| Literature DB >> 35796778 |
Kathrin Heinrich1,2, Lisa Miller-Phillips3,4, Frank Ziemann3,4, Korbinian Hasselmann3, Katharina Rühlmann5, Madeleine Flach5, Dorottya Biro5, Michael von Bergwelt-Baildon3,4, Julian Holch3,4, Tobias Herold3,4, Louisa von Baumgarten6, Philipp A Greif3,4, Irmela Jeremias4,7,8, Rachel Wuerstlein9, Jozefina Casuscelli10, Christine Spitzweg11, Max Seidensticker12, Bernhard Renz13, Stefanie Corradini14, Philipp Baumeister15, Elisabetta Goni16, Amanda Tufman17, Andreas Jung5,18, Jörg Kumbrink5,18, Thomas Kirchner5,18, Frederick Klauschen4,5,18, Klaus H Metzeler4,19, Volker Heinemann3,4,5, C Benedikt Westphalen20,21,22.
Abstract
PURPOSE: In 2016, the University of Munich Molecular Tumor Board (MTB) was implemented to initiate a precision oncology program. This review of cases was conducted to assess clinical implications and functionality of the program, to identify current limitations and to inform future directions of these efforts.Entities:
Keywords: Comprehensive genomic profiling; Molecular tumor board; Personalized medicine; Precision oncology; Targeted therapy
Year: 2022 PMID: 35796778 PMCID: PMC9261163 DOI: 10.1007/s00432-022-04165-0
Source DB: PubMed Journal: J Cancer Res Clin Oncol ISSN: 0171-5216 Impact factor: 4.322
Baseline characteristics of the first 1000 patients
| Baseline characteristics ( | |
| Age | Median: 56 years; range 14–86 years |
| Gender | 383 (42.0%) male, 534 (58.0%) female |
| Diagnosis (selected); | |
| Breast cancer | 148 (16.2) |
| Biliary tract cancer | 56 (6.1) |
| Cervical cancer | 15 (1.6) |
| Colorectal cancer | 100 (10.9) |
| Cancer of unknown primary | 42 (4.6) |
| Gastric cancer | 14 (1.5) |
| Glioblastoma | 15 (1.6) |
| Head and neck cancer | 11 (1.2) |
| Melanoma | 15 (1.6) |
| Neuroendocrine malignancies | 23 (2.5) |
| NSCLC | 61 (6.7) |
| Ovarian cancer | 37 (4.0) |
| Pancreatic cancer | 71 (7.8) |
| Prostate cancer | 10 (1.1) |
| Sarcoma | 54 (5.9) |
| Thyroid cancer | 46 (5.0) |
| Urinary tract cancer | 24 (2.7) |
Fig. 1Consort diagram of the first 1000 patients
Outcome in regard to the respective panel
| Panel | Total number | Actionable/Recommendation for therapy | Alteration, not druggable | No alteration | Testing not successful |
|---|---|---|---|---|---|
| In-house | 337 | 122 (36.2%) | 57 (16.9%) | 99 (29.4%) | 59 (17.5%) |
| In-house (after application of the comprehensive (OCAv3) and TMB panel) | 434 | 173 (40.0%) | 163 (37.6%) | 61 (14.1%) | 37 (8.5%) |
| Commercial | 94 | 51 (54.3%) | 32 (34.0%) | 2 (2.1%) | 9 (9.6%) |
Therapeutic recommendations of the first 1000 patients
| Therapeutic recommendation | Frequency (%) | |
|---|---|---|
| TKI | 95 | 25.3 |
| mTOR inhibitor | 74 | 19.7 |
| Immune-checkpoint-inhibitor | 40 | 10.6 |
| PARP inhibitor | 34 | 9.0 |
| Clinical trial | 32 | 8.5 |
| BRAF/MEK inhibition | 27 | 7.2 |
| Genetic counselling | 27 | 7.2 |
| Monoclonal antibody | 23 | 6.1 |
| IDH1/2 inhibitor | 17 | 4.5 |
| Additional diagnostics | 14 | 3.8 |
| Alpelisib | 10 | 2.7 |
| Trametinib/hydroxychloroquine | 10 | 2.7 |
| Androgen-receptor blockade | 9 | 2.4 |
| CDK4/6 inhibitor | 9 | 2.4 |
| Change in management | 3 | 0.8 |
TKI tyrosine kinase inhibitor, mTOR mechanistic target of rapamycin, PARP poly-[ADP-ribose-]polymerase, IDH isocitrat-dehydrogenase; CDK cyclin-dependent kinase
Not actionable alterations
| Alteration | Frequency (%) | |
|---|---|---|
| TMB LOW | 157 | 61.1 |
| KRAS | 97 | 37.8 |
| TP53 | 86 | 33.5 |
| PIK3CA | 17 | 6.6 |
| MYC | 14 | 5.4 |
| ARID1A/2 | 10 | 2.8 |
| CDKN2A | 10 | 2.8 |
| NRAS | 9 | 3.5 |
| SMAD 4 | 8 | 3.1 |
| APC | 7 | 2.7 |
| CTNNB1 | 5 | 2.9 |
| BRAF (non V600E) | 3 | 1.2 |
| PTEN | 3 | 1.2 |
Results of analysis of tumor mutational burden in 233 patients
| TMB | % | |
|---|---|---|
| Low* (≤ 5 mutations/Mb) | 157 | 67.38 |
| Intermediate* (> 5/ < 20 mutations/Mb) | 65 | 27.90 |
| High* (≥ 20/ < 50 mutations/Mb) | 9 | 3.86 |
| Very high* (≥ 50 mutations/Mb) | 2 | 0.86 |
| < 10 Mutations/Mb | 208 | 89.27 |
| ≥ 10 Mutations/Mb | 25 | 10.73 |
*Definition according to Riviere et al. (2020)
Fig. 2Outcome of NGS regarding diagnosis; alphabetical order; Absolute numbers for respective category in bars