, have challenged the development of targeted therapies (Kalimutho et al. 2015; Bianchini et al. 2016; Garrido-Castro, Lin, and Polyak 2019), leaving TNBC with limited treatment option.
12
Table 1.2 : Potentially targetable pathway in TNBC based on NGS
studies.
Abbreviations: Mut, gene mutation; gain, gene copy-number gain (<5 but more than 2 copies); amp, gene amplification (≥5 copies and/or gene-specific and centromeric probe ratio >2). Note: Frequencies (%) of alterations are included when available. References: ref.1, (Koboldt et al. 2012); ref.2, (Pereira et al. 2016); ref.3, (Balko et al.
2014). Table adapted from (Garrido-Castro, Lin, and Polyak 2019).
3.1.
The molecular heterogeneity of TNBC
The identification of PAM50 intrinsic subtypes had a great impact on understanding BC heterogeneity and predicting response to therapy (Prat et al. 2012). Disagreeing to the initial principle that TNBC would be synonymous of BL BC, this classification wasn’t enough to grasp entirely the TNBC molecular heterogeneity. Around 50 to 80% of TNBC exhibits a BL phenotype while approximately 75% of BL tumors are TNBC (Figure 1.4) (Prat et al. 2014). Because TNBC has also been associated with high variability in clonal and mutational evolution (Shah et al. 2012), efforts were put in order to characterize this molecular heterogeneity.
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Figure 1.4 : TNBC and BL molecular heterogeneity.
Distribution of intrinsic PAM50 subtypes within TNBC (A) and distribution of TNBC among BL BC (B). Figure adapted from (Garrido-Castro, Lin, and Polyak 2019).
In 2011, Lehmann et al. highlighted for the first time the substantial molecular heterogeneity characterizing TNBC tumors (Lehmann et al. 2011). Following the work done by Perou et al. leading to BC classification into distinct intrinsic subtypes, unsupervised clustering of ~600 TNBC gene expression profile was performed to get insight into TNBC molecular heterogeneity. This study showed that TNBC could be subdivided into six stable molecular subtypes, namely: Basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL) and luminal androgen receptor (LAR). BL1 tumors were associated with DNA damage response and cell cycle processes while BL2 were more associated with growth factor and myoepithelial markers. The IM and LAR subtypes were enriched for immune
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14 signatures and the androgen signaling pathway, respectively. Finally, both M and MSL tumors were associated with epithelial to mesenchymal transition (EMT) markers. These two subtypes differ by the overexpression of the Wnt/Notch pathway for M tumors and growth factors and angiogenesis for the MSL subtype (Figure 1.5).
Figure 1.5 : Molecular portrait of TNBC molecular heterogeneity.
Molecular classification of TNBC based on the unsupervised clustering of 587 TNBC gene expression profiles divided into a training and validation set from (Lehmann et al.
2011). Each TNBC molecular subtype is associated with distinct probability of pCR to
standard chemotherapy treatment (Masuda et al. 2013).
Theses subtypes were characterized by distinct genes expression profile and gene ontologies. Potential molecular drivers were also identified within each subtype providing evidence that gene expression signatures in TNBC may inform therapy selection and hence guide the development of effective targeted therapy in this disease. BL1 and BL2 subtypes, exhibiting higher expression of cell cycle and DNA damage response genes, were shown to be sensitive to platinum therapy, whereas M and MSL subtypes, enriched for epithelial-mesenchymal transition and growth factors, were sensitive to Src and PI3K/mTOR signaling inhibition (Lehmann et al. 2011, 2014). In a retrospective single-institutional analysis of patients treated with standard neoadjuvant chemotherapy, Masuda et al. have examined the relationship between
TNBC BL1 BL2 IM LAR MSL M
15 pathological complete response (pCR) and clinical outcome by TNBC subtypes using both PAM50 and Lehmann classification and found the latter to be a better predictor of pCR (Masuda et al. 2013). Survival data were not available for all subtypes owing to the small study sample size, but it was determined that BL1 was associated with highest pCR rate, whereas BL2 and LAR subtypes had the lowest response rates (pCR of 0 and 10%, respectively) (Figure 1.5). While this study is limited by its small size and retrospective nature, it raises important considerations for the potential use of molecular subtyping to better decipher the clinical heterogeneity of TNBC. Stratification into subtypes with different biology and clinical course may help identify patients in whom the absence of pCR does not equate into poor outcome.
In 2015, another group confirmed the existence of at least four of these subtypes using DNA and RNA profiling of ~200 TNBC tumors, namely: Basal-like immune-activated (BLIA), Basal-like immune-suppressed (BLIS), Mesenchymal (MES) and the Luminal androgen receptor (LAR) (Burstein et al. 2015). These subtypes were associated with distinct gene expression pattern, molecular pathways, CNA profiles and clinical outcomes. Altogether, these two studies highlight the substantial heterogeneity characterizing TNBC which could lead to a TNBC subtype-centered treatment care at the transcriptional level.
3.2.
The need for better stratification
TP53 (~70-80%) and PIK3CA (10-20%) mutations are the most frequent alterations found within TNBC. All other alterations are present at a low (1-5%) to very low frequency (<1%) in TNBC, with several actionable targets with available drugs (Figure
1.4 & Figure 1.6) (Kalimutho et al. 2015). Given this substantial heterogeneity,
16
Figure 1.6 : Molecular alteration characterized within TNBC with
potential target inhibitors.
Pathway centric view of molecular alteration under clinical investigation in early, locally advance and/or metastatic TNBC settings. Figure reprinted from (Kalimutho et al. 2015).
The vascular endothelial growth factor (VEGF) gene is known to be overexpressed in TNBC and plays a central role in tumor proliferation, invasion and metastasis (Ribatti et al. 2016). Several randomized phase III trials evaluated the effect of VEGFR inhibitors (sunitinib) (Barrios et al. 2010) or PDGFR inhibitors (sorafenib) (J. Baselga et al. 2014) as well as low-dose cyclophosphamide and methotrexate maintenance (known to have an anti-angiogenic activity) (Colleoni et al. 2016) in BC patients. All these trials failed to show significant treatment benefit overall and within TNBC patients.
Another well-characterized overexpressed gene is the epidermal growth factor receptor (EGFR), which is present in 50-60% of TNBC (H. S. Park et al. 2014; Nakai, Hung, and Yamaguchi 2016) and has a critical role in proliferation and migration. In
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17 phase I and II trials, this overexpression did not help to identify TNBC patients most likely to benefit from EGFR (cetuximab or panitumumab) (Yardley et al. 2016; Nabholtz et al. 2016) or tyrosine kinase (lapatinib) inhibitors (Di Leo et al. 2008).
As already stated, the PI3K/AKT/mTOR signaling pathway is hyperactivated in about 10-20% of TNBC patients, including mainly PIK3CA mutations, but also PTEN loss and AKT amplification (Shah et al. 2012; Koboldt et al. 2012; Pereira et al. 2016). The two phase II randomized trials I-SPY 2 and LOTUS (Tripathy et al. 2015; Kim et al. 2017), assessing the clinical activity of AKT inhibitors, showed significant survival improvement in selected TNBC patients with activated PI3K/AKT pathway and this led to on-going phase III trial evaluating the efficacy of ipatasertib + paclitaxel versus placebo + paclitaxel in locally advanced or metastatic TNBC (ClinicalTrials.gov identifier: NCT03337724).
Approximately 30% of TNBC have been reported to be positive for androgen receptor expression (AR, defined by ≥1% of tumor cell nuclei IHC staining) (L. C. Collins et al. 2011). Several phase II studies, assessing the clinical activity of AR blockade such as bicalutamide (Gucalp et al. 2013) or enzalutamide (Traina et al. 2018), showed promising results with 20% to 30% of AR-positive metastatic TNBC presenting stable disease at 6 months. These results led to the development of a phase III trial evaluating the efficacy and safety of bicalutamide with conventional chemotherapy, in patients with AR-positive metastatic TNBC (ClinicalTrials.gov identifier: NCT03055312).
Other molecular targets, such as MEK (Brufsky et al. 2016), mTOR (I. H. Park et al. 2018) or HDAC (Connolly et al. 2017) inhibitors have yielded conflicting results to date in TNBC whereas several molecular aberrations in locally advanced and metastatic TNBC are currently under investigation as potential therapeutic targets.
18 therapies in unselected TNBC patients, there has been a research impetus towards optimizing treatment.
4. The tumor microenvironment in TNBC
The immune system has been acknowledged as an important factor in oncology for more than a century (Coley 1891; Ehrlich 1909). However, it is only recently that cancer immunotherapy has demonstrated remarkable clinical benefit in different refractory malignancies and clinicians working in the BC field are now turning their attention to this promising treatment solution.
4.1.
The composition of the tumor micro-environment
In solid tumors, cancers are not only constituted of tumor cells, but are complex organs composed of several sorts of cell types, such as distinct immune sub-populations, normal and cancer-associated fibroblasts (CAFs) as well as blood and lymphatic vessels (figure 1.7) (Quail and Joyce 2013). In 2011, Hanahan and Weinberg revisited and extended their concept of cancer hallmark with four more characteristics (Hanahan and Weinberg 2011) highlighting the importance of the tumor microenvironment (TME) and tumors’ ability to escape immune surveillance.
Figure 1.7 : The tumor micro-environment contexture.
19
CAFs, cancer-associated fibroblast; Treg, regulatory T cell; MDSC, myeloid-derived suppressor cell; ECM, extra-cellular matrix. Figure reprinted from (Quail and Joyce
2013).
Solid tumors are extremely heterogeneous in regard to their TME composition, and it is especially true for the immune compartment. The density, location and organization of these immune cells are referred to as the “immune contexture” (Fridman et al. 2012). Immune cells can be categorized as anti- and pro-tumoral immunity (Ostrand-Rosenberg 2008). The anti-tumoral immunity is composed of immune cells, such as, cytotoxic T cells, helper T cells, natural killer (NKs) cells, dendritic cells (DCs), or anti-tumorigenic macrophages (type M1), responsible of the tumor cell elimination. On the opposite, the pro-tumoral immunity is composed of immune cells, such as, T regulatory (Tregs) cells, myeloid-derived suppressor cells (MDSCs) or pro-tumorigenic macrophages (type M2), sustaining tumor growth, invasion and metastasis (Quail and Joyce 2013). The balance between anti- and pro-tumorigenic immunity within the tumor has been shown to drive the patient’s response to treatment (Salgado et al. 2015), however, they are not the only actor. CAFs are one of the most abundant stromal components in the TME and play an essential role in the making and the remodeling of the extracellular matrix (ECM) as well as interacting with other cells within the TME with the secretion of growth factors, cytokines and chemokines (Cirri and Chiarugi 2011). Taken together, these data emphasize the complex and interconnected TME, as a critical factor of tumor progression and response to treatment.
4.2.
The prognostic and predictive value of tumor infiltrating
lymphocytes
20 migrate to lymph nodes in order to present the antigen to T cells, resulting in the priming and activation of effector T cell responses against cancer-specific antigens. Activated effector T cells will then journey through the bloodstream and infiltrate the tumor. Once within the tumor, these cytotoxic cells will specifically recognize tumor presenting the neoantigen on their MHC through binding with the T cell receptor (TCR) and kill the target by releasing cytotoxic enzymes. The death of these tumor cells releases additional tumor-associated antigens that will reactivate the cancer-immunity cycle.
Figure 1.8 : The cancer immunity cycle.
Schematic representation of the cancer-immunity cycle model proposed by (Chen
and Mellman 2013). Figure adapted from (Sahin and Türeci 2018).
The tight association between tumor infiltrating lymphocytes (TILs) and BC survival has been reported for the first time by Sistrunk and McCarty in 1922 (Sistrunk and Maccarty 1922). Since this study, several reports have repeatedly highlighted TILs predictive and prognostic value in BC, and more specifically in TNBC treated either in the adjuvant or the neoadjuvant setting (Table 1.3). Moreover, gene expression studies have also linked the expression of several immune markers associated with
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21 different immune cell types with the response to chemotherapy in TNBC (Desmedt et al. 2008; Gu-Trantien et al. 2013; Denkert et al. 2015; Callari et al. 2016). Altogether, these data imply the synergistic role of the immune system in the response to chemotherapy, with the treatment-induced immunogenic tumor cell death activating the cancer-immunity cycle.
Study TNBC
definition
Systemic
treatment TIL assessment
Association with outcome
TILs and preoperative chemotherapy response Denkert et al. (2015)
(1) TNBC Neoadjuvant CT
sTILs as continuous and categorical marker to define LPBC versus non-LPBC Higher pCR rate in LPBC group Issa-Nummer et al. (2013) (2) ER-negative/ HER2-negative Neoadjuvant CT
TILs as categorical marker to
define LPBC versus non-LPBC No significant association Baseline TILs and prognosis
Loi et al. (2013) (3) ER-negative/ HER2-negative Adjuvant CT
sTILs and iTILs as continuous and categorical marker to define LPBC versus non-LPBC
Better DFS and OS in LPBC group
Dieci et al. (2015) (4) ER-negative/ HER2-negative Untreated and adjuvant CT
sTILs and iTILs as continuous marker to define LPBC versus non-LPBC sTILs
Lower risk of death in LPBC group
Loi et al. (2014) (5) TNBC Adjuvant CT sTILs as continous marker to define LPBC versus non-LPBC Lower risk of distant relapse in LPBC group
Adams et al. (2014) (6) TNBC Adjuvant CT
sTILs as continuous and categorical marker to define LPBC versus non-LPBC
Lower risk of distant relapse and death in LPBC group Post-treatment TILs and prognosis
Loi et al. (2016) (7) TNBC Neoadjuvant CT
sTILs assessed in residual disease as continuous and categorical marker to define three groups with high, intermediate and low sTIL content
Lower risk of recurrence and death in high sTIL group in residual disease
Dieci et al. (2014) (8) TNBC Neoadjuvant CT (some also post-surgery CT)
sTILs and iTILs assessed in the residual disease as continuous and categorical marker to define LPBC versus non-LPBC
Lower risk of distant relapse and death in LPBC group
Table 1.3 : Prognostic and predictive value of TILs in TNBC.
22 al. 2013); 4, (M. V. Dieci et al. 2015); 5, (Sherene Loi et al. 2014); 6, (Sylvia Adams et al. 2014); 7, (Sherene Loi et al. 2016); 8, (M. V. Dieci, Criscitiello, et al. 2014).Table
reprinted from (Bianchini et al. 2016).
4.3.
Promising success of cancer immunotherapy
TILs presence within the tumors is not always associated with clinical benefit. Immunosuppressive pathways are often activated by tumor cells to evade immune surveillance, thus hijacking the cancer immunity cycle (Figure 1.8) and escaping the cytotoxic anti-tumor specific response. This mechanism is also referred to as immunoediting (Zitvogel, Tesniere, and Kroemer 2006). Under normal physiological conditions, immune checkpoints act as immune regulators, promoting self-tolerance and preventing autoimmunity (Nishimura et al. 1999, 2001). However, when expressed on the tumor cell surface, these immune checkpoints act as a repressor of effector T lymphocyte thus helping cancer cells to evade cytotoxic immune response (Figure 1.9) (Wei, Duffy, and Allison 2018). Blocking these immune checkpoints with monoclonal antibodies appeared as a promising treatment solution for cancer patients (Pardoll 2012).
CTLA4 is the first immune checkpoint receptor to be clinically targeted (Hodi et al. 2003). CTLA4 and CD28 are two highly homologous receptors with opposite activity and binding both to B7 ligands present on DCs (Bour-Jordan et al. 2011) (Figure 1.9). The binding of CD28 receptor on the effector T lymphocyte to B7 ligands enhances T cell amplification and T cell cytotoxic activation. However, the binding of CTLA4 to B7 ligands will repress T cell activity. CTLA4 has also been shown to promote pro-tumorigenic immunity through Tregs stimulation (Buchbinder et al. 2015).
23
Figure 1.9: Immune check inhibitors in cancer treatment.
The interplay between the immune system and cancer cells within the tumor microenvironment. Presentation of immune checkpoints blockade with monoclonal antibodies restoring cytotoxic T lymphocytes antitumor activity and relieving immunosuppression. Abbreviations: CTLA-4, cytotoxic T-lymphocyte antigen 4; CTLs, cytotoxic T lymphocytes; DC, dendritic cell; MHC, major histocompatibility complex; PD-1, programmed cell death-1; PD-L1, programmed cell death-1 ligand; TCR, T cell receptor; Tregs, regulatory T cells. Figure reprinted from (Ayoub, Al-Shami, and
Yaghan 2019).
24 immunogenic BC subtype, but early phase trials with ICB alone demonstrated disappointing results, with an overall response rate ranging from 5 to 20% (Table 1.4). Nevertheless, when ICB was used in combination with another treatment, the overall response rate rose up to 25-40% (Table 1.4). In March 2019, atezolizumab (anti-PD-L1) was approved in combination with nab-paclitaxel for patients with metastatic TNBC, following the positive phase III trial (IMpassion130, NCT02425891) (P. Schmid et al. 2018) thus increasing the enthusiasm for investigating immunotherapy agents to treat patients with metastatic BC.
Still, several patients did not derive benefit from ICB therapies, therefore, biomarkers are needed in order to better identify patients who may benefit the most from ICB therapies. Tumor mutational burden (Havel et al. 2015) and microsatellite instability (Lemery, Keegan, and Pazdur 2017) have been characterized as biomarkers in other solid tumors, but these genomic alterations are not frequent in TNBC. While PD-L1 tumor expression has appeared as a potential biomarker for BC (Table 1.4), the use of multiple antibodies assessing PD-L1 expression between studies has challenged any proper conclusion (Sylvia Adams et al. 2019; P. Schmid et al. 2018; S Loi, Adams, et al. 2017). TILs levels have also emerged as a promising biomarker in an exploratory analysis, but large prospective studies are needed for further confirmation before implementing TILs in routine clinical practice.
25
Source (Name) Phase
Population and Line of Therapy
No.
Patients Safety Profile ORR, % ClinicalTrials.gov
Monotherapy Pembrolizumab; KEYNOTE-012 (1) 1b mTNBC, PD-L1+, 15.6% 1st line 27 15.6% Gr 3-5 AEs; 5 Gr 3 AEs: anemia, aseptic meningitis, lymphopenia, headache, pyrexia; 1 death (DIC)
18.5 NCT01848834 Atezolizumab (2) 1a mTNBC, PD-L1+, later expanded to include PD-L1−, 17% 1st line 115 11% Gr 3/4 treatment-related AEs, 2% Gr 5 treatment-related AEs (1 pulmonary
hypertension, 1 not specified)
10 (12 for PD-L1+, 0 for PD-L1-) NCT01375842 Pembrolizumab; KEYNOTE-086 Cohort A (3) 2 mTNBC, PD-L1+/−, 2nd line and beyond 170
12% with Gr 3-4 AEs; 19% with irAEs of any grade, of which 1.2% were Gr 3-4 (most common: hypothyroidism/ hyperthyroidism, pneumonitis) 4.7 (4.8 PD-L1+, 4.7 PD-L1-) NCT02447003 Pembrolizumab; KEYNOTE-086 Cohort B (4) 2 mTNBC, PD-L1+, 1st line 84 10% Gr 3-4 AEs; no
discontinuations or deaths due of treatment-related Aes 23.1 NCT02447003 Combination Therapy Atezolizumab + nab-paclitaxel (5) 1b mTNBC, PD-L1 +/−, 1st to 3rd line 33 73% Gr 3-4 treatment-related AEs, most common: neutropenia, anemia, thrombocytopenia, diarrhea, pneumonia. 39 NCT01633970 Pembrolizumab + eribulin mesylate; ENHANCE (6) 1b/2 mTNBC, PD-L1 +/−, 1st to 3rd line 107 No DLTs, 66.7% Gr 3-4 AEs; most common, neutropenia and fatigue; most common immune-related Aes hypo/hyperthyroidism, rash, hyperglycemia, and pneumonitis 26.4 NCT02513472 Niraparib + pembrolizumab; TOPACIO (7) 2 mTNBC, 1st to 3rd line 46
Gr 3-4 AEs were fatigue (7%), anemia (15%), and thrombopenia (13%) 28 (Includes unconfirmed responses) NCT02657889 Atezolizumab + nab-paclitaxel (8) 3 mTNBC, or locally advanced, 451
Gr 3-4 AEs were neutropenia (8%) and peripheral neuropathy (6%)
56.0 NCT02425891
Table 1.4 : Reported Trials of Anti–CTLA-4 or Anti–PD1/PD-L1 ICB in
Metastatic TNBC.
Data retrieved from reported trials of ICB in metastatic TNBC as of June 30, 2018. References : 1, (Nanda et al. 2016); 2, (Emens et al. 2019); 3, (S Adams, Schmid, et
al. 2018); 4, (S Adams, Loi, et al. 2018); 5, (Sylvia Adams et al. 2018); 6, (Tolaney et al. 2018); 7, (Vinayak et al. 2018); 8, (P. Schmid et al. 2018). Abbreviations: AE,
26
ORR, objective response rate; PD-L1, PD-1 ligand 1.Table adapted from (Sylvia
Adams et al. 2019).
Justification and aims of the thesis
As previously stated, when starting this research program:
• TNBC was defined as a biologically aggressive disease with limited treatment options.
• Large scale genomic studies had already reported the mutational and copy-number landscape of TNBC.
• Genome-wide profiling and hierarchical clustering analyses had improved our understanding of the biological complexity and diversity characterizing TNBC. • The TME, and particularly the immune component, was recognized as an
important player associated with clinical outcome and therapy response. • TNBC has been characterized by higher mutational burden and higher TILs
levels as compared to other molecular subtypes.
• The promising treatment success of immune checkpoint blockade in melanoma and lung cancer opened the door for the development of IBC therapy in TNBC.
Despite these revolutionary improvements, several issues remained to be addressed with regards to the genomic characterization of TNBC molecular subtypes and their tumor microenvironment.
In chapter 1 of this thesis, we sought to address the following research questions:
Main research question:
1) Which mutational profiles and copy number aberrations are associated with each of the TNBC molecular subtypes?
Specific research questions:
27 2) Are they associated with specific clinic-pathologic characteristics and
histotypes?
3) Are they associated with distinct clinical outcome?
In chapter 2 of this thesis, we sought to address the following research questions:
Main research question:
1) Which TME components and immune cell populations are characterizing each TNBC molecular subtypes?
Specific research questions:
1) Are TNBC molecular subtypes associated with different spatial immune organization?
2) Are TME components, immune cell populations and spatial immune organization associated with clinical outcome?
3) Which genomic alterations and TME processes are associated with immune escape mechanism?
28
Chapter 1: Characterization of TNBC genomic and
transcriptomic heterogeneity
My contribution to this study involves: • Conceptualization and Design • Methodology
• Formal Analysis
• Literature search and Interpretation • Statistical analyses
• Visualization
• Writing of the manuscript
• Presentation of the results at the following meeting:
o U-CRC Symposium, September 2017, Brussels, BE
o EMBL Cancer Genomics, November 2017, Heidelberg, DE
o San Antonio Breast Cancer Symposium (SABCS), December 2017, San Antonio, TX, US
o Télévie Seminar, February 2018, Brussels, BE
o American Association for Cancer Research (AACR), April 2018, Chicago, IL, US
This research work is related to the following publication:
Bareche Y., Venet D., Ignatiadis M., Aftimos P., Piccart M., Rothe F., Sotiriou C.
Unravelling triple-negative breast cancer molecular heterogeneity using an integrative multiomic analysis.
Manuscript available online at : https://doi.org/10.1093/annonc/mdy024
29
1. Introduction
Triple negative breast cancer (TNBC), defined by the lack of expression of estrogen receptor (ER) and progesterone receptor (PR) and the absence of HER2 overexpression and amplification, represents about 10-20% (Bauer et al. 2007) of all breast cancers. TNBCs overall portend worse prognosis compared with other types of breast cancer with increased likelihood of early distant recurrence and death (Bauer et al. 2007). Beyond PAM50-based classification, recent efforts of genome-wide profiling have led to the recognition of 6 stable molecular subtypes of TNBC as described by Lehmann et al. (Lehmann et al. 2011) namely basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), luminal androgen receptor (LAR), mesenchymal (M) and mesenchymal stem-like (MSL). A more recent and partially overlapping classification segregated TNBC into four subtypes: like/immune-suppressed (BLIS), basal-like/immune activated (BLIA), LAR and mesenchymal (MES) (Burstein et al. 2015). In a retrospective study, Masuda et al. (Masuda et al. 2013) have examined the relationship between pathological complete response (pCR) and clinical outcome by TNBC subtypes using both PAM50 and Lehmann’s classification and found the latter to be a better predictor of pCR (Lehmann et al. 2014). While Masuda’s study is limited by its small size and retrospective nature, it raises important considerations for the potential use of molecular subtyping to better decipher the clinical heterogeneity of TNBC.
Based on these data and in view of the limited clinical benefit with targeted therapies in unselected TNBC patients, there has been a research impetus towards optimizing treatment through molecular subtyping (José Baselga et al. 2013). Little is known about the potential driving molecular events within each subtype and further insight into their underlying genomic alterations is needed. TNBCs are significantly associated with BRCA1 germline mutations and high levels of genomic instability,
TP53 (82%) and PIK3CA (10%) being the two most frequently mutated somatic genes
30 therapeutic targets in TNBC, chemotherapy still is the only standard treatment option for those patients.
Here, we aimed to study the genomic aberrations that drive each of the TNBC molecular subtypes as defined by Lehmann et al. by applying an integrative analysis combining somatic mutations, copy number aberrations and gene expression profiles of 550 TNBC derived from METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) (Curtis et al. 2012) and the TCGA (The Cancer Genome Atlas) (Koboldt et al. 2012) consortia. To our knowledge, this is the largest study that aims to in depth characterize TNBC subtype-specific alterations, with the ultimate goal to provide novel genomic-driven therapeutic strategies.
2. Materials & Methods
Data acquisition
The breast cancer dataset disclosed by the METABRIC study is hosted by the European Bioinformatics Institute (EBI) and deposited in the European Genome-Phenome Archive (EGA) at http://www.ebi.ac.uk/ega/, under accession number EGAS00000000083. It contains cDNA and normalized RNA microarray profiling of 1992 fresh-frozen breast cancer samples performed on the Affymetrix SNP 6.0 arrays and the Illumina HT-12 v3 arrays respectively. METABRIC somatic mutation profiles, performed on Illumina HiSeq 2,000, of 2,433 breast cancer patients were retrieved from Pereira et al. (Pereira et al. 2016) (available at http://github.com/cclab-brca).
The breast cancer dataset disclosed by the TCGA study is hosted by the Broad Institute and deposited in the FIREHOSE Broad GDAC at https://gdac.broadinstitute.org. It contains cDNA and raw count from mRNA-sequencing of 1093 breast cancer samples performed on the Affymetrix SNP 6.0 arrays and the Illumina HiSeq 2,000 respectively. Moreover, TCGA whole exome somatic mutation profiles, performed on Illumina GAIIx, of 977 breast cancer patients were also retrieved from GDAC platform.
