The lung cancer program of Alliance Against Cancer. Ruggero De Maria, President

Transcription

1 The lung cancer program of Alliance Against Cancer Ruggero De Maria, President

2 Alliance Against Cancer (ACC) was established in 2002 by the Italian Ministry of Health with the task of promoting an active collaboration among Italian cancer institutes/associations through the exchange of information, knowledge, data, scientific results and human resources, and to respond in an appropriate and harmonised way to the building of the Italian and European strategy in oncology, at the level of both patient treatment and research. With more than 3,000 beds, about 250,000 patients treated annually, and more than 5,000 scientists and clinicians working on different aspects of oncology, ACC develops new approaches in prevention, early diagnosis, treatment and rehabilitation, while promoting the diffusion of state of the art cancer care in Italy.

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4 NUMBERS Research performance of ACC Institutes Number of publications: > 5,000 / year Impact Factor: > 20,000 / year Research Grants: ~215,000,000 Euros (2014) New cancer patients every year: ~ 90,000 Active Clinical Trials: >1,000 (2014)

5 MAJOR PUBBLICATIONS (2016)

6 Information is the first therapy for cancer patients 39 information points in the major cancer centers Help line Mon-Fri from 9 a.m. to 7 p.m. AIMaC Information therapy forumtumore.aimac.it 34 booklet

7 28 Partners 19 Countries 15 Member states 3 Associated countries 1 Third country

8 May 2, 2016

9 MD Anderson Global MDACC Sister Institutions Co-Branded Institutions

10 IST IEO INT ISS IDI Humanitas Pascale HSR Besta Maugeri CRO IOV IOR IRE CROB Bari OPBG Meldola Reggio Emilia Gaslini IRCC Candiolo Lung Breast Colon ACC Working Groups Brain Melanoma Sarcoma

11 ACC Program for Precision Medicine Preclinical To develop a preclinical platform for in vitro screening of cancer drugs followed by simulation of clinical trials in avatar models to provide solid bases for effective clinical trials Clinical Phase I To validate among ACC Institutes proprietary NGS panels able to detect all actionable and major driver alterations, with the aim of obtaining a low-cost full characterization of all the tumors requiring molecular diagnostics while fostering innovative clinical trials Clinical Phase II To transfer the last innovations on liquid biopsy and transcriptome analysis to the routine diagnostics, while analyzing innovative biomarkers for immunotherapy

12 MRI Diagnosis and tissue banking H&E NGS Tumor dissociation and culture DNA RNA Biomarker-driven preclinical trials Tumorsphere expansion Banking GBM, Sarcoma, Melanoma, Colorectal, Lung, Breast and Renal carcinomas

13 Precision Medicine challenging and opportunities About 5% of Cancer Patients can be treated with the available FDA approved drugs, based on analyses of driver mutations However 40.2% of Patients can be treated with FDA approved drugs after repurposing 73.3% Patients (33.1% Further Patients) can be treated with drugs in Clinical Trials 39% of Patients can Receive Combination Therapies using FDAapproved Drugs + Drugs in Clinical Trials

14 : 315 genes, 28 gene fusions

15 Oncomine is an available Low-Cost Lung Cancer Panel Ion AmpliSeq Colon and Lung Cancer Panel Covers known (> 500) and novel mutations in 91 hotspot regions in 22 genes relevant to colon and lung tumorigenesis: KRAS, EGFR, BRAF, PIK3CA, AKT1, ERBB2, PTEN, NRAS, STK11, MEK1, ALK, DDR2, CTNNB1, MET, TP53, SMAD4, FBXW7, FGFR3, NOTCH1, ERBB4, FGFR1, FGFR2 Ion AmpliSeq RNA Fusion Lung Cancer Research Panel ALK ROS1 RET NTRK1 EML4, HIP1, KIF5B, KLC1, TPR CD74, EXR, GOPC, LRIG3, SDC4, SLC34A2, TPM3 CCDC6, CUX1, KIF5B CEL, NFASC, IRF2BP2, TFG, QSTM1, SSBP2, DYNC2H1, CD74, MPRIP

16 How to approach the Precision Medicine issue in oncology?

17 ACC Genomics The Lung-Oncochip 1. Genes (all coding sequences, amplifications, deletions ) Total: 182 Actionables: 164 Drivers: Translocations/Rearrangements (RNA) - 93 Total fusion targets - 52 Actionable partner pairs (including ALK, RET, ROS1) 3. Germline Variants - Identified in the pharmgkb database - 87 genes; (141 variants)

18 A. Actionable Genes: ACC-Lung Panel: 182 full Genes ABL1 BRCA2 DNMT3A FGFR4 JAK2 MMP2 PDGFRA RARA TYK2 ABL2 BTK DOT1L FH JAK3 MPL PDGFRB RET VEGFA AKR1B1 CBFB EGFR FKBP5 KDR MST1R PGF RICTOR VEGFB AKT1 CCND1 EPHA1 FLCN KIT MTOR PGR ROS1 YES1 AKT2 CCND2 EPHA2 FLT1 KMT2A MYCN PIK3C2B RUNX1 AKT3 CCND3 EPHA3 FLT3 LCK MYD88 PIK3CA SLTM ALK CCNE1 EPHA4 FLT4 LYN NCOR2 PIK3CB SMARCB1 APC CDK4 EPHB2 FUS MAP2K1 NF1 PIK3CD SMO AR CDK6 ERBB2 FYN MAP2K2 NF2 PIK3R1 SMOX ARAF CDKN1B ERBB3 GNA11 MAP2K4 NOTCH1 PIK3R2 SRC ATM CDKN2A ERBB4 GNAQ MAP3K1 NOTCH2 PIP5K1A STK11 ATR CDKN2B ERCC2 HDAC9 MAP3K11 NOTCH3 PLCG2 STK4 AURKA CDKN2C ESR1 HGF MAP3K4 NOTCH4 PML SYK AURKB CHEK2 EZH2 HRAS MAP4K1 NPM1 PRKCZ TAOK1 AURKC CSF1R FBXW7 HSP90AA1 MAPK1 NRAS PTCH1 TAOK2 BAP1 CSF3R FGF3 IDH1 MAPK8 NTRK1 PTEN TEK BCL2 CTNNB1 FGF4 IDH2 MDM2 NTRK2 PTPN11 TET2 BCR DDR1 FGFR1 IGF1R MET NTRK3 RAC1 TOP2A BRAF DDR2 FGFR2 IGF2 MGMT PALB2 RAD50 TSC1 BRCA1 DNMT1 FGFR3 JAK1 MITF PDGFB RAF1 TSC2 B. Driver Genes: 164 genes with the following characteristics: FDA approved drug targets Clinical Trial drug targets In any cancer ACVR1B EPB41L3 KRAS PIK3CA SLTM ARID1A ERBB2 LRP1B POTEF SMARCA4 ATM FBXW7 MAP2K1 PTEN STK11 BRAF FGFR2 NAV3 RB1 TP53 CDKN2A HRAS NF1 RBM10 U2AF1 CRIPAK KEAP1 NFE2L2 SETBP1 EGFR KMT2D NOTCH1 SETD2 33 genes identified as driver by at least 2 out of 6 bioinformatics pipeline on LUSC, LUAD and NSCLC cancer types: DOTS-Finder, MuSiC, MutSig, OncodriveCLUST/FM, TUSONexplorer, Vogelstein approach (used as confirmation) In bold, genes that are both driver and actionable

19 ACC-Lung Panel: 93 fusion genes Actionable Translocations and Rearrangements EML4-ALK CTNNA3-IGF2 NF1-PSDM11 CLTC-ROS1 HIP1-ALK EML4-ALK ECHDC1-FYN CTNNA3-IGF2 CD74-NTRK1 NF1-PSDM11 GOPC-ROS1 CLTC-ROS1 KIF5B-ALK HIP1-ALK RBM14-FGF3 ECHDC1-FYN MPRIP-NTRK1 CD74-NTRK1 LRIG3-ROS1 GOPC-ROS1 KLC1-ALK KIF5B-ALK FGFR2-KIAA1967 RBM14-FGF3 BFSP2-PIK3CB MPRIP-NTRK1 SDC4-ROS1 LRIG3-ROS1 PTPN3-ALK KLC1-ALK FGFR3-TACC3 FGFR2-KIAA1967 PIP5K1A-C1orf56 BFSP2-PIK3CB SLC34A2-ROS1 SDC4-ROS1 STRN-ALK PTPN3-ALK IKZF3-NF1 FGFR3-TACC3 SKI-PRKCZ PIP5K1A-C1orf56 KIF5B-RET SLC34A2-ROS1 TFG-ALK STRN-ALK JARID2-ATM IKZF3-NF1 CCDC6-RET SKI-PRKCZ TPM3-ROS1 KIF5B-RET TPR-ALK TFG-ALK KDM2A-DDR2 JARID2-ATM CUX1-RET CCDC6-RET SCAF11-PDGFRA TPM3-ROS1 TPR-ALK KDM2A-DDR2 CUX1-RET SCAF11-PDGFRA BRAF-SLC6A4 KIAA0182-NOTCH3 NCO4-RET SPINT2-MAP4K1 CAPZA2-MET KIF5B-MET TRIM33-RET STK11-HMHA1 KRAS-CDH13 LRP1B-HECW2 CCDC6-ROS TAOK1-ZNF385C CDKN2A-FOCAD MDM2-NUP107 CD74-ROS1 TRIM24-NTRK2 FGFR2-CIT MST1R-FNDC3B EZR-ROS1 USP32-EPHA1 52 actionable partner pairs (at least one of the genes is actionable) derived from lung cancer data: Atlas of Genetics and Cytogenetics in Oncology and Haematology Vogelstein, Science 2013 COSMIC To be performed on cdna

20 ACC-Lung Panel: 87 pharmacogenomic variants ABCB1 CYP19A1 EPHX1 HSPA5 RRM2 TMEM43,XPC ABCB1 CYP19A1 EPHX1 HSPA5 RRM2 TMEM43,XPC ABCC1 CYP1A1 ERBB2 HTR2A RRM2B TP53 ABCC2 CYP1B1 ERCC1 KLC1,XRCC3 SHMT1 TPMT ABCC3 CYP2B6 ERCC2 LRP2 SLC10A2 TYMS ABCC4 CYP2C19 ERCC2,KLC3 MTHFR SLC19A1 UGT1A1 ABCG2 CYP2C8 ESR1 NAT2 SLC22A12 UGT1A4 AKT1 CYP2D6 FCGR3A NOS3 SLC22A16 UGT1A8 BAIAP3 CYP2E1 FDPS NQO1 SLC22A2 UGT1A9 C8orf34 CYP3A4 FMO3 NQO2 SLC28A3 UMPS CASP7 CYP3A5 GALNT14 NRP2 SLC31A1 XRCC1 CBR1 DCBLD1 GRIN2B PARD3B SLC6A4 XRCC4 CBR3 DPYD GSTP1 PIK3CA SLCO1B1 ZNF423 CCND1 DSCAM HLA-DQB1 PRDX4 SLCO1B3 CDA EGF HMGCR RHBDF2 SOD2 COMT EGFR HNMT RRM1 SULT2B1 Clinical variants extracted from pharmacogenomics database (PharmGKB) Only point variant included in assay Sometimes overlapping with full genes (and included in full-gene design)

21 PERCENTAGE OF COVERED Pts. AVERAGE NR OF ACTIONABLE MUTATIONS PER Pt. The ACC Lung-Cancer Chip ACC Genomics 164 Actionable Genes 52 Actionable Fusions 96 Actionable SNPs 18 Driver Genes 5 Driver Fusions Cost: <300 per patient 99% Patients with 1 actionable mutation Actionable mutations per patient ACC: ~4.5 80% ACC: 97% 60% Foundation One: 93% Foundation One: ~3.5 40% 20% Oncomine: 25% Oncomine: 0.25 GENOMIC SPACE GENOMIC SPACE Kb: Kb: In silico screening of ~500 lung-cancer patients

22 Gene Mutated tumors ACC Genomics Most frequently mutated ACTIONABLE genes in lung ADC ACC Foundation One Oncomine STK11 80 yes yes yes EGFR 73 yes yes yes NF1 64 yes yes KDR 50 yes yes NOTCH4 49 yes ERBB4 47 yes yes yes ATM 46 yes yes HDAC9 46 yes HGF 46 yes yes EPHA3 45 yes yes NTRK3 42 yes yes BRAF 41 yes yes yes CHEK2 39 yes yes PDGFRA 38 yes yes ALK 33 yes yes yes NOTCH2 32 yes yes

23 ACC Genomics Most frequently mutated DRIVER genes in lung ADC Gene Mutated tumors ACC Foundation One Oncomine TP yes yes yes LRP1B 192 yes yes KRAS 164 yes yes yes NAV3 118 yes KEAP1 96 yes yes SETBP1 59 yes KMT2D 51 yes yes ARID1A 44 yes yes SMARCA4 43 yes yes RBM10 36 yes yes SETD2 33 yes yes RB1 31 yes yes EPB41L3 30 yes

24 Validation of the Alliance Against Cancer lung panel in 1,000 patients with Non Small Cell Lung Cancer

25 Secondary Objectives 1. To determine the percentage of enrolled NSCLC patients with an adequate amount of tissue to perform molecular analyses with standard methods. 2. To investigate the percentage of cases evaluated by NGS > (at least 90%). 3. To evaluate the frequency of the oncogenic alterations identified by NGS in comparison with the data from literature. 4. To evaluate the frequency of the oncogenic alterations (presence of KRAS mutations, ROS-1 fusions, MET exon 14 skipping mutations and BRAF mutations), identified by NGS, in comparison with standard methods.

26 Secondary Objectives 5. To evaluate the proportion of patients carrying mutations, for which an approved targeted therapy is available, according to National and International Guidelines, that have received the recommend therapy > (at least 90%). 6. To evaluate the performance of each center in terms of sensitivity, specificity and percentage of successful molecular characterization by NGS of EGFR activating mutations and EML4-ALK rearrangements. 7. To measure the time interval to obtain the molecular results (within 4 weeks from the first patient s visit to biopsy and within 10 working days from the biopsy to the final written report of molecular analyses performed). 8. To measure OS and PFS

27 Clinical Value of the Germline Actionable-Genome

28 The ACC-GerSom Gene-Panel: the phylosophy 1. Genomic profile of every patient (tumor+germline) Identification of actionable somatic mutations Identification of germline pharmacogenomic variants Identification of drivergene mutations Treatmentstratification Drug-toxicity New stratification markers Identification of Germline cancer-risk variants 2. Mapping of each risk-variant In family members Cancerprevention plans Registry of Somatic cancer-mutations Registry of Germline cancer-risk variants

29 Second Gene-Panel Strategy (Onco-Chip) The Actionable Genome (same for all tumor types) Somatic Actionability (164 genes) Germline Actionability (126 genes) Drug- Toxicity Actionability (87 genes) The Tumor-Driver Genome (tumor-type specific) Drivers identified with public and proprietary bioinformatic pipelines (385 genes) bp 9 Cancer-types: Breast, Ovary cancer Lung Cancer Colo-rectal Cancer Prostate cancer Melanomas Renal Cancer Glioblastomas Neuroendocrine tumors

30 GERMSOM Panel 1 ABL2 DOT1L JAK3 PDGFRB VEGFA NCOR1 MED12 FAM175A XPC AKR1B1 EGFR KDR PGF VEGFB RBMX U2AF1 GEN1 FANCE AKT1 EPHA1 KIT PGR YES1 SF3B2 BCOR HOXB13 WRN AKT2 EPHA2 KMT2A PIK3C2B ELANE SPEN WT1 MRE11A NBN AKT3 EPHA3 LCK PIK3CA SMARCE1 TBL1XR1 CEBPA PALLD EXT1 ALK EPHA4 LYN PIK3CB TP53 TBX3 DDX3X RAD51 RECQL4 APC EPHB2 MAP2K1 PIK3CD ARID1A ZFP36L1 WAS XRCC2 FANCG AR ERBB2 MAP2K2 PIK3R1 KMT2D CDH1 SMARCA4 XRCC3 FANCC ARAF ERBB3 MAP2K4 PIK3R2 NFE2L2 MSH3 HLA-A COL7A1 XPA ATM ERBB4 MAP3K1 PIP5K1A RB1 POLD1 MYC DOCK8 BMPR1A ATR ERCC2 MAP3K11 PLCG2 SETD2 RMRP TERT HFE CDKN1C AURKA ESR1 MAP3K4 PML KDM6A ACVR1B KMT2C POLH FANCF AURKB EZH2 MAP4K1 PRKCZ STAG2 RBM10 SMAD4 SDHA EXT2 AURKC FBXW7 MAPK1 PTCH1 ELF3 AMER1 RNF43 SLC25A13 DDB2 BAP1 FGF3 MAPK8 PTEN ARHGAP35 AXIN2 LRP1B SRY CEP57 BCL2 FGF4 MDM2 PTPN11 EP300 B2M CBL TGFBR1 ERCC5 BCR FGFR1 MET RAC1 FAT1 PCBP1 ITK SDHB FANCM BRAF FGFR2 MGMT RAD50 FRG1B SMAD2 CRIPAK MUTYH BUB1B BRCA1 FGFR3 MITF RAF1 IL32 SOX9 KEAP1 SDHC FANCI BRCA2 FGFR4 MMP2 RARA MGAM TCF7L2 NAV3 EPCAM BLM BTK FH MPL RET GBA CDC27 POTEF MSH2 SLX4 CBFB FKBP5 MST1R RICTOR RPSAP58 ACVR2A SETBP1 TMEM127 ERCC4 CCND1 FLCN MTOR ROS1 FAH HMBS DIS3L2 PMS1 FANCA CCND2 FLT1 MYCN RUNX1 SLC9A9 UROD EPB41L3 MLH1 PRKAR1A CCND3 FLT3 MYD88 SLTM GJB2 AJUBA ARID1B GATA2 FANCB CCNE1 FLT4 NCOR2 SMARCB1 MTAP CYLD PPP6C PHOX2B GPC3 CDK4 FUS NF1 SMO TRIM37 HLA-B STK19 PMS2 MSH6 CDK6 FYN NF2 SMOX KRAS NSD1 CDK12 SBDS AIP CDKN1B GNA11 NOTCH1 SRC AOAH RASA1 PRSS1 SUFU CDKN2A GNAQ NOTCH2 STK11 ARID2 RHOA DKC1 SDHAF2 CDKN2B HDAC9 NOTCH3 STK4 ATN1 TGFBR2 MEN1 SDHD CDKN2C HGF NOTCH4 SYK CASP8 ZNF750 OR2T35 RAD51D CHEK2 HRAS NPM1 TAOK1 CTCF GUSB SPOP RAD51C CSF1R HSP90AA1 NRAS TAOK2 FOXA1 KDM5C PGM5 BRIP1 CSF3R IDH1 NTRK1 TEK FZR1 PBRM1 DICER1 RHBDF2 CTNNB1 IDH2 NTRK2 TET2 GATA3 VHL PPM1D PRF1 DDR1 IGF1R NTRK3 TOP2A HIST1H3B FAS MAX CDC73 DDR2 IGF2 PALB2 TSC1 HNF1A SERPINA1 POLE FANCL DNMT1 JAK1 PDGFB TSC2 MED23 SH2D1A SOS1 ERCC3 DNMT3A JAK2 PDGFRA TYK2 MYB STAT3 BARD1 FANCD2 ACTIONABLE ALL BLADDER BRAIN BREAST COLON HEAD AND NECK KIDNEY LEUKEMIA LIMPH NODES LIVER MELANOMA OVARY PANCREAS PROSTATE STOMACH THYROID UTERUS RISK

31 The ACC Prescription Database: Goals Genomic database Create large-scale data resources of genomic information: Mutational Database Actionability infos Link the genomic database to: to EMRs and Health-System databases to research pipelines Interface with Doctors Create widely accepted and controlled vocabularies (ontologies) for clinical phenotypes Generate standards and pipelines for data interpretation and clinical decision support (automated decision support tools for clinicians) Interface with patients Train patients in using genomic information Give patients access to the latest experimental protocols and drugs

32 THE ACC PRESCRIPTION DATABASE Id: entry id Type: type of mutation (SNV, CAN, germline, translocation or other) Disease: the disease for which that relationship is observed Drug name: merges drug category/drug name Exact alteration: site of mutation if SNV, amp o del se CAN, translocation partner se translocation Act status: the contest in which the relationship is observed Act type: predicts sensitivity or resistance, prognostic (nothing else) Source: one of the four original databases (Cancer Discovery, Mills, Target (Broad), Intogen) or ACC References: Pubmed ID of the supporting paper Clinical trials: clinical trials.gov ID of available trials (in development)

33 Cancer stem cells Shared Database and Biobank WG GLIOMA Enrollment Avatar Omics analysis Preclinical validation Genomics-driven clinical trial

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