Data-Driven Approaches to Identify the Regulators of the Anticancer Immune Response

Data-driven Approaches to Identify the Regulators of the Anticancer Immune Response

Peng Jiang, Ph.D. July 9th, 2020 Bioinformatics Training and Education Program Is my result supported by other datasets?

2 Tumor Immune Evasion

T lymphocyte Cancer cell T lymphocyte Cancer cell

3 Knowledge database

• Focused Presentation • iATLAS (Thorsson et al., Immunity 2018) • TCIA (Charoentong et al., Cell Report 2017)

• Customized Analysis • TIDE (Jiang et al., Nature Medicine 2018) • TISIDB (Ru et al., Bioinformatics 2019)

4 iATLAS: Flagship website of TCGA Study the immune ecosystem from bulk tumor profiles

5 iATLAS: Flagship website of TCGA

Major result to browser, the six clusters they defined

6 Select subtypes to browse TCGA association

7 Knowledge database

• Focused Presentation • iATLAS (Thorsson et al., Immunity 2018) • TCIA (Charoentong et al., Cell Report 2017)

• Customized Analysis • TIDE (Jiang et al., Nature Medicine 2018) • TISIDB (Ru et al., Bioinformatics 2019)

8 TIDE: Tumor Immune Dysfunction and Exclusion http://tide.dfci.harvard.edu Cyotoxic T Lymphocyte

T cell Dysfunction T cell Exclusion CTL high tumor CTL low tumor

(Gajewski, Schreiber, Fu et al., 2013) 9 TIDE: Tumor Immune Dysfunction and Exclusion http://tide.dfci.harvard.edu

10 Gene Query

11 Gene Query: Example TGFB1

12 T-cell Dysfunction in CTL-high Tumors

Cyotoxic T Lymphocyte

T cell Dysfunction T cell Exclusion CTL high tumor CTL low tumor 13 High level of cytotoxic T lymphocyte indicates better survival

TCGA melanoma Cytotoxic T Lymphocyte (CTL) Mark genes: p = 0.0028 CD8A, CD8B, GZMA, GZMB, High CTL n=88 PRF1 urvival Fraction S

0.2Low 0.6 CTL 1.0 n=229 0 100 200 300 Month

14 Association between CTL and survival depends on TGFB1 level

p = 0.0028

TGFB1 high High CTL TGFB1 low (1SD above mean ) n=88 (all others)

p = 0.78 p = 0.0016 urvival Fraction

Low CTL S

0.2Low 0.6 CTL 1.0 n=19 High CTL n=229 n=71 High CTL 0 100 200 300 n=17 Month urvival Fraction urvival Fraction S S 0.2 0.6 1.0 0.2Low 0.6 CTL 1.0 n=210 0 100 200 0 100 200 300 Month Month 15 T-cell dysfunction test in a linear model Death Hazard

TGFB1 expression

CTL (Cytotoxic T Lymphocyte) Hazard = a * CTL + b * P + d * CTL * P

T cell dysfunction score = d / StdErr(d)

16 Antagonistic Interaction between CTL and TGFB1

Coef Stderr Z Pr(>|z|) Age 0.02 0.01 3.55 3.78E-04 Gender 0.02 0.17 0.12 9.07E-01 Stage 0.29 0.09 3.31 9.34E-04 CTL -0.50 0.15 -3.32 9.05E-04 TGFB1 -0.10 0.10 -1.04 3.00E-01 CTL * TGFB1 0.11 0.03 3.47 5.18E-04

Hazard = c1*Age + c2*Gender + c3*Stage + a*CTL + b*TGFB1 + d * CTL * TGFB1 Background Interaction 17 Interaction between variables matters!

TGFB1 encodes an immuno-suppressive cytokine (Sharma et al., 2017)

Coef StdErr Z-score Pr(>|z|) Coef StdErr Z-score Pr(>|z|) Age 0.02 0.01 3.95 7.85E-05 Age 0.02 0.01 3.55 3.79E-04 Gender 0.00 0.17 0.00 9.97E-01 Gender 0.02 0.17 0.12 9.08E-01 Stage 0.29 0.09 3.31 9.34E-04 Stage 0.30 0.09 3.36 7.74E-04 CTL -0.54 0.17 -3.29 9.95E-04 TGFB1 -0.13 0.09 -1.42 1.55E-01 TGFB1 -0.10 0.10 -0.97 3.31E-01 TGFB1 alone associate with lower death risk CTL*TGFB1 0.13 0.04 3.47 5.28E-04 Correlation(TGFB1, CTL) = 0.39 TGFB1 antagonizes the effect of cytotoxic T lymphocyte

18 Synergistic Interaction between CTL and SOX10

Coef Stderr Z Pr(>|z|) Age 0.02 0.01 3.26 1.11E-03 Gender 0.03 0.17 0.15 8.80E-01 Stage 0.29 0.09 3.33 8.63E-04 CTL -0.79 0.21 -3.79 1.51E-04 SOX10 -0.01 0.10 -0.11 9.10E-01 CTL * SOX10 -0.59 0.16 -3.69 2.23E-04

Hazard = c1*Age + c2*Gender + c3*Stage + a*CTL + b*SOX10 + d * CTL * SOX10 Background Interaction SOX10 level in cancer cells can promote the T-cell mediated tumor killing (Patel et al., 2017; Khong et al., 2002). 19 The p-value histogram should have a significant peak

Melanoma (SKCM) 1.0 Glioblastoma (GBM) ) % ( 6 0.

2 Non-significant Percentage Percentage 0. 0.2 0.6 1.0

0.0 0.4 0.8 0.0 0.4 0.8 p−value p−value

20 Large-scale Cancer Genomics Data Cohorts

PRECOG 135 cohorts 18K samples METABRIC

49 cohorts, 13K samples 21 Five datasets with significant p-value peaks

Melanoma (SKCM) Breast (TNBC) Neuroblastoma (NB) ) % ( Percentage Percentage 0.2 0.6 1.0 0.2 0.6 1.0 0.2 0.6 1.0

0.0 0.4 0.8 0.0 0.4 0.8 0.0 0.4 0.8 p−value p−value p−value

Endometrial (UCEC) Leukemia (AML) Percentage (%) Percentage 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0

0.0 0.4 0.8 0.0 0.4 0.8 p−value p−value 22 Genes with Significant T Dysfunction Scores

Endometrial (UCEC) Breast (TNBC) Neuroblastoma (NB) Leukemia (AML) Melanoma (SKCM) Average Profile TMEM130 LIME1 NUB1 DIDO1 WFS1 MAL KHNYN XCL1 WNT10A ADAMTS17 NAV2 SFN COL11A2 VSNL1 GUCA1A USH1G TMPRSS3 DNAJC30 NDST4 SERPINB9 COQ10A FAM65B LRMP KCNA5 ARID3B STARD3 PNMT ANKRD29 TGFB1 NFATC3 SPN ELMO1 ADGRG5 POU2F2 NLRC5 ZNFX1 PD-L1 TREML1 ICOS BTN3A1 GPR155 RORA IL2RB CD5 VOPP1 NDST3 ADAM19 KCNMA1 Z-score ( d / SE ) SE / d ( Z-score Endometrial (UCEC) 4 0 -4 Breast (TNBC) Neuroblastoma (NB) Leukemia (AML) Melanoma (SKCM)

Average Profile AMPD3 LRRC15 ZBTB7A IL21R ZBTB32 RHOF PIM2 FCRL5 LY9 PVRIG CD6 SPOCK2 KLRB1 KCNA3 GRK2 PDE1A BMP4 CACNA2D2 AXIN2 OSTC SEC23A SNRNP48 PPP3CA DDX59 SESTD1 ST3GAL6 TIPRL COPG1 NDUFS2 PEX13 HSDL2 DXO LMBRD2 PHGDH COPB2 PGM3 PAPSS1 ATP5C1 HDAC2 TRIT1 CCDC43 SOX10

23 Correlation with Cytotoxic T Lymphocyte Level

r= 0.385 , p= 1.18e−12

● 8 6

● ●

4 ●

● ● ● ●

TGFB1 ● ● ● ● ●● ● ● ● ●

2 ● ●● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ●● ● ●● ● ●●●● ● ● ● ● ● ●●●●● ● ●●●●●●●● ●●● ●● ● ● ● ● ●●●● ●● ● ● ●● 0 ● ●● ●●● ● ● ●●●●●●● ●● ● ● ● ● ● ●●●●●●●●● ● ● ● ● ●●●●●●●●●● ● ●●●● ●●●●● ●●● ● ● ●●●●●●●●●● ●●●●●●●●●●● ●●●●●●●●● ● ●●●●● ●● ●●●● 0 1 2 3 4 5 6 CTL

24 Correlation with Death Risk

Continuous z= −1.42 , p= 0.155 1.0 0.8 0.6 0.4 Survival Fraction 0.2 TGFB1 Top (n=268) TGFB1 Bottom (n=49) 0.0 0 50 100 200 300 OS (month)

25 Association with T cell Dysfunction : Core

Continuous z= 3.47 , p= 0.000518 TGFB1 High TGFB1 Low 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 Survival Fraction Survival Fraction 0.2 0.2 CTL Top (n=32) CTL Top (n=127) CTL Bottom (n=32) CTL Bottom (n=126) 0.0 0.0 0 50 100 200 300 0 50 100 150 200 250 OS (month) OS (month)

26 Association with T cell Dysfunction : More

Continuous z= 3 , p= 0.00272 TGFB1 High TGFB1 Low 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 Survival Fraction Survival Fraction 0.2 0.2 CTL Top (n=7) CTL Top (n=45) CTL Bottom (n=7) CTL Bottom (n=45) 0.0 0.0 0 10 20 30 40 0 50 100 150 OS OS

27 Exclusion : Example PTGS2

28 Tumor Immune Evasion through T-cell Exclusion

Cyotoxic T Lymphocyte

T cell Dysfunction T cell Exclusion

CTL high tumor CTL low tumor 29 Immunosuppressive Cell Types in T-cell Exclusion T cell exclusion factors Infiltration High Low

Correlation genome-wide

Cell signature Tumor expression (Gajewski, Schreiber, Fu et al., 2013) (Dougan et al., 2017) 30 Immunosuppressive cell signatures predict T-cell exclusion in tumors

Myeloid derived suppressor Tumor assoc macrophage Cancer assoc fibroblast Average signature

● ● ● ● R= −0.634 R= −0.718 R= −0.387 R= −0.722 ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● TL ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● C ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●●●● ●● ● ●● ● ●● ●●●● ● ● ●● ●● ●●●● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●●●● ●● ●● ● ●●● ●●●●● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ●●● ●● ● ●● ● ● ●●● ● ●● ● ●●● ● ● ● ●●●●● ● ● ● ● ●●●●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●●● ●●●● ● ● ● ● ●● ●●● ● ●● ● ●● ●●●●●●●●● ● ● 0123456 ● ●● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ●●●●●●●●●●● ●●● ● ● ● ●● ●●● ●●● ●●●● ●● ●●●●●● ● ● ●● ●●●●● ●●●●● ●● ●● ● ● ● ● ●● ●●●●●●●●●● ●●●● ●● ●●● ●●● ●●●●●●●●●●●● ● ●● ● ● ● ● ● ●● ● ●●●● ●● ●●●● ●● ● ● ● ●●●●●●●●●●●● ● ● ●● ● ● ● ●● ●●●●●●●●●● ●●● ●●● ● ● ● ●● ● ●●●●●●●●●●●●●●●● ●● ●● ●● ● ● ●● ●● ●●●●●●●●●●●●●●● ●●●● ●● ●●●●●●●●●●●●●●●●●●●● ●●● ● ●●● ●● ● ●●●●●●●●●●●●●●●●●●●●●● ●● ●● ●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●● ● ●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● −0.3 −0.1 0.1 −0.15 −0.05 0.05 −0.2 0.0 0.2 −0.3 −0.1 0.1 Corr(Tumor, Immune suppressive cell signature)

CTL : expression average of CD8A, CD8B, GZMA, GZMB, PRF1 31 Immunosuppressive cell signatures predict T-cell exclusion in all solid tumors ) % ( 43 solid tumor types Percentage Percentage 0 10 20 30 −1.0 0.0 1.0 Corr(CTL, Exclusion score) 32 Exclusion : Example PTGS2

33 Two Categories of Tumor Immune Evasion

Cyotoxic T Lymphocyte

T cell Dysfunction T cell Exclusion CTL high tumor CTL low tumor

(Gajewski, Schreiber, Fu et al., 2013) 34 TIDE: Tumor Immune Dysfunction and Exclusion

35 Prediction with a Two-Part Procedure CD8A, CD8B, GZMA, GZMB, PRF1

CTL All positive Others CTL high CTL low

Corr Corr Dysfunction Exclusion signature signature

36 TIDE Scores of Pre-Treatment Tumors

Hugo et al., 2016 n = 25 VanAllen et al., 2015 n = 42 Prat et al., 2017 n = 33 PD1 RNASeq CTLA4 RNASeq PD1 NanoString iction score d

high low high low high low 10 1 2 3 10 1 2 3 10 1 2 3 TIDE pre − − CTL − CTL CTL

Responder Non-responder Long−survival 37 Receiver Operating Characteristic (ROC) Curve

A B AUC : Area Under ROC Curve True positive rate True positive rate

False positive rate False positive rate Biomarker A is better than Biomarker B From 0 to 1 with random performance as 0.5 38 TIDE outperforms other biomarkers

PD1 RNASeq CTLA4 RNASeq PD1 NanoString

TIDE TIDE PDL1 PDL1 TIDE IFNG IFNG PDL1 True positive rate positive True Mutation Mutation IFNG 0.0 0.4 0.8 0.0 0.4 0.8 0.00.2 0.4 0.8 0.6 1.0 0.2 0.6 1.0 0.2 0.6 1.0 False positive rate False positive rate False positive rate

39 TIDE outperforms other biomarkers

PD1 RNASeq CTLA4 RNASeq PD1 NanoString 0.8 0.8 0.8 0.7 0.7 0.7 C AU 0.6 0.6 0.6 0.5 0.5 0.5 IPS IPS CD8 CD8 CD8 TIDE TIDE TIDE IFNG IFNG PDL1 PDL1 PDL1 SCNA SCNA IPS Mutation Mutation IFNG T.Clonality TIDE.Adeno T.Clonality TIDE.Melanoma TIDE.Squamous Mutation: nonsynonymous mutation load; PDL1: PD-L1 expression; CD8: CD8A + CD8B expression; IFNG: interferon gamma response expression signature (Ayers et al. 2017); T.Clonality: Clonality of T cell receptor; IPS: ImmunePhenoScore (Charoentong et al. 2017); SCNA: variation of copy number alterations (Davoli et al. 2017). 40 TIDE can predict patient overall survival

PD1 RNASeq CTLA4 RNASeq PD1 NanoString p = 0.011 p = 0.001 p = 0.042 Bottom

Bottom Bottom

urvival Fraction Top

S Top Top 0.0 0.4 0.8 0.0 0.4 0.8 0.0200 0.4600 0.8 1000 500 1000 1500 5 15 25 35 OS (day) OS (day) PFS (month)

41 Independent validation of TIDE

Eva Pérez-Guijarro et al., Nature Medicine 2020 @ Glenn Merlino Lab

Double-blinded test @ BMS064 42 Immunotherapy Clinical Cohorts Example : STAT1

43 CRISPR Screens Example: STAT1

44 CRISPR screen for T-cell mediated tumor killing

Pan*, Kobayashi*, Jiang* et al., Science, 2018 45 Gene Prioritization Putting everything together

46 Gene Prioritization

47 Biomarker Evaluation

48 Biomarker Comparison

49 Biomarker Comparison : Survival Plot

50 Some online demo

51 2: Cell Signaling Research Platform

https://cellsig-dev.ccr.cancer.gov (temporary domain, only within NIH firewall)

52 Data Collection

SRA GEO Controlled Vocabulary ENA AE Meta Data

Automatic Processing Curate Proofread Treatment, Condition, Dose, Duration

Expression matrix + logFC Genes

53 GDF15 GDF3 BMP2 FGF17 IL11 IFNG logFC LIF OSM 4 IL32 IL36G CCL5 PTGS2 (PGE2) CXCL8 2 CXCL2 CXCL3 IL1A IL1B CSF2 (GMCSF) 0 CCL20 CXCL1 CCL2 Response within WNT5A IL12B (IL12,IL23) −2 CCL23 EBI3 (IL35) INHBA (Activin A/AB, Inhibin A) signaling molecules CCL7 CXCL5 −4 CXCL6 IL6 CCL18 IL1RN (IL1RA) CCL24 CCL17 CCL22 GDF5 ANGPTL1 LGALS9 (Galectin 9) CD274 (PDL1) TNFSF10 (TRAIL) CXCL10 CXCL11 CCL8 Ligand CXCL9 Receptor CXCR4 (MIF,CXCL12) FGFR3 (FGFs) TNFRSF12A (TWEAK) IL7R (IL7,TSLP) FPR2 (CCL23) IL2 IL4 LIF NO IL21 IL10 IL15 IL12 IL36 IL27 IFN1 IL1B IL1A IFNL IFNG FGF2 IL17A PGE2 TNFA BMP2 BMP6 BMP4 BDNF 54 MCSF TGFB1 CD40L TGFB3 VEGFA GMCSF Activin A Gene Query

55 Signal Prediction

56 Signal Prediction input = a1 s1 + ... + an sn

signaling molecules 57 Application on Single Cell RNA-Seq

58 Application on Spatial Transcriptomics

59 Some online demo

60 Postdoc mentors Current Lab Members Beibei Ru Yu Zhang

Kai Wucherpfennig X. Shirley Liu • Deng Pan • Shengqing Gu • Aya Kobayashi Jie Yuan (expected on June, 2020) • Jingxin Fu

CDSL PIs

Eytan Ruppin S. Cenk Sahinalp Sridhar Hannenhalli Branch Chief