| Literature DB >> 33096476 |
Suzy M Scholl1, Jonas Beal2, Leanne de Koning3, Elodie Girard2, Marina Popovic4, Anne de la Rochefordière5, Fabrice Lecuru6, Virginie Fourchotte6, Charlotte Ngo7, Anne Floquet8, Els Mjj Berns9, Gemma Kenter10, Pierre Gestraud2, Heiko von der Leyen11, Charlotte Lecerf12, Vincent Puard3, Sergio Roman Roman3, Aurelien Latouche13, Attila Kereszt14, Balazs Balint15, Roman Rouzier16, Maud Kamal12.
Abstract
BACKGROUND: Cervical cancer (CC) remains a leading cause of gynaecological cancer-related mortality world wide and constitutes the third most common malignancy in women. The RAIDs consortium (http://www.raids-fp7.eu/) conducted a prospective European study [BioRAIDs (NCT02428842)] with the objective to stratify CC patients for innovative treatments. A "metagene" of genomic markers in the PI3K pathway and epigenetic regulators had been previously associated with poor outcome [2].Entities:
Keywords: Beta-catenin pβ-cat552 and pβ-cat675; Cervical cancer; Molecular and protein biomarkers for chemo-radiation efficiency; Molecular landscape
Mesh:
Substances:
Year: 2020 PMID: 33096476 PMCID: PMC7581879 DOI: 10.1016/j.ebiom.2020.103049
Source DB: PubMed Journal: EBioMedicine ISSN: 2352-3964 Impact factor: 8.143
Patients’ characteristics of the different BioRAIDs subpopulations.
| Clinical data | Mutation data | Mutation and CNV data | RPPA data | Mutation, CNV and RPPA data | ||
|---|---|---|---|---|---|---|
| 376 | 181 | 146 | 135 | 89 | ||
| 290 (77%) | 133 (73%) | 110 (75%) | 97 (72%) | 66 (74%) | ||
| 86 (23%) | 48 (27%) | 36 (25%) | 38 (28%) | 23 (26%) | ||
| 108 (29%) | 60 (33%) | 50 (34%) | 46 (34%) | 32 (36%) | ||
| 104 (28%) | 54 (30%) | 41 (28%) | 42 (31%) | 27 (30%) | ||
| 220 (58%) | 126 (70%) | 104 (71%) | 93 (69%) | 62 (70%) | ||
| 52 (14%) | 1 (<1%) | 1 (<1%) | 0 | 0 | ||
| 308 (82%) | 148 (82%) | 118 (81%) | 112 (83%) | 72 (81%) | ||
| 43 (11%) | 19 (10%) | 16 (11%) | 15 (11%) | 11 (12%) | ||
| 15 (4%) | 11 (6%) | 9 (6%) | 7 (5%) | 5 (6%) | ||
| 9 (2%) | 3 (10%) | 3 (2%) | 1 (<1%) | 1 (1%) | ||
| 1 (<1%) | 0 | 0 | 0 | 0 | ||
| 242 (64%) | 112 (62%) | 89 (61%) | 89 (66%) | 55 (62%) | ||
| 76 (20%) | 35 (19%) | 30 (21%) | 21 (16%) | 16 (18%) | ||
| 58 (15%) | 34 (19%) | 27 (18%) | 25 (19%) | 18 (20%) | ||
CNV= copy number variation; FIGO 2018 integrating lymph nodes status under IIIC; PFS=Progression free survival; HPV type (based on hybridisation test)2: Clade 9 (HPV 16.31.33.35.52.58) & Clade 7 (HPV 18.39.45.59.68); NACT=Neaodjuvant chemotherapy, NA=not available.
Fig. 1Venn diagram illustrating the number of patients for the different combinations of omics types and clinical data. (CNV = copy number variation; RPPA = reverse phase protein array).
Fig. 2Coefficient values in Cox boost of frequent genetic variants associated with worse or better prognosis using all available omics types (mutational, copy number variation and RRPA variables) clinical data (panel a); clinical data with RPPA variables (panel b); gene and copy number variants and mutations (panel c) and mutations only (panel d). (mut = mutations; CNV = copy number variations; RPPA = reverse phase protein array).
Fig. 3Expression levels of phosphobeta-catenin-Ser552 (panel a) and 14–3–3 protein (panel b), IDO (panel c) and phosphobeta-catenin-Ser675 (panel d) per RPPA cluster.
Fig. 4Kaplan Meier progression free survival curves as a function of tumour heterogeneity at start (number of mutations per patients from a defined list of genes), limited to the following molecular alterations that were detected in a sizable proportion of patients (>5%). The probability of survival is the probability that the members of each group did not experience death or relapse at each time point.
Fig. 5Pattern of frequencies of molecular alterations of significance by individual patient. Panel a with mutations only and Panel b integrating mutations and CNV.