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heterogeneity Population > patient > tissue > genome - - PowerPoint PPT Presentation

Oct 17, 2022 253 likes •673 views

Dissecting cancer heterogeneity Population > patient > tissue > genome Florian Markowetz CRUK Cambridge Institute www.markowetzlab.org Heterogeneity in cancer

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Florian Markowetz CRUK Cambridge Institute www.markowetzlab.org

Dissecting cancer heterogeneity

Population > patient > tissue > genome

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Heterogeneity in cancer

Inter-patient population subtypes Intra-patient spatial, temporal Intra-tumor tissue Intra-tumor genetic

                  

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Systems Genetics of Cancer

  • What are prognostic subtypes of cancer?
  • Which genetic events drive tumour

development?

  • What are markers to predict disease

progression?

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Population heterogeneity

                  

Curtis et al, Nature 2012

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METABRIC – genomic and transcriptional landscape of breast cancer

Dataset 1 ~1000 samples Dataset 2 ~1000 samples

mRNA Copy number changes miRNA SNPs Histopathology Clinical information

~400 paired normals

https://www.ebi.ac.uk/ega/studies/EGAS00000000083

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Intra-patient heterogeneity Spatial and temporal heterogeneity in ovarian cancer predicts survival

                  

Schwarz et al, submitted

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Intra-patient heterogeneity in HGSOC

OV03/04 study

  • 17 patients
  • 3-30 samples per patient
  • Biopsy, surgery and relapse
  • Pre- and post-chemotherapy

HGSOC

  • Multiple metastases
  • Good initial response
  • Often resistant relapse
  • Genomic rearrangements
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Cancer is a disease of the genome

http://www.nasa.gov/images/content/514467main_41s_factoring_DNA_1024.jpg
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Cancer is a disease of the tissue

http://clincancerres.aacrjournals.org/content/18/16/4266/F3.expansion.html
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Hanahan and Weinberg (2001)

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Genomics Tissue

Comprehensive portraits of cancer

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DNA RNA Protein ChIP

Van’t Veer et al (2002) http://ms.lbl.gov Ross-Innes et al (2012)

Tumors are complex tissues

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Intra-tumor heterogeneity Quantitative image analysis of cellular heterogeneity complements genomics

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Yuan et al, Science Trans Med 2012

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Automated image analysis

Supervised classification Spatial smoothing Cell types and location

H&E Yinyin Yuan

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CRImage

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Man vs Machine

Raza Ali

(Caldas lab)
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Quantitative analysis of tumour composition

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Spatial features of tissue

  • rganisation

Spatial statistics (K-score) Uniform Clustered

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Spatial features of tissue

  • rganisation
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SLIDE 23 Rimm D L Sci Transl Med 2011;3:108fs8-108fs8
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Spatial features of tumour tissue

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Morphological heterogeneity

Morphological features S tandard Deviation Median S kewness P rognosis S ignaling pathways Genomic aberrations A H&E C ancer S tromal Lymphocyte

Morphological features

  • 1. Fraction of pixels outside of the circle with radius effr
  • 2. Shape factor,
  • 3. 1st Hus translation/scale/rotation invariant moment
  • 4. Eccentricity calculated based on geometric information
  • 5. Eccentricity calculated based on image moments

Yinyin Yuan

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Morpho-genomic subtypes

Yinyin Yuan

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Morphology <-> Gene expression

median_C-g.I1 MTERFD1 HSF1 EXOSC4 UTP23 sd_C-g.ecc sd_C-g.I2 sd_C-m.ecc median_C-g.acirc ATAD2 MTBP FAM91A1 BOP1 MCM4 TOP1MT MCM10 CCNE2 MASTL RECQL4 RAD51AP1 NCAPD2 PDSS1 C19orf2 SLMO2 CMAS DSCC1 CSE1L PHF20L1 GINS4 FOXM1 CASP2 HINT3 POP1 GMPS YWHAZ TUBG1 skewness_C-g.ecc HAGH

Yinyin Yuan

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JAM3 – driver of cell morphology

Yinyin Yuan Chris Bakal

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Xin Wang

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Anne Trinh Stainings in tissue microarrays Comparison to gene expression classifier

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Spatial features are predictive

Anne Trinh

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SLIDE 32 Feedback Box: * Instructions * Errors Plotting Area Image Properties Cell Count: * Background Thresholding * H-minima Watershed * SVM Cell Classification Regional Segmentation: * Label Dataset (file or directly) * KMeans-MRF (grayscale & RGB) Save: * mat file * csv & images * classifiers Load & Preprocess: * Load TMA * Detect Outline * Find Brown Area View Results

ASUMT: A Still Unnamed MATLAB Toolbox

Anne Trinh

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ER+ ERBB2 ampl HER2 expr

IFISH = IF + FISH

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Go IFISH: a toolbox for semi-automated detection of nuclei, membrane and spots Anne Trinh

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Single cell analysis

  • f stain intensities

HER 2 E R ERB B2 Anne Trinh

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Key collaboration partners

  • Carlos Caldas, Raza Ali, Suet-Feung Chin,

Oscar Rueda, Stefan Gräf @ University of Cambridge

  • Yinyin Yuan + lab

@ Institute for Cancer Research

  • Chris Bakal + lab

@ Institute for Cancer Research

  • JP Medema, Louis Vermeulen

@ Amsterdam Medical Center

  • Anne-Lise Børresen-Dale, Hege Russnes, Inga

Hansine Rye @ Oslo University

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the team

Alumni: Xin Wang Yinyin Yuan Roland Schwarz Mauro Castro Gökmen Altay

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Carlos Caldas

  • Breast Cancer

Functional Genomics

  • Cambridge

Breast Cancer Research Unit

Paul Pharoah

Strangeways Laboratories, Cambridge

  • Genetic

epidemiology

Stephen Friend

Sage Bionetworks

Jason Carroll

  • ER biology
  • ChIP-seq in

tumors

FMlab

Doug Fearon

  • Tumor

immunology

  • Tumor

microenvironment

CRUK Cambridge Institute

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Florian Markowetz CRUK Cambridge Institute www.markowetzlab.org

Dissecting cancer heterogeneity

Thank you !

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Systems Genetics = genome × phenotypes × conditions