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Oncology oracle
- 2. Use cell line data to better match cancer patients and therapies
- 3. Cell lines are a rich source of drug sensitivity data, enables the training of 98 individual drug models 39, 571 Unique Drug - Cell line Sensitivity Values Predict 675 Human cancer cell lines with RNA-seq Gene Expression
- 4. Linear models provide ease of interpretability and transparency Drug Sensitivity Linear Model
- 5. Ridge regression keeps only a few significant genes in model Drug Sensitivity Linear Model Ridge Regression
- 6. Automatic Relevance Determnation Regression tunes model sparsity from data Drug Sensitivity Linear Model Ridge Regression Automatic Relevance Determination Regression
- 7. Start with only the most highly varying genes 8000 of the highest varying genes in the Genentech data were chosen as features to reduce noise and for computational tractability Drug Sensitivity Linear Model Ridge Regression Automatic Relevance Determination Regression
- 8. Docetaxel model separates sensitive and resistant breast cancer patients with 0.87 ROC AUC False Positive Rate True Positive Rate Increasing Predicted Docetaxel Sensitivity 91% of sensitive patients treated 85% of resistant patients avoid unnecessary treatment
- 9. Model predicts Cisplatin response in Lung Squamous Cell Carcinoma
- 10. About Me ©2014NatureAmerica,Inc.Allrightsreserved. T E C H N I C A L R E P O RT S Functional interpretation of genomic variation is critical to understanding human disease, but it remains difficult to predict the effects of specific mutations on protein interaction networks and the phenotypes they regulate. We describe an analytical framework based on multiscale statistical mechanics that integrates genomic and biophysical data to model the human SH2-phosphoprotein network in normal and cancer cells. We apply our approach to data in The Cancer Genome Atlas (TCGA) and test model predictions experimentally. We find that mutations mapping to phosphoproteins often create new interactions but that mutations altering SH2 domains result almost exclusively in loss of interactions. Some of these mutations eliminate all interactions, but many cause more selective loss, thereby rewiring specific edges in highly connected subnetworks. Moreover, idiosyncratic mutations appear to be as functionally consequential as recurrent mutations. By synthesizing genomic, structural and biochemical data, our framework represents a new approach to the interpretation of genetic variation. TCGA and similar projects have generated extensive data on the muta - tional landscape of tumors1. To understand the functional consequences of these mutations, it is necessary to ascertain how they alter the pro- tein-protein interaction (PPI) networks involved in regulating cellular Low-throughput methods such as fluorescence polarization spec- troscopy provide precise interaction data on a few dozen PIDs and ligands but cannot easily be scaled to the full proteome2, whereas high-throughput array-based methods provide greater scale but suffer from systematic artifacts and high false positive and false negative rates, resulting in data sets that only partly agree2. An additional challenge is modeling the effects of mutations for pro- teins with multiple binding domains and/or multiple sites of phos- phorylation9, a reality for most signaling proteins (for example, the CRK oncoprotein). Existing methods are either limited to individual domains10–13 or are insufficiently precise to discern the effects of single- residue changes14,15. The MSM framework we have developed combines genomic, bind- ing and structural data and reconciles inconsistencies within and among data sets to generate PID networks for normal and cancer cells. We develop a bottom-up first-principles approach, involving a single mathematical equation based on statistical mechanical ensem- bles, that models domains, proteins and networks, and we then apply this approach to the analysis of SH2 networks and mutations found in TCGA16. We validate newly predicted interactions experimentally and demonstrate the sensitivity of MSM to single-residue mutations that cause subtle changes in binding affinity. Our analysis provides mechanistic insights into an important PID cancer network and vali- dates a computational approach to PID networks that can be applied A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks Mohammed AlQuraishi1–3, Grigoriy Koytiger1,3, Anne Jenney1, Gavin MacBeath2 & Peter K Sorger1 PTPN11 (SHP2)GRB2 STAT5A STAT6 STAT1 STAT4 STAT2 STAT3STA P1TENC1 TNS1 TNS3 TNS4 RIN 1 SH2D5 SH2B1 SH2B2 SH2B3 SHC1 SHC3 PLCG1 PLCG2 BCAR3 SH2D3C SH2D3A FER FES CRKCRKLMATKHSH2DSH2D2A SHBSHFSHDSHEFGRYES1 SRC H C K LCK LYN BLK SLA2 PTK6 PTPN6 PTPN11 ABL1 GRAP GRB2 NCK1 NCK2 PIK3R1 PIK3R3 PIK3R22NHC ITK TXK BTK TEC BMX SYK ZAP70 GRB7 GRB14 GRB10 SH2D1A SH2D1B IN PPL1 BLNK LCP2 DAPP1VAV1VAV3VAV2RASA1SH3BP2 JAK2 JAK3 SUPT6H CBL ANKS1A ANKS1B GULP1 NUMB NUMBL DAB1 DAB2 APBA1 APBA2 APBA3 CCM 2 D O K 1 DO K2 DO K4 DOK6 DOK5FRS3IRS1 IRS4 APBB1APBB2APBB3 SHC2 APPL1 EPS8L2 STAT5A STAT6 STAT1 STAT4 STAT2 STAT3STA P1TENC1 TNS1 TNS3 TNS4 RIN 1 SH2D5 SH2B1 SH2B2 SH2B3 SHC1 SHC3 PLCG1 PLCG2 BCAR3 SH2D3C SH2D3A FER FES CRKCRKLMATKHSH2DSH2D2A SHBSHFSHDSHEFGRYES1 SRC H C K LCK LYN BLK SLA2 PTK6 PTPN6 PTPN11 ABL1 GRAP GRB2 NCK1 NCK2 PIK3R1 PIK3R3 PIK3R2CHN2 ITK TXK BTK TEC BMX SYK ZAP70 GRB7 GRB14 GRB10 SH2D1A SH2D1B IN PPL1 BLNK LCP2 DAPP1VAV1VAV3VAV2RASA1SH3BP2 JAK2 JAK3 SUPT6H CBL ANKS1A ANKS1B GULP1 NUMB NUMBL DAB1 DAB2 APBA1 APBA2 APBA3 CCM 2 D O K 1 DO K2 DO K4 DOK6 DOK5FRS3IRS1 IRS4 APBB1APBB2APBB3 SHC2 APPL1 EPS8L2 Ph.D. Harvard University Department of Chemistry and Chemical Biology Postdoc Harvard Medical School Department of Systems Biology Management Consultant Dean & Co.
- 11. Model predicts Gemcitabine response in Lung Cancer Patients
- 12. Drug Train Size Spearman r Spearman p 17-AAG 401 0.35 0.00 681640 282 0.17 0.01 A-443654 107 0.04 0.66 A-770041 107 0.30 0.00 ABT-263 294 0.15 0.01 ABT-888 296 0.09 0.14 AEW541 281 0.14 0.02 AG-014699 293 0.27 0.00 AICAR 296 0.29 0.00 AKT inhibitor VIII 301 0.25 0.00 AMG-706 294 0.20 0.00 AP-24534 301 0.17 0.00 AS601245 300 0.12 0.03 ATRA 296 0.20 0.00 AUY922 300 0.09 0.12 AZ628 106 0.30 0.00 AZD-0530 109 0.13 0.17 AZD-2281 296 0.21 0.00 AZD0530 282 0.27 0.00 AZD6244 394 0.17 0.00 AZD6482 283 0.21 0.00 AZD7762 296 0.25 0.00 AZD8055 292 0.24 0.00 Afatinib 240 0.18 0.01 Axitinib 296 0.18 0.00 BAY 61-3606 300 0.22 0.00 BI-2536 107 0.03 0.73 BIBW2992 296 0.33 0.00 BIRB 0796 294 0.14 0.02 BMS-509744 107 0.17 0.09 BMS-536924 107 0.41 0.00 BMS-708163 294 0.19 0.00 BMS-754807 300 0.12 0.04 BX-795 293 0.37 0.00 Bexarotene 303 0.11 0.06 Bicalutamide 306 0.16 0.01 Bleomycin 300 0.38 0.00 Bortezomib 122 0.00 0.96 Bosutinib 298 0.25 0.00 Drug Train Size Spearman rSpearman p Bryostatin 1 300 0.13 0.02 CCT007093 294 0.05 0.37 CCT018159 287 0.21 0.00 CEP-701 296 0.25 0.00 CGP-082996 107 0.25 0.01 CGP-60474 107 -0.07 0.49 CHIR-99021 301 0.05 0.36 CI-1040 294 0.49 0.00 CMK 107 0.08 0.40 Camptothecin 296 0.24 0.00 Cisplatin 296 0.14 0.01 Cyclopamine 106 0.13 0.18 Cytarabine 299 0.28 0.00 DMOG 301 0.27 0.00 Dasatinib 117 0.33 0.00 Docetaxel 305 0.46 0.00 Doxorubicin 330 0.12 0.03 EHT 1864 294 0.11 0.06 Elesclomol 296 0.28 0.00 Embelin 300 0.18 0.00 Epothilone B 300 0.23 0.00 Erlotinib 346 0.29 0.00 Etoposide 323 0.15 0.01 FH535 300 0.21 0.00 FTI-277 301 0.03 0.60 GDC-0449 296 -0.03 0.61 GDC0941 291 0.12 0.04 GNF-2 107 -0.04 0.71 GSK-1904529A 300 0.14 0.01 GSK-650394 300 0.23 0.00 GSK269962A 106 -0.05 0.64 GW 441756 296 0.18 0.00 GW843682X 107 0.07 0.44 Gefitinib 300 0.26 0.00 Gemcitabine 316 0.12 0.04 IPA-3 300 0.30 0.00 Imatinib 128 0.18 0.04 Irinotecan 165 0.13 0.09 JNJ-26854165 293 0.34 0.00
- 13. Drug Train Size Spearman rSpearman p JNK Inhibitor VIII294 0.11 0.06 JNK-9L 301 0.04 0.52 JW-7-52-1 107 -0.03 0.77 KIN001-135 107 0.09 0.38 KU-55933 293 0.20 0.00 L-685458 275 0.22 0.00 LAQ824 300 -0.03 0.57 LBW242 281 -0.04 0.48 LFM-A13 300 0.03 0.58 Lapatinib 354 0.32 0.00 Lenalidomide 297 0.00 0.93 MG-132 107 -0.23 0.02 MK-2206 275 0.16 0.01 MS-275 105 0.12 0.24 Methotrexate 304 0.25 0.00 Midostaurin 301 0.24 0.00 Mitomycin C 316 0.12 0.03 NSC-87877 301 0.19 0.00 NU-7441 293 0.10 0.08 NVP-BEZ235 292 0.14 0.01 NVP-TAE684 109 0.20 0.03 Nilotinib 383 0.07 0.15 Nutlin-3 282 0.23 0.00 Nutlin-3a 294 0.33 0.00 OSI-906 300 0.25 0.00 OSU-03012 300 0.23 0.00 Obatoclax Mesylate300 0.35 0.00 PAC-1 300 0.16 0.01 PD-0325901 401 0.49 0.00 PD-0332991 378 0.22 0.00 PD-173074 294 -0.06 0.31 PF-02341066 109 0.01 0.91 PF-4708671 293 -0.03 0.62 PF-562271 301 0.23 0.00 PF2341066 282 0.24 0.00 PHA-665752 348 0.21 0.00 PLX4720 398 0.24 0.00 Paclitaxel 369 0.17 0.00 Panobinostat 278 0.44 0.00 Drug Train Size Spearman rSpearman p Parthenolide 106 0.07 0.49 Pazopanib 304 0.06 0.27 Ponatinib 273 0.14 0.02 Pyrimethamine 106 0.07 0.46 QS11 300 0.11 0.06 RAF265 258 -0.02 0.77 RDEA119 292 0.51 0.00 RO-3306 294 0.14 0.02 Rapamycin 109 0.02 0.85 Roscovitine 105 -0.06 0.54 S-Trityl-L-cysteine107 0.05 0.62 SB 216763 281 0.08 0.17 SB590885 282 0.19 0.00 SL 0101-1 291 0.00 0.99 Salubrinal 106 -0.18 0.07 Shikonin 301 0.21 0.00 Sirolimus 106 0.05 0.63 Sorafenib 353 0.02 0.73 Sunitinib 125 0.31 0.00 TAE684 282 0.14 0.02 TGX221 107 0.11 0.25 TKI258 282 0.09 0.13 TW 37 293 0.24 0.00 Temsirolimus 298 0.27 0.00 Thapsigargin 300 0.30 0.00 Tipifarnib 300 0.08 0.17 Topotecan 295 0.28 0.00 VX-680 107 -0.03 0.75 VX-702 296 -0.08 0.18 Vinblastine 306 0.29 0.00 Vinorelbine 312 0.15 0.01 Vorinostat 307 0.23 0.00 WH-4-023 107 0.18 0.07 WZ-1-84 107 0.19 0.05 XMD8-85 106 0.36 0.00 Z-LLNle-CHO 107 -0.17 0.07 ZD-6474 277 0.13 0.04 ZM-447439 283 0.02 0.77
- 14. Rescaling Breast Cancer dataset NCI-60 U95 Array Clincal U95 Array Combat Batch Correct NCI-60 U95 Array Clincal U95 ArrayNCI-60 RNA-Seq Fit Gene Specific Linear Model Apply Linear Rescaling Clincal “RNA-Seq” Prune genes with correlation p<0.001
- 15. ARD Regression y λ ~ N (0, λIn) w w ~ N (0, α ) α X y = Xw + λ1 λ2 p(λ) = Γ(λ1, λ2) γi 1 γi 2 p(αi ) = Γ(γi 1, γi 2) Drug Sensitivity Linear Model Ridge Regression Automatic Relevance Determination Regression