| Literature DB >> 34646088 |
Dora Čerina1, Višnja Matković2, Kristina Katić2, Ingrid Belac Lovasić3, Robert Šeparović4, Ivana Canjko5, Blanka Jakšić6, Ana Fröbe6, Stjepko Pleština7, Žarko Bajić8, Eduard Vrdoljak1.
Abstract
Comprehensive genomic profiling (CGP) is gradually becoming an inevitable part of the everyday oncology clinical practice. The interpretation and optimal implementation of the results is one of the hot topics of modern-day oncology. According to the recent findings, uterine cancer harbors a high level of gene alterations but is still insufficiently explored. The primary goal of this project was to assess the proportion of patients with targetable mutations. Also, the aim was to define and emphasize potential opportunities as well as the problems we have faced in the first year of testing on the national level. We performed a multicentric, retrospective, nested cross-sectional analysis on the total population of Croatian patients with advanced/metastatic uterine cancer where the tumor CGP was performed during 2020. CGP of the tumor tissue of 32 patients revealed clinically relevant genomic alterations (CRGA) in 27 patients (84%) with a median of 3 (IQR 1-4) CRGA per patient. The most common CRGAs were those of phosphatide-inositol-3 kinases (PIK3) in 22 patients (69%), with 13/22 (59%) of those patients harboring PIK3CA mutation. The next most common CGRAs were ARID1A and PTEN mutations in 13 (41%) and 11 (34%) patients, respectively. Microsatellite status was determined as stable in 21 patients (66%) and highly unstable in 10 patients (31%). A high tumor mutational burden (≥10Muts/Mb) was reported in 12 patients (38%). CGP analysis reported some kind of targeted therapy for 28 patients (88%). CGP determined clinically relevant genomic alterations in the significant majority of patients with metastatic uterine cancer, defining it as a rich ground for further positioning and development of precision oncology.Entities:
Keywords: genomic profiling; mutation; precision oncology; targeted therapy; uterine cancer
Mesh:
Year: 2021 PMID: 34646088 PMCID: PMC8504363 DOI: 10.3389/pore.2021.1609963
Source DB: PubMed Journal: Pathol Oncol Res ISSN: 1219-4956 Impact factor: 3.201
Patients characteristics, disease status, and therapy received prior to comprehensive genomic profiling.
| All patients ( | ||
|---|---|---|
| n | (%) | |
| Age at diagnosis of metastatic disease, median (IQR) | 65 | (59–68) |
| Year of initial diagnosis | ||
| 2010–2017 | 8 | (25) |
| 2018 | 6 | (19) |
| 2019 | 10 | (31) |
| 2020 | 8 | (25) |
| Year of metastatic disease | ||
| 2017–2018 | 2 | (6) |
| 2019 | 12 | (39) |
| 2020 | 17 | (55) |
| Metastatic disease at initial diagnosis | 10 | (31) |
| Time from initial diagnosis to metastatic disease (months), median (IQR) | 8 | (2–19) |
| FIGO classification stage at diagnosis | ||
| I | 10 | (31) |
| II | 4 | (13) |
| III | 8 | (25) |
| IV | 10 | (31) |
| Histological subtypes | ||
| endometrial carcinoma | ||
| grade 1 | 5 | (16) |
| grade 2 | 9 | (28) |
| grade 3 | 2 | (6) |
| serous adenocarcinoma | 6 | (19) |
| clear cell carcinoma | 1 | (3) |
| mixed types | ||
| endometrial + serious adenocarcinoma | 1 | (3) |
| endometrial + clear cell carcinoma | 1 | (3) |
| uterine sarcoma | 2 | (6) |
| leimyosarcoma | 3 | (9) |
| carcinosarcoma | 2 | (6) |
| Number of previous treatment lines for metastatic disease | ||
| 1 | 12 | (39) |
| 2 | 11 | (35) |
| 3 | 6 | (19) |
| 4 | 2 | 6) |
| ECOG performance status before CGP | ||
| 0 | 25 | (78) |
| 1 | 6 | (19) |
| 2 | 1 | (3) |
Data are presented as number (percentage) of patients if not stated otherwise.
CGP, comprehensive genomic profiling; IQR, interquartile range.
Data were missing for date of metastatic disease and number of previous treatment lines for metastatic disease in 1 (3%) patient.
Total is <100% due to a rounding error.
The results of the comprehensive genomic profiling.
| All patients ( | ||
|---|---|---|
| n | (%) | |
| Time from the metastatic disease to the | 6 | (2–14) |
| CGP (months), median (IKR) | ||
| Genomic alterations | ||
| any genomic alteration | 31 | (97) |
| clinically relevant | 27 | (84) |
| clinically not relevant | 30 | (94) |
| Number of genomic alterations, median (IQR) | ||
| total number | 6 | (4–7) |
| clinically relevant | 3 | (1–4) |
| clinically not relevant | 3 | (1–5) |
| Number of clinically relevant genomic alterations | ||
| 0 | 5 | (16) |
| 1 | 5 | (16) |
| 2–3 | 10 | (31) |
| 4–5 | 8 | (25) |
| 6–13 | 4 | (13) |
| Clinically relevant genomic alterations | ||
| PIK3 pathway | 22 | (69) |
| ARID1A | 13 | (41) |
| PTEN | 11 | (34) |
| KRAS | 5 | (16) |
| PALB | 4 | (13) |
| Each of BRCA2, CTNNB1 | 3 | (9) |
| ERBB2, NRAS, RNF43 | ||
| Each of AKT1, FBXW7, FGFR2, PTCH1 | 2 | (6) |
| Each of ALK, ATM, C378R, C83fs*16 | 1 | (3) |
| CCND1, CDK-4, FANCL, FGFR1, G132V | ||
| KEAP1, MDM, MDM2, MET, MTOR, NF1 | ||
| NF2, PIK3CB, Q1835*, Q546K, R93Q | ||
| RAD54L, STK11 | ||
| Microsatellite status | ||
| stable | 21 | (66) |
| high instability | 10 | (31) |
| not determined | 1 | (3) |
| Tumor mutational burden (TMB), median (IQR) | 5 | (2–18) |
| Tumor mutational burden (TMB) | ||
| not high | 19 | (59) |
| high (≥10 mutations/Mb) | 12 | (38) |
| not determined | 1 | (3) |
Data are presented as number (percentage) of patients if not stated otherwise
CGP, comprehensive genomic profiling; IQR, interquartile range.
Total is >100% due to a rounding error.
Suggested treatment options based on the comprehensive genomic profiling.
| Suggested treatment | Targeted biomarker | Prevalence | |
|---|---|---|---|
| n | (%) | ||
| Immune check-point inhibitors (nivolumab, avelumab, atezolizumab, pembrolizumab, durvalumab, cemiplimab) | MSI-high | 10 | (31) |
| TMB>10 | 12 | (38) | |
| mTOR inhibitors (everolimus, temsirolimus) | PIK3-kinases | 22 | (69) |
| AKT1 | 2 | (6) | |
| NF2 | 1 | (3) | |
| PTEN | 11 | (34) | |
| CTNNB1 | 3 | (9) | |
| FBXW7 | 2 | (6) | |
| STK11 | 1 | (3) | |
| PIK3 inhibitor (alpelisib) | PIK3CA | 13 | (41) |
| PARP inhibitors | PALB2 | 4 | (13) |
| (olaparib, niraparib, talazoparib, rucaparib) | BRCA2 | 3 | (9) |
| MEK inhibitors | NRAS | 3 | (9) |
| (binimetinib, cobimetinib, trametinib) | NF1 | 1 | (3) |
| EGFR/HER2 TKIs (afatinib, lapatinib, dacomitinib, neratinib) | ERBB2 | 3 | (9) |
| antiHER-2 monoclonal antibodies (trastuzumab, pertuzumab, trastuzumabemtansine) | ERBB2 | 3 | (9) |
| Hedgehog pathway inhibitors (sonidegib, vismodegib) | PTCH1 | 2 | (6) |
| TKI (pazopanib) | FGFR2 | 2 | (6) |
| CDK 4/6 inhibitors (palbociclib, ribociclib) | CDK4 | 1 | (3) |