Clasificación Molecular del Cáncer de Próstata. JM Piulats

Transcription

1 Clasificación Molecular del Cáncer de Próstata JM Piulats

2 Introduction The Gleason score is the major method for prostate cancer tissue grading and the most important prognostic factor in this disease. However, the Gleason score does not provide information on therapy selection. Patients are currently grouped by clinical stage or treatment status. This approach does not have a mechanistic foundation that can guide the proper sequences or combinations of molecularly targeted therapies.

3 Tumor Heterogeneity CS Cooper et al. Nature Genet 47, (2015) L Wei et al. Eur Urol 71, (2017)

4 Tumor Heterogeneity CS Cooper et al. Nature Genet 47, (2015) L Wei et al. Eur Urol 71, (2017) Mutations present at high levels in morphologically normal tissue distant from cancer. Existence of ongoing mutational processes consistent with field effects. Earlier or separate clonal expansions.

5 A Kumar et al. Nature Med 22, (2016) Tumor Heterogeneity There is limited diversity among metastases within an individual.

6 Basic Biology

7 Basic Biology Cancer Genome Atlas Research Network. Cell 163, (2015) D Robinson et al. Cell 161, (2015)

8 Basic Biology

9 Basic Biology

10 Prostate Cancer Datasets > 1800 patients

11 Cancer Genome Atlas Research Network. Cell 163, (2015) D Robinson et al. Cell 161, (2015) Prostate Cancer Datasets 333 patients Primary Tumors 150 patients (mostly pre-abi/enza) Metastatic tumors PSA Recurrence 11.74% Median Follow-up close to 2 years TCGA (n) ECDT (n) Whole-exome Seq Yes (333) Yes (150) Copy-number Changes Yes (333) Yes (150) DNA-methyla on Yes (333) No Low pass (100) High pass (19) No RNA-seq Yes (333) Yes (150) microrna-seq Yes (330) No Reverse-phase Protein-array Yes (152) No Whole-genome Seq DNA-based RNA-based Protein-based

12 Cancer Genome Atlas Research Network. Cell 163, (2015) D Robinson et al. Cell 161, (2015) Prostate Cancer Datasets

13 Cancer Genome Atlas Research Network. Cell 163, (2015) D Robinson et al. Cell 161, (2015) Prostate Cancer Datasets

14 Cancer Genome Atlas Research Network. Cell 163, (2015) TCGA Primary Tumor - HSPC

15 Cancer Genome Atlas Research Network. Cell 163, (2015) ETS negative ETS positive TCGA Primary Tumor - HSPC

16 Cancer Genome Atlas Research Network. Cell 163, (2015) TCGA Primary Tumor - HSPC ETS negative ETS positive PI3K and TP53 mutations TMPRSS2-ERG presence in: TUR associated with risk of death from PCa. Active surveillance associated with increased risk of progression. Associated with younger age at diagnosis.

17 Cancer Genome Atlas Research Network. Cell 163, (2015) ETS positive TCGA Primary Tumor - HSPC SPOP wt SPOP mutant ETS negative CHD1 and SPINK1 No clear association with clinical or pathological parameters. Tumors among highest androgen receptor transcriptional activity. Modulates DNA-Ds break repair, associated with genomic instability, and sensitizes to PARPi.

18 Cancer Genome Atlas Research Network. Cell 163, (2015) ETS negative ETS positive TCGA Primary Tumor - HSPC 10% SPINK1 Mutually exclusive with ERG rearrangements. Shorter time to BQ recurrence than SPINK1-. Independent predictor for clinical recurrence.

19 Cancer Genome Atlas Research Network. Cell 163, (2015) ETS negative ETS positive TCGA Primary Tumor - HSPC Tumors among highest transcriptional activity. androgen receptor

20 Cancer Genome Atlas Research Network. Cell 163, (2015) ETS negative ETS positive TCGA Primary Tumor - HSPC Methylator phenotype. Actionable mutation. Early age of onset.

21 Cancer Genome Atlas Research Network. Cell 163, (2015) TCGA Primary Tumor - HSPC No ERG rearrangement With ERG rearrangement

22 Cancer Genome Atlas Research Network. Cell 163, (2015) TCGA Primary Tumor - HSPC No ERG rearrangement With ERG rearrangement

23 D Robinson et al. Cell 161, (2015) SU2C Metastases - CRPC

24 D Robinson et al. Cell 161, (2015) SU2C Metastases - CRPC

25 Expression Arrays

26 S You et al. Cancer Res 76, (2016) Expression Arrays

27 S You et al. Cancer Res 76, (2016) Expression Arrays

28 S You et al. Cancer Res 76, (2016) Expression Arrays

29 S You et al. Cancer Res 76, (2016) Expression Arrays

30 S You et al. Cancer Res 76, (2016) Expression Arrays Investigate Gene Sets Curated Gene Sets Genes down-regulated upon IL6 deprivation. BRCA2 network of transcripts. Lung cancer poor survival. High grade breast cancer. NRAS signaling. Cervical cancer proliferation. Dividing cells. CHECK2 network of transcripts. KRAS signaling. Down-regulated after acute transplant rejection. Transport of inorganic cations and aa. Up-regulated in GIST. Good survival in HCC. Down-regulated in PC. Down-regulated during differentiation. Up-regulated in CD31- stem cells. Down-regulated in basal breast cancer. Down-regulated in KRAS signal.

31 Conclusions: When comparing primary tumors, molecular classification might hint small differences in prognostic between different groups. Molecular classification gets blurry when comparing samples from mcrpc. Importance of tumor biopsies. Molecular classification still not useful to decide patient treatment. But close?. Intra-patient tumor heterogeneity seems not to be a problem when comparing between metastases. Liquid biopsy?.