clasificación molecular del cáncer de próstataclasificación molecular del cáncer de próstata...

Clasificación Molecular delCáncer de Próstata

Clasificación Molecular delCáncer de Próstata

JM Piulats

• The Gleason score is the major method for prostate cancer tissue grading and the mostimportant 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 propersequences or combinations of molecularly targeted therapies.

Introduction

• The Gleason score is the major method for prostate cancer tissue grading and the mostimportant 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 propersequences or combinations of molecularly targeted therapies.

Tumor HeterogeneityCS Cooper et al. Nature Genet 47, 367-372 (2015)

L Wei et al. Eur Urol 71, 183-192 (2017)

Tumor HeterogeneityCS Cooper et al. Nature Genet 47, 367-372 (2015)

L Wei et al. Eur Urol 71, 183-192 (2017)

• Mutations present at highlevels in morphologicallynormal tissue distant fromcancer.

• Existence of ongoingmutational processesconsistent with field effects.

• Earlier or separate clonalexpansions.

• Mutations present at highlevels in morphologicallynormal tissue distant fromcancer.

• Existence of ongoingmutational processesconsistent with field effects.

• Earlier or separate clonalexpansions.

Tumor HeterogeneityA Kumar et al. Nature Med 22, 369-378 (2016)

• There is limited diversity among metastases within an individual.

Basic Biology

Cancer Genome Atlas Research Network. Cell 163, 1011-1025 (2015)D Robinson et al. Cell 161, 1215-1228 (2015)Basic Biology

Basic Biology

114

57

17661

12

150

499

> 1800 patients

Prostate Cancer Datasets

112

57

477216

333

333 patientsPrimary Tumors

PSA Recurrence 11.74%Median Follow-up close to 2 years

150 patients (mostly pre-ABI/ENZA)Metastatic tumors

Cancer Genome Atlas Research Network. Cell 163, 1011-1025 (2015)D Robinson et al. Cell 161, 1215-1228 (2015)Prostate Cancer Datasets

333 patientsPrimary Tumors

PSA Recurrence 11.74%Median Follow-up close to 2 years

DNA-based

RNA-based

Protein-based

TCGA (n) ECDT (n)

Whole-exome Seq Yes (333) Yes (150)

Copy-number Changes Yes (333) Yes (150)

DNA-methyla on Yes (333) No

Whole-genome Seq 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

Cancer Genome Atlas Research Network. Cell 163, 1011-1025 (2015)D Robinson et al. Cell 161, 1215-1228 (2015)Prostate Cancer Datasets

Cancer Genome Atlas Research Network. Cell 163, 1011-1025 (2015)

TCGA Primary Tumor - HSPC



ETS p

ositi

veET

S neg

ativ

e



ETS p

ositi

ve

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.

ETS n

egat

ive



ETS p

ositi

ve

SPOP wt SPOP mutant

ETS n

egat

ive

CHD1 and SPINK1

• No clear association with clinical or pathologicalparameters.

• Tumors among highest androgen receptortranscriptional activity.

• Modulates DNA-Ds break repair, associated withgenomic instability, and sensitizes to PARPi.



ETS p

ositi

veET

S neg

ativ

e 10% SPINK1 • Mutually exclusive with ERG rearrangements.• Shorter time to BQ recurrence than SPINK1-.• Independent predictor for clinical recurrence.



ETS p

ositi

veET

S neg

ativ

e

Tumors among highest androgen receptortranscriptional activity.



ETS p

ositi

veET

S neg

ativ

e

• Methylator phenotype.• Actionable mutation.• Early age of onset.


TCGA Primary Tumor - HSPCNo ERG

rearrangementWith ERG

rearrangement

D Robinson et al. Cell 161, 1215-1228 (2015)

SU2C Metastases - CRPC

Expression Arrays

S You et al. Cancer Res 76, 4948-4958 (2016)

Expression Arrays

S You et al. Cancer Res 76, 4948-4958 (2016)

Expression Arrays

Investigate Gene SetsCurated 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.

• 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.

Conclusions:

• When comparing primary tumors, molecular classification might hint small differencesin prognostic between different groups.

• Molecular classification gets blurry when comparing samples from mCRPC. Importanceof tumor biopsies.

• Molecular classification still not useful to decide patient treatment. But close?.

• Intra-patient tumor heterogeneity seems not to be a problem when comparingbetween metastases. Liquid biopsy?.

• When comparing primary tumors, molecular classification might hint small differencesin prognostic between different groups.

• Molecular classification gets blurry when comparing samples from mCRPC. Importanceof tumor biopsies.

• Molecular classification still not useful to decide patient treatment. But close?.

• Intra-patient tumor heterogeneity seems not to be a problem when comparingbetween metastases. Liquid biopsy?.

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